A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer

• Published in: BioMed research international Vol. 2020; no. 2020; pp. 1 - 12
• Main Authors: Li, Qingguo, Cai, Sanjun, Liu, Qi, Shan, Zezhi, Luo, Dakui, Li, Xinxiang
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc
• Subjects: Accuracy; Adjuvant treatment; Analysis; Biomarkers; Cancer; Care and treatment; Chemotherapy; Colon; Colon cancer; Colonic Neoplasms - diagnosis; Colonic Neoplasms - genetics; Colonic Neoplasms - metabolism; Colorectal cancer; Datasets; Diagnosis; Gender; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Genes; Genetic aspects; Genomes; Genotypes; Humans; Kaplan-Meier Estimate; Medical prognosis; Metabolic disorders; Metabolism; Metabolomics; Metastasis; Multivariate Analysis; Nomograms; Prognosis; Proportional Hazards Models; Regression analysis; Reproducibility of Results; Risk; Risk Factors; Risk groups; ROC Curve; Signal Transduction - genetics; Survival Analysis; China
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2020/4845360

A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.