Identifying individualized risk subpathways reveals pan-cancer molecular classification based on multi-omics data

Computational and Structural Biotechnology Journal

Yan Cao¹  Yunpeng Zhang²  Chunlong Zhang³  Yingqi Xu³  Congxue Hu³  Feng Li³  Desi Shang³  Yanjun Xu³  Xia Li³  Jingwen Wang³  Xuan Zheng³  Wanqi Mi³  Yang Cao⁴ 

[1] Corresponding authors at: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China (Y. Zhang);Department of Urology, Harbin Medical University Cancer Hospital, Harbin, China (Y. Cao);College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China;Harbin Medical University Cancer Hospital, Harbin, China;

关键词: Individualized risk subpathway;    Pathway analysis;    Multi-omics;    Pan-cancer;   

DOI  :  

来源: DOAJ

【 摘 要 】

Cancer is a highly heterogeneous disease with different functional disorders among individuals. The initiation and progression of cancer is usually related to dysregulation of local regions within pathways. Identification of individualized risk pathways is crucial for revealing the mechanisms of tumorigenesis and heterogeneity. However, approach that focused on mining patient-specific risk subpathway regions is still lacking. Here, we developed an individualized cancer risk subpathway identification method that was referred as InCRiS by integrating multi-omics data. Then, the method was applied to nearly 3000 samples across 9 TCGA cancer types and its robustness and reliability were evaluated. Dissecting dysregulated subpathways in these tumor samples revealed several key regions which may play oncogenic roles in cancer. The construction of risk subpathway dysregulation profile of pan-cancers revealed 11 pan-cancer molecular classification (InCRiS subtypes) with significantly different clinical outcomes. Moreover, subpathway regions that tend to be disordered in individuals of specific subtypes were examined for understanding the pathogenesis of tumor and some key genes such as CTNNB1, EP300 and PRKDC were nominated in different subtypes. In summary, the proposed method and resulting data presented useful resources to study the mechanism of tumor heterogeneity and to discovery novel therapeutic targets for precise treatment of cancer.

【 授权许可】

Unknown