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pmid:40565608

Bioinformatics-Driven Identification of Ferroptosis-Related Gene Signatures Distinguishing Active and Latent Tuberculosis.

Rakesh Arya · Hemlata Shakya · Viplov Kumar Biswas · Gyanendra Kumar · Sumendra Yogarayan · Harish Kumar Shakya · Jong-Joo Kim

Genes · 2025 doi:10.3390/genes16060716

Paper

complete

Experiment

complete

Data

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Analysis

partial

Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:40565608
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2026-11-01T20:30:39Z
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title
Bioinformatics-Driven Identification of Ferroptosis-Related Gene Signatures Distinguishing Active and Latent Tuberculosis.
doi
10.3390/genes16060716
pmid
40565608
authors array · 7 items
[0]
Rakesh Arya
[1]
Hemlata Shakya
[2]
Viplov Kumar Biswas
[3]
Gyanendra Kumar
[4]
Sumendra Yogarayan
[5]
Harish Kumar Shakya
[6]
Jong-Joo Kim
journal
Genes
year
2025
abstract
BACKGROUND: Tuberculosis (TB) remains a major global public health challenge, and diagnosing it can be difficult due to issues such as distinguishing active TB from latent TB infection (LTBI), as well as the sample collection process, which is often time-consuming and lacks sensitivity and specificity. Ferroptosis is emerging as an important factor in TB pathogenesis; however, its underlying molecular mechanisms are not fully understood. Thus, there is a critical need to establish ferroptosis-related diagnostic biomarkers for tuberculosis (TB). METHODS: This study aimed to identify and validate potential ferroptosis-related genes in TB infection while enhancing clinical diagnostic accuracy through bioinformatics-driven gene identification. The microarray expression profile dataset GSE28623 from the Gene Expression Omnibus (GEO) database was used to identify ferroptosis-related differentially expressed genes (FR-DEGs) associated with TB. Subsequently, these genes were used for immune cell infiltration, Gene Set Enrichment Analysis (GSEA), functional enrichment and correlation analyses. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA) and validated in independent datasets GSE37250, GSE39940, GSE19437, and GSE31348. RESULTS: A total of 21 FR-DEGs were identified. Among them, four hub genes (ACSL1, PARP9, TLR4, and ATG3) were identified as diagnostic biomarkers. These biomarkers were enriched in immune-response related pathways and were validated. Immune cell infiltration, GSEA, functional enrichment and correlation analyses revealed that multiple immune cell types could be activated by FR-DEGs. Throughout anti-TB therapy, the expression of the four hub gene signatures significantly decreased in patients cured of TB. CONCLUSIONS: In conclusion, ferroptosis plays a key role in TB pathogenesis. These four hub gene signatures are linked with TB treatment effectiveness and show promise as biomarkers for differentiating TB from LTBI.
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Gene expression analysis was conducted using GEO2R, an NCBI-built web tool (https://www.ncbi.nlm.nih.gov/geo/geo2r, accessed on 18 April 2024) [24], to identify differentially expressed genes (DEGs) between TB and LTBI groups. WGCNA was employed to identify highly relevant gene modules associated with TB [29].
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sections[7] "2.5. Differential Gene Expression Analysis Using GEO2R" paragraph 2
quote
Gene expression analysis was conducted using GEO2R, an NCBI-built web tool (https://www.ncbi.nlm.nih.gov/geo/geo2r, accessed on 18 April 2024) [24], to identify differentially expressed genes (DEGs) between TB and LTBI groups.
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sections[10] "2.8. Weighted Gene Co-Expression Network Analysis (WGCNA)" paragraph 2
quote
WGCNA was employed to identify highly relevant gene modules associated with TB [29].
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Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024).
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Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024).
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Among them, four hub genes (ACSL1, PARP9, TLR4, and ATG3) were identified as diagnostic biomarkers.
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Among them, four hub genes (ACSL1, PARP9, TLR4, and ATG3) were identified as diagnostic biomarkers.
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Among them, four hub genes (ACSL1, PARP9, TLR4, and ATG3) were identified as diagnostic biomarkers.
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Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024).
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Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024).
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Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024).
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Pathway and functional association analysis of whole blood gene expression profiles reveal functional networks underlying TB pathogenesis. [Agilent-014850]
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Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024).
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[100]
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[106]
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[110]
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[111]
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[114]
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[115]
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[118]
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[119]
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[120]
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[125]
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[127]
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[128]
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[130]
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[131]
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[140]
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[141]
