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pmid:34213077

IRF1 as a potential biomarker in Mycobacterium tuberculosis infection.

Liwei Wu · Qiliang Cheng · Zilu Wen · Yanzheng Song · Yijun Zhu · Lin Wang

Journal of cellular and molecular medicine · 2021 doi:10.1111/jcmm.16756

Paper

complete

Experiment

partial

Data

partial

Analysis

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Execution

absent

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pmid:34213077
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title
IRF1 as a potential biomarker in Mycobacterium tuberculosis infection.
doi
10.1111/jcmm.16756
pmid
34213077
authors array · 6 items
[0]
Liwei Wu
[1]
Qiliang Cheng
[2]
Zilu Wen
[3]
Yanzheng Song
[4]
Yijun Zhu
[5]
Lin Wang
journal
Journal of cellular and molecular medicine
year
2021
abstract
Pulmonary tuberculosis (PTB) is a major global public health problem. The purpose of this study was to find biomarkers that can be used to diagnose tuberculosis. We used four NCBI GEO data sets to conduct analysis. Among the four data sets, GSE139825 is lung tissue microarray, and GSE83456, GSE19491 and GSE50834 are blood microarray. The differential genes of GSE139825 and GSE83456 were 68 and 226, and intersection genes were 11. Gene ontology (GO) analyses of 11 intersection genes revealed that the changes were mostly enriched in regulation of leucocyte cell-cell adhesion and regulation of T-cell activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs revealed that the host response in TB strongly involves cytokine-cytokine receptor interactions and folate biosynthesis. In order to further narrow the range of biomarkers, we used protein-protein interaction to establish a hub gene network of two data sets and a network of 11 candidate genes. Eventually, IRF1 was selected as a biomarker. As validation, IRF1 levels were shown to be up-regulated in patients with TB relative to healthy controls in data sets GSE19491 and GSE50834. Additionally, IRF1 levels were measured in the new patient samples using ELISA. IRF1 was seen to be significantly up-regulated in patients with TB compared with healthy controls with an AUC of 0.801. These results collectively indicate that IRF1 could serve as a new biomarker for the diagnosis of pulmonary tuberculosis.
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Student t test was used to test the difference between the two groups.
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The data that support the findings of this study are openly available in NCBI GEO database at https://www.ncbi.nlm.nih.gov/geo/
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Human alveolar and splenic macrophage populations display a disctinct transcriptomic response to infection with Mycobacterium tuberculosis
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[497]
GSM550400
sample_count
498
group_sizes
null
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null
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6947
taxon
Homo sapiens
title
Blood Transcriptional Profiles in Human Active and Latent Tuberculosis
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
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structure object · 15 fields
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[43]
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title
Gene expression analysis of PBMC from HIV and HIV/TB co-infected patients
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
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The data that support the findings of this study are openly available in NCBI GEO database at https://www.ncbi.nlm.nih.gov/geo/
structure object · 15 fields
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GEO accession URL
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The data that support the findings of this study are openly available in NCBI GEO database at https://www.ncbi.nlm.nih.gov/geo/
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sample_accessions array · 202 items
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[27]
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[28]
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[29]
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[30]
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[44]
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[45]
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[47]
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[49]
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[50]
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[51]
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[52]
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[53]
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[54]
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[55]
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[56]
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[57]
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[58]
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[59]
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[60]
GSM2203600
[61]
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[62]
GSM2203602
[63]
GSM2203603
[64]
GSM2203604
[65]
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[66]
GSM2203606
[67]
GSM2203607
[68]
GSM2203608
[69]
GSM2203609
[70]
GSM2203610
[71]
GSM2203611
[72]
GSM2203612
[73]
GSM2203613
[74]
GSM2203614
[75]
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[76]
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[77]
GSM2203617
[78]
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[79]
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[80]
