A lipid metabolism-related gene signature for risk stratification and prognosis prediction in patients with breast cancer.

Breast cancer (BC) is among the cancers with the highest incidence rates. Although multiple therapies are available, there is an unmet need for prediction of prognoses and treatment responses. Increasing evidence has shown that lipid metabolism is important for the development of BC. The tumor-promoting role of lipid metabolism in BC has inspired us to build a model to predict prognosis and stratify patients using lipid metabolism-related genes (LMRGs) that may reflect the underlying biological mechanisms of BC. We identified a list of genes involved in lipid metabolism that were associated with the overall survival of BC. The above genes were selected by the least absolute shrinkage and selection operator (LASSO) method to avoid overfitting, and the stepwise Cox proportional hazards regression model was applied. The BC cohort of the Cancer Genome Atlas was divided into a training cohort and a test cohort at a ratio of 1:1. A six-gene signature, comprising APOC3, CEL, CPT1A, JAK2, NFKBIA, and PLA2G1B, was developed using the training cohort. There was a clear distinction in overall survival between low- and high-risk patients in the training cohort, the test cohort, various validation cohorts, and different clinical subgroups. Then, immune cell infiltration analysis, GO and KEGG analyses were performed. Enrichment analyses were applied to explore the possible underlying mechanisms. We also analyzed the susceptibility of patients to predefined drugs in different risk groups in an attempt to identify potential therapeutic drugs. Carnitine palmitoyl transferase 1A (CPT1A), one of the signature genes, is a key enzyme in lipid metabolism that has been related to cancer progression. Therefore, we analyzed the prognostic values of CPT1A in public cohorts and our independent BC cohort by performing immunohistochemistry. CPT1A was significantly related to overall survival in patients with BC in the cohorts. In general, the LMRG signature can predict overall survival and potential immunotherapy response in patients with BC, including triple-negative BC. The findings have highlighted the role of lipid metabolism and CPT1A in BC, showing the implications for further research, and the signature is a potential tool for prognosis prediction and may help clinicians with clinical decisions.