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pmid:39766817

Exploring Immune Cell Infiltration and Small Molecule Compounds for Ulcerative Colitis Treatment.

Yi Lu · Dongqing Lu · Chujie Li · Luping Chen

Genes · 2024 doi:10.3390/genes15121548

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

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:39766817
kind
science-record
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sha256:4556aebb472fa55aeb7c9e5114e6d1bf3d83f9a02383333e665d29fb18934456
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https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T20:30:05Z
updated_at
2026-11-01T22:13:30Z
generator object · 2 fields
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corpus-assay-engine
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paper object · 10 fields
state
complete
title
Exploring Immune Cell Infiltration and Small Molecule Compounds for Ulcerative Colitis Treatment.
doi
10.3390/genes15121548
pmid
39766817
authors array · 4 items
[0]
Yi Lu
[1]
Dongqing Lu
[2]
Chujie Li
[3]
Luping Chen
journal
Genes
year
2024
abstract
BACKGROUND/OBJECTIVES: Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD) with a relapsing nature and complex etiology. Bioinformatics analysis has been widely applied to investigate various diseases. This study aimed to identify crucial differentially expressed genes (DEGs) and explore potential therapeutic agents for UC. METHODS: The GSE47908 and GSE55306 colon tissue transcriptome gene datasets were downloaded from the Gene Expression Omnibus-NCBI (GEO) database. GEO2R and Gene Set Enrichment Analysis (GSEA) were used to screen for DEGs in patients with UC compared to the normal population based on weighted gene co-expression network analysis (WGCNA). GO-BP analysis and KEGG enrichment analysis were performed on the intersecting differential genes via the Metascape website, while hub genes were analyzed by STRING11.0 and Cytoscape3.7.1. The expression of hub genes was verified in the dataset GSE38713 colon tissue specimens. Finally, the gene expression profiles of the validation set were analyzed by immuno-infiltration through the ImmuCellAI online tool, and the CMap database was used to screen for negatively correlated small molecule compounds. RESULTS: A total of 595 and 926 genes were screened by analysis of GSE47908 and GSE55306 datasets, respectively. Combined WGCNA hub module intersection yielded 12 hub genes (CXCL8, IL1β, CXCL1, CCL20, CXCL2, CXCR2, LCN2, SELL, AGT, LILRB3, MMP3, IDO1) associated with the pathogenesis of UC. GSEA analysis yielded intersecting pathways for both datasets (colorectal cancer pathway, base excision repair, cell cycle, apoptosis). GO-BP and KEGG enrichment analyses were performed to obtain key biological processes (inflammatory response, response to bacteria, leukocyte activation involved in the immune response, leukocyte-cell adhesion, apoptosis, positive regulation of immune effector processes) and key signaling pathways (cytokine-cytokine receptor interactions, IBD, NOD-like receptor signaling pathways). The immune cell infiltration analysis suggested that the incidence of UC was mainly related to the increase in CD4+T cells, depletion of T cells, T follicular helper cells, natural killer cells, γδ T cells and the decrease in CD8 naive T cells, helper T cells 17 and effector T cells. The CMap database results showed that small molecule compounds such as vorinostat, roxarsone, and wortmannin may be therapeutic candidates for UC. CONCLUSIONS: This study not only aids in early prediction and prevention but also provides novel insights into the pathogenesis and treatment of UC.
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GEO2R and Gene Set Enrichment Analysis (GSEA) were used to screen for DEGs in patients with UC compared to the normal population based on weighted gene co-expression network analysis (WGCNA).
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sections[0] "Abstract" paragraph 1
quote
GEO2R and Gene Set Enrichment Analysis (GSEA) were used to screen for DEGs in patients with UC compared to the normal population based on weighted gene co-expression network analysis (WGCNA).
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The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.
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Combined WGCNA hub module intersection yielded 12 hub genes (CXCL8, IL1β, CXCL1, CCL20, CXCL2, CXCR2, LCN2, SELL, AGT, LILRB3, MMP3, IDO1) associated with the pathogenesis of UC.
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sections[0] "Abstract" paragraph 1
quote
Combined WGCNA hub module intersection yielded 12 hub genes (CXCL8, IL1β, CXCL1, CCL20, CXCL2, CXCR2, LCN2, SELL, AGT, LILRB3, MMP3, IDO1) associated with the pathogenesis of UC.
