| Integrating Multi-Omics Approaches for Cancer Biomarker Discovery: A Bioinformatics Perspective |
| Paper ID : 1241-IGA |
| Authors |
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Parinaz Khanjanpoor *, Hesam Aminian Department of Health and Science, School of Medicine, University of Piedmont Orientale (UPO), Novara, Italy |
| Abstract |
| Background and Aim: Integrating multi-omics approaches has become pivotal in cancer biomarker discovery, offering a comprehensive view of tumor biology by combining genomics, transcriptomics, epigenomics, proteomics, and metabolomics data. This review aims to explore bioinformatics strategies for multi-omics data integration and their application in identifying robust cancer biomarkers for diagnosis, prognosis, and personalized therapy. Materials and Methods: A systematic literature search was conducted using keywords such as "multi-omics," "cancer biomarker," "bioinformatics," and "data integration" across databases including PubMed, Nature, and Frontiers journals. Inclusion criteria were peer-reviewed articles published in English from 2015 to 2025. Results: Multi-omics integration leverages diverse high-throughput datasets to construct comprehensive molecular profiles of tumors. Techniques like Similarity Network Fusion (SNF) and Ranked SNF enable the fusion of patient similarity matrices across data types to identify key biomarker candidates. Network-based analyses reveal regulatory interactions among genes, miRNAs, and transcription factors, highlighting hub nodes implicated in cancer progression. Bioinformatics tools and public repositories facilitate data processing, visualization, and pathway analysis. Multi-omics biomarkers have demonstrated improved accuracy in predicting treatment response and survival across various cancers, including neuroblastoma, ovarian, and esophageal cancers. Challenges include data heterogeneity, integration complexity, and clinical validation. Advances in machine learning and AI are enhancing biomarker discovery by handling high-dimensional data and uncovering novel signatures. Conclusion: Bioinformatics-driven multi-omics integration represents a powerful approach for cancer biomarker discovery, enabling deeper insights into tumor biology and precision oncology. Continued methodological improvements and clinical validation will accelerate the translation of multi-omics biomarkers into personalized cancer diagnosis and therapy |
| Keywords |
| Multi-omics integration, Cancer biomarker discovery, Bioinformatics |
| Status: Accepted |
