ArticleComputational and mathematical methods in medicine2016
Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer.
Article in Computational and mathematical methods in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 20 citations in OpenAlex.
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- Elevated CDC45 Expression Predicts Poorer Overall Survival Prognoses and Worse Immune Responses for Kidney Renal Clear Cell Carcinoma via Single-Cell and Bulk RNA-Sequencing.Biochemical genetics · 2024Article
- Prognostic value of HPV 16/18 genotyping and geminin mRNA quantification in low-grade cervical squamous intraepithelial lesion.Bioengineered · 2021Article
- Identification of potential core genes and pathways predicting pathogenesis in head and neck squamous cell carcinoma.Bioscience reports · 2021Article
- Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.PloS one · 2021Article
- Identification of Core Genes Involved in the Progression of Cervical Cancer Using an Integrative mRNA Analysis.International journal of molecular sciences · 2020Article
- A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study.Computational and mathematical methods in medicine · 2020Article
- Systematic prediction of target genes and pathways in cervical cancer from microRNA expression data.Oncology letters · 2018Article
- Identification and validation of the role of matrix metalloproteinase-1 in cervical cancer.International journal of oncology · 2018Article
- Modeling the dynamics of chromosomal alteration progression in cervical cancer: A computational model.PloS one · 2017Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe objective of our study was to predicate candidate genes in cervical cancer (CC) using a network-based strategy and to understand the pathogenic process of CC.
methodsA pathogenic network of CC was extracted based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed.
resultsIn this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes, VIM had the highest weight value. Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism.
conclusionCandidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application.
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