Identification of pivotal genes associated with the prognosis of gastric carcinoma through integrated analysis.

Zhenchao Ma,Lixin Ru,Weihua Zhu,Jianwei Xu

doi:10.1042/bsr20203676

Zhenchao Ma, Lixin Ru + Show 2 more

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https://doi.org/10.1042/bsr20203676

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Abstract

Purpose: Detecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values.Methods: Microarray datasets from the Gene Expression Omnibus (GEO) (GSE54129) and The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) datasets were applied for common differentially co-expressed genes using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Functional enrichment analysis and protein–protein interaction (PPI) network analysis of differentially co-expressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, Gene Set Enrichment Analysis (GSEA) was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated.Results: Sixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell–cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and serpin family E member 1 (SERPINE1) was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that extracellular matrix (ECM) receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8+ T cell and T-cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment (TME) in GC.Conclusions: Our analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC.

Highlights

Gastric cancer (GC) is an aggressive solid tumor malignancy with approximately 27600 estimated new cases and 11010 estimated deaths in 2020, which causes a huge socioeconomic burden to patients and their families [1]
To detect the functional module in stomach adenocarcinoma (STAD), two gene co-expression networks were established through the Weighted Gene Co-expression Network Analysis (WGCNA) package based on the GSE54129 and The Cancer Genome Atlas (TCGA)-STAD datasets
According to the outcomes of Gene Set Enrichment Analysis (GSEA), we found that focal adhesion, extracellular matrix (ECM) receptor interaction, leukocyte transendothelial migration, regulation of actin cytoskeleton, MAPK signaling pathway, and pathways in cancers were highly associated with the expression of SERPINE1 (Figure 8A-J)

Summary

Introduction

Gastric cancer (GC) is an aggressive solid tumor malignancy with approximately 27600 estimated new cases and 11010 estimated deaths in 2020, which causes a huge socioeconomic burden to patients and their families [1]. The therapeutic strategies for GC include surgery, radiotherapy, neoadjuvant chemotherapy and immunotherapy, and the survival rate for patients with early GC is nearly 90% [3]. It is extremely necessary to detect candidate diagnostic and prognostic indicators and therapeutic targets for GC patients. With the rapid progress of genomic technology, gene expression profiles are usually used through bioinformatics methods to explore the underlying molecular mechanisms of tumors and find cancer-specific indicators [5]. Differential gene expression analysis is commonly adopted to analyze transcriptomics datasets, and this is conducive to exploring the underlying biological and molecular mechanisms of tumors and detecting quantitative differences between the gene expression levels of experimental and control cohorts [8]

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