Identification of prognosis-related genes in the tumor microenvironment of stomach adenocarcinoma by TCGA and GEO datasets

Abstract

Accumulating evidence has demonstrated that tumor microenvironment (TME) plays a crucial role in stomach adenocarcinoma (STAD) development, progression, prognosis and immunotherapeutic responses. How the genes in TME interact and behave is extremely crucial for tumor investigation. In the present study, we used gene expression data of STAD available from TCGA and GEO datasets to infer tumor purity using ESTIMATE algorithms, and predicted the associations between tumor purity and clinical features and clinical outcomes. Next, we calculated the differentially expressed genes (DEGs) from the comparisons of immune and stromal scores, and postulated key biological processes and pathways that the DEGs mainly involved in. Then, we analyzed the prognostic values of DEGs in TCGA dataset, and validated the results by GEO dataset. Finally, we used CIBERSORT computational algorithm to estimate the 22 tumor infiltrating immune cells (TIICs) subsets in STAD tissues. We found that stromal and immune scores were significantly correlated with STAD subtypes, clinical stages, Helicobacter polyri infection, and stromal scores could predict the clinical outcomes in STAD patients. Moreover, we screened 307 common DEGs in TCGA and GSE51105 datasets. In the prognosis analyses, we only found OGN, JAM2, RERG, OLFML2B, and ADAMTS1 genes were significantly associated with overall survival in TCGA and GSE84437 datasets, and these genes were correlated with the fractions of T cells, B cells, macrophages, monocytes, NK cells and DC cells, respectively. Our comprehensive analyses for transcriptional data not only improved the understanding of characteristics of TME, but also provided the targets for individual therapy in STAD.

Drug metabolism‐related eight‐gene signature can predict the prognosis of gastric adenocarcinoma

“…The tumor microenvironment significantly contributes to the occurrence, progression, prognosis, and immunotherapy response of gastric adenocarcinoma. 6 Gastric adenocarcinoma is a chronic gastritis caused by Helicobacter pylori and is often characterized by the infiltration of immune cells, including granulocytes, macrophages, and T lymphocytes. 40 Therefore, the current study explored the relationship between prognostic risk and immune cell infiltration.…”

“… 4 Therefore, numerous studies have been conducted to explore prognostic biomarkers in an attempt to improve the clinical outcome of patients with gastric adenocarcinoma. Among them, Yao and Ren et al evaluated the importance of immune microenvironment‐related genes in gastric adenocarcinoma prognosis by mining public databases 5 , 6 . However, a more comprehensive understanding of tumorigenesis mechanisms and the exploration of potential prognostic biomarkers from multiple perspectives are still required.…”

High Expression of ERK-related RASGRF2 predicts Poor prognosis in patients with Stomach Adenocarcinoma and correlates with M2 macrophage

“…It has recently been proposed that tumor microenvironments, especially immune factors within such environments, play an important role in STAD progression 41 , 42 . Accumulating evidence indicates that tumor microenvironment immune subtypes can predict clinical responses to immunotherapeutic strategies across a variety of tumor types 41 , 43 . The present study demonstrated that high RASGRF2 expression correlates significantly and positively with gene signature-estimated Tem, macrophage, pDC, and NK cell infiltration into the tumor.…”

Immune Checkpoint Gene Expression Profiling Identifies Programmed Cell Death Ligand-1 Centered Immunologic Subtypes of Oral and Squamous Cell Carcinoma With Favorable Survival

“…The TCGA is well-established as the most comprehensive cancer genomics program, which has produced, evaluated, and made public data related to the genomic sequencing, expression, methylation, and copy number variation of over 11,000 patients with cancer who have been diagnosed with more than 30 distinct forms of cancer (17). Many previous bioinformatics studies have followed the traditional study design of analyzing the TCGA data and followed by the verification of the computationally predicted results using GEO data sets (19)(20)(21)(22)(23)(24). The GEO database is a freely accessible resource for the functional genomics data that contain original data sets from tens of thousands of published microarray or sequencing experiments (25).…”