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Home Science News Cancer

A systematic exploration of key candidate genes and pathways in the biogenesis of human gastric cancer

April 29, 2024
in Cancer
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A systematic exploration of key candidate genes and pathways in the biogenesis of human gastric cancer
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Gastric cancer (GC) is a prevalent gastrointestinal malignancy, yet its early detection remains hindered due to the lack of available genetic markers. This study aimed to identify alternative genetic markers for the early prognosis and prevention of GC.

Background and Aims

Gastric cancer (GC) is a prevalent gastrointestinal malignancy, yet its early detection remains hindered due to the lack of available genetic markers. This study aimed to identify alternative genetic markers for the early prognosis and prevention of GC.

 

Methods

This objective was achieved through the analysis of differentially expressed genes (DEGs) from three datasets obtained from the Gene Expression Omnibus (GEO). By doing so, our goal was to identify hub genes associated with gastric adenocarcinoma that could serve as potential biomarkers for the early detection and management of GC. Three GEO datasets (GSE172032, GSE179581, and GSE181492), consisting of 10 normal and 10 GC samples were analyzed using the Galaxy web server. The visualizations of DEGs, including heatmaps, volcano plots, and MD plots, were generated via the same tool. ShinyGO performed Gene Ontology and KEGG enrichment analysis, while NetworkAnalyst identified a protein-protein interaction (PPI) network and screened 10 potential hub genes. Kaplan Meier plotter was used to analyze overall survival analysis for key hub genes, and NetworkAnalyst was used to assess protein-drug interactions for the top 10 hub genes.

 

Results

A total of 1,079 common DEGs emerged across datasets, concentrating on significant GC-related pathways. Ten hub genes (H2BC21, H3C12, H2BC17, H3C2, H3C10, ERBB4, H2AC8, H3C8, H2BC14, and MAPT) were found to be linked to GC via PPI analysis. Survival analysis revealed that higher expression levels of ERBB4 and MAPT were associated with poor overall survival in GC patients. Furthermore, protein-drug interaction analysis revealed that the protein product of the MAPT gene exhibited a robust connection with drug compounds, specifically docetaxel and paclitaxel. These findings suggested that these drugs have the potential to inhibit the function of MAPT.

 

Conclusions

This study identified 1079 DEGs, with 638 upregulated and 441 downregulated, between human GC tissues and matched adjacent normal tissue specimens based on the GSE172032, GSE179581, and GSE181492 datasets. Further analysis of DEGs suggested that three types of hub genes namely, H3 Clustered Histone genes (H3C2, H3C8, H3C10, H3C12), HER4, and MAPT, could play critical roles in the progression of GC. The strong association of these predicted hub genes with the progression of GC has been identified in many studies by researchers. In summary, the present study provides a comprehensive analysis of key candidate genes and pathways in human GC using a bioinformatics approach. The identified hub genes and pathways provide valuable insights into the molecular mechanisms underlying GC development and progression and may serve as potential therapeutic targets and biomarkers for the early detection of GC.

 

Full text

 

The study was recently published in the Journal of Translational Gastroenterology.

Journal of Translational Gastroenterology (JTG) dedicates to improving clinical diagnosis and treatment, advancing understanding of the molecular mechanisms, and promoting translation from bench to bedside of gastrointestinal, hepatobiliary, and pancreatic diseases. The aim of JTG is to provide a forum for the exchange of ideas and concepts on basic, translational, and clinical aspects of gastroenterology, and promote cross-disciplinary research and collaboration.

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Journal

Journal of Translational Gastroenterology

DOI

10.14218/JTG.2023.00072

Article Title

A Systematic Exploration of Key Candidate Genes and Pathways in the Biogenesis of Human Gastric Cancer: A Comprehensive Bioinformatics Investigation

Article Publication Date

9-Feb-2024

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