Machine learning and gene network integration reveal prognostic subnetworks and biomarkers in pancreatic cancer
This study identified novel regulatory cancer networks and associated biomarkers for pancreatic cancer prognosis and diagnosis by integrating WGCNA with ML, DGE, pathway, and survival analyses. An interactive web portal to explore the full results and visualizations is available at pc-biomarkers.de. Future work will further validate these biomarkers to improve early detection, prognosis, and treatment strategies.
Authors
- Maximilian Rudert (ORCID: https://orcid.org/0000-0003-4982-6594)
- Gudrun Dandekar (ORCID: https://orcid.org/0000-0001-6127-5794)
- Elena Bencúrová (ORCID: https://orcid.org/0000-0003-4268-1812)
- Rana Salihoğlu (ORCID: https://orcid.org/0000-0003-3277-2446)
- Regina Ebert (ORCID: https://orcid.org/0000-0002-8192-869X)
- Thomas Dandekar (ORCID: https://orcid.org/0000-0003-1886-7625)
- J. A. Nieves
Institutions
- University of Würzburg (DE)
- Klinik König-Ludwig Haus (DE)
- Universitätsklinikum Würzburg (DE)
Publication Details
- Journal
- Computational and Structural Biotechnology Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.csbj.2025.09.028
- Citations
- 1
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 1.05