Revealing a cancer-associated fibroblast-based risk signature for pancreatic adenocarcinoma through single-cell and bulk RNA-seq analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39332020", "kind": "science-record", "revision": 3, "content_hash": "sha256:80e31c3e82381c8d2a58ab9b33f9f45e2c12ba84088f67b3a133a53dfc357fc4", "created_at": "2026-11-01T20:29:31Z", "updated_at": "2026-11-01T22:12:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Revealing a cancer-associated fibroblast-based risk signature for pancreatic adenocarcinoma through single-cell and bulk RNA-seq analysis.", "doi": "10.18632/aging.206043", "pmid": "39332020", "authors": ["Jing Ma", "Zhinan Chen", "Limin Hou"], "journal": "Aging", "year": 2024, "abstract": "PURPOSE: Proliferation of stromal connective tissue is a hallmark of pancreatic adenocarcinoma (PAAD). The engagement of activated cancer-associated fibroblasts (CAFs) contributes to the progression of PAAD through their involvement in tumor fibrogenesis. However, the prognostic significance of CAF-based risk signature in PAAD has not been explored.\nMETHODS: The single-cell RNA sequencing (scRNA-seq) data sourced from GSE155698 within the Gene Expression Omnibus (GEO) database was supplemented by bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and microarray data retrieved from the GEO database. The scRNA-seq data underwent processing via the Seurat package to identify distinct CAF clusters utilizing specific CAF markers. Differential gene expression analysis between normal and tumor samples was conducted within the TCGA-PAAD cohort. Univariate Cox regression analysis pinpointed genes associated with CAF clusters, identifying prognostic CAF-related genes. These genes were utilized in LASSO regression to craft a predictive risk signature. Subsequently, integrating clinicopathological traits and the risk signature, a nomogram model was constructed.\nRESULTS: Our scRNA-seq analysis unveiled four distinct CAF clusters in PAAD, with two linked to PAAD prognosis. Among 207 identified DEGs, 148 exhibited significant correlation with these CAF clusters, forming the basis of a seven-gene risk signature. This signature emerged as an independent predictor in multivariate analysis for PAAD and demonstrated predictive efficacy in immunotherapeutic outcomes. Additionally, a novel nomogram, integrating age and the CAF-based risk signature, exhibited robust predictability and reliability in prognosticating PAAD. Moreover, the risk signature displayed substantial correlations with stromal and immune scores, as well as specific immune cell types.\nCONCLUSIONS: The prognosis of PAAD can be accurately predicted using the CAF-based risk signature, and a thorough analysis of the PAAD CAF signature may aid in deciphering the patient's immunotherapy response and presenting fresh cancer treatment options.", "claims": [{"id": "claim-method", "statement": "Further prognosis-associated genes were identified through univariate Cox regression analysis, considering a significance threshold of P < 0.05. To reduce the pool of prognostic genes, initial analysis involved least absolute shrinkage and selection operator (LASSO) Cox regression, yielding seven genes. Subsequently, a multivariate Cox regression employing stepwise regression constructed the risk signature, calculating coefficients via the formula: risk score = Σβi*Expi.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39332020", "locator": "sections[5] \"Identification of prognostic genes\" paragraph 2", "quote": "Further prognosis-associated genes were identified through univariate Cox regression analysis, considering a significance threshold of P < 0.05. To reduce the pool of prognostic genes, initial analysis involved least absolute shrinkage and selection operator (LASSO) Cox regression, yielding seven genes. Subsequently, a multivariate Cox regression employing stepwise regression constructed the risk signature, calculating coefficients via the formula: risk score = Σβi*Expi."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. The data can be found in GEO and TCGA databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39332020", "locator": "sections[10] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. The data can be found in GEO and TCGA databases."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The seven-gene CAF-based risk signature was an independent predictor of PAAD prognosis and demonstrated predictive efficacy for immunotherapeutic outcomes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39332020", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "This signature emerged as an independent predictor in multivariate analysis for PAAD and demonstrated predictive efficacy in immunotherapeutic outcomes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The seven-gene CAF-based risk signature was an independent predictor of PAAD prognosis and demonstrated predictive efficacy for immunotherapeutic outcomes.", "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:39332020"], "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:GSE155698"], "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:GSE155698"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39332020", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.206043", "sha256": "sha256:75432e1b5eb2439313ba90c7862b3ebc2887799700478ab3704c934c5475c4cd", "bytes": 49241, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.18632/aging.206043", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11466480", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE155698", "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=GSE155698", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE155698", "version": null, "note": "Publicly available datasets were analyzed in this study. 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METHODS: The single-cell RNA sequencing (scRNA-seq) data sourced from GSE155698 with", "ibus (GEO) database was supplemented by bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and"], "order_basis": "paper_text_order:77", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["in PAAD has not been explored. METHODS: The single-cell RNA sequencing (scRNA-seq) data sourced from GSE155698 with", "ed. METHODS: The single-cell RNA sequencing (scRNA-seq) data sourced from GSE155698 within the Gene", "ay data retrieved from the GEO database. The scRNA-seq data underwent processing via the Seurat pac", "nomogram model was constructed. 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