Research Paper Volume 15, Issue 11 pp 5164—5189
Identification of a novel MSI-related ceRNA network for predicting the prognosis and immunotherapy response of gastric cancer
- 1 Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710032, Shaanxi Province, China
- 2 Department of Biomedical Engineering, Air Force Hospital of Eastern Theater Command, Nanjing 210001, Jiangsu Province, China
- 3 State Key Laboratory of Cancer Biology and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi’an 710032, Shaanxi Province, China
Received: October 31, 2022 Accepted: May 3, 2023 Published: June 12, 2023
https://doi.org/10.18632/aging.204794How to Cite
Copyright: © 2023 Zhang 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: Mounting evidence has underscored the pivotal role of the competitive endogenous RNA (ceRNA) regulatory networks among various cancers. However, the behavior characteristics and complexity of the ceRNA network in Gastric cancer (GC) remains unclear. In this study, we aimed to clarify a Microsatellite instability (MSI)-related ceRNA regulatory network and identify potential prognostic markers associated with GC.
Methods and Results: We extracted transcriptome data of GC patients from The Cancer Genome Atlas (TCGA) and identified differentially expressed lncRNAs, miRNAs and mRNAs based on MSI status. A hub ceRNA network including 1 lncRNAs (MIR99AHG), 2 miRNAs and 26 mRNAs specific to MSI was established in GC. We further constructed a prognostic model with seven target mRNAs by Lasso Cox regression, which yielded AUC values of 0.76. The prognostic model was further validated in an external independent dataset that integrated three GEO datasets. The characterization of immune cell infiltration and immunotherapy effects between high-risk and low-risk groups were then analyzed. Immune cell infiltration was significantly different between high- and low-risk groups based on risk scores. GC patients with lower risk scores correlated with better immune checkpoint inhibitor therapy (ICI) response. We further validated the expression and regulatory relationship of the ceRNA network in vitro experiments, and also confirmed the relationship between MIR99AHG and PD-L1.
Conclusions: Our research provides in-depth insights on the role of MSI-related ceRNA in GC and the prognosis and ICIs therapy response of GC patients can be assessed by the risk model based on MSI-related ceRNA network.