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
- 1 The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou 730000, Gansu, China
- 2 General Surgery Clinical Medical Center, Gansu Provincial Hospital, Lanzhou 730000, Gansu, China
- 3 First Clinical Medical College, Lanzhou University, Lanzhou 730000, Gansu, China
- 4 Graduate School, Ning Xia Medical University, Yinchuan 750004, Ning Xia, China
- 5 Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou 730000, Gansu, China
- 6 Intelligent Medical Laboratory, Gansu Provincial Hospital, Lanzhou 730000, Gansu, China
Received: January 19, 2021 Accepted: July 8, 2021 Published: July 27, 2021https://doi.org/10.18632/aging.203352
How to Cite
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.
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.