A novel 8-genome instability-associated lncRNAs signature predicting prognosis and drug sensitivity in gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35696730", "kind": "science-record", "revision": 4, "content_hash": "sha256:3dc387ffd31565c42ed27dbcbc33653954a1c7724101c2b519281f65f0f9aa2a", "created_at": "2026-11-01T18:14:40Z", "updated_at": "2026-11-01T22:06:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel 8-genome instability-associated lncRNAs signature predicting prognosis and drug sensitivity in gastric cancer.", "doi": "10.1177/03946320221103195", "pmid": "35696730", "authors": ["Changhong Yi", "Xiulan Zhang", "Xia Chen", "Birun Huang", "Jing Song", "Minghui Ma", "Xiaolu Yuan", "Chaohao Zhang"], "journal": "International journal of immunopathology and pharmacology", "year": 2022, "abstract": "BACKGROUND: Genome instability lncRNA (GILnc) is prevalently related with gastric cancer (GC) pathophysiology. However, the study on the relationship GILnc and prognosis and drug sensitivity of GC remains scarce.\nMETHOD: We extracted expression data of 375 GC patients from TCGA cohort and 205 GC patients from GSE26942 cohort. Then, lncRNA was separated from expression data, and systematically characterized the 8 marker lncRNAs using the LASSO method. Next, we constructed a GILnc model (GILnc score) to quantify the GILnc index of each GC patient. Finally, we analyzed the relationship between GILnc score and clinical traits including survival outcomes, TP53, and drug sensitivity of GC.\nRESULTS: Based on a computational frame, 205 GILncs in GC has been identified. Then, a 8 GILncs was successfully established to predict overall survival in GC patients based on LASSO analysis, divided GC samples into high GILnc score and low GILnc score groups with significantly different outcome and was validated in multiple independent patient cohorts. Furthermore, GILnc model is better than the prediction performance of two recently published lncRNA signatures, and the high GILnc score group was more sensitive to mitomycin. Besides, the GILnc score has greater prognostic significance than TP53 mutation status alone and is capable of identifying intermediate subtype group existing with partial TP53 functionality in TP53 wild-type patients. Finally, GILnc signature as verified in GSE26942.\nCONCLUSION: We applied bioinformatics approaches to suggest that a 8 GILnc signature could serve as prognostic biomarkers, and provide a novel direction to explore the pathogenesis of GC.", "claims": [{"id": "claim-method", "statement": "In order to predict the clinical outcomes of GC with GILncs signature, we applied LASSO Cox regression algorithm to the 205 GILncs in the training set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35696730", "locator": "sections[11] \"Development of a GILncs signature for prognosis of patients with GC in the training set\" paragraph 2", "quote": "In order to predict the clinical outcomes of GC with GILncs signature, we applied LASSO Cox regression algorithm to the 205 GILncs in the training set."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used in the study can be downloaded from the TCGA data repository (https://gdac.broadinstitute.org/; accessed 26 May 2021) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26942; accessed 26 May 2021).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35696730", "locator": "sections[25] \"Data availability\" paragraph 2", "quote": "All data used in the study can be downloaded from the TCGA data repository (https://gdac.broadinstitute.org/; accessed 26 May 2021) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26942; accessed 26 May 2021)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Then, a 8 GILncs was successfully established to predict overall survival in GC patients based on LASSO analysis, divided GC samples into high GILnc score and low GILnc score groups with significantly different outcome and was validated in multiple independent patient cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35696730", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Then, a 8 GILncs was successfully established to predict overall survival in GC patients based on LASSO analysis, divided GC samples into high GILnc score and low GILnc score groups with significantly different outcome and was validated in multiple independent patient cohorts."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Then, a 8 GILncs was successfully established to predict overall survival in GC patients based on LASSO analysis, divided GC samples into high GILnc score and low GILnc score groups with significantly different outcome and was validated in multiple independent patient cohorts.", "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:35696730"], "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:GSE26942"], "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:GSE26942"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35696730", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1177/03946320221103195", "sha256": 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