ArticleJournal of oncology2022
Identification of Signature Genes in the PD-1 Relative Gastric Cancer Using a Combined Analysis of Gene Expression and Methylation Data.
Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The morbidity and mortality rates for gastric cancer (GC) rank second among all cancers, indicating the serious threat it poses to human health, as well as human life. This study aims to identify the pathways and genes as well as investigate the molecular mechanisms of tumor-related genes in gastric cancer (GC). Method: We compared differentially expressed genes (DEGs) and differentially methylated genes (DMGs) in gastric cancer and normal tissue samples using The Cancer Genome Atlas (TCGA) data. The Kyoto Encyclopedia of Gene and Genome (KEGG) and the Gene Ontology (GO) enrichment analysis' pathway annotations were conducted on DMGs and DEGs using a clusterProfiler R package to identify the important functions, as well as the biological processes and pathways involved. The intersection of the two was chosen and defined as differentially methylated and expressed genes (DMEGs). For DMEGs, we used the principal component analysis (PCA) to differentiate gastric cancer from adjacent samples. The linear discriminant analysis method was applied to categorize the samples using DMEGs methylation data and DMEGs expression profiles data and was validated using the leave-one-out cross-validation (LOOCV) method. We plotted the ROC curve for the classification and calculated the AUC (area under the ROC curve) value for a more intuitive view of the classification effect. We also used the NetworkAnalyst 3.0 tool to analyze DMEGs, using DrugBank to acquire information on protein-drug interactions and generate a network map of gene-drug interactions. Results: We identified a total of 971 DMGs in 188 PD-1 negative and 187 PD-1 positive gastric cancer samples obtained from TCGA. The KEGG and GO enrichment analysis showed the involvement of the regulation of ion transmembrane transport, collagen-containing extracellular matrix, cell-cell junction, and peptidase regulator activity. We simultaneously obtained 1,189 DEGs, out of which 986 were downregulated, while 203 were upregulated in tumors. The enriched analysis of the GO's and KEGG's pathways indicated that the most significant pathways included an intestinal immune network for IgA production, Conclusion: These results may provide potential molecular biological therapy for PD-1 negative gastric cancer.
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