Clinical Efficacy of Neoadjuvant Chemotherapy plus Modified Radical Mastectomy for Stage II-III Breast Cancer Patients and Its Influence on Serum Tumor Markers.

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.

Causal effects of genetically predicted endometriosis on breast cancer: a two-sample Mendelian randomization study.

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.

Artificial intelligence in breast cancer: application and future perspectives.

Breast cancer is one of the most common cancers and is one of the leading causes of cancer-related deaths in women worldwide. Early diagnosis and treatment are the key for a favorable prognosis. The application of artificial intelligence technology in the medical field is increasingly extensive, including image analysis, automated diagnosis, intelligent pharmaceutical system, personalized treatment and so on. AI-based breast cancer imaging, pathology and adjuvant therapy technology cannot only reduce the workload of clinicians, but also continuously improve the accuracy and sensitivity of breast cancer diagnosis and treatment. This paper reviews the application of AI in breast cancer, as well as looks ahead and poses challenges to the future development of AI for breast cancer detection and therapeutic, so as to provide ideas for future research.

An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer.

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 autophagy-related genes were obtained from the human autophagy database (HADb). ARlncRNAs were identified by conducting co­expression 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 immune­related 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.