ArticleExperimental and therapeutic medicine2020
Integrative module analysis of HCC gene expression landscapes.
Article in Experimental and therapeutic medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Integrative multi-platform meta-analysis of hepatocellular carcinoma gene expression profiles for identifying prognostic and diagnostic biomarkers.Genes & diseases · 2023Article
- Detection of key mRNAs in liver tissue of hepatocellular carcinoma patients based on machine learning and bioinformatics analysis.MethodsX · 2023Article
- Gene Expression Profiling of Glioblastoma to Recognize Potential Biomarker Candidates.Frontiers in genetics · 2022Article
- Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis.Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite hepatocellular carcinoma (HCC) being a common cancer globally, its initiation and progression are not well understood. The present study was designed to investigate the hub genes and biological processes of HCC, which change substantially during its progression. Three gene expression profiles of 480 patients with HCC were obtained from the Gene Expression Omnibus database. Subsequent to performing functional annotations and constructing protein-protein interaction (PPI) networks, 657 differentially expressed genes were identified, which were subsequently used to screen candidate hub genes. PPI networks were modularized using the weighted gene correlation network analysis algorithm, the topological overlapping matrix and the hierarchical cluster tree, which were utilized via STRING. Clinical data obtained from The Cancer Genome Atlas were then analyzed to validate the experiments performed using six hub genes. Additionally, a transcription factor and microRNA-mRNA network were constructed to determine the potential regulatory mechanisms of six hub genes. The results revealed that the oxidation-reduction process and cell cycle associated processes were markedly involved in HCC progression. Six highly expressed genes, including cyclin B2, cell division cycle 20, mitotic arrest deficient 2 like 1, minichromosome maintenance complex component 2, centromere protein F and BUB mitotic checkpoint serine/threonine kinase B, were confirmed as hub genes and validated via experiments associated with cell division. These hub genes are necessary for confirmatory experiments and may be used in clinical gene therapy as biomarkers or drug targets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.