A three-lncRNA expression signature associated with the prognosis of gastric cancer patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:28444881", "kind": "science-record", "revision": 7, "content_hash": "sha256:af84679d786d29231e88b8dfed9260e0aff93a87ddca3a61b4e6b083a10346eb", "created_at": "2026-11-01T15:40:25Z", "updated_at": "2026-11-01T21:20:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A three-lncRNA expression signature associated with the prognosis of gastric cancer patients.", "doi": "10.1002/cam4.1047", "pmid": "28444881", "authors": ["Peng Song", "Bo Jiang", "Zhijian Liu", "Jie Ding", "Song Liu", "Wenxian Guan"], "journal": "Cancer medicine", "year": 2017, "abstract": "Long noncoding RNAs (lncRNAs) have emerged as essential players in gene regulation. An ever-increasing number of lncRNAs has been found to be associated with the biogenesis and prognosis of gastric cancer (GC). We aimed to develop an lncRNA signature with prognostic value for survival outcomes of GC. Using an lncRNA mining approach, we analyzed the lncRNA expression profiles of 492 GC patients from the Gene Expression Omnibus (GEO), which consisted of the GSE62254 set (N = 300) and the GSE15459 set (N = 192). The associations between the lncRNAs' expression and survival outcome were evaluated. A set of three lncRNAs (LINC01140, TGFB2-OT1, and RP11-347C12.10) was identified to significantly correlate with overall survival. These lncRNAs were then combined to form a single prognostic signature. Based on this three-lncRNA expression signature, patients in the GSE62254 set were classified into high- and low-risk subgroups with significantly different overall survival (hazard ratio [HR] = 1.93, P < 0.001) and disease-free survival (HR = 1.91, P < 0.001). Good reproducibility for the prognostic value of this lncRNA signature was confirmed in the GSE15459 set. Further analysis showed that the prognostic value of this signature was independent of some clinical characteristics. Gene set enrichment analysis indicated that high-risk scores positively related to several molecular pathways of cancer metastasis. Our results suggest that this innovative lncRNA expression signature may be a useful biomarker for the prognosis of patients with GC based on bioinformatics analysis.", "claims": [{"id": "claim-method", "statement": "A multivariate stepwise Cox regression analysis was performed to evaluate the correlation between variables, including selected demographic characteristics and clinical features, risk scores (as continuous variables) and GC survival.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:28444881", "locator": "sections[13] \"Stepwise Cox regression model for survival\" paragraph 2", "quote": "A multivariate stepwise Cox regression analysis was performed to evaluate the correlation between variables, including selected demographic characteristics and clinical features, risk scores (as continuous variables) and GC survival."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GC gene expression data used in this study were obtained from publicly available GEO databases. To evaluate the association between lncRNA expression signatures and GC survival, we selected the microarray expression profiles based on three criteria: (1) the profiles should be generated by the Affymetrix HG‐U133 Plus 2.0 Array (GPL570 platform), (2) the corresponding clinical data, such as histological classification and follow‐up information, were available online, and (3) the sample size was >100. This resulted in two sets (GSE62254 and GSE15459) that were screened in our study. Raw CEL files of the expression for the two GEO datasets were downloaded, and the Robust Multichip Average (RMA) algorithm was performed for background‐adjustment, quantile normalization, and log‐transformation by the R package “affy” 12.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:28444881", "locator": "sections[3] \"GC data sets\" paragraph 2", "quote": "The GC gene expression data used in this study were obtained from publicly available GEO databases."}, {"asset": "source:pmid:28444881", "locator": "sections[3] \"GC data sets\" paragraph 2", "quote": "To evaluate the association between lncRNA expression signatures and GC survival, we selected the microarray expression profiles based on three criteria: (1) the profiles should be generated by the Affymetrix HG‐U133 Plus 2.0 Array (GPL570 platform), (2) the corresponding clinical data, such as histological classification and follow‐up information, were available online, and (3) the sample size was >100."}, {"asset": "source:pmid:28444881", "locator": "sections[3] \"GC data sets\" paragraph 2", "quote": "This resulted in two sets (GSE62254 and GSE15459) that were screened in our study."