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Combined WGCNA hub module intersection yielded 12 hub genes (CXCL8, IL1β, CXCL1, CCL20, CXCL2, CXCR2, LCN2, SELL, AGT, LILRB3, MMP3, IDO1) associated with the pathogenesis of UC.
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the published estimate and its evidence
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570
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Homo sapiens
title
Expression data from intestinal mucosa of patients with UC
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null
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The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.
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The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.
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GSM1162262
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GSM1162275
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GSM1162283
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GSM1162286
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null
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570
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Homo sapiens
title
Transcriptional analysis of left-sided colitis, pancolitis and ulcerative colitis-associated dysplasia
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2026-11-01T15:39:54Z
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GSM1289176
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GSM1289192
[25]
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GSM1289199
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GSM1289200
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GSM1289203
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GSM1289205
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GSM1289206
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GSM1289207
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40
group_sizes
null
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null
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14951
taxon
Homo sapiens
title
Differential Gene Expression between Active and Quiescent Stages of Ulcerative Colitis: Insights into a Distinctive Pathogenesis Profile
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2026-11-01T17:30:42Z
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The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.
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The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.
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10942;11154;13304
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Drosophila melanogaster; Homo sapiens
title
ChIP-nexus: a novel ChIP-exo protocol for improved detection of in vivo transcription factor binding footprints
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GEO2R and Gene Set Enrichment Analysis (GSEA) were used to screen for DEGs in patients with UC compared to the normal population based on weighted gene co-expression network analysis (WGCNA).
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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:39766817
parent_hash
sha256:6701a8dc2d1fd8e10f0ecaa079998fa43cd56cc4a5578c0f11d8580d1fcec87f
fork_of
null
gaps array · 13 items
[0]
asset:data:geo:GSE38713 licence not read
[1]
asset:data:geo:GSE38713 not downloaded
[2]
asset:data:geo:GSE47908 licence not read
[3]
asset:data:geo:GSE47908 not downloaded
[4]
asset:data:geo:GSE53306 licence not read
[5]
asset:data:geo:GSE53306 not downloaded
[6]
asset:data:geo:GSE55306 licence not read
[7]
asset:data:geo:GSE55306 not downloaded
[8]
execution.not_run
[9]
method.reader_named_unmapped
[10]
review.human_critique
[11]
review.independent_reader
[12]
review.machine_critique
assays array · 3 items
[0] object · 11 fields
role
data_generation
definition_id
clinical_cohort
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.85
evidence array · 1 item
[0]
lysis (GSEA) were used to screen for DEGs in patients with UC compared to the normal population based o
order_basis
paper_text_order:621
id
assay:1
[1] object · 11 fields
role
data_generation
definition_id
apoptosis_assay
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.93
evidence array · 2 items
[0]
r pathway, base excision repair, cell cycle, apoptosis). GO-BP and KEGG enrichment analyses were pe
[1]
he immune response, leukocyte-cell adhesion, apoptosis, positive regulation of immune effector proc
order_basis
paper_text_order:1655
id
assay:2
[2] object · 11 fields
role
analysis
definition_id
network_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
PPI
order_basis
consumes_earlier_output
id
assay:3
