Revealing platelet-related subtypes and prognostic signature in pancreatic adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37198621", "kind": "science-record", "revision": 3, "content_hash": "sha256:da5530ed72dd641bd346e3b6258bf32a0ddb1ac22e6feb576ed9221af57a2979", "created_at": "2026-11-01T19:01:53Z", "updated_at": "2026-11-01T22:09:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Revealing platelet-related subtypes and prognostic signature in pancreatic adenocarcinoma.", "doi": "10.1186/s12920-023-01530-x", "pmid": "37198621", "authors": ["Jian-Gang Zhao", "Yu-Jie Li", "Yong Wu", "Ke Zhang", "Lin-Jia Peng", "Hao Chen"], "journal": "BMC medical genomics", "year": 2023, "abstract": "BACKGROUND: Pancreatic adenocarcinoma (PDAC) is a malignant tumor with high heterogeneity and poor prognosis. In this study, we sought to identify the value of platelet-related genes in prognosis and heterogeneity of PDAC through multiple transcriptomic methods.\nMETHODS: Based on datasets from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), platelet-related genes were screened out, and the TCGA cohort (n = 171) was identified into two subtypes by unsupervised clustering. The platelet-related risk score model (PLRScore) was constructed by univariate Cox and LASSO regression, and the predictive ability was evaluated by Kaplan-Meier test and time-dependent receiver operating characteristic (ROC) curves. The results were validated in two other external validation sets, ICGC-CA (n = 140) and GSE62452 (n = 66). Furthermore, predictive nomogram containing clinical characteristics and PLRScore was established. In addition, we determined the possible correlation between PLRScore and immune infiltration and response of immunotherapy. Finally, we analyzed the heterogeneity of our signature in various types of cells using single-cell analysis.\nRESULTS: Platelet-related subtypes that have significant difference of overall survival and immune states (p < 0.05) were identified. PLRScore model based on four-gene signature (CEP55, LAMA3, CA12, SCN8A) was constructed to predict patient prognosis. The AUCs of training cohort were 0.697, 0.687 and 0.675 for 1-, 3-and 5-year, respectively. Further evaluation of the validation cohorts yielded similar results. In addition, PLRScore was associated with immune cell infiltration and immune checkpoint expression, and had promising ability to predict response to immunotherapy of PDAC.\nCONCLUSIONS: In this study, the platelet-related subtypes were identified and the four-gene signature was constructed and validated. It may provide new insights into the therapeutic decision-making and molecular targets of PDAC.", "claims": [{"id": "claim-method", "statement": "The platelet-related risk score model (PLRScore) was constructed by univariate Cox and LASSO regression, and the predictive ability was evaluated by Kaplan-Meier test and time-dependent receiver operating characteristic (ROC) curves.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37198621", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The platelet-related risk score model (PLRScore) was constructed by univariate Cox and LASSO regression, and the predictive ability was evaluated by Kaplan-Meier test and time-dependent receiver operating characteristic (ROC) curves."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analysed during the current study are available in the TCGA, https://portal.gdc.cancer.gov/projects/TCGA-PAAD/; GTEx, https://gtexportal.org/; GSE160252, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE160252; GSE62452, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62452; GSE78220, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78220; ICGC, https://dcc.icgc.org/; GSA, https://ngdc.cncb.ac.cn/gsa/browse/CRA001160.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37198621", "locator": "sections[25] \"Data availability\" paragraph 2", "quote": "The datasets generated and/or analysed during the current study are available in the TCGA, https://portal.gdc.cancer.gov/projects/TCGA-PAAD/; GTEx, https://gtexportal.org/; GSE160252, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE160252; GSE62452, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62452; GSE78220, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78220; ICGC, https://dcc.icgc.org/; GSA, https://ngdc.cncb.ac.cn/gsa/browse/CRA001160."}], "state": "complete"}, {"id": "claim-target-result", "statement": "PLRScore model based on four-gene signature (CEP55, LAMA3, CA12, SCN8A) was constructed to predict patient prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37198621", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "PLRScore model based on four-gene signature (CEP55, LAMA3, CA12, SCN8A) was constructed to predict patient prognosis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "PLRScore model based on four-gene signature (CEP55, LAMA3, CA12, SCN8A) was constructed to predict patient prognosis.", "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:37198621"], "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:GSE160252"], "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:GSE160252"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37198621", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-023-01530-x", "sha256": "sha256:c66db40dfee031bb9af49c6f6d52578ac06ac6ba31c203d3a6f3fc3cbb86e57e", "bytes": 39277, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12920-023-01530-x", "version": null, "note": "Full article fetched from PMC; 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