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[81]
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[82]
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[83]
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[84]
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[85]
GSM2203625
[86]
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[87]
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[88]
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[89]
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[90]
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[91]
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[92]
GSM2203632
[93]
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[94]
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[95]
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[96]
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[97]
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[98]
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[99]
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[100]
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[101]
GSM2203641
[102]
GSM2203642
[103]
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[104]
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[105]
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[106]
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[107]
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[108]
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[109]
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[110]
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[112]
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[113]
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[114]
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[115]
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[116]
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[117]
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[118]
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[119]
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[120]
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[121]
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[122]
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[123]
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[124]
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[125]
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[126]
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[127]
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[128]
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[129]
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[130]
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[187]
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[197]
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GSM2203740
[201]
GSM2203741
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null
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10558
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Homo sapiens
title
The transcriptional signature of active tuberculosis reflects symptom status in extra-pulmonary and pulmonary tuberculosis
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
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The GO and KEGG analysis of DEGs in GSE139825
structure object · 5 fields
pmcid
PMC8335664
section_index
13
section_title
Enrichment analyses of DEGs
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5
table object · 8 fields
label
TABLE 1
caption
The GO and KEGG analysis of DEGs in GSE139825
rows array · 21 items
[0] array · 5 items
[0]
Category
[1]
Description
[2]
Count
[3]
Log10(P)
[4]
Log10(q)
[1] array · 5 items
[0]
GO Biological Processes
[1]
response to lipopolysaccharide
[2]
26
[3]
−31.99
[4]
−27.62
[2] array · 5 items
[0]
GO Biological Processes
[1]
cellular response to interleukin‐1
[2]
18
[3]
−23.71
[4]
−20.13
[3] array · 5 items
[0]
GO Biological Processes
[1]
positive regulation of cytokine production
[2]
20
[3]
−19.21
[4]
−16.02
[4] array · 5 items
[0]
GO Biological Processes
[1]
defence response to other organism
[2]
21
[3]
−18.08
[4]
−15.04
[5] array · 5 items
[0]
GO Biological Processes
[1]
maintenance of location
[2]
14
[3]
−13.39
[4]
−10.72
[6] array · 5 items
[0]
GO Biological Processes
[1]
interleukin‐1 beta production
[2]
10
[3]
−12.98
[4]
−10.37
[7] array · 5 items
[0]
GO Biological Processes
[1]
positive regulation of MAPK cascade
[2]
16
[3]
−12.57
[4]
−9.99
[8] array · 5 items
[0]
GO Biological Processes
[1]
positive regulation of leucocyte migration
[2]
10
[3]
−11.73
[4]
−9.23
[9] array · 5 items
[0]
GO Biological Processes
[1]
regulation of multi‐organism process
[2]
13
[3]
−10.5
[4]
−8.1
[10] array · 5 items
[0]
GO Biological Processes
[1]
lipopolysaccharide‐mediated signalling pathway
[2]
7
[3]
−9.95
[4]
−7.61
[11] array · 5 items
[0]
GO Biological Processes
[1]
nitric oxide biosynthetic process
[2]
7
[3]
−9.04
[4]
−6.79
[12] array · 5 items
[0]
GO Biological Processes
[1]
response to mechanical stimulus
[2]
9
[3]
−8.69
[4]
−6.47
[13] array · 5 items
[0]
GO Biological Processes
[1]
regulation of interleukin‐2 production
[2]
6
[3]
−8.68
[4]
−6.46
[14] array · 5 items
[0]
GO Biological Processes
[1]
response to inorganic substance
[2]
12
[3]
−7.95
[4]
−5.83
[15] array · 5 items
[0]
GO Biological Processes
[1]
negative regulation of cytokine production
[2]
9
[3]
−6.56
[4]
−4.62
[16] array · 5 items
[0]
GO Cellular Components
[1]
external side of plasma membrane
[2]
10
[3]
−7.19
[4]
−5.15
[17] array · 5 items
[0]
GO Molecular Functions
[1]
CXCR chemokine receptor binding
[2]
4
[3]
−7.02
[4]
−5.01
[18] array · 5 items
[0]
KEGG Pathway
[1]
