AUTHOR=Kim Yongkang , Lee Sungyoung , Jang Jin-Young , Lee Seungyeoun , Park Taesung 

TITLE=Identifying miRNA-mRNA Integration Set Associated With Survival Time

JOURNAL=Frontiers in Genetics

VOLUME=12

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.634922

DOI=10.3389/fgene.2021.634922

ISSN=1664-8021

ABSTRACT=<p>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 <italic>mimi-surv</italic>, which accounts for the biological relationship, to identify such integrated markers more efficiently. Through simulation studies, we found that the statistical power of <italic>mimi-surv</italic> be better than other models. Application to real datasets from Seoul National University Hospital and The Cancer Genome Atlas demonstrated that <italic>mimi-surv</italic> successfully identified miRNA-mRNA integrations sets associated with progression-free survival of pancreatic ductal adenocarcinoma (PDAC) patients. Only <italic>mimi-surv</italic> found miR-96, a previously unidentified PDAC-related miRNA in these two real datasets. Furthermore, <italic>mimi-surv</italic> was shown to identify more PDAC related miRNAs than other methods because it used the known structure for miRNA-mRNA regularization. An implementation of <italic>mimi-surv</italic> is available at <ext-link ext-link-type="uri" xlink:href="http://statgen.snu.ac.kr/software/mimi-surv" xmlns:xlink="http://www.w3.org/1999/xlink">http://statgen.snu.ac.kr/software/mimi-surv</ext-link>.</p>