Identification of Stemness Characteristics Associated With the Immune Microenvironment and Prognosis in Gastric Cancer

Deli Mao; Zhijun Zhou; Shenglei Song; Dongsheng Li; Yulong He; Zhewei Wei; Changhua Zhang

doi:10.3389/fonc.2021.626961

Identification of Stemness Characteristics Associated With the Immune Microenvironment and Prognosis in Gastric Cancer

Front Oncol. 2021 Mar 3:11:626961. doi: 10.3389/fonc.2021.626961. eCollection 2021.

Authors

Deli Mao¹, Zhijun Zhou², Shenglei Song¹, Dongsheng Li¹, Yulong He¹, Zhewei Wei³, Changhua Zhang¹

Affiliations

¹ Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
² Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
³ Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

Abstract

Background: Gastric cancer (GC) is a highly heterogeneous disease. In recent years, the prognostic value of the mRNA expression-based stemness index (mRNAsi) across cancers has been reported. We intended to identify stemness index-associated genes (SI-genes) for clinical characteristic, gene mutation status, immune response, and tumor microenvironment evaluation as well as risk stratification and survival prediction.

Methods: The correlations between the mRNAsi and GC prognosis, clinical characteristics, gene mutation status, immune cell infiltration and tumor microenvironment were evaluated. Weighted gene correlation network analysis (WGCNA) was performed to identify SI-genes from differentially expressed genes (DEGs) in The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was employed to calculate the sample SI-gene-based ssGSEA score according to the SI-genes. Then, the correlations between the ssGSEA score and GC prognosis, clinical characteristics, gene mutation status, immune cell infiltration and tumor microenvironment were analyzed. Finally, the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used to construct a prognostic signature with prognostic SI-genes. The ssGSEA score and prognostic signature were validated using the Gene Expression Omnibus (GEO) database.

Results: The mRNAsi could predict overall survival (OS), clinical characteristics, the gene mutation status, immune cell infiltration, and the tumor microenvironment composition. Fourteen positive SI-genes and 178 negative SI-genes were screened out using WGCNA. The ssGSEA score, similar to the mRNAsi, was found to be closely related to OS, clinical characteristics, the gene mutation status, immune cell infiltration, and the tumor microenvironment composition. Finally, a prognostic signature based on 18 prognostic SI-genes was verified to more accurately predict GC 1-year, 3-year, and 5-year OS than traditional clinical prediction models.

Conclusion: The ssGSEA score and prognostic signature based on 18 prognostic SI-genes are of great value for immune response evaluation, risk stratification and survival prediction in GC and suggest that stemness features are crucial drivers of GC progression.

Keywords: gastric cancer; immune response; prognostic signature; stemness index; tumor microenvironment.