Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods
Our integrated framework provides a biologically grounded, interpretable, and highly accurate approach for tumor tissue-of-origin classification. The identified miRNA biomarkers demonstrate strong translational potential, supported by clinical trial overlap, drug sensitivity data, and survival analyses. This work highlights the power of combining miRNA network biology with ML to improve precision oncology diagnostics and supports future development of liquid biopsy-based cancer classification.
Authors
- Masuma Khatun (ORCID: https://orcid.org/0000-0003-2976-2319)
- Vijayachitra Modhukur (ORCID: https://orcid.org/0000-0002-7123-9903)
- Ankita Lawarde (ORCID: https://orcid.org/0000-0001-7572-4431)
- Prakash Lingasamy (ORCID: https://orcid.org/0000-0002-8826-4531)
- Andres Salumets (ORCID: https://orcid.org/0000-0002-1251-8160)
Institutions
- Karolinska University Hospital (SE)
- University of Helsinki (FI)
- Helsinki University Hospital (FI)
- Competence Centre on Health Technologies (Estonia) (EE)
- University of Tartu (EE)
Publication Details
- Journal
- Frontiers in Bioinformatics
- Published
- 2025-05-06
- DOI
- https://doi.org/10.3389/fbinf.2025.1571476
- Citations
- 3
- Primary Topic
- Cancer-related molecular mechanisms research
- Type
- article
- Field-Weighted Citation Impact
- 2.81