Cargando…

Identification of Diagnostic Markers Correlated With HIV(+) Immune Non-response Based on Bioinformatics Analysis

Background: 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...

Descripción completa

Detalles Bibliográficos
Autores principales: Bai, Ruojing, Li, Zhen, Hou, Yuying, Lv, Shiyun, Wang, Ran, Hua, Wei, Wu, Hao, Dai, Lili
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727996/
https://www.ncbi.nlm.nih.gov/pubmed/35004856
http://dx.doi.org/10.3389/fmolb.2021.809085
_version_ 1784626633718628352
author Bai, Ruojing
Li, Zhen
Hou, Yuying
Lv, Shiyun
Wang, Ran
Hua, Wei
Wu, Hao
Dai, Lili
author_facet Bai, Ruojing
Li, Zhen
Hou, Yuying
Lv, Shiyun
Wang, Ran
Hua, Wei
Wu, Hao
Dai, Lili
author_sort Bai, Ruojing
collection PubMed
description Background: 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. Methods: 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. Results: 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. Conclusion: 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.
format Online
Article
Text
id pubmed-8727996
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-87279962022-01-06 Identification of Diagnostic Markers Correlated With HIV(+) Immune Non-response Based on Bioinformatics Analysis Bai, Ruojing Li, Zhen Hou, Yuying Lv, Shiyun Wang, Ran Hua, Wei Wu, Hao Dai, Lili Front Mol Biosci Molecular Biosciences Background: 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. Methods: 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. Results: 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. Conclusion: 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. Frontiers Media S.A. 2021-12-22 /pmc/articles/PMC8727996/ /pubmed/35004856 http://dx.doi.org/10.3389/fmolb.2021.809085 Text en Copyright © 2021 Bai, Li, Hou, Lv, Wang, Hua, Wu and Dai. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Bai, Ruojing
Li, Zhen
Hou, Yuying
Lv, Shiyun
Wang, Ran
Hua, Wei
Wu, Hao
Dai, Lili
Identification of Diagnostic Markers Correlated With HIV(+) Immune Non-response Based on Bioinformatics Analysis
title Identification of Diagnostic Markers Correlated With HIV(+) Immune Non-response Based on Bioinformatics Analysis
title_full Identification of Diagnostic Markers Correlated With HIV(+) Immune Non-response Based on Bioinformatics Analysis
title_fullStr Identification of Diagnostic Markers Correlated With HIV(+) Immune Non-response Based on Bioinformatics Analysis
title_full_unstemmed Identification of Diagnostic Markers Correlated With HIV(+) Immune Non-response Based on Bioinformatics Analysis
title_short Identification of Diagnostic Markers Correlated With HIV(+) Immune Non-response Based on Bioinformatics Analysis
title_sort identification of diagnostic markers correlated with hiv(+) immune non-response based on bioinformatics analysis
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727996/
https://www.ncbi.nlm.nih.gov/pubmed/35004856
http://dx.doi.org/10.3389/fmolb.2021.809085
work_keys_str_mv AT bairuojing identificationofdiagnosticmarkerscorrelatedwithhivimmunenonresponsebasedonbioinformaticsanalysis
AT lizhen identificationofdiagnosticmarkerscorrelatedwithhivimmunenonresponsebasedonbioinformaticsanalysis
AT houyuying identificationofdiagnosticmarkerscorrelatedwithhivimmunenonresponsebasedonbioinformaticsanalysis
AT lvshiyun identificationofdiagnosticmarkerscorrelatedwithhivimmunenonresponsebasedonbioinformaticsanalysis
AT wangran identificationofdiagnosticmarkerscorrelatedwithhivimmunenonresponsebasedonbioinformaticsanalysis
AT huawei identificationofdiagnosticmarkerscorrelatedwithhivimmunenonresponsebasedonbioinformaticsanalysis
AT wuhao identificationofdiagnosticmarkerscorrelatedwithhivimmunenonresponsebasedonbioinformaticsanalysis
AT dailili identificationofdiagnosticmarkerscorrelatedwithhivimmunenonresponsebasedonbioinformaticsanalysis