A Lipid Metabolism-Related Genes Prognosis Biomarker Associated With The Tumor Immune Microenvironment in Colorectal Carcinoma
Abstract Background: Lipid metabolic reprogramming was considered as a new hallmark of malignant tumors. It has been reported to play a crucial biological role in cell proliferation, energy homeostasis and signal-transduction. However, the important value of lipid metabolism-related genes(LMRGs) in prognostic prediction and the tumor immune microenvironment has not been explored by large sample studies in colorectal cancer(CRC). Methods: In this study, the lipid metabolism status of 1086 CRC samples was analyzed using RNA expression profiles and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, of which the former was determined as training set and the latter as validation set. The risk signature was constructed by the univariate Cox regression and Least Absolute Shrinkage and Selection Operator(LASSO) COX regression. The patients were stratified into high- and low-risk groups according to the median value of the risk score. Immune and mutation landscape between low- and high-risk CRC patients were also explored. Additionally, we established a nomogram integrating the risk signature and clinical factors to improve risk assessment of CRC patients. Results: A four LMRGs signature, including PROCA1, CCKBR, CPT2 and FDFT1, was constructed to predict the prognosis of CRC. The risk signature as an independent prognostic factor for CRC was associated with a variety of parameters. Survival analysis showed that patients with low risk score had a better prognosis. There were different immune landscapes between low and high-risk CRC patients, especially in monocytes, dendritic cells, M0 and M2-like macrophages. Patients in the low-risk group were more likely to have higher tumor mutation burden, stem cell characteristics and level of PD-L1 expression. In addition, it was found that genes that played crucial biological functions in tumorigenesis (including TP53, PI3K and MUC16) had significant differences in mutation frequency between two groups. Conclusion: A lipid metabolism-related risk signature for predicting the prognosis of CRC was identified in this study. Furthermore, this prognostic signature may be a potential biomarker for predicting the efficacy of chemotherapy and anti-PD-L1 therapy in CRC.