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[143]
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[144]
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[147]
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[150]
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[151]
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[160]
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[162]
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[179]
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[204]
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[223]
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[224]
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[225]
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[227]
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[228]
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[234]
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[236]
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[237]
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[238]
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[239]
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[240]
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[244]
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[268]
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[405]
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[536]
GSM914889
sample_count
537
group_sizes
null
group_sizes_basis
null
platform
10558
taxon
Homo sapiens
title
Genome-wide transcriptional profiling of HIV positive and negative adults with active tuberculosis, latent TB infection and other diseases
resolution_checked_at
2026-11-01T17:30:42Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[5] object · 15 fields
id
data:geo:GSE39940
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=GSE39940
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE39940
version
null
note
Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024).
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024).
access_statement_kind
data_availability_statement
sample_accessions array · 334 items
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[110]
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[111]
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[149]
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[150]
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[151]
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Genome wide transcriptional profiling of HIV positive and negative children with active tuberculosis, latent TB infection and other diseases from South Africa and Malawi
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Gene expression analysis was conducted using GEO2R, an NCBI-built web tool (https://www.ncbi.nlm.nih.gov/geo/geo2r, accessed on 18 April 2024) [24], to identify differentially expressed genes (DEGs) between TB and LTBI groups. WGCNA was employed to identify highly relevant gene modules associated with TB [29].
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Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024).
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40565608", "kind": "science-record", "revision": 2, "content_hash": "sha256:562b407e08242918ee52a56884eadc5a357e03d2ff53463aba429e1a461146e2", "created_at": "2026-11-01T20:30:39Z", "updated_at": "2026-11-01T21:35:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics-Driven Identification of Ferroptosis-Related Gene Signatures Distinguishing Active and Latent Tuberculosis.", "doi": "10.3390/genes16060716", "pmid": "40565608", "authors": ["Rakesh Arya", "Hemlata Shakya", "Viplov Kumar Biswas", "Gyanendra Kumar", "Sumendra Yogarayan", "Harish Kumar Shakya", "Jong-Joo Kim"], "journal": "Genes", "year": 2025, "abstract": "BACKGROUND: Tuberculosis (TB) remains a major global public health challenge, and diagnosing it can be difficult due to issues such as distinguishing active TB from latent TB infection (LTBI), as well as the sample collection process, which is often time-consuming and lacks sensitivity and specificity. Ferroptosis is emerging as an important factor in TB pathogenesis; however, its underlying molecular mechanisms are not fully understood. Thus, there is a critical need to establish ferroptosis-related diagnostic biomarkers for tuberculosis (TB).\nMETHODS: This study aimed to identify and validate potential ferroptosis-related genes in TB infection while enhancing clinical diagnostic accuracy through bioinformatics-driven gene identification. The microarray expression profile dataset GSE28623 from the Gene Expression Omnibus (GEO) database was used to identify ferroptosis-related differentially expressed genes (FR-DEGs) associated with TB. Subsequently, these genes were used for immune cell infiltration, Gene Set Enrichment Analysis (GSEA), functional enrichment and correlation analyses. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA) and validated in independent datasets GSE37250, GSE39940, GSE19437, and GSE31348.\nRESULTS: A total of 21 FR-DEGs were identified. Among them, four hub genes (ACSL1, PARP9, TLR4, and ATG3) were identified as diagnostic biomarkers. These biomarkers were enriched in immune-response related pathways and were validated. Immune cell infiltration, GSEA, functional enrichment and correlation analyses revealed that multiple immune cell types could be activated by FR-DEGs. Throughout anti-TB therapy, the expression of the four hub gene signatures significantly decreased in patients cured of TB.\nCONCLUSIONS: In conclusion, ferroptosis plays a key role in TB pathogenesis. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE19439"], "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:GSE19439"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40565608", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/genes16060716", "sha256": 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The microarray expression profile dataset GSE28623 from the", "al Analysis Raw data matrix files, including microarray annotations, were downloaded from the Nation"], "order_basis": "paper_text_order:832", "id": "assay:1"}]}