Search

Purpose: Cutaneous metastasis (CM) accounts for 5–30% of patients with breast cancer (BC) and presents unfavorable response to treatment and poor prognosis. A better understanding of the molecular alterations involved in metastasis is essential, which would help identify diagnostic and efficacy biomarkers for CM. Materials: : We retrospectively reviewed a total of 13 patients with histological or cytological diagnosis of breast cancer and CM. Clinical information was extracted from the medical records. The mutational landscape of matched primary tumors with their lymph nodes or CM tissues were analyzed using next-generation sequencing (NGS) of 425 cancer-relevant genes. All tissues were also analyzed by immunohistochemistry (IHC). The association of prognosis with various clinical and molecular factors was also evaluated. Results: More than half of the patients were Ki67 low (< 50%, 53.7%). Most patients (12, 92.3%) had other metastasis sites other than skin. The median time from diagnosis to the presentation of CM (T1) was 15 months (range: 0–94 months) and the median time from CM to death (T2) was 13 months (range 1–78). The most frequently altered genes across the three types of tissues were TP53 (69.6%, 16/23), PIK3CA (34.8%, 8/23), and MYC (26.1%). The number of alterations in CM tends to be higher than in primary tumors (median 8 vs. 6, P = 0.077). Copy number loss in STK11, copy number gain in FGFR4, TERT, AR, FLT4 and VEGFA and mutations in ATRX, SRC, AMER1 and RAD51C were significantly enriched in CM (all P < 0.05). Ki67 high group (> 50%) showed significantly shorter T1 than the Ki67 low group (≤ 50%) (median 12.5 vs. 50.0 months, P = 0.036). TP53, PIK3CA mutations, and TERT amplification group were associated with inferior T2 (median 11 vs. 36 months, P = 0.065; 8 vs. 36 months, P = 0.013, 7 vs. 36 months, P = 0.003, respectively). All p values were not adjusted. Conclusion: We compared the genomic features of primary breast cancer tissues with their corresponding CM tissues and discussed potential genes and pathways that may contribute to the skin metastasis of advanced breast cancers patients. TP53, PIK3CA mutant, and TERT amplification may serve as biomarkers for poor prognosis for CM patients. [ABSTRACT FROM AUTHOR]

Background: The axillary lymph-node metastatic burden is closely associated with treatment decisions and prognosis in breast cancer patients. This study aimed to explore the value of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT)–based radiomics in combination with ultrasound and clinical pathological features for predicting axillary lymph-node metastatic burden in breast cancer. Methods: A retrospective analysis was conducted and involved 124 patients with pathologically confirmed early-stage breast cancer who had undergone 18F-FDG PET/CT examination. The ultrasound, PET/CT, and clinical pathological features of all patients were analysed, and radiomic features from PET images were extracted to establish a multi-parameter predictive model. Results: The ultrasound lymph-node positivity rate and PET lymph-node positivity rate in the high nodal burden group were significantly higher than those in the low nodal burden group (χ2 = 19.867, p < 0.001; χ2 = 33.025, p < 0.001). There was a statistically significant difference in the PET-based radiomics score (RS) for predicting axillary lymph-node burden between the high and low lymph-node burden groups. (-1.04 ± 0.41 vs. -1.47 ± 0.41, t = -4.775, p < 0.001). The ultrasound lymph-node positivity (US_LNM) (odds ratio [OR] = 3.264, 95% confidence interval [CI] = 1.022–10.423), PET lymph-node positivity (PET_LNM) (OR = 14.242, 95% CI = 2.960–68.524), and RS (OR = 5.244, 95% CI = 3.16–20.896) are all independent factors associated with high lymph-node burden (p < 0.05). The area under the curve (AUC) of the multi-parameter (MultiP) model was 0.895, which was superior to those of US_LNM, PET_LNM, and RS models (AUC = 0.703, 0.814, 0.773, respectively), with statistically significant differences (Z = 2.888, 3.208, 3.804, respectively; p = 0.004, 0.002, < 0.001, respectively). Decision curve analysis indicated that the MultiP model provided a higher net benefit for all patients. Conclusion: A MultiP model based on PET-based radiomics was able to effectively predict axillary lymph-node metastatic burden in breast cancer. Trial registration: This study was registered with ClinicalTrials.gov (registration number: NCT05826197) on May 7, 2023. [ABSTRACT FROM AUTHOR]

Background: Obesity is associated with an increased breast cancer risk in postmenopausal women and may contribute to worse outcomes. Black women experience higher obesity and breast cancer mortality rates than non-Black women. We examined associations between race, obesity, and clinical tumor stage with breast cancer prognosis. Methods: We conducted a prospective cohort study in 1,110 breast cancer patients, using univariable and multivariable Cox regression analyses to evaluate the effects of obesity, race/ethnicity, and clinical tumor stage on progression-free and overall survival (PFS and OS). Results: 22% of participants were Black, 64% were Hispanic White, and 14% were non-Hispanic White or another race. 39% of participants were obese (body mass index [BMI] ≥ 30 kg/m2). In univariable analyses, tumor stage III-IV was associated with worse PFS and OS compared to tumor stage 0-II (hazard ratio [HR] = 4.68, 95% confidence interval [CI] = 3.52–6.22 for PFS and HR = 5.92, 95% CI = 4.00-8.77 for OS). Multivariable analysis revealed an association between Black race and worse PFS in obese (HR = 2.19, 95% CI = 1.06–4.51) and non-obese (HR = 2.11, 95% CI = 1.05–4.21) women with tumors staged 0-II. Obesity alone was not associated with worse PFS or OS. Conclusions: Results suggest a complex interrelationship between obesity and race in breast cancer prognosis. The association between the Black race and worse PFS in tumor stages 0-II underscores the importance of early intervention in this group. Future studies are warranted to evaluate whether alternative measures of body composition and biomarkers are better prognostic indicators than BMI among Black breast cancer survivors. [ABSTRACT FROM AUTHOR]

Background: Developing countries have a significantly higher incidence of breast cancer in patients younger than 40 years as compared to developed countries. This study aimed to examine if young age at diagnosis is an independent prognostic factor for worse survival outcomes in breast cancer as well as the effect of age on Disease-free survival (DFS) and local recurrence free survival (LRFS) after adjusting for various tumor characteristics, local and systemic treatments. Methods: This is a secondary analysis of prospective cohort of patients from two existing databases. We identified patients with breast cancer aged 40 years or less and we matched them to those older than 40 years. We also matched based on stage and molecular subtypes. In cohort 1, we matched at a ratio of 1:1, while in cohort 2 we matched at a ratio of 1:3. Results: In cohort 1, Disease-free survival (DFS) at 5 years was significantly shorter for those younger than 40 years (75.6% and 92.7% respectively; p < 0.03). On multivariate analysis, only chemotherapy was found to be significant, while age was not found to be an independent predictor of prognosis. Local recurrence free survival at 5 years was similar between both age categories. Only hormonal therapy is a significant predictor for LRFS at 5 years. In the second cohort, DFS and LRFS at 3 years were similar between those younger and those older than 40 years. On multivariate analysis, no factor including age was found to be an independent predictor of prognosis. Conclusion: Data in the literature is controversial on the effect of young age on breast cancer prognosis. Our findings could not demonstrate that age is an independent prognostic factor in our population. There is a need for outcomes from larger, prospective series that have longer follow-ups and more data from our region. [ABSTRACT FROM AUTHOR]

Background: Among the most common forms of cancer worldwide, breast cancer posed a serious threat to women. Recent research revealed a lack of oxygen, known as hypoxia, was crucial in forming breast cancer. This research aimed to create a robust signature with hypoxia-related genes to predict the prognosis of breast cancer patients. The function of hypoxia genes was further studied through cell line experiments. Materials and methods: In the bioinformatic part, transcriptome and clinical information of breast cancer were obtained from The Cancer Genome Atlas(TCGA). Hypoxia-related genes were downloaded from the Genecards Platform. Differentially expressed hypoxia-related genes (DEHRGs) were identified. The TCGA filtered data was evenly split, ensuring a 1:1 distribution between the training and testing sets. Prognostic-related DEHRGs were identified through Cox regression. The signature was established through the training set. Then, it was validated using the test set and external validation set GSE131769 from Gene Expression Omnibus (GEO). The nomogram was created by incorporating the signature and clinicopathological characteristics. The predictive value of the nomogram was evaluated by C-index and receiver operating characteristiccurve. Immune microenvironment and mutation burden were also examined. In the experiment part, the function of the two most significant hypoxia-related genes were further explored by cell-line experiments. Results: In the bioinformatic part, 141 up-regulated and 157 down-regulated DEHRGs were screened out. A prognostic signature was constructed containing nine hypoxia genes (ALOX15B, CA9, CD24, CHEK1, FOXM1, HOTAIR, KCNJ11, NEDD9, PSME2) in the training set. Low-risk patients exhibited a much more favorable prognosis than higher-risk ones (P < 0.001). The signature was double-validated in the test set and GSE131769 (P = 0.006 and P = 0.001). The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817. Patients in the high-risk group had a higher tumor mutation burden when compared to the low-risk group. In the experiment part, the down-regulation of PSME2 inhibited cell growth ability and clone formation capability of breast cancer cells, while the down-regulation of KCNJ11 did not have any functions. Conclusion: Based on 9 DEHRGs, a reliable signature was established through the bioinformatic method. It could accurately predict the prognosis of breast cancer patients. Cell line experiment indicated that PSME2 played a protective role. Summarily, we provided a new insight to predict the prognosis of breast cancer by hypoxia-related genes. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is a major global health concern, and there is a continuous search for novel biomarkers to predict its prognosis. The mitochondrial protein NDUFAF6, previously studied in liver cancer, is now being investigated for its role in breast cancer. This study aims to explore the expression and functional significance of NDUFAF6 in breast cancer using various databases and experimental models. Methods: We analyzed breast cancer samples from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Human Protein Atlas (HPA) databases, supplemented with immunohistochemistry (IHC) staining to assess NDUFAF6 expression. A breast cancer cell xenograft mouse model was used to evaluate tumor growth, apoptosis, and NDUFAF6 expression. Survival probabilities were estimated through Kaplan–Meier plots and Cox regression analysis. A Protein–Protein Interaction (PPI) network was constructed, and differentially expressed genes related to NDUFAF6 were analyzed using GO, KEGG, and GSEA. The relationship between NDUFAF6 expression, immune checkpoints, and immune infiltration was also evaluated. Results: NDUFAF6 was found to be overexpressed in breast cancer patients and in the xenograft mouse model. Its expression correlated with worse clinical features and prognosis. NDUFAF6 expression was an independent predictor of breast cancer outcomes in both univariate and multivariate analyses. Functionally, NDUFAF6 is implicated in several immune-related pathways. Crucially, NDUFAF6 expression correlated with various immune infiltrating cells and checkpoints, particularly promoting PD-L1 expression by inhibiting the NRF2 signaling pathway. Conclusion: The study establishes NDUFAF6 as a potential prognostic biomarker in breast cancer. Its mechanism of action, involving the inhibition of NRF2 to upregulate PD-L1, highlights its significance in the disease's progression and potential as a target for immunotherapy. [ABSTRACT FROM AUTHOR]

