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Breast cancer is the most common cause of female morbidity and death worldwide. Compared with other cancers, early detection of breast cancer is more helpful to improve the prognosis of patients. In order to achieve early diagnosis and treatment, clinical treatment requires rapid and accurate diagnosis. Therefore, the development of an automatic detection system for breast cancer suitable for patient imaging is of great significance for assisting clinical treatment. Accurate classification of pathological images plays a key role in computer-aided medical diagnosis and prognosis. However, in the automatic recognition and classification methods of breast cancer pathological images, the scale information, the loss of image information caused by insufficient feature fusion, and the enormous structure of the model may lead to inaccurate or inefficient classification. To minimize the impact, we proposed a lightweight PCSAM-ResCBAM model based on two-stage convolutional neural network. The model included a Parallel Convolution Scale Attention Module network (PCSAM-Net) and a Residual Convolutional Block Attention Module network (ResCBAM-Net). The first-level convolutional network was built through a 4-layer PCSAM module to achieve prediction and classification of patches extracted from images. To optimize the network's ability to represent global features of images, we proposed a tiled feature fusion method to fuse patch features from the same image, and proposed a residual convolutional attention module. Based on the above, the second-level convolutional network was constructed to achieve predictive classification of images. We evaluated the performance of our proposed model on the ICIAR2018 dataset and the BreakHis dataset, respectively. Furthermore, through model ablation studies, we found that scale attention and dilated convolution play an important role in improving model performance. Our proposed model outperforms the existing state-of-the-art models on 200 × and 400 × magnification datasets with a maximum accuracy of 98.74 %.

In patients with esophageal squamous cell carcinoma (ESCC), the incidence and mortality rate of ESCC in our country are also higher than those in the rest of the world. Despite advances in the treatment department method, patient survival rates have not obviously improved, which often leads to treatment obstruction and cancer repeat. ESCC has special cells called cancer stem-like cells (CSLCs) with self-renewal and differentiation ability, which reflect the development process and prognosis of cancer. In this review, we evaluated CSLCs, which are identified from the expression of cell surface markers in ESCC. By inciting EMTs to participate in tumor migration and invasion, stem cells promote tumor redifferentiation. Some factors can inhibit the migration and invasion of ESCC via the EMT-related pathway. We here summarize the research progress on the surface markers of CSLCs, EMT pathway, and the microenvironment in the process of tumor growth. Thus, these data may be more valuable for clinical applications.

ObjectivesThis study aimed to establish and validate a prognostic model based on magnetic resonance imaging and clinical features to predict the survival time of patients with glioblastoma multiforme (GBM).MethodsIn this study, a convolutional denoising autoencoder (DAE) network combined with the loss function of the Cox proportional hazard regression model was used to extract features for survival prediction. In addition, the Kaplan–Meier curve, the Schoenfeld residual analysis, the time-dependent receiver operating characteristic curve, the nomogram, and the calibration curve were performed to assess the survival prediction ability.ResultsThe concordance index (C-index) of the survival prediction model, which combines the DAE and the Cox proportional hazard regression model, reached 0.78 in the training set, 0.75 in the validation set, and 0.74 in the test set. Patients were divided into high- and low-risk groups based on the median prognostic index (PI). Kaplan–Meier curve was used for survival analysis (p = < 2e-16 in the training set, p = 3e-04 in the validation set, and p = 0.007 in the test set), which showed that the survival probability of different groups was significantly different, and the PI of the network played an influential role in the prediction of survival probability. In the residual verification of the PI, the fitting curve of the scatter plot was roughly parallel to the x-axis, and the p-value of the test was 0.11, proving that the PI and survival time were independent of each other and the survival prediction ability of the PI was less affected than survival time. The areas under the curve of the training set were 0.843, 0.871, 0.903, and 0.941; those of the validation set were 0.687, 0.895, 1.000, and 0.967; and those of the test set were 0.757, 0.852, 0.683, and 0.898.ConclusionThe survival prediction model, which combines the DAE and the Cox proportional hazard regression model, can effectively predict the prognosis of patients with GBM.

To construct a prognostic model for preoperative prediction on computed tomography (CT) images of esophageal squamous cell carcinoma (ESCC), we created radiomics signature with high throughput radiomics features extracted from CT images of 272 patients (204 in training and 68 in validation cohort). Multivariable logistic regression was applied to build the radiomics signature and the predictive nomogram model, which was composed of radiomics signature, traditional TNM stage, and clinical features. A total of 21 radiomics features were selected from 954 to build a radiomics signature which was significantly associated with progression-free survival (p < 0.001). The area under the curve of performance was 0.878 (95% CI: 0.831–0.924) for the training cohort and 0.857 (95% CI: 0.767–0.947) for the validation cohort. The radscore of signatures' combination showed significant discrimination for survival status. Radiomics nomogram combined radscore with TNM staging and showed considerable improvement over TNM staging alone in the training cohort (C-index, 0.770 vs. 0.603; p < 0.05), and it is the same with clinical data (C-index, 0.792 vs. 0.680; p < 0.05), which were confirmed in the validation cohort. Decision curve analysis showed that the model would receive a benefit when the threshold probability was between 0 and 0.9. Collectively, multiparametric CT-based radiomics nomograms provided improved prognostic ability in ESCC.

