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pmid:37576152

Identification of Hub Genes in the Pathogenesis of Bronchiolitis Obliterans via Bioinformatic Analysis and Experimental Verification.

Zhongji Wu · Xiaowen Chen · Kangkang Zhang · Zhenwei Liu · Haidi Zhang · Zhaocong Zheng · Xiaodie Zhang · Yubiao Chen · Yinghui Peng · Hui Li · Kaiyin Huang · Sixiang Tang · Li Zhao · Dehui Chen

Journal of inflammation research · 2023 doi:10.2147/JIR.S419845

Paper

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Experiment

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Data

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Analysis

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:37576152
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science-record
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2026-11-01T19:02:21Z
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2026-11-01T22:52:30Z
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paper object · 10 fields
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title
Identification of Hub Genes in the Pathogenesis of Bronchiolitis Obliterans via Bioinformatic Analysis and Experimental Verification.
doi
10.2147/JIR.S419845
pmid
37576152
authors array · 14 items
[0]
Zhongji Wu
[1]
Xiaowen Chen
[2]
Kangkang Zhang
[3]
Zhenwei Liu
[4]
Haidi Zhang
[5]
Zhaocong Zheng
[6]
Xiaodie Zhang
[7]
Yubiao Chen
[8]
Yinghui Peng
[9]
Hui Li
[10]
Kaiyin Huang
[11]
Sixiang Tang
[12]
Li Zhao
[13]
Dehui Chen
journal
Journal of inflammation research
year
2023
abstract
Background: Bronchiolitis obliterans (BO) is a chronic disease that can arise as a complication of severe childhood pneumonia and can also impact the long-term survival of patients after lung transplantation. However, the precise molecular mechanism underlying BO remains unclear. We aimed to identify BO-associated hub genes and their molecular mechanisms. Methods: BO-associated transcriptome datasets (GSE52761, GSE137169, and GSE94557) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Additional bioinformatics analyses, such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI) analyses, were performed to determine functional roles and DEG-associated regulatory networks. Prediction of hub genes using the 12 algorithms available in the Cytohubba plugin of Cytoscape software was also performed. Verification was performed using the BO mouse model. Results: Our results revealed 57 DEGs associated with BO, of which 18 were down-regulated and 39 were up-regulated. The Cytohubba plugin data further narrowed down the 57 DEGs into 9 prominent hub genes (CCR2, CD1D, GM2A, TFEC, MPEG1, CTSS, GPNMB, BIRC2, and CTSZ). Genes such as CCR2, TFEC, MPEG1, CTSS, and CTSZ were dysregulated in 2,3-butanedione-induced BO mice, whereas TFEC, CTSS, and CTSZ were dysregulated in nitric acid-induced BO mouse models. Conclusion: Our study identified and validated four novel BO biomarkers, which may allow further investigation into the development of distinct BO diagnostic markers and novel therapeutic avenues.
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First, we transformed the matrix data into log2 values for data reduction; then, we utilized the R software package Limma (version 3.40.6) for data normalization and differential analysis.
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sections[7] "Data Processing and Differentially Expressed Genes Identification" paragraph 2
quote
First, we transformed the matrix data into log2 values for data reduction; then, we utilized the R software package Limma (version 3.40.6) for data normalization and differential analysis.
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The datasets were named GSE52761, GSE137169, and GSE94557 and involved the use of BO models in Wistar rats, in situ lung transplantation in C57 mice, and whole blood transcriptome sequencing in humans after lung transplantation, respectively.
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sections[16] "Source of the Datasets" paragraph 2
quote
The datasets were named GSE52761, GSE137169, and GSE94557 and involved the use of BO models in Wistar rats, in situ lung transplantation in C57 mice, and whole blood transcriptome sequencing in humans after lung transplantation, respectively.
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The analysis identified 57 BO-associated DEGs, comprising 18 downregulated and 39 upregulated genes.
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Our results revealed 57 DEGs associated with BO, of which 18 were down-regulated and 39 were up-regulated.
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The analysis identified 57 BO-associated DEGs, comprising 18 downregulated and 39 upregulated genes.
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Gene analysis during the development of obliterative bronchiolitis in a murine orthotopic lung transplantation model
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Gene expression in 2,3-Pentanedione Induced Bronchiolitis Oblliterans
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Whole blood gene expression from lung transplanted patients with chronic lung allograft dysfunction
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First, we transformed the matrix data into log2 values for data reduction; then, we utilized the R software package Limma (version 3.40.6) for data normalization and differential analysis.
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pmid:37576152
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sha256:ad43a5613353e2a05759b537746ba4ebd3e7db29e2760f09ccf0fe41c1f58250
fork_of
null
gaps array · 10 items
[0]
asset:data:geo:GSE137169 licence not read
[1]
asset:data:geo:GSE137169 not downloaded
[2]
asset:data:geo:GSE52761 licence not read
[3]
asset:data:geo:GSE52761 not downloaded
[4]
asset:data:geo:GSE94557 licence not read
[5]
asset:data:geo:GSE94557 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 · 4 items
