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pmid:35712711

Regulatory T Cell-Related Gene Indicators in Pulmonary Hypertension.

Yan Liu · Jun-Zhuo Shi · Rong Jiang · Shao-Fei Liu · Yang-Yang He · Emiel P C van der Vorst · Christian Weber · Yvonne Döring · Yi Yan

Frontiers in pharmacology · 2022 doi:10.3389/fphar.2022.908783

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complete

Experiment

complete

Data

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Analysis

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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:35712711
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science-record
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2026-11-01T18:14:42Z
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2026-11-01T22:06:48Z
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title
Regulatory T Cell-Related Gene Indicators in Pulmonary Hypertension.
doi
10.3389/fphar.2022.908783
pmid
35712711
authors array · 9 items
[0]
Yan Liu
[1]
Jun-Zhuo Shi
[2]
Rong Jiang
[3]
Shao-Fei Liu
[4]
Yang-Yang He
[5]
Emiel P C van der Vorst
[6]
Christian Weber
[7]
Yvonne Döring
[8]
Yi Yan
journal
Frontiers in pharmacology
year
2022
abstract
Objective: Regulatory T cells (Tregs) are critical immune modulators to maintain immune homeostasis and limit pulmonary hypertension (PH). This study was aimed to identify Treg-related genes (TRGs) in PH. Methods: The gene expression profile from lungs of PH patients was retrieved from the Gene Expression Omnibus (GEO) database. The abundance of Tregs was estimated by the xCell algorithm, the correlation of which with differentially expressed genes (DEGs) was performed. DEGs with a |Pearson correlation coefficient| >0.4 were identified as TRGs. Functional annotation and the protein-protein interaction (PPI) network were analyzed. A gene signature for 25 hub TRGs (TRGscore) was generated by a single sample scoring method to determine its accuracy to distinguish PH from control subjects. TRGs were validated in datasets of transcriptional profiling of PH cohorts and in lung tissues of experimental PH mice. Results: A total of 819 DEGs were identified in lungs of 58 PAH patients compared to that of 25 control subjects of dataset GSE117261. In total, 165 of all these DEGs were correlated with the abundance of Tregs and identified as TRGs, with 90 upregulated genes and 75 downregulated genes compared to that of control subjects. The upregulated TRGs were enriched in negative regulation of multiple pathways, such as cAMP-mediated signaling and I-kappaB kinase/NF-kappaB signaling, and regulated by multiple genes encoding transcriptional factors including HIF1A. Furthermore, 25 hub genes categorized into three clusters out of 165 TRGs were derived, and we identified 27 potential drugs targeting 10 hub TRGs. The TRGscore based on 25 hub TRGs was higher in PH patients and could distinguish PH from control subjects (all AUC >0.7). Among them, 10 genes including NCF2, MNDA/Ifi211, HCK, FGR, CSF3R, AQP9, S100A8, G6PD/G6pdx, PGD, and TXNRD1 were significantly reduced in lungs of severe PH patients of dataset GSE24988 as well as in lungs of hypoxic PH mice compared to corresponding controls. Conclusion: Our finding will shed some light on the Treg-associated therapeutic targets in the progression of PH and emphasize on TRGscore as a novel indicator for PH.
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The relationship of all DEGs with the abundance of Tregs was further investigated by Pearson correlation analysis.
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sections[5] "Identification of Treg-Related Genes and the Functional Enrichment" paragraph 2
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The relationship of all DEGs with the abundance of Tregs was further investigated by Pearson correlation analysis.
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sections[25] "Data Availability Statement" paragraph 2
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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 in the article/Supplementary Material.
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The TRGscore based on 25 hub TRGs was higher in PH patients and could distinguish PH from control subjects (all AUC >0.7).
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The TRGscore based on 25 hub TRGs was higher in PH patients and could distinguish PH from control subjects (all AUC >0.7).
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The TRGscore based on 25 hub TRGs was higher in PH patients and could distinguish PH from control subjects (all AUC >0.7).
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Gene expression profiling of pulmonary arterial hypertension
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Systems Analysis of the Human Pulmonary Arterial Hypertension Lung Transcriptome
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title
Gene expression profiles based on Pulmonary Artery Pressures in Pulmonary Fibrosis
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agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
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 object · 4 fields
parent_id
pmid:35712711
parent_hash
sha256:43d42891ff513d98319cada2202a9b9e344773829b142ea4f111579eacaf06eb
fork_of
null
gaps array · 10 items
[0]
asset:data:geo:GSE113439 licence not read
[1]
asset:data:geo:GSE113439 not downloaded
[2]
asset:data:geo:GSE117261 licence not read
[3]
asset:data:geo:GSE117261 not downloaded
[4]
asset:data:geo:GSE24988 licence not read
[5]
asset:data:geo:GSE24988 not downloaded
[6]
execution.not_run
[7]
review.human_critique
[8]
review.independent_reader
[9]
review.machine_critique
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_id
animal_model
