Identifying miRNA-Gene Common and Specific Regulatory Modules for Cancer Subtyping by a High-Order Graph Matching Model

• Published in: IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 20; no. 1; pp. 421 - 431
• Main Authors: Chen, Jiazhou, Han, Guoqiang, Xu, Aodan, Akutsu, Tatsuya, Cai, Hongmin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2023; Institute of Electrical and Electronics Engineers (IEEE); The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adenocarcinoma; Bioinformatics; Biological system modeling; Cancer; Cancer subtyping; common and specific modules; Contamination; Genomics; Graph matching; Heavy chains; high-order graph matching; Immunoglobulins; miRNA; miRNA-gene regulatory modules; Modules; Molecular modelling; Pollution measurement; Regulatory mechanisms (biology); Tensors; Tumorigenesis; Tumors
• ISSN: 1545-5963; 1557-9964; 2374-0043; 1557-9964
• DOI: 10.1109/TCBB.2022.3161635

Identifying regulatory modules between miRNAs and genes is crucial in cancer research. It promotes a comprehensive understanding of the molecular mechanisms of cancer. The genomic data collected from subjects usually relate to different cancer statuses, such as different TNM Classifications of Malignant Tumors (TNM) or histological subtypes. Simple integrated analyses generally identify the core of the tumorigenesis (common modules) but miss the subtype-specific regulatory mechanisms (specific modules). In contrast, separate analyses can only report the differences and ignore important common modules. Therefore, there is an urgent need to develop a novel method to jointly analyze miRNA and gene data of different cancer statuses to identify common and specific modules. To that end, we developed a H igh- O rder G raph M atching model to identify C ommon and S pecific modules (HOGMCS) between miRNA and gene data of different cancer statuses. We first demonstrate the superiority of HOGMCS through a comparison with four state-of-the-art techniques using a set of simulated data. Then, we apply HOGMCS on stomach adenocarcinoma data with four TNM stages and two histological types, and breast invasive carcinoma data with four PAM50 subtypes. The experimental results demonstrate that HOGMCS can accurately extract common and subtype-specific miRNA-gene regulatory modules, where many identified miRNA-gene interactions have been confirmed in several public databases.