Identification of a Novel Amino Acid Metabolism-Related Gene Risk Signature as a Prognostic and Immunotherapeutic Efficiency Predictor for Colorectal Cancer

Background: The importance of amino acid metabolism in multiple cancers was investigated by accumulating researches. But the role of amino acid metabolism-related genes (AAMRGs) played in the colorectal cancer (CRC) progression remains unclear. Methods: The clinical information and RNA sequencing of CRC were acquired from The Cancer Genome Atlas (TCGA) databases and the Gene Expression Omnibus (GEO) databases, and amino acid metabolic gene data were downloaded from a published article in the journal Cell Reports. The Cox-LASSO analysis was adopted to establish a AAMRG prognostic signature. Kaplan–Meier (K-M) survival curve and receiver operating characteristic (ROC) curve were adopted to estimate the prognostic capacity of our risk signature. RT‒qPCR was adopted to estimate the expression of AAMRGs in clinical samples. Moreover, gene set enrichment analysis (GSEA) was adopted and according to result of GSEA, further investigation was conducted in immune infiltration, somatic mutation, drug sensitivity and EMT. Result: The activity of amino acid metabolism was significantly increased in CRC. A 10-AAMRG prognostic signature was established and the CRC samples were classified into two groups (high-risk and low-risk). Risk score was analyzed to be an independent factor of CRC to affect cancer progression. The result of RT-qPCR showed that among 10 AAMRGs, the expression abundances of MRPS23 and TRAP1 in CRC tissues and adjacent normal colorectal tissues were both upregulated. According to GSEA outcomes and further analyses, significant differences were observed in immune infiltration and EMT between two risk groups. Furthermore, analysis of drug sensitivity illustrated that some chemotherapy drugs had higher IC50 values in low-risk group, including bexarotene, bicalutamide and imatinib. Conclusion: The 10-AAMRG signature we built is a prospective biomarker, which has great potentiality to predict CRC therapeutic responses and clinical prognosis.