Abstract The expression level of SLC35A3 is associated with the prognosis of many cancers, but its role in colorectal cancer (CRC) is unclear. The purpose of our study was to elucidate the role of SLC35A3 in CRC. The expression levels of SLC35A3 in CRC were evaluated through tumor immune resource assessment (TIMER), The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), Human Protein Atlas (HPA), qRT-PCR, and immunohistochemical evaluation. TCGA, GEO, and ICGC databases were used to analyze the diagnostic and prognostic value of SLC35A3 in CRC. A overall survival (OS) model was constructed and validated based on the expression level of SLC35A3 and multivariable analysis results. The cBioPortal tool was used to analyze SLC35A3 mutation in CRC. The UALCAN tool was used to analyze the promoter methylation level of SLC35A3 in colorectal cancer. In addition, the role of SLC35A3 in CRC was determined through GO analysis, KEGG analysis, gene set enrichment analysis (GSEA), immune infiltration analysis, and immune checkpoint correlation analysis. In vitro experiments validated the function of SLC35A3 in colorectal cancer cells. Compared with adjacent normal tissues and colonic epithelial cells, the expression of SLC35A3 was decreased in CRC tissues and CRC cell lines. Low expression of SLC35A3 was associated with N stage, pathological stage, and lymphatic infiltration, and it was unfavorable for OS, disease-specific survival (DSS), recurrence-free survival (RFS), and post-progression survival (PPS). According to the Receiver Operating Characteristic (ROC) analysis, SLC35A3 is a potential important diagnostic biomarker for CRC patients. The nomograph based on the expression level of SLC35A3 showed a better predictive model for OS than single prognostic factors and TNM staging. SLC35A3 has multiple types of mutations in CRC, and its promoter methylation level is significantly decreased. GO and KEGG analysis indicated that SLC35A3 may be involved in transmembrane transport protein activity, cell communication, and interaction with neurotransmitter receptors. GSEA revealed that SLC35A3 may be involved in energy metabolism, DNA repair, and cancer pathways. In addition, SLC35A3 was closely related to immune cell infiltration and immune checkpoint expression. Immunohistochemistry confirmed the positive correlation between SLC35A3 and helper T cell infiltration. In vitro experiments showed that overexpression of SLC35A3 inhibited the proliferation and invasion capability of colorectal cancer cells and promoted apoptosis. The results of this study indicate that decreased expression of SLC35A3 is closely associated with poor prognosis and immune cell infiltration in colorectal cancer, and it can serve as a promising independent prognostic biomarker and potential therapeutic target.

The Mediterranean diet (MD) had a protective effect on cancer prevention. However, there has not been a systematic evaluation of the diet's comprehensive effects on metabolic indices and quality of life in cancer patients. The present meta-analysis summarizes for the first time the effects of MD on metabolic indices and quality of life in cancer patients. We conducted a randomized controlled trial (RCT) search on PubMed, Embase, and Cochrane Library, which recruited cancer patients receiving the MD intervention. In this meta-analysis, nine RCTs were included. Two authors independently extracted the data and checked the accuracy of the results. Compared with the control group, MD reduced the body weight (p

