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Computational single-cell methods for predicting cancer risk
1CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Biochemical Society Transactions
|June 10, 2024
Summary
Predicting cancer risk is challenging. New computational methods using single-cell data can identify cells likely to become cancerous, improving early detection and prevention strategies.
Area of Science:
- Computational biology
- Systems biology
- Cancer research
Background:
- Cancer risk prediction is a significant challenge despite biotechnological advances.
- Improved prediction is crucial for enhancing prevention, early detection, and survival rates.
- Current computational and experimental methods face limitations.
Purpose of the Study:
- To summarize emerging computational challenges and advances in cancer risk prediction.
- To focus on computational strategies utilizing single-cell data for cancer risk assessment.
- To introduce novel bottom-up network modeling approaches for estimating cancer stemness and dedifferentiation.
Main Methods:
- Utilizing single-cell omics data (scRNA-seq, snRNA-seq).
- Employing bottom-up network modeling from a systems-biological perspective.
- Describing two methods: diffusion network entropy (tissue/lineage-independent) and transcription factor regulons (tissue/lineage-specific).
Main Results:
- The developed computational tools successfully delineate the heterogeneous inter-cellular cancer-risk landscape.
- These methods can identify cells with a higher likelihood of developing into cancerous cells.
- Application to pre-invasive cancer stages demonstrated efficacy in risk assessment.
Conclusions:
- Bottom-up systems biological modeling of single-cell omic data represents a novel computational paradigm.
- This approach promises to advance the development of preventive and early cancer detection strategies.
- It facilitates more accurate cancer-risk prediction at the single-cell level.
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