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Background <p>Gestational diabetes mellitus (GDM) is a metabolic disorder during pregnancy. Identifying the molecular signatures and specific biomarkers of GDM might provide novel clues for GDM prognosis and targeted therapy. This study aimed to identify biological markers for GDM and explore their underlying molecular mechanisms.</p> Methods <p>To identify key genes and signaling pathways in GDM, the next generation sequencing (NGS) dataset GSE154377 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between GDM and normal control samples were identified using DESeq2 in R bioconductor package. Using the DEGs, we further accomplished a series of gene ontology (GO) and REACTOME pathway enrichment analyses. Protein–protein interaction (PPI) network was borrowed using the IID interactome database and visualized using Cytoscape software. The most key modules from the PPI network were selected for GO and REACTOME pathway enrichment analysis. A miRNA-hub gene regulatory network and TF-hub gene regulatory network were generated depending on essential hub genes and visualized using Cytoscape software. A receiver operating characteristic curve (ROC) analysis for hub genes was performed to diagnose GDM.</p> Results <p>In total, 953 DEGs were identified, of which 478were up regulated genes and 475 were down regulated genes. GO and REACTOME pathway enrichment analysis results revealed that the up regulated genes were mainly enriched in multicellular organismal process and formation of the cornified envelope whereas downregulated genes were mainly enriched in hemostasis and cell activation. Through analyzing the PPI network, we screened hub genes TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB by the Cytoscape software. The regulatory network analysis revealed that microRNAs (miRNAs) include hsa-mir-198 and hsa-mir-582-5p, and transcription factors (TFs) include CREM and EP300 might be involved in the development of GDM. ROC analysis demonstrated that the hub genes screened for GDM was of good diagnostic significance.</p> Conclusions <p>Overall, these results thus highlight a range of novel signaling pathways and key genes that are linked to the advancement and progression of GDM, providing a list of important diagnostic and prognostic molecular markers that have the potential to aid in the clinical diagnosis and treatment of GDM.</p>

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Bioinformatics analysis of next generation sequencing data to diagnose crucial and novel genes in gestational diabetes mellitus

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月4日

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  • Varun Alur,
  • Basavaraj Vastrad,
  • Varshita Raju,
  • Chanabasayya Vastrad,
  • Shivakumar Kotturshetti

摘要

Background

Gestational diabetes mellitus (GDM) is a metabolic disorder during pregnancy. Identifying the molecular signatures and specific biomarkers of GDM might provide novel clues for GDM prognosis and targeted therapy. This study aimed to identify biological markers for GDM and explore their underlying molecular mechanisms.

Methods

To identify key genes and signaling pathways in GDM, the next generation sequencing (NGS) dataset GSE154377 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between GDM and normal control samples were identified using DESeq2 in R bioconductor package. Using the DEGs, we further accomplished a series of gene ontology (GO) and REACTOME pathway enrichment analyses. Protein–protein interaction (PPI) network was borrowed using the IID interactome database and visualized using Cytoscape software. The most key modules from the PPI network were selected for GO and REACTOME pathway enrichment analysis. A miRNA-hub gene regulatory network and TF-hub gene regulatory network were generated depending on essential hub genes and visualized using Cytoscape software. A receiver operating characteristic curve (ROC) analysis for hub genes was performed to diagnose GDM.

Results

In total, 953 DEGs were identified, of which 478were up regulated genes and 475 were down regulated genes. GO and REACTOME pathway enrichment analysis results revealed that the up regulated genes were mainly enriched in multicellular organismal process and formation of the cornified envelope whereas downregulated genes were mainly enriched in hemostasis and cell activation. Through analyzing the PPI network, we screened hub genes TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB by the Cytoscape software. The regulatory network analysis revealed that microRNAs (miRNAs) include hsa-mir-198 and hsa-mir-582-5p, and transcription factors (TFs) include CREM and EP300 might be involved in the development of GDM. ROC analysis demonstrated that the hub genes screened for GDM was of good diagnostic significance.

Conclusions

Overall, these results thus highlight a range of novel signaling pathways and key genes that are linked to the advancement and progression of GDM, providing a list of important diagnostic and prognostic molecular markers that have the potential to aid in the clinical diagnosis and treatment of GDM.