Oxidative phosphorylation-related genes for prognosis and tumor microenvironment in breast cancer

Oxidative phosphorylation (OXPHOS) is a major energy resource occurring in mitochondria. Targeting OXPHOS-related genes has emerged as potential targets for cancer therapy. This study aimed to explore the significance of OXPHOS-related genes in breast cancer (BRCA).

Differentially expressed genes (DEGs) related to OXPHOS in BRCA were identified using packages of Limma and VennDiagram using the data from public databases. A prognostic model based on differentially expressed OXPHOS-related genes was constructed using least absolute shrinkage and selection operator Cox regression analyses and then evaluated through Kaplan-Meier and receiver operator characteristic (ROC) curves. Additionally, gene set variate analysis (GSVA) and gene set enrichment analysis (GSEA) were performed to explore the potential pathways involved in BRCA. Furthermore, the tumor microenvironment (TME) difference between low- and high-risk BRCA groups was investigated. The prognostic significance of hub genes was then examined. We conducted a protein-protein interaction (PPI) network to uncover the potential gene interactions and identify key genes, whose expressions were validated in cells.

Our analyses revealed 234 differentially expressed OXPHOS-related genes, from which a nine-gene (ATP5PF, FOXP3, IGF2, IREB2, MIEF2, NOTCH1, PDE12, SHC1, and UCP3) prognostic model was constructed. Patients in the high-risk group exhibited poorer survival outcomes and a suppressed immune microenvironment compared to the low-risk group. Additionally, except for IGF2, abnormal expression levels of hub genes were significantly associated with poor prognosis of BRCA patients. GSVA and GSEA highlighted the involvement of TME-related pathways, such as transforming growth factor beta (TGF-β) and mechanistic target of rapamycin (mTOR) signaling pathways. PPI network identified 4 common genes that interacted with all hub genes. The in vitro experiment on the key genes showed a consistent result with the bioinformatics finding.

Our study provides insights into the prognostic biomarkers and molecular mechanisms in BRCA, offering potential therapeutic avenues and guiding personalized treatment strategies for improved patient outcomes.