Abstract
A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/malikyousef/miRcorrNet.
Highlights
MiRNAs are short non-coding RNAs of approximately 22 nucleotides and they have active role in controlling downstream proteomic profiles (Bartel, 2018)
The tool detects groups, which are later subject to the Rank procedure
The most distinctive feature of miRcorrNet is its ability to classify case and control samples with an efficient performance using the acquired miRNA–mRNA groups. Those groups of genes and their associated miRNAs may serve as a biomarker for the specific disease under investigation
Summary
MiRNAs are short non-coding RNAs of approximately 22 nucleotides and they have active role in controlling downstream proteomic profiles (Bartel, 2018). It is predicted that approximately 30% of human genes (Lewis, Burge & Bartel, 2005) and most cellular processes, including cell proliferation, apoptosis, necrosis, autophagy and stress responses, are regulated by miRNAs (Keller et al, 2011) (Ivanov, Liu & Bartsch, 2016). Since these processes are critical in carcinogenesis and tumor progression (Ling, Fabbri & Calin, 2013), miRNAs can be used as biomarkers for various cancer types, to predict the likelihood of cancer development and progression
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
