Title: A framework for identifying colorectal cancer genes in detected protein complexes

Authors: Xiwei Tang; Mingcai Zheng; Bihai Zhao; Guosheng Huang

Addresses: School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, Hunan Sheng, China ' School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, Hunan Sheng, China ' School of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410022, Hunan Sheng, China; Hunan Provincial Key Laboratory of Nutrition and Quality Control of Aquatic Animals, School of Biological and Environmental Engineering, Changsha University, Changsha 410022, Hunan Sheng, China ' School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, Hunan Sheng, China

Abstract: Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and has been the fourth leading cause of cancer-related death worldwide. Inherited factors have a significant role in the CRC growth and progression. Identification of mutation genes leading to CRC remains an arduous task. Compared with the biologically experimental technologies for detecting potential CRC genes, the computational methods are more worthwhile and more efficient. In the study, a new method called ICGPC is proposed to discover latent CRC susceptibility genes. ICGPC takes full advantage of all kinds of biological information closely related to CRC like protein-protein interactions, Sub-Cellular Localisation (SCL) and protein complexes. ICGPC discovers a list of potential proteins encoded by CRC-causal genes. The literature study method is used to evaluate the top proteins in the list and determine that out of 30 novel proteins, 10 proteins are closely associated to CRC. Especially, the top five proteins, i.e., PCNA, CDC20, CCNB1, FZR1 and CDC27, are the most promising candidates and biologists should pay more attention to them.

Keywords: colorectal cancer gene; protein complex; protein-protein interaction; protein sub-cellular localisation.

DOI: 10.1504/IJDMB.2019.100628

International Journal of Data Mining and Bioinformatics, 2019 Vol.22 No.3, pp.265 - 279

Received: 22 May 2019
Accepted: 23 May 2019
Published online: 05 Jul 2019 *

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