ArticleBMC bioinformatics2021
Bioinformatics screening of biomarkers related to liver cancer.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A GIMAP family signature for prognostic prediction and immune characterization in hepatocellular carcinoma patients.Discover oncology · 2026Article
- Mitochondrial permeability transition drives the expression, identification and validation of necrosis-related genes in prognostic risk models of hepatocellular carcinoma.Translational cancer research · 2025Article
- Advances and challenges in molecular understanding, early detection, and targeted treatment of liver cancer.World journal of hepatology · 2025Review
- Article
- The positive feedback loop of MAD2L1/TYK2/STAT3 induces progression in B-cell acute lymphoblastic leukaemia.Journal of cancer research and clinical oncology · 2023Article
- Non-steroidal anti-inflammatory drugs and biomarkers: A new paradigm in colorectal cancer.Frontiers in medicine · 2023Review
- Upregulation of CCNB2 and Its Perspective Mechanisms in Cerebral Ischemic Stroke and All Subtypes of Lung Cancer: A Comprehensive Study.Frontiers in integrative neuroscience · 2022Article
- Integrated analysis of necroptosis-related genes for evaluating immune infiltration and colon cancer prognosis.Frontiers in immunology · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundLiver cancer is a common malignant tumor in China, with high mortality. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, mRNA quantification and the clinical data of liver cancer. However, the hub genes, which can be served as biomarkers for diagnosis and treatment of early liver cancer, are not well screened.
resultsHere we present a new method for getting 6 key genes, aiming to diagnose and treat the early liver cancer. We firstly analyzed the different expression microarrays based on TCGA database, and a total of 1564 differentially expressed genes were obtained, of which 1400 were up-regulated and 164 were down-regulated. Furthermore, these differentially expressed genes were studied by using GO and KEGG enrichment analysis, a PPI network was constructed based on the STRING database, and 15 hub genes were obtained. Finally, 15 hub genes were verified by applying the survival analysis method on Oncomine database, and 6 key genes were ultimately identified, including PLK1, CDC20, CCNB2, BUB1, MAD2L1 and CCNA2. The robustness analysis of four independent data sets verifies the accuracy of the key gene's classification of the data set.
conclusionsAlthough there are complicated differences between cancer and normal cells in gene functions, cancer cells could be differentiated in case that a group of special genes expresses abnormally. Here we presented a new method to identify the 6 key genes for diagnosis and treatment of early liver cancer, and these key genes can help us understand the pathogenesis of liver cancer more deeply.
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