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Updated: Oct 18, 2025
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
Published on: July 22, 2020
Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis
Xiaolong Chen1, Zhixiong Xia2, Yafeng Wan3
1National Key Clinical Department, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background:
Hepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven genes and potential drugs in HCC.
Methods:
Three mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, Gene Ontology terms analysis and Kyoto encyclopedia of genes and genomes enrichment analysis, construction of protein-protein interaction network. The expression levels of hub genes were validated based on The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of HCC patients were further conducted by Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis. DGIdb database was performed to search the candidate drugs for HCC.
Results:
A total of 197 DEGs were identified. The protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes software, 10 genes were selected by Cytoscape plugin cytoHubba and served as hub genes. These 10 genes were all closely related to the survival of HCC patients. DGIdb database predicted 29 small molecules as the possible drugs for treating HCC.
Conclusion:
Our study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in the future.
Insights
This study identifies 10 key genes and 29 potential drugs for hepatocellular carcinoma (HCC) treatment. These findings offer new insights into HCC mechanisms and may improve future therapies.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality globally.
- The underlying molecular mechanisms driving HCC development remain largely undefined.
- Understanding these mechanisms is crucial for developing effective therapeutic strategies.
Purpose of the Study:
- To identify differentially expressed genes (DEGs) and key driver genes in HCC.
- To explore potential therapeutic targets and candidate drugs for HCC treatment.
- To elucidate novel insights into HCC pathogenesis.
Main Methods:
- Analysis of three mRNA expression datasets to identify DEGs and hub genes.
- Construction of a protein-protein interaction network and enrichment analyses (GO, KEGG).
- Validation of hub gene expression using TCGA, GEPIA, and Human Protein Atlas; survival analysis using Kaplan-Meier plotter and GEPIA; drug discovery using DGIdb.
Main Results:
- Identified 197 DEGs in HCC, with 10 selected as hub genes crucial for patient survival.
- Constructed a protein-protein interaction network to visualize gene relationships.
- Predicted 29 small molecules as potential therapeutic agents for HCC.
Conclusions:
- The study provides novel insights into the pathogenesis of hepatocellular carcinoma.
- Identified hub genes and candidate drugs offer promising avenues for future HCC therapy.
- These findings may enhance the efficacy of HCC treatment.
