Identification of key genes participating in copper-diethyldithiocarbamate-related cell death process and predicting the development of prostate cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39361158", "kind": "science-record", "revision": 3, "content_hash": "sha256:47a2fde2a3c9d995f4f6b12fcc97707423a0e7dab3455e4fa32769ae5aa89a98", "created_at": "2026-11-01T20:29:34Z", "updated_at": "2026-11-01T22:12:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key genes participating in copper-diethyldithiocarbamate-related cell death process and predicting the development of prostate cancer.", "doi": "10.1007/s12672-024-01390-6", "pmid": "39361158", "authors": ["Xin'an Wang", "Chengdang Xu", "Junjie Ma", "Xiao Wang", "Xi Chen"], "journal": "Discover oncology", "year": 2024, "abstract": "Copper (Cu) is used as a cofactor in all organisms, and yet it can be toxic at high intracellular concentrations, causing cell death. Diethyldithiocarbamate (DDC) is a Cu ionophore that can transport Cu effectively into the cell. Copper-diethyldithiocarbamate (Cu-DDC) can treat prostate cancer (PCa) and may correlate with the cell death process. However, the specific Cu-DDC-related cell death genes in PCa are still unknown. Information about the Cu-DDC-related cell death genes was obtained from a previous study. Concurrently, the RNA expression profiles and clinical data were downloaded from public databases such as GEO, TCGA, and CPGEA. Using data from TCGA database, the logistic and lasso regression models were generated using R software. The influence of these genes in affecting PCa progression and prognosis was analyzed. Finally, the expression of these genes was verified in clinical samples. We found five Cu-DDC-related cell death genes associated with the occurrence of PCa from GSE35988, a gene dataset, namely, CDKN2A, PRC1, CDK1, SOX2, and ZNF365. CDKN2A, PRC1, and CDK1 are known to influence PCa patients' disease-free survival (DFS) status and were overexpressed, whereas SOX2 and ZNF365 were under-expressed in PCa in the different databases. Some of these genes can affect PCa progression. Consistent with the database results, the mRNA and protein expression of CDKN2A, PRC1, and CDK1 was also higher in clinical samples. In conclusion, we identified five hub genes which are important for Cu-DDC-related cell death process that can predict the development of PCa.", "claims": [{"id": "claim-method", "statement": "Lasso regression model was used to estimate the roles of these genes in affecting the prognosis of patients with PCa.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39361158", "locator": "sections[7] \"Survival analysis\" paragraph 2", "quote": "Lasso regression model was used to estimate the roles of these genes in affecting the prognosis of patients with PCa."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data from public databases can be acquired from TCGA (http://portal.gdc.cancer.gov), GEO (https://www.ncbi.nlm.nih.gov/gds), and CPGEA (http://www.cpgea.com/) databases. Other data can get from the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39361158", "locator": "sections[31] \"Data availability\" paragraph 2", "quote": "The data from public databases can be acquired from TCGA (http://portal.gdc.cancer.gov), GEO (https://www.ncbi.nlm.nih.gov/gds), and CPGEA (http://www.cpgea.com/) databases. Other data can get from the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The lasso regression model identified five Cu-DDC-related cell death genes whose combined expression significantly influenced patients’ disease-free survival in PCa (P = 1.1e-06).", "kind": "quantitative", "target": {"value": 1.0, "unit": "p-value", "tolerance": null, "source": "We found that these genes effectively influenced the DFS in patients with PCa with a P = 1.1e-06 (Fig. 4C)."}, "evidence": [{"asset": "source:pmid:39361158", "locator": "sections[20] \"Prognostic value of Cu-DDC-related cell death genes in PCa\" paragraph 2", "quote": "Using microarray and clinical data from the TCGA database, we constructed a lasso regression model, wherein we found that these genes affected the patients’ DFS status."}, {"asset": "source:pmid:39361158", "locator": "sections[20] \"Prognostic value of Cu-DDC-related cell death genes in PCa\" paragraph 2", "quote": "We found that these genes effectively influenced the DFS in patients with PCa with a P = 1.1e-06 (Fig. 4C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The lasso regression model identified five Cu-DDC-related cell death genes whose combined expression significantly influenced patients’ disease-free survival in PCa (P = 1.1e-06).", "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:39361158"], "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:GSE35988"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:student_t", "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:GSE35988"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:student_t"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39361158", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-024-01390-6", "sha256": "sha256:896965502af117dbf2760ac0efe78550f3f81017a664abf260bd0e5da65dff05", "bytes": 40181, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12672-024-01390-6", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11450124", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE35988", "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=GSE35988", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE35988", "version": null, "note": "The data from public databases can be acquired from TCGA (http://portal.gdc.cancer.gov), GEO (https://www.ncbi.nlm.nih.gov/gds), and CPGEA (http://www.cpgea.com/) databases. 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