Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitus
Background: Intrauterine exposure to gestational diabetes mellitus (GDM) poses significant risks to fetal development and future metabolic health. Despite its clinical importance, the role of microRNAs (miRNAs) in fetoplacental vascular endothelial cell (VEC) programming in the context of GDM remain...
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Elsevier
2025-03-01
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| Series: | Biochemistry and Biophysics Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405580824002528 |
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| author | Yulan Lu Chunhong Liu Xiaoxia Pang Xinghong Chen Chunfang Wang Huatuo Huang |
| author_facet | Yulan Lu Chunhong Liu Xiaoxia Pang Xinghong Chen Chunfang Wang Huatuo Huang |
| author_sort | Yulan Lu |
| collection | DOAJ |
| description | Background: Intrauterine exposure to gestational diabetes mellitus (GDM) poses significant risks to fetal development and future metabolic health. Despite its clinical importance, the role of microRNAs (miRNAs) in fetoplacental vascular endothelial cell (VEC) programming in the context of GDM remains elusive. This study aims to identify signature miRNA genes involved in this process using bioinformatics analysis via multiple algorithms. Methods: The dataset used in this study was acquired from Gene Expression Omnibus (GEO). Firstly, differentially expressed miRNA genes (DEMGs) were evaluated using limma package. Thereafter, an enrichment analysis of DEMGs was performed. Then, the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) were used as the other algorithms for screening candidate signature miRNA genes. Genes from the intersection of limma, LASSO, and SVM genes were used as the final signature miRNA genes. The receiver operator characteristic curve (ROC), the nomogram diagram, gene set enrichment analysis (GSEA), and signature miRNAs-target genes interaction network were implemented further to explore the features and functions of signature genes. Results: A total of 32 DEMGs, with 21 upregulated and 11 downregulated miRNA genes, were obtained from limma analysis. LASSO and SVM analyses identified 15 and 12 candidate signature miRNA genes, respectively. After the intersection of genes from limma, LASSO, and SVM analyses, MIR34A and MIR186 were found as the final signature genes related to fetoplacental VEC programming. MIR34A and MIR186 were highly expressed and were associated with an increased risk of fetoplacental VEC programming in GDM mothers. The area under the curve (AUC) of ROC for MIR34A and MIR186 were 0.960 and 0.935, respectively. GSEA analysis revealed that these signature genes positively participate in cellular processes related to VEC migration, cell differentiation, angiogenesis, programmed cell death, and inflammatory response. Finally, miRNAs-target genes interaction network analysis provides the interaction of signature miRNAs and their critical target genes, which may help further studies for miR-34a and miR-186 in GDM. Conclusions: MIR34A and MIR186 are novel signature miRNA genes related to fetoplacental VEC programming that may represent critical genes associated with placental function and fetal programming under GDM conditions. |
| format | Article |
| id | doaj-art-75c2b4f2097f4726b29b141ead7dd3ab |
| institution | OA Journals |
| issn | 2405-5808 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Biochemistry and Biophysics Reports |
| spelling | doaj-art-75c2b4f2097f4726b29b141ead7dd3ab2025-08-20T02:03:07ZengElsevierBiochemistry and Biophysics Reports2405-58082025-03-014110188810.1016/j.bbrep.2024.101888Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitusYulan Lu0Chunhong Liu1Xiaoxia Pang2Xinghong Chen3Chunfang Wang4Huatuo Huang5Center of Reproduction Medical, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, 533000, ChinaCenter for Medical Laboratory Science, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, 533000, China; Key Laboratory of Research and Development on Clinical Molecular Diagnosis for High-Incidence Diseases of Baise, Guangxi, 533000, China; Key Laboratory of Research on Clinical Molecular Diagnosis for High Incidence Diseases in Western Guangxi of Guangxi Higher Education Institutions, Guangxi, 533000, ChinaCenter for Medical Laboratory Science, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, 533000, China; Key Laboratory of Research and Development on Clinical Molecular Diagnosis for High-Incidence Diseases of Baise, Guangxi, 533000, China; Key Laboratory of Research on Clinical Molecular Diagnosis for High Incidence Diseases in Western Guangxi of Guangxi Higher Education Institutions, Guangxi, 533000, ChinaCenter of Reproduction