In:
Frontiers in Genetics, Frontiers Media SA, Vol. 13 ( 2022-5-18)
Abstract:
Background: Gastric carcinoma (GC) is a carcinoma with a high incidence rate, and it is a deadly carcinoma globally. An effective tool, that is, able to predict different survival outcomes for GC patients receiving individualized treatments is deeply needed. Methods: In total, data from 975 GC patients were collected from TCGA-STAD, GSE15459, and GSE84437. Then, we performed a comprehensive unsupervised clustering analysis based on 54 TGFβ-pathway-related genes and correlated these patterns with tumor microenvironment (TME) cell-infiltrating characteristics. WGCNA was then applied to find the module that had the closest relation with these patterns. The least absolute shrinkage and selection operator (LASSO) algorithm was combined with cross validation to narrow down variables and random survival forest (RSF) was used to create a risk score. Results: We identified two different TGFβ regulation patterns and named them as TGFβ Cluster 1 and Cluster 2. TGFβ Cluster 1 was linked to significantly poorer survival outcomes and represented an inflamed TME subtype of GC. Using WGCNA, a module (magenta) with the closest association with the TGFβ clusters was identified. After narrowing down the gene list by univariate Cox regression analysis, the LASSO algorithm and cross validation, four of the 243 genes in the magenta module were applied to build a risk score. The group with a higher risk score exhibited a considerably poorer survival outcome with high predictive accuracy. The risk score remained an independent risk factor in multivariate Cox analysis. Moreover, we validated this risk score using GSE15459 and GSE84437. Furthermore, we found that the group with a higher risk score represented an inflamed TME according to the evidence that the risk score was remarkably correlated with several steps of cancer immunity cycles and a majority of the infiltrating immune cells. Consistently, the risk score was significantly related to immune checkpoint genes and T cell–inflamed gene expression profiles (GEPs), indicating the value of predicting immunotherapy. Conclusions: We have developed and validated a TGFβ-associated signature, that is, capable of predicting the survival outcome as well as depicting the TME immune characteristics of GC. In summary, this signature may contribute to precision medicine for GC.
Type of Medium:
Online Resource
ISSN:
1664-8021
DOI:
10.3389/fgene.2022.818378
DOI:
10.3389/fgene.2022.818378.s001
DOI:
10.3389/fgene.2022.818378.s002
DOI:
10.3389/fgene.2022.818378.s003
DOI:
10.3389/fgene.2022.818378.s004
DOI:
10.3389/fgene.2022.818378.s005
DOI:
10.3389/fgene.2022.818378.s006
DOI:
10.3389/fgene.2022.818378.s007
DOI:
10.3389/fgene.2022.818378.s008
DOI:
10.3389/fgene.2022.818378.s009
DOI:
10.3389/fgene.2022.818378.s010
DOI:
10.3389/fgene.2022.818378.s011
DOI:
10.3389/fgene.2022.818378.s012
DOI:
10.3389/fgene.2022.818378.s013
DOI:
10.3389/fgene.2022.818378.s014
DOI:
10.3389/fgene.2022.818378.s015
DOI:
10.3389/fgene.2022.818378.s016
DOI:
10.3389/fgene.2022.818378.s017
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2022
detail.hit.zdb_id:
2606823-0
Bookmarklink