Predicting survival and immune status of breast cancer patients based on prognostic features related to PANoptosis

Breast cancer (BRCA) is a prevalent female malignancy. PANoptosis, integrating diverse cell death traits, is pivotal in BRCA, thus necessitating deeper study.

Data from Gene Expression Omnibus (GEO, GSE180286 and GSE20685) and The Cancer Genome Atlas (TCGA) were analyzed. Weighted gene co-expression network analysis (WGCNA) identified PANoptosis-related genes in BRCA patients from TCGA. Further refinement of these module genes was conducted through univariate Cox regression, LASSO regression (glmnet package), and stepwise multivariate regression analysis to derive the final biomarkers. Based on these biomarkers, a risk model was established, and in-vitro experiments (wound healing assay, Transwell assay, and qRT-PCR) were carried out to validate the accuracy of these biomarkers. The MCPcounter package and the oncoPredict package were used to assess immune cell infiltration and sensitivity to drugs in BRCA patients, respectively.

This study identified 8 biomarkers (ACY3, CD83, CXCL13, KLHDC7B, NR1H3, SMCO4, TRPM2, and UPP1) and established a risk model. In-vitro experiments revealed significant differences in biomarker expression between BRCA cells and the control group, with TRPM2 knockdown inhibiting BRCA cell migration and invasion. Enrichment analysis showed metabolic pathways were activated in high-risk group. Additionally, immune analysis showed lower immune cell enrichment and significant enrichment of fibroblasts in the high-risk group. Drug sensitivity analysis linked 13 drugs to RiskScore. Finally, single-cell analysis identified six cell types (including cancer stem cells, fibroblasts, T-cells, macrophages, B/Plasma cells, and endothelial cells) for BRCA and found that macrophages had higher PANoptosis activity.

The current research introduces a novel model for BRCA prognosis analysis but also provides a fresh perspective on BRCA treatment strategies.