NOD‐like receptor signalling pathway
[2]
16
[3]
−20.73
[4]
−17.36
[19] array · 5 items
[0]
KEGG Pathway
[1]
Influenza A
[2]
10
[3]
−10.87
[4]
−8.44
[20] array · 5 items
[0]
KEGG Pathway
[1]
Transcriptional mis regulation in cancer
[2]
7
[3]
−6.55
[4]
−4.61
locator
section[13]/table-wrap[1]#jcmm16756-tbl-0001
[6] object · 16 fields
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asset:table:pmid:34213077:2
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jats-table
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read_table
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note
The GO and KEGG analysis of DEGs in GSE83456
structure object · 5 fields
pmcid
PMC8335664
section_index
13
section_title
Enrichment analyses of DEGs
row_count
21
column_count
5
table object · 8 fields
label
TABLE 2
caption
The GO and KEGG analysis of DEGs in GSE83456
rows array · 21 items
[0] array · 5 items
[0]
Category
[1]
Description
[2]
Count
[3]
Log10(P)
[4]
Log10(q)
[1] array · 5 items
[0]
GO Biological Processes
[1]
defence response to other organism
[2]
41
[3]
−25.22
[4]
−20.86
[2] array · 5 items
[0]
GO Biological Processes
[1]
regulation of innate immune response
[2]
35
[3]
−23.45
[4]
−19.39
[3] array · 5 items
[0]
GO Biological Processes
[1]
response to bacterium
[2]
34
[3]
−15.67
[4]
−12.31
[4] array · 5 items
[0]
GO Biological Processes
[1]
regulation of inflammatory response
[2]
27
[3]
−13.54
[4]
−10.22
[5] array · 5 items
[0]
GO Biological Processes
[1]
response to interferon‐gamma
[2]
18
[3]
−13.36
[4]
−10.07
[6] array · 5 items
[0]
GO Biological Processes
[1]
myeloid leucocyte activation
[2]
29
[3]
−13.03
[4]
−9.78
[7] array · 5 items
[0]
KEGG Pathway
[1]
Systemic lupus erythematosus
[2]
15
[3]
−12.72
[4]
−9.5
[8] array · 5 items
[0]
GO Biological Processes
[1]
regulation of immune effector process
[2]
23
[3]
−11.39
[4]
−8.26
[9] array · 5 items
[0]
GO Biological Processes
[1]
regulation of cell activation
[2]
25
[3]
−10.05
[4]
−7.23
[10] array · 5 items
[0]
KEGG Pathway
[1]
NOD‐like receptor signalling pathway
[2]
14
[3]
−10.04
[4]
−7.23
[11] array · 5 items
[0]
GO Biological Processes
[1]
regulation of response to biotic stimulus
[2]
13
[3]
−9.75
[4]
−7
[12] array · 5 items
[0]
GO Biological Processes
[1]
regulation of response to cytokine stimulus
[2]
14
[3]
−9.22
[4]
−6.54
[13] array · 5 items
[0]
GO Biological Processes
[1]
positive regulation of interferon‐beta production
[2]
7
[3]
−8.52
[4]
−5.9
[14] array · 5 items
[0]
GO Biological Processes
[1]
cytokine secretion
[2]
11
[3]
−8.06
[4]
−5.52
[15] array · 5 items
[0]
GO Biological Processes
[1]
response to lipopolysaccharide
[2]
16
[3]
−7.94
[4]
−5.41
[16] array · 5 items
[0]
GO Biological Processes
[1]
positive regulation of I‐kappa B kinase/NF‐kappa B signalling
[2]
12
[3]
−7.44
[4]
−5
[17] array · 5 items
[0]
KEGG Pathway
[1]
Pertussis
[2]
8
[3]
−6.8
[4]
−4.44
[18] array · 5 items
[0]
GO Biological Processes
[1]
response to interferon‐beta
[2]
6
[3]
−6.76
[4]
−4.4
[19] array · 5 items
[0]
GO Biological Processes
[1]
regulation of interleukin‐12 production
[2]
7
[3]
−6.59
[4]
−4.25
[20] array · 5 items
[0]
GO Biological Processes
[1]
response to tumour necrosis factor
[2]
14
[3]
−6.53
[4]
−4.21
locator
section[13]/table-wrap[2]#jcmm16756-tbl-0002
gaps array · 8 items
[0]
asset:data:geo:GSE139825 not downloaded
[1]
asset:data:geo:GSE139825 licence not read
[2]
asset:data:geo:GSE19491 not downloaded
[3]
asset:data:geo:GSE19491 licence not read
[4]
asset:data:geo:GSE50834 not downloaded
[5]
asset:data:geo:GSE50834 licence not read
[6]
asset:data:geo:GSE83456 not downloaded
[7]
asset:data:geo:GSE83456 licence not read
analysis object · 4 fields
state
partial
methods array · 2 items
[0] object · 10 fields
id
method-1
family
group_comparison
specific
student_t_test
label
Group comparison
dsl_operations array · 0 items
missing_operations array · 1 item
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student_t
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
1
band
verbatim
quote
Student t test was used to test the difference between the two groups.
[1] object · 10 fields
id
method-2
family
network_analysis
specific
ppi_network
label
Network / pathway analysis
dsl_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
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edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
protein-protein interaction
targets array · 0 items
gaps array · 1 item
[0]
method.student_t
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
false
required_operations array · 0 items
missing_operations array · 1 item
[0]
student_t
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: student_t_test.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 4 items
[0]
GSE139825
[1]
GSE83456
[2]
GSE19491
[3]
GSE50834
resolved_accessions array · 4 items
[0]
GSE139825
[1]
GSE83456
[2]
GSE19491
[3]
GSE50834
statements array · 1 item
[0]
The data that support the findings of this study are openly available in NCBI GEO database at https://www.ncbi.nlm.nih.gov/geo/
basis
The paper names GSE139825, GSE83456, GSE19491, GSE50834 in NCBI GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 0 items
assets array · 4 items
[0]
source:pmid:34213077
[1]
data:geo:GSE139825
[2]
asset:table:pmid:34213077:1
[3]
asset:table:pmid:34213077:2
steps array · 0 items
must_build object · 5 fields
dsl_operations array · 1 item
[0] object · 3 fields
operation
student_t
priority
null
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE139825
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-10-31T18:31:41Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
15754
completion_tokens
1434
cached_tokens
784
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
