ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers

Ankita Lawarde; Edris Sharif Rahmani; Adhiraj Nath; Darja Lavogina; Jana Jaal; Andres Salumets; Vijayachitra Modhukur

doi:10.1016/j.ncrna.2023.10.006

ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers

Noncoding RNA Res. 2023 Oct 17;9(1):125-140. doi: 10.1016/j.ncrna.2023.10.006. eCollection 2024 Mar.

Authors

Ankita Lawarde^{1

2}, Edris Sharif Rahmani¹, Adhiraj Nath³, Darja Lavogina^{1

4

5}, Jana Jaal^{4

6}, Andres Salumets^{1

2

7}, Vijayachitra Modhukur^{1

2}

Affiliations

¹ Competence Centre on Health Technologies, Tartu, Estonia.
² Department of Obstetrics and Gynecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia.
³ Bioengineering Research Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, North Guwahati, Assam, India.
⁴ Institute of Clinical Medicine, Faculty of Medicine, University of Tartu, Estonia.
⁵ Institute of Chemistry, University of Tartu, Estonia.
⁶ Haematology and Oncology Clinic, Tartu University Hospital, Tartu, Estonia.
⁷ Division of Obstetrics and Gynecology, Department of Clinical Science, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden.

Abstract

Background: MicroRNAs (miRNAs) are key regulators of gene expression that have been implicated in gynecological and breast cancers. Understanding the cancer stage-wise expression patterns of miRNAs and their interactions with other RNA molecules in cancer is crucial to improve cancer diagnosis and treatment planning. Comprehensive web tools that integrate data on the transcriptome, circulating miRNAs, and their validated targets to derive beneficial conclusions in cancer research are lacking.

Methods: Using the Shiny R package, we developed a web tool called ExplORRNet that integrates transcriptomic profiles from The Cancer Genome Atlas and miRNA expression data derived from various sources, including tissues, cell lines, exosomes, serum, and plasma, available in the Gene Expression Omnibus database. Differential expression analyses between normal and tumor tissue samples as well as different stages of cancer, accompanied by gene enrichment and survival analyses, can be performed using specialized R packages. Additionally, a miRNA-messenger RNA (mRNA)-long non-coding RNA (lncRNA) networks are constructed to identify regulatory modules.

Results: Our tool identifies cancer stage-wise differentially regulated miRNAs, mRNAs, and lncRNAs in gynecological and breast cancers. Survival analysis identifies miRNAs associated with patient survival, and functional enrichment analysis provides insights into dysregulated miRNA-related biological processes and pathways. The miRNA-mRNA-lncRNA networks highlight interconnected regulatory molecular modules driving cancer progression. Case studies demonstrate the utility of the ExplORRNet for studying gynecological and breast cancers.

Conclusion: ExplORRNet is an intuitive and user-friendly web tool that provides a deeper understanding of dysregulated miRNAs and their functional implications in gynecological and breast cancers. We hope our ExplORRNet tool has potential utility among the clinical and basic researchers and will be beneficial to the entire cancer genomics community to encourage and facilitate mining the rapidly growing public databases to progress the field of precision oncology. The ExplORRNet is available at https://mirna.cs.ut.ee.

Keywords: Clinical stage; Gynecological cancer; Network; R shiny; TCGA; miRNA.