Abstract
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.
Highlights
Colon cancer (CC) is the third most common cancer worldwide
A total of 471 CC samples and 349 normal colon samples, including 853 metabolism-related genes were included in the final analysis
Transcriptome change profiling was performed between CC samples and normal colon samples
Summary
Colon cancer (CC) is the third most common cancer worldwide. Radical resection is considered the primary therapeutic strategy for the management of CC, followed by radiotherapy and chemotherapy. The tumor, lymph node, metastasis (TNM) staging system has been used as the standard classification for predicting the recurrence in patients with CC [2]. This system is not ideal for the prognostic prediction and clinical management of CC. Efforts have been made to develop new methods that could improve prognostic prediction and participate in making individualized decision using clinicopathologic characteristics and molecular biomarkers [3,4,5] Among these new tools, gene score signatures based on integrated data analysis appear as a promising approach
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