ArticleBMC cancer2024
Identification of key genes associated with cervical cancer based on bioinformatics analysis.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MicroRNA Dysregulation in HPV-Driven Cervical Cancer: A Review of Oncoprotein-Targeted Signaling Pathways.Life (Basel, Switzerland) · 2026Review
- Identification and validation of stemness-associated hub genes in cervical cancer: a bioinformatics and experimental study.World journal of surgical oncology · 2026Article
- AGPAT family members act as potential predictive biomarkers and therapeutic targets in cervical cancer.Discover oncology · 2026Article
- Precision Biomarker Identification in Gynecological Cancers Using Coexpression Networks and Attention-Based LSTM in Healthcare 4.0.Diagnostics (Basel, Switzerland) · 2026Article
- Gramine Suppresses Cervical Cancer by Targeting CDK2: Integrated Omics-Pharmacology and In Vitro Evidence.Current issues in molecular biology · 2026Article
- CERV-Score: A Hybrid Machine Learning Framework for Cervical Cancer Risk Prediction Using Integrated Clinical and Genomic Data.International journal of telemedicine and applications · 2026Article
- Publication trends and hotspots for cervical cancer screening biomarker: a bibliometric analysis.Discover oncology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundCervical cancer has extremely high morbidity and mortality, and its pathogenesis is still in the exploratory stage. This study aimed to screen and identify differentially expressed genes (DEGs) related to cervical cancer through bioinformatics analysis.
methodsGSE63514 and GSE67522 were selected from the GEO database to screen DEGs. Then GO and KEGG analysis were performed on DEGs. PPI network of DEGs was constructed through STRING website, and the hub genes were found through 12 algorithms of Cytoscape software. Meanwhile, GSE30656 was selected from the GEO database to screen DEMs. Target genes of DEMs were screened through TagetScan, miRTarBase and miRDB. Next, the hub genes screened from DEGs were merged with the target genes screened from DEMs. Finally, ROC curve and nomogram analysis were performed to assess the predictive capabilities of the hub genes. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry.
resultsSix hub genes, TOP2A, AURKA, CCNA2, IVL, KRT1, and IGFBP5, were mined through the protein-protein interaction network. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry, and it was found that TOP2A, AURKA as well as CCNA2 were overexpressed and IGFBP5 was low expression in cervical cancer.
conclusionsThis study showed that TOP2A, AURKA, CCNA2 and IGFBP5 screened through bioinformatics analysis were significantly differentially expressed in cervical cancer samples compared with normal samples, which might be biomarkers of cervical cancer.
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