Background: The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients. Methods: In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs. Results: Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648–3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3. Conclusion: Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients. [ABSTRACT FROM AUTHOR]

Human endogenous retroviruses (HERVs) are the remnants of ancient retroviral infections integrated into the human genome. Although most HERVs are silenced or rendered inactive by various regulatory mechanisms, they retain the potential to influence the nearby genes. We analyzed the regulatory map of 91 HERV-Ks on neighboring genes in human breast cancer and investigated the impact of HERV-Ks on the tumor microenvironment (TME) and prognosis of breast cancer. Nine RNA-seq datasets were obtained from GEO and NCBI SRA. Differentially expressed genes and HERV-Ks were analyzed using DESeq2. Validation of high-risk prognostic candidate genes using TCGA data. These included Overall survival (multivariate Cox regression model), immune infiltration analysis (TIMER), tumor mutation burden (maftools), and drug sensitivity analysis (GSCA). A total of 88 candidate genes related to breast cancer prognosis were screened, of which CD48, SLAMF7, SLAMF1, IGLL1, IGHA1, and LRRC8A were key genes. Functionally, these six key genes were significantly enriched in some immune function-related pathways, which may be associated with poor prognosis for breast cancer (p = 0.00016), and the expression levels of these genes were significantly correlated with the sensitivity of breast cancer treatment-related drugs. Mechanistically, they may influence breast cancer development by modulating the infiltration of various immune cells into the TME. We further experimentally validated these genes to confirm the results obtained from bioinformatics analysis. This study represents the first report on the regulatory potential of HERV-K in the neighboring breast cancer genome. We identified three key HERV-Ks and five neighboring genes that hold promise as novel targets for future interventions and treatments for breast cancer. [ABSTRACT FROM AUTHOR]

Background: Malignant progression is the major cause of poor prognosis in breast cancer (BC) patients. Plasma exosomal miRNAs have been reported to be involved in tumor progression, but their roles in BC remain unclear. Methods: We performed plasma exosomal miRNA sequencing on 45 individuals, including healthy controls and nonmetastatic and metastatic BC patients. We examined the correlation between miRNA expression in tumor tissues and plasma exosomes in BC patients by qRT‒PCR. The effects of exosomal miR-361-3p on BC cells were determined by CellTiter-Glo, migration and wound healing assays. The target genes of miR-361-3p and downstream pathways were explored by dual-luciferase reporter assay, RNA knockdown, rescue experiments, and western blotting. We utilized murine xenograft model to further assess the impact of plasma exosomal miR-361-3p on the malignant progression of BC. Results: We found that the expression level of plasma exosomal miR-361-3p gradually increased with malignant progression in BC patients, and the expression of miR-361-3p in plasma exosomes and BC tissues was positively correlated. Consistently, exosomal miR-361-3p enhanced the migration and proliferation of two BC cell lines, MDA-MB-231 and SK-BR-3. Furthermore, our data showed that miR-361-3p inhibited two novel target genes, ETV7 and BATF2, to activate the PAI-1/ERK pathway, leading to increased BC cell viability. Finally, the consistency of the in vivo experimental results supported that elevated plasma exosomal miR-361-3p promote the malignant progression of BC. Conclusions: We found for the first time that plasma exosomal miR-361-3p was associated with malignant progression in BC patients. Mechanistically, exosomal miR-361-3p can enhance the migration and proliferation of BC cells by targeting the ETV7 and BATF2/PAI-1/ERK pathways. Our data suggest that plasma exosomal miR-361-3p has the potential to serve as a biomarker for predicting malignant progression in BC patients. [ABSTRACT FROM AUTHOR]

Background: Menopausal status has a known relationship with the levels of estrogen, progesterone, and other sex hormones, potentially influencing the activity of ER, PR, and many other signaling pathways involved in the initiation and progression of breast cancer. However, the differences between premenopausal and postmenopausal breast cancer patients at the molecular level are unclear. Methods: We retrieved eight datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) associated with menopausal status in breast cancer patients were identified using the MAMA and LIMMA methods. Based on these validated DEGs, we performed Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein–protein interaction (PPI) networks were constructed. We used DrugBank data to investigate which of these validated DEGs are targetable. Survival analysis was performed to explore the influence of these genes on breast cancer patient prognosis. Results: We identified 762 DEGs associated with menopausal status in breast cancer patients. PPI network analysis indicated that these genes are primarily involved in pathways such as the cell cycle, oocyte meiosis and progesterone-mediated oocyte maturation pathways. Notably, several genes played roles in multiple signaling pathways and were associated with patient survival. These genes were also observed to be targetable according to the DrugBank database. Conclusion: We identified DEGs associated with menopausal status in breast cancer patients. The association of these genes with several key pathways may promote understanding of the complex characterizations of breast cancer. Our findings offer valuable insights for developing new therapeutic strategies tailored to the menopausal status of breast cancer patients. [ABSTRACT FROM AUTHOR]

Background: Malnutrition is associated with poor overall survival (OS) in breast cancer patients; however, the most predictive nutritional indicators for the prognosis of patients with breast cancer are not well-established. This study aimed to compare the predictive effects of common nutritional indicators on OS and to refine existing nutritional indicators, thereby identifying a more effective nutritional evaluation indicator for predicting the prognosis in breast cancer patients. Methods: This prospective study analyzed data from 776 breast cancer patients enrolled in the "Investigation on Nutritional Status and its Clinical Outcome of Common Cancers" (INSCOC) project, which was conducted in 40 hospitals in China. We used the time-dependent receiver operating characteristic curve (ROC), Kaplan–Meier survival curve, and Cox regression analysis to evaluate the predictive effects of several nutritional assessments. These assessments included the patient-generated subjective nutrition assessment (PGSGA), the global leadership initiative on malnutrition (GLIM), the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI). Utilizing machine learning, these nutritional indicators were screened through single-factor analysis, and relatively important variables were selected to modify the PNI. The modified PNI, termed the cholesterol-modified prognostic nutritional index (CPNI), was evaluated for its predictive effect on the prognosis of patients. Results: Among the nutritional assessments (including PGSGA, GLIM, CONUT, NRI, and PNI), PNI showed the highest predictive ability for patient prognosis (time-dependent ROC = 0.58). CPNI, which evolved from PNI, emerged as the superior nutritional index for OS in breast cancer patients, with the time-dependent ROC of 0.65. It also acted as an independent risk factor for mortality (p < 0.05). Moreover, the risk of malnutrition and mortality was observed to increase gradually among both premenopausal and postmenopausal age women, as well as among women categorized as non-overweight, overweight, and obese. Conclusions: The CPNI proves to be an effective nutritional assessment tool for predicting the prognosis of patients with breast cancer. [ABSTRACT FROM AUTHOR]

Purpose: To explore the potential role of signal transducer and activator of transcription 5A (STAT5A) in the metastasis of breast cancer, and its mechanism of regulation underlying. Methods and results: TCGA datasets were used to evaluate the expression of STAT5A in normal and different cancerous tissues through TIMER2.0, indicating that STAT5A level was decreased in breast cancer tissues compared with normal ones. Gene Set Enrichment Analysis predicted that STAT5A was associated with the activation of immune cells and cell cycle process. We further demonstrated that the infiltration of immune cells was positively associated with STAT5A level. Influorescence staining revealed the expression and distribution of F-actin was regulated by STAT5A, while colony formation assay, wound healing and transwell assays predicted the inhibitory role of STAT5A in the colony formation, migratory and invasive abilities in breast cancer cells. In addition, overexpression of the Notch3 intracellular domain (N3ICD), the active form of Notch3, resulted in the increased expression of STAT5A. Conversely, silencing of Notch3 expression by siNotch3 decreased STAT5A expression, supporting that STAT5A expression is positively associated with Notch3 in human breast cancer cell lines and breast cancer tissues. Mechanistically, chromatin immunoprecipitation showed that Notch3 was directly bound to the STAT5A promoter and induced the expression of STAT5A. Moreover, overexpressing STAT5A partially reversed the enhanced mobility of breast cancer cells following Notch3 silencing. Low expression of Notch3 and STAT5A predicted poorer prognosis of patients with breast cancer. Conclusion: The present study demonstrates that Notch3 inhibits metastasis in breast cancer through inducing transcriptionally STAT5A, which was associated with tumor-infiltrating immune cells, providing a novel strategy to treat breast cancer. [ABSTRACT FROM AUTHOR]

Tumor cell-released LC3+ extracellular vesicles (LC3+ EVs) participate in immunosuppression during autophagy and contribute to the occurrence and development of breast cancer. In view of the strong association between the LC3+ EVs and breast cancer, developing an effective strategy for the quantitative detection of LC3+ EVs levels with high sensitivity to identify LC3+ EVs as new biomarkers for accurate diagnosis of breast cancer is crucial, but yet not been reported. Herein, an ultrasensitive electrochemical immunosensor is presented for the quantitative determination of LC3+ EVs using a three-dimensional graphene oxide hydrogel-methylene blue composite as a redox probe, showing a low detection limit and a wide linear range. With this immunosensor, the expression levels of LC3+ EVs in various practical sample groups including different cancer cell lines, the peripheral blood of tumor-bearing mice before and after immunotherapy, and the peripheral blood from breast cancer patients with different subtypes and stages were clearly distinguished. This study demonstrated that LC3+ EVs were superior as biomarkers for the accurate diagnosis of breast cancer compared to traditional biomarkers, particularly for cancer subtype discrimination. This work would provide a new noninvasive detection tool for the early diagnosis and prognosis assessment of breast cancer in clinics. [ABSTRACT FROM AUTHOR]

