A Robust Circular RNA-Associated Three-Gene Prognostic Signature for Patients with Gastric Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33969120", "kind": "science-record", "revision": 8, "content_hash": "sha256:6ce2e4215b8a096a00e9523d5726af0ba8c4bf910c5f074880af36f1bc2df298", "created_at": "2026-11-01T15:41:35Z", "updated_at": "2026-11-01T22:46:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Robust Circular RNA-Associated Three-Gene Prognostic Signature for Patients with Gastric Cancer.", "doi": "10.1155/2021/6633289", "pmid": "33969120", "authors": ["Yang Li", "Rui Li", "Xiuli Wang", "Yuan Yuan", "Yangmei Zhang"], "journal": "BioMed research international", "year": 2021, "abstract": "Accumulating evidence has demonstrated that circular RNAs (circRNAs) play vital roles in cancer progression. However, the underlying molecular mechanisms of circRNAs remain poorly elucidated in gastric cancer (GC). The main purpose of present study is to explore the underlying regulatory mechanism by constructing a circRNA-associated competitive endogenous RNA (ceRNA) network and further establish a robust prognostic signature for patients with GC. Based on expression data of circRNA, microRNA, and mRNA derived from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, a circRNA-associated ceRNA network, containing 15 cirRNAs, 9 microRNAs, and 35 mRNAs, was constructed using the Starbase database. Functional enrichment analysis showed that the ceRNA network might be involved in many cancer-related pathways, such as regulation of transcription from RNA polymerase II promoter, mesodermal cell differentiation, and focal adhesion. A protein-protein interaction network was constructed based on genes within the circRNA-associated ceRNA network. We found that six of ten hub genes within the PPI network were significantly associated with overall survival (OS). Thus, using the LASSO method, we constructed a three-gene prognostic signature based on TCGA-GC cohort, which could classify GC patients into low-risk and high-risk groups with significant difference in OS (HR = 1.9, 95%CI = 1.14-3.2, and log-rank p = 0.001). The prognostic performance of the three-gene signature was verified in GSE15459 (HR = 1.9, 95%CI = 1.27-3.0, and log - rank p = 2.2E - 05) and GSE84437 (HR = 1.5, 95%CI = 1.17-2.0, and log - rank p = 6.3E - 04). Multivariate Cox analysis further revealed that the three-gene prognostic signature could serve as an independent risk factor for OS. Taken together, our findings contribute to a better understanding of the underlying mechanisms of circRNAs in GC progression. Furthermore, a robust prognostic signature is meaningful to facilitate individualized treatment for patients with GC.", "claims": [{"id": "claim-method", "statement": "The Kaplan-Meier method was used to assess the differences in survival time of low- and high-risk GC patients, and the log-rank test was used to determine the statistical significance of observed differences between groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33969120", "locator": "sections[8] \"2.6. Statistical Analysis\" paragraph 2", "quote": "The Kaplan-Meier method was used to assess the differences in survival time of low- and high-risk GC patients, and the log-rank test was used to determine the statistical significance of observed differences between groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data sets analyzed in this study were downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/) public databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33969120", "locator": "sections[19] \"Data Availability\" paragraph 2", "quote": "All data sets analyzed in this study were downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/) public databases."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33969120"], "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:GSE15459"], "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:GSE15459"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33969120", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2021/6633289", "sha256": 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