Integrated weighted gene co-expression network analysis uncovers STAT1(signal transducer and activator of transcription 1) and IFI44L (interferon-induced protein 44-like) as key genes in pulmonary arterial hypertension

Yang, Han; Lü, Yang; Yang, Hongmin; Zhu, Yaoxi; Tang, Yaohan; Li, Lixia; Liu, Changhu; Yuan, Jing

doi:10.1080/21655979.2021.1972200

Cited by 8 publications

(4 citation statements)

References 45 publications

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“…Apo lipoprotein E (APOE) ( Rao et al, 2022 ), Angiotensinogen II Type 1 Receptor (AGTR1) ( Wulandari et al, 2018 ), Angiotensin-converting enzyme (ACE) ( Ya et al, 2019 ), methylenetetrahydrofolate reductase (MTHFR) ( Garakanidze et al, 2018 ), endothelin1 (EDN1) ( Yang et al, 2021 ), ring finger protein 213 (RNF213) ( Suzuki et al, 2018 ), Alpha-Adducin 1 (ADD1) ( Yermolenko et al, 2021 ), tumor necrosis factor (TNF) ( Khlynova et al, 2020 ) and T-box transcription factor 2 (TBX2) ( Abramova et al, 2020 ) involve in the AH process and the overexpression of them can cause amplification in AH.…”

Section: Discussionmentioning

confidence: 99%

“…MiR-663 demonstrated the most tendency to NOS2 which ameliorates AH. on the other side, early studies have shown that this microRNA decreases arterial blood pressure in patients as this microRNA has exerted suppressive effects on AH by inhibition of TGF-β and subsequently, oppression of TGF-β1/smad2/3 signaling, vascular remodeling (Zhu et al, 2020;Li et al, 2021). Furthermore, in a study performed on 91 Caucasian participants from Eastern Siberia, it was found out that rs2779249 and rs229718 polymorphism of the NOS2 gene were associated with AH (Alyabyeva et al, 2023).…”

Section: Mir-663 Has the Most Binding Affinity To Nitric Oxide Syntha...mentioning

confidence: 99%

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Genetic and epigenetic factors of arterial hypertension: a bibliometric- and in-silico-based analyses

Karabaeva,

Vochshenkova,

Zare

et al. 2023

Front. Mol. Biosci.

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Introduction: Arterial hypertension (AH) is a pervasive global health concern with multifaceted origins encompassing both genetic and environmental components. Previous research has firmly established the association between AH and diverse genetic factors. Consequently, scientists have conducted extensive genetic investigations in recent years to unravel the intricate pathophysiology of AH.Methods: In this study, we conducted a comprehensive bibliometric analysis employing VOSviewer software to identify the most noteworthy genetic factors that have been the focal point of numerous investigations within the AH field in recent years. Our analysis revealed genes and microRNAs intricately linked to AH, underscoring their pivotal roles in this condition. Additionally, we performed molecular docking analyses to ascertain microRNAs with the highest binding affinity to these identified genes. Furthermore, we constructed a network to elucidate the in-silico-based functional interactions between the identified microRNAs and genes, shedding light on their potential roles in AH pathogenesis.Results: Notably, this pioneering in silico examination of genetic factors associated with AH promises novel insights into our understanding of this complex condition. Our findings prominently highlight miR-7110-5p, miR-7110-3p, miR-663, miR-328-3p, and miR-140-5p as microRNAs exhibiting a remarkable affinity for target genes. These microRNAs hold promise as valuable diagnostic and therapeutic factors, offering new avenues for the diagnosis and treatment of AH in the foreseeable future.Conclusion: In summary, this research underscores the critical importance of genetic factors in AH and, through in silico analyses, identifies specific microRNAs with significant potential for further investigation and clinical applications in AH management.

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Section: Discussionmentioning

confidence: 99%

Section: Mir-663 Has the Most Binding Affinity To Nitric Oxide Syntha...mentioning

confidence: 99%

Genetic and epigenetic factors of arterial hypertension: a bibliometric- and in-silico-based analyses

Karabaeva,

Vochshenkova,

Zare

et al. 2023

Front. Mol. Biosci.

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“…A previous study found that LMNA (lamin A/C) [85], SLC11A2 [163], CRTC2 [164], TBK1 [165], GRN (granulin precursor) [166], CTSD (cathepsin D) [167], STARD10 [168], PGRMC1 [169], TFE3 [170], POR (cytochrome p450 oxidoreductase) [171], SESN1 [172], IL15 [173], PIK3R1 [134], OSM (oncostatin M) [98], SOCS3 [174], USP21 [100], GLUL (glutamate-ammonia ligase) [175], IL1R1 [176], TP53 [177], PPM1A [178], CTBP1 [179], DNAJC3 [180], ATP6 [181], DDX21 [182], COX2 [183], RACK1 [184], ND1 [158], CCND2 [185] and COX1 [186] are positively correlated with type 2 diabetes mellitus, suggesting its potential as a biomarker for type 2 diabetes mellitus. LMNA (lamin A/C) [187], PFKFB3 [188], TRAF2 [189], ADORA2B [190], ACADS (acyl-CoA dehydrogenase short chain) [191], IRF7 [192], ASL (argininosuccinatelyase) [193], IL15 [194], PIK3R1 [195], OSM (oncostatin M) [196], SOCS3 [197], CHD9 [198], IFI44L [199], GNLY (granulysin) [200], FLNB (filamin B) [201], IL1R1 [202], SETD3 [203], TP53 [204], BRD4 [205], COX2 [206], HSP90AB1 [207], SLC7A1 [208], ND2 [209] and COX1 [210] have been reported to be associated with hypertension. LMNA (lamin A/C) [211], INPP5K [212], SCYL1 [213], AKR7A2 [214], TRAF2 [215], SLC11A2 [216], NEU1 [217], SNAP29 [218], DUSP22 [219], P2RX4 [220], ADORA2B [221], NAXD (NAD(P)HX dehydratase) [222], FEZ1 [223], TBK1 [224], GRN (granulin precursor) [225], ATG4D [226], CTSD (cathepsin D) [227], PPP2R5D […”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Vastrad¹,

