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Published on: November 5, 2019
A contrast set mining based approach for cancer subtype analysis
A M Trasierras1, J M Luna2, S Ventura2
1Department of Computer Science and Numerical Analysis, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), Spain; Maimonides Biomedical Research Institute of Cordoba, IMIBIC, University of Cordoba, Córdoba, 14071, Spain; Phytoplant Research S.L.U, Departamento Tecnología y Control, Rabanales 21-Parque Científico Tecnológico de Córdoba, Calle Astrónoma Cecilia Payne, Córdoba, Spain.
This study uses contrast set mining on cancer transcriptomic data to uncover molecular mechanisms driving cancer subtypes. The findings reveal specific genetic relationships and gene expression patterns linked to cancer progression and survival.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Personalized cancer therapies require understanding common and unique cancer subtype characteristics.
- Current predictive approaches lack insight into underlying molecular mechanisms.
- A descriptive approach is needed to identify cancer-driving molecular mechanisms without prior validation.
Purpose of the Study:
- To apply contrast set mining to cancer transcriptomic data for discovering high-order molecular relationships.
- To identify descriptive genetic relationships and affected functional pathways in various cancer subtypes.
- To provide a foundation for novel personalized therapies by elucidating cancer's molecular basis.
Main Methods:
- Utilized contrast set mining on RNA-Seq gene expression data from breast, kidney, and colon cancer subtypes.
- Divided gene expression databases by cancer subtype to detect subtype-specific gene group associations.
- Validated findings through literature research and assessed prognostic value via survival analysis.
Main Results:
- Extracted highly specific genetic relationships linked to functional pathways affected in cancer subtypes.
- Identified gene expression patterns associated with patient survival across different cancer types.
- Discovered novel gene associations with potential as cancer biomarkers and targets for future research.
Conclusions:
- Contrast set mining effectively reveals molecular mechanisms and high-order relationships in cancer transcriptomic data.
- The identified genetic relationships and survival patterns offer new insights into cancer heterogeneity.
- This approach provides a valuable starting point for developing targeted cancer therapies and biomarker discovery.
