A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer
Zhang L, Fan S, Vera J, Lai X (2023)
Publication Type: Journal article
Publication year: 2023
Journal
Book Volume: 21
Pages Range: 34-45
DOI: 10.1016/j.csbj.2022.11.037
Abstract
Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.
Authors with CRIS profile
Julio Vera González
Professur für Tumorimmunologie (Schwerpunkt Systembiologie)
Xin Lai
Department of Dermatology
Involved external institutions
China (CN)How to cite
APA:
Zhang, L., Fan, S., Vera, J., & Lai, X. (2023). A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer. Computational and Structural Biotechnology Journal, 21, 34-45. https://dx.doi.org/10.1016/j.csbj.2022.11.037
MLA:
Zhang, Le, et al. "A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer." Computational and Structural Biotechnology Journal 21 (2023): 34-45.
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