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Integrated Analysis Identifies an Immune-Based Prognostic Signature for the Mesenchymal Identity in Gastric Cancer.

ABSTRACT: Background:Gastric cancer (GC) has been divided into four molecular subtypes, of which the mesenchymal subtype has the poorest survival. Our goal is to develop a prognostic signature by integrating the immune system and molecular modalities involved in the mesenchymal subtype. Methods:The gene expression profiles collected from 6 public datasets were applied to this study, including 1,221 samples totally. Network analysis was applied to integrate the mesenchymal modalities and immune signature to establish an immune-based prognostic signature for GC (IPSGC). Results:We identified six immune genes as key factors of the mesenchymal subtype and established the IPSGC. The IPSGC can significantly divide patients into high- and low-risk groups in terms of overall survival (OS) and relapse-free survival (RFS) in discovery (OS: P < 0.001) and 5 independent validation sets (OS range: P = 0.05 to P < 0.001; RFS range: P = 0.03 to P < 0.001). Further, in multivariate analysis, the IPSGC remained an independent predictor of prognosis and performed better efficiency compared to clinical characteristics. Moreover, macrophage M2 was significantly enriched in the high-risk group, while plasma cells were enriched in the low-risk group. Conclusions:We propose an immune-based signature identified by network analysis, which is a promising prognostic biomarker and help for the selection of GC patients who might benefit from more rigorous therapies. Further prospective studies are warranted to test and validate its efficiency for clinical application.

SUBMITTER: Peng D

PROVIDER: S-EPMC7171688 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Integrated Analysis Identifies an Immune-Based Prognostic Signature for the Mesenchymal Identity in Gastric Cancer.

Peng Dongzhu D Gu Bin B Ruan Liming L Zhang Xingguo X Shu Peng P

BioMed research international 20200409

<h4>Background</h4>Gastric cancer (GC) has been divided into four molecular subtypes, of which the mesenchymal subtype has the poorest survival. Our goal is to develop a prognostic signature by integrating the immune system and molecular modalities involved in the mesenchymal subtype.<h4>Methods</h4>The gene expression profiles collected from 6 public datasets were applied to this study, including 1,221 samples totally. Network analysis was applied to integrate the mesenchymal modalities and imm ...[more]

PMID: 32352015

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Project description:BackgroundAdvanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient subgroups but biomarkers remain challenging. Traditional classification of gastric cancer (GC) is based on genomic profiling and molecular features. Therefore, it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling.MethodsSingle-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm were applied to identify hub-network of immune-related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub-gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular function of hub gene in vitro.ResultsThree immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets. Compared to Immunity_L, the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and Molecular Complex Detection (MCODE) algorithm. Furthermore, ADAM like decysin 1 (ADAMDEC1) was identified as a seed gene among hub-network genes which is highly associated with favorable prognosis in both GSE62254 and external validation datasets. In addition, high expression of ADAMDEC1 correlated with immunotherapeutic response in IMvigor210 datasets. In vitro, ADAMDEC1 was confirmed as a potential protein in regulating proliferation and migration of gastric cancer cell. Deficiency of ADAMDEC1 of gastric cancer cell also associated with high expression of PD-L1 and Jurkat T cell apoptosis.ConclusionsWe identified immune-related subtypes and key tumor microenvironment marker in AGC which might facilitate the development of novel immune therapeutic targets.

Dataset Information

Integrated Analysis Identifies an Immune-Based Prognostic Signature for the Mesenchymal Identity in Gastric Cancer.

Publications

Integrated Analysis Identifies an Immune-Based Prognostic Signature for the Mesenchymal Identity in Gastric Cancer.

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