Identifying miRNA-mRNA Integration Set Associated With Survival Time

Yongkang Kim; Sungyoung Lee; Jin-Young Jang; Seungyeoun Lee; Taesung Park

doi:10.3389/fgene.2021.634922

Identifying miRNA-mRNA Integration Set Associated With Survival Time

Front Genet. 2021 Jun 29:12:634922. doi: 10.3389/fgene.2021.634922. eCollection 2021.

Authors

Yongkang Kim¹, Sungyoung Lee^{2

3}, Jin-Young Jang⁴, Seungyeoun Lee⁵, Taesung Park^{1

6}

Affiliations

¹ Department of Statistics, Seoul National University, Seoul, South Korea.
² Center for Precision Medicine, Seoul National University Hospital, Seoul, South Korea.
³ Department of Genomic Medicine, Seoul National University Hospital, Seoul, South Korea.
⁴ Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea.
⁵ Department of Mathematics and Statistics, Sejong University, Seoul, South Korea.
⁶ Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.

Abstract

In the "personalized medicine" era, one of the most difficult problems is identification of combined markers from different omics platforms. Many methods have been developed to identify candidate markers for each type of omics data, but few methods facilitate the identification of multiple markers on multi-omics platforms. microRNAs (miRNAs) is well known to affect only indirectly phenotypes by regulating mRNA expression and/or protein translation. To take into account this knowledge into practice, we suggest a miRNA-mRNA integration model for survival time analysis, called mimi-surv, which accounts for the biological relationship, to identify such integrated markers more efficiently. Through simulation studies, we found that the statistical power of mimi-surv be better than other models. Application to real datasets from Seoul National University Hospital and The Cancer Genome Atlas demonstrated that mimi-surv successfully identified miRNA-mRNA integrations sets associated with progression-free survival of pancreatic ductal adenocarcinoma (PDAC) patients. Only mimi-surv found miR-96, a previously unidentified PDAC-related miRNA in these two real datasets. Furthermore, mimi-surv was shown to identify more PDAC related miRNAs than other methods because it used the known structure for miRNA-mRNA regularization. An implementation of mimi-surv is available at http://statgen.snu.ac.kr/software/mimi-surv.

Keywords: The Cancer Genome Atlas; miRNA-mRNA integration; pancreatic ductal adenocarcinoma; personalized medicine; statistical method.