CoMI: consensus mutual information for tissue-specific gene signatures

• Published in: BMC bioinformatics Vol. 22; no. Suppl 10; p. 624
• Main Authors: Huang, Sing-Han, Lo, Yu-Shu, Luo, Yong-Chun, Chuang, Yi-Hsuan, Lee, Jung-Yu, Yang, Jinn-Moon
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 19.04.2022; BioMed Central; BMC
• Subjects: Consensus; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Genetic aspects; Health aspects; Humans; Neoplasms - genetics; Omics data; Precision Medicine; Prognostic gene signature; Tissue-specific gene signature; Tissues; United States; Taiwan; Prognostic gene signature; Tissue-specific gene signature; Omics data
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-022-04682-2

The gene signatures have been considered as a promising early diagnosis and prognostic analysis to identify disease subtypes and to determine subsequent treatments. Tissue-specific gene signatures of a specific disease are an emergency requirement for precision medicine to improve the accuracy and reduce the side effects. Currently, many approaches have been proposed for identifying gene signatures for diagnosis and prognostic. However, they often lack of tissue-specific gene signatures. Here, we propose a new method, consensus mutual information (CoMI) for analyzing omics data and discovering gene signatures. CoMI can identify differentially expressed genes in multiple cancer omics data for reflecting both cancer-related and tissue-specific signatures, such as Cell growth and death in multiple cancers, Xenobiotics biodegradation and metabolism in LIHC, and Nervous system in GBM. Our method identified 50-gene signatures effectively distinguishing the GBM patients into high- and low-risk groups (log-rank p = 0.006) for diagnosis and prognosis. Our results demonstrate that CoMI can identify significant and consistent gene signatures with tissue-specific properties and can predict clinical outcomes for interested diseases. We believe that CoMI is useful for analyzing omics data and discovering gene signatures of diseases.