ArticleScientific reports2023
Identification of diagnostic biomarkers via weighted correlation network analysis in colorectal cancer using a system biology approach.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 15 citations in OpenAlex.
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- Gene expression and immune cell heterogeneity in inbred Amur tiger.BMC genomics · 2026Article
- Article
- Integrative Bioinformatics and Experimental Validation Establish CCNB1 as a Potential Biomarker for Diagnosis and Prognosis in Colorectal Cancer.Current issues in molecular biology · 2025Article
- Exploring the hub gene CERS6 as a therapeutic target in type 1 diabetes through a bioinformatics and network analyst approach.Scientific reports · 2025Article
- MicroRNA bioinformatics in precision oncology: an integrated pipeline from NGS to AI-based target discovery.Journal of applied genetics · 2025Review
- Machine learning and gene network integration reveal prognostic subnetworks and biomarkers in pancreatic cancer.Computational and structural biotechnology journal · 2025Article
- Deciphering molecular landscape of breast cancer progression and insights from functional genomics and therapeutic explorations followed by in vitro validation.Scientific reports · 2024Article
- Tissue-specific atlas of trans-models for gene regulation elucidates complex regulation patterns.BMC genomics · 2024Article
- System biology approach to identify the novel biomarkers in glioblastoma multiforme tumors by using computational analysis.Frontiers in pharmacology · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is the third most frequent cancer to be diagnosed in both females and males necessitating identification of effective biomarkers. An in-silico system biology approach called weighted gene co-expression network analysis (WGCNA) can be used to examine gene expression in a complicated network of regulatory genes. In the current study, the co-expression network of DEGs connected to CRC and their target genes was built using the WGCNA algorithm. GO and KEGG pathway analysis were carried out to learn more about the biological role of the DEmRNAs. These findings revealed that the genes were mostly enriched in the biological processes that were involved in the regulation of hormone levels, extracellular matrix organization, and extracellular structure organization. The intersection of genes between hub genes and DEmRNAs showed that DKC1, PA2G4, LYAR and NOLC1 were the clinically final hub genes of CRC.
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