ArticleFrontiers in oncology2021
Construction of Gene Modules and Analysis of Prognostic Biomarkers for Cervical Cancer by Weighted Gene Co-Expression Network Analysis.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 20 citations in OpenAlex.
- Identification of a biologically coherent three gene immune signature predictive of immunotherapy benefit in gastric adenocarcinoma.Discover oncology · 2026Article
- Expression, potential biological behaviour and clinical significance of MCM3 in pancreatic adenocarcinoma: a comprehensive study integrating high throughput sequencing, CRISPR screening and in-house immunohistochemistry.Annals of medicine · 2024Article
- Identification of angiogenesis-related genes and molecular subtypes for psoriasis based on random forest algorithm.Clinical and experimental immunology · 2024Article
- Clinical significance and immune landscape of a novel ferroptosis-related prognosis signature in osteosarcoma.BMC cancer · 2023Article
- Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers.TheScientificWorldJournal · 2023Article
- Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies.Discover oncology · 2022Article
- Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer.International journal of molecular sciences · 2022Article
- Network Analysis Reveals Different Cellulose Degradation Strategies AcrossFrontiers in genetics · 2022Article
- Identification of Circular RNA-Based Immunomodulatory Networks in Colorectal Cancer.Frontiers in oncology · 2021Article
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3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite advances in the understanding of neoplasm, patients with cervical cancer still have a poor prognosis. Identifying prognostic markers of cervical cancer may enable early detection of recurrence and more effective treatment.
methodsGene expression profiling data were acquired from the Gene Expression Omnibus database. After data normalization, genes with large variation were screened out. Next, we built co-expression modules by using weighted gene co-expression network analysis to investigate the relationship between the modules and clinical traits related to cervical cancer progression. Functional enrichment analysis was also applied on these co-expressed genes. We integrated the genes into a human protein-protein interaction (PPI) network to expand seed genes and build a co-expression network. For further analysis of the dataset, the Cancer Genome Atlas (TCGA) database was used to identify seed genes and their correlation to cervical cancer prognosis. Verification was further conducted by qPCR and the Human Protein Atlas (HPA) database to measure the expression of hub genes.
resultsUsing WGCNA, we identified 25 co-expression modules from 10,016 genes in 128 human cervical cancer samples. After functional enrichment analysis, the magenta, brown, and darkred modules were selected as the three most correlated modules for cancer progression. Additionally, seed genes in the three modules were combined with a PPI network to identify 31 tumor-specific genes. Hierarchical clustering and Gepia results indicated that the expression quantity of hub genes NDC80, TIPIN, MCM3, MCM6, POLA1, and PRC1 may determine the prognosis of cervical cancer. Finally, TIPIN and POLA1 were further filtered by a LASSO model. In addition, their expression was identified by immunohistochemistry in HPA database as well as a biological experiment.
conclusionOur research provides a co-expression network of gene modules and identifies TIPIN and POLA1 as stable potential prognostic biomarkers for cervical cancer.
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