ObjectiveBazhen Decoction (BZD) is a common adjuvant therapy drug for colorectal cancer (CRC), although its anti-tumor mechanism is unknown. This study aims to explore the core components, key targets, and potential mechanisms of BZD treatment for CRC.MethodsThe Traditional Chinese Medicine Systems Pharmacology (TCMSP) was employed to acquire the BZD’s active ingredient and targets. Meanwhile, the Drugbank, Therapeutic Target Database (TTD), DisGeNET, and GeneCards databases were used to retrieve pertinent targets for CRC. The Venn plot was used to obtain intersection targets. Cytoscape software was used to construct an “herb-ingredient-target” network and identify core targets. GO and KEGG pathway enrichment analyses were conducted using R language software. Molecular docking of key ingredients and core targets of drugs was accomplished using PyMol and Autodock Vina software. Cell and animal research confirmed Bazhen Decoction efficacy and mechanism in treating colorectal cancer.ResultsBZD comprises 173 effective active ingredients. Using four databases, 761 targets related to CRC were identified. The intersection of BZD and CRC yielded 98 targets, which were utilized to construct the “herb-ingredient-target” network. The four key effector components with the most targets were quercetin, kaempferol, licochalcone A, and naringenin. Protein-protein interaction (PPI) analysis revealed that the core targets of BZD in treating CRC were AKT1, MYC, CASP3, ESR1, EGFR, HIF-1A, VEGFR, JUN, INS, and STAT3. The findings from molecular docking suggest that the core ingredient exhibits favorable binding potential with the core target. Furthermore, the GO and KEGG enrichment analysis demonstrates that BZD can modulate multiple signaling pathways related to CRC, like the T cell receptor, PI3K-Akt, apoptosis, P53, and VEGF signaling pathway. In vitro, studies have shown that BZD dose-dependently inhibits colon cancer cell growth and invasion and promotes apoptosis. Animal experiments have shown that BZD treatment can reverse abnormal expression of PI3K, AKT, MYC, EGFR, HIF-1A, VEGFR, JUN, STAT3, CASP3, and TP53 genes. BZD also increases the ratio of CD4+ T cells to CD8+ T cells in the spleen and tumor tissues, boosting IFN-γ expression, essential for anti-tumor immunity. Furthermore, BZD has the potential to downregulate the PD-1 expression on T cell surfaces, indicating its ability to effectively restore T cell function by inhibiting immune checkpoints. The results of HE staining suggest that BZD exhibits favorable safety profiles.ConclusionBZD treats CRC through multiple components, targets, and metabolic pathways. BZD can reverse the abnormal expression of genes such as PI3K, AKT, MYC, EGFR, HIF-1A, VEGFR, JUN, STAT3, CASP3, and TP53, and suppresses the progression of colorectal cancer by regulating signaling pathways such as PI3K-AKT, P53, and VEGF. Furthermore, BZD can increase the number of T cells and promote T cell activation in tumor-bearing mice, enhancing the immune function against colorectal cancer. Among them, quercetin, kaempferol, licochalcone A, naringenin, and formaronetin are more highly predictive components related to the T cell activation in colorectal cancer mice. This study is of great significance for the development of novel anti-cancer drugs. It highlights the importance of network pharmacology-based approaches in studying complex traditional Chinese medicine formulations.

Abstract To describe the preoperative nutritional status of Low-grade Appendiceal Mucinous Neoplasms (LAMNs) and identify prognostic factors for survival. Medical records from 165 patients with LAMNs who attended the Aerospace Center Hospital, Beijing, China between January 2017, and December 2018 were retrospectively reviewed. Survival analysis was performed with the Kaplan–Meier method, the log-rank test, and a Cox proportional hazards model. Among 165 patients, 59 (36%) were male and 106 (64%) were female. Patient’s median age was 58 years (range 20 to 78 years). Univariate analysis indicated that gender, weight loss, prior surgical score (PSS), red blood cell, albumin, peritoneal cancer index (PCI), completeness of cytoreduction (CCR), and hyperthermic intraperitoneal chemotherapy (HIPEC) were related to prognosis. Multivariate analysis showed that PSS, CCR and HIPEC were independent predictors of prognosis. The preoperative nutritional status of patients plays an important role in predicting prognosis. Patients can benefit from a complete cytoreductive surgery (CCRS) and HIPEC in an experienced institution for the first medical treatment.

BackgroundPseudomyxoma peritonei (PMP) is a rare disease, with the overall survival (OS) influenced by many factors. To date, no ascites characteristics have been reported to predict OS of patients with PMP. The present study therefore aims to describe the ascites characteristics for PMP and identify prognostic factors for survival.MethodsBetween June 2010 and June 2020, 473 PMP patients who underwent cytoreductive surgery and hyperthermic intraperitoneal chemotherapy were included in a retrospective study. Survival analysis was performed with the Kaplan–Meier method by the log-rank test and a Cox proportional hazards model. Associations between categorical variables were analyzed using the chi-squared test.ResultsAmong all included patients, 61% were women. The median OS was 47 months (range, 4–124 months) at the last follow-up in December 2020. Ascites characteristics can be divided into light blood ascites, “Jelly” mucus ascites, and faint yellow and clear ascites. Multivariate Cox analysis showed that the degree of radical surgery, ascites characteristics, and pathological grade were independently associated with OS in PMP patients. The chi-squared test documented that faint yellow “Jelly” ascites were related to low-grade PMP and light blood ascites were associated with high-grade PMP (P