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MiniMax-M3.1-Flash
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minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
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null
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gaps array · 1 item
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execution.not_run
provenance object · 7 fields
entities array · 3 items
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id
pmid:34213077
type
paper
label
pmid:34213077
[1] object · 3 fields
id
source:pmid:34213077
type
manuscript
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PMID 34213077 full text
[2] object · 3 fields
id
data:geo:GSE139825
type
dataset
label
GEO GSE139825
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:34213077
type
paper
label
pmid:34213077
[1] object · 3 fields
id
source:pmid:34213077
type
manuscript
label
PMID 34213077 full text
[2] object · 3 fields
id
data:geo:GSE139825
type
dataset
label
GEO GSE139825
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-34213077
type
extraction
used array · 1 item
[0]
source:pmid:34213077
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE139825
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accession_resolution
used array · 1 item
[0]
data:geo:GSE139825
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agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
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software
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Corpus assay engine
[1] object · 3 fields
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agent:minimax-reader
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software
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MiniMax literature reader
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dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
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lineage object · 4 fields
parent_id
pmid:34213077
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sha256:5fea1673ba97a5720571289d85fe8d3fb77c1a86df6a7dada9880ce7f64a31e5
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null
gaps array · 14 items
[0]
asset:data:geo:GSE139825 licence not read
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asset:data:geo:GSE139825 not downloaded
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[3]
asset:data:geo:GSE19491 not downloaded
[4]
asset:data:geo:GSE50834 licence not read
[5]
asset:data:geo:GSE50834 not downloaded
[6]
asset:data:geo:GSE83456 licence not read
[7]
asset:data:geo:GSE83456 not downloaded
[8]
execution.not_run
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[10]
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step:run_analysis blocked on method:student_t
assays array · 2 items
[0] object · 11 fields
role
data_generation
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99e1195c7d6f
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support_state
partial
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assay_lexicon
99e1195c7d6f
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0.93
evidence array · 1 item
[0]
re measured in the new patient samples using ELISA. IRF1 was seen to be significantly up-regula
order_basis
paper_text_order:1274
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assay:1
[1] object · 11 fields
role
analysis
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f2132e96ae6c7a4e
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f2132e96ae6c7a4e
evidence array · 1 item
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Student t test was used to test the difference between the two groups.
order_basis
consumes_earlier_output
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34213077", "kind": "science-record", "revision": 7, "content_hash": "sha256:1b60d2604214c56866779741ceee4cf7bf64f5ef2495133b31fcba8ccb4fd4c2", "created_at": "2026-11-01T15:41:41Z", "updated_at": "2026-11-01T22:04:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "IRF1 as a potential biomarker in Mycobacterium tuberculosis infection.", "doi": "10.1111/jcmm.16756", "pmid": "34213077", "authors": ["Liwei Wu", "Qiliang Cheng", "Zilu Wen", "Yanzheng Song", "Yijun Zhu", "Lin Wang"], "journal": "Journal of cellular and molecular medicine", "year": 2021, "abstract": "Pulmonary tuberculosis (PTB) is a major global public health problem. The purpose of this study was to find biomarkers that can be used to diagnose tuberculosis. We used four NCBI GEO data sets to conduct analysis. Among the four data sets, GSE139825 is lung tissue microarray, and GSE83456, GSE19491 and GSE50834 are blood microarray. The differential genes of GSE139825 and GSE83456 were 68 and 226, and intersection genes were 11. Gene ontology (GO) analyses of 11 intersection genes revealed that the changes were mostly enriched in regulation of leucocyte cell-cell adhesion and regulation of T-cell activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs revealed that the host response in TB strongly involves cytokine-cytokine receptor interactions and folate biosynthesis. In order to further narrow the range of biomarkers, we used protein-protein interaction to establish a hub gene network of two data sets and a network of 11 candidate genes. Eventually, IRF1 was selected as a biomarker. As validation, IRF1 levels were shown to be up-regulated in patients with TB relative to healthy controls in data sets GSE19491 and GSE50834. Additionally, IRF1 levels were measured in the new patient samples using ELISA. IRF1 was seen to be significantly up-regulated in patients with TB compared with healthy controls with an AUC of 0.801. 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