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pmid:39121828
Transcriptomic profiling identifies a nucleotide metabolism-related signature with prognostic power in gliomas.
Ai-Qin Chen · Qi-Xuan Jiang · Yong-Jian Zhu · Qiang-Wei Wang
Translational oncology · 2024 doi:10.1016/j.tranon.2024.102068
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Experiment
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Complete file
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We aimed to comprehensively analyze the nucleotide metabolism-related gene set and clinical significance in gliomas.\nMETHODS: The RNA sequencing data of 702 gliomas from the Cancer Genome Atlas (TCGA) dataset were included as the training set, and the RNA sequencing data from the other three datasets (CGGA, GSE16011, and Rembrandt) were used as independent validation sets. Survival curve, Cox regression analysis, time-dependent ROC curve and nomogram model were performed to evaluate prognostic power of signature. R language was the main tool for bioinformatic analysis and graphical work.\nRESULTS: Based on the expression profiles of nucleotide metabolism-related genes, consensus clustering identified two robust clusters with different prognosis. We then developed a nucleotide metabolism-related signature that was closely related to clinical, pathological, and genomic characteristics of gliomas. And ROC curve showed that our signature was a potential biomarker for mesenchymal subtype. Survival curve and Cox regression analysis revealed signature as an independent prognostic factor for gliomas. In addition, we constructed a nomogram model to predict individual survival. Finally, functional analysis showed that nucleotide metabolism not only affected cell division and cell cycle, but also was associated with immune response in gliomas.\nCONCLUSION: We developed a nucleotide metabolism-related signature to predict prognosis and provided new insights into the role of nucleotide metabolism in gliomas.", "claims": [{"id": "claim-method", "statement": "In addition, univariate and multivariate Cox regression analysis showed that the signature risk score (HR, 2.589; 95 % CI, 1.649–4.063; p = 3.54E-05) was an independent prognostic indicator of glioma in TCGA dataset (Table 1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39121828", "locator": "sections[13] \"Prognostic analysis of nucleotide metabolism-related signature\" paragraph 2", "quote": "In addition, univariate and multivariate Cox regression analysis showed that the signature risk score (HR, 2.589; 95 % CI, 1.649–4.063; p = 3.54E-05) was an independent prognostic indicator of glioma in TCGA dataset (Table 1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "RNA sequencing data from TCGA, CGGA, GSE16011 and Rembrandt datasets are available in a public, open access repository. 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Our single-cell sequencing data is unsuitable to post due to containing sensitive patient information."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The nucleotide metabolism-related signature was an independent prognostic factor for gliomas.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39121828", "locator": "sections[0] \"Abstract\" paragraph 9", "quote": "Survival curve and Cox regression analysis revealed signature as an independent prognostic factor for gliomas."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The nucleotide metabolism-related signature was an independent prognostic factor for gliomas.", "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:39121828"], "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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