Bai Ruojing, Li Zhen, Hou Yuying, Lv Shiyun, Wang Ran, Hua Wei, Wu Hao, Dai Lili
Beijing Key Laboratory for HIV/AIDS Research, Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Institute of Neurology, Tianjin Third Central Hospital Affiliated to Nankai University, Tianjin, China.
Front Mol Biosci. 2021 Dec 22;8:809085. doi: 10.3389/fmolb.2021.809085. eCollection 2021.
HIV-infected immunological non-responders (INRs) are characterized by their inability to reconstitute CD4 T cell pools after antiretroviral therapy. The risk of non-AIDS-related diseases in INRs is increased, and the outcome and prognosis of INRs are inferior to that of immunological responders (IRs). However, few markers can be used to define INRs precisely. In this study, we aim to identify further potential diagnostic markers associated with INRs through bioinformatic analyses of public datasets. This study retrieved the microarray data sets of GSE106792 and GSE77939 from the Gene Expression Omnibus (GEO) database. After merging two microarray data and adjusting the batch effect, differentially expressed genes (DEGs) were identified. Gene Ontology (GO) resource and Kyoto Encyclopedia of Genes and Genomes (KEGG) resource were conducted to analyze the biological process and functional enrichment. We performed receiver operating characteristic (ROC) curves to filtrate potential diagnostic markers for INRs. Gene Set Enrichment Analysis (GSEA) was conducted to perform the pathway enrichment analysis of individual genes. Single sample GSEA (ssGSEA) was performed to assess scores of immune cells within INRs and IRs. The correlations between the diagnostic markers and differential immune cells were examined by conducting Spearman's rank correlation analysis. Subsequently, miRNA-mRNA-TF interaction networks in accordance with the potential diagnostic markers were built with Cytoscape. We finally verified the mRNA expression of the diagnostic markers in clinical samples of INRs and IRs by performing RT-qPCR. We identified 52 DEGs in the samples of peripheral blood mononuclear cells (PBMC) between INRs and IRs. A few inflammatory and immune-related pathways, including chronic inflammatory response, T cell receptor signaling pathway, were enriched. FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1 were considered as potential diagnostic markers. ssGSEA results showed that the IRs had significantly higher enrichment scores of seven immune cells compared with IRs. The miRNA-mRNA-TF network was constructed with 97 miRNAs, 6 diagnostic markers, and 26 TFs, which implied a possible regulatory relationship. The six potential crucial genes, FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1, may be associated with clinical diagnosis in INRs. Our study provided new insights into diagnostic and therapeutic targets.
HIV感染的免疫无反应者(INR)的特征是在抗逆转录病毒治疗后无法重建CD4 T细胞库。INR中非艾滋病相关疾病的风险增加,其结局和预后比免疫反应者(IR)差。然而,几乎没有标志物可用于精确界定INR。在本研究中,我们旨在通过对公共数据集进行生物信息学分析,确定与INR相关的更多潜在诊断标志物。本研究从基因表达综合数据库(GEO)中检索了GSE106792和GSE77939的微阵列数据集。合并两个微阵列数据并调整批次效应后,鉴定出差异表达基因(DEG)。利用基因本体论(GO)资源和京都基因与基因组百科全书(KEGG)资源进行生物学过程和功能富集分析。我们绘制了受试者工作特征(ROC)曲线,以筛选INR的潜在诊断标志物。进行基因集富集分析(GSEA)以对单个基因进行通路富集分析。进行单样本GSEA(ssGSEA)以评估INR和IR中免疫细胞的分数。通过Spearman等级相关分析检查诊断标志物与差异免疫细胞之间的相关性。随后,用Cytoscape构建与潜在诊断标志物一致的miRNA-mRNA-TF相互作用网络。我们最终通过RT-qPCR验证了INR和IR临床样本中诊断标志物的mRNA表达。我们在INR和IR的外周血单个核细胞(PBMC)样本中鉴定出52个DEG。富集了一些炎症和免疫相关通路,包括慢性炎症反应、T细胞受体信号通路。FAM120AOS、LTA、FAM179B、JUN、PTMA和SH3YL1被视为潜在诊断标志物。ssGSEA结果显示,与IR相比,IR的七种免疫细胞富集分数显著更高。用97个miRNA、6个诊断标志物和26个TF构建了miRNA-mRNA-TF网络,这暗示了一种可能的调控关系。六个潜在的关键基因FAM120AOS、LTA、FAM179B、JUN、PTMA和SH3YL1可能与INR的临床诊断相关。我们的研究为诊断和治疗靶点提供了新的见解。