A novel immune-related radioresistant lncRNAs signature based model for risk stratification and prognosis prediction in esophageal squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36147506", "kind": "science-record", "revision": 4, "content_hash": "sha256:56f660218f27ab06410f241efc0a1c8020420f9c04be876a7d7aa119bbe8cee1", "created_at": "2026-11-01T18:15:26Z", "updated_at": "2026-11-01T22:07:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel immune-related radioresistant lncRNAs signature based model for risk stratification and prognosis prediction in esophageal squamous cell carcinoma.", "doi": "10.3389/fgene.2022.921902", "pmid": "36147506", "authors": ["Jianqing Zheng", "Xiaohui Chen", "Bifen Huang", "Jiancheng Li"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background and purpose: Radioresistance remains a major reason of radiotherapeutic failure in esophageal squamous cell carcinoma (ESCC). Our study is to screen the immune-related long non-coding RNA (ir-lncRNAs) of radiation-resistant ESCC (rr-ESCC) via Gene Expression Omnibus (GEO) database and to construct a prognostic risk model. Methods: Microarray data (GSE45670) related to radioresistance of ESCC was downloaded from GEO. Based on pathologic responses after chemoradiotherapy, patients were divided into a non-responder (17 samples) and responder group (11 samples), and the difference in expression profiles of ir-lncRNAs were compared therein. Ir-lncRNA pairs were constructed for the differentially expressed lncRNAs as prognostic variables, and the microarray dataset (GSE53625) was downloaded from GEO to verify the effect of ir-lncRNA pairs on the long-term survival of ESCC. After modelling, patients are divided into high- and low-risk groups according to prognostic risk scores, and the outcomes were compared within groups based on the COX proportional hazards model. The different expression of ir-lncRNAs were validated using ECA 109 and ECA 109R cell lines via RT-qPCR. Results: 26 ir-lncRNA genes were screened in the GSE45670 dataset with differential expression, and 180 ir-lncRNA pairs were constructed. After matching with ir-lncRNA pairs constructed by GSE53625, six ir-lncRNA pairs had a significant impact on the prognosis of ESCC from univariate analysis model, of which three ir-lncRNA pairs were significantly associated with prognosis in multivariate COX analysis. These three lncRNA pairs were used as prognostic indicators to construct a prognostic risk model, and the predicted risk scores were calculated. With a median value of 2.371, the patients were divided into two groups. The overall survival (OS) in the high-risk group was significantly worse than that in the low-risk group (p < 0.001). The 1-, 2-, and 3-year prediction performance of this risk-model was 0.666, 0.702, and 0.686, respectively. In the validation setting, three ir-lncRNAs were significantly up-regulated, while two ir-lncRNAs were obviouly down-regulated in the responder group. Conclusion: Ir-lncRNAs may be involved in the biological regulation of radioresistance in patients with ESCC; and the prognostic risk-model, established by three ir-lncRNAs pairs has important clinical value in predicting the prognosis of patients with rr-ESCC.", "claims": [{"id": "claim-method", "statement": "Univariate and multivariate Cox regression survival analyses were performed using the “survival” package in R 4.1.2 software to screen ir-lncRNAs with significant impact on prognosis. The least absolute shrinkage and selection operator (LASSO) regression analysis was carried out to narrow down the prognostically significant ir-lncRNA pairs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36147506", "locator": "sections[5] \"Construction of the predictive model\" paragraph 3", "quote": "Univariate and multivariate Cox regression survival analyses were performed using the “survival” package in R 4.1.2 software to screen ir-lncRNAs with significant impact on prognosis. The least absolute shrinkage and selection operator (LASSO) regression analysis was carried out to narrow down the prognostically significant ir-lncRNA pairs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36147506", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A prognostic risk model based on three immune-related lncRNA pairs separated ESCC patients into high- and low-risk groups with significantly different overall survival (p < 0.001).", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "The overall survival (OS) in the high-risk group was significantly worse than that in the low-risk group (p < 0.001)."}, "evidence": [{"asset": "source:pmid:36147506", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "These three lncRNA pairs were used as prognostic indicators to construct a prognostic risk model, and the predicted risk scores were calculated."}, {"asset": "source:pmid:36147506", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The overall survival (OS) in the high-risk group was significantly worse than that in the low-risk group (p < 0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A prognostic risk model based on three immune-related lncRNA pairs separated ESCC patients into high- and low-risk groups with significantly different overall survival (p < 0.001).", "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:36147506"], "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:GSE45670"], "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:GSE45670"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36147506", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.921902", "sha256": 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Methods: Microarray data (GSE45670) related to radioresistance o", "sed lncRNAs as prognostic variables, and the microarray dataset (GSE53625) was downloaded from GEO t"], "order_basis": "paper_text_order:456", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ed using ECA 109 and ECA 109R cell lines via RT-qPCR. Results: 26 ir-lncRNA genes were screened i", "using ECA 109 and ECA 109R cell lines via RT-qPCR. Results: 26 ir-lncRNA genes were screened i", "expression levels of ir-lncRNAs detected by RT-qPCR, *p < 0.05, **p < 0.01, ***p < 0.001. Click", "pression levels of ir-lncRNAs detected by RT-qPCR, *p < 0.05, **p < 0.01, ***p < 0.001. Click"], "order_basis": "paper_text_order:1294", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Univariate and multivariate Cox regression survival analyses were performed using the “survival” package in R 4.1.2 software to screen ir-lncRNAs with significant impact on prognosis. The least absolute shrinkage and selection operator (LASSO) regression analysis was carried out to narrow down the prognostically significant ir-lncRNA pairs."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}