Abstract
The infiltration degree of immune and stromal cells has been shown clinically significant in tumor microenvironment (TME). However, the utility of stromal and immune components in Gastric cancer (GC) has not been investigated in detail. In the present study, ESTIMATE and CIBERSORT algorithms were applied to calculate the immune/stromal scores and the proportion of tumor-infiltrating immune cell (TIC) in GC cohort, including 415 cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were screened by Cox proportional hazard regression analysis and protein–protein interaction (PPI) network construction. Then ADAMTS12 was regarded as one of the most predictive factors. Further analysis showed that ADAMTS12 expression was significantly higher in tumor samples and correlated with poor prognosis. Gene Set Enrichment Analysis (GSEA) indicated that in high ADAMTS12 expression group gene sets were mainly enriched in cancer and immune-related activities. In the low ADAMTS12 expression group, the genes were enriched in the oxidative phosphorylation pathway. CIBERSORT analysis for the proportion of TICs revealed that ADAMTS12 expression was positively correlated with Macrophages M0/M1/M2 and negatively correlated with T cells follicular helper. Therefore, ADAMTS12 might be a tumor promoter and responsible for TME status and tumor energy metabolic conversion.
Highlights
The infiltration degree of immune and stromal cells has been shown clinically significant in tumor microenvironment (TME)
Immune scores and stromal scores are significantly associated with clinicopathological parameters and prognosis of Gastric cancer (GC)
Based on the ESTIMATE algorithm, the immune scores distributed from − 1184.83 to 2826.73 and the stromal scores ranged from − 1838.38 to 2085.81
Summary
The infiltration degree of immune and stromal cells has been shown clinically significant in tumor microenvironment (TME). ESTIMATE and CIBERSORT algorithms were applied to calculate the immune/stromal scores and the proportion of tumor-infiltrating immune cell (TIC) in GC cohort, including 415 cases from The Cancer Genome Atlas (TCGA) database. To predict the purity of tumor tissues, Yoshihara et al.[9] had created an algorithm mode called ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data), basing on gene expression data from the TCGA database. By taking use of both ESTIMATE algorithm and CIBERSORT computational methods, based on TCGA database, for the first time, we extracted a set of microenvironment-related genes, which could predict poor prognosis in GC patients. By further comparing differentially expressed genes (DEGs), we confirmed that ADAMTS12 could be a potential prognostic factor and might be responsible for the change of TME status in GC
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