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pmid:36845395

Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs.

Siqi Deng · Shijie Shen · Keyu Liu · Saeed El-Ashram · Abdulaziz Alouffi · Beniamino Terzo Cenci-Goga · Guomin Ye · Chengzhang Cao · Tingting Luo · Hui Zhang · Weimin Li · Siyuan Li · Wanjiang Zhang · Jiangdong Wu · Chuangfu Chen

Frontiers in genetics · 2023 doi:10.3389/fgene.2023.1041892

Paper

complete

Experiment

partial

Data

partial

Analysis

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Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:36845395
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science-record
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sha256:0d039c3637d14f1b61d65bf3564e47e3109032a4822592c174b3f23364d4dea8
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2026-11-01T19:01:28Z
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2026-11-01T22:08:56Z
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title
Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs.
doi
10.3389/fgene.2023.1041892
pmid
36845395
authors array · 15 items
[0]
Siqi Deng
[1]
Shijie Shen
[2]
Keyu Liu
[3]
Saeed El-Ashram
[4]
Abdulaziz Alouffi
[5]
Beniamino Terzo Cenci-Goga
[6]
Guomin Ye
[7]
Chengzhang Cao
[8]
Tingting Luo
[9]
Hui Zhang
[10]
Weimin Li
[11]
Siyuan Li
[12]
Wanjiang Zhang
[13]
Jiangdong Wu
[14]
Chuangfu Chen
journal
Frontiers in genetics
year
2023
abstract
Tuberculosis (TB) is a common infectious disease linked to host genetics and the innate immune response. It is vital to investigate new molecular mechanisms and efficient biomarkers for Tuberculosis because the pathophysiology of the disease is still unclear, and there aren't any precise diagnostic tools. This study downloaded three blood datasets from the GEO database, two of which (GSE19435 and 83456) were used to build a weighted gene co-expression network for searching hub genes associated with macrophage M1 by the CIBERSORT and WGCNA algorithms. Furthermore, 994 differentially expressed genes (DEGs) were extracted from healthy and TB samples, four of which were associated with macrophage M1, naming RTP4, CXCL10, CD38, and IFI44. They were confirmed as upregulation in TB samples by external dataset validation (GSE34608) and quantitative real-time PCR analysis (qRT-PCR). CMap was used to predict potential therapeutic compounds for tuberculosis using 300 differentially expressed genes (150 downregulated and 150 upregulated genes), and six small molecules (RWJ-21757, phenamil, benzanthrone, TG-101348, metyrapone, and WT-161) with a higher confidence value were extracted. We used in-depth bioinformatics analysis to investigate significant macrophage M1-related genes and promising anti-Tuberculosis therapeutic compounds. However, more clinical trials were necessary to determine their effect on Tuberculosis.
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The datasets presented in this study can be found in online repositories. The names of the repositories and accession number can be found in the article/Supplementary Material.
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GSE19435 and GSE83456 yielded 994 DEGs (666 upregulated and 328 downregulated genes).
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sections[17] "Identification of DEGs" paragraph 2
quote
GSE19435 and GSE83456 yielded 994 DEGs (666 upregulated and 328 downregulated genes)
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GSE19435 and GSE83456 yielded 994 DEGs (666 upregulated and 328 downregulated genes).
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Transcriptional profiles in Blood of patients with Tuberculosis - Longitudinal Study
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Gene and microRNA expression in pulmonary tuberculosis and sarcoidosis
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The transcriptional signature of active tuberculosis reflects symptom status in extra-pulmonary and pulmonary tuberculosis
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The datasets presented in this study can be found in online repositories. The names of the repositories and accession number can be found in the article/Supplementary Material.
basis
The paper names GSE19435, GSE83456, GSE34608 in NCBI-GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36845395", "kind": "science-record", "revision": 3, "content_hash": "sha256:0d039c3637d14f1b61d65bf3564e47e3109032a4822592c174b3f23364d4dea8", "created_at": "2026-11-01T19:01:28Z", "updated_at": "2026-11-01T22:08:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs.", "doi": "10.3389/fgene.2023.1041892", "pmid": "36845395", "authors": ["Siqi Deng", "Shijie Shen", "Keyu Liu", "Saeed El-Ashram", "Abdulaziz Alouffi", "Beniamino Terzo Cenci-Goga", "Guomin Ye", "Chengzhang Cao", "Tingting Luo", "Hui Zhang", "Weimin Li", "Siyuan Li", "Wanjiang Zhang", "Jiangdong Wu", "Chuangfu Chen"], "journal": "Frontiers in genetics", "year": 2023, "abstract": "Tuberculosis (TB) is a common infectious disease linked to host genetics and the innate immune response. It is vital to investigate new molecular mechanisms and efficient biomarkers for Tuberculosis because the pathophysiology of the disease is still unclear, and there aren't any precise diagnostic tools. This study downloaded three blood datasets from the GEO database, two of which (GSE19435 and 83456) were used to build a weighted gene co-expression network for searching hub genes associated with macrophage M1 by the CIBERSORT and WGCNA algorithms. Furthermore, 994 differentially expressed genes (DEGs) were extracted from healthy and TB samples, four of which were associated with macrophage M1, naming RTP4, CXCL10, CD38, and IFI44. They were confirmed as upregulation in TB samples by external dataset validation (GSE34608) and quantitative real-time PCR analysis (qRT-PCR). CMap was used to predict potential therapeutic compounds for tuberculosis using 300 differentially expressed genes (150 downregulated and 150 upregulated genes), and six small molecules (RWJ-21757, phenamil, benzanthrone, TG-101348, metyrapone, and WT-161) with a higher confidence value were extracted. We used in-depth bioinformatics analysis to investigate significant macrophage M1-related genes and promising anti-Tuberculosis therapeutic compounds. However, more clinical trials were necessary to determine their effect on Tuberculosis.", "claims": [{"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:36845395", "data:geo:GSE19435"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "group_comparison_mann_whitney", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE19435"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:31:58Z", "independence": "single_reader"}, "evidence_check": {"method": null, "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 16295, "completion_tokens": 1743, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:36845395", "type": "paper", "label": "pmid:36845395"}, {"id": "source:pmid:36845395", "type": "manuscript", "label": "PMID 36845395 full text"}, {"id": "data:geo:GSE19435", "type": "dataset", "label": "GEO GSE19435"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36845395", "type": "paper", "label": "pmid:36845395"}, {"id": "source:pmid:36845395", "type": "manuscript", "label": "PMID 36845395 full text"}, {"id": "data:geo:GSE19435", "type": "dataset", "label": "GEO GSE19435"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36845395", "type": "extraction", "used": ["source:pmid:36845395"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE19435", "type": "accession_resolution", "used": ["data:geo:GSE19435"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:36845395", "parent_hash": "sha256:917c13dce5ac289462f94db9e73d737bcdfe839285fb4d74725d9af8eda1df08", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE19435 licence not read", "asset:data:geo:GSE19435 not downloaded", "asset:data:geo:GSE34608 licence not read", "asset:data:geo:GSE34608 not downloaded", "asset:data:geo:GSE83456 licence not read", "asset:data:geo:GSE83456 not downloaded", "execution.not_run", "method.group_comparison_mann_whitney", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:group_comparison_mann_whitney"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["a. (B) The boxplot showed a batch-normalized microarray profile using “sva” and “limma” packages. (C"], "order_basis": "paper_text_order:4769", "id": "assay:1"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Mann-Whitney"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}