Identification of Stemness Characteristics Associated With the Immune Microenvironment and Prognosis in Gastric Cancer - Mao, Deli; Zhou, Zhijun; Song, Shenglei; Li, Dongsheng; He, Yulong; Wei, Zhewei; Zhang, Changhua (Frontiers in Oncology, 2021)
Background Gastric cancer (GC) is a highly heterogeneous disease. In recent years, the prognostic value of mRNA expression-based stemness index (mRNAsi) in the pan-cancer has been reported. We intend to identify stemness index-associated genes (SI-genes) for clinical characteristics, genes mutation status, immune response, and tumor microenvironment evaluation as well as risk stratification, survival prediction. Methods The correlation between mRNAsi and GC prognosis, clinical characteristics, gene mutation status, immune cell infiltration as well as tumor microenvironment was evaluated. The weighted gene correlation network analysis (WGCNA) was performed to identify SI-genes from differentially expressed genes (DEGs) of the Cancer Genome Atlas (TCGA). Single sample Gene Set Enrichment Analysis (ssGSEA) was employed to calculate the sample SI-genes-based ssGSEA score according to SI-genes. Then, the correlation between ssGSEA score and GC prognosis, clinical characteristics, gene mutation status, immune cell infiltration as well as tumor microenvironment was analyzed. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) cox regression algorithm was used to construct the prognostic signature with prognostic SI-genes. The ssGSEA score and prognostic signature were validated using the Gene Expression Omnibus (GEO) database. Results mRNAsi can predict overall survival (OS), clinical characteristics, gene mutation status, immune cell infiltration, and the composition of the tumor microenvironment. 14 positive SI-genes and 178 negative SI-genes were screened out using the WGCNA. The ssGSEA score, like mRNAsi, was found to be closely related to OS, clinical characteristics, gene mutation status, immune cell infiltration, and the composition of the tumor microenvironment. Finally, the prognostic signature based on 18 prognostic SI-genes was verified to have a more accurate prediction of GC 1-year, 3-year, and 5-year OS capabilities compared to traditional clinical prediction models. Conclusion The ssGSEA score and prognostic signature based on 18 prognostic SI-genes are of great value for the immune response evaluation, risk stratification and survival prediction of GC and suggests that stemness features are crucial drivers in GC progression.