Background: Metaplastic breast cancer(MBC) is a specific pathological type of invasive breast cancer. There are few studies related to MBC due to its rarity. This study aimed to analyse the differences in clinicopathological characteristics and prognosis between Metaplastic breast cancer and triple-negative invasive ductal carcinoma (TN-IDC). Methods: We retrospectively compared the clinicopathological characteristics of patients diagnosed with MBC and TN-IDC at the Fourth Hospital of Hebei Medical University between 2011 and 2020 in a 1:2 ratio. The log-rank test was used to compare the two groups' disease-free survival (DFS) and overall survival (OS). For MBCs, we performed univariate and multivariate analyses using the Cox proportional hazards model to determine the characteristics that impacted OS and DFS. Results: A total of 81 patients with MBC and 162 patients with TN-IDC were included in this study. At initial diagnosis, MBC patients had larger tumour diameters(P = 0.03) and fewer positive lymph nodes (P = 0.04). Patients with MBC were more likely to have organ metastases after surgery (P = 0.03). Despite receiving the same treatment, MBC patients had worse DFS (HR = 1.66, 95%CI 0.90–3.08, P = 0.11) and OS (HR = 1.98, 95% CI 1.03–3.81, P = 0.04), and OS was statistically significant. Positive lymph nodes at initial diagnosis were associated with worse DFS (HR = 3.98, 95%CI 1.05–15.12, P = 0.04) and OS (HR = 3.70, 95%CI 1.03–13.34, P = 0.04) for patients with MBC. The efficacy of platinum-based agents is insensitive for MBC patients receiving chemotherapy. In addition, patients treated with preoperative chemotherapy had worse DFS compared to patients treated with postoperative chemotherapy (HR = 3.51, 95%CI 1.05–11.75, P = 0.04). Conclusions: The clinicopathological characteristics and prognosis of MBC and TN-IDC differ in many ways. Further studies are required to determine suitable treatment guidelines for patients with MBC. [ABSTRACT FROM AUTHOR]

Background: The tumor microenvironment and intercellular communication between solid tumors and the surrounding stroma play crucial roles in cancer initiation, progression, and prognosis. Radiomics provides clinically relevant information from radiological images; however, its biological implications in uncovering tumor pathophysiology driven by cellular heterogeneity between the tumor and stroma are largely unknown. We aimed to identify radiogenomic signatures of cellular tumor-stroma heterogeneity (TSH) to improve breast cancer management and prognosis analysis. Methods: This retrospective multicohort study included five datasets. Cell subpopulations were estimated using bulk gene expression data, and the relative difference in cell subpopulations between the tumor and stroma was used as a biomarker to categorize patients into good- and poor-survival groups. A radiogenomic signature-based model utilizing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was developed to target TSH, and its clinical significance in relation to survival outcomes was independently validated. Results: The final cohorts of 1330 women were included for cellular TSH biomarker identification (n = 112, mean age, 57.3 years ± 14.6) and validation (n = 886, mean age, 58.9 years ± 13.1), radiogenomic signature of TSH identification (n = 91, mean age, 55.5 years ± 11.4), and prognostic (n = 241) assessments. The cytotoxic lymphocyte biomarker differentiated patients into good- and poor-survival groups (p < 0.0001) and was independently validated (p = 0.014). The good survival group exhibited denser cell interconnections. The radiogenomic signature of TSH was identified and showed a positive association with overall survival (p = 0.038) and recurrence-free survival (p = 3 × 10–4). Conclusion: Radiogenomic signatures provide insights into prognostic factors that reflect the imbalanced tumor-stroma environment, thereby presenting breast cancer-specific biological implications and prognostic significance. [ABSTRACT FROM AUTHOR]

Background: For many years, the role of the microbiome in tumor progression, particularly the tumor microbiome, was largely overlooked. The connection between the tumor microbiome and the tumor genome still requires further investigation. Methods: The TCGA microbiome and genome data were obtained from Haziza et al.'s article and UCSC Xena database, respectively. Separate WGCNA networks were constructed for the tumor microbiome and genomic data after filtering the datasets. Correlation analysis between the microbial and mRNA modules was conducted to identify oncogenome associated microbiome module (OAM) modules, with three microbial modules selected for each tumor type. Reactome analysis was used to enrich biological processes. Machine learning techniques were implemented to explore the tumor type-specific enrichment and prognostic value of OAM, as well as the ability of the tumor microbiome to differentiate TP53 mutations. Results: We constructed a total of 182 tumor microbiome and 570 mRNA WGCNA modules. Our results show that there is a correlation between tumor microbiome and tumor genome. Gene enrichment analysis results suggest that the genes in the mRNA module with the highest correlation with the tumor microbiome group are mainly enriched in infection, transcriptional regulation by TP53 and antigen presentation. The correlation analysis of OAM with CD8+ T cells or TAM1 cells suggests the existence of many microbiota that may be involved in tumor immune suppression or promotion, such as Williamsia in breast cancer, Biostraticola in stomach cancer, Megasphaera in cervical cancer and Lottiidibacillus in ovarian cancer. In addition, the results show that the microbiome-genome prognostic model has good predictive value for short-term prognosis. The analysis of tumor TP53 mutations shows that tumor microbiota has a certain ability to distinguish TP53 mutations, with an AUROC value of 0.755. The tumor microbiota with high importance scores are Corallococcus, Bacillus and Saezia. Finally, we identified a potential anti-cancer microbiota, Tissierella, which has been shown to be associated with improved prognosis in tumors including breast cancer, lung adenocarcinoma and gastric cancer. Conclusion: There is an association between the tumor microbiome and the tumor genome, and the existence of this association is not accidental and could change the landscape of tumor research. [ABSTRACT FROM AUTHOR]

Background: For many years, the role of the microbiome in tumor progression, particularly the tumor microbiome, was largely overlooked. The connection between the tumor microbiome and the tumor genome still requires further investigation. Methods: The TCGA microbiome and genome data were obtained from Haziza et al.'s article and UCSC Xena database, respectively. Separate WGCNA networks were constructed for the tumor microbiome and genomic data after filtering the datasets. Correlation analysis between the microbial and mRNA modules was conducted to identify oncogenome associated microbiome module (OAM) modules, with three microbial modules selected for each tumor type. Reactome analysis was used to enrich biological processes. Machine learning techniques were implemented to explore the tumor type-specific enrichment and prognostic value of OAM, as well as the ability of the tumor microbiome to differentiate TP53 mutations. Results: We constructed a total of 182 tumor microbiome and 570 mRNA WGCNA modules. Our results show that there is a correlation between tumor microbiome and tumor genome. Gene enrichment analysis results suggest that the genes in the mRNA module with the highest correlation with the tumor microbiome group are mainly enriched in infection, transcriptional regulation by TP53 and antigen presentation. The correlation analysis of OAM with CD8+ T cells or TAM1 cells suggests the existence of many microbiota that may be involved in tumor immune suppression or promotion, such as Williamsia in breast cancer, Biostraticola in stomach cancer, Megasphaera in cervical cancer and Lottiidibacillus in ovarian cancer. In addition, the results show that the microbiome-genome prognostic model has good predictive value for short-term prognosis. The analysis of tumor TP53 mutations shows that tumor microbiota has a certain ability to distinguish TP53 mutations, with an AUROC value of 0.755. The tumor microbiota with high importance scores are Corallococcus, Bacillus and Saezia. Finally, we identified a potential anti-cancer microbiota, Tissierella, which has been shown to be associated with improved prognosis in tumors including breast cancer, lung adenocarcinoma and gastric cancer. Conclusion: There is an association between the tumor microbiome and the tumor genome, and the existence of this association is not accidental and could change the landscape of tumor research. [ABSTRACT FROM AUTHOR]

The primary aim of this study is to critically evaluate and comment on the research presented in the article titled "A Novel Super-Enhancer-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer" by Wu et al. Our specific objectives include assessing the methodology employed by the authors, particularly in regard to the utilization of a super-enhancer-related gene signature for breast cancer prognosis prediction. We propose the necessity of subgroup analysis to effectively address the heterogeneity in breast cancer subtypes, which is crucial for the applicability of the SERGs across diverse breast cancer cases. Additionally, we suggest conducting a more comprehensive immune panel study to deepen the understanding of how the immune microenvironment impacts breast cancer prognosis. Our commentary seeks to provide valuable insights into the strengths and weaknesses of the study, contributing to a more comprehensive understanding of its findings and potential clinical implications. [ABSTRACT FROM AUTHOR]

Introduction: Low serum selenium and altered tumour RNA expression of certain selenoproteins are associated with a poor breast cancer prognosis. Selenoprotein expression stringently depends on selenium availability, hence circulating selenium may interact with tumour selenoprotein expression. However, there is no matched analysis to date. Methods: This study included 1453 patients with newly diagnosed breast cancer from the multicentric prospective Sweden Cancerome Analysis Network – Breast study. Total serum selenium, selenoprotein P and glutathione peroxidase 3 were analysed at time of diagnosis. Bulk RNA-sequencing was conducted in matched tumour tissues. Fully adjusted Cox regression models with an interaction term were employed to detect dose-dependent interactions of circulating selenium with the associations of tumour selenoprotein mRNA expression and mortality. Results: 237 deaths were recorded within ~ 9 years follow-up. All three serum selenium biomarkers correlated positively (p < 0.001). All selenoproteins except for GPX6 were expressed in tumour tissues. Single cell RNA-sequencing revealed a heterogeneous expression pattern in the tumour microenvironment. Circulating selenium correlated positively with tumour SELENOW and SELENON expression (p < 0.001). In fully adjusted models, the associations of DIO1, DIO3 and SELENOM with mortality were dose-dependently modified by serum selenium (p < 0.001, p = 0.020, p = 0.038, respectively). With increasing selenium, DIO1 and SELENOM associated with lower, whereas DIO3 expression associated with higher mortality. Association of DIO1 with lower mortality was only apparent in patients with high selenium [above median (70.36 µg/L)], and the HR (95%CI) for one-unit increase in log(FPKM + 1) was 0.70 (0.50–0.98). Conclusions: This first unbiased analysis of serum selenium with the breast cancer selenotranscriptome identified an effect-modification of selenium on the associations of DIO1, SELENOM, and DIO3 with prognosis. Selenium substitution in patients with DIO1-expressing tumours merits consideration to improve survival. [ABSTRACT FROM AUTHOR]

