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IntroductionTriple-negative breast cancer (TNBC) is linked to a poorer outlook, heightened aggressiveness relative to other breast cancer variants, and limited treatment choices. The absence of conventional treatment methods makes TNBC patients susceptible to metastasis. The objective of this research was to assess the clinical and pathological traits of TNBC patients, predict the influence of risk elements on their outlook, and create a prediction model to assist doctors in treating TNBC patients and enhancing their prognosis.MethodsWe included 23,394 individuals with complete baseline clinical data and survival information who were diagnosed with primary TNBC between 2010 and 2015 based on the SEER database. External validation utilised a group from The Affiliated Lihuili Hospital of Ningbo University. Independent risk factors linked to TNBC prognosis were identified through univariate, multivariate, and least absolute shrinkage and selection operator regression methods. These characteristics were chosen as parameters to develop 3- and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) nomogram models. Model accuracy was assessed using calibration curves, consistency indices (C-indices), receiver operating characteristic curves (ROCs), and decision curve analyses (DCAs). Finally, TNBC patients were divided into groups of high, medium, and low risk, employing the nomogram model for conducting a Kaplan-Meier survival analysis.ResultsIn the training cohort, variables such as age at diagnosis, marital status, grade, T stage, N stage, M stage, surgery, radiation, and chemotherapy were linked to OS and BCSS. For the nomogram, the C-indices stood at 0.762, 0.747, and 0.764 in forecasting OS across the training, internal validation, and external validation groups, respectively. Additionally, the C-index values for the training, internal validation, and external validation groups in BCSS prediction stood at 0.793, 0.755, and 0.811, in that order. The findings revealed that the calibration of our nomogram model was successful, and the time-variant ROC curves highlighted its effectiveness in clinical settings. Ultimately, the clinical DCA showcased the prospective clinical advantages of the suggested model. Furthermore, the online version was simple to use, and nomogram classification may enhance the differentiation of TNBC prognosis and distinguish risk groups more accurately.ConclusionThese nomograms are precise tools for assessing risk in patients with TNBC and forecasting survival. They can help doctors identify prognostic markers and create more effective treatment plans for patients with TNBC, providing more accurate assessments of their 3- and 5-year OS and BCSS.

Abstract This study used a Mendelian randomization (MR) approach to investigate the causal relationship between genetically predicted endometriosis (EMS) and breast cancer risk. A total of 122,977 cases and 105,974 controls were included in the analysis, with gene-level summary data obtained from the Breast Cancer Association Consortium (BCAC). An inverse variance-weighting approach was applied to assess the causal relationship between EMS and breast cancer risk, and weighted median and MR-Egger regression methods were used to evaluate pleiotropy. Results showed a causal relationship between EMS and a decreased risk of overall breast cancer (odds ratio [OR] 0.95; 95% CI 0.90–0.99, p = 0.02). Furthermore, EMS was associated with a lower risk for estrogen receptor (ER)-positive breast cancer in a subgroup analysis based on immunohistochemistry type (OR 0.91; 95% CI 0.86–0.97, p = 0.005). However, there was no causal association between ER-negative breast cancer and survival (OR 1.00; 95% CI 0.94–1.06, p = 0.89). Pleiotropy was not observed. These findings provide evidence of a relationship between EMS and reduced breast cancer risk in invasive breast cancer overall and specific tissue types, and support the results of a previous observational study. Further research is needed to elucidate the mechanisms underlying this association.

Potential cancer therapy can be accomplished by utilizing nano-based platforms that supply facilitated drug penetration inside cancer cells. In this paper, an intelligent therapeutic nano-based system derived from metal–organic framework (MOF) core–shell hybrids with the capacity of potential drug loading, tumor microenvironment-triggered drug release as well as promising cell penetration was developed. The core–shell iron oxide-platinium@MOF/epirubicin (Fe3O4-Pt@MOF/EPI) nanospheres were constructed, where these nanoplatforms endow the system with the capability of pH-responsive drug release. The synthesized Fe3O4-Pt@MOF/EPI nanoparticles were characterized using different well-known techniques. The Fe3O4-Pt@MOF/EPI nanospheres were shown to have a dried size of around 50 nm (evidenced by SEM analysis), a spherical/core–shell/porous structure (evidenced by TEM), and an average hydrodynamic size of 92.89 nm (evidenced by DLS). Additionally, TGA analysis revealed that Fe3O4-Pt@MOF/EPI nanospheres had a weight loss at temperatures between 220 and 450 °C associated with the removal of MOF and EPI from the structure of core–shell nanospheres. The N2 adsorption–desorption data also reflected the porosity of core–shell Fe3O4-Pt@MOF nanospheres by indicating type IV behavior with an apparent hysteresis loop in the range of 0.38–0.98. Furthermore, XRD analysis disclosed the changes in the peak intensity at positions of 57.2° and 39.5°, which indicated the effects of loaded MOF on Fe3O4-Pt nanosphere. Moreover, core–shell Fe3O4-Pt@MOF/EPI nanospheres showed high loading capacity and drug release in a pH-responsive manner. Cell viability and cellular uptake assays on mouse fibroblast (NIH3T3) and triple-negative 4 T1 breast (TNFB) tumors showed that the core–shell Fe3O4-Pt@MOF/EPI nanospheres effectively inhibited TNFB cancer cells proliferation through inducing higher cell penetration compared to free EPI while having good biocompatibility against NIH3T3 cells. In conclusion, the present study may provide useful information about the development of efficient anticancer platforms against breast cancer cells, while further in vivo and pre-clinical assays are required to support this study.

