Single-cell RNA sequencing and AlphaFold 3 insights into cytokine signaling and its role in uveal melanoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39916959", "kind": "science-record", "revision": 3, "content_hash": "sha256:cf4788faf21db9a73fa4b45d02e3950488a54bb82a2ba2327a1b54abf7f2e534", "created_at": "2026-11-01T20:30:11Z", "updated_at": "2026-11-01T22:13:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single-cell RNA sequencing and AlphaFold 3 insights into cytokine signaling and its role in uveal melanoma.", "doi": "10.3389/fimmu.2024.1458041", "pmid": "39916959", "authors": ["Hongyan Sun", "Cunzi Li", "Zuhui Pu", "Ying Lu", "Zijing Wu", "Lan Zhou", "Hongzhan Lin", "Yumo Wang", "Tao Zi", "Lisha Mou", "Ming-Ming Yang"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Background: Uveal melanoma (UVM) is a form of eye cancer with a poor prognosis, particularly in metastatic patients. This study aimed to elucidate the cellular heterogeneity within UVM and identify prognostic biomarkers.\nMethods: We performed single-cell RNA sequencing (scRNA-seq) on primary and metastatic UVM samples. A UVM-specific gene signature was constructed using LASSO regression and validated via ROC curve analysis in the TCGA-UVM and GSE84976 cohorts. AlphaFold 3 was used to predict the 3D structures of key proteins. T-cell populations were analyzed using pseudotime trajectory mapping and interaction network visualization. CRISPR-Cas9 screening analysis was conducted to identify hub genes and cytokine pathways that may serve as therapeutic targets. Additionally, we constructed the Dictionary of Immune Responses to Cytokines at single-cell resolution to evaluate cytokine signatures.\nResults: ScRNA-seq revealed five major cell types within UVMs and subdivided them into seven distinct subtypes. Cytokine signaling analysis revealed differential expression of cytokine signaling in immune-related genes (CSIRGs) across these subtypes in primary and metastatic tumors. The UVM-specific gene signature demonstrated high predictive accuracy in ROC curve analysis and was associated with overall survival in Kaplan-Meier survival analyses. Additionally, AlphaFold 3 predicted the 3D structures of key proteins with high confidence. T-cell population analysis revealed complex developmental pathways and interaction networks in UVM. Myeloid-derived suppressor cells (MDSCs) were found to be increased in metastatic UVM, correlating with the enrichment of GM-CSF. CRISPR-Cas9 screening analysis identified hub genes and cytokine pathways with low gene effect scores across cell lines, indicating their potential importance in UVM.\nConclusion: This study identified critical cellular subtypes and prognostic biomarkers in UVM, shedding light on targeted therapies. The insights into cytokine signaling and T-cell dynamics within the UVM microenvironment provide a foundation for developing personalized therapeutic strategies to improve patient outcomes.", "claims": [{"id": "claim-method", "statement": "Kaplan–Meier survival curves were generated to compare overall survival between the CSIRG-high-risk and CSIRG-low-risk groups in both cohorts. Log-rank tests were used to determine the statistical significance of the differences in survival between the two groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39916959", "locator": "sections[15] \"Predictive accuracy and survival analysis of the CSIRG signature in UVM cohorts\" paragraph 3", "quote": "Kaplan–Meier survival curves were generated to compare overall survival between the CSIRG-high-risk and CSIRG-low-risk groups in both cohorts. 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Methods: We performed single-cell RNA sequencing (scRNA-seq) on primary and metastatic UVM sa", "acteristics of UVM, we performed single-cell RNA sequencing (scRNA-seq) analysis of primary and metastat"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Single-cell RNA sequencing and AlphaFold 3 insights into cytokine signa", "prognostic biomarkers. Methods: We performed single-cell RNA sequencing (scRNA-seq) on primary and metastatic UVM sa", "ds: We performed single-cell RNA sequencing (scRNA-seq) on primary and metastatic UVM samples. A UV", "on to evaluate cytokine signatures. 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