ReviewFrontiers in oncology2018
Emerging Biomarkers in Bladder Cancer Identified by Network Analysis of Transcriptomic Data.
Review in Frontiers in oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Detection of a group of classic serum tumor markers (CSTMs) aids in the differential diagnosis and disease assessment of upper tract urothelial carcinoma (UTUC).Translational andrology and urology · 2026Article
- Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification.Discover oncology · 2025Review
- Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches.Discover oncology · 2025Article
- Gene co-expression network and differential expression analyses reveal key genes for weaning weight in Simmental-Holstein crossbred cattle.Animal biotechnology · 2024Article
- Article
- Tozasertib activates anti-tumor immunity through decreasing regulatory T cells in melanoma.Neoplasia (New York, N.Y.) · 2024Article
- Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2023Article
- Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods.Scientific reports · 2023Article
- Identification of Hub Genes Associated with Gastric Cancer via Bioinformatics Analysis and Validation Studies.International journal of general medicine · 2023Article
- Identification and verification of novel immune-related ferroptosis signature with excellent prognostic predictive and clinical guidance value in hepatocellular carcinoma.Frontiers in genetics · 2023Article
- Identification of Key Genes as Early Warning Signals of Acute Myocardial Infarction Based on Weighted Gene Correlation Network Analysis and Dynamic Network Biomarker Algorithm.Frontiers in immunology · 2022Article
- CASP5 and CR1 as potential biomarkers for Kawasaki disease: an Integrated Bioinformatics-Experimental Study.BMC pediatrics · 2021Article
- Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis.Bioengineered · 2021Article
- KIF4A promotes the development of bladder cancer by transcriptionally activating the expression of CDCA3.International journal of molecular medicine · 2021Article
- HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening.BMC musculoskeletal disorders · 2021Article
- Article
- Identification of Immune-Cell-Related Prognostic Biomarkers of Esophageal Squamous Cell Carcinoma Based on Tumor Microenvironment.Frontiers in oncology · 2021Article
- A Co-Expression Network Reveals the Potential Regulatory Mechanism of lncRNAs in Relapsed Hepatocellular Carcinoma.Frontiers in oncology · 2021Article
- Article
- Selection of endogenous control genes for normalising gene expression data derived from formalin-fixed paraffin-embedded tumour tissue.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bladder cancer is a very common malignancy. Although new treatment strategies have been developed, the identification of new therapeutic targets and reliable diagnostic/prognostic biomarkers for bladder cancer remains a priority. Generally, they are found among differentially expressed genes between patients and healthy subjects or among patients with different tumor stages. However, the classical approach includes processing these data taking into consideration only the expression of each single gene regardless of the expression of other genes. These complex gene interaction networks can be revealed by a recently developed systems biology approach called Weighted Gene Co-expression Network Analysis (WGCNA). It takes into account the expression of all genes assessed in an experiment in order to reveal the clusters of co-expressed genes (modules) that, very probably, are also co-regulated. If some genes are co-expressed in controls but not in pathological samples, it can be hypothesized that a regulatory mechanism was altered and that it could be the cause or the effect of the disease. Therefore, genes within these modules could play a role in cancer and thus be considered as potential therapeutic targets or diagnostic/prognostic biomarkers. Here, we have reviewed all the studies where WGCNA has been applied to gene expression data from bladder cancer patients. We have shown the importance of this new approach in identifying candidate biomarkers and therapeutic targets. They include both genes and miRNAs and some of them have already been identified in the literature to have a role in bladder cancer initiation, progression, metastasis, and patient survival.
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