Elucidating the Gene Signatures and Immune Cell Types in HIV-Infected Immunological Non-Responders by Bioinformatics Analyses.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36514742", "kind": "science-record", "revision": 4, "content_hash": "sha256:cf11ecb2ef67753dc812d4101d3c85d23e749c55713ac5a6466d5ea3d2444ea9", "created_at": "2026-11-01T19:01:01Z", "updated_at": "2026-11-01T22:50:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Elucidating the Gene Signatures and Immune Cell Types in HIV-Infected Immunological Non-Responders by Bioinformatics Analyses.", "doi": "10.2147/IJGM.S390642", "pmid": "36514742", "authors": ["Qing Xiao", "Junyan Han", "Fengting Yu", "Liting Yan", "Qun Li", "Xiaojie Lao", "Hongxin Zhao", "Fujie Zhang"], "journal": "International journal of general medicine", "year": 2022, "abstract": "Objective: Numerous studies have reported on the pathogenesis of poor immune reconstitution (PIR) after antiretroviral treatment in human immunodeficiency virus (HIV) patients. However, fewer studies focused on both immune-related genes (IRGs) and immune cells, and the correlation between IRGs and immune cells was evaluated via bioinformatics analyses.\nMethods: Gene expression profiling of GSE143742 from the Gene Expression Omnibus (GEO) database was analyzed to get differentially expressed immune-related genes (DEIRGs). The enrichment analysis and protein-protein interaction (PPI) networks of DEIRGs were established. The relative fractions of 22 immune cell types were detected using the \"CIBERSORT\". The correlation analysis between DEIRGs and immune cells was constructed to discover the potential IRGs associated with immune cells. A logistic regression diagnostic model was built, and a receiver operating characteristic (ROC) curve was performed to evaluate the model's diagnostic efficacy. The CMap database was used to find molecules with therapeutic potential. RT-qPCR was used to verify the expression of the hub DEIRGs.\nResults: We identified eight types of significantly changed immune cells and five hub IRGs in INRs. The DEIRGs were mainly enriched in lymphocyte activation, receptor-ligand activity, and T cell receptor signaling pathway. The correlation analysis showed that the expression of TNF, CXCR4 and TFRC correlate with CD8 cells, resting mast cells, activated NK cells, and naïve CD4 cells in INRs. Meanwhile, TFRC and IL7R relate to activated NK cells and resting memory CD4 cells respectively in IRs. A diagnostic model was constructed using multiple logistic regression and nine small molecules were identified as possible drugs.\nConclusion: In this study, we suggested that the process of PIR might be related to TNF, CXCR4, TFRC, CD48, and IL7R. And these IRGs play roles in regulating immune-competent cells. And our constructed diagnostic model has excellent effectiveness. Moreover, some small-molecule drugs are screened to alleviate PIR.", "claims": [{"id": "claim-method", "statement": "The genes that showed different expressions between INR and IR samples were identified by the R package “linear models for microarray analysis (LIMMA)”.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36514742", "locator": "sections[5] \"Identification of Differentially Expressed Immune-Related Genes (DEIRGs)\" paragraph 2", "quote": "The genes that showed different expressions between INR and IR samples were identified by the R package “linear models for microarray analysis (LIMMA)”."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143742 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106792.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36514742", "locator": "sections[28] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143742 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106792."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The area under the ROC curve (AUC) of the training data was 0.922 (95% CI, 0.845–1.000) (Figure 7A).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The area under the ROC curve (AUC) of the training data was 0.922 (95% CI, 0.845–1.000) (Figure 7A)."}, "evidence": [{"asset": "source:pmid:36514742", "locator": "sections[20] \"Diagnostic Model Development and Its Validation\" paragraph 2", "quote": "The area under the ROC curve (AUC) of the training data was 0.922 (95% CI, 0.845–1.000) (Figure 7A)."}, {"asset": "source:pmid:36514742", "locator": "sections[20] \"Diagnostic Model Development and Its Validation\" paragraph 2", "quote": "For external validation, the area under the ROC curve was 0.889 (95% CI, 0.694–1.000) (Figure 7B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The area under the ROC curve (AUC) of the training data was 0.922 (95% CI, 0.845–1.000) (Figure 7A).", "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:36514742"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE106792"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE106792"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36514742", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/IJGM.S390642", "sha256": "sha256:9c9c63c3e6d1b0e58efeb367709669a8302842c00c2e9965bb8d08d5657fad55", "bytes": 46090, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/IJGM.S390642", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9741855", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE106792", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106792", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE106792", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143742 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106792.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4273542", "GSM4273543", "GSM4273544", "GSM4273545", "GSM4273546", "GSM4273547", "GSM4273548", "GSM4273549", "GSM4273550", "GSM4273551", "GSM4273552", "GSM4273553", "GSM4273554", "GSM4273555", "GSM4273556", "GSM4273557", "GSM4273558", "GSM4273559", "GSM4273560", "GSM4273561", "GSM4273562", "GSM4273563", "GSM4273564", "GSM4273565", "GSM4273566", "GSM4273567", "GSM4273568", "GSM4273569", "GSM4273570", "GSM4273571", "GSM4273572", "GSM4273573", "GSM4273574", "GSM4273575", "GSM4273576", "GSM4273577", "GSM4273578", "GSM4273579", "GSM4273580", "GSM4273581", "GSM4273582", "GSM4273583", "GSM4273584", "GSM4273585", "GSM4273586", "GSM4273587", "GSM4273588", "GSM4273589", "GSM4273590", "GSM4273591", "GSM4273592", "GSM4273593", "GSM4273594", "GSM4273595", "GSM4273596", "GSM4273597", "GSM4273598", "GSM4273599", "GSM4273600", "GSM4273601", "GSM4273602"], "sample_count": 61, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "Senescence and TGF-β producing Tregs drive HIV persistence and impaired immune homeostasis", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE106792 not downloaded", "asset:data:geo:GSE106792 licence not read", "asset:data:geo:GSE143742 not downloaded", "asset:data:geo:GSE143742 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The genes that showed different expressions between INR and IR samples were identified by the R package “linear models for microarray analysis (LIMMA)”."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.7, "band": "medium", "quote": "logistic regression"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman correlation"}}, {"id": "method-5", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "build_graph", "expected_value": 95.0, "unit": "per cent", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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