A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33614260", "kind": "science-record", "revision": 7, "content_hash": "sha256:87f351849ffb51815288e6c818c8f09dca33acee777b900170f20769e2dad3aa", "created_at": "2026-11-01T15:41:26Z", "updated_at": "2026-11-01T22:03:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets.", "doi": "10.7717/peerj.10556", "pmid": "33614260", "authors": ["Yiguo Wu", "Junping Deng", "Shuhui Lai", "Yujuan You", "Jing Wu"], "journal": "PeerJ", "year": 2021, "abstract": "BACKGROUND: Gastric cancer (GC) is one of the most common carcinomas of the digestive tract, and the prognosis for these patients may be poor. There is evidence that some long non-coding RNAs(lncRNAs) can predict the prognosis of patients with GC. However, few lncRNA signatures have been used to predict prognosis. Herein, we aimed to construct a risk score model based on the expression of five lncRNAs to predict the prognosis of patients with GC and provide new potential therapeutic targets.\nMETHODS: We performed differentially expressed and survival analyses to identify differentially expressed survival-ralated lncRNAs by using GC patient expression profile data from The Cancer Genome Atlas (TCGA) database. We then established a formula including five lncRNAs to predict the prognosis of patients with GC. In addition, to verify the prognostic value of this risk score model, two independent Gene Expression Omnibus (GEO) datasets, GSE62254 (N = 300) and GSE15459 (N = 200), were employed as validation groups.\nRESULTS: Based on the characteristics of five lncRNAs, patients with GC were divided into high or low risk subgroups. The prognostic value of the risk score model with five lncRNAs was confirmed in both TCGA and the two independent GEO datasets. Furthermore, stratification analysis results showed that this model had an independent prognostic value in patients with stage II-IV GC. We constructed a nomogram model combining clinical factors and the five lncRNAs to increase the accuracy of prognostic prediction. Enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the five lncRNAs are associated with multiple cancer occurrence and progression-related pathways.\nCONCLUSION: The risk score model including five lncRNAs can predict the prognosis of patients with GC, especially those with stage II-IV, and may provide potential therapeutic targets in future.", "claims": [{"id": "claim-method", "statement": "We used Kaplan–Meier analysis to compare the survival of the two groups and time-dependent receiver operating characteristic (ROC) curves to assess our lncRNA-based risk model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33614260", "locator": "sections[6] \"Validation of the lncRNA-based model for prognostic prediction\" paragraph 2", "quote": "We used Kaplan–Meier analysis to compare the survival of the two groups and time-dependent receiver operating characteristic (ROC) curves to assess our lncRNA-based risk model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data are available at UCSC Xena (https://xenabrowser.net/) and at NCBI GEO: GSE62254 and GSE15459.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33614260", "locator": "sections[19] \"Additional Information and Declarations\" paragraph 4", "quote": "Data are available at UCSC Xena (https://xenabrowser.net/) and at NCBI GEO: GSE62254 and GSE15459."}], "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:33614260"], "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:33614260", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.10556", "sha256": 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"verified", "reader": "read_table", "reader_status": "available", "identifier": "section[10]/table-wrap[3]#table-3", "version": null, "note": "The schematic workflow of the present study.", "structure": {"pmcid": "PMC7879943", "section_index": 10, "section_title": "Identification of five prognostic lncRNAs", "row_count": 0, "column_count": 0}, "table": {"label": "Table 3", "caption": "The schematic workflow of the present study.", "rows": [], "row_count": 0, "column_count": 0, "numeric_cells": 0, "header_rows": 0, "locator": "section[10]/table-wrap[3]#table-3"}}, {"id": "asset:table:pmid:33614260:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:e8e6b2ade6eb804a98c6bd37083c7df0bdac3cdce49ddf5c6b214931753c31d8", "bytes": 239, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[15]/table-wrap[1]#table-4", "version": null, "note": "The association between five-lncRNA signature and OS of GC patients in training group.", "structure": {"pmcid": "PMC7879943", "section_index": 15, "section_title": "Discussion", "row_count": 0, "column_count": 0}, "table": {"label": "Table 4", "caption": "The association between five-lncRNA signature and OS of GC patients in training group.", "rows": [], "row_count": 0, "column_count": 0, "numeric_cells": 0, "header_rows": 0, "locator": "section[15]/table-wrap[1]#table-4"}}], "gaps": ["asset:data:geo:GSE15459 not downloaded", "asset:data:geo:GSE15459 licence not read", "asset:data:geo:GSE62254 not downloaded", "asset:data:geo:GSE62254 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", 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