Development of a 3-MicroRNA Signature and Nomogram for Predicting the Survival of Patients with Uveal Melanoma Based on TCGA and GEO Databases.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36457950", "kind": "science-record", "revision": 2, "content_hash": "sha256:6fc945903aa24966cd5c851f716170c05b1a0d385335f1e650af373003327e25", "created_at": "2026-11-01T19:00:56Z", "updated_at": "2026-11-01T21:29:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development of a 3-MicroRNA Signature and Nomogram for Predicting the Survival of Patients with Uveal Melanoma Based on TCGA and GEO Databases.", "doi": "10.1155/2022/9724160", "pmid": "36457950", "authors": ["Jun Zuo", "Hongquan Ye", "Jing Tang", "Jianqun Lu", "Qi Wan"], "journal": "Journal of ophthalmology", "year": 2022, "abstract": "Background: The aim of this study was to apply bioinformatic analysis to develop a robust miRNA signature and construct a nomogram model in uveal melanoma (UM) to improve prognosis prediction.\nMethods: miRNA and mRNA sequencing data for 80 UM patients were obtained from The Cancer Genome Atlas (TCGA) database. The patients were further randomly assigned to a training set (n = 40, used to identify key miRNAs) and a testing set (n = 40, used to internally verify the signature). Then, miRNAs data of GSE84976 and GSE68828 were downloaded from Gene Expression Omnibus (GEO) database for outside verification. Combining univariate analysis and LASSO methods for identifying a robust miRNA biomarker in training set and the signature was validated in testing set and outside dataset. A prognostic nomogram was constructed and combined with decision curve as well as reduction curve analyses to assess the application of clinical usefulness. Finally, we constructed a miRNA-mRNA regulator network in UM and conducted pathway enrichment analysis according to the mRNAs in the network.\nResults: In total, a 3-miRNA was identified and validated that can robustly predict UM patients' survival. According to univariate and multivariate cox analyses, age at diagnosis, tumor node metastasis (TNM) classification, stage, and the 3-miRNA signature significantly correlated with the survival outcomes. These characteristics were used to establish nomogram. The nomogram worked well for predicting 1 and 3 years of overall survival time. The decision curve of nomogram revealed a good clinical usefulness of our nomogram. What's more, a miRNA-mRNA network was constructed. Pathway enrichment showed that this network was largely involved in mRNA processing, the mRNA surveillance pathway, the spliceosome, and so on.\nConclusions: We developed a 3-miRNA biomarker and constructed a prognostic nomogram, which may afford a quantitative tool for predicting the survival of UM. Our finding also provided some new potential targets for the treatment of UM.", "claims": [{"id": "claim-method", "statement": "To explore the associations between microRNA and the overall survival of UM patients, univariate cox regression was applied to select the potential prognostic microRNAs and mRNAs (p value <0.05). Afterwards, the LASSO method was used to develop prognostic model with these prognostic microRNAs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36457950", "locator": "sections[4] \"2.2. Development and Validation of MicroRNA Signature\" paragraph 2", "quote": "To explore the associations between microRNA and the overall survival of UM patients, univariate cox regression was applied to select the potential prognostic microRNAs and mRNAs (p value <0.05). Afterwards, the LASSO method was used to develop prognostic model with these prognostic microRNAs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated for this study can be found in the GEO databases (GSE84976 and GSE68828; https://www.ncbi.nlm.nih.gov/geo/) and UCSC Xena website (TCGA-UVM; https://gdc.xenahubs.net).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36457950", "locator": "sections[19] \"Data Availability\" paragraph 2", "quote": "The datasets generated for this study can be found in the GEO databases (GSE84976 and GSE68828; https://www.ncbi.nlm.nih.gov/geo/) and UCSC Xena website (TCGA-UVM; https://gdc.xenahubs.net)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We developed a 3-miRNA biomarker and constructed a prognostic nomogram, which may afford a quantitative tool for predicting the survival of UM.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36457950", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We developed a 3-miRNA biomarker and constructed a prognostic nomogram, which may afford a quantitative tool for predicting the survival of UM."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We developed a 3-miRNA biomarker and constructed a prognostic nomogram, which may afford a quantitative tool for predicting the survival of UM.", "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:36457950"], "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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