Background: Breast cancers with 1–10% cell staining for estrogen receptor (ER) present particular clinical features. The clinical data of estrogen receptor expression level and treatment effect are limited, particularly regarding chemotherapy benefit. We evaluated the pathologic response to neoadjuvant chemotherapy (NAC) in ER low positive tumors (ER staining 1-10%) and compared it with ER > 10% positive tumors (ER staining > 10%) and ER-negative tumors. We further explored the differences in recurrence and survival with respect to the ER expression level. Method: Patients with stages II and III HER2-negative primary breast cancer who received neoadjuvant chemotherapy followed by definitive surgery were categorized according to their ER percentages into three groups: ER-negative, ER low positive, and ER > 10% positive. Logistic regression models were used to assess the association between each variable and pathologic complete response (pCR). Kaplan‒Meier analysis was used to estimate survival outcomes. Cox models were used to adjust for patient and tumor characteristics. Results: A total of 241 patients were analyzed. Of all patients included, 22 (9.1%) had ER low positive tumors, 159 (66.0%) had ER > 10% positive tumors, and 60 (24.9%) were ER-negative. Low ER positivity was significantly associated with a higher pCR rate than ER > 10% positivity (OR, 0.249; 95% CI, 0.067–0.923; P = 0.038). After a median follow-up time of 32 months, the disease-free survival (DFS) and overall survival (OS) of the patients with ER low positive tumors were significantly worse than those of the patients with ER > 10% positive tumors but similar to those with ER-negative tumors. After adjustment for covariates, ER low positive tumors were significantly associated with worse DFS than ER > 10% positive tumors. Conclusion: Our results indicated that ER low positive breast cancer presents a better response to neoadjuvant chemotherapy and significantly worse prognosis for patients than those with ER > 10% positive tumors, but similar to the ER-negative group. These data support that this category of patients behaves clinically like patients with ER-negative breast cancer and should be treated differently from patients with ER > 10% positive tumors. Further prospective study is needed. [ABSTRACT FROM AUTHOR]

Background: Heterogeneous tumor cells are thought to be a significant factor in the failure of endocrine therapy in estrogen receptor-positive (ER+) cancers. Culturing patient-derived breast cancer cells (PDBCCs) provides an invaluable tool in pre-clinical and translational research for the heterogeneity of cancer cells. This study aimed to investigate the effects of different media components and culture methods on the BCSC-associated immunophenotypes and gene expression in ER + PDBCCs. Methods: Ten patients with ER + breast cancer were employed in this study, six of whom had neoadjuvant chemotherapy and four of whom did not. PDBCCs were isolated by enzymatic methods using collagen I and hyaluronidase. PDBCCs were grown as monolayers in mediums with different compositions and as multicellular spheroid in a suspended condition. Collagen I-coated plate and ultralow attachment plate coated with polymer-X were used for monolayer and spheroid culture. Flow cytometry, immunofluorescent staining, RT-PCR, and RNA-sequencing were employed to examine the immunophenotype and genetic profile of PDBCCs. Results: More than 95% of PDBCCs sustain EpCAM high/+/fibroblast marker- phenotypes in monolayer conditions by subculturing 3–4 times. A83-01 removal induced senescent cells with high β-galactosidase activity. PDBCCs grown as monolayers were characterized by the majority of cells having an EpCAM+/CD49f + phenotype. Compared to full media in monolayer culture, EGF removal increased EpCAM+/CD49f − phenotype (13.8-fold, p = 0.028), whereas R-spondin removal reduced it (0.8-fold, p = 0.02). A83-01 removal increased EpCAM+/CD24 + phenotype (1.82-fold, p = 0.023) and decreased EpCAM low/-/CD44+/CD24- phenotype (0.45-fold, p = 0.026). Compared to monolayer, spheroid resulted in a significant increase in the population with EpCAM-/CD49+ (14.6-fold, p = 0.006) and EpCAM low/-/CD44+/CD24- phenotypes (4.16-fold, p = 0.022) and ALDH high activity (9.66-fold, p = 0.037). ALDH1A and EMT-related genes were upregulated. In RNA-sequencing analysis between spheroids and monolayers, a total of 561 differentially expressed genes (2-fold change, p < 0.05) were enriched in 27 KEGG pathways including signaling pathways regulating pluripotency of stem cells. In a recurrence-free survival analysis based on the Kaplan-Meier Plotter database of the up-and down-regulated genes identified in spheroids, 15 up-, and 14 down-regulated genes were associated with poor prognosis of breast cancer patients. Conclusion: The media composition and spheroid culture method change in the BCSCs and EMT markers of PDBCCs, implying the importance of defining the media composition and culture method for studying PDBCCs in vitro. [ABSTRACT FROM AUTHOR]

Background: Growing evidence has shown that gut microbiome composition is associated with breast cancer (BC), but the causality remains unknown. We aimed to investigate the link between BC prognosis and the gut microbiome at various oestrogen receptor (ER) statuses. Methods: We performed a genome-wide association study (GWAS) to analyse the gut microbiome of BC patients, the dataset for which was collected by the Breast Cancer Association Consortium (BCAC). The analysis was executed mainly via inverse variance weighting (IVW); the Mendelian randomization (MR) results were verified by heterogeneity tests, sensitivity analysis, and pleiotropy analysis. Results: Our findings identified nine causal relationships between the gut microbiome and total BC cases, with ten and nine causal relationships between the gut microbiome and ER-negative (ER-) and ER-positive (ER+) BC, respectively. The family Ruminococcaceae and genus Parabacteroides were most apparent among the three categories. Moreover, the genus Desulfovibrio was expressed in ER- BC and total BC, whereas the genera Sellimonas, Adlercreutzia and Rikenellaceae appeared in the relationship between ER + BC and total BC. Conclusion: Our MR inquiry confirmed that the gut microbiota is causally related to BC. This further explains the link between specific bacteria for prognosis of BC at different ER statuses. Considering that potential weak instrument bias impacts the findings and that the results are limited to European females due to data constraints, further validation is crucial. [ABSTRACT FROM AUTHOR]

Background: The prognostic role of either forkhead box A1 (FOXA1) or anterior gradient 2 (AGR2) in breast cancer has been found separately. Considering that there were interplays between them depending on ER status, we aimed to assess the statistical interaction between AGR2 and FOXA1 on breast cancer prognosis and examine the prognostic role of the combination of them by ER status. Methods: AGR2 and FOXA1 expression in tumor tissues were evaluated with tissue microarrays by immunohistochemistry in 915 breast cancer patients with follow up data. The expression levels of these two markers were treated as binary variables, and many different cutoff values were tried for each marker. Survival and Cox proportional hazard analyses were used to evaluate the relationship between AGR2, FOXA1 and prognosis, and the statistical interaction between them on the prognosis was assessed on multiplicative scale. Results: Statistical interaction between AGR2 and FOXA1 on the PFS was significant with all the cutoff points in ER-positive breast cancer patients but not ER-negative ones. Among ER-positive patients, the poor prognostic role of the high level of FOXA1 was significant only in patients with the low level of AGR2, and vice versa. When AGR2 and FOXA1 were considered together, patients with low levels of both markers had significantly longer PFS compared with all other groups. Conclusions: There was a statistical interaction between AGR2 and FOXA1 on the prognosis of ER-positive breast cancer. The combination of AGR2 and FOXA1 was a more useful marker for the prognosis of ER-positive breast cancer patients. [ABSTRACT FROM AUTHOR]

Background: For many years, the role of the microbiome in tumor progression, particularly the tumor microbiome, was largely overlooked. The connection between the tumor microbiome and the tumor genome still requires further investigation. Methods: The TCGA microbiome and genome data were obtained from Haziza et al.'s article and UCSC Xena database, respectively. Separate WGCNA networks were constructed for the tumor microbiome and genomic data after filtering the datasets. Correlation analysis between the microbial and mRNA modules was conducted to identify oncogenome associated microbiome module (OAM) modules, with three microbial modules selected for each tumor type. Reactome analysis was used to enrich biological processes. Machine learning techniques were implemented to explore the tumor type-specific enrichment and prognostic value of OAM, as well as the ability of the tumor microbiome to differentiate TP53 mutations. Results: We constructed a total of 182 tumor microbiome and 570 mRNA WGCNA modules. Our results show that there is a correlation between tumor microbiome and tumor genome. Gene enrichment analysis results suggest that the genes in the mRNA module with the highest correlation with the tumor microbiome group are mainly enriched in infection, transcriptional regulation by TP53 and antigen presentation. The correlation analysis of OAM with CD8+ T cells or TAM1 cells suggests the existence of many microbiota that may be involved in tumor immune suppression or promotion, such as Williamsia in breast cancer, Biostraticola in stomach cancer, Megasphaera in cervical cancer and Lottiidibacillus in ovarian cancer. In addition, the results show that the microbiome-genome prognostic model has good predictive value for short-term prognosis. The analysis of tumor TP53 mutations shows that tumor microbiota has a certain ability to distinguish TP53 mutations, with an AUROC value of 0.755. The tumor microbiota with high importance scores are Corallococcus, Bacillus and Saezia. Finally, we identified a potential anti-cancer microbiota, Tissierella, which has been shown to be associated with improved prognosis in tumors including breast cancer, lung adenocarcinoma and gastric cancer. Conclusion: There is an association between the tumor microbiome and the tumor genome, and the existence of this association is not accidental and could change the landscape of tumor research. [ABSTRACT FROM AUTHOR]

Background: Women with breast cancer have different chances of surviving their disease, depending on where they live. Variations in survival may stem from unequal access to prompt diagnosis, treatment and care. Implementation of the right to health may help remedy such inequalities. The right to health is enshrined in international human rights law, notably Article 12 of the International Covenant on Economic, Social and Cultural Rights. A human rights-based approach to health requires a robust, just and efficient health system, with access to adequate health services and medicines on a non-discriminatory basis. However, it may prove challenging for health policymakers and cancer management specialists to implement and monitor this right in national health systems. Method: This article presents the results of a Delphi study designed to select indicators of implementation of the right to health to inform breast cancer care and management. In a systematic process, 13 experts examined an initial list of 151 indicators. Results: After two rounds, 54 indicators were selected by consensus, three were rejected, three were added, and 97 remained open for debate. For breast cancer, right-to-health features selected as worth implementing and monitoring included the formal recognition of the right to health in breast cancer strategies; a population-based screening programme, prompt diagnosis, strong referral systems and limited waiting times; the provision of palliative, survivorship and end-of-life care; the availability, accessibility, acceptability and quality (AAAQ) of breast cancer services and medicines; the provision of a system of accountability; and the collection of anonymised individual data to target patterns of discrimination. Conclusion: We propose a set of indicators as a guide for health policy experts seeking to design national cancer plans that are based on a human rights-based approach to health, and for cancer specialists aiming to implement principles of the right to health in their practice. The 54 indicators selected may be used in High-Income Countries, or member states of the OECD who also have signed the International Covenant on Economic, Social and Cultural Rights to monitor progress towards implementation of the right to health for women with breast cancer. [ABSTRACT FROM AUTHOR]

