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pmid:38935691

Computational reassessment of RNA-seq data reveals key genes in active tuberculosis.

Rakesh Arya · Hemlata Shakya · Reetika Chaurasia · Surendra Kumar · Joseph M Vinetz · Jong Joo Kim

PloS one · 2024 doi:10.1371/journal.pone.0305582

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Experiment

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:38935691
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science-record
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2026-11-01T20:29:00Z
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2026-11-01T22:54:35Z
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paper object · 10 fields
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title
Computational reassessment of RNA-seq data reveals key genes in active tuberculosis.
doi
10.1371/journal.pone.0305582
pmid
38935691
authors array · 6 items
[0]
Rakesh Arya
[1]
Hemlata Shakya
[2]
Reetika Chaurasia
[3]
Surendra Kumar
[4]
Joseph M Vinetz
[5]
Jong Joo Kim
journal
PloS one
year
2024
abstract
BACKGROUND: Tuberculosis is a serious life-threatening disease among the top global health challenges and rapid and effective diagnostic biomarkers are vital for early diagnosis especially given the increasing prevalence of multidrug resistance. METHODS: Two human whole blood microarray datasets, GSE42826 and GSE42830 were retrieved from publicly available gene expression omnibus (GEO) database. Deregulated genes (DEGs) were identified using GEO2R online tool and Gene Ontology (GO), protein-protein interaction (PPI) network analysis was performed using Metascape and STRING databases. Significant genes (n = 8) were identified using T-test/ANOVA and Molecular Complex Detection (MCODE) score ≥10, which was validated in GSE34608 dataset. The diagnostic potential of three biomarkers was assessed using Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) plot. The transcriptional levels of these genes were also examined in a separate dataset GSE31348, to monitor the patterns of variation during tuberculosis treatment. RESULTS: A total of 62 common DEGs (57 upregulated, 7 downregulated genes) were identified in two discovery datasets. GO functions and pathway enrichment analysis shed light on the functional roles of these DEGs in immune response and type-II interferon signaling. The genes in Module-1 (n = 18) were linked to innate immune response, interferon-gamma signaling. The common genes (n = 8) were validated in GSE34608 dataset, that corroborates the results obtained from discovery sets. The gene expression levels demonstrated responsiveness to Mtb infection during anti-TB therapy in GSE31348 dataset. In GSE34608 dataset, the expression levels of three specific genes, GBP5, IFITM3, and EPSTI1, emerged as potential diagnostic makers. In combination, these genes scored remarkable diagnostic performance with 100% sensitivity and 89% specificity, resulting in an impressive Area Under Curve (AUC) of 0.958. However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity. CONCLUSIONS: The study presents valuable insights into the critical gene network perturbed during tuberculosis. These genes are determinants for assessing the effectiveness of an anti-TB response and distinguishing between active TB and healthy individuals. GBP5, IFITM3 and EPSTI1 emerged as candidate core genes in TB and holds potential as novel molecular targets for the development of interventions in the treatment of TB.
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The significant genes were selected based on two criteria of common top 8 proteins in the heatmap analysis by T-test/ANOVA in MetaboAnalyst and MCODE score ≥10.
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sections[7] "Validation of important genes and ROC analysis" paragraph 2
quote
The significant genes were selected based on two criteria of common top 8 proteins in the heatmap analysis by T-test/ANOVA in MetaboAnalyst and MCODE score ≥10.
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Availability of data and materials: Datasets including GSE42826, GSE42830, GSE34608 and GSE31348 were downloaded from NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo).
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sections[18] "Data Availability" paragraph 2
quote
Availability of data and materials: Datasets including GSE42826, GSE42830, GSE34608 and GSE31348 were downloaded from NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo).
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GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity.
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However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity.
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sections[0] "Abstract" paragraph 3
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However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity.
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GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity.
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Tuberculosis Patients Blood Gene Expression Through Treatment
