Published online Sep 15, 2020. doi: 10.4251/wjgo.v12.i9.975
Peer-review started: April 5, 2020
First decision: May 15, 2020
Revised: May 15, 2020
Accepted: August 1, 2020
Article in press: August 1, 2020
Published online: September 15, 2020
With the popularization and advancement of high throughput sequencing technologies, there is a real possibility of establishing multiple-gene signatures based on data integration and bioinformatics analysis in cancer research. The present study aimed to identify prognostic biomarkers for gastric carcinoma (GC) patients using comprehensive bioinformatics analyses.
GC is one of the most aggressive primary digestive tumors. It has unsatisfactory therapeutic outcomes and is difficult to diagnose early. Therefore, it is necessary to search for a multiple-gene signature-derived model for predicting prognosis and accurately identifying anti-cancer targeted therapies to improve the prognostic stratification and personalized therapy for GC patients.
We aimed to explore the potential multiple-gene prognostic biomarkers and effective therapeutic targets for GC. In this study, we performed integrated bioinformatics analysis to establish a nine-gene risk score model (COL8A1, CTHRC1, COL5A2, SERPINE1, COL1A2, FNDC1 AADAC, MAOA, and MAMDC2) associated with prognosis and treatment response in GC patients. The nine-gene signature-derived risk score allows to predict GC prognosis and might prove useful for guiding therapeutic strategies for GC patients.
Differentially expressed genes (DEGs) were screened using gene expression data from The Cancer Genome Atlas and GEO databases for GC. Overlapping DEGs were analyzed using univariate and multivariate Cox regression analyses. A risk score model was then constructed and signature prognostic values were validated utilizing an independent GEO dataset (GSE15459). CBioPortal, GEPIA, and KM-plotter databases were used to analyze each gene in the risk score model. Gene set enrichment analysis and the connectivity map database were used to predict high-risk score-associated pathways and therapeutic small molecule drugs, respectively.
A total of 95 overlapping DEGs were found and a nine-gene signature (COL8A1, CTHRC1, COL5A2, AADAC, MAMDC2, SERPINE1, MAOA, COL1A2, and FNDC1) was constructed for the GC prognosis prediction. Receiver operating characteristic curve performance in the training dataset (The Cancer Genome Atlas- stomach adenocarcinoma) and validation dataset (GSE15459) demonstrated a robust prognostic value of the risk score model. Multiple database analyses for each gene provided evidence to further understand the nine-gene signature. Gene set enrichment analysis showed that the high-risk group was enriched in multiple cancer-related pathways. Moreover, several new small molecule drugs for potential treatment of GC were identified.
Using a series of comprehensive bioinformatics analyses and validations, a novel nine-gene signature was constructed. The signature-derived risk score model had a robust prognostic capacity and therapeutic response in GC. Several small molecule drugs were identified to serve as potential therapeutic candidates for GC using bioinformatics. Further experimental studies are necessary to validate these findings and to elucidate the mechanisms for GC-related signaling pathways.
Multiple-gene assays are of great importance for precision medicine of GC patients. To further verify the prognostic capacity of the nine-gene signature, our future study may pay more attention to exploring the potential regulatory mechanisms how the nine-gene signature affects the development of GC.