[0]
med. Verification was performed using the BO mouse model. Results: Our results revealed 57 DEGs assoc
[1]
med. Verification was performed using the BO mouse model. Results: Our results revealed 57 DEGs
[2]
were dysregulated in nitric acid-induced BO mouse models. Conclusion: Our study identified and
[3]
ood ad libitum. The ethics protocols for the mouse models were approved by The First Affiliated
order_basis
paper_text_order:1053
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
differential_expression
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f2132e96ae6c7a4e
required_operations array · 3 items
[0]
normalise_counts
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differential_expression_fit
[2]
fdr_bh
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
First, we transformed the matrix data into log2 values for data reduction; then, we utilized the R software package Limma (version 3.40.6) for data normalization and differential 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:37576152", "kind": "science-record", "revision": 5, "content_hash": "sha256:f2766fb275d7fba9a67728a04cecc55901181e1e25aac01a4cf1b115782dd2af", "created_at": "2026-11-01T19:02:21Z", "updated_at": "2026-11-01T22:52:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Hub Genes in the Pathogenesis of Bronchiolitis Obliterans via Bioinformatic Analysis and Experimental Verification.", "doi": "10.2147/JIR.S419845", "pmid": "37576152", "authors": ["Zhongji Wu", "Xiaowen Chen", "Kangkang Zhang", "Zhenwei Liu", "Haidi Zhang", "Zhaocong Zheng", "Xiaodie Zhang", "Yubiao Chen", "Yinghui Peng", "Hui Li", "Kaiyin Huang", "Sixiang Tang", "Li Zhao", "Dehui Chen"], "journal": "Journal of inflammation research", "year": 2023, "abstract": "Background: Bronchiolitis obliterans (BO) is a chronic disease that can arise as a complication of severe childhood pneumonia and can also impact the long-term survival of patients after lung transplantation. However, the precise molecular mechanism underlying BO remains unclear. We aimed to identify BO-associated hub genes and their molecular mechanisms.\nMethods: BO-associated transcriptome datasets (GSE52761, GSE137169, and GSE94557) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Additional bioinformatics analyses, such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI) analyses, were performed to determine functional roles and DEG-associated regulatory networks. Prediction of hub genes using the 12 algorithms available in the Cytohubba plugin of Cytoscape software was also performed. Verification was performed using the BO mouse model.\nResults: Our results revealed 57 DEGs associated with BO, of which 18 were down-regulated and 39 were up-regulated. The Cytohubba plugin data further narrowed down the 57 DEGs into 9 prominent hub genes (CCR2, CD1D, GM2A, TFEC, MPEG1, CTSS, GPNMB, BIRC2, and CTSZ). Genes such as CCR2, TFEC, MPEG1, CTSS, and CTSZ were dysregulated in 2,3-butanedione-induced BO mice, whereas TFEC, CTSS, and CTSZ were dysregulated in nitric acid-induced BO mouse models.\nConclusion: Our study identified and validated four novel BO biomarkers, which may allow further investigation into the development of distinct BO diagnostic markers and novel therapeutic avenues.", "claims": [{"id": "claim-method", "statement": "First, we transformed the matrix data into log2 values for data reduction; then, we utilized the R software package Limma (version 3.40.6) for data normalization and differential analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37576152", "locator": "sections[7] \"Data Processing and Differentially Expressed Genes Identification\" paragraph 2", "quote": "First, we transformed the matrix data into log2 values for data reduction; then, we utilized the R software package Limma (version 3.40.6) for data normalization and differential analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets were named GSE52761, GSE137169, and GSE94557 and involved the use of BO models in Wistar rats, in situ lung transplantation in C57 mice, and whole blood transcriptome sequencing in humans after lung transplantation, respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37576152", "locator": "sections[16] \"Source of the Datasets\" paragraph 2", "quote": "The datasets were named GSE52761, GSE137169, and GSE94557 and involved the use of BO models in Wistar rats, in situ lung transplantation in C57 mice, and whole blood transcriptome sequencing in humans after lung transplantation, respectively."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The analysis identified 57 BO-associated DEGs, comprising 18 downregulated and 39 upregulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37576152", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Our results revealed 57 DEGs associated with BO, of which 18 were down-regulated and 39 were up-regulated."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The analysis identified 57 BO-associated DEGs, comprising 18 downregulated and 39 upregulated genes.", "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:37576152"], "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:GSE137169"], "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:GSE137169"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37576152", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JIR.S419845", "sha256": 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE52761", "GSE137169", "GSE94557"], "resolved_accessions": ["GSE52761", "GSE137169", "GSE94557"], "statements": ["The datasets were named GSE52761, GSE137169, and GSE94557 and involved the use of BO models in Wistar rats, in situ lung transplantation in C57 mice, and whole blood transcriptome sequencing in humans after lung transplantation, respectively."], "basis": "The paper names GSE52761, GSE137169, GSE94557 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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