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39766817", "kind": "science-record", "revision": 3, "content_hash": "sha256:4556aebb472fa55aeb7c9e5114e6d1bf3d83f9a02383333e665d29fb18934456", "created_at": "2026-11-01T20:30:05Z", "updated_at": "2026-11-01T22:13:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring Immune Cell Infiltration and Small Molecule Compounds for Ulcerative Colitis Treatment.", "doi": "10.3390/genes15121548", "pmid": "39766817", "authors": ["Yi Lu", "Dongqing Lu", "Chujie Li", "Luping Chen"], "journal": "Genes", "year": 2024, "abstract": "BACKGROUND/OBJECTIVES: Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD) with a relapsing nature and complex etiology. Bioinformatics analysis has been widely applied to investigate various diseases. This study aimed to identify crucial differentially expressed genes (DEGs) and explore potential therapeutic agents for UC.\nMETHODS: The GSE47908 and GSE55306 colon tissue transcriptome gene datasets were downloaded from the Gene Expression Omnibus-NCBI (GEO) database. GEO2R and Gene Set Enrichment Analysis (GSEA) were used to screen for DEGs in patients with UC compared to the normal population based on weighted gene co-expression network analysis (WGCNA). GO-BP analysis and KEGG enrichment analysis were performed on the intersecting differential genes via the Metascape website, while hub genes were analyzed by STRING11.0 and Cytoscape3.7.1. The expression of hub genes was verified in the dataset GSE38713 colon tissue specimens. Finally, the gene expression profiles of the validation set were analyzed by immuno-infiltration through the ImmuCellAI online tool, and the CMap database was used to screen for negatively correlated small molecule compounds.\nRESULTS: A total of 595 and 926 genes were screened by analysis of GSE47908 and GSE55306 datasets, respectively. Combined WGCNA hub module intersection yielded 12 hub genes (CXCL8, IL1β, CXCL1, CCL20, CXCL2, CXCR2, LCN2, SELL, AGT, LILRB3, MMP3, IDO1) associated with the pathogenesis of UC. GSEA analysis yielded intersecting pathways for both datasets (colorectal cancer pathway, base excision repair, cell cycle, apoptosis). GO-BP and KEGG enrichment analyses were performed to obtain key biological processes (inflammatory response, response to bacteria, leukocyte activation involved in the immune response, leukocyte-cell adhesion, apoptosis, positive regulation of immune effector processes) and key signaling pathways (cytokine-cytokine receptor interactions, IBD, NOD-like receptor signaling pathways). The immune cell infiltration analysis suggested that the incidence of UC was mainly related to the increase in CD4+T cells, depletion of T cells, T follicular helper cells, natural killer cells, γδ T cells and the decrease in CD8 naive T cells, helper T cells 17 and effector T cells. The CMap database results showed that small molecule compounds such as vorinostat, roxarsone, and wortmannin may be therapeutic candidates for UC.\nCONCLUSIONS: This study not only aids in early prediction and prevention but also provides novel insights into the pathogenesis and treatment of UC.", "claims": [{"id": "claim-method", "statement": "GEO2R and Gene Set Enrichment Analysis (GSEA) were used to screen for DEGs in patients with UC compared to the normal population based on weighted gene co-expression network analysis (WGCNA).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39766817", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "GEO2R and Gene Set Enrichment Analysis (GSEA) were used to screen for DEGs in patients with UC compared to the normal population based on weighted gene co-expression network analysis (WGCNA)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39766817", "locator": "sections[25] \"notes\" paragraph 1", "quote": "The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Combined WGCNA hub module intersection yielded 12 hub genes (CXCL8, IL1β, CXCL1, CCL20, CXCL2, CXCR2, LCN2, SELL, AGT, LILRB3, MMP3, IDO1) associated with the pathogenesis of UC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39766817", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Combined WGCNA hub module intersection yielded 12 hub genes (CXCL8, IL1β, CXCL1, CCL20, CXCL2, CXCR2, LCN2, SELL, AGT, LILRB3, MMP3, IDO1) associated with the pathogenesis of UC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Combined WGCNA hub module intersection yielded 12 hub genes (CXCL8, IL1β, CXCL1, CCL20, CXCL2, CXCR2, LCN2, SELL, AGT, LILRB3, MMP3, IDO1) associated with the pathogenesis of UC.", "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:39766817"], "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:GSE38713"], "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:GSE38713"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39766817", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/genes15121548", "sha256": 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