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Advancing cancer driver gene identification through an integrative network and pathway approach.
Song, Junrong; Song, Zhiming; Gong, Yuanli; Ge, Lichang; Lou, Wenlu.
Afiliación
  • Song J; The School of Information, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, PR China; Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, PR China. Electronic address: 916525667@qq.com.
  • Song Z; The School of Information, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, PR China; Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, PR China.
  • Gong Y; The School of Information, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, PR China.
  • Ge L; The School of Information, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, PR China.
  • Lou W; Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, PR China; The School of Business, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, PR China.
J Biomed Inform ; : 104729, 2024 Sep 19.
Article en En | MEDLINE | ID: mdl-39306314
ABSTRACT

OBJECTIVE:

Cancer is a complex genetic disease characterized by the accumulation of various mutations, with driver genes playing a crucial role in cancer initiation and progression. Distinguishing driver genes from passenger mutations is essential for understanding cancer biology and discovering therapeutic targets. However, the majority of existing methods ignore the mutational heterogeneity and commonalities among patients, which hinders the identification of driver genes more effectively.

METHODS:

This study introduces MCSdriver, a novel computational model that integrates network and pathway information to prioritize the identification of cancer driver genes. MCSdriver employs a bidirectional random walk algorithm to quantify the mutual exclusivity and functional relationships between mutated genes within patient cohorts. It calculates similarity scores based on a mutual exclusivity-weighted network and pathway coverage patterns, accounting for patient-specific heterogeneity and molecular profile similarity.

RESULTS:

This approach enhances the accuracy and quality of driver gene identification. MCSdriver demonstrates superior performance in identifying cancer driver genes across four cancer types from The Cancer Genome Atlas, showing a higher F-score, Recall and Precision compared to existing ranking list-based and module-based models.

CONCLUSION:

The MCSdriver model not only outperforms other models in identifying known cancer driver genes but also effectively identifies novel driver genes involved in cancer-related biological processes. The model's consideration of patient-specific heterogeneity and similarity in molecular profiles significantly enhances the accuracy and quality of driver gene identification. Validation through Gene Ontology enrichment analysis and literature mining further underscores its potential application value in personalized cancer therapy, offering a promising tool for advancing our understanding and treatment of cancer.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Biomed Inform Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Biomed Inform Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos