Dataset Information

A transcriptional co-expression network-based approach to identify prognostic biomarkers in gastric carcinoma.

ABSTRACT: Background:Gastric carcinoma is a very diverse disease. The progression of gastric carcinoma is influenced by complicated gene networks. This study aims to investigate the actual and potential prognostic biomarkers related to survival in gastric carcinoma patients to further our understanding of tumor biology. Methods:A weighted gene co-expression network analysis was performed with a transcriptome dataset to identify networks and hub genes relevant to gastric carcinoma prognosis. Data was obtained from 300 primary gastric carcinomas (GSE62254). A validation dataset (GSE34942 and GSE15459) and TCGA dataset confirmed the results. Gene ontology, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene set enrichment analysis (GSEA) were performed to identify the clusters responsible for the biological processes and pathways of this disease. Results:A brown transcriptional module enriched in the organizational process of the extracellular matrix was significantly correlated with overall survival (HR = 1.586, p = 0.005, 95% CI [1.149-2.189]) and disease-free survival (HR = 1.544, p = 0.008, 95% CI [1.119-2.131]). These observations were confirmed in the validation dataset (HR = 1.664, p = 0.006, 95% CI [1.155-2.398] in overall survival). Ten hub genes were identified and confirmed in the validation dataset from this brown module; five key biomarkers (COL8A1, FRMD6, TIMP2, CNRIP1 and GPR124 (ADGRA2)) were identified for further research in microsatellite instability (MSI) and epithelial-tomesenchymal transition (MSS/EMT) gastric carcinoma molecular subtypes. A high expression of these genes indicated a poor prognosis. Conclusion:A transcriptional co-expression network-based approach was used to identify prognostic biomarkers in gastric carcinoma. This method may have potential for use in personalized therapies, however, large-scale randomized controlled clinical trials and replication experiments are needed before these key biomarkers can be applied clinically.

SUBMITTER: Liu D

PROVIDER: S-EPMC7025707 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A transcriptional co-expression network-based approach to identify prognostic biomarkers in gastric carcinoma.

Liu Danqi D Zhou Boting B Liu Rangru R

PeerJ 20200214

<h4>Background</h4>Gastric carcinoma is a very diverse disease. The progression of gastric carcinoma is influenced by complicated gene networks. This study aims to investigate the actual and potential prognostic biomarkers related to survival in gastric carcinoma patients to further our understanding of tumor biology.<h4>Methods</h4>A weighted gene co-expression network analysis was performed with a transcriptome dataset to identify networks and hub genes relevant to gastric carcinoma prognosis. ...[more]

PMID: 32095347

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Project description:BackgroundGastric cancer (GC) is one of the most common cancer worldwide. It is essential to identify non-invasive diagnostic and prognostic biomarkers of GC. The aim of the present study was to screen candidate biomarkers associated with the pathogenesis and prognosis of GC by a novel strategy.MethodsThe expression level of gene higher in cancer than in adjacent non-cancer tissue was defined as "positive", and the top 5% genes with "positive rate" were filtered out as candidate diagnostic biomarkers in three Gene Expression Omnibus (GEO) datasets. Further, a prognostic risk model was constructed by multivariate Cox regression analysis in GEO dataset and validated in The Cancer Genome Atlas (TCGA). The expression level of candidate biomarkers was determined in serum and serum-derived exosomes of GC patients. Moreover, the effect of biomarkers in exosomes on migration of GC cells was analyzed by transwell assay.ResultsTen candidate biomarkers (AGT, SERPINH1, WNT2, LIPG, PLAU, COL1A1, MMP7, MXRA5, CXCL1 and COL11A1) were identified with efficient diagnostic value in GC. A prognostic gene signature consisted of AGT, SERPINH1 and MMP7 was constructed and showed a good performance in predicting overall survivals in TCGA. Consistently, serum levels of the three biomarkers also showed high sensitivity and specificity in distinguishing GC patients from controls. In addition, the expression level of the three biomarkers were associated with malignant degree and decreased after surgery in GC patients. Moreover, the expression level of AGT and MMP7 in exosomes correlated positively with serum level. The exosomes derived from serum of GC patients can promote migration of SGC-7901 cells. After neutralized the expression level of three proteins in exosomes with antibodies, the migration of GC cells was obviously suppressed.ConclusionsOur findings provided a novel strategy to identify diagnostic biomarkers based on public datasets, and suggested that the three-gene signature was a candidate diagnostic and prognostic biomarker for patients with GC.

Dataset Information

A transcriptional co-expression network-based approach to identify prognostic biomarkers in gastric carcinoma.

Publications

A transcriptional co-expression network-based approach to identify prognostic biomarkers in gastric carcinoma.

Similar Datasets

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