}, {"asset": "source:pmid:28444881", "locator": "sections[4] \"Microarray expression processing and lncRNA profile mining\" paragraph 2", "quote": "Raw CEL files of the expression for the two GEO datasets were downloaded, and the Robust Multichip Average (RMA) algorithm was performed for background‐adjustment, quantile normalization, and log‐transformation by the R package “affy” 12."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Mining lncRNA expression profiles from the public GEO microarray datasets GSE62254 and GSE15459, with univariate Cox regression, random survival forests, and multivariable Cox regression, identified a three-lncRNA prognostic signature for gastric cancer.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:28444881", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Using an lncRNA mining approach, we analyzed the lncRNA expression profiles of 492 GC patients from the Gene Expression Omnibus (GEO), which consisted of the GSE62254 set (N = 300) and the GSE15459 set (N = 192)."}, {"asset": "source:pmid:28444881", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A set of three lncRNAs (LINC01140, TGFB2‐OT1, and RP11‐347C12.10) was identified to significantly correlate with overall survival."}, {"asset": "source:pmid:28444881", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "These lncRNAs were then combined to form a single prognostic signature."}, {"asset": "source:pmid:28444881", "locator": "sections[9] \"Identification of prognostic lncRNAs\" paragraph 2", "quote": "After subjecting the ncRNA expression data to univariable Cox regression analysis by BRB‐Array Tools, we identified 85 and 21 ncRNAs that strongly correlated with the patients’ OS from the two sets, respectively."}, {"asset": "source:pmid:28444881", "locator": "sections[9] \"Identification of prognostic lncRNAs\" paragraph 2", "quote": "To make the model more practical, RSF were performed on the basis of the 11 lncRNAs, resulting in three lncRNAs remaining in the model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Mining lncRNA expression profiles from the public GEO microarray datasets GSE62254 and GSE15459, with univariate Cox regression, random survival forests, and multivariable Cox regression, identified a three-lncRNA prognostic signature for gastric 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:28444881"], "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. 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To evaluate the association between lncRNA expression signatures and GC survival, we selected the microarray expression profiles based on three criteria: (1) the profiles should be generated by the Affymetrix HG‐U133 Plus 2.0 Array (GPL570 platform), (2) the corresponding clinical data, such as histological classification and follow‐up information, were available online, and (3) the sample size was >100. This resulted in two sets (GSE62254 and GSE15459) that were screened in our study. Raw CEL files of the expression for the two GEO datasets were downloaded, and the Robust Multichip Average (RMA) algorithm was performed for background‐adjustment, quantile normalization, and log‐transformation by the R package “affy” 12.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The GC gene expression data used in this study were obtained from publicly available GEO databases. To evaluate the association between lncRNA expression signatures and GC survival, we selected the microarray expression profiles based on three criteria: (1) the profiles should be generated by the Affymetrix HG‐U133 Plus 2.0 Array (GPL570 platform), (2) the corresponding clinical data, such as histological classification and follow‐up information, were available online, and (3) the sample size was >100. This resulted in two sets (GSE62254 and GSE15459) that were screened in our study. Raw CEL files of the expression for the two GEO datasets were downloaded, and the Robust Multichip Average (RMA) algorithm was performed for background‐adjustment, quantile normalization, and log‐transformation by the R package “affy” 