The combined prognostic model of copper-dependent to predict the prognosis of pancreatic cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36035166", "kind": "science-record", "revision": 3, "content_hash": "sha256:8cbe605ab731a78dd33eaec4c6ef939d41bfdf6095a506ac7a5b558806d576c7", "created_at": "2026-11-01T18:15:12Z", "updated_at": "2026-11-01T21:28:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The combined prognostic model of copper-dependent to predict the prognosis of pancreatic cancer.", "doi": "10.3389/fgene.2022.978988", "pmid": "36035166", "authors": ["Xiao Guan", "Na Lu", "Jianping Zhang"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Purpose: To assess the prognostic value of copper-dependent genes, copper-dependent-related genes (CDRG), and CDRG-associated immune-infiltrating cells (CIC) for pancreatic cancer. Methods: CDRG were obtained by single-cell analysis of the GSE156405 dataset in the Gene Expression Omnibus (GEO) database. In a ratio of 7:3, we randomly divided the Cancer Genome Atlas (TCGA) cohort into a training cohort and a test cohort. Tumor samples from the GSE62452 dataset were used as the validation cohort. CIBERSORT was used to obtain the immune cell infiltration. We identified the prognostic CDRG and CIC by Cox regression and the least absolute selection operator (LASSO) method. The clinical significance of these prognostic models was assessed using survival analysis, immunological microenvironment analysis, and drug sensitivity analysis. Results: 536 CDRG were obtained by single-cell sequencing analysis. We discovered that elevated LIPT1 expression was associated with a worse prognosis in pancreatic cancer patients. EPS8, CASC8, TATDN1, NT5E, and LDHA comprised the CDRG-based prognostic model. High infiltration of Macrophages.M2 in pancreatic cancer patients results in poor survival. The combined prognostic model showed great predictive performance, with the area under the curve (AUC) values being basically between 0.7 and 0.9 in all three cohorts. Conclusion: We found a cohort of CDRG and CIC in patients with pancreatic cancer. The combined prognostic model provided new insights into the prognosis and treatment of pancreatic cancer.", "claims": [{"id": "claim-method", "statement": "We identified the prognostic CDRG and CIC by Cox regression and the least absolute selection operator (LASSO) method.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36035166", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We identified the prognostic CDRG and CIC by Cox regression and the least absolute selection operator (LASSO) method."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36035166", "locator": "sections[29] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The combined prognostic model showed great predictive performance, with the area under the curve (AUC) values being basically between 0.7 and 0.9 in all three cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36035166", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The combined prognostic model showed great predictive performance, with the area under the curve (AUC) values being basically between 0.7 and 0.9 in all three cohorts."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The combined prognostic model showed great predictive performance, with the area under the curve (AUC) values being basically between 0.7 and 0.9 in all three cohorts.", "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:36035166"], "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:GSE156405"], "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:GSE156405"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36035166", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.978988", "sha256": 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