Identification of Key Biological Processes, Pathways, Networks, and Genes with Potential Prognostic Values in Hepatocellular Carcinoma Using a Bioinformatics Approach
| Autor: | Huijie Lu, Qianlin Zhu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Cancer Research Carcinoma Hepatocellular Chromosomal Proteins Non-Histone Biology Bioinformatics Mitochondrial Proteins 03 medical and health sciences 0302 clinical medicine Drug Discovery DSN1 Gene expression Biomarkers Tumor medicine Humans Gene Regulatory Networks Radiology Nuclear Medicine and imaging Protein Interaction Maps KEGG Gene Survival analysis Pharmacology Gene Expression Profiling Liver Neoplasms Computational Biology General Medicine Cell cycle Prognosis medicine.disease digestive system diseases Mitochondria Gene Expression Regulation Neoplastic Gene Ontology 030104 developmental biology Oncology 030220 oncology & carcinogenesis Hepatocellular carcinoma Liver cancer Microtubule-Associated Proteins |
| Zdroj: | Cancer Biotherapy and Radiopharmaceuticals. 36:837-849 |
| ISSN: | 1557-8852 1084-9785 |
| DOI: | 10.1089/cbr.2019.3327 |
| Popis: | Aim: Hepatocellular carcinoma (HCC), as one primary liver cancer type, accounts for 75%-85% of liver cancer cases. HCC is the second leading cause of cancer death in East Asia and sub-Saharan Africa and the sixth most common in western countries. Identification of key genes would facilitate the development of therapies and improve the prognosis outcomes of HCC patients. This study was to identify the key biological processes, pathways, and key genes in HCC. Methods: Data were downloaded from Broad GDAC. Differentially expressed genes (DEGs) and weighted gene coexpression network (WGCNA) were analyzed by DESeq2 and WGCNA, respectively. Gene ontology (GO) and KEGG enrichment analyses were performed on all DEGs and the coexpressed genes in two significant modules. Kaplan-Meier plotter online database was used to identify the potential prognostic genes in HCC. Finally, GEO database was used to validate the analysis of gene expression of Broad GDAC data. Results: The authors identified the dark gray and red modules as the significant modules in HCC based on WGCNA. GO and KEGG enrichment of the two significant modules identified the mitochondrion-mediated metabolic processes and pathways, and the cell cycle as the key biological processes and pathways in HCC. Subsequently, 28 hub genes were screened out by constructing protein-protein interaction network using Metascape. Finally, three genes (NDUFAF6, CKAP5, and DSN1 genes) were identified to be potential prognostic and key genes in HCC based on Kaplan-Meier survival analysis, GEO dataset validation, and literature review. Conclusions: The authors found that mitochondrion-mediated metabolic processes and the cell cycle were the key biological processes and pathways in HCC. NDUFAF6, CKAP5, and DSN1 genes were valuable genes with the potential to be prognosis biomarkers and targeted therapies in HCC. |
| Databáze: | OpenAIRE |
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