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Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease.

Helena Lucena-Padros1, Nereida Bravo-Gil1,2, Cristina Tous1,2

  • 1Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain.

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|February 24, 2024
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Summary

This study identifies new candidate genes and microRNAs (miRNAs) for Hirschsprung's disease (HSCR) using a computational multi-omics approach. These findings offer novel insights into HSCR pathogenesis and potential biomarkers for the rare developmental disorder.

Keywords:
Hirschsprung’s diseaseenteric neuropathynetworks analysisomics expression datasystem biology

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Area of Science:

  • Genetics and Developmental Biology
  • Computational Biology and Bioinformatics
  • Gastroenterology

Background:

  • Hirschsprung's disease (HSCR) is a rare congenital disorder characterized by the absence of enteric ganglia in the intestinal tract.
  • The genetic basis of HSCR is complex, with mutations in the RET proto-oncogene being a primary cause, yet its full pathogenesis remains unclear.
  • Understanding the molecular mechanisms underlying HSCR is crucial for developing effective diagnostic and therapeutic strategies.

Purpose of the Study:

  • To identify novel genes and microRNAs (miRNAs) associated with Hirschsprung's disease (HSCR) using a computational multi-omics approach.
  • To discover potential biomarkers for HSCR through network analysis and clustering.
  • To elucidate new aspects of HSCR pathogenesis by analyzing gene-disease and miRNA-target interactions.

Main Methods:

  • Applied a computational strategy integrating multi-omics data, including protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks.
  • Utilized DPClusO and BiClusO algorithms for network analysis and miRNA-BD for screening miRNA biomarker potential.
  • Performed functional enrichment analysis (Gene Ontology, pathways) on identified candidate genes.

Main Results:

  • Identified 55 significant gene-disease modules, proposing 178 novel candidate genes and two biological pathways implicated in HSCR.
  • Discovered 12 key miRNAs with significant biomarker potential from a set of 137 predicted HSCR-associated miRNAs.
  • Functional analysis confirmed the relevance of identified candidates to HSCR-related biological processes.

Conclusions:

  • The computational multi-omics approach successfully identified new candidate genes and miRNAs relevant to Hirschsprung's disease (HSCR) pathogenesis.
  • The study provides novel insights into the molecular underpinnings of HSCR and highlights potential biomarkers for future clinical investigation.
  • Further molecular experiments are warranted to validate these computational findings and advance clinical applications for HSCR.