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Integrated Bioinformatic Analysis Reveals the Underlying Molecular Mechanism of and Potential Drugs for Pulmonary Arterial Hypertension

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Integrated Bioinformatic Analysis Reveals the Underlying Molecular Mechanism of and Potential Drugs for Pulmonary Arterial Hypertension www.aging-us.com AGING 2021, Vol. 13, No. 10 Research Paper Integrated bioinformatic analysis reveals the underlying molecular mechanism of and potential drugs for pulmonary arterial hypertension Haoru Dong2,*, Xiuchun Li1,*, Mengsi Cai1,*, Chi Zhang2, Weiqi Mao2, Ying Wang2, Qian Xu2, Mayun Chen1, Liangxing Wang1,&, Xiaoying Huang1 1Division of Pulmonary Medicine, The First Affiliated Hospital of Wenzhou Medical University, Key Laboratory of Heart and Lung, Wenzhou 325000, Zhejiang, P.R. China 2The First Clinical Medical College, Wenzhou Medical University, Wenzhou 325000, Zhejiang, P.R. China *Equal contribution Correspondence to: Xiaoying Huang, Liangxing Wang; email: [email protected], https://orcid.org/0000-0003-4854-0922; [email protected], https://orcid.org/0000-0001-7214-2786 Keywords: PAH, DEGs, hub gene, molecular docking, potential drugs, bioinformatics Received: November 18, 2020 Accepted: March 4, 2021 Published: May 18, 2021 Copyright: © 2021 Dong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Pulmonary arterial hypertension (PAH) is a devastating cardiovascular disease without a clear mechanism or drugs for treatment. Therefore, it is crucial to reveal the underlying molecular mechanism and identify potential drugs for PAH. In this study, we first integrated three human lung tissue datasets (GSE113439, GSE53408, GSE117261) from GEO. A total of 151 differentially expressed genes (DEGs) were screened, followed by KEGG and GO enrichment analyses and PPI network construction. Five hub genes (CSF3R, NT5E, ANGPT2, FGF7, and CXCL9) were identified by Cytoscape (Cytohubba). GSEA and GSVA were performed for each hub gene to uncover the potential mechanism. Moreover, to repurpose known and therapeutic drugs, the CMap database was retrieved, and nine candidate compounds (lypressin, ruxolitinib, triclabendazole, L-BSO, tiaprofenic acid, AT-9283, QL-X-138, huperzine-a, and L-741742) with a high level of confidence were obtained. Then ruxolitinib was selected to perform molecular docking simulations with ANGPT2, FGF7, NT5E, CSF3R, JAK1, JAK2, JAK3, TYK2. A certain concentration of ruxolitinib could inhibit the proliferation and migration of rat pulmonary artery smooth muscle cells (rPASMCs) in vitro. Together, these analyses principally identified CSF3R, NT5E, ANGPT2, FGF7 and CXCL9 as candidate biomarkers of PAH, and ruxolitinib might exert promising therapeutic action for PAH. INTRODUCTION in the development of PAH in recent years [3], the underlying molecular mechanism of PAH remains Pulmonary arterial hypertension (PAH) is a serious unclear. In terms of PAH therapy, the curative effects of cardiovascular disease leading to right heart failure and specific drugs related to the prostacyclin pathway [4], eventually death [1]. The main pathophysiology of PAH endothelin pathway [5] or nitric oxide pathway [6] are is incrassation of the medial and intimal layers of the not satisfactory following the support of clinical trials pulmonary arterial wall, which might result in increased consisting of all PAH subtypes. Therefore, investigation pulmonary vascular resistance and haemodynamic of the underlying mechanisms of PAH and a search for derangements [2]. Although many genes and related auxiliary potential drugs for its treatment are still biological processes have been reported to be involved desperately needed. www.aging-us.com 14234 AGING Recently, there has been a growing trend for PAH application in PAH therapy. Ultimately, in vitro research to utilize high-throughput technologies to experiments verified the effects of ruxolitinib in PAH. explore novel diagnostic or prognostic biomarkers and therapeutic targets. For example, Wang et al. reported RESULTS that YWHAB was a diagnostic biomarker for idiopathic pulmonary arterial hypertension [3], and Zhu et al. Identification of DEGs related to PAH uncovered that miR-140-5p and TNF-α might be therapeutic targets for PAH [7]. Recently, many The study design is illustrated in Figure 1. We bioinformatics tools were developed and FocusHeuristics downloaded the raw data of GSE113439, GSE53408, is a competitive approach to explore disease-associated and GSE117261 from the Gene Expression Omnibus genes [8]. On the other hand, “Cytohubba”, a plug-in of (GEO; https://www.ncbi.nlm.nih.gov/gds/) database Cytoscape, is a useful and user-friendly tool to obtain hub [15]. Supplementary Table 1 shows the general genes in biological network and has been widely applied characteristics of the three datasets. Next, they