Differentiated serum levels of Krüppel-like factors 2 and 4, sP-selectin, and sE-selectin in patients with gestational diabetes mellitus

Zhang, Hongmei; Chen, Zhigao; Wang, Xiaoling

doi:10.1080/09513590.2022.2164762

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…As a negative regulator of adipogenesis, KLF2 is highly expressed in preadipocytes and regulates glucolipid metabolism and insulin sensitivity by directly inhibiting the PPARγ2 promoter activity ( 119 ). Furthermore, although KLFs 2, 4, and 9 have been strongly associated with Gestational Diabetes Mellitus (GDM) development, the specific regulatory roles of KLFs 2 and 4 remain unclear, necessitating additional research in a larger patient population ( 121 , 125 ). Research indicates that KLF9 plays a crucial role in regulating hepatic glucose metabolism.…”

Section: Klfs and Cardiomyopathymentioning

confidence: 99%

Krüpple-like factors in cardiomyopathy: emerging player and therapeutic opportunities

Gui,

Liu,

Jin

et al. 2024

Front. Cardiovasc. Med.

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Cardiomyopathy, a heterogeneous pathological condition characterized by changes in cardiac structure or function, represents a significant risk factor for the prevalence and mortality of cardiovascular disease (CVD). Research conducted over the years has led to the modification of definition and classification of cardiomyopathy. Herein, we reviewed seven of the most common types of cardiomyopathies, including Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC), diabetic cardiomyopathy, Dilated Cardiomyopathy (DCM), desmin-associated cardiomyopathy, Hypertrophic Cardiomyopathy (HCM), Ischemic Cardiomyopathy (ICM), and obesity cardiomyopathy, focusing on their definitions, epidemiology, and influencing factors. Cardiomyopathies manifest in various ways ranging from microscopic alterations in cardiomyocytes, to tissue hypoperfusion, cardiac failure, and arrhythmias caused by electrical conduction abnormalities. As pleiotropic Transcription Factors (TFs), the Krüppel-Like Factors (KLFs), a family of zinc finger proteins, are involved in regulating the setting and development of cardiomyopathies, and play critical roles in associated biological processes, including Oxidative Stress (OS), inflammatory reactions, myocardial hypertrophy and fibrosis, and cellular autophagy and apoptosis, particularly in diabetic cardiomyopathy. However, research into KLFs in cardiomyopathy is still in its early stages, and the pathophysiologic mechanisms of some KLF members in various types of cardiomyopathies remain unclear. This article reviews the roles and recent research advances in KLFs, specifically those targeting and regulating several cardiomyopathy-associated processes.

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Section: Klfs and Cardiomyopathymentioning

confidence: 99%

Krüpple-like factors in cardiomyopathy: emerging player and therapeutic opportunities

Gui,

Liu,

Jin

et al. 2024

Front. Cardiovasc. Med.

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Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Next generation sequencing (NGS) dataset GSE243039 was obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein-protein interaction (PPI) network was constructed and modules were analysed using the Human Integrated Protein-Protein Interaction rEference (HIPIE) database and Cytoscape software, and hub genes were identified. Subsequantely, a network between miRNAs and hub genes, and network between TFss and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve (ROC) analysis was used to validate the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including VCAM1, SNCA, PRKCB, ADRB2, FOXQ1, MDFI, ACTBL2, PRKD1, DAPK1 and ACTC1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including TCF3 and CLOCK, were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment, and monitoring of endometriosis.

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Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

Vastrad,

Vastrad

2024

Egypt J Med Hum Genet

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Background Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Methods Next-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes. Results A total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. Conclusions This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

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Differentiated serum levels of Krüppel-like factors 2 and 4, sP-selectin, and sE-selectin in patients with gestational diabetes mellitus

Cited by 3 publications

References 23 publications

Krüpple-like factors in cardiomyopathy: emerging player and therapeutic opportunities

Krüpple-like factors in cardiomyopathy: emerging player and therapeutic opportunities

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

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