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pmid:38510272
Elucidating the role of TWIST1 in ulcerative colitis: a comprehensive bioinformatics and machine learning approach.
Wenjie Ou · Zhaoxue Qi · Ning Liu · Junzi Zhang · Xuguang Mi · Yuan Song · Yanqiu Fang · Baiying Cui · Junjie Hou · Zhixin Yuan
Frontiers in genetics · 2024 doi:10.3389/fgene.2024.1296570
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Experiment
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Complete file
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Prolonged inflammation can lead to colitis-associated colorectal cancer (CAC). While the exact cause of UC remains unknown, this study aims to investigate the role of the TWIST1 gene in UC. Methods: Second-generation sequencing data from adult UC patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and characteristic genes were selected using machine learning and Lasso regression. The Receiver Operating Characteristic (ROC) curve assessed TWIST1's potential as a diagnostic factor (AUC score). Enriched pathways were analyzed, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA). Functional mechanisms of marker genes were predicted, considering immune cell infiltration and the competing endogenous RNA (ceRNA) network. Results: We found 530 DEGs, with 341 upregulated and 189 downregulated genes. TWIST1 emerged as one of four potential UC biomarkers via machine learning. TWIST1 expression significantly differed in two datasets, GSE193677 and GSE83687, suggesting its diagnostic potential (AUC = 0.717 in GSE193677, AUC = 0.897 in GSE83687). Enrichment analysis indicated DEGs associated with TWIST1 were involved in processes like leukocyte migration, humoral immune response, and cell chemotaxis. Immune cell infiltration analysis revealed higher rates of M0 macrophages and resting NK cells in the high TWIST1 expression group, while TWIST1 expression correlated positively with M2 macrophages and resting NK cell infiltration. We constructed a ceRNA regulatory network involving 1 mRNA, 7 miRNAs, and 32 long non-coding RNAs (lncRNAs) to explore TWIST1's regulatory mechanism. Conclusion: TWIST1 plays a significant role in UC and has potential as a diagnostic marker. This study sheds light on UC's molecular mechanisms and underscores TWIST1's importance in its progression. Further research is needed to validate these findings in diverse populations and investigate TWIST1 as a therapeutic target in UC.", "claims": [{"id": "claim-method", "statement": "In the GSE193677 and GSE83687 datasets, t-test was employed to compare the expression levels of TWIST1 between the UC experimental group and the control group.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38510272", "locator": "sections[6] \"2.4 Validation of TWIST1 expression and diagnostic value\" paragraph 2", "quote": "In the GSE193677 and GSE83687 datasets, t-test was employed to compare the expression levels of TWIST1 between the UC experimental group and the control group."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data presented in this study are deposited in the GitHub repository, accessible at: https://github.com/xia-wanqiu/single_gene_analysis_for_UC.git. The sample data, GSE193677 and GSE83687, presented in this study are sourced from public databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38510272", "locator": "sections[18] \"Data availability statement\" paragraph 2", "quote": "The data presented in this study are deposited in the GitHub repository, accessible at: https://github.com/xia-wanqiu/single_gene_analysis_for_UC.git. The sample data, GSE193677 and GSE83687, presented in this study are sourced from public databases."}], "state": "complete"}, {"id": "claim-target-result", "statement": "TWIST1 showed diagnostic potential for ulcerative colitis, with AUC values of 0.717 in GSE193677 and 0.897 in GSE83687.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "ROC curves were generated using data from GSE193677 and GSE83687, revealing TWIST1’s AUC to be 0.717 (95% confidence interval: 0.658–0.774) and 0.897 (95% confidence interval: 0.804–0.970) in GSE193677 and GSE83687, respectively (Figures 4C, D)."}, "evidence": [{"asset": "source:pmid:38510272", "locator": "sections[12] \"3.2 Machine learning to screen potential biomarkers and its diagnostic value\" paragraph 4", "quote": "ROC curves were generated using data from GSE193677 and GSE83687, revealing TWIST1’s AUC to be 0.717 (95% confidence interval: 0.658–0.774) and 0.897 (95% confidence interval: 0.804–0.970) in GSE193677 and GSE83687, respectively (Figures 4C, D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "TWIST1 showed diagnostic potential for ulcerative colitis, with AUC values of 0.717 in GSE193677 and 0.897 in GSE83687.", "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:38510272"], "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:GSE193677"], "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:GSE193677"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38510272", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2024.1296570", "sha256": "sha256:ee23fb4e442f0cd336cb5f478a7fd6731f8deb67be2d10123bb334dd7df6728d", "bytes": 29018, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2024.1296570", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10952112", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE193677", "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=GSE193677", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE193677", "version": null, "note": "The data presented in this study are deposited in the GitHub repository, accessible at: https://github.com/xia-wanqiu/single_gene_analysis_for_UC.git. 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data presented in this study are deposited in the GitHub repository, accessible at: https://github.com/xia-wanqiu/single_gene_analysis_for_UC.git. The sample data, GSE193677 and GSE83687, presented in this study are sourced from public databases.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data presented in this study are deposited in the GitHub repository, accessible at: https://github.com/xia-wanqiu/single_gene_analysis_for_UC.git. 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