ArticleAging2019
Identification of hub genes in prostate cancer using robust rank aggregation and weighted gene co-expression network analysis.
Article in Aging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 1 of them a synthesis that pooled it.
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Who cites it
80 citing papers in PubMed, 1 synthesis or guideline pooled it, 133 citations in OpenAlex.
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- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.Nucleic acids research · 2025Article
- RNA-Seq Uncovers Association of Endocrine-Disrupting Chemicals with Hub Genes and Transcription Factors in Aggressive Prostate Cancer.International journal of molecular sciences · 2025Article
- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.bioRxiv : the preprint server for biology · 2025Article
- Rank Aggregation Methods and Tools in Genomic Data Analysis.Current genomics · 2025Review
- Identification of Novel lncRNAs Related to Colorectal Cancer Through Bioinformatics Analysis.BioMed research international · 2025Article
- Aberrant DNA polymerase beta expression is associated with dysregulated tumor immune microenvironment and its prognostic value in gastric cancer.Clinical and experimental medicine · 2024Article
- Identification of the Key GeneInternational journal of molecular sciences · 2024Article
- Mechanism and role of nuclear laminin B1 in cell senescence and malignant tumors.Cell death discovery · 2024Review
- Uncovering the shared neuro-immune-related regulatory mechanisms between spinal cord injury and osteoarthritis.Heliyon · 2024Article
- Bioinformatics and Machine Learning Methods Identified MGST1 and QPCT as Novel Biomarkers for Severe Acute Pancreatitis.Molecular biotechnology · 2024Article
- Predicting prostate cancer recurrence: Introducing PCRPS, an advanced online web server.Heliyon · 2024Article
- The Effect of HMGB1 and HMGB2 on Transcriptional Regulation Differs in Neuroendocrine and Adenocarcinoma Models of Prostate Cancer.International journal of molecular sciences · 2024Article
- Bridging the Gap Between Imaging and Molecular Characterization: Current Understanding of Radiomics and Radiogenomics in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2024Review
- Differentially expressed platelet activation-related genes in dogs with stage B2 myxomatous mitral valve disease.BMC veterinary research · 2023Article
- Centralspindlin proteins Pavarotti and Tumbleweed along with WASH regulate nuclear envelope budding.The Journal of cell biology · 2023Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The pathogenic mechanisms of prostate cancer (PCa) remain to be defined. In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate 10 eligible PCa microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between tumor samples and normal, matched specimens. To explore potential associations between gene sets and PCa clinical features and to identify hub genes, we utilized WGCNA to construct gene co-expression networks incorporating the DEGs screened with the use of RRA. From the key module, we selected LMNB1, TK1, ZWINT, and RACGAP1 for validation. We found that these genes were up-regulated in PCa samples, and higher expression levels were associated with higher Gleason scores and tumor grades. Moreover, ROC and K-M plots indicated these genes had good diagnostic and prognostic value for PCa. On the other hand, methylation analyses suggested that the abnormal up-regulation of these four genes likely resulted from hypomethylation, while GSEA and GSVA for single hub gene revealed they all had a close association with proliferation of PCa cells. These findings provide new insight into PCa pathogenesis, and identify LMNB1, TK1, RACGAP1 and ZWINT as candidate biomarkers for diagnosis and prognosis of PCa.
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