Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.

Abstract

Background: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer (BRCA) with limited therapeutic targets. This study aimed to identify T cell-related signatures for TNBC diagnosis and prognosis.

Methods: Clinical data and transcriptomic profiles were obtained from the TCGA-BRCA dataset, and single-cell RNA sequencing (scRNA-seq) data were downloaded from the GEO database. Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. Immune cell infiltration patterns were analyzed between high- and low-LR score groups, and Kaplan-Meier analysis evaluated the prognostic significance of hub genes. Functional enrichment and pathway analysis were performed using GSEA, and scRNA-seq data further explored hub gene-related pathways in immune cells.

Results: Three hub genes (CACNA1H, KCNJ11, and S100B) were identified with strong diagnostic and prognostic relevance in TNBC. The LR model based on these genes achieved an AUC of 0.917 in diagnosing TNBC from other BRCA subtypes. Low LR scores were associated with poorer overall survival and reduced immune cell infiltration, particularly CD8 T cells and cytotoxic lymphocytes. S100B showed strong associations with the cytokine-cytokine receptor interaction pathway, JAK-STAT signaling, and T cell receptor signaling.

Conclusion: CACNA1H, KCNJ11, and S100B are potential diagnostic and prognostic biomarkers in TNBC. Their immune-related functions highlight their potential for guiding targeted immunotherapy strategies.