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pmid:35517818

A Series of Genes for Predicting Responses to Anti-Tumor Necrosis Factor α Therapy in Crohn's Disease.

Kai Nie · Chao Zhang · Minzi Deng · Weiwei Luo · Kejia Ma · Jiahao Xu · Xing Wu · Yuanyuan Yang · Xiaoyan Wang

Frontiers in pharmacology · 2022 doi:10.3389/fphar.2022.870796

Paper

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:35517818
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2026-11-01T18:14:24Z
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title
A Series of Genes for Predicting Responses to Anti-Tumor Necrosis Factor α Therapy in Crohn's Disease.
doi
10.3389/fphar.2022.870796
pmid
35517818
authors array · 9 items
[0]
Kai Nie
[1]
Chao Zhang
[2]
Minzi Deng
[3]
Weiwei Luo
[4]
Kejia Ma
[5]
Jiahao Xu
[6]
Xing Wu
[7]
Yuanyuan Yang
[8]
Xiaoyan Wang
journal
Frontiers in pharmacology
year
2022
abstract
Background: Patients with Crohn's disease (CD) experience severely reduced quality of life, particularly those who do not respond to conventional therapies. Antitumor necrosis factor (TNF)α is commonly used as first-line therapy; however, many patients remain unresponsive to this treatment, and the identification of response predictors could facilitate the improvement of therapeutic strategies. Methods: We screened Gene Expression Omnibus (GEO) microarray cohorts with different anti-TNFα responses in patients with CD (discovery cohort) and explored the hub genes. The finding was confirmed in independent validation cohorts, and multiple algorithms and in vitro cellular models were performed to further validate the core predictor. Results: We screened four discovery datasets. Differentially expressed genes between anti-TNFα responders and nonresponders were confirmed in each cohort. Gene ontology enrichment revealed that innate immunity was involved in the anti-TNFα response in patients with CD. Prediction analysis of microarrays provided the minimum misclassification of genes, and the constructed network containing the hub genes supported the core status of TLR2. Furthermore, GSEA also supports TLR2 as the core predictor. The top hub genes were then validated in the validation cohort (GSE159034; p < 0.05). Furthermore, ROC analyses demonstrated the significant predictive value of TLR2 (AUC: 0.829), TREM1 (AUC: 0.844), and CXCR1 (AUC: 0.841). Moreover, TLR2 expression in monocytes affected the immune-epithelial inflammatory response and epithelial barrier during lipopolysaccharide-induced inflammation (p < 0.05). Conclusion: Bioinformatics and experimental research identified TLR2, TREM1, CXCR1, FPR1, and FPR2 as promising candidates for predicting the anti-TNFα response in patients with Crohn's disease and especially TLR2 as a core predictor.
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We performed differential expression analyses between responders and nonresponders to infliximab therapy using the limma R package (Team, 2013; Ritchie et al., 2015).
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sections[4] "Common Differential Network Exploration" paragraph 2
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We performed differential expression analyses between responders and nonresponders to infliximab therapy using the limma R package (Team, 2013; Ritchie et al., 2015).
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Publicly available datasets were analyzed in this study. The datasets (GSE16879, GSE52746, GSE107865, GSE111761, GSE159034) for this study can be found in the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Single-cell data of Crohn’s disease entitled “PREDICT 2021 paper: CD” is available on the single-cell portal (https://singlecell.broadinstitute.org/single_cell).
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sections[16] "Data Availability Statement" paragraph 2
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Publicly available datasets were analyzed in this study. The datasets (GSE16879, GSE52746, GSE107865, GSE111761, GSE159034) for this study can be found in the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Single-cell data of Crohn’s disease entitled “PREDICT 2021 paper: CD” is available on the single-cell portal (https://singlecell.broadinstitute.org/single_cell).
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Furthermore, ROC analyses demonstrated the significant predictive value of TLR2 (AUC: 0.829), TREM1 (AUC: 0.844), and CXCR1 (AUC: 0.841).
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Furthermore, ROC analyses demonstrated the significant predictive value of TLR2 (AUC: 0.829), TREM1 (AUC: 0.844), and CXCR1 (AUC: 0.841).
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Furthermore, ROC analyses demonstrated the significant predictive value of TLR2 (AUC: 0.829), TREM1 (AUC: 0.844), and CXCR1 (AUC: 0.841).
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Publicly available datasets were analyzed in this study. The datasets (GSE16879, GSE52746, GSE107865, GSE111761, GSE159034) for this study can be found in the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Single-cell data of Crohn’s disease entitled “PREDICT 2021 paper: CD” is available on the single-cell portal (https://singlecell.broadinstitute.org/single_cell).
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Publicly available datasets were analyzed in this study. The datasets (GSE16879, GSE52746, GSE107865, GSE111761, GSE159034) for this study can be found in the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Single-cell data of Crohn’s disease entitled “PREDICT 2021 paper: CD” is available on the single-cell portal (https://singlecell.broadinstitute.org/single_cell).
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We performed differential expression analyses between responders and nonresponders to infliximab therapy using the limma R package (Team, 2013; Ritchie et al., 2015).
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capability object · 3 fields
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gwas
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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: limma_voom.
capability object · 4 fields
tier
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capability:computational_analysis
availability_state
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The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
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GSE16879
