A novel tumor-derived exosomal gene signature predicts prognosis in patients with pancreatic cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39262474", "kind": "science-record", "revision": 3, "content_hash": "sha256:9e540f733ba4c321b1095edbe6492f6383956cedd7b4feb77613a0211f5adadd", "created_at": "2026-11-01T20:29:25Z", "updated_at": "2026-11-01T22:12:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel tumor-derived exosomal gene signature predicts prognosis in patients with pancreatic cancer.", "doi": "10.21037/tcr-23-2354", "pmid": "39262474", "authors": ["Yang Wang", "Chao Liang", "Xinbo Liu", "Shu-Qun Cheng"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Pancreatic cancer is a devastating disease with poor prognosis. Accumulating evidence has shown that exosomes and their cargo have the potential to mediate the progression of pancreatic cancer and are promising non-invasive biomarkers for the early detection and prognosis of this malignancy. This study aimed to construct a gene signature from tumor-derived exosomes with high prognostic capacity for pancreatic cancer using bioinformatics analysis.\nMethods: Gene expression data of solid pancreatic cancer tumors and blood-derived exosome tissues were downloaded from The Cancer Genome Atlas (TCGA) and ExoRBase 2.0. Overlapping differentially expressed genes (DEGs) in the two datasets were analyzed, followed by functional enrichment analysis, protein-protein interaction networks, and weighted gene co-expression network analysis (WGCNA). Using the least absolute shrinkage and selection operator (LASSO) regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was constructed based on the TCGA dataset, which was validated by an external validation dataset, GSE62452. The prognostic power of this gene signature and its relationship with various pathways and immune cell infiltration were analyzed.\nResults: A total of 166 overlapping DEGs were identified from the two datasets, which were markedly enriched in functions and pathways associated with the cell cycle. Two key modules and corresponding 70 exosomal DEGs were identified using WGCNA. Using LASSO Cox regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was built using six exosomal DEGs (ARNTL2, FHL2, KRT19, MMP1, CDCA5, and KIF11), which showed high predictive performance for prognosis in both the training and validation datasets. In addition, this prognostic signature is associated with the differential activation of several pathways, such as the cell cycle, and the infiltration of some immune cells, such as Tregs and CD8+ T cells.\nConclusions: This study established a six-exosome gene signature that can accurately predict the prognosis of pancreatic cancer.", "claims": [{"id": "claim-method", "statement": "By further screening with the least absolute shrinkage and selection operator (LASSO) Cox regression analysis using R3.6.1 lars package (version 1.2) (28), the prognostic signature was built by DEGs screened by LASSO regression analysis and its risk score was calculated as follows: risk score = ∑ Coef DEGs × Exp DEGs, where Coef DEGs represents the regression coefficient of DEGs and the Exp DEGs indicates the expression level of DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39262474", "locator": "sections[8] \"Construction of the prognostic signature\" paragraph 2", "quote": "By further screening with the least absolute shrinkage and selection operator (LASSO) Cox regression analysis using R3.6.1 lars package (version 1.2) (28), the prognostic signature was built by DEGs screened by LASSO regression analysis and its risk score was calculated as follows: risk score = ∑ Coef DEGs × Exp DEGs, where Coef DEGs represents the regression coefficient of DEGs and the Exp DEGs indicates the expression level of DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The pancreatic cancer transcriptome data in TCGA, normalized with log2 (fpkm+1) transformation, were downloaded from the UCSC Xena database on October 20, 2023.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39262474", "locator": "sections[3] \"Data source and data preprocessing\" paragraph 2", "quote": "The pancreatic cancer transcriptome data in TCGA, normalized with log2 (fpkm+1) transformation, were downloaded from the UCSC Xena database on October 20, 2023."}], "state": "complete"}, {"id": "claim-target-result", "statement": "This study established a six-exosome gene signature that can accurately predict the prognosis of pancreatic cancer.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39262474", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "This study established a six-exosome gene signature that can accurately predict the prognosis of pancreatic cancer."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "This study established a six-exosome gene signature that can accurately predict the prognosis of pancreatic cancer.", "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:39262474"], "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:GSE62452"], "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:GSE62452"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39262474", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-23-2354", "sha256": 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