Updated perspective of EPAS1 and the role in pulmonary hypertension

Wang, Na; Hua, Jing; Fu, Yucan; An, Jun; Chen, Xiangyu; Wang, Chuancui; Zheng, Yanghong; Wang, Feilong; Ji, Yingqun; Li, Qiang

doi:10.3389/fcell.2023.1125723

Cited by 11 publications

(10 citation statements)

References 114 publications

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“…EPAS1/HIF-2α has been proven to have relationship with the TME [73]. Moreover, EPAS1/HIF-2α has also been demonstrated to regulate or take part in many tumor-related pathways, such as the VEGF and PI3K-Akt pathways [74,75]. Furthermore, the degradation of HIF-2α is induced by products of the VHL gene [76].…”

Section: Discussionmentioning

confidence: 99%

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis

Wang,

Chen,

Tang

et al. 2024

IJMS

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Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, single-cell transcriptome sequencing (scRNA-seq) data from six patients downloaded from the GEO database were adopted to describe the tumor microenvironment (TME) of ccRCC, including its T cells, tumor-associated macrophages (TAMs), endothelial cells (ECs), and cancer-associated fibroblasts (CAFs). Based on the differential typing of the TME, we identified tumor cell-specific regulatory programs that are mediated by three key transcription factors (TFs), whilst the TF EPAS1/HIF-2α was identified via drug virtual screening through our analysis of ccRCC’s protein structure. Then, a combined deep graph neural network and machine learning algorithm were used to select anti-ccRCC compounds from bioactive compound libraries, including the FDA-approved drug library, natural product library, and human endogenous metabolite compound library. Finally, five compounds were obtained, including two FDA-approved drugs (flufenamic acid and fludarabine), one endogenous metabolite, one immunology/inflammation-related compound, and one inhibitor of DNA methyltransferase (N4-methylcytidine, a cytosine nucleoside analogue that, like zebularine, has the mechanism of inhibiting DNA methyltransferase). Based on the tumor microenvironment characteristics of ccRCC, five ccRCC-specific compounds were identified, which would give direction of the clinical treatment for ccRCC patients.

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Section: Discussionmentioning

confidence: 99%

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis

Wang,

Chen,

Tang

et al. 2024

IJMS

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“…WNT7A [445], SLC6A4 [446], BDNF (brain derived neurotrophic factor) [447], CXCL10 [448], NEK7 [449], CYP1B1 [450], ABCA3 [451], TRIB3 [452], PCSK9 [453], FGF2 [454], ACKR4 [455], FASN (fatty acid synthase) [456], VIP (vasoactive intestinal peptide) [457], KL (klotho) [458], BMPR2 [459], APOA1 [323], TLR3 [460], CCR2 [461], TLR7 [462], CAV1 [463], WWC2 [464], TFPI (tissue factor pathway inhibitor) [465], EPAS1 [466] and CCDC40 [467] could serve as a potential therapeutic for pulmonary hypertension treatment. A previous study reported that the genes include CX3CR1 [468], CSF2 [469], CLDN18 [470], TRIM58 [471], PF4 [472], FFAR2 [473], MPO (myeloperoxidase) [474], CD5L [475], SH3GL2 [476], ITGA2B [477], S100A8 [478], VEGFD (vascular endothelial growth factor D) [479], CXCL11 [480], IL1A [481], WNT7A [482], SSTR1 [483], AQP4 [484], SCD (stearoyl-CoA desaturase) [485], SLC6A4 [486], BDNF (brain derived neurotrophic factor) [487], C...…”

Section: Discussionmentioning

confidence: 99%

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analysis of DEGs were performed. Then, a protein protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst database was used to construct the targeted microRNAs (miRNAs) and transcription factors (TFs) of the hub genes. Finally receiver operating characteristic (ROC) curve analysis was used to validates the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8 and EFHC2 were selected. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

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“…Unfortunately, the interventional management of vascular remodeling is not welldelineated. Hypoxia plays a significant role in the pathogenesis of PAH, and numerous studies have shown the relationship between PAH and the hypoxia-inducible factors family, especially HIF 1A 1,26,27,28 .…”

Section: Introductionmentioning

confidence: 99%

“…Clinically, PAH is categorized into five groups: (i) pulmonary arterial hypertension; (ii) PAH associated with left heart disease; (iii) PAH associated with lung disease and/or hypoxia; (iv) PAH associated with pulmonary artery obstructions; and (v) PAH with unclear or multifactorial mechanisms. The diagnostic criteria for PAH were updated as mean pulmonary artery pressure (mPAP) >20 mmHg at rest during right heart catheterization according to the 2022 European Society of Cardiology/European Respiratory Society PAH guidelines 1,4 .…”

Section: Introductionmentioning

confidence: 99%

“…The primary pathophysiology of pulmonary vascular remodeling involves (i) intimal endothelial cell proliferation; (ii) apoptotic resistance; (iii) medial pulmonary artery smooth muscle cell (PASMCs) hypertrophy and proliferation; (iv) adventitial fibroblast proliferation and activation; with (v) excessive extracellular matrix deposition; and (vi) interstitial or perivascular inflammatory infiltration. PAH patients have a low 5-year survival rate and high mortality, about 57%, due to the complexity of etiologies and limited interventions for irreversible pulmonary vascular remodeling 1,30 .…”

Section: Introductionmentioning

confidence: 99%

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Exposure to the Interior Environment of Water-Damaged Buildings Can Activate Hif 1a, Induce Proliferative Physiology and Impair Mitochondrial Metabolism: Implications for Hypoxic Pulmonary Hypertension in Chronic Inflammatory Response Syndrome

Shoemaker

2024

MRAJ

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Hypoxia inducible factor 1A (HIF 1A) is an oxygen sensing nuclear transcription factor that regulates oxygen homeostasis in many illnesses ranging from, but not limited to cancer, heart failure, premature infants to viral infections. We report here measurement of HIF 1A using transcriptomics as a biomarker in Chronic Inflammatory Response Syndrome (CIRS), a systemic inflammatory and metabolic illness characterized by a multisystem, multi-symptom illness acquired following exposure to the interior environment of water-damaged buildings (WDB). Pulmonary artery hypertension (PAH) is well established but treatment is problematic, depending on its physiologic basis. Reduction of HIF 1A by other modalities does not approach the reduction of HIF 1A reported here. As shown by current literature, reduction of HIF 1A is crucial in PAH therapy. The additional problem of proliferative physiology in CIRS and PAH suggests a commonality of pathogenesis, with reduction of HIF 1A providing a salutary response. By first reviewing the diverse pathophysiology of PAH, we present data that provide a basis for the demonstrated efficacy of our treatment protocol which was used sequentially in CIRS patients to reduce HIF 1A and to improve molecular hypometabolism, suggesting a basis for a novel approach to treatment of PAH.

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Updated perspective of EPAS1 and the role in pulmonary hypertension

Cited by 11 publications

References 114 publications

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Exposure to the Interior Environment of Water-Damaged Buildings Can Activate Hif 1a, Induce Proliferative Physiology and Impair Mitochondrial Metabolism: Implications for Hypoxic Pulmonary Hypertension in Chronic Inflammatory Response Syndrome

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