IntroductionBreast cancer (BRCA) is the most common malignancy among women worldwide. It was widely accepted that autophagy and the tumor immune microenvironment play an important role in the biological process of BRCA. Long non-coding RNAs (lncRNAs), as vital regulatory molecules, are involved in the occurrence and development of BRCA. The aim of this study was to assess the prognosis of BRCA by constructing an autophagy-related lncRNA (ARlncRNA) prognostic model and to provide individualized guidance for the treatment of BRCA.MethodsThe clinical data and transcriptome data of patients with BRCA were acquired from the Cancer Genome Atlas database (TCGA), and autophagy-related genes were obtained from the human autophagy database (HADb). ARlncRNAs were identified by conducting co‑expression analysis. Univariate and multivariate Cox regression analysis were performed to construct an ARlncRNA prognostic model. The prognostic model was evaluated by Kaplan–Meier survival analysis, plotting risk curve, Independent prognostic analysis, clinical correlation analysis and plotting ROC curves. Finally, the tumor immune microenvironment of the prognostic model was studied.Results10 ARlncRNAs(AC090912.1, LINC01871, AL358472.3, AL122010.1, SEMA3B-AS1, BAIAP2-DT, MAPT-AS1, DNAH10OS, AC015819.1, AC090198.1) were included in the model. Kaplan–Meier survival analysis of the prognostic model showed that the overall survival(OS) of the low-risk group was significantly better than that of the high-risk group (p< 0.001). Multivariate Cox regression analyses suggested that the prognostic model was an independent prognostic factor for BRCA (HR = 1.788, CI = 1.534–2.084, p < 0.001). ROCs of 1-, 3- and 5-year survival revealed that the AUC values of the prognostic model were all > 0.7, with values of 0.779, 0.746, and 0.731, respectively. In addition, Gene Set Enrichment Analysis (GSEA) suggested that several tumor-related pathways were enriched in the high-risk group, while several immune‑related pathways were enriched in the low-risk group. Patients in the low-risk group had higher immune scores and their immune cells and immune pathways were more active. Patients in the low-risk group had higher PD-1 and CTLA-4 levels and received more benefits from immune checkpoint inhibitors (ICIs) therapy.DiscussionThe ARlncRNA prognostic model showed good performance in predicting the prognosis of patients with BRCA and is of great significance to guide the individualized treatment of these patients.

Objective • This research aims to assess the clinical efficacy of neoadjuvant chemotherapy (NACT) in combination with modified radical mastectomy (MRM) for stage II-III breast cancer (BC) patients and its impact on serum tumor markers (STMs). Methods • The study included 119 stage II-III BC patients treated between June 2018 and June 2021. Among them, 55 cases underwent MRM (reference group), while 64 cases received NACT followed by MRM (research group). We compared intraoperative parameters (blood loss, operation time, hospital stay), clinical outcomes, the incidence of postoperative adverse events (AEs), changes in STMs (CA125, CA153, CEA), and one-year postoperative quality of life (QOL). Results • In comparison to the reference group, the research group exhibited significantly lower intraoperative blood loss, shorter operation times, reduced hospital stays, and higher rates of disease remission. Notably, the research group experienced a lower overall incidence of AEs, including skin flap necrosis, subscalp effusion, infection, and upper limb lymphedema. Postoperatively, all STMs in the research group exhibited statistically significant reductions and were lower than those in the reference group. Additionally, all QOL subscales demonstrated improvements and higher scores in the research group. Conclusions • NACT followed by MRM represents an effective approach for enhancing surgical outcomes and clinical efficacy in stage II-III BC patients. This combination therapy also reduces the risk of postoperative AEs and leads to favorable changes in STMs and postoperative QOL levels. [ABSTRACT FROM AUTHOR]

