Identification and validation of a prognostic signature of drug resistance and mitochondrial energy metabolism-related differentially expressed genes for breast cancer

Background: Drug resistance constitutes one of the principal causes of poor prognosis in breast cancer patients. Cancer cells can survive independently of the energy provided by mitochondria; however, they are incapable of synthesizing new DNA strands without mitochondrial involvement.This may suggest that mitochondrial energy metabolism could be related to drug resistance. Hence, drug resistance and mitochondrial energy metabolism-related differentially expressed genes (DMRDEGs) may emerge as candidates for novel cancer biomarkers. This study endeavors to assess the viability of DMRDEGs as biomarkers or therapeutic targets for breast cancer. Methods: We utilized the DRESIS database and MSigDB to identify genes related to drug resistance. Additionally, we sourced genes associated with mitochondrial energy metabolism from GeneCards and extant literature. By merging these genes with the differentially expressed genes observed in normal and tumor tissues from the TCGA-BRCA and GEO databases, we successfully identified the DMRDEGs. Employing unsupervised consensus Clustering, we divided breast cancer patients into two distinct groups based on the DMRDEGs. Consequently, we identified four hub genes to formulate a prognostic model, applying Cox regression, LASSO regression, and Random Forest methods. Furthermore, we examined the immune infiltration and tumor mutation burden of the genes within our model and scrutinized the divergences in the immune microenvironment between high- and low-risk groups. Small hairpin RNA and lentiviral plasmids were designed for the stable transfection of breast cancer cell lines MDA-MB-231 and HCC1806. By conducting clone formation, scratch test and transwell assays, we initiated a preliminary investigation into the mechanistic roles of AIFM1. Results: We utilized DMRDEGs to develop a prognostic model that includes four mRNAs for breast cancer, which, by combining various clinical features and critical breast cancer facets, proved to be remarkably effective in forecasting patient outcomes. Additionally, AIFM1 appeared to enhance the proliferation, migration, and invasiveness of the breast cancer cell lines MDA-MB-231 and HCC1806. Conclusions: DMRDEGs have the potential to act as diagnostic markers and therapeutic targets for breast cancer. Within the mutated associated genes, ATP7B, FUS, AIFM1, and PPARG could serve as early diagnostic indicators, and notably, AIFM1 may present itself as a promising therapeutic target.