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利用生物信息学分析鉴定特发性肺动脉高压中的潜在生物标志物和免疫浸润特征

Identification of Potential Biomarkers and Immune Infiltration Characteristics in Idiopathic Pulmonary Arterial Hypertension Using Bioinformatics Analysis.

作者信息

Zeng Haowei, Liu Xiaoqin, Zhang Yushun

机构信息

Department of Structural Heart Disease, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

出版信息

Front Cardiovasc Med. 2021 Feb 1;8:624714. doi: 10.3389/fcvm.2021.624714. eCollection 2021.

Abstract

Idiopathic pulmonary arterial hypertension (IPAH) is a rare but severe lung disorder, which may lead to heart failure and early mortality. However, little is known about the etiology of IPAH. Thus, the present study aimed to establish the differentially expressed genes (DEGs) between IPAH and normal tissues, which may serve as potential prognostic markers in IPAH. Furthermore, we utilized a versatile computational method, CIBERSORT to identify immune cell infiltration characteristics in IPAH. The GSE117261 and GSE48149 datasets were obtained from the Gene Expression Omnibus database. The GSE117261 dataset was adopted to screen DEGs between IPAH and the control groups with the criterion of |log2 fold change| ≥ 1, adjusted < 0.05, and to further explore their potential biological functions via Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes Pathway analysis, and Gene Set Enrichment Analysis. Moreover, the support vector machine (SVM)-recursive feature elimination and the least absolute shrinkage and selection operator regression model were performed jointly to identify the best potential biomarkers. Then we built a regression model based on these selected variables. The GSE48149 dataset was used as a validation cohort to appraise the diagnostic efficacy of the SVM classifier by receiver operating characteristic (ROC) analysis. Finally, immune infiltration was explored by CIBERSORT in IPAH. We further analyzed the correlation between potential biomarkers and immune cells. In total, 75 DEGs were identified; 40 were downregulated, and 35 genes were upregulated. Functional enrichment analysis found a significantly enrichment in heme binding, inflammation, chemokines, cytokine activity, and abnormal glycometabolism. , and were identified as the best potential biomarkers with an area under the ROC curve (AUC) of 1 (95%CI = 0.937-1.000, specificity = 100%, sensitivity = 100%) in the discovery cohort and 1(95%CI = 0.805-1.000, specificity = 100%, sensitivity = 100%) in the validation cohort. Moreover, immune infiltration analysis by CIBERSORT showed a higher level of CD8+ T cells, resting memory CD4+ T cells, gamma delta T cells, M1 macrophages, resting mast cells, as well as a lower level of naïve CD4+ T cells, monocytes, M0 macrophages, activated mast cells, and neutrophils in IPAH compared with the control group. In addition, , and were correlated with immune cells. , and were identified as potential biomarkers to discriminate IPAH from the control. There was an obvious difference in immune infiltration between patient with IPAH and normal groups.

摘要

特发性肺动脉高压(IPAH)是一种罕见但严重的肺部疾病,可能导致心力衰竭和早期死亡。然而,人们对IPAH的病因知之甚少。因此,本研究旨在确定IPAH与正常组织之间的差异表达基因(DEGs),这些基因可能作为IPAH的潜在预后标志物。此外,我们利用一种通用的计算方法CIBERSORT来识别IPAH中的免疫细胞浸润特征。GSE117261和GSE48149数据集从基因表达综合数据库中获得。采用GSE117261数据集,以|log2倍数变化|≥1、校正后P<0.05为标准筛选IPAH与对照组之间的DEGs,并通过基因本体分析、京都基因与基因组百科全书通路分析和基因集富集分析进一步探索其潜在的生物学功能。此外,联合进行支持向量机(SVM)递归特征消除和最小绝对收缩和选择算子回归模型,以识别最佳潜在生物标志物。然后我们基于这些选定的变量建立了一个回归模型。GSE48149数据集用作验证队列,通过受试者工作特征(ROC)分析评估SVM分类器的诊断效能。最后,通过CIBERSORT探索IPAH中的免疫浸润情况。我们进一步分析了潜在生物标志物与免疫细胞之间的相关性。总共鉴定出75个DEGs;40个下调,35个基因上调。功能富集分析发现血红素结合、炎症、趋化因子、细胞因子活性和糖代谢异常方面有显著富集。在发现队列中,[具体基因1]、[具体基因2]和[具体基因3]被确定为最佳潜在生物标志物,ROC曲线下面积(AUC)为1(95%CI = 0.937 - 1.000,特异性 = 100%,敏感性 = 100%),在验证队列中AUC为1(95%CI = 0.805 - 1.000,特异性 = 100%,敏感性 = 100%)。此外,CIBERSORT进行的免疫浸润分析显示,与对照组相比,IPAH中CD8 + T细胞、静息记忆CD4 + T细胞、γδT细胞和M1巨噬细胞、静息肥大细胞水平较高,而幼稚CD4 + T细胞水平较低,单核细胞、M0巨噬细胞、活化肥大细胞和中性粒细胞水平较低。此外,[具体基因1]、[具体基因2]和[具体基因3]与免疫细胞相关。[具体基因1]、[具体基因2]和[具体基因3]被确定为区分IPAH与对照组的潜在生物标志物。IPAH患者与正常组之间的免疫浸润存在明显差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad84/7882500/8b73609390db/fcvm-08-624714-g0001.jpg

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