Seven-gene signature on tumor microenvironment for predicting the prognosis of patients with pancreatic cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33968691", "kind": "science-record", "revision": 3, "content_hash": "sha256:382ca8e8556c06d4bae7686e7855839a08b817ecae404e41a50f782b5dd727a0", "created_at": "2026-11-01T17:35:15Z", "updated_at": "2026-11-01T21:25:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Seven-gene signature on tumor microenvironment for predicting the prognosis of patients with pancreatic cancer.", "doi": "10.21037/gs-21-28", "pmid": "33968691", "authors": ["Bin Yang", "Jinghua Xie", "Zhiguo Li", "Dan Su", "Longfa Lin", "Xiaofeng Guo", "Zhiqiang Fu", "Quanbo Zhou", "Yanan Lu"], "journal": "Gland surgery", "year": 2021, "abstract": "BACKGROUND: The aim of the present study was to construct a novel gene signature on the tumor microenvironment (TME) to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).\nMETHODS: We downloaded gene expression profiles and clinical information of PDAC from The Cancer Genome Atlas (TCGA) datasets, as well as Gene Expression Omnibus (GEO) datasets (GSE78229, GSE62452, and GSE28735). Differentially expressed genes were generated by comparing high versus low score groups of immune/stromal subgroups based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Subsequently, a prognostic risk score model was constructed and validated through univariate and multivariate Cox regression analyses. Finally, functional enrichment analysis and protein-protein interactions were performed to predict the functional implication of the prognostic model.\nRESULTS: We picked out 1,797 upregulated genes in immune groups and stromal groups. Through further analysis, we constructed a 7-gene signature on the TME. The risk score from the model effectively differentiated patients into high-risk and low-risk groups with different overall survival and was validated by GEO datasets. A functional analysis suggested that 7 selected genes and their co-expressed genes were mainly enriched in immune response, extracellular structure organization, and cell adhesion molecule binding.\nCONCLUSIONS: Our results showed that the 7-gene model on the TME can be used to assess the prognosis of patients with PDAC.", "claims": [{"id": "claim-method", "statement": "Subsequently, a prognostic risk score model was constructed and validated through univariate and multivariate Cox regression analyses.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33968691", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Subsequently, a prognostic risk score model was constructed and validated through univariate and multivariate Cox regression analyses."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We downloaded gene expression profiles and clinical information of PDAC from The Cancer Genome Atlas (TCGA) datasets, as well as Gene Expression Omnibus (GEO) datasets (GSE78229, GSE62452, and GSE28735).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33968691", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We downloaded gene expression profiles and clinical information of PDAC from The Cancer Genome Atlas (TCGA) datasets, as well as Gene Expression Omnibus (GEO) datasets (GSE78229, GSE62452, and GSE28735)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study constructed a 7-gene tumor-microenvironment signature whose risk score differentiated PDAC patients into high-risk and low-risk groups with different overall survival and was validated using GEO datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:33968691", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Through further analysis, we constructed a 7-gene signature on the TME. The risk score from the model effectively differentiated patients into high-risk and low-risk groups with different overall survival and was validated by GEO datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study constructed a 7-gene tumor-microenvironment signature whose risk score differentiated PDAC patients into high-risk and low-risk groups with different overall survival and was validated using GEO datasets.", "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:33968691"], "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:GSE28735"], "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:GSE28735"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33968691", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/gs-21-28", "sha256": 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