ArticleFrontiers in genetics2021
Identification of Survival-Associated Hub Genes in Pancreatic Adenocarcinoma Based on WGCNA.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Machine learning and gene network integration reveal prognostic subnetworks and biomarkers in pancreatic cancer.Computational and structural biotechnology journal · 2025Article
- Knockdown of CENPM activates cGAS-STING pathway to inhibit ovarian cancer by promoting pyroptosis.BMC cancer · 2024Article
- WD Repeat and HMG Box DNA Binding Protein 1: An Oncoprotein at the Hub of Tumorigenesis and a Novel Therapeutic Target.International journal of molecular sciences · 2023Review
- miR-24-3p Regulates Epithelial-Mesenchymal Transition and the Malignant Phenotype of Pancreatic Adenocarcinoma by Regulating ASF1B Expression.Biochemical genetics · 2023Article
- Weighted gene co-expression network analysis revealed T cell differentiation associated with the age-related phenotypes in COVID-19 patients.BMC medical genomics · 2023Article
- Weighted correlation network analysis identifies multiple susceptibility loci for low-grade glioma.Cancer medicine · 2023Article
- Identification of Potential Diagnostic Genes of HIV-Infected Immunological Non-Responders on Bioinformatics Analysis.Journal of inflammation research · 2023Article
- The Involvement of WDHD1 in the Occurrence of Esophageal Cancer as a Downstream Target of PI3K/AKT Pathway.Journal of oncology · 2022Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic adenocarcinoma is one of the leading causes of cancer-related death worldwide. Since little clinical symptoms were shown in the early period of pancreatic adenocarcinoma, most patients were found to carry metastases when diagnosis. The lack of effective diagnosis biomarkers and therapeutic targets makes pancreatic adenocarcinoma difficult to screen and cure. The fundamental problem is we know very little about the regulatory mechanisms during carcinogenesis. Here, we employed weighted gene co-expression network analysis (WGCNA) to build gene interaction network using expression profile of pancreatic adenocarcinoma from The Cancer Genome Atlas (TCGA). STRING was used for the construction and visualization of biological networks. A total of 22 modules were detected in the network, among which yellow and pink modules showed the most significant associations with pancreatic adenocarcinoma. Dozens of new genes including PKMYT1, WDHD1, ASF1B, and RAD18 were identified. Further survival analysis yielded their valuable effects on the diagnosis and treatment of pancreatic adenocarcinoma. Our study pioneered network-based algorithm in the application of tumor etiology and discovered several promising regulators for pancreatic adenocarcinoma detection and therapy.
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