Background: Surgical resection remains the primary treatment for gastrointestinal (GI) cancer, omega-3 polyunsaturated fatty acids (n-3 PUFAs) have been reported to improve the prognosis of patients undergoing gastrointestinal tumor surgery. This meta-analysis aims to explore the efficacy of n-3 PUFAs on GI cancer patients undergoing surgery. Methods: A systematic search of PubMed, Cochrane Library databases, EMBASE (until December 2021) was conducted. PRISMA checklist was followed. The data were analyzed by RevMan v5.3.0. Results: A total of ten RCTs articles including 663 patients were studied. The analysis demonstrated that the n-3 PUFAs group significantly reduced levels of interleukin-6 (IL-6) (P = 0.001), C-reactive protein (CRP) (P

Objective: The ameliorative effect of taurine on diabetes has received extensive attention in recent years. Despite promising data from animal studies, the efficacy of taurine supplementation in human studies has been inconsistent. We thus did a meta-analysis of randomized controlled trials to assess the effect of taurine supplement on glycemic indices, serum lipids, blood pressure, body composition in patients with diabetes. Methods: We systematically searched PubMed, Embase, Cochrane, Web of Science, FDA.gov, and ClinicalTrials.gov for randomized controlled trials (published from inception to January 15, 2022; no language restrictions) about the effect of taurine supplement on diabetes. Values of Standardized Mean Differences (SMD) were determined for continuous outcomes. Results: Of 2206 identified studies, 5 randomized controlled trials were eligible and were included in our analysis (N = 209 participants). Compared with the control group, taurine could significantly reduce HbA1c (SMD −0.41[95% CI: −0.74, −0.09], p = 0.01), Fasting Blood Sugar (SMD − 1.28[95% CI: −2.42, −0.14], p = 0.03) and HOMA-IR (SMD − 0.64[95% CI: −1.22, −0.06], p = 0.03). In addition, taurine also reduced Insulin (SMD −0.48 [95% CI: −0.99, 0.03], p = 0.06) and TG (SMD −0.26 [95% CI: −0.55, 0.02], p = 0.07), but did not reach statistical significance. Conclusions: Taurine supplementation is beneficial in reducing glycemic indices, such as HbA1c, Fasting Blood Sugar, HOMA-IR in diabetic patients, but has no significant effect on serum lipids, blood pressure and body composition in diabetic patients. Taurine emerges as a new option for the management of patients with diabetes. Further studies are needed to understand the potential effect of taurine in diabetic patients.

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is increasing worldwide, contributing to 70–85% of kidney cancer cases. Ferroptosis is a novel type of programmed cell death and could predict prognoses in cancers. Here, we developed a ferroptosis-related long non-coding RNA (FRlncRNA) signature to improve the prognostic prediction of ccRCC.Methods: The transcriptome profiles of FRlncRNAs and clinical data of ccRCC were obtained from The Cancer Genome Atlas and ICGC databases. Patients were randomly assigned to training cohorts, testing cohorts, and overall cohorts. The FRlncRNA signature was constructed by Lasso regression and Cox regression analysis, and Kaplan–Meier (K-M) analysis was used to access the prognosis of each group. The accuracy of this signature was evaluated by the receiver operating characteristic (ROC) curve. The visualization of functional enrichment was carried out by the gene set enrichment analysis (GSEA). Internal and external datasets were performed to verify the FRlncRNA signature.Results: A FRlncRNA signature comprising eight lncRNAs (AL590094.1, LINC00460, LINC00944, AC024060.1, HOXB-AS4, LINC01615, EPB41L4A-DT, and LINC01550) was identified. Patients were divided into low- and high-risk groups according to the median risk score, in which the high-risk group owned a dramatical shorter survival time than that of the low-risk group. Through ROC analysis, it was found that this signature had a greater predictive capability than traditional evaluation methods. The risk score was an independent risk factor for overall survival suggested by multivariate Cox analysis (HR = 1.065, 95%CI = 1.036–1.095, and p < 0.001). We constructed a clinically predictive nomogram based on this signature and its clinical features, which is of accurate prediction about the survival rate of patients. The GSEA showed that primary pathways were the P53 signaling pathway and tumor necrosis factor–mediated signaling pathway. The major FRlncRNAs (LINC00460, LINC00944, LINC01550, and EPB41L4A-DT) were verified with the prognosis of ccRCC in the GEPIA and K-M Plotter databases. Their major target genes (BNIP3, RRM2, and GOT1) were closely related to the stage, grade, and survival outcomes of ccRCC by the validation of multiple databases. Additionally, we found two groups had a significant distinct pattern of immune function, immune checkpoint, and immune infiltration, which may lead to different survival benefits.Conclusions: The FRlncRNA signature was accurate and act as reliable tools for predicting clinical outcomes and the immune microenvironment of patients with ccRCC, which may be molecular biomarkers and therapeutic targets.