A Novel Four Genes of Prognostic Signature for Uveal Melanoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35422861", "kind": "science-record", "revision": 3, "content_hash": "sha256:f71e376b7fdf23499ee47bbb96acc84d74e10743822ebe3979b41a56cf69d575", "created_at": "2026-11-01T18:14:15Z", "updated_at": "2026-11-01T21:27:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Four Genes of Prognostic Signature for Uveal Melanoma.", "doi": "10.1155/2022/8281067", "pmid": "35422861", "authors": ["Yan Liu", "Huibin Du", "Qi Wan", "Yan He", "Wei Lu", "Wenhao Wang", "Xiaohui Lv"], "journal": "Journal of oncology", "year": 2022, "abstract": "Autophagy and immunity play critical roles in various cancers, but the prognostic impact of autophagy and immunity for uveal melanoma (UM) remains lacking. Therefore, the RNA sequencing of data in the TCGA-UVM dataset was downloaded from UCSC Xena database. The prognostic autophagy- and immunity-related genes (AIRGs) were selected via univariate Cox regression. Next, we applied LASSO method to construct four genes of signature in the TCGA-UVM and verified in another two GEO datasets (GSE84976 and GSE22138). This signature intimately associated with overall survival (OS) time and metastasis-free survival (MFS) time of UM, which could be considered as a prognostic indicator. Besides, by applying risk assessment, the patients of UM can be divided into two subgroups (high/low risk) with different survival time, distinct clinical outcomes, and immune microenvironments. Gene set enrichment analysis (GSEA) manifested that cancer hallmark epithelial-mesenchymal transition and KRAS pathways were positively activated in the high-risk group. Moreover, the high-risk group could be more sensitive to chemotherapies than the low-risk group. Thus, our finding suggested that the four genes of signature closely linked with UM risk and survival can afford more accurate survival prediction and potential therapeutic targets for clinical application.", "claims": [{"id": "claim-method", "statement": "The univariate cox regression and Benjamini and Hochberg (BH) correcting methods were used to select prognostic AIRGs (adjust p values < 0.05 and HR > = 1.10 | HR < = 0.90). Next, we used LASSO algorithm to develop risk model via the prognostic AIRGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35422861", "locator": "sections[5] \"2.3. Identification of Prognostic AIRG Model\" paragraph 2", "quote": "The univariate cox regression and Benjamini and Hochberg (BH) correcting methods were used to select prognostic AIRGs (adjust p values < 0.05 and HR > = 1.10 | HR < = 0.90). Next, we used LASSO algorithm to develop risk model via the prognostic AIRGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated for this study can be found in the GEO database (GSE22138 and GSE84976; https://www.ncbi.nlm.nih.gov/geo/) and UCSC Xena website (TCGA-UVM; https://gdc. http://xenahubs.net).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35422861", "locator": "sections[20] \"Data Availability\" paragraph 2", "quote": "The datasets generated for this study can be found in the GEO database (GSE22138 and GSE84976; https://www.ncbi.nlm.nih.gov/geo/) and UCSC Xena website (TCGA-UVM; https://gdc. http://xenahubs.net)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A four-gene prognostic signature comprising PRKCD, MPL, EREG, and JAG2 classified uveal melanoma patients into high- and low-risk groups with significant survival differences and independently predicted overall or metastasis-free survival.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "According to the multivariate Cox regression analyses, only the risk score was stably remained independent prediction for OS or MFS in the TCGA-UVM dataset (HR = 34.951, 95% CI = 4.891–249.759, p < 0.001), GSE22138 dataset (HR = 45.623, 95% CI = 10.025–564.298, p < 0.001), and GSE84976 dataset (HR = 3.532, 95% CI = 1.783–9.276, p = 0.042)."}, "evidence": [{"asset": "source:pmid:35422861", "locator": "sections[12] \"3.2. Discovery and Validation of the Four Genes of Prognostic Signature\" paragraph 2", "quote": "Moreover, based on the 5-year AUCs of different gene combinations, we found that 4 genes of signature (PRKCD, MPL, EREG, and JAG2) were arrived the max value of 0.829 (Figures 2(d) and 2(e))."}, {"asset": "source:pmid:35422861", "locator": "sections[12] \"3.2. Discovery and Validation of the Four Genes of Prognostic Signature\" paragraph 2", "quote": "The curves of the Kaplan-Meier (KM) survival analysis manifested that high-risk patients have worse prognosis than those in the low-risk with log-rank test p value < 0.001 (Figure 3(a))."}, {"asset": "source:pmid:35422861", "locator": "sections[13] \"3.3. Performance Evaluation of Four Genes Signature\" paragraph 2", "quote": "According to the multivariate Cox regression analyses, only the risk score was stably remained independent prediction for OS or MFS in the TCGA-UVM dataset (HR = 34.951, 95% CI = 4.891–249.759, p < 0.001), GSE22138 dataset (HR = 45.623, 95% CI = 10.025–564.298, p < 0.001), and GSE84976 dataset (HR = 3.532, 95% CI = 1.783–9.276, p = 0.042)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A four-gene prognostic signature comprising PRKCD, MPL, EREG, and JAG2 classified uveal melanoma patients into high- and low-risk groups with significant survival differences and independently predicted overall or metastasis-free survival.", "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:35422861"], "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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Therefore, the RNA sequencing of data in the TCGA-UVM dataset was download"], "order_basis": "paper_text_order:189", "id": "assay:1"}, {"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": ["The univariate cox regression and Benjamini and Hochberg (BH) correcting methods were used to select prognostic AIRGs (adjust p values < 0.05 and HR > = 1.10 | HR < = 0.90). Next, we used LASSO algorithm to develop risk model via the prognostic AIRGs."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}