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pmid:38034382

Identification CCL2,CXCR2,S100A9 of the immune-related gene markers and immune infiltration characteristics of inflammatory bowel disease and heart failure via bioinformatics analysis and machine learning.

Xu Luo · Rui Wang · Xin Zhang · Xin Wen · Siwei Deng · Wen Xie

Frontiers in cardiovascular medicine · 2023 doi:10.3389/fcvm.2023.1268675

Paper

complete

Experiment

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Data

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Analysis

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Execution

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title
Identification CCL2,CXCR2,S100A9 of the immune-related gene markers and immune infiltration characteristics of inflammatory bowel disease and heart failure via bioinformatics analysis and machine learning.
doi
10.3389/fcvm.2023.1268675
pmid
38034382
authors array · 6 items
[0]
Xu Luo
[1]
Rui Wang
[2]
Xin Zhang
[3]
Xin Wen
[4]
Siwei Deng
[5]
Wen Xie
journal
Frontiers in cardiovascular medicine
year
2023
abstract
Background: Recently, heart failure (HF) and inflammatory bowel disease (IBD) have been considered to be related diseases with increasing incidence rates; both diseases are related to immunity. This study aims to analyze and identify immune-related gene (IRG) markers of HF and IBD through bioinformatics and machine learning (ML) methods and to explore their immune infiltration characteristics. Methods: This study used gene expressiondata (GSE120895, GSE21610, GSE4183) from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs) and compare them with IRGs from the ImmPort database to obtain differentially expressed immune-related genes (DIRGs). Functional enrichment analysis of IRGs was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, three machine models and protein-protein interactions (PPIs) were established to identify diagnostic biomarkers. The receiver operating characteristic (ROC) curves were applied to evaluate the diagnostic value of the candidate biomarkersin the validation set (GSE1145, GSE36807) and obtain their correlations with immune cells through the Spearman algorithm. Finally, the CIBERSORT algorithm was used to evaluate the immune cell infiltration of the two diseases. Results: Thirty-four DIRGs were screened and GO and KEGG analysis results showed that these genes are mainly related to inflammatory and immune responses. CCL2, CXCR2 and S100A9 were identified as biomarkers.The immune correlation results indicated in both diseases that CCL2 is positively correlated with mast cell activation, CXCR2 is positively correlated with neutrophils and S100A9 is positively correlated with neutrophils and mast cell activation. Analysis of immune characteristics showed that macrophages M2, macrophages M0 and neutrophils were present in both diseases. Conclusions: CCL2, CXCR2 and S100A9 are promising biomarkers that will become potential immunogenetic biomarkers for diagnosing comorbidities of HF and IBD. macrophages M2, macrophages M0, neutrophil-mediated inflammation and immune regulation play important roles in the development of HF and IBD and may become diagnostic and therapeutic targets.
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The diagnostic validity of the selected characteristic genes was evaluated with the ROCR package of R and MedCalc software using receiver operating characteristic(ROC) curves.
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
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CCL2, CXCR2 and S100A9 were identified as biomarkers.
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The diagnostic validity of the selected characteristic genes was evaluated with the ROCR package of R and MedCalc software using receiver operating characteristic(ROC) curves.
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gaps array · 1 item
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method.reader_named_unmapped
capability object · 3 fields
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capability:computational_analysis
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candidate
Automated Assays Laravel application and simulated adapters
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basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: ppi_network.
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tier
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[0]
GSE120895
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GSE4183
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GSE120895
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GSE21610
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GSE4183
[3]
GSE1145
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GSE36807
statements array · 1 item
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
basis
The paper names GSE120895, GSE21610, GSE4183, GSE1145, GSE36807 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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review object · 5 fields
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MiniMax-M3.1-Flash
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minimax
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minimax
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independence
single_reader
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method
verbatim
availability
verbatim
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5
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9541
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2096
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paper
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source:pmid:38034382