Purpose: Myelin and lymphocyte protein 2 (MAL2) is mainly involved in endocytosis under physiological conditions and mediates the transport of materials across the membranes of cell and organelle. It has been reported that MAL2 is significantly upregulated in diverse cancers. This study aimed to investigate the role of MAL2 in breast cancer (BC). Methods: Bioinformatics analysis and Immunohistochemical assay were applied to detect the correlation between MAL2 expression in breast cancer tissues and the prognosis of breast cancer patients. Functional experiments were carried out to investigate the role of MAL2 in vitro and in vivo. The molecular mechanisms involved in MAL2-induced β-catenin and c-Myc expression and β-catenin/c-Myc-mediated enhancement of BC progression were confirmed by western blot, β-catenin inhibitor and agonist, Co-IP and immunofluorescence colocalization assays. Results: Results from the cancer genome atlas (TCGA) and clinical samples confirmed a significant upregulation of MAL2 in BC tissues than in adjacent non-tumor tissues. High expression of MAL2 was associated with worse prognosis. Functional experiments demonstrated that MAL2 knockdown reduced the migration and invasion associating with EMT, increased the apoptosis of BC cells in vitro and reduced the metastatic capacity in vivo. Mechanistically, MAL2 interacts with β-catenin in BC cells. MAL2 silencing reduced the expression of β-catenin and c-Myc, while the β-catenin agonist SKL2001 partially rescued the downregulation of c-Myc and inhibition of migration and invasion caused by MAL2 knockdown in BC cells. Conclusion: These observations provided evidence that MAL2 acted as a potential tumor promoter by regulating EMT and β-catenin/c-Myc axis, suggesting potential implications for anti-metastatic therapy for BC. [ABSTRACT FROM AUTHOR]

Background: Breast cancer brain metastases (BCBM) are highly heterogenous with widely differing survival. The prognosis of the oligometastatic breast cancer (BC) patients with brain metastases (BM) has not been well studied. We aimed to investigate the prognosis of BCBM patients with limited intracranial and extracranial metastatic lesions. Methods: Four hundred and forty-five BCBM patients treated between 1st January 2008 and 31st December 2018 at our institute were included. Clinical characteristics and treatment information were obtained from patient's medical records. The updated breast Graded Prognostic Assessment (Breast GPA) was calculated. Results: The median OS after diagnosis of BM were 15.9 months. Median OS for patients with GPA 0–1.0, 1.5–2, 2.5–3 and 3.5–4 were 6.9, 14.2, 21.8, 42.6 months respectively. The total number of intracranial and extracranial metastatic lesions, in addition to the Breast GPA, salvage local therapy and systemic therapy (anti-HER2 therapy, chemotherapy and endocrine therapy) were demonstrated to be associated with prognosis. One hundred and thirteen patients (25.4%) had 1–5 total metastatic lesions at BM diagnosis. Patients with 1–5 total metastatic lesions had a significantly longer median OS of 24.3 months compared to those with greater than 5 total metastatic lesions with a median OS of 12.2 months (P < 0.001; multivariate HR 0.55, 95% CI, 0.43–0.72). Among the patients with 1–5 metastatic lesions, median OS for GPA 0–1.0 was 9.8 months, compared to 22.8, 28.8 and 71.0 for GPA 1.5–2.0, 2.5–3.0 and 3.5–4.0 respectively, which is much longer than the corresponding patients with greater than 5 total metastatic lesions, with medium OS of 6.8, 11.6, 18.6 and 42.6 months respectively for GPA 0–1.0, 1.5–2.0, 2.5–3.0 and 3.5–4.0. Conclusions: The patients with 1–5 total metastatic lesions demonstrated better OS. The prognostic value of the Breast GPA and the survival benefit of salvage local therapy and continuation of systemic therapy after BM were confirmed. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is a malignant tumour that seriously threatens women's life and health and exhibits high inter-individual heterogeneity, emphasising the need for more in-depth research on its pathogenesis. While internal 7-methylguanosine (m7G) modifications affect RNA processing and function and are believed to be involved in human diseases, little is currently known about the role of m7G modification in breast cancer. Methods and Results: We elucidated the expression, copy number variation incidence and prognostic value of 24 m7G-related genes (m7GRGs) in breast cancer. Subsequently, based on the expression of these 24 m7GRGs, consensus clustering was used to divide tumour samples from the TCGA-BRCA dataset into four subtypes based on significant differences in their immune cell infiltration and stromal scores. Differentially expressed genes between subtypes were mainly enriched in immune-related pathways such as 'Ribosome', 'TNF signalling pathway' and 'Salmonella infection'. Support vector machines and multivariate Cox regression analysis were applied based on these 24 m7GRGs, and four m7GRGs—AGO2, EIF4E3, DPCS and EIF4E—were identified for constructing the prediction model. An ROC curve indicated that a nomogram model based on the risk model and clinical factors had strong ability to predict the prognosis of breast cancer. The prognoses of patients in the high- and low-TMB groups were significantly different (p = 0.03). Moreover, the four-gene signature was able to predict the response to chemotherapy. Conclusions: In conclusion, we identified four different subtypes of breast cancer with significant differences in the immune microenvironment and pathways. We elucidated prognostic biomarkers associated with breast cancer and constructed a prognostic model involving four m7GRGs. In addition, we predicted the candidate drugs related to breast cancer based on the prognosis model. [ABSTRACT FROM AUTHOR]

Background: Breast cancer brain metastases (BCBM) are the most fatal, with limited survival in all breast cancer distant metastases. These patients are deemed to be incurable. Thus, survival time is their foremost concern. However, there is a lack of accurate prediction models in the clinic. What's more, primary surgery for BCBM patients is still controversial. Methods: The data used for analysis in this study was obtained from the SEER database (2010–2019). We made a COX regression analysis to identify prognostic factors of BCBM patients. Through cross-validation, we constructed XGBoost models to predict survival in patients with BCBM. Meanwhile, a BCBM cohort from our hospital was used to validate our models. We also investigated the prognosis of patients treated with surgery or not, using propensity score matching and K–M survival analysis. Our results were further validated by subgroup COX analysis in patients with different molecular subtypes. Results: The XGBoost models we created had high precision and correctness, and they were the most accurate models to predict the survival of BCBM patients (6-month AUC = 0.824, 1-year AUC = 0.813, 2-year AUC = 0.800 and 3-year survival AUC = 0.803). Moreover, the models still exhibited good performance in an externally independent dataset (6-month: AUC = 0.820; 1-year: AUC = 0.732; 2-year: AUC = 0.795; 3-year: AUC = 0.936). Then we used Shiny-Web tool to make our models be easily used from website. Interestingly, we found that the BCBM patients with an annual income of over USD$70,000 had better BCSS (HR = 0.523, 95%CI 0.273–0.999, P < 0.05) than those with less than USD$40,000. The results showed that in all distant metastasis sites, only lung metastasis was an independent poor prognostic factor for patients with BCBM (OS: HR = 1.606, 95%CI 1.157–2.230, P < 0.01; BCSS: HR = 1.698, 95%CI 1.219–2.365, P < 0.01), while bone, liver, distant lymph nodes and other metastases were not. We also found that surgical treatment significantly improved both OS and BCSS in BCBM patients with the HER2 + molecular subtypes and was beneficial to OS of the HR−/HER2− subtype. In contrast, surgery could not help BCBM patients with HR + /HER2− subtype improve their prognosis (OS: HR = 0.887, 95%CI 0.608–1.293, P = 0.510; BCSS: HR = 0.909, 95%CI 0.604–1.368, P = 0.630). Conclusion: We analyzed the clinical features of BCBM patients and constructed 4 machine-learning prognostic models to predict their survival. Our validation results indicate that these models should be highly reproducible in patients with BCBM. We also identified potential prognostic factors for BCBM patients and suggested that primary surgery might improve the survival of BCBM patients with HER2 + and triple-negative subtypes. [ABSTRACT FROM AUTHOR]

Background: Reproductive tract infections influenced a series of inflammatory processes which involved in the development of breast cancer, while the processes were largely affected by estrogen. The present study aimed to explore the associations of breast cancer risk and prognosis with reproductive tract infections and the modification effects of estrogen exposure. Methods: We collected history of reproductive tract infections, menstruation and reproduction from 1003 cases and 1107 controls and a cohort of 4264 breast cancer patients during 2008–2018 in Guangzhou, China. We used logistic regression model to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for risk; Cox model was applied to estimate the hazard ratios (HRs) and 95% CIs for progression-free survival (PFS) and overall survival (OS). Results: It was found that previous reproductive tract infections were negatively associated with breast cancer risk (OR = 0.80, 95%CI, 0.65–0.98), particularly for patients with more menstrual cycles (OR = 0.74, 95%CI, 0.57–0.96). Patients with previous reproductive tract infections experienced better OS (HR = 0.61; 95% CI, 0.40–0.94) and PFS (HR = 0.84; 95% CI, 0.65–1.09). This protective effect on PFS was only found in patients with more menstrual cycles (HR = 0.52, 95% CI:0.34–0.79, Pinteraction = 0.015). Conclusions: The findings suggested that reproductive tract infections may be protective for the initiation and development of breast cancer, particularly for women with a longer interval of lifetime estrogen exposure. [ABSTRACT FROM AUTHOR]

Purpose: Multiple genome-wide and candidate-gene association studies have been conducted to search for common risk variants of breast cancer. Recent large meta-analyses and consolidating evidence have highlighted the role of the caspase-8 gene in breast cancer pathogenesis. Therefore, this study aimed to identify common variations and haplotypes associated with risk and overall survival of breast cancer with respect to underlying susceptibility variants in the CASP8 gene region in a group of the Iranian population. Methods: In a case-control study with a total of 1008 samples (455 cases and 553 controls), genotyping of 12 candidate polymorphisms, consisting of rs3834129, rs2037815, rs7608692, rs12990906, rs3769821, rs6435074, rs3754934, rs3817578, rs10931936, rs1045485, rs1045487, and rs13113, were performed using PCR-based methods, including ARMS-PCR, AS-PCR, RFLP-PCR, HRM-PCR, and TaqMan-PCR. Results: rs3834129, rs3754934, rs12990906, and rs10931936 were associated with the risk and overall survival of breast cancer. Several haplotypes were also identified an associated with a higher risk of breast cancer, including a three-SNP haplotype rs3817578-rs10931936-rs1045485 [p < 0.001, OR = 1.78(1.32–2.41)]. rs3754934-C allele showed an association with a lower risk of death in all patients [p = 0.022; HR = 0.46(0.23–0.89)] and in the hormone-receptor-positive group [p = 0.038; HR = 0.37(0.14–0.95)], as well as CC genotype in the hormone-receptor-positive group [p = 0.002; HR = 0.09(0.02–0.43)]. Conclusion: The present study suggests a diagnostic and prognostic role of CASP8 gene variations in breast cancer. The risky haplotypes are likely to have one or more underlying breast cancer susceptibility alleles. Understanding the mode of action of these alleles will aid individual-level risk prediction. It also may help identify at-risk patients to provide them with better surveillance. [ABSTRACT FROM AUTHOR]

