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Identification of potential gene markers in gestational diabetes mellitus.

ABSTRACT: This study aims to investigate underlying mechanisms of gestational diabetes mellitus (GDM). In this work, the GSE70493 dataset from GDM and control samples was acquired from Gene Expression Omnibus (GEO) database. Afterward, differentially expressed genes (DEGs) were screened between GDM and control samples. Subsequently, functional enrichment analysis and protein-protein interaction (PPI) network analysis of these DEGs were carried out. Furthermore, significant sub-modules were identified, and the functional analysis was also performed. Finally, we undertook a quantitative real-time polymerase chain reaction (qRT-PCR) with the purpose of confirming several key genes in GDM development. There were totally 528 up-regulated and 684 down-regulated DEGs between GDM and healthy samples. The functional analyses suggested that the above genes were dramatically enriched in type 1 diabetes mellitus (T1DM) process and immune-related pathways. Moreover, PPI analysis revealed that several members of human leukocyte antigen (HLA) superfamily, including down-regulated HLA-DQA1, HLA-DRB1, HLA-DPA1, and HLA-DQB1 served as hub genes. In addition, six significant sub-clusters were extracted and functional analysis suggested that these four genes in sub-module 1 were also associated with immune and T1DM-related pathways. Finally, they were also confirmed by qRT-PCR array. Besides, the four members of HLA superfamily might be implicated with molecular mechanisms of GDM, contributing to a deeper understanding of GDM development.

SUBMITTER: Tang W

PROVIDER: S-EPMC9279970 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Type 2 diabetes mellitus (T2DM) is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. In the present study, microarray data (GSE201966) of β‑cell enriched tissue obtained by laser capture microdissection were downloaded, including 10 control and 10 type 2 diabetic subjects. A comprehensive bioinformatics analysis of microarray data in the context of protein‑protein interaction (PPI) networks was employed, combined with subcellular location information to mine the potential candidate genes for T2DM and provide further insight on the possible mechanisms involved. First, differential analysis screened 108 differentially expressed genes. Then, 83 candidate genes were identified in the layered network in the context of PPI via network analysis, which were either directly or indirectly linked to T2DM. Of those genes obtained through literature retrieval analysis, 27 of 83 were involved with the development of T2DM; however, the rest of the 56 genes need to be verified by experiments. The functional analysis of candidate genes involved in a number of biological activities, demonstrated that 46 upregulated candidate genes were involved in 'inflammatory response' and 'lipid metabolic process', and 37 downregulated candidate genes were involved in 'positive regulation of cell death' and 'positive regulation of cell proliferation'. These candidate genes were also involved in different signaling pathways associated with 'PI3K/Akt signaling pathway', 'Rap1 signaling pathway', 'Ras signaling pathway' and 'MAPK signaling pathway', which are highly associated with the development of T2DM. Furthermore, a microRNA (miR)‑target gene regulatory network and a transcription factor‑target gene regulatory network were constructed based on miRNet and NetworkAnalyst databases, respectively. Notably, hsa‑miR‑192‑5p, hsa‑miR‑124‑5p and hsa‑miR‑335‑5p appeared to be involved in T2DM by potentially regulating the expression of various candidate genes, including procollagen C‑endopeptidase enhancer 2, connective tissue growth factor and family with sequence similarity 105, member A, protein phosphatase 1 regulatory inhibitor subunit 1 A and C‑C motif chemokine receptor 4. Smad5 and Bcl6, as transcription factors, are regulated by ankyrin repeat domain 23 and transmembrane protein 37, respectively, which might also be used in the molecular diagnosis and targeted therapy of T2DM. Taken together, the results of the present study may offer insight for future genomic‑based individualized treatment of T2DM and help determine the underlying molecular mechanisms that lead to T2DM.

Project description:Due to many adverse effects of gestational diabetes mellitus (GDM) on the mother and fetus, its diagnosis is crucial. The presence of GDM can be confirmed by an abnormal fasting plasma glucose level (aFPG) and/or oral glucose tolerance test (OGTT) performed mostly between 24 and 28 gestational week. Both aFPG and abnormal glucose tolerance (aGT) are used to diagnose GDM. In comparison to measurement of FPG, OGTT is time-consuming, usually inconvenient for the patient, and very often needs to be repeated. Therefore, it is necessary to seek tests that will be helpful and convenient to diagnose GDM. For this reason, we investigated the differences in fasting serum metabolites between GDM women with abnGM and normal FPG (aGT-GDM group), with aFPG and normal glucose metabolism (aFPG-GDM group) as well as pregnant women with normal glucose tolerance (NGT) being a control group. Serum metabolites were measured by an untargeted approach using gas chromatography-mass spectrometry (GC-MS). In the discovery phase, fasting serum samples collected from 79 pregnant women (aFPG-GDM, n = 24; aGT-GDM, n = 26; NGT, n = 29) between 24 and 28 weeks of gestation (gwk) were fingerprinted. A set of metabolites (α-hydroxybutyric acid (α-HB), β-hydroxybutyric acid (β-HB), and several fatty acids) significant in aGT-GDM vs NGT but not significant in aFPG-GDM vs NGT comparison in the discovery phase was selected for validation. These metabolites were quantified by a targeted GC-MS method in a validation cohort consisted of 163 pregnant women (aFPG-GDM, n = 51; aGT-GDM, n = 44; and NGT, n = 68). Targeted analyses were also performed on the serum collected from 92 healthy women in the first trimester (8-14 gwk) who were NGT at this time, but in the second trimester (24-28 gwk) they were diagnosed with GDM. It was found that α-HB, β-HB, and several fatty acids were associated with aGT-GDM. A combination of α-HB, β-HB, and myristic acid was found highly specific and sensitive for the diagnosis of GDM manifested by aGT-GDM (AUC = 0.828) or to select women at a risk of aGT-GDM in the first trimester (AUC = 0.791). Our findings provide new potential markers of GDM and may have implications for its early diagnosis.

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Identification of potential gene markers in gestational diabetes mellitus.

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