Exploring the prognostic value of T cell exhaustion and mitochondrial dysfunction related genes in breast cancer through bioinformatics analysis and RT-qPCR validation

Breast cancer (BRCA) is a complex cancer with heterogeneous molecular mechanisms. This study aimed to identify prognostic genes related to T cell exhaustion and mitochondrial dysfunction in BRCA, and to construct a prognostic model. First, transcriptomic and clinical data for both tumor and normal samples were retrieved from public databases. Next, differentially expressed genes (DEGs) were identified. These DEGs were then intersected with 2030 mitochondrial-related genes and 683 T cell exhaustion-related genes obtained from relevant databases to pinpoint candidate genes. Moreover, regression analyses were carried out to refine the prognostic genes. A risk model was established to assess the risk score of BRCA patients. Cox regression analyses were utilized to determine the independent prognostic factors. Then a prognostic model was constructed. In addition, immune infiltration, drug sensitivity, and single-cell transcriptomics were integrated to dissect mechanisms. The expression of these genes in BRCA was validated by quantitative reverse transcription polymerase chain reaction (RT-qPCR). From 5041 DEGs, regression identified 7 prognostic genes (BCL2A1, GZMB, IRF7, MTHFD2, TFRC, JUN, and PPP1R15A). The accurate risk model stratified patients: high-risk correlated with suppressed immunity (p < 2.2e-16), elevated TIDE (p = 5.4e-14), and higher CI.1040 IC50 (cor = 0.63, p < 0.0001). Single-cell analysis revealed 6 types and MIF-(CD74 + CD44) crosstalk. RT-qPCR confirmed MTHFD2, TFRC, IRF7, BCL2A1 upregulation in tumor (p < 0.05). Risk score, age, race, N/M-stage were independent factors. Seven prognostic genes effectively predicted BRCA prognosis with independent prognostic factors.