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Publicly available datasets were analyzed in this study. The datasets (GSE16879, GSE52746, GSE107865, GSE111761, GSE159034) for this study can be found in the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Single-cell data of Crohn’s disease entitled “PREDICT 2021 paper: CD” is available on the single-cell portal (https://singlecell.broadinstitute.org/single_cell).
basis
The paper names GSE16879, GSE52746, GSE107865, GSE111761, GSE159034 in Gene Expression Omnibus database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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source:pmid:35517818
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run_analysis
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data:geo:GSE107865
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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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single_reader
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PMID 35517818 full text
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s: We screened Gene Expression Omnibus (GEO) microarray cohorts with different anti-TNFα responses i
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We performed differential expression analyses between responders and nonresponders to infliximab therapy using the limma R package (Team, 2013; Ritchie et al., 2015).
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35517818", "kind": "science-record", "revision": 5, "content_hash": "sha256:c71045ed322b31dc0522d75ce6fe65f4bb8f5695d22399c773029e4ea726c99d", "created_at": "2026-11-01T18:14:24Z", "updated_at": "2026-11-01T22:49:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Series of Genes for Predicting Responses to Anti-Tumor Necrosis Factor α Therapy in Crohn's Disease.", "doi": "10.3389/fphar.2022.870796", "pmid": "35517818", "authors": ["Kai Nie", "Chao Zhang", "Minzi Deng", "Weiwei Luo", "Kejia Ma", "Jiahao Xu", "Xing Wu", "Yuanyuan Yang", "Xiaoyan Wang"], "journal": "Frontiers in pharmacology", "year": 2022, "abstract": "Background: Patients with Crohn's disease (CD) experience severely reduced quality of life, particularly those who do not respond to conventional therapies. Antitumor necrosis factor (TNF)α is commonly used as first-line therapy; however, many patients remain unresponsive to this treatment, and the identification of response predictors could facilitate the improvement of therapeutic strategies. Methods: We screened Gene Expression Omnibus (GEO) microarray cohorts with different anti-TNFα responses in patients with CD (discovery cohort) and explored the hub genes. The finding was confirmed in independent validation cohorts, and multiple algorithms and in vitro cellular models were performed to further validate the core predictor. Results: We screened four discovery datasets. Differentially expressed genes between anti-TNFα responders and nonresponders were confirmed in each cohort. Gene ontology enrichment revealed that innate immunity was involved in the anti-TNFα response in patients with CD. Prediction analysis of microarrays provided the minimum misclassification of genes, and the constructed network containing the hub genes supported the core status of TLR2. Furthermore, GSEA also supports TLR2 as the core predictor. The top hub genes were then validated in the validation cohort (GSE159034; p < 0.05). Furthermore, ROC analyses demonstrated the significant predictive value of TLR2 (AUC: 0.829), TREM1 (AUC: 0.844), and CXCR1 (AUC: 0.841). Moreover, TLR2 expression in monocytes affected the immune-epithelial inflammatory response and epithelial barrier during lipopolysaccharide-induced inflammation (p < 0.05). Conclusion: Bioinformatics and experimental research identified TLR2, TREM1, CXCR1, FPR1, and FPR2 as promising candidates for predicting the anti-TNFα response in patients with Crohn's disease and especially TLR2 as a core predictor.", "claims": [{"id": "claim-method", "statement": "We performed differential expression analyses between responders and nonresponders to infliximab therapy using the limma R package (Team, 2013; Ritchie et al., 2015).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35517818", "locator": "sections[4] \"Common Differential Network Exploration\" paragraph 2", "quote": "We performed differential expression analyses between responders and nonresponders to infliximab therapy using the limma R package (Team, 2013; Ritchie et al., 2015)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. The datasets (GSE16879, GSE52746, GSE107865, GSE111761, GSE159034) for this study can be found in the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Single-cell data of Crohn’s disease entitled “PREDICT 2021 paper: CD” is available on the single-cell portal (https://singlecell.broadinstitute.org/single_cell).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35517818", "locator": "sections[16] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. The datasets (GSE16879, GSE52746, GSE107865, GSE111761, GSE159034) for this study can be found in the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Single-cell data of Crohn’s disease entitled “PREDICT 2021 paper: CD” is available on the single-cell portal (https://singlecell.broadinstitute.org/single_cell)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Furthermore, ROC analyses demonstrated the significant predictive value of TLR2 (AUC: 0.829), TREM1 (AUC: 0.844), and CXCR1 (AUC: 0.841).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35517818", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Furthermore, ROC analyses demonstrated the significant predictive value of TLR2 (AUC: 0.829), TREM1 (AUC: 0.844), and CXCR1 (AUC: 0.841)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Furthermore, ROC analyses demonstrated the significant predictive value of TLR2 (AUC: 0.829), TREM1 (AUC: 0.844), and CXCR1 (AUC: 0.841).", "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:35517818"], "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:GSE107865"], "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:GSE107865"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35517818", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2022.870796", "sha256": "sha256:261d4ccbc3cc7fd90add60ee966367936786cb9b940bef423fc002c06365a6ae", "bytes": 53475, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fphar.2022.870796", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9065476", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE107865", "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=GSE107865", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE107865", "version": null, "note": "Publicly available datasets were analyzed in this study. 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