12.", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM387788", "GSM387789", "GSM387790", "GSM387791", "GSM387792", "GSM387793", "GSM387794", "GSM387795", "GSM387796", "GSM387797", "GSM387798", "GSM387799", "GSM387800", "GSM387801", "GSM387802", "GSM387803", "GSM387804", "GSM387805", "GSM387806", "GSM387807", "GSM387808", "GSM387809", "GSM387810", "GSM387811", "GSM387812", "GSM387813", "GSM387814", "GSM387815", "GSM387816", "GSM387817", "GSM387818", "GSM387819", "GSM387820", "GSM387821", "GSM387822", "GSM387823", "GSM387824", "GSM387825", "GSM387826", "GSM387827", "GSM387828", "GSM387829", "GSM387830", "GSM387831", "GSM387832", "GSM387833", "GSM387834", "GSM387835", "GSM387836", "GSM387837", "GSM387838", "GSM387839", "GSM387840", "GSM387841", "GSM387842", "GSM387843", "GSM387844", "GSM387845", "GSM387846", "GSM387847", "GSM387848", "GSM387849", "GSM387850", "GSM387851", "GSM387852", "GSM387853", "GSM387854", "GSM387855", "GSM387856", "GSM387857", "GSM387858", "GSM387859", "GSM387860", "GSM387861", "GSM387862", "GSM387863", "GSM387864", "GSM387865", "GSM387866", "GSM387867", "GSM387868", "GSM387869", "GSM387870", "GSM387871", "GSM387872", "GSM387873", "GSM387874", "GSM387875", "GSM387876", "GSM387877", "GSM387878", "GSM387879", "GSM387880", "GSM387881", "GSM387882", "GSM387883", "GSM387884", "GSM387885", "GSM387886", "GSM387887", "GSM387888", "GSM387889", "GSM387890", "GSM387891", "GSM387892", "GSM387893", "GSM387894", "GSM387895", "GSM387896", "GSM387897", "GSM387898", "GSM387899", "GSM387900", "GSM387901", "GSM387902", "GSM387903", "GSM387904", "GSM387905", "GSM387906", "GSM387907", "GSM387908", "GSM387909", "GSM387910", "GSM387911", "GSM387912", "GSM387913", "GSM387914", "GSM387915", "GSM387916", "GSM387917", "GSM387918", "GSM387919", "GSM387920", "GSM387921", "GSM387922", "GSM387923", "GSM387924", "GSM387925", "GSM387926", "GSM387927", "GSM387928", "GSM387929", "GSM387930", "GSM387931", "GSM387932", "GSM387933", "GSM387934", "GSM387935", "GSM387936", "GSM387937", "GSM387938", "GSM387939", "GSM387940", "GSM387941", "GSM387942", "GSM387943", "GSM387944", "GSM387945", "GSM387946", "GSM387947", "GSM387948", "GSM387949", "GSM387950", "GSM387951", "GSM387952", "GSM387953", "GSM387954", "GSM387955", "GSM387956", "GSM387957", "GSM387958", "GSM387959", "GSM387960", "GSM387961", "GSM387962", "GSM387963", "GSM387964", "GSM387965", "GSM387966", "GSM387967", "GSM387968", "GSM387969", "GSM387970", "GSM387971", "GSM387972", "GSM387973", "GSM387974", "GSM387975", "GSM387976", "GSM387977", "GSM387978", "GSM387979", "GSM387980", "GSM387981", "GSM387982", "GSM387983", "GSM387984", "GSM387985", "GSM387986", "GSM387987"], "sample_count": 200, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Gastric Cancer Project '08 (Singapore Patient Cohort)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE62254", "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=GSE62254", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE62254", "version": null, "note": "The GC gene expression data used in this study were obtained from publicly available GEO databases. To evaluate the association between lncRNA expression signatures and GC survival, we selected the microarray expression profiles based on three criteria: (1) the profiles should be generated by the Affymetrix HG‐U133 Plus 2.0 Array (GPL570 platform), (2) the corresponding clinical data, such as histological classification and follow‐up information, were available online, and (3) the sample size was >100. This resulted in two sets (GSE62254 and GSE15459) that were screened in our study. Raw CEL files of the expression for the two GEO datasets were downloaded, and the Robust Multichip Average (RMA) algorithm was performed for background‐adjustment, quantile normalization, and log‐transformation by the R package “affy” 12.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The GC gene expression data used in this study were obtained from publicly available GEO databases. To evaluate the association between lncRNA expression signatures and GC survival, we selected the microarray expression profiles based on three criteria: (1) the profiles should be generated by the Affymetrix HG‐U133 Plus 2.0 Array (GPL570 platform), (2) the corresponding clinical data, such as histological classification and follow‐up information, were available online, and (3) the sample size was >100. This resulted in two sets (GSE62254 and GSE15459) that were screened in our study. Raw CEL files of the expression for the two GEO datasets were downloaded, and the Robust Multichip Average (RMA) algorithm was performed for background‐adjustment, quantile normalization, and log‐transformation by the R package “affy” 12.", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM1523727", "GSM1523728", "GSM1523729", "GSM1523744", "GSM1523745", "GSM1523746", "GSM1523747", "GSM1523748", "GSM1523765", "GSM1523768", "GSM1523769", "GSM1523770", "GSM1523771", "GSM1523772", "GSM1523773", "GSM1523774", "GSM1523775", "GSM1523776", "GSM1523777", "GSM1523778", "GSM1523779", "GSM1523780", "GSM1523781", "GSM1523782", "GSM1523783", "GSM1523784", "GSM1523785", "GSM1523786", "GSM1523787", "GSM1523788", "GSM1523789", "GSM1523790", "GSM1523791", "GSM1523792", "GSM1523793", "GSM1523794", "GSM1523795", "GSM1523796", "GSM1523797", "GSM1523798", "GSM1523799", "GSM1523800", "GSM1523801", "GSM1523802", "GSM1523803", "GSM1523804", "GSM1523805", "GSM1523806", 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