The integration of single-cell sequencing, TCGA, and GEO data analysis revealed that PRRT3-AS1 is a biomarker and therapeutic target of SKCM.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36211371", "kind": "science-record", "revision": 3, "content_hash": "sha256:f73f04908059514e38411649fc0c21e160a6c79196426abd5226f347104ae954", "created_at": "2026-11-01T18:15:32Z", "updated_at": "2026-11-01T21:29:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The integration of single-cell sequencing, TCGA, and GEO data analysis revealed that PRRT3-AS1 is a biomarker and therapeutic target of SKCM.", "doi": "10.3389/fimmu.2022.919145", "pmid": "36211371", "authors": ["Wancong Zhang", "Xuqi Xie", "Zijian Huang", "Xiaoping Zhong", "Yang Liu", "Kit-Leong Cheong", "Jianda Zhou", "Shijie Tang"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Introduction: Skin cutaneous melanoma (SKCM) is the world's fourth deadliest cancer, and advanced SKCM leads to a poor prognosis. Novel biomarkers for SKCM diagnosis and prognosis are urgently needed. Long non-coding RNAs (lncRNAs) provide various biological functions and have been proved to play a significant role in tumor progression. Single-cell RNA sequencing (scRNA-seq) enables genome analysis at the single-cell level. This study explored prognostic lncRNAs in SKCM based on scRNA-seq and bulk RNA sequencing data.\nMaterials and methods: The TCGA cohort and melanoma samples in the GEO database (GSE72056, GSE19234, GSE15605, GSE7553, and GSE81383) were included in this study. Marker genes were filtered, and ensemble lncRNAs were annotated. The clinical significance of selected lncRNAs was verified through TCGA and GEO dataset analysis. SiRNA transfection, wound-healing and transwell assays were performed to evaluate the effect of PRRT3-AS1 on cellular function. Immune infiltration of the selected lncRNAs was also exhibited.\nResults: A 5-marker-lncRNAs