Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36568076", "kind": "science-record", "revision": 3, "content_hash": "sha256:df636a2f57b6051c3169d7ceec0b361a2d15cbefeab00240a546c88c0066b04e", "created_at": "2026-11-01T19:01:07Z", "updated_at": "2026-11-01T22:08:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma.", "doi": "10.3389/fendo.2022.1056310", "pmid": "36568076", "authors": ["Hao Chi", "Gaoge Peng", "Jinyan Yang", "Jinhao Zhang", "Guobin Song", "Xixi Xie", "Dorothee Franziska Strohmer", "Guichuan Lai", "Songyun Zhao", "Rui Wang", "Fang Yang", "Gang Tian"], "journal": "Frontiers in endocrinology", "year": 2022, "abstract": "Background: Uveal melanoma (UVM) is the most common primary intraocular malignancy in adults and is highly metastatic, resulting in a poor patient prognosis. Sphingolipid metabolism plays an important role in tumor development, diagnosis, and prognosis. This study aimed to establish a reliable signature based on sphingolipid metabolism genes (SMGs), thus providing a new perspective for assessing immunotherapy response and prognosis in patients with UVM.\nMethods: In this study, SMGs were used to classify UVM from the TCGA-UVM and GEO cohorts. Genes significantly associated with prognosis in UVM patients were screened using univariate cox regression analysis. The most significantly characterized genes were obtained by machine learning, and 4-SMGs prognosis signature was constructed by stepwise multifactorial cox. External validation was performed in the GSE84976 cohort. The level of immune infiltration of 4-SMGs in high- and low-risk patients was analyzed by platforms such as CIBERSORT. The prediction of 4-SMGs on immunotherapy and immune checkpoint blockade (ICB) response in UVM patients was assessed by ImmuCellAI and TIP portals.\nResults: 4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis. Multivariate analysis found that the model was an independent predictor of UVM, with patients in the low-risk group having higher overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores had good prognostic power. The high-risk group showed better results when receiving immunotherapy.\nConclusions: 4-SMGs signature and nomogram showed excellent predictive performance and provided a new perspective for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology studies.", "claims": [{"id": "claim-method", "statement": "We conducted univariate and multivariate Cox regression analyses to assess risk score as an independent prognostic factor.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36568076", "locator": "sections[7] \"Independent prognostic analysis and nomogram construction\" paragraph 2", "quote": "We conducted univariate and multivariate Cox regression analyses to assess risk score as an independent prognostic factor."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are publicly available. This data can be found here: https://github.com/zsylittlecloud/R-code-for-1056310-Machine-learning-to-construct-sphingolipid-metabolism-genes-signature-to-chara.git.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36568076", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are publicly available. This data can be found here: https://github.com/zsylittlecloud/R-code-for-1056310-Machine-learning-to-construct-sphingolipid-metabolism-genes-signature-to-chara.git."}], "state": "complete"}, {"id": "claim-target-result", "statement": "4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36568076", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis.", "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:36568076"], "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:GSE84976"], "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:GSE84976"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36568076", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fendo.2022.1056310", "sha256": "sha256:fa8463e56c62c31fcf6a6fbd452611f7f390045328b0e2930348d6a2430bebea", "bytes": 47155, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fendo.2022.1056310", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9772281", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE84976", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84976", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE84976", "version": null, "note": "The original contributions presented in the study are publicly available. This data can be found here: https://github.com/zsylittlecloud/R-code-for-1056310-Machine-learning-to-construct-sphingolipid-metabolism-genes-signature-to-chara.git.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are publicly available. 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