Vastrad²

2022

Preprint

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Type 1 diabetes mellitus (T1DM) comprise the most common forms of autoimmune disease. The aim of this investigation was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of T1DM. The next generation sequencing (NGS) dataset GSE182870 was downloaded from the gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the functional enrichment, gene ontology (GO) and REACTOME pathway of the differentially expressed genes. Protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network, and TF-hub gene regulatory network were conducted via comprehensive target prediction and network analyses. Finally, hub genes were validated by using receiver operating characteristic curve analysis. A total of 860 DEGs were screened out from NGS dataset, among which 477 genes were up regulated and 383 genes were down regulated. GO enrichment analysis indicated that up regulated genes were mainly involved in cellular metabolic process, intracellular anatomical structure and catalytic activity, and the down regulated genes were significantly enriched in cellular nitrogen compound biosynthetic process, protein containing complex and heterocyclic compound binding. REACTOME pathway enrichment analysis showed that the up regulated genes were mainly enriched in metabolism of carbohydrates and the down regulated genes were significantly enriched in metabolism of RNA. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed, and hub genes (MAPK14, RHOC, MAD2L1, TAF1, TRAF2, HSP90AA1, TP53, HSP90AB1, UBA52 and RACK1) were identified. This study provides further insights into the underlying pathogenesis of T1DM.

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“…Liu et al [578], Park et al [579], Yang et al [580], Yu et al [581] and Forero-Delgadillo et al [582] mentioned that hsa-mir-21-5p, STAT3, STAT1, GATA2 and PAX2 were related to other kidney diseases. Studies have indicated that hsa-mir-21-5p [583], STAT3 [584], STAT1 [585] and GATA2 [586] plays a substantial role in hypertension. Ortiz-Dosal et al [587], Su et al [588] and Cox et al [589] demonstrated that the altered expression of hsa-mir-21-5p, STAT3 and STAT1 are associated with progression of obesity.…”

Section: Discussionmentioning

confidence: 99%

Identification of biomarkers and pathways for focal segmental glomerulosclerosis using next generation sequencing data and bioinformatics analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Focal segmental glomerulosclerosis (FSGS) is a renal disease leading threat to human health around the world. Here we aimed to explore novel molecular and potential therapeutic targets in FSGS through adopting integrated bioinformatics tools. Next generation sequencing (NGS) data of GSE197307 was available from Gene Expression Omnibus (GEO) database. Furthermore, differentially expressed genes (DEGs) were screened using the DESeq2 package in R software. Gene ontology (GO) and REACTOME pathway enrichment analyses of DEGs were performed via g:Profiler. Then, the protein-protein interaction (PPI) network, miRNA- hub gene regulatory network and TF-hub gene regulatory network were constructed using the HIPPIE, miRNet and NetworkAnalyst databases. Hub genes were validated using the receiver operating characteristic curve (ROC) analysis. By performing DEGs analysis, 488 up regulated genes and 488 down regulated genes were successfully identified from GSE197307, respectively. And they were mainly enriched in the terms of multicellular organismal process, cell periphery, protein binding, metabolism, immune system process, signaling receptor binding and immune system. Based on the data of protein-protein interaction (PPI), miRNA-hub gene regulatory network and TF-hub gene regulatory network, the top hub genes were ranked, including ILK, DDX5, MATR3, ALB, FOS, MKI67, PLK1, TNF, LCK and GTSE1. Bioinformatics analysis of NGS data identified signaling pathways and hub genes, potentially representing molecular mechanisms for the occurrence, progression, and risk prediction in FSGS.

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Integrated weighted gene co-expression network analysis uncovers STAT1(signal transducer and activator of transcription 1) and IFI44L (interferon-induced protein 44-like) as key genes in pulmonary arterial hypertension

Abstract: 2021) Integrated weighted gene co-expression network analysis uncovers STAT1(signal transducer and activator of transcription 1) and IFI44L (interferon-induced protein 44-like) as key genes in pulmonary arterial hypertension,

Cited by 8 publications

References 45 publications

Genetic and epigenetic factors of arterial hypertension: a bibliometric- and in-silico-based analyses

Genetic and epigenetic factors of arterial hypertension: a bibliometric- and in-silico-based analyses

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Identification of biomarkers and pathways for focal segmental glomerulosclerosis using next generation sequencing data and bioinformatics analysis

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