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Identification of prognostic gene expression signatures based on the tumor microenvironment characterization of gastric cancer.

Authors :
Sang Q
Dai W
Yu J
Chen Y
Fan Z
Liu J
Li F
Li J
Wu X
Hou J
Yu B
Feng H
Zhu ZG
Su L
Li YY
Liu B
Source :
Frontiers in immunology [Front Immunol] 2022 Aug 12; Vol. 13, pp. 983632. Date of Electronic Publication: 2022 Aug 12 (Print Publication: 2022).
Publication Year :
2022

Abstract

Increasing evidence has elucidated that the tumor microenvironment (TME) shows a strong association with tumor progression and therapeutic outcome. We comprehensively estimated the TME infiltration patterns of 111 gastric cancer (GC) and 21 normal stomach mucosa samples based on bulk transcriptomic profiles based on which GC could be clustered as three subtypes, TME-Stromal, TME-Mix, and TME-Immune. The expression data of TME-relevant genes were utilized to build a GC prognostic model-GC_Score. Among the three GC TME subtypes, TME-Stomal displayed the worst prognosis and the highest GC_Score, while TME-Immune had the best prognosis and the lowest GC_Score. Connective tissue growth factor (CTGF), the highest weighted gene in the GC_Score, was found to be overexpressed in GC. In addition, CTGF exhibited a significant correlation with the abundance of fibroblasts. CTGF has the potential to induce transdifferentiation of peritumoral fibroblasts (PTFs) to cancer-associated fibroblasts (CAFs). Beyond characterizing TME subtypes associated with clinical outcomes, we correlated TME infiltration to molecular features and explored their functional relevance, which helps to get a better understanding of carcinogenesis and therapeutic response and provide novel strategies for tumor treatments.<br />Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (Copyright © 2022 Sang, Dai, Yu, Chen, Fan, Liu, Li, Li, Wu, Hou, Yu, Feng, Zhu, Su, Li and Liu.)

Details

Language :
English
ISSN :
1664-3224
Volume :
13
Database :
MEDLINE
Journal :
Frontiers in immunology
Publication Type :
Academic Journal
Accession number :
36032070
Full Text :
https://doi.org/10.3389/fimmu.2022.983632