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. 2021 Dec 22:8:809085.
doi: 10.3389/fmolb.2021.809085. eCollection 2021.

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

Ruojing Bai  1 , Zhen Li  1 , Yuying Hou  2 , Shiyun Lv  1 , Ran Wang  1 , Wei Hua  3 , Hao Wu  1 , Lili Dai  3
Affiliations

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

Ruojing Bai et al. Front Mol Biosci. .

Abstract

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.

Keywords: INRs; IRs; bioinformatic gene analysis; diagnostic markers; gene expression omnibus.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Identification of DEGs between INRs and 17 IRs. (A,B) Volcano plot (A) and Heatmap (B) presented the expression of DEGs. (C) The chromosomal locations and expression patterns of DEGs.
FIGURE 2
FIGURE 2
Go annotation an KEGG enrichment analysis of DEGs. (A–C) The top 10 terms of BP (A), CC (B), and MF (C) of GO annotation were enriched by DEGs. (D) The top 10 KEGG pathways enriched by DEGs.
FIGURE 3
FIGURE 3
Identification of diagnostic markers by ROC analysis. (A–F) ROC curves showed the AUC values of FAM120AOS (A), LTA (B), FAM179B (C), JUN (D), PTMA (E), SH3YL1 (F).
FIGURE 4
FIGURE 4
The results of enrichment analyses of diagnostic markers. (A) HALLMARK_TGF_BETA_SIGNALING pathway enriched by LTA. (B–C) TREG_VS_TCONV_UP and TREG_VS_TCONV_DN pathway enriched by PTMA (B) and LTA (C). (D) The expression of genes in the HALLMARK_TGF_BETA_SIGNALING pathway between INRs and IRs. (E) The expression of genes in TREG_VS_TCONV_UP and TREG_VS_TCONV_DN pathway between INRs and IRs.
FIGURE 5
FIGURE 5
Correlations between diagnostic markers and immune cells. (A) The discrepancy of enrichment scores for 28 immune cells between the INRs and IRs samples. (B) The correlation heatmap of 6 diagnostic markers and 7 differential immune cells.
FIGURE 6
FIGURE 6
The regulating mechanisms of diagnostic markers. (A) The miRNA mRNA regulatory network comprising 97 miRNAs, 6 diagnostic markers, and 106 edges. (B) The mRNA-TF regulatory network comprising 38 mRNA TF pairs. (C) The miRNA-mRNA-TF network of 6 diagnostic markers.
FIGURE 7
FIGURE 7
The mRNA expression of diagnostic markers in clinical 10 INRs and 10 IRs samples were detected by RT-PCR. The expression of FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1 (A–F) between the INRs and IRs samples (*p < 0.05, **p < 0.01, ***p < 0.001). (G) The expression of the diagnostic markers in merged GEO series (****p < 0.0001).
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References

    1. Akira S., Uematsu S., Takeuchi O. (2006). Pathogen Recognition and Innate Immunity. Cell. 124, 783–801. 10.1016/j.cell.2006.02.015 - DOI - PubMed
    1. Bai R.-J., Dai L.-L., Wu H. (2020). Advances and Challenges in Antiretroviral Therapy for Acquired Immunodeficiency Syndrome. Chin. Med. J. (Engl.) 133, 2775–2777. 10.1097/CM9.0000000000001226 - DOI - PMC - PubMed
    1. Baker J. V., Peng G., Rapkin J., Abrams D. I., Silverberg M. J., MacArthur R. D., et al. (2008a). CD4+ Count and Risk of Non-AIDS Diseases Following Initial Treatment for HIV Infection. AIDS 22, 841–848. 10.1097/QAD.0b013e3282f7cb76 - DOI - PMC - PubMed
    1. Baker J. V., Peng G., Rapkin J., Krason D., Reilly C., Cavert W. P., et al. (2008b). Poor Initial CD4+ Recovery with Antiretroviral Therapy Prolongs Immune Depletion and Increases Risk for AIDS and Non-AIDS Diseases. J. Acquir. Immune. Defic. Syndr. 48, 541–546. 10.1097/QAI.0b013e31817bebb3 - DOI - PMC - PubMed
    1. Bindea G., Mlecnik B., Tosolini M., Kirilovsky A., Waldner M., Obenauf A. C., et al. (2013). Spatiotemporal Dynamics of Intratumoral Immune Cells Reveal the Immune Landscape in Human Cancer. Immunity 39, 782–795. 10.1016/j.immuni.2013.10.003 - DOI - PubMed