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Research Paper|Volume 11, Issue 19|pp 8710—8727

Association of a novel seven-gene expression signature with the disease prognosis in colon cancer patients

Haojie Yang1, Hua Liu1, Hong-Cheng Lin2, Dan Gan1, Wei Jin1, Can Cui1, Yixin Yan3, Yiming Qian3, Changpeng Han1, Zhenyi Wang1
  • 1Department of Coloproctology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
  • 2Department of Coloproctology, The Sixth Affiliated Hospital of Sun Yat-sen University, Gastrointestinal and Anal Hospital of Sun Yat-sen University, Guangzhou 510655, China
  • 3Department of Emergency Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
* Equal contribution
Received: July 12, 2019Accepted: October 7, 2019Published: October 15, 2019
https://doi.org/10.18632/aging.102365

Copyright © 2019 Yang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Older patients who are diagnosed with colon cancer face unique challenges, specifically regarding to cancer treatment. The aim of this study was to identify prognostic signatures to predicting prognosis in colon cancer patients through a detailed transcriptomic analysis. RNA-seq expression profile, miRNA expression profile, and clinical phenotype information of all the samples of TCGA colon adenocarcinoma were downloaded and differentially expressed mRNAs (DEMs), differentially expressed lncRNAs (DELs) and differentially expressed miRNAs (DEMis) were identified. A competing endogenous RNA (ceRNA) network was constructed further and DEMs related with prognosis in the ceRNA network was screened using Cox regression analysis. Risk score models for predicting the prognosis of colon cancer patients were built using these DEMs. A total of 1476 DEMs, 9 DELs, and 243 DEMis between the tumor and normal samples were identified and functional enrichment analyses showed that the DEMs were significantly enriched in the nervous system development, ribosome biogenesis pathways in eukaryotes, and drug metabolism cytochrome P450. Twelve DEMs related with prognosis were screened from the ceRNA network. Thereafter, the risk score models of prognostic DEMs were obtained, involving seven DEMs (SGCG, CLDN23, SLC4A4, CCDC78, SLC17A7, OTOP3, and SMPDL3A). Additionally, cancer stage was identified as a prognostic clinical factor. This prognostic signature was further validated in two independent datasets. Our study developed a seven-mRNA and one-clinical factor signature that are associated with prognosis in colon cancer patients, which may serve as possible biomarkers and therapeutic targets in the future.