Implications of CRNDE in prognosis, tumor immunity, and therapeutic sensitivity in low grade glioma patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37194105", "kind": "science-record", "revision": 3, "content_hash": "sha256:0ad7221f89f36b42992cb17f696cd58911ec7385b7a4f9ccab80e742bc0e7d4e", "created_at": "2026-11-01T19:01:53Z", "updated_at": "2026-11-01T22:09:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Implications of CRNDE in prognosis, tumor immunity, and therapeutic sensitivity in low grade glioma patients.", "doi": "10.1186/s12935-023-02930-w", "pmid": "37194105", "authors": ["Chen Yang", "Yingchuan Jiang", "Fan Hu", "Qiuping Li", "Biao Qi"], "journal": "Cancer cell international", "year": 2023, "abstract": "BACKGROUND: Colorectal tumor differentially expressed (CRNDE) is specifically expressed in human brains and is the most highly expressed lncRNA in gliomas. Nevertheless, its implications in low grade glioma (LGG) are still indistinct. This study presented systematic analyses of CRNDE in LGG biology.\nMETHODS: We retrospectively retrieved TCGA, CGGC and GSE16011 LGG cohorts. Survival analysis was conducted for evaluating the prognostic significance of CRNDE in LGG. A CRNDE-based nomogram was established, and its predictive performance was verified. Signaling pathways underlying CRNDE were analyzed through ssGSEA and GSEA approaches. The abundance of immune cells and activity of cancer-immunity cycle were estimated with ssGSEA approach. Immune checkpoints, HLAs, chemokines, and immunotherapeutic response indicators (TIDE, and TMB) was quantified. U251 and SW1088 cells were transfected with specific shRNAs of CRNDE, and flow cytometry (apoptosis) and western blot (β-catenin and Wnt5a) assays were conducted.\nRESULTS: Up-regulated CRNDE was found in LGG, and was linked to unfavorable clinical outcomes. The CRNDE-based nomogram enabled to accurately predict patients' prognosis. High CRNDE expression was linked to more genomic variations, activity of tumorigenic pathways, tumor immunity (increase in infiltration of immune cells, expression of immune checkpoints, HLAs and chemokines, and cancer-immunity cycle), and therapeutic sensitivity. CRNDE knockdown mitigated malignant phenotypes of LGG cells.\nCONCLUSIONS: Our study determined CRNDE as a novel predictor for patient prognosis, tumor immunity and therapeutic response in LGG. Assessment of CRNDE expression is a promising approach for predicting the therapeutic benefits of LGG patients.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression models were established for computing hazard ratio (HR) and confidence interval (CI) of CRNDE expression. 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CRNDE expression and clinical variables were employed for univariable and multivariate models."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are included within the supplementary information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37194105", "locator": "sections[30] \"Availability of data and materials\" paragraph 2", "quote": "The data used to support the findings of this study are included within the supplementary information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Our study determined CRNDE as a novel predictor for patient prognosis, tumor immunity and therapeutic response in LGG.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37194105", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Our study determined CRNDE as a novel predictor for patient prognosis, tumor immunity and therapeutic response in LGG."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Our study determined CRNDE as a novel predictor for patient prognosis, tumor immunity and therapeutic response in LGG.", "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:37194105"], "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:GSE16011"], "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:GSE16011"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37194105", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12935-023-02930-w", "sha256": 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