Telomere-related prognostic biomarkers for survival assessments in pancreatic cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37391503", "kind": "science-record", "revision": 3, "content_hash": "sha256:8dec45ed1e9e228c14a5d06bef22b4765d237b009f990ba4e2a47be3498e9615", "created_at": "2026-11-01T19:02:09Z", "updated_at": "2026-11-01T22:09:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Telomere-related prognostic biomarkers for survival assessments in pancreatic cancer.", "doi": "10.1038/s41598-023-37836-0", "pmid": "37391503", "authors": ["Shengyang Chen", "Shuiquan Hu", "Baizhong Zhou", "Bingbing Cheng", "Hao Tong", "Dongchao Su", "Xiaoyong Li", "Yanjun Chen", "Genhao Zhang"], "journal": "Scientific reports", "year": 2023, "abstract": "Human telomeres are linked to genetic instability and a higher risk of developing cancer. Therefore, to improve the dismal prognosis of pancreatic cancer patients, a thorough investigation of the association between telomere-related genes and pancreatic cancer is required. Combat from the R package \"SVA\" was performed to correct the batch effects between the TCGA-PAAD and GTEx datasets. After differentially expressed genes (DEGs) were assessed, we constructed a prognostic risk model through univariate Cox regression, LASSO-Cox regression, and multivariate Cox regression analysis. Data from the ICGC, GSE62452, GSE71729, and GSE78229 cohorts were used as test cohorts for validating the prognostic signature. The major impact of the signature on the tumor microenvironment and its response to immune checkpoint drugs was also evaluated. Finally, PAAD tissue microarrays were fabricated and immunohistochemistry was performed to explore the expression of this signature in clinical samples. After calculating 502 telomere-associated DEGs, we constructed a three-gene prognostic signature (DSG2, LDHA, and RACGAP1) that can be effectively applied to the prognostic classification of pancreatic cancer patients in multiple datasets, including TCGA, ICGC, GSE62452, GSE71729, and GSE78229 cohorts. In addition, we have screened a variety of tumor-sensitive drugs targeting this signature. Finally, we also found that protein levels of DSG2, LDHA, and RACGAP1 were upregulated in pancreatic cancer tissues compared to normal tissues by immunohistochemistry analysis. We established and validated a telomere gene-related prognostic signature for pancreatic cancer and confirmed the upregulation of DSG2, LDHA, and RACGAP1 expression in clinical samples, which may provide new ideas for individualized immunotherapy.", "claims": [{"id": "claim-method", "statement": "After differentially expressed genes (DEGs) were assessed, we constructed a prognostic risk model through univariate Cox regression, LASSO-Cox regression, and multivariate Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37391503", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "After differentially expressed genes (DEGs) were assessed, we constructed a prognostic risk model through univariate Cox regression, LASSO-Cox regression, and multivariate Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study (including TCGA-PAAD, GTEx, ICGC-PACA-CN, GSE62452, GSE71729, and GSE78229 cohorts) are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37391503", "locator": "sections[26] \"Data availability\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study (including TCGA-PAAD, GTEx, ICGC-PACA-CN, GSE62452, GSE71729, and GSE78229 cohorts) are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "With AUCs at 1-, 2-, and 3-year of 0.718, 0.679, and 0.651, this classifier displayed high predictive performance (Fig. 1H).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37391503", "locator": "sections[13] \"Establishment of telomere-related subtypes\" paragraph 2", "quote": "With AUCs at 1-, 2-, and 3-year of 0.718, 0.679, and 0.651, this classifier displayed high predictive performance (Fig. 1H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "With AUCs at 1-, 2-, and 3-year of 0.718, 0.679, and 0.651, this classifier displayed high predictive performance (Fig. 1H).", "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:37391503"], "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. 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