Integrative bioinformatics analysis and experimental validation of key biomarkers for risk stratification in primary biliary cholangitis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37784152", "kind": "science-record", "revision": 4, "content_hash": "sha256:94a946173dbb6eca7519fa68e102606a267fd04107d495801c16cbdeff73c397", "created_at": "2026-11-01T19:02:38Z", "updated_at": "2026-11-01T22:52:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative bioinformatics analysis and experimental validation of key biomarkers for risk stratification in primary biliary cholangitis.", "doi": "10.1186/s13075-023-03163-y", "pmid": "37784152", "authors": ["Siyuan Tian", "Yinan Hu", "Miao Zhang", "Kemei Wang", "Guanya Guo", "Bo Li", "Yulong Shang", "Ying Han"], "journal": "Arthritis research & therapy", "year": 2023, "abstract": "BACKGROUND: Primary biliary cholangitis (PBC) is an autoimmune liver disease, whose etiology is yet to be fully elucidated. Currently, ursodeoxycholic acid (UDCA) is the only first-line drug. However, 40% of PBC patients respond poorly to it and carry a potential risk of disease progression. So, in this study, we aimed to explore new biomarkers for risk stratification in PBC patients to enhance treatment.\nMETHODS: We first downloaded the clinical characteristics and microarray datasets of PBC patients from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Hub genes were further validated in multiple public datasets and PBC mouse model. Furthermore, we also verified the expression of the hub genes and developed a predictive model in our clinical specimens.\nRESULTS: A total of 166 DEGs were identified in the GSE79850 dataset, including 95 upregulated and 71 downregulated genes. Enrichment analysis indicated that DEGs were significantly enriched in inflammatory or immune-related process. Among these DEGs, 15 risk-related genes were recognized and further validated in the GSE119600 cohort. Then, TXNIP, CD44, ENTPD1, and PDGFRB were identified as candidate hub genes. Finally, we proceeded to the next screening with these four genes in our serum samples and developed a three-gene panel. The gene panel could effectively identify those patients at risk of disease progression, yielding an AUC of 0.777 (95% CI, 0.657-0.870).\nCONCLUSIONS: In summary, combining bioinformatics analysis and experiment validation, we identified TXNIP, CD44, and ENTPD1 as promising biomarkers for risk stratification in PBC patients.", "claims": [{"id": "claim-method", "statement": "A logistic regression algorithm was used to construct the predictive model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37784152", "locator": "sections[10] \"Statistical analysis\" paragraph 2", "quote": "A logistic regression algorithm was used to construct the predictive model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data in this study that support the findings are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/) with the following data accession number(s): GSE79850 and GSE119600.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37784152", "locator": "sections[25] \"Availability of data and materials\" paragraph 2", "quote": "The data in this study that support the findings are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/) with the following data accession number(s): GSE79850 and GSE119600."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The three-gene panel could effectively identify PBC patients at risk of disease progression, yielding an AUC of 0.777 (95% CI, 0.657–0.870).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The gene panel could effectively identify those patients at risk of disease progression, yielding an AUC of 0.777 (95% CI, 0.657–0.870)."}, "evidence": [{"asset": "source:pmid:37784152", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The gene panel could effectively identify those patients at risk of disease progression, yielding an AUC of 0.777 (95% CI, 0.657–0.870)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The three-gene panel could effectively identify PBC patients at risk of disease progression, yielding an AUC of 0.777 (95% CI, 0.657–0.870).", "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:37784152"], "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:GSE119600"], "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:GSE119600"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37784152", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s13075-023-03163-y", "sha256": 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Furthermore, we also verified the expressio", "alidated in multiple public datasets and PBC mouse model. Furthermore, we also verified the exp", "Methods Experimental animals Our dnTGF-βRII mice were a generous gift from Pro. ZX Lian (Guangdong", "ng Provincial People’s Hospital). dnTGF-βRII mice were bred onto a C57BL/6 (B6) strain background a"], "order_basis": "paper_text_order:810", "id": "assay:1"}, {"role": "data_generation", "definition_id": "clinical_cohort", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ey genes in the PBC, we collected peripheral blood samples from 66 PBC patients in our center. We firstly co", "B CD44, C ENTPD1, and D PDGFRB in peripheral blood samples from 66 patients. E The area under the curve (AUC"], "order_basis": "paper_text_order:7953", "id": "assay:2"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ry Table 1. Primers used in the Quantitative real-time PCR."], "order_basis": "paper_text_order:11851", "id": "assay:3"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["A logistic regression algorithm was used to construct the predictive model."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}