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Research Paper|Volume 13, Issue 14|pp 19028—19047

Development and validation of prognostic model based on the analysis of autophagy-related genes in colon cancer

Yongfeng Wang1,2,5, Kaili Lin4, Tianchun Xu6, Liuli Wang2,3, Liangyin Fu1, Guangming Zhang1, Jing Ai1, Yajun Jiao4, Rongrong Zhu1, Xiaoyong Han4, Hui Cai1,2,5
  • 1The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou 730000, Gansu, China
  • 2General Surgery Clinical Medical Center, Gansu Provincial Hospital, Lanzhou 730000, Gansu, China
  • 3First Clinical Medical College, Lanzhou University, Lanzhou 730000, Gansu, China
  • 4Graduate School, Ning Xia Medical University, Yinchuan 750004, Ning Xia, China
  • 5Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou 730000, Gansu, China
  • 6Intelligent Medical Laboratory, Gansu Provincial Hospital, Lanzhou 730000, Gansu, China
* Co-first authors
Received: January 19, 2021Accepted: July 8, 2021Published: July 27, 2021
https://doi.org/10.18632/aging.203352

Copyright: © 2021 Wang 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

Background: Autophagy, a process of self-digestion, is closely related to multiple biological processes of colon cancer. This study aimed to construct and evaluate prognostic signature of autophagy-related genes (ARGs) to predict overall survival (OS) in colon cancer patients.

Materials and Methods: First, a total of 234 ARGs were downloaded via The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, differentially expressed ARGs were identified in colon cancer. The univariate and multivariate Cox regression analysis was performed to screen prognostic ARGs to construct the prognostic model. The feasibility of the prognostic model was evaluated using receiver operating characteristic curves and Kaplan-Meier curves. A prognostic model integrating the gene signature with clinical parameters was established with a nomogram.

Results: We developed an autophagy risk signature based on the 6 ARGs (ULK3, ATG101, MAP1LC3C, TSC1, DAPK1, and SERPINA1). The risk score was positively correlated with poor outcome and could independently predict prognosis. Furthermore, the autophagy-related signature could effectively reflect the levels of immune cell type fractions and indicate an immunosuppressive microenvironment.

Conclusion: We innovatively identified and validated 6 autophagy-related gene signature that can independently predict prognosis and reflect overall immune response intensity in the colon cancer microenvironment.