Identification and validation of xenobiotic metabolism-associated prognostic signature based on five genes to evaluate immune microenvironment in colon cancer.

Abstract

Xenobiotic metabolism plays an important role in the progression of colon cancer; however, little is known about its related biomarkers. This study sought to construct a prognostic model related to xenobiotic metabolism in colon cancer, and further reveal the characteristics of tumor immune microenvironment based on the prognostic model. Transcriptome data of 41 normal colon tissues and 473 colon tumor tissues and the clinical features of 452 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Data on xenobiotic metabolism genes (XMGs) were obtained from the hallmark xenobiotic metabolism set of the Molecular Signatures Database (MSigDB) and articles. Additionally, data on differential XMGs in colon cancer were acquired for a functional enrichment analysis by R software. An XMG prognostic model was constructed by a Cox regression analysis, and evaluated using Kaplan-Meier survival curves, risk curves, receiver operating characteristic (ROC) curves, and an independent prognostic analysis in a training cohort and validation cohort. Moreover, tumor immune infiltration and negative regulatory immune genes of cancer-immunity cycle (CIC), including immune checkpoints and immune cytokines, were further analyzed between low- and high-risk groups in both the training and validation cohorts. Differences with P value <0.05 were interpreted as statistically significant. A total of 126 differential XMGs were distinguished in the colon cancer data set, which were mainly enriched in the metabolism pathways of drugs and nutrients. There were 5 optimized genes (i.e., CYP2W1, GSTM1, TGFB2, MPP2, and ACOX1) used to construct the prognosis model, which effectively predicted prognosis and had good ROC curves. Between low- and high-risk groups, there were significant differences in abundance for T cells CD4 memory resting and T cells regulatory (Tregs), and expression of PDCD1, LAG3, NOS3, TGFB1, and ICAM1 in the training cohort and validation cohort. The XMGs in the prognostic model have a good prediction effect on the prognosis of colon cancer patients. The T cells CD4 memory resting, and Tregs, immune checkpoints PDCD1 and LAG3, and CIC negative regulatory immune cytokines NOS3, TGFB1, and ICAM1 are closely associated with xenobiotic metabolism.