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pmid:35004856

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

Ruojing Bai · Zhen Li · Yuying Hou · Shiyun Lv · Ran Wang · Wei Hua · Hao Wu · Lili Dai

Frontiers in molecular biosciences · 2021 doi:10.3389/fmolb.2021.809085

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Experiment

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title
Identification of Diagnostic Markers Correlated With HIV+ Immune Non-response Based on Bioinformatics Analysis.
doi
10.3389/fmolb.2021.809085
pmid
35004856
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[0]
Ruojing Bai
[1]
Zhen Li
[2]
Yuying Hou
[3]
Shiyun Lv
[4]
Ran Wang
[5]
Wei Hua
[6]
Hao Wu
[7]
Lili Dai
journal
Frontiers in molecular biosciences
year
2021
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.
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The DEGs were determined between INRs and IRs samples with the “limma” R package (Ritchie et al., 2015; Phipson et al., 2016).
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sections[5] "Identification of Differentially Expressed Genes" paragraph 2
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The DEGs were determined between INRs and IRs samples with the “limma” R package (Ritchie et al., 2015; Phipson et al., 2016).
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
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ROC analysis identified six genes—FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1—with AUC values greater than 0.9 as potential diagnostic markers distinguishing INRs from IRs.
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sections[15] "Identification of the Diagnostic Markers and Gene Set Enrichment Analysis" paragraph 2
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ROC analysis was conducted on the 52 DEGs and excavated six genes (FAM120AOS, LTA, FAM179B, JUN, PTMA, SH3YL1) with AUC values over 0.9 (Figures 3A–F), thereby illustrating that the mentioned genes exhibited a powerful discrimination ability to discriminate INRs samples from the IRs samples.
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ROC analysis identified six genes—FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1—with AUC values greater than 0.9 as potential diagnostic markers distinguishing INRs from IRs.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35004856", "kind": "science-record", "revision": 5, "content_hash": "sha256:6c9ed4b61db22f13c197eb99c9d257e615a4f532733ddce1b746985b6aebe7a5", "created_at": "2026-11-01T17:36:44Z", "updated_at": "2026-11-01T22:48:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Diagnostic Markers Correlated With HIV+ Immune Non-response Based on Bioinformatics Analysis.", "doi": "10.3389/fmolb.2021.809085", "pmid": "35004856", "authors": ["Ruojing Bai", "Zhen Li", "Yuying Hou", "Shiyun Lv", "Ran Wang", "Wei Hua", "Hao Wu", "Lili Dai"], "journal": "Frontiers in molecular biosciences", "year": 2021, "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.", "claims": [{"id": "claim-method", "statement": "The DEGs were determined between INRs and IRs samples with the “limma” R package (Ritchie et al., 2015; Phipson et al., 2016).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35004856", "locator": "sections[5] \"Identification of Differentially Expressed Genes\" paragraph 2", "quote": "The DEGs were determined between INRs and IRs samples with the “limma” R package (Ritchie et al., 2015; Phipson et al., 2016)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35004856", "locator": "sections[21] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC analysis identified six genes—FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1—with AUC values greater than 0.9 as potential diagnostic markers distinguishing INRs from IRs.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35004856", "locator": "sections[15] \"Identification of the Diagnostic Markers and Gene Set Enrichment Analysis\" paragraph 2", "quote": "ROC analysis was conducted on the 52 DEGs and excavated six genes (FAM120AOS, LTA, FAM179B, JUN, PTMA, SH3YL1) with AUC values over 0.9 (Figures 3A–F), thereby illustrating that the mentioned genes exhibited a powerful discrimination ability to discriminate INRs samples from the IRs samples."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ROC analysis identified six genes—FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1—with AUC values greater than 0.9 as potential diagnostic markers distinguishing INRs from IRs.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35004856"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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