Background: The kinesin-13 family member 2C (KIF2C) is a versatile protein participating in many biological processes. KIF2C is frequently up-regulated in multiple types of cancer and is associated with cancer development. However, the role of KIF2C in immune cell infiltration of tumor microenvironment and immunotherapy in breast cancer remains unclear. Methods: The expression of KIF2C was analyzed using Tumor Immune Estimation Resource (TIMER) database and further verified by immunohistochemical staining in human breast cancer tissues. The correlation between KIF2C expression and clinical parameters, the impact of KIF2C on clinical prognosis and independent prognostic factors were analyzed by using TCGA database, the Kaplan-Meier plotter, and Univariate and multivariate Cox analyses, respectively. The nomograms were constructed according to independent prognostic factors and validated with C-index, calibration curves, ROC curves, and decision curve analysis. A gene set enrichment analysis (GSEA) was performed to explore the underlying molecular mechanisms of KIF2C. The degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA (ssGSEA). The Tumor mutational burden and Tumor Immune Dysfunction and Rejection (TIDE) were used to analyze immunotherapeutic efficiency. Finally, the KIF2C-related competing endogenous RNA (ceRNA) network was constructed to predict the putative regulatory mechanisms of KIF2C. Results: KIF2C was remarkably up-regulated in 18 different types of cancers, including breast cancer. Kaplan-Meier survival analysis showed that high KIF2C expression was associated with poor overall survival (OS). KIF2C expression was associated with clinical parameters such as age, TMN stage, T status, and molecular subtypes. We identified age, stage, estrogen receptor (ER) and KIF2C expression as OS-related independent prognosis factors for breast cancer. An OS-related nomogram was developed based on these independent prognosis factors and displayed good predicting ability for OS of breast cancer patients. Finally, our results revealed that KIF2C was significantly related to immune cell infiltration, tumor mutational burden, and immunotherapy in patients with breast cancer. Conclusion: KIF2C was overexpressed in breast cancer and was positively correlated with immune cell infiltration and immunotherapy response. Therefore, KIF2C can serve as a potential biomarker for prognosis and immunotherapy in breast cancer. [ABSTRACT FROM AUTHOR]

Objective: To investigate how the negative regulation of CD44st by miR-138-5p affects the invasive ability of breast cancer cell lines and prognosis in postoperative breast cancer patients. Methods: RT-PCR, qRT-PCR, and western blot assays were used to detect the expression of CD44s, CD44v6, and CD44st at both mRNA and protein levels. The expression of miR-138-5p in breast cancer cell lines was also evaluated. The binding ability of miR-138-5p to CD44st was determined via a dual-luciferase assay. The CD44 protein expression in breast cancer tissues was detected using immunohistochemistry. A Transwell assay was used to detect the invasive ability of tumor cells. The correlation between CD44st and miR-138-5p mRNA expression in breast cancer tissues was evaluated using qRT-PCR, and the relationship between clinicopathological features was statistically analyzed. Results: CD44s and CD44v6 were highly expressed in MDAMB-231 cell line, while CD44st was highly expressed in MCF-7/Adr and Skbr-3 cells. None of the CD44 isoforms were expressed in MCF-7 cells. The miR-138-5p was highly expressed in MCF-7 cells, but not in MCF-7/Adr, Skbr-3, and MDAMB-231 cells. The dual-luciferase assay suggested that miR-138-5p could bind to wild-type CD44st 3'-UTR, miR-138-5p overexpression significantly inhibited the expression level of CD44 protein in MCF-7/Adr cells, and miR-138-5p + CD44st (3'-UTR)-treated MCF-7/Adr and Skbr-3 cells were significantly less invasive than those in the control group (P < 0.05). RT-PCR results for 80 postoperative breast cancer patients showed that the mRNA expression rate for CD44st was higher in cancer tissues than in paracancerous tissues, and the expression rate of miR-138-5p was higher in paracancerous tissues than in cancerous tissues (P < 0.01). In cancer tissues, CD44st was negatively correlated with miR-138-5p expression, with correlation coefficient r = -0.76 (Pearson's correlation), coefficient of determination R2 = 0.573, F = 106.89, and P < 0.001. The median overall survival value for patients in the low miR-138-5p expression group was 40.39 months [95% confidence interval (CI): 35.59–45.18 months] and 56.30 months (95% CI: 54.38–58.21 months) for patients in the high-expression group, with a log rank (Mantel-Cox) of 13.120, one degree of freedom, and P < 0.001. Conclusion: In breast cancer cell lines, miR-138-5p negatively regulated expression of CD44st and affected the invasive ability of tumor cells and patient prognosis after breast cancer surgery. [ABSTRACT FROM AUTHOR]

Background: Ropivacaine, a local anesthetic, exhibits anti-tumor effects in various cancer types. However, its specific functions and the molecular mechanisms involved in breast cancer cell stemness remain elusive. Methods: The effects of ropivacaine on breast cancer stemness were investigated by in vitro and in vivo assays (i.e., FACs, MTT assay, mammosphere formation assay, transwell assays, western blot, and xenograft model). RNA-seq, bioinformatics analysis, Western blot, Luciferase reporter assay, and CHIP assay were used to explore the mechanistic roles of ropivacaine subsequently. Results: Our study showed that ropivacaine remarkably suppressed stem cells-like properties of breast cancer cells both in vitro and in vivo. RNA-seq analysis identified GGT1 as the downstream target gene responding to ropivacaine. High GGT1 levels are positively associated with a poor prognosis in breast cancer. Ropivacaine inhibited GGT1 expression by interacting with the catalytic domain of AKT1 directly to impair its kinase activity with resultant inactivation of NF-κB. Interestingly, NF-κB can bind to the promoter region of GGT1. KEGG and GSEA analysis indicated silence of GGT1 inhibited activation of NF-κB signaling pathway. Depletion of GGT1 diminished stem phenotypes of breast cancer cells, indicating the formation of NF-κB /AKT1/GGT1/NF-κB positive feedback loop in the regulation of ropivacaine-repressed stemness in breast cancer cells. Conclusion: Our finding revealed that local anesthetic ropivacaine attenuated breast cancer stemness through AKT1/GGT1/NF-κB signaling pathway, suggesting the potential clinical value of ropivacaine in breast cancer treatment. [ABSTRACT FROM AUTHOR]

Background: Lipid metabolism disorders may be involved in the occurrence and development of breast cancer. This study aimed to investigate the serum lipid changes during neoadjuvant chemotherapy for breast cancer and the effect of dyslipidemia on the prognosis of breast cancer patients. Methods: We collected the data from 312 breast cancer patients who underwent surgery after receiving standard neoadjuvant therapy. χ2 test and T-test were employed to analyze the effect of chemotherapy on the serum lipid metabolism of patients. The effects of dyslipidemia on the disease-free survival (DFS) of patients with breast cancer were analyzed by χ2 test and COX regression analysis. Results: A total of 56 out of 312 patients (17.9%) suffered from relapse. The baseline serum lipid level of the patients was significantly correlated with their age and body mass index (BMI) (p < 0.05). Chemotherapy increased the levels of triglycerides, total cholesterol, and low-density lipoprotein cholesterol, but decreased the level of high-density lipoprotein cholesterol (p < 0.001). Preoperative dyslipidemia was significantly associated with the axillary pCR rate (p < 0.05). COX regression analysis revealed that the full-course serum lipid level (HR = 1.896 [95%CI 1.069–3.360]; p = 0.029), N stage (HR = 4.416 [95%CI 2.348–8.308]; p < 0.001) and the total pCR rate (HR = 4.319 [95%CI 1.029–18.135]; p = 0.046) acted as prognostic factors affecting DFS in breast cancer. The relapse rate in patients with a high level of total cholesterol was higher than that in patients with a high level of triglycerides (61.9% vs 30.0%; p < 0.05). Conclusions: Dyslipidemia deteriorated after chemotherapy. The full-course serum lipid level may thus serve as a blood marker for predicting breast cancer prognosis. Serum lipids should therefore be closely monitored in breast cancer patients throughout the treatment course, and patients with dyslipidemia should be treated in a timely manner. [ABSTRACT FROM AUTHOR]

Current clinical tools for breast cancer (BC) diagnosis are insufficient but liquid biopsy of different bodily fluids has recently emerged as a minimally invasive strategy that provides a real-time snapshot of tumour biomarkers for early diagnosis, active surveillance of progression, and post-treatment recurrence. Extracellular vesicles (EVs) are nano-sized membranous structures 50–1000 nm in diameter that are released by cells into biological fluids. EVs contain proteins, nucleic acids, and lipids which play pivotal roles in tumourigenesis and metastasis through cell-to-cell communication. Proteins and miRNAs from small EVs (sEV), which range in size from 50–150 nm, are being investigated as a potential source for novel BC biomarkers using mass spectrometry-based proteomics and next-generation sequencing. This review covers recent developments in sEV isolation and single sEV analysis technologies and summarises the sEV protein and miRNA biomarkers identified for BC diagnosis, prognosis, and chemoresistance. The limitations of current sEV biomarker research are discussed along with future perspective applications. [ABSTRACT FROM AUTHOR]

Background: The tumor microenvironment (TME) in breast cancer plays a vital role in occurrence, development, and therapeutic responses. However, immune and stroma constituents in the TME are major obstacles to understanding and treating breast cancer. We evaluated the significance of TME-related genes in breast cancer. Methods: Invasive breast cancer (BRCA) samples were retrieved from the TCGA and GEO databases. Stroma and immune scores of samples as well as the proportion of tumor infiltrating immune cells (TICs) were calculated using the ESTIMATE and CIBERSORT algorithms. TME-related differentially expressed genes (DEGs) were analyzed by a protein interaction (PPI) network and univariate Cox regression to determine CD1C as a hub gene. Subsequently, the prognostic value of CD1C, its response to immunotherapy, and its mechanism in the TME were further studied. Results: In BRCA, DEGs were determined to identify CD1C as a hub gene. The expression level of CD1C in BRCA patients was verified based on the TCGA database, polymerase chain reaction (PCR) results, and western blot analysis. Immunohistochemical staining (IHC) results revealed a correlation between prognosis, clinical features, and CD1C expression in BRCA. Enrichment analysis of GSEA and GSVA showed that CD1C participates in immune-associated signaling pathways. CIBERSORT showed that CD1C levels were associated with tumor immune infiltrating cells (TILs), such as different kinds of T cells. Gene co-expression analysis showed that CD1C and the majority of immune-associated genes were co-expressed in BRCA. In renal cell carcinoma, patients with a high expression of CD1C had a better immunotherapy effect. Conclusion: CD1C is an important part of the TME and participates in immune activity regulation in breast tumors. CD1C is expected to become a prognostic marker and a new treatment target for breast cancer. [ABSTRACT FROM AUTHOR]

