Prognostic Value of Fatty Acid Metabolism-Related Genes in Colorectal Cancer

Purpose: Colon cancer (CC), a malignancy with high global incidence and mortality, remains a major public health burden. As a pivotal aspect of tumor metabolic reprogramming, fatty acid metabolism has drawn significant research interest. This study was designed to elucidate the relationship between fatty acid metabolism-related gene expression and prognosis in patients with CC. Method: We obtained the mRNA expression profiles and corresponding clinical information of colon cancers from The Cancer Genome Atlas (TCGA) database. Expression data of fatty acid metabolism-related genes and survival data were extracted for subsequent analysis. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were employed to identify fatty acid metabolism-related genes associated with prognosis in CC patients. Subsequently, a prognostic model based on these six genes was constructed to predict survival probability. Patients were stratified into high-risk and low-risk groups based on the model. Differences between the two groups were analyzed, including gene set enrichment analysis (GSEA), immune cell infiltration, immunotherapy efficacy, and immune checkpoint expression levels. Furthermore, a novel nomogram incorporating the risk score, age, gender, and clinical stage was developed to predict individual patient outcome. Finally, the expression levels of the identified risk genes were validated in cell lines using quantitative real-time PCR (qRT-PCR). Results: 449 CC and 41 normal samples were included in this study. A prognostic model based on six fatty acid metabolism-related genes (ENO3, ELOVL3, ACOT11, ALAD, ELOVL6, ACADL) were built to evaluate the prognosis of CC patients. Patients in the high-risk group had poorer overall survival than those in the low-risk group (P < 0.001), with AUC value of 0.701. M0 macrophage infiltration and T helper cells were higher in the high-risk group, and regulatory T cells (Tregs) and infiltration of natural killer cell (NK) cells was less. The expression levels of PD-1, LAG3, and CTLA4 were higher in high-risk patients, and the high-risk group had a higher TIDE score, indicating a worse response to immunotherapy. The Calibration plots, receiver operating characteristic (ROC) curve, and Decision Curve Analysis (DCA) all showed that the nomogram method can accurately predict the survival rate of CC patients. In addition, qRT-PCR showed downregulated expression of ACOT11, ALAD, and ACADL, and upregulated expression of ELOVL6 and ENO3 in all colon cancer cell lines tested. There was no significant difference in the expression level of ELOVL3 between colon cancer cells and colon epithelial lines. Conclusion: Targeting fatty acid metabolism-related genes represents a promising therapeutic strategy for colon cancer (CC) that could pave the way for personalized treatment and enhanced patient survival.