Background: More and more evidence suggests that cancer is a mitochondrial metabolic disease recently and mitochondria dysfunction is critical to tumorigenesis. As a gatekeeper of mitochondria, the voltage-dependent anion channel 1 (VDAC1) is associated with the development of breast cancer (BC). However, its potential mechanism and clinical significance remain unclear; thus, in this research, we aimed to explore it. Methods: VDAC1 expression in BC tissues and normal tissues was obtained from The Cancer Genome Atlas (TCGA) and validated by datasets from the gene expression omnibus (GEO) database. Then, the relationships between VDAC1 expression and clinicopathological features were analyzed. Receiver operating characteristics (ROC) curves were used to identify the diagnostic value of VDAC1. The prognostic value was evaluated by Kaplan-Meier survival curves and Cox regression analysis. VDAC1 with its co-expression genes were subjected to enrichment analysis to explore potential mechanisms in BC and the protein-protein interaction (PPI) network was constructed. At last, the association between VDAC1 expression and infiltration levels of immune cell infiltration by various methods, as well as their corresponding markers, was analyzed. We also analyzed the correction between VDAC1 expression and eight immune checkpoint genes and the tumor immune dysfunction and exclusion (TIDE) scores of each BC sample in TCGA were calculated and the differences between high and low VDAC1 expression groups were analyzed. Results: VDAC1 expression was remarkably elevated in BC (p < 0.001), and high expression of VDAC1 was associated with the positive expression of ER (p = 0.004), PR (p = 0.033), and HER2 (p = 0.001). ROC analysis suggested that VDAC1 had diagnosed value in BC. The Kaplan-Meier analysis suggested that higher expression of VDAC1 was associated with shorter overall survival (OS), and further Cox regression analysis revealed that VDAC1 was an independent factor of unfavorable prognosis in BC patients. Enrichment analysis of VDAC1 and its co-expression suggested that VDAC1 was related to the regulation of mitochondrial energy metabolism and protein modification, and the HIF-1 singing pathway might be the potential mechanism in BC. Notably, we found that VDAC1 expression was infiltration levels of most types of immune cells, as well as the expression of marker genes of immune cells. The ICGs PDCD1, CTLA4, LAG3, SIGLEC15, and TIGIT were negatively corrected with VDAC1 expression in BC. TIDE scores between the low and high expression groups showed no difference. Conclusion: Overexpressed VDAC1 in BC could be severed as a novel biomarker for diagnosis and VDAC1 was an independent factor for adverse prognosis prediction. Our study revealed that VDAC1 might inhibit tumor immunity and might be a novel therapeutic target in BC.

Axillary lymph node dissection (ALND) is a standard procedure for early-stage breast cancer (BC) patients with three or more positive sentinel lymph nodes (SLNs). However, ALND can lead to significant postoperative complications without always providing additional clinical benefits. This study aims to develop machine-learning (ML) models to predict non-sentinel lymph node (non-SLN) metastasis in Chinese BC patients with three or more positive SLNs, potentially allowing the omission of ALND. Data from 2217 BC patients who underwent SLN biopsy at Shantou University Medical College were analyzed, with 634 having positive SLNs. Patients were categorized into those with ≤ 2 positive SLNs and those with ≥ 3 positive SLNs. We applied nine ML algorithms to predict non-SLN metastasis. Model performance was evaluated using ROC curves, precision-recall curves, and calibration curves. Decision Curve Analysis (DCA) assessed the clinical utility of the models. The RF model showed superior predictive performance, achieving an AUC of 0.987 in the training set and 0.828 in the validation set. Key predictive features included size of positive SLNs, tumor size, number of SLNs, and ER status. In external validation, the RF model achieved an AUC of 0.870, demonstrating robust predictive capabilities. The developed RF model accurately predicts non-SLN metastasis in BC patients with ≥ 3 positive SLNs, suggesting that ALND might be avoided in selected patients by applying additional axillary radiotherapy. This approach could reduce the incidence of postoperative complications and improve patient quality of life. Further validation in prospective clinical trials is warranted.

The growing body of evidence suggests that breast cancer (BC) who achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) may experience a more favorable prognosis. The objective of this study is to investigate the correlation between clinicopathologic parameters of locally advanced breast cancer (LABC) patients and the outcomes of NAC, with the aim of identifying predictive indicators for pCR. Additionally, we seek to examine the conversion of IHC markers in pCR patients following NAC and its impact on the prognosis of BC patients. We conducted a study involving 126 patients with LABC. Clinicopathological parameters associated with pCR were subjected to univariate and multivariate analysis. Kaplan–Meier (KM) curves and the log-rank test were used to compare the statistical difference in prognosis in different groups of patients. Additionally, we used difference and consistency tests to examine the conversion of immunohistochemistry (IHC) markers following NAC. The status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and molecular subtypes of BC were associated with pCR in the univariate analysis (all P < .05), which may be potential markers to predict pCR. HER2 was identified as an independent factor for predicting pCR in the multivariate analysis. The pCR rate of HER2-positive patients who received NAC combined targeted therapy was higher than that of patients who only received NAC (P = .003). The disease-free survival (DFS) rate of TNBC patients who achieved pCR was significantly higher than that of non-pCR TNBC patients (P = .026). The IHC marker conversion after NAC mainly existed in PR (P = .041). Ki67 expression decreased in the luminal B subtype and increased in the HER2 enriched subtype after NAC (all P < .001). Patients with Ki67 expression change after NAC had longer overall survival (OS) and DFS than unchanged patients (all P < .05). HER2-positive is an independent indicator for predicting pCR, and HE2-positive patients who received NAC combined targeted therapy were favorable to achieving pCR. IHC markers of BC patients exhibit varying degrees of alterations after NAC, and changes in Ki67 expression after NAC could serve as a marker to predict a better prognosis.

