ArticleExperimental and therapeutic medicine2018
Identification of key candidate genes and pathways in hepatocellular carcinoma by integrated bioinformatical analysis.
Article in Experimental and therapeutic medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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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
20 citing papers in PubMed, 20 citations in OpenAlex.
- Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.PloS one · 2025Article
- Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2024Article
- Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine.Cancer informatics · 2024Article
- Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning.Scientific reports · 2023Article
- HFE promotes mitotic cell division through recruitment of cytokinetic abscission machinery in hepatocellular carcinoma.Oncogene · 2022Article
- Identification and Validation of Hub Genes with Poor Prognosis in Hepatocellular Carcinoma by Integrated Bioinformatical Analysis.International journal of general medicine · 2022Article
- Network analysis of hepatocellular carcinoma liquid biopsies augmented by single-cell sequencing data.Frontiers in genetics · 2022Article
- Upregulated TUBG1 expression is correlated with poor prognosis in hepatocellular carcinoma.PeerJ · 2022Article
- Loss of WNK1 Suppressed the Malignant Behaviors of Hepatocellular Carcinoma Cells by Promoting Autophagy and Activating AMPK Pathway.Disease markers · 2022Article
- Meta-analysis of transcriptomics data identifies potential biomarkers and their associated regulatory networks in gallbladder cancer.Gastroenterology and hepatology from bed to bench · 2022Review
- Identification and validation of real hub genes in hepatocellular carcinoma based on weighted gene co-expression network analysis.Cancer biomarkers : section A of Disease markers · 2022Article
- Identification of Key Genes for Hepatitis Delta Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2021Article
- Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis.BioMed research international · 2021Article
- System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration.Molecular diagnosis & therapy · 2021Review
- Cellular Mechanisms of Liver Fibrosis.Frontiers in pharmacology · 2021Review
- Bioinformatics Analysis based on Multiple Databases Identifies Hub Genes Associated with Hepatocellular Carcinoma.Current genomics · 2019Article
- Article
- Comprehensive investigation of key biomarkers and pathways in hepatitis B virus-related hepatocellular carcinoma.Journal of Cancer · 2019Article
- Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis.International journal of genomics · 2019Article
- Cytochrome P450 family members are associated with fast-growing hepatocellular carcinoma and patient survival: An integrated analysis of gene expression profiles.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology AssociationArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is one of the most common malignant neoplasms worldwide, however the underlying mechanisms and gene signatures of HCC are unknown. In the present study the profile datasets of four cohorts were integrated to elucidate the pathways and candidate genes of HCC. The expression profiles GSE25097, GSE45267, GSE57957 and GSE62232 were downloaded from the Gene Expression Omnibus database, including 436 HCC and 94 normal liver tissues. A total of 185 differentially expressed genes (DEGs) were identified in HCC, including 92 upregulated genes and 92 downregulated genes. Gene ontology (GO) was performed, which revealed that the upregulated DEGs were primarily enriched in cell division, mitotic nuclear division, mitotic cytokinesis and G1/S transition of the mitotic cell cycle. Pathway enrichment was analyzed based on the Kyoto Encyclopedia of Genes and Genomes database to assess the functional relevance of DEGs. The most significant module was selected from protein-protein interactions and 15 important hub genes were identified. The sub-networks of hub genes were involved in cell division, p53 signaling, and T lymphotropic virus type I infection signaling pathways. In conclusion, the present study revealed that the identified DEG candidate genes may promote the understanding of the cause and molecular mechanisms underlying the development of HCC and that these candidates and signal pathways may be potential targets of clinical therapy for HCC.
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