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Availability of data and materials: Datasets including GSE42826, GSE42830, GSE34608 and GSE31348 were downloaded from NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo).
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Human whole blood microarray study to compare patients with tuberculosis, sarcoidosis, pneumonia, and lung cancer (training)
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2026-11-01T20:16:08Z
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Computational reassessment of RNA-seq data reveals key genes in active tuberculosi
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g resistance. METHODS: Two human whole blood microarray datasets, GSE42826 and GSE42830 were retriev
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38935691", "kind": "science-record", "revision": 4, "content_hash": "sha256:36ca77938c7ccda8457b3080aa7e3ba839bd5f7260d3e965770d132cd50acb75", "created_at": "2026-11-01T20:29:00Z", "updated_at": "2026-11-01T22:54:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Computational reassessment of RNA-seq data reveals key genes in active tuberculosis.", "doi": "10.1371/journal.pone.0305582", "pmid": "38935691", "authors": ["Rakesh Arya", "Hemlata Shakya", "Reetika Chaurasia", "Surendra Kumar", "Joseph M Vinetz", "Jong Joo Kim"], "journal": "PloS one", "year": 2024, "abstract": "BACKGROUND: Tuberculosis is a serious life-threatening disease among the top global health challenges and rapid and effective diagnostic biomarkers are vital for early diagnosis especially given the increasing prevalence of multidrug resistance.\nMETHODS: Two human whole blood microarray datasets, GSE42826 and GSE42830 were retrieved from publicly available gene expression omnibus (GEO) database. Deregulated genes (DEGs) were identified using GEO2R online tool and Gene Ontology (GO), protein-protein interaction (PPI) network analysis was performed using Metascape and STRING databases. Significant genes (n = 8) were identified using T-test/ANOVA and Molecular Complex Detection (MCODE) score ≥10, which was validated in GSE34608 dataset. The diagnostic potential of three biomarkers was assessed using Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) plot. The transcriptional levels of these genes were also examined in a separate dataset GSE31348, to monitor the patterns of variation during tuberculosis treatment.\nRESULTS: A total of 62 common DEGs (57 upregulated, 7 downregulated genes) were identified in two discovery datasets. GO functions and pathway enrichment analysis shed light on the functional roles of these DEGs in immune response and type-II interferon signaling. The genes in Module-1 (n = 18) were linked to innate immune response, interferon-gamma signaling. The common genes (n = 8) were validated in GSE34608 dataset, that corroborates the results obtained from discovery sets. The gene expression levels demonstrated responsiveness to Mtb infection during anti-TB therapy in GSE31348 dataset. In GSE34608 dataset, the expression levels of three specific genes, GBP5, IFITM3, and EPSTI1, emerged as potential diagnostic makers. In combination, these genes scored remarkable diagnostic performance with 100% sensitivity and 89% specificity, resulting in an impressive Area Under Curve (AUC) of 0.958. However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity.\nCONCLUSIONS: The study presents valuable insights into the critical gene network perturbed during tuberculosis. These genes are determinants for assessing the effectiveness of an anti-TB response and distinguishing between active TB and healthy individuals. GBP5, IFITM3 and EPSTI1 emerged as candidate core genes in TB and holds potential as novel molecular targets for the development of interventions in the treatment of TB.", "claims": [{"id": "claim-method", "statement": "The significant genes were selected based on two criteria of common top 8 proteins in the heatmap analysis by T-test/ANOVA in MetaboAnalyst and MCODE score ≥10.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38935691", "locator": "sections[7] \"Validation of important genes and ROC analysis\" paragraph 2", "quote": "The significant genes were selected based on two criteria of common top 8 proteins in the heatmap analysis by T-test/ANOVA in MetaboAnalyst and MCODE score ≥10."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Availability of data and materials: Datasets including GSE42826, GSE42830, GSE34608 and GSE31348 were downloaded from NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38935691", "locator": "sections[18] \"Data Availability\" paragraph 2", "quote": "Availability of data and materials: Datasets including GSE42826, GSE42830, GSE34608 and GSE31348 were downloaded from NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity.", "kind": "quantitative", "target": {"value": 0.986, "unit": "auc", "tolerance": null, "source": "However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity."}, "evidence": [{"asset": "source:pmid:38935691", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity.", "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:38935691"], "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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