Background: The prognosis of patients with breast cancer liver metastasis (BCLM) is generally poor, and there are no specific treatment guidelines. Accurate prognostic tools are needed to estimate survival and support individualized management.Methods: The study cohort consisted of 4,817 patients diagnosed with BCLM from the SEER database spanning 2010 to 2020. Candidate predictors were screened using univariate and multivariate Cox regression. Five machine-learning algorithms—Random Forest (RF), Logistic Regression, XGBoost, Decision Tree, and Gradient Boosting—were trained to predict 6-month, 1-, 3-, and 5-year overall survival (OS) and breast cancer–specific survival (BCSS). Labels at each horizon were handled with inverse probability-of-censoring weighting, and performance was assessed with IPCW-AUC, accuracy, F1 score, calibration, and decision curve analysis. External validation included 124 BCLM patients from two Chinese hospitals. To evaluate the effect of primary tumor surgery (PTS), we modeled PTS as a time-dependent exposure and performed time-dependent Cox analyses with time-varying effects, Simon–Makuch curves, piecewise Cox modeling, 2-month landmark analysis, and E-value calculations.Results: RF was the top performer for both OS and BCSS across horizons (training AUCs = 0.840–0.899; internal test AUCs = 0.763–0.787), with good calibration and net benefit. External validation showed consistent discrimination (AUCs 0.779–0.815). SHAP analyses highlighted chemotherapy, age, subtype, and surgery as dominant contributors. In time-dependent analyses, PTS was associated with reduced risks of death (OS: HR 0.80, 95% CI 0.72–0.88) and breast cancer–specific mortality (BCSS: HR 0.77, 95% CI 0.69–0.86); findings were directionally consistent in piecewise and landmark analyses, and E-values (≥1.81) supported robustness to moderate unmeasured confounding.Conclusion: We developed and externally validated robust RF-based models for predicting OS and BCSS in BCLM. Our results indicate that PTS is associated with longer survival and lower breast cancer-specific mortality in carefully selected patients, supporting consideration within individualized, multidisciplinary decision-making.

Background: Breast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide. Natural killer (NK) cells play a crucial role in the innate immune system and exhibit significant anti-tumor activity. However, the role of NK cell-related genes (NRGs) in BC diagnosis and prognosis remains underexplored. With the advent of machine learning (ML) techniques, predictive modeling based on NRGs may offer a new avenue for precision oncology.Methods: We collected transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were identified, and key prognostic NRGs were selected using univariate and multivariate Cox regression analyses. We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. Additionally, a prognostic risk model was developed using LASSO-Cox regression, and its performance was validated in independent cohorts. To explore the potential mechanisms underlying the prognostic differences between high-risk and low-risk patient groups, as well as their drug treatment sensitivities, we conducted functional enrichment analysis, tumor microenvironment analysis, immunotherapy prediction, drug sensitivity analysis, and mutation analysis.Results: ULBP2, CCL5, PRDX1, IL21, NFATC2, CD2, and VAV3 were identified as key NRGs for the construction of ML models. Among the 12 ML diagnostic models, the Random Forest (RF) model demonstrated the best performance, which demonstrated robust performance in distinguishing BC from normal tissues in both training (TCGA) and validation (GEO) cohorts. In terms of the prognostic model, the risk score based on LASSO-Cox regression effectively distinguished between high-risk and low-risk patients, with patients in the high-risk group exhibiting significantly poorer overall survival (OS) compared to those in the low-risk group, and was validated in the GEO cohorts. Patients in the high-risk group displayed increased tumor proliferation, immune evasion, and reduced immune cell infiltration, correlating with poorer prognosis and lower response rates to immunotherapy. Furthermore, drug sensitivity analysis indicated that high-risk patients were more sensitive to Thapsigargin, Docetaxel, AKT inhibitor VIII, Pyrimethamine, and Epothilone B, while showing higher resistance to drugs such as I-BET-762, PHA-665752, and Belinostat.Conclusion: This study provides a comprehensive analysis of NRGs in BC and establishes reliable ML-based diagnostic and prognostic models. The findings highlight the clinical relevance of NRGs in BC progression, immune regulation, and therapy response, offering potential targets for personalized treatment strategies.

Purpose Ferroptosis and cuproptosis are both metal-dependent regulated cell death that play an important role in cancer. However, the expression patterns and the prognostic values of ferroptosis-related genes (FRGs) associated with cuproptosis in breast cancer (BC) are largely unknown. This study aims to explore the prognostic value of cuproptosis-related FRGs and their relationship with tumor microenvironments in BC. Methods The clinical and RNA sequencing data of BC patients from TCGA, METABRIC and GEO databases were analyzed. The least absolute shrinkage and selection operator regression analysis was used to establish prognostic signatures based on cuproptosis-related FRGs. The overall survival between risk subgroups was assessed by Kaplan-Meier analysis. The changes in risk score during neoadjuvant chemotherapy, and differences in immune cells, immune checkpoints, and drug sensitivity between risk subgroups were also analyzed in this study. Results A successful development of a prognostic signature based on cuproptosis-related FRGs in the TCGA cohort was achieved and it was validated in the METABRIC cohort. Gene set enrichment analysis results revealed the enrichment of steroid biosynthesis and ABC transporters in the high-risk group. Moreover, the signature was also found to be associated with immune cells and immune checkpoints. Lower risk score in patients after neoadjuvant chemotherapy and higher sensitivity of the high-risk group to AKT inhibitor VIII and cisplatin was also observed. Conclusion Cuproptosis-related FRGs can be used as a novel prognostic signature for predicting the overall survival of BC patients. This can provide meaningful insights into the selection of immunotherapy and antitumor drugs for BC.