Machine Learning Links Non-Canonical TCA To Malignancy
Lin et al. (published December 18, 2025) present a computational study combining constraint-based metabolic modeling and machine learning to profile non-canonical TCA cycle activity across more than 500 cancer cell lines. They define Cycle Propensity and Cycle Flux Intensity, show high cycle engagement correlates with decreased oxygen consumption and increased lactate (Warburg phenotype), and report gene signatures and DepMap-linked vulnerabilities predicting cycle activity.
Key Points
- 1Quantified non-canonical TCA cycle activity across over 500 cancer cell lines using constraint-based modeling
- 2Linked high cycle engagement to increased NADPH and α-ketoglutarate production and Warburg-like metabolic shifts
- 3Derived gene signatures predicting cycle activity, revealing vulnerabilities and candidate targets from DepMap dependencies
Scoring Rationale
Integrative, peer-reviewed modeling and ML deliver actionable insights; score limited by focus on cancer-specific metabolic pathway.
Sources
Public references used for this report.
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