Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35845587", "kind": "science-record", "revision": 4, "content_hash": "sha256:0bb0fcdfd379d175c8b619592013bf83f79702e59d31b27fa72cb92c7a299d99", "created_at": "2026-11-01T18:14:55Z", "updated_at": "2026-11-01T22:07:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer.", "doi": "10.1155/2022/3660110", "pmid": "35845587", "authors": ["Xiaoguang Wang", "Man Ni", "Daxiong Han"], "journal": "Evidence-based complementary and alternative medicine : eCAM", "year": 2022, "abstract": "Objective: Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis.\nMethods: Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan-Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases.\nResults: Univariate Cox regression and Kaplan-Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan-Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa (p < 0.05). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells.\nConclusions: The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.", "claims": [{"id": "claim-method", "statement": "The stepAIC algorithm [23] was a stepwise logistic regression method. We used the stepAIC algorithm to identify prognostic genes in the top 20 hub nodes (multivariate Cox regression analysis) and construct a prognostic risk model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35845587", "locator": "sections[7] \"2.5. Construction and Assessment of the Prognostic Risk Model\" paragraph 2", "quote": "The stepAIC algorithm [23] was a stepwise logistic regression method. We used the stepAIC algorithm to identify prognostic genes in the top 20 hub nodes (multivariate Cox regression analysis) and construct a prognostic risk model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35845587", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "The data used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A five-gene prognostic risk model was established and significantly correlated with survival in pancreatic cancer patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35845587", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A prognostic risk model was established using the signature of five genes."}, {"asset": "source:pmid:35845587", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The prognostic risk model was significantly correlated with the survival in PCa patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A five-gene prognostic risk model was established and significantly correlated with survival in pancreatic cancer patients.", "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:35845587"], "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:35845587", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/3660110", "sha256": "sha256:efced82c11f510d9f012835251c6d7e9c22cf34474edfeffd38e0ec21e659b1d", "bytes": 42614, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/3660110", "version": null, "note": "Full article fetched from PMC; 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