Purpose: Despite the poor prognosis of triple-negative breast cancer (TNBC), it has been demonstrated that neoadjuvant immunotherapy in combination with chemotherapy can improve the pathologic complete response (pCR) rate and/or long-term outcome of TNBC. However, there have been no real-world studies reporting on the effectiveness of neoadjuvant checkpoint inhibitors in early TNBC. Methods: Between November 2019 and December 2021, 63 early TNBC patients treated with anti-PD-1 antibodies (pembrolizumab or camrelizumab) or anti-PD-L1 antibody (atezolizumab) in combination with chemotherapy at seven institutions were included. PCR1 defined as ypT0/Tis and ypN0 was the primary endpoint. Secondary endpoints included pCR2 defined as ypT0/Tis, overall response rate (ORR), disease-free survival (DFS), drug-related adverse events (AEs) and biomarkers. Results: Among the patients in the current study, 34.9% of patients were able to achieve pCR1, and 47.6% of patients had achieved pCR2. The ORR was 82.5%. 33 patients with non-pCR2 tumors were found to have a median DFS of 20.7 months (95% CI 16.3 months-not reached). The DFS of patients with pCR2 and non-pCR2 after neoadjuvant therapy was significantly different (HR = 0.28, 95% CI 0.10–0.79; P = 0.038). The most common AEs were nausea (63.4%), fatigue (42.7%), leucopenia (30.0%) and elevated transaminase (11.7%). Conclusion: It is possible to achieve a meaningful pCR rate and DFS by combining neoadjuvant checkpoint blockade with chemotherapy in patients with high-risk TNBC. Compared to clinical trials, however, there was a slightly lower pCR rate in this multicentered real-world study. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common malignancy and the second leading cause of cancer-related death in women. Recent studies have indicated that aberrant activation of Rho GTPases relates to the malignant properties of breast cancer cells. As the guanine nucleotide exchange factor of Rho GTPases, the role of ECT2 (epithelial cell transforming 2) in breast cancer is still unclear. Tissue microarrays and multiple public databases were utilized to investigate the relationship between ECT2 level and clinical-pathological features of breast cancer patients. Kaplan Meier-plotter online tool and tissue microarray with survival information were used to investigate the predictive value for breast cancer. Here, we found increased ECT2 level was highly associated with advanced TNM stage, poor differentiation, and loss of hormone receptors of breast cancer. Gene expression profile showed that ECT2 level was closely correlated to cell-proliferation-associated pathways. Integration analysis using public databases and tissue microarray indicated that high ECT2 was an adverse prognostic factor for breast cancer patients. We believe the ECT2 level might be a valuable complement for commercially available predictors such as the 21 genes test. Furthermore, ECT2 would be a novel target for drug development for breast cancer. [ABSTRACT FROM AUTHOR]

Background: Loss of G2-specific E3-like (G2E3) protein sensitizes tumor cells to chemotherapy. However, the role of G2E3 in breast cancer development and patient's prognosis is unclear. Here, we explored the expression, prognostic significance, and regulatory pathway of G2E3 in breast cancer. Methods: TCGA and UALCAN database were utilized to explore G2E3 expression in breast cancer and normal tissues and its expression in breast cancer based on clinicopathological characteristics, respectively. The Kaplan–Meier plotter database was utilized to determine the effect of G2E3 on the prognosis of breast cancer patients. RT-PCR was utilized to validate the G2E3 expression in cancerous and normal breast tissues. Immunohistochemistry analysis was utilized to validate the prognostic effect of G2E3 expression in breast cancer patients and the relationship between G2E3 expression and lymphocyte infiltration levels. Receiver operating characteristic (ROC) curves were also generated to validate the diagnostic value of G2E3 expression in recurrence/distant organ metastasis and death. The STRING database, DAVID database, and Sanger-box tools were utilized to perform GO functional, KEGG pathway enrichment, and GSEA analysis. The TISIDB database was utilized to determine the relationship between G2E3 expression and tumor immunity. Finally, CTD database was utilized to screen for potential therapeutic compounds that could reduce the G2E3 mRNA expression. Results: TCGA data presented that G2E3 expression was higher in breast cancer tissues than in normal breast tissues. This result was further validated by RT-PCR (P = 0.003). The Kaplan–Meier plotter database suggested that patients with high G2E3 mRNA expression had significantly shorter RFS and OS than patients with low G2E3 mRNA expression. Immunohistochemistry analysis of 156 breast cancer clinical specimens also validated patients with G2E3-positive expression had a significantly shorter DFS and OS than patients with G2E3-negative expression. Thus, G2E3 expression was an independent prognostic predictor of DFS and OS. The G2E3-positive expression also has a high diagnostic value for recurrence/distant organ metastasis and death. GSEA analysis revealed that G2E3 might be enriched in the E2F, PI3K/AKT/mTOR signaling, DNA repair pathways, and other cancer-related signaling pathways. The TISIDB database showed that G2E3 expression was significantly negatively associated with lymphocyte infiltration. This result was further validated in clinical breast cancer samples (P = 0.048; R = −0.158). Using the CTD database, we found that (+)-JQ1 compound, 1,2-dimethylhydrazine, and other compounds may decrease the G2E3 mRNA expression. These compounds could serve as potential therapeutic compounds for the clinical treatment of breast cancer. Conclusions: G2E3 expression was higher in breast cancer tissues than in normal tissues. G2E3-positive expression was related to a worse survival outcome in patients with breast cancer. Genes co-expressed with G2E3 may be enriched in the breast cancer-related signaling pathways. The G2E3 expression was significantly negatively associated with lymphocyte infiltration. G2E3 may serve as a novel prognostic biomarker and therapeutic target for breast cancer. [ABSTRACT FROM AUTHOR]

Obesity plays an important role in the development and progression of breast cancer via various oncogenic pathways. However, the biological mechanisms underlying this relationship are not fully understood. Moreover, it is unclear whether obesity-related and further associated biomarkers could be suitable targets for lifestyle interventions. This systematic review was conducted to examine relationships between obesity-related blood parameters and prognosis for breast cancer survivors enrolled in lifestyle intervention studies. A systematic, computerized literature search was conducted from inception through August 26th, 2020 in PubMed, EMBASE, and CENTRAL. The focus was on observational data from randomized controlled lifestyle intervention trials investigating associations between selected baseline biomarkers, measured in remission, and breast cancer recurrence, breast cancer mortality and/or all-cause mortality. Four studies with data from 5234 women met the inclusion criteria.Studies herein provide moderate evidence that bioavailable or serum testosterone may be positively linked to breast cancer recurrence and inversely linked to disease-free survival. Limited evidence suggests no associations with circulating estradiol or insulin levels on prognosis outcomes, whereas HDL cholesterol was inversely associated with breast cancer recurrence. For some other biomarkers, such as growth factors, adipokines, and CRP, the evidence for associations with disease prognosis was too weak to draw conclusions.Overall, despite potential candidates, there is insufficient evidence to confirm or refute that obesity-related biomarkers and sex hormones have a prognostic value for breast cancer survival. More longitudinal studies in breast cancer survivors to examine the clinical utility of obesity-related biomarkers are needed. [ABSTRACT FROM AUTHOR]

Background: Cellular experiments revealed that a decreased histone H3 lysine 9 trimethylation (H3K9me3) level was associated with the upregulation of oncogenes in breast cancer cells. Moreover, the role of H3K9me3 in breast cancer was closely associated with estrogen receptor (ER) status. Therefore, we aimed to examine the prognostic value of H3K9me3 on breast cancer by ER status. The level of H3K9me3 in tumors were evaluated with tissue microarrays by immunohistochemistry for 917 women diagnosed with primary invasive breast cancer. Hazard ratios (HRs) and their 95% confidence intervals (CIs) for overall survival (OS) and progression-free survival (PFS) were estimated using Cox regression models. Interaction between H3K9me3 and ER on the prognosis was assessed on multiplicative scale. Results: The level of H3K9me3 in tumor tissues was lower than that in adjacent tissues. The high level of H3K9me3 was associated with a better OS (HR = 0.43, 95% CI: 0.21–0.86) and PFS (HR = 0.49, 95% CI: 0.29–0.81) among only ER-positive but not ER-negative tumors. Moreover, the interaction between the level of H3K9me3 and ER status (negative and positive) on the prognosis was significant (Pinteraction = 0.011 for OS; Pinteraction = 0.022 for PFS). Furthermore, the ER-positive tumors were stratified by ER-low and ER-high positive tumors, and the prognostic role of H3K9me3 was significant among only ER-high positive patients (HR = 0.34, 95% CI: 0.13–0.85 for OS; HR = 0.47, 95% CI: 0.26–0.86 for PFS). Conclusions: Our study showed that the prognostic value of H3K9me3 on breast cancer was related to ER status and expression level, and the high level of H3K9me3 was associated with a better prognosis among ER-positive tumors, particularly ER-high positive tumors. [ABSTRACT FROM AUTHOR]

Background: As the second cause of cancer death in women, breast cancer has become a worldwide priority. Previous studies based on tumour cell lines demonstrated that arachidonic acid (AA) and its metabolites promote cancer development. However, recent studies based on the tumour microenvironment revealed the antitumour effect of AA metabolism. Therefore, it is essential to reevaluate and elucidate the effect of AA metabolism on breast cancer. Methods: Raw data were obtained from The Cancer Genome Atlas (TCGA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) databases. The AA metabolic score of each sample, enrichment of differentially expressed genes (DEGs) and immune infiltration were analysed by bioinformatics. Cox regression and least absolute shrinkage and selection operator regression were performed to establish an AA metabolism prognostic signature. An AA metabolism related nomogram for predicting the survival probability of patients was built. Result: AA metabolism was related to good prognosis in the TCGA-BRCA and METABRIC cohort. DEGs enrichment suggested that the upregulated DEGs of the high AA metabolism group were significantly enriched in immune-related pathways. The high AA metabolism group was infiltrated with more CD8+ T cells and activated NK cells. An AA metabolic signature (SPINK8, KLRB1, APOD and PIGR) was constructed for breast cancer prognosis. Conclusion: The study indicated that a high level of AA metabolism may be a biomarker for good prognosis in breast cancer, providing a possible explanation for the discouraging effect of cyclooxygenase inhibitors in cancer therapy. Moreover, a novel AA metabolic prognostic signature was constructed in the study, providing a novel strategy for breast cancer. [ABSTRACT FROM AUTHOR]

