Integrated single-cell and bulk RNA sequencing revealed the molecular characteristics and prognostic roles of neutrophils in pancreatic cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37728418", "kind": "science-record", "revision": 3, "content_hash": "sha256:090c39f8fc8435ffe9028ed9942771929c969ff9cc886f1282e9111a5cf76484", "created_at": "2026-11-01T19:02:33Z", "updated_at": "2026-11-01T22:10:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated single-cell and bulk RNA sequencing revealed the molecular characteristics and prognostic roles of neutrophils in pancreatic cancer.", "doi": "10.18632/aging.205044", "pmid": "37728418", "authors": ["Biao Zhang", "Jiaao Sun", "Hewen Guan", "Hui Guo", "Bingqian Huang", "Xu Chen", "Feng Chen", "Qihang Yuan"], "journal": "Aging", "year": 2023, "abstract": "Pancreatic cancer, one of the most prevalent tumors of the digestive system, has a dismal prognosis. Cancer of the pancreas is distinguished by an inflammatory tumor microenvironment rich in fibroblasts and different immune cells. Neutrophils are important immune cells that infiltrate the microenvironment of pancreatic cancer tumors. The purpose of this work was to examine the complex mechanism by which neutrophils influence the carcinogenesis and development of pancreatic cancer and to construct a survival prediction model based on neutrophil marker genes. We incorporated the GSE111672 dataset, comprising RNA expression data from 27,000 cells obtained from 3 patients with PC, and conducted single-cell data analysis. Thorough investigation of pancreatic cancer single-cell RNA sequencing data found 350 neutrophil marker genes. Using The Cancer Genome Atlas (TCGA), GSE28735, GSE62452, GSE57495, and GSE85916 datasets to gather pancreatic cancer tissue transcriptome data, and consistent clustering was used to identify two categories for analyzing the influence of neutrophils on pancreatic cancer. Using the Random Forest algorithm and Cox regression analysis, a survival prediction model for pancreatic cancer was developed, the model showed independent performance for survival prognosis, clinic pathological features, immune infiltration, and drug sensitivity. Multivariate Cox analysis findings revealed that the risk scores derived from predictive models is independent prognostic markers for pancreatic patients. In conclusion, based on neutrophil marker genes, this research created a molecular typing and prognostic grading system for pancreatic cancer, this system was very accurate in predicting the prognosis, tumor immune microenvironment status, and pharmacological treatment responsiveness of pancreatic cancer patients.", "claims": [{"id": "claim-method", "statement": "To further investigate the predictive usefulness of NRGs in PC and quantify the NRG score in each patient, univariate Cox regression analysis was used to screen NRGs linked with patient outcomes. Random survival forests-variable hunting (RSFVH) was used to screen and filter genes [49, 50]. A prognostic risk score model for PC was developed using a multivariable Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37728418", "locator": "sections[8] \"Construction and validation of the prognostic risk score model\" paragraph 2", "quote": "To further investigate the predictive usefulness of NRGs in PC and quantify the NRG score in each patient, univariate Cox regression analysis was used to screen NRGs linked with patient outcomes. Random survival forests-variable hunting (RSFVH) was used to screen and filter genes [49, 50]. A prognostic risk score model for PC was developed using a multivariable Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in this work may be found in the Supplementary Materials or contact with the first author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37728418", "locator": "sections[15] \"Availability of data and materials\" paragraph 2", "quote": "The datasets analyzed in this work may be found in the Supplementary Materials or contact with the first author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study developed a three-gene prognostic risk model for pancreatic cancer comprising LDHA, IL1R2, and TM4SF1.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37728418", "locator": "sections[20] \"Establishment of the PC prognostic risk model based on NRGs\" paragraph 2", "quote": "The optimal gene combination (LDHA, IL1R2, and TM4SF1) was determined (Figure 7C), and a predictive model was created using multivariate Cox regression."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study developed a three-gene prognostic risk model for pancreatic cancer comprising LDHA, IL1R2, and TM4SF1.", "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:37728418"], "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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