Medical, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, 533000, ChinaCenter for Medical Laboratory Science, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, 533000, China; Key Laboratory of Research and Development on Clinical Molecular Diagnosis for High-Incidence Diseases of Baise, Guangxi, 533000, China; Key Laboratory of Research on Clinical Molecular Diagnosis for High Incidence Diseases in Western Guangxi of Guangxi Higher Education Institutions, Guangxi, 533000, China; Corresponding author. Center for Medical Laboratory Science, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, 533000, China.Center for Medical Laboratory Science, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, 533000, China; Key Laboratory of Research and Development on Clinical Molecular Diagnosis for High-Incidence Diseases of Baise, Guangxi, 533000, China; Key Laboratory of Research on Clinical Molecular Diagnosis for High Incidence Diseases in Western Guangxi of Guangxi Higher Education Institutions, Guangxi, 533000, China; Corresponding author. Key Laboratory of Research on Clinical Molecular Diagnosis for High Incidence Diseases in Western Guangxi of Guangxi Higher Education Institutions, Guangxi, 533000, China.Background: Intrauterine exposure to gestational diabetes mellitus (GDM) poses significant risks to fetal development and future metabolic health. Despite its clinical importance, the role of microRNAs (miRNAs) in fetoplacental vascular endothelial cell (VEC) programming in the context of GDM remains elusive. This study aims to identify signature miRNA genes involved in this process using bioinformatics analysis via multiple algorithms. Methods: The dataset used in this study was acquired from Gene Expression Omnibus (GEO). Firstly, differentially expressed miRNA genes (DEMGs) were evaluated using limma package. Thereafter, an enrichment analysis of DEMGs was performed. Then, the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) were used as the other algorithms for screening candidate signature miRNA genes. Genes from the intersection of limma, LASSO, and SVM genes were used as the final signature miRNA genes. The receiver operator characteristic curve (ROC), the nomogram diagram, gene set enrichment analysis (GSEA), and signature miRNAs-target genes interaction network were implemented further to explore the features and functions of signature genes. Results: A total of 32 DEMGs, with 21 upregulated and 11 downregulated miRNA genes, were obtained from limma analysis. LASSO and SVM analyses identified 15 and 12 candidate signature miRNA genes, respectively. After the intersection of genes from limma, LASSO, and SVM analyses, MIR34A and MIR186 were found as the final signature genes related to fetoplacental VEC programming. MIR34A and MIR186 were highly expressed and were associated with an increased risk of fetoplacental VEC programming in GDM mothers. The area under the curve (AUC) of ROC for MIR34A and MIR186 were 0.960 and 0.935, respectively. GSEA analysis revealed that these signature genes positively participate in cellular processes related to VEC migration, cell differentiation, angiogenesis, programmed cell death, and inflammatory response. Finally, miRNAs-target genes interaction network analysis provides the interaction of signature miRNAs and their critical target genes, which may help further studies for miR-34a and miR-186 in GDM. Conclusions: MIR34A and MIR186 are novel signature miRNA genes related to fetoplacental VEC programming that may represent critical genes associated with placental function and fetal programming under GDM conditions.http://www.sciencedirect.com/science/article/pii/S2405580824002528PregnancyPlacentaBioinformaticsFetusVascular endothelial cellsDevelopment programming |
| spellingShingle | Yulan Lu Chunhong Liu Xiaoxia Pang Xinghong Chen Chunfang Wang Huatuo Huang Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitus Biochemistry and Biophysics Reports Pregnancy Placenta Bioinformatics Fetus Vascular endothelial cells Development programming |
| title | Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitus |
| title_full | Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitus |
| title_fullStr | Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitus |
| title_full_unstemmed | Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitus |
| title_short | Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitus |
| title_sort | bioinformatic identification of signature mirnas associated with fetoplacental vascular dysfunction in gestational diabetes mellitus |
| topic | Pregnancy Placenta Bioinformatics Fetus Vascular endothelial cells Development programming |
| url | http://www.sciencedirect.com/science/article/pii/S2405580824002528 |
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