Background: PDZ-binding kinase/T-lymphokine-activated killer cell-derived protein kinase (PBK/TOPK) is a potential prognostic indicator for patients with breast cancer. The objective of the present study was to explore the relationship between PBK/TOPK expression and clinicopathological indicators as well as the survival of patients with breast cancer. Methods: Immunohistochemical staining was used to detect the expression of PBK/TOPK in 202 cases of breast cancer tissues. The relationship between PBK/TOPK and clinicopathological parameters was evaluated using Spearman's rank-order correlation. The difference in PBK/TOPK expression among different molecular types was analyzed with the chi-square test. Kaplan-Meier analysis was used to create a survival curve and the log rank test was used to analyze the overall survival (OS) and disease-free survival (DFS). Prognostic correlation was assessed using univariate and multivariate Cox regression analyses. Results: Among 202 breast cancer samples, PBK/TOPK was expressed ("+" and "++") in 182 samples (90.1%). In addition, the histological grade, TNM stages, lymph node metastasis, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and Ki-67 were positively associated with PBK/TOPK expression. With regard to the molecular type, the expression of PBK/TOPK is different. The expression level of PBK/TOPK was negatively correlated with both the OS and DFS of breast cancer patients. The difference in the above results is meaningful (P < 0.05). Conclusions: PBK/TOPK is overexpressed in breast cancer, and the expression is closely related to the clinicopathological characteristics of the disease. Breast cancer patients with high expression of PBK/TOPK have a poor prognosis. Therefore, healthcare providers can optimize breast cancer management using this indicator. [ABSTRACT FROM AUTHOR]

Background: Dysregulation of ACTA2-AS1 and miR-532-5p and their functions in various cancers have been widely reported. Their potential of serving as biomarkers in triple-negative breast cancer (TNBC) remains unknown. This study aimed to evaluate the function of ACTA2-AS1 and miR-532-5p and their potential of serving as biomarkers in TNBC. Results: The TNBC tissues were collected from 119 patients, where the reduced level of ACTA2-AS1 and increased level of miR-532-5p were observed by PCR and showed a significantly negative correlation (P < 0.001). Both ACTA2-AS1 and miR-532-5p were closely associated with the malignant development and poor prognosis of TNBC patients. Moreover, in TNBC cell, overexpressing ACTA2-AS1 was found to suppress cell proliferation and metastasis, which was reversed by the upregulation of miR-532-5p. Conclusions: ACTA2-AS1 and miR-532-5p could act as biomarkers of TNBC predicting the progression and prognosis of patients. ACTA2-AS1 served as a tumor suppressor of TNBC which was mediated by miR-532-5p. [ABSTRACT FROM AUTHOR]

Background: There is a growing body of evidence from biological experiments suggesting that microRNAs (miRNAs) play a significant regulatory role in both diverse cellular activities and pathological processes. Exploring miRNA-disease associations not only can decipher pathogenic mechanisms but also provide treatment solutions for diseases. As it is inefficient to identify undiscovered relationships between diseases and miRNAs using biotechnology, an explosion of computational methods have been advanced. However, the prediction accuracy of existing models is hampered by the sparsity of known association network and single-category feature, which is hard to model the complicated relationships between diseases and miRNAs. Results: In this study, we advance a new computational framework (GATMDA) to discover unknown miRNA-disease associations based on graph attention network with multi-source information, which effectively fuses linear and non-linear features. In our method, the linear features of diseases and miRNAs are constructed by disease-lncRNA correlation profiles and miRNA-lncRNA correlation profiles, respectively. Then, the graph attention network is employed to extract the non-linear features of diseases and miRNAs by aggregating information of each neighbor with different weights. Finally, the random forest algorithm is applied to infer the disease-miRNA correlation pairs through fusing linear and non-linear features of diseases and miRNAs. As a result, GATMDA achieves impressive performance: an average AUC of 0.9566 with five-fold cross validation, which is superior to other previous models. In addition, case studies conducted on breast cancer, colon cancer and lymphoma indicate that 50, 50 and 48 out of the top fifty prioritized candidates are verified by biological experiments. Conclusions: The extensive experimental results justify the accuracy and utility of GATMDA and we could anticipate that it may regard as a utility tool for identifying unobserved disease-miRNA relationships. [ABSTRACT FROM AUTHOR]

Background: Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer with poor prognosis and limited treatment. As a major component of the tumor microenvironment, tumor-associated macrophages (TAMs) play an important role in facilitating the aggressive behavior of TNBC. This study aimed to explore the novel mechanism of TAMs in the regulation of epithelial–mesenchymal transition (EMT) and cancer stem cell (CSC) properties in TNBC. Methods: Expression of the M2-like macrophage marker CD163 was evaluated by immunohistochemistry in human breast cancer tissues. The phenotype of M2 macrophages polarized from Tohoku-Hospital-Pediatrics-1 (THP1) cells was verified by flow cytometry. Transwell assays, wound healing assays, western blotting, flow cytometry, ELISA, quantitative polymerase chain reaction (qPCR), luciferase reporter gene assays, and immunofluorescence assays were conducted to investigate the mechanism by which TAMs regulate EMT and CSC properties in BT549 and HCC1937 cells. Results: Clinically, we observed a high infiltration of M2-like tumor-associated macrophages in TNBC tissues and confirmed that TAMs were associated with unfavorable prognosis in TNBC patients. Moreover, we found that conditioned medium from M2 macrophages (M2-CM) markedly promoted EMT and CSC properties in BT549 and HCC1937 cells. Mechanistically, we demonstrated that chemokine (C–C motif) ligand 2 (CCL2) secretion by TAMs activated Akt signaling, which in turn increased the expression and nuclear localization of β-catenin. Furthermore, β-catenin knockdown reversed TAM-induced EMT and CSC properties. Conclusions: This study provides a novel mechanism by which TAMs promote EMT and enhance CSC properties in TNBC via activation of CCL2/AKT/β-catenin signaling, which may offer new strategies for the diagnosis and treatment of TNBC. 5r3JY8ktnQBgogUH1DXeQZ Video Abstract [ABSTRACT FROM AUTHOR]

Background: Data transformations are commonly used in bioinformatics data processing in the context of data projection and clustering. The most used Euclidean metric is not scale invariant and therefore occasionally inappropriate for complex, e.g., multimodal distributed variables and may negatively affect the results of cluster analysis. Specifically, the squaring function in the definition of the Euclidean distance as the square root of the sum of squared differences between data points has the consequence that the value 1 implicitly defines a limit for distances within clusters versus distances between (inter-) clusters. Methods: The Euclidean distances within a standard normal distribution (N(0,1)) follow a N(0, 2 ) distribution. The EDO-transformation of a variable X is proposed as E D O = X / (2 · s) following modeling of the standard deviation s by a mixture of Gaussians and selecting the dominant modes via item categorization. The method was compared in artificial and biomedical datasets with clustering of untransformed data, z-transformed data, and the recently proposed pooled variable scaling. Results: A simulation study and applications to known real data examples showed that the proposed EDO scaling method is generally useful. The clustering results in terms of cluster accuracy, adjusted Rand index and Dunn's index outperformed the classical alternatives. Finally, the EDO transformation was applied to cluster a high-dimensional genomic dataset consisting of gene expression data for multiple samples of breast cancer tissues, and the proposed approach gave better results than classical methods and was compared with pooled variable scaling. Conclusions: For multivariate procedures of data analysis, it is proposed to use the EDO transformation as a better alternative to the established z-standardization, especially for nontrivially distributed data. The "EDOtrans" R package is available at https://cran.r-project.org/package=EDOtrans. [ABSTRACT FROM AUTHOR]

Background: Data transformations are commonly used in bioinformatics data processing in the context of data projection and clustering. The most used Euclidean metric is not scale invariant and therefore occasionally inappropriate for complex, e.g., multimodal distributed variables and may negatively affect the results of cluster analysis. Specifically, the squaring function in the definition of the Euclidean distance as the square root of the sum of squared differences between data points has the consequence that the value 1 implicitly defines a limit for distances within clusters versus distances between (inter-) clusters. Methods: The Euclidean distances within a standard normal distribution (N(0,1)) follow a N(0, 2 ) distribution. The EDO-transformation of a variable X is proposed as E D O = X / (2 · s) following modeling of the standard deviation s by a mixture of Gaussians and selecting the dominant modes via item categorization. The method was compared in artificial and biomedical datasets with clustering of untransformed data, z-transformed data, and the recently proposed pooled variable scaling. Results: A simulation study and applications to known real data examples showed that the proposed EDO scaling method is generally useful. The clustering results in terms of cluster accuracy, adjusted Rand index and Dunn's index outperformed the classical alternatives. Finally, the EDO transformation was applied to cluster a high-dimensional genomic dataset consisting of gene expression data for multiple samples of breast cancer tissues, and the proposed approach gave better results than classical methods and was compared with pooled variable scaling. Conclusions: For multivariate procedures of data analysis, it is proposed to use the EDO transformation as a better alternative to the established z-standardization, especially for nontrivially distributed data. The "EDOtrans" R package is available at https://cran.r-project.org/package=EDOtrans. [ABSTRACT FROM AUTHOR]

Much higher risk of cancer has been found in diabetes patients. Insulin receptor (IR) and insulin-like growth factor 1 receptor (IGF1R) have been extensively studied in both breast cancer and diabetes therapies. Interestingly, a recent study proposed that IR/IGF1R ratio is an important factor for breast cancer prognosis. Women with higher IR/IGF1R ratio showed poor breast cancer prognosis as well as hyperinsulinemia. Here, we propose a novel mechanism that oncogenic protein TRIP-Br1 renders breast cancer cells and insulin deficient mice to have higher IR/IGF1R ratio by positively and negatively regulating IR and IGF1R expression at the protein level, respectively. TRIP-Br1 repressed IR degradation by suppressing its ubiquitination. Meanwhile, TRIP-Br1 directly interacts with both IGF1R and NEDD4-1 E3 ubiquitin ligase, in which TRIP-Br1/NEDD4-1 degrades IGF1R via ubiquitin/proteasome system. TRIP-Br1-mediated higher IR/IGF1R ratio enhanced the proliferation and survival of breast cancer cells. In conclusion, current study may provide an important information in the regulatory mechanism of how breast cancer cells have acquired higher IR/IGF1R ratio. [ABSTRACT FROM AUTHOR]