MSICKB: A Curated Knowledgebase for Exploring Molecular Heterogeneity and Biomarker Discovery in Microsatellite Instability Cancers
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Background
Microsatellite instability (MSI) is a critical form of genomic instability and a key biomarker for predicting patient prognosis and response to immunotherapy in cancer. However, significant heterogeneity in clinical outcomes exists even among patients with MSI cancers of the same type, suggesting the presence of distinct molecular subtypes and the need for more personalized biomarkers. The systematic exploration of this heterogeneity is currently hindered by the lack of a specialized, multi-dimensional data resource. This raises a critical scientific question: building upon the established link between MSI status and therapeutic efficacy, how can we leverage a multi-dimensional, deeply curated data resource to precisely characterize the molecular heterogeneity of MSI cancers and discover personalized biomarkers for precision oncology?
Methods
To address this question, we developed the Microsatellite Instability Cancer Knowledgebase (MSICKB). We systematically curated MSI-related data from 492 publications spanning from 1997 to 2024, covering 31 cancer types. The curated data encompasses four key dimensions: Genetic & Molecular Features, Clinicopathological Features, Therapeutic Response, and Prognostic Factors. Furthermore, we constructed and analyzed a comprehensive gene-disease network to systematically characterize the molecular landscape of MSI cancers.
Results
MSICKB( http://www.sysbio.org.cn/MSICKB/ ) curates a total of 1,382 MSI-related features from 492 publications, covering 31 cancer types. The knowledgebase includes four major categories: Genetic & Molecular Features, Clinicopathological Features, Prognostic Factors, and Therapeutic Response. An application analysis of the curated data revealed that the constructed gene-disease network exhibits a scale-free-like topology, indicating a hierarchical organization governed by a few highly connected hub nodes. Further investigation of the 14 identified hub genes showed significant functional enrichment in core MSI-related pathways, including DNA mismatch repair, immune checkpoint regulation, and key oncogenic signaling pathways.
Conclusion
MSICKB provides an integrated, multi-dimensional data resource that facilitates the exploration of the molecular heterogeneity landscape and common regulatory principles within MSI cancers. It serves as a valuable platform for identifying novel molecular subtypes and biomarkers. In the future, the continuous evolution of MSICKB will support the development of clinical prediction models, assist in the advancement of intelligent medicine, and ultimately promote the progress of precision diagnostics and therapeutics for MSI cancers.