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 3 items
[0]
2020). A total of 16 male 8-week-old C57BL/6 mice were provided by Vital River Laboratories Co., Lt
[1]
5 ± 2°C, and 50 ± 5% relative humidity). The mice were randomly kept either in the hypoxia chamber
[2]
ondition). The feeding, water, and cages for mice were changed twice a week. Identification of TRGs
order_basis
paper_text_order:2681
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
correlation_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
pearson_r
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
The relationship of all DEGs with the abundance of Tregs was further investigated by Pearson correlation analysis.
order_basis
consumes_earlier_output
id
assay:2
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35712711", "kind": "science-record", "revision": 4, "content_hash": "sha256:0adb38235613cbd0f9e6f41cd211e7a9c18985f564d948c89b17780bbb8cc946", "created_at": "2026-11-01T18:14:42Z", "updated_at": "2026-11-01T22:06:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Regulatory T Cell-Related Gene Indicators in Pulmonary Hypertension.", "doi": "10.3389/fphar.2022.908783", "pmid": "35712711", "authors": ["Yan Liu", "Jun-Zhuo Shi", "Rong Jiang", "Shao-Fei Liu", "Yang-Yang He", "Emiel P C van der Vorst", "Christian Weber", "Yvonne Döring", "Yi Yan"], "journal": "Frontiers in pharmacology", "year": 2022, "abstract": "Objective: Regulatory T cells (Tregs) are critical immune modulators to maintain immune homeostasis and limit pulmonary hypertension (PH). This study was aimed to identify Treg-related genes (TRGs) in PH. Methods: The gene expression profile from lungs of PH patients was retrieved from the Gene Expression Omnibus (GEO) database. The abundance of Tregs was estimated by the xCell algorithm, the correlation of which with differentially expressed genes (DEGs) was performed. DEGs with a |Pearson correlation coefficient| >0.4 were identified as TRGs. Functional annotation and the protein-protein interaction (PPI) network were analyzed. A gene signature for 25 hub TRGs (TRGscore) was generated by a single sample scoring method to determine its accuracy to distinguish PH from control subjects. TRGs were validated in datasets of transcriptional profiling of PH cohorts and in lung tissues of experimental PH mice. Results: A total of 819 DEGs were identified in lungs of 58 PAH patients compared to that of 25 control subjects of dataset GSE117261. In total, 165 of all these DEGs were correlated with the abundance of Tregs and identified as TRGs, with 90 upregulated genes and 75 downregulated genes compared to that of control subjects. The upregulated TRGs were enriched in negative regulation of multiple pathways, such as cAMP-mediated signaling and I-kappaB kinase/NF-kappaB signaling, and regulated by multiple genes encoding transcriptional factors including HIF1A. Furthermore, 25 hub genes categorized into three clusters out of 165 TRGs were derived, and we identified 27 potential drugs targeting 10 hub TRGs. The TRGscore based on 25 hub TRGs was higher in PH patients and could distinguish PH from control subjects (all AUC >0.7). Among them, 10 genes including NCF2, MNDA/Ifi211, HCK, FGR, CSF3R, AQP9, S100A8, G6PD/G6pdx, PGD, and TXNRD1 were significantly reduced in lungs of severe PH patients of dataset GSE24988 as well as in lungs of hypoxic PH mice compared to corresponding controls. Conclusion: Our finding will shed some light on the Treg-associated therapeutic targets in the progression of PH and emphasize on TRGscore as a novel indicator for PH.", "claims": [{"id": "claim-method", "statement": "The relationship of all DEGs with the abundance of Tregs was further investigated by Pearson correlation analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35712711", "locator": "sections[5] \"Identification of Treg-Related Genes and the Functional Enrichment\" paragraph 2", "quote": "The relationship of all DEGs with the abundance of Tregs was further investigated by Pearson correlation analysis."}], "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 in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35712711", "locator": "sections[25] \"Data Availability 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 in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The TRGscore based on 25 hub TRGs was higher in PH patients and could distinguish PH from control subjects (all AUC >0.7).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35712711", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The TRGscore based on 25 hub TRGs was higher in PH patients and could distinguish PH from control subjects (all AUC >0.7)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The TRGscore based on 25 hub TRGs was higher in PH patients and could distinguish PH from control subjects (all AUC >0.7).", "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:35712711"], "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:GSE113439"], "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:GSE113439"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35712711", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2022.908783", "sha256": "sha256:7fa0d44fc213939ad8b2674c8b3df6bae2cadc1c6e9944b3bf1e08ea56cff8db", "bytes": 52249, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fphar.2022.908783", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9197497", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE113439", "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=GSE113439", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE113439", "version": null, "note": "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 in the article/Supplementary Material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. 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