were [9]. However, the limited sample sizes make these studies inputted into R by “affy” package and the missing data inconsistent and can lead to unconvincing conclusions were filled by “impute” package [16]. Then, “sva” about biological functions. Therefore, it is much more package was used to remove batch effects [17]. The Q- critical to elucidate the underlying molecular mechanism Q plot shows the effect of batch removal of PAH, which will in turn provide possible routes on (Supplementary Figure 1). The heterogeneity of the which potential treatments can be designed. datasets before and after batch effect removal was examined by “pca” package (Figures 2A, 2B). To Regarding PAH treatment, to develop a novel drug annotate the data, we used R software to match each requires substantial costs and a lengthy process of probeset to the corresponding gene symbol according to ensuring its safety and tolerance in the human body [10, “hugene10sttranscriptcluster.db” package. If multiple 11]. By contrast, repurposing a non-novel drug with probesets mapped to a same gene symbol, the maximum precise and new mode of action is relatively more cost- expression value was selected. In total, 18,820 genes efficient and time-saving. Recently, an increasing were obtained. number of free databases and online tools have been developed, which could help our repositioning of To identify the DEGs, the 132 samples were divided known drugs. For example, the Connectivity map into 47 control samples (CON group) and 85 pulmonary (CMap; https://clue.io/) database serves as a potentially arterial hypertension samples (PAH group). “Limma” useful tool for drug screening that can predict molecular package was utilized to conduct the differential gene targeted agents based on DEGs [12]. Combined with expression analysis, and a total of 151 significant DEGs high-throughput data, it facilitates the repurposing of (103 upregulated and 48 downregulated) were identified known drugs which has passed the toxicology and (adjusted P value < 0.05, FC > 1.5; Supplementary dosage analyses. Recently, a study reanalysed CMap Table 2) [18]. All DEGs were used to perform whole-genome transcriptome data by combining 26 hierarchical clustering analysis, and the heatmap similarity scores with 6 different heuristics. It provided showed evidently different expression between the two an insight to find a known drug by comparing the groups (Figure 2C). effects on the transcriptomes [13]. Then, molecular docking simulation and in vitro experiments can be GO and KEGG pathway enrichment analyses of performed to verify the prediction results. DEGs In the present study, we integrated 3 datasets containing We carried out GO enrichment analysis on DEGs with 132 human lung tissue samples to obtain DEGs. Then, the help of “clusterprofiler” package to explore their Kyoto Encyclopedia of Genes and Genomes (KEGG) biological features [19]. Biological process (BP) terms and Gene Ontology (GO) enrichment analyses were showed that the DEGs were enriched in “regulation performed based on the DEGs. After obtaining the hub of inflammatory response”, “peptidyl-tyrosine genes from the protein-protein interaction (PPI) phosphorylation” and “peptidyl-tyrosine modification” network, Gene set enrichment analysis (GSEA) and (Figure 3A), suggesting that the inflammatory response gene set variation analysis (GSVA) were utilized to might play an important role in the development of further reveal the potential biological functions of the PAH. In terms of cellular component (CC), the terms hub genes [14]. Then, the CMap database was “extracellular matrix”, “membrane raft” and “membrane employed to search for potential compounds for PAH microdomain” were significantly enriched (Figure 3B). treatment. Molecular docking simulation between Therefore, we hypothesized that the DEGs mainly candidate compound and the proteins encoded by hub played roles in the extracellular matrix. The major genes was conducted to validate their prospective enriched molecular function (MF) terms of the DEGs www.aging-us.com 14235 AGING were “receptor ligand activity”, “integrin binding” and EcCentricity algorithms, the number of obtained “sulfur compound binding” (Figure 3C). candidate genes was over 40 (Supplementary Table 3)). Then we intersected the results of 12 algorithms. Moreover, to explore the more profound function of Finally, five hub genes (CSF3R, NT5E, ANGPT2, DEGs, we conducted KEGG pathway enrichment FGF7, and CXCL9) were obtained (Figure 4B and analysis with “clusterprofiler” package [19]. Based on Supplementary Table 4). The five hub genes are the results, the following pathways were significantly labelled in the volcano plot to show their expression enriched in DEGs: “PI3K-AKT signalling pathway”, levels (Figure 4C). “focal adhesion” and “hypertrophic cardiomyopathy”
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