A DCS-related lncRNA signature predicts the prognosis and chemotherapeutic response of patients with gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35993308", "kind": "science-record", "revision": 3, "content_hash": "sha256:5c2d48125cdbc5541c6ba071b34be903bfde01a04d54b0263ab642371404101c", "created_at": "2026-11-01T18:15:09Z", "updated_at": "2026-11-01T21:28:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A DCS-related lncRNA signature predicts the prognosis and chemotherapeutic response of patients with gastric cancer.", "doi": "10.1042/BSR20220989", "pmid": "35993308", "authors": ["Yang Zhang", "Leyan Li", "Yi Tu", "Zongfeng Feng", "Zhengrong Li", "Yi Cao", "Yong Li"], "journal": "Bioscience reports", "year": 2022, "abstract": "The combination of docetaxel, cisplatin, and S-1 (DCS) is a common chemotherapy regimen for patients with gastric cancer (GC). However, studies on long noncoding RNAs (lncRNAs) associated with the chemotherapeutic response to and prognosis after DCS remain lacking. The aim of the present study was to identify DCS mRNAs-lncRNAs associated with chemotherapy response and prognosis in GC patients. In the present study, we identified 548 lncRNAs associated with these 16 mRNAs in the TCGA and GSE31811 datasets. Eleven lncRNAs were used to construct a prognostic signature by least absolute shrinkage and selection operator (LASSO) regression. A model including the 11 lncRNAs (LINC02532, AC007277.1, AC005324.4, AL512506.1, AC068790.7, AC022509.2, AC113139.1, LINC00106, AC005165.1, MIR100HG, and UBE2R2-AS1) associated with the prognosis of GC was constructed. The signature was validated in the TCGA database, model comparison, and qRT-PCR experiments. The results showed that the risk signature was a more effective prognostic factor for GC patients. Furthermore, the results showed that this model can well predicting chemotherapy drug response and immune infiltration of GC patients. In addition, our experimental results indicated that lower expression levels of LINC00106 and UBE2R2-AS1 predicted worse drug resistance in AGS/DDP cells. The experimental results agreed with the predictions. Furthermore, knockdown of LINC00106 or UBE2R2-AS1 can significantly enhanced the proliferation and migration of GC AGS cells in vitro. In conclusion, a novel DCS therapy-related lncRNA signature may become a new strategy to predict chemotherapy response and prognosis in GC patients. LINC00106 and UBE2R2-AS1 may exhibit a tumor suppressive function in GC.", "claims": [{"id": "claim-method", "statement": "A model incorporating 11 lncRNAs with optimal GC correlation and prognosis was constructed based on LASSO Cox regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35993308", "locator": "sections[10] \"Establishment of the DCS therapy-associated lncRNAs prognostic risk model\" paragraph 2", "quote": "A model incorporating 11 lncRNAs with optimal GC correlation and prognosis was constructed based on LASSO Cox regression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used in the study can be downloaded from the TCGA (https://tcga-data.nci.nih.gov/tcga/) database and the GEO (https://www.ncbi.nlm.nih.gov/geo). The other datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35993308", "locator": "sections[34] \"Data Availability\" paragraph 2", "quote": "All data used in the study can be downloaded from the TCGA (https://tcga-data.nci.nih.gov/tcga/) database and the GEO (https://www.ncbi.nlm.nih.gov/geo). The other datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The results showed that the risk signature was a more effective prognostic factor for GC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35993308", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The results showed that the risk signature was a more effective prognostic factor for GC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The results showed that the risk signature was a more effective prognostic factor for GC 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:35993308"], "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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