A mitochondrial lipid metabolism-related gene signature predicts prognosis and immune landscape in colorectal cancer.

Colorectal cancer (CRC) is a highly aggressive gastrointestinal malignancy with significant global health consequences. While mitochondrial lipid metabolism genes are known to influence CRC progression, their prognostic relevance remains inadequately explored. This study systematically evaluated the expression profiles and prognostic significance of mitochondrial lipid metabolism-related genes in CRC patients. A risk model was constructed using data from the TCGA and GEO databases. Additionally, we examined the tumor microenvironment (TME), immune cell infiltration, tumor mutation burden, microsatellite instability (MSI), and drug sensitivity. Key genes associated with core mitochondrial lipid metabolism were identified and functionally validated through a series of in vitro cellular experiments. Mitochondrial lipid metabolism-associated genes were identified, including ABHD4, ABHD8, HDHD5, PNPLA4, GK5, CPT2, YJEFN3, CRYAB, HSPA1A, MAPK1, ATG7, HDAC3, and ACAT2. A nomogram integrating the risk score with key clinical variables (pTNM stage and age) was developed to predict patient outcomes. Significant variations in immune cell infiltration were observed between risk groups. Immune microenvironment analysis revealed significant differences in immune cell infiltration between risk groups, and the risk score was significantly correlated with the expression of TME-related genes and immune checkpoint molecules, indicating a markedly immunosuppressive microenvironment in the high-risk group. Additionally, TIDE analysis showed that combining the risk score with immune, stromal scores and MSI could more effectively predict the benefit of immunotherapy. Furthermore, in vitro experiments demonstrated that knockdown of two key genes, ABHD4 and YJEFN3, significantly suppressed CRC cell proliferation, migration, and invasion, supporting their potential oncogenic roles. This mitochondrial lipid metabolism-based risk model represents a promising prognostic biomarker, offering potential guidance for personalized therapeutic strategies in CRC management.