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PMID 38034382 full text
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data:geo:GSE1145
type
dataset
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GEO GSE1145
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activity-reading-sci-2026-11-01
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paper
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pmid:38034382
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source:pmid:38034382
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PMID 38034382 full text
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data:geo:GSE1145
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dataset
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GEO GSE1145
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agent:corpus-assay-engine
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used array · 1 item
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agent:minimax-reader
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activity-resolve-GSE1145
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agent:corpus-assay-engine
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agent:corpus-assay-engine
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agent:minimax-reader
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dsl_catalogue_fingerprint
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asset:data:geo:GSE36807 licence not 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:38034382", "kind": "science-record", "revision": 3, "content_hash": "sha256:321fa8e6c47431917332d55827ec498e93c58ec5d50c0f48afe49ff9b4d5c645", "created_at": "2026-11-01T19:02:58Z", "updated_at": "2026-11-01T22:10:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification CCL2,CXCR2,S100A9 of the immune-related gene markers and immune infiltration characteristics of inflammatory bowel disease and heart failure via bioinformatics analysis and machine learning.", "doi": "10.3389/fcvm.2023.1268675", "pmid": "38034382", "authors": ["Xu Luo", "Rui Wang", "Xin Zhang", "Xin Wen", "Siwei Deng", "Wen Xie"], "journal": "Frontiers in cardiovascular medicine", "year": 2023, "abstract": "Background: Recently, heart failure (HF) and inflammatory bowel disease (IBD) have been considered to be related diseases with increasing incidence rates; both diseases are related to immunity. This study aims to analyze and identify immune-related gene (IRG) markers of HF and IBD through bioinformatics and machine learning (ML) methods and to explore their immune infiltration characteristics.\nMethods: This study used gene expressiondata (GSE120895, GSE21610, GSE4183) from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs) and compare them with IRGs from the ImmPort database to obtain differentially expressed immune-related genes (DIRGs). Functional enrichment analysis of IRGs was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, three machine models and protein-protein interactions (PPIs) were established to identify diagnostic biomarkers. The receiver operating characteristic (ROC) curves were applied to evaluate the diagnostic value of the candidate biomarkersin the validation set (GSE1145, GSE36807) and obtain their correlations with immune cells through the Spearman algorithm. Finally, the CIBERSORT algorithm was used to evaluate the immune cell infiltration of the two diseases.\nResults: Thirty-four DIRGs were screened and GO and KEGG analysis results showed that these genes are mainly related to inflammatory and immune responses. CCL2, CXCR2 and S100A9 were identified as biomarkers.The immune correlation results indicated in both diseases that CCL2 is positively correlated with mast cell activation, CXCR2 is positively correlated with neutrophils and S100A9 is positively correlated with neutrophils and mast cell activation. Analysis of immune characteristics showed that macrophages M2, macrophages M0 and neutrophils were present in both diseases.\nConclusions: CCL2, CXCR2 and S100A9 are promising biomarkers that will become potential immunogenetic biomarkers for diagnosing comorbidities of HF and IBD. macrophages M2, macrophages M0, neutrophil-mediated inflammation and immune regulation play important roles in the development of HF and IBD and may become diagnostic and therapeutic targets.", "claims": [{"id": "claim-method", "statement": "The diagnostic validity of the selected characteristic genes was evaluated with the ROCR package of R and MedCalc software using receiver operating characteristic(ROC) curves.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38034382", "locator": "sections[6] \"Verification of diagnostic markers\" paragraph 2", "quote": "The diagnostic validity of the selected characteristic genes was evaluated with the ROCR package of R and MedCalc software using receiver operating characteristic(ROC) curves."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38034382", "locator": "sections[18] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "CCL2, CXCR2 and S100A9 were identified as biomarkers.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38034382", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "CCL2, CXCR2 and S100A9 were identified as biomarkers."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "CCL2, CXCR2 and S100A9 were identified as biomarkers.", "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:38034382"], "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:GSE1145"], "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:GSE1145"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38034382", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcvm.2023.1268675", "sha256": 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