Abstract Background and Objective Azithromycin is the first azalide antibiotic that is related to the macrolide family of antibiotics. Bioequivalence studies in China are initiated by the National Medical Products Administration (NMPA), which supports a generic consistency evaluation program for ensuring that generic products manufactured in China meet the required standards and provide equivalent therapeutic effects to their reference products. This study aimed to assess the bioequivalence of two azithromycin tablets under both fasting and fed conditions in healthy Chinese volunteers. Methods This was a single-center, open-label, single-dose, randomized, three-way crossover trial with two independent groups (fasting group and fed group). A total of 72 healthy Chinese subjects (36 subjects in the fasting state and 36 subjects in the fed state) were enrolled and randomized to treatment. Blood samples were collected from 0 to 120 h after a single oral dose of a 250-mg generic azithromycin tablet (test, T) or branded azithromycin tablet (reference, R). The plasma concentrations of azithromycin were determined by high-performance liquid chromatography–tandem mass spectrometry (HPLC‒MS/MS). A non-compartmental analysis method was used to estimate the pharmacokinetic parameters. Adverse events were documented. Results In a fasting state, the bioequivalence of maximum plasma concentration (C max) was evaluated using the reference-scaled average bioequivalence (RSABE) approach (within-subject standard deviation, S WR > 0.294), and the bioequivalence of area under the concentration–time curve from time 0 to the time of the last measurable plasma concentration (AUC0–t ) and area under the concentration–time curve from time 0 extrapolated to infinity (AUC0–∞ ) were evaluated by the average bioequivalence (ABE) method (S WR < 0.294). The geometric mean ratio (GMR) of T/R for C max was 106.49%, while the 95% upper confidence bound was < 0. The GMRs of AUC0–t and AUC0–∞ were 103.34% and 101.28%, and the 90% confidence intervals (CIs) of the test/reference were 95.90–111.35%/94.85–108.15%, respectively. In the fed state, the RSABE approach was applied to estimate the bioequivalence of C max (S WR >0.294), and the ABE approach was applied to estimate the bioequivalence of AUC0–t and AUC0–∞ (SWR < 0.294). The GMR for C max was 99.80%, while the 95% upper confidence bound value was < 0. The GMRs of AUC0–t and AUC0–∞ were 97.07% and 98.15%, and the 90% CIs of the T/R were 90.02–104.68% and 90.66–106.25%, respectively. All adverse events were mild and transient. Conclusions The trial indicated that the test and the reference azithromycin tablets were bioequivalent and well tolerated in healthy Chinese volunteers under both fasting and fed conditions. Trial Registration Clinicaltrials, ChiCTR2300071630 (retrospectively registered in 19/05/2023).

Introduction: Breast cancer (BRCA) is the most common malignancy among women worldwide. It was widely accepted that autophagy and the tumor immune microenvironment play an important role in the biological process of BRCA. Long non-coding RNAs (lncRNAs), as vital regulatory molecules, are involved in the occurrence and development of BRCA. The aim of this study was to assess the prognosis of BRCA by constructing an autophagy-related lncRNA (ARlncRNA) prognostic model and to provide individualized guidance for the treatment of BRCA. Methods: The clinical data and transcriptome data of patients with BRCA were acquired from the Cancer Genome Atlas database (TCGA), and autophagyrelated genes were obtained from the human autophagy database (HADb). ARlncRNAs were identified by conducting co‑expression analysis. Univariate and multivariate Cox regression analysis were performed to construct an ARlncRNA prognostic model. The prognostic model was evaluated by Kaplan–Meier survival analysis, plotting risk curve, Independent prognostic analysis, clinical correlation analysis and plotting ROC curves. Finally, the tumor immune microenvironment of the prognostic model was studied. Results: 10 ARlncRNAs(AC090912.1, LINC01871, AL358472.3, AL122010.1, SEMA3B-AS1, BAIAP2-DT, MAPT-AS1, DNAH10OS, AC015819.1, AC090198.1) were included in the model. Kaplan–Meier survival analysis of the prognostic model showed that the overall survival(OS) of the low-risk group was significantly better than that of the high-risk group (p< 0.001). Multivariate Cox regression analyses suggested that the prognostic model was an independent prognostic factor for BRCA (HR = 1.788, CI = 1.534–2.084, p < 0.001). ROCs of 1-, 3- and 5-year survival revealed that the AUC values of the prognostic model were all > 0.7, with values of 0.779, 0.746, and 0.731, respectively. In addition, Gene Set Enrichment Analysis (GSEA) suggested that several tumor-related pathways were enriched in the high-risk group, while several immune‑related pathways were enriched in the low-risk group. Patients in the low-risk group had higher immune scores and their immune cells and immune pathways were more active. Patients in the low-risk group had higher PD-1 and CTLA-4 levels and received more benefits from immune checkpoint inhibitors (ICIs) therapy. Discussion: The ARlncRNA prognostic model showed good performance in predicting the prognosis of patients with BRCA and is of great significance to guide the individualized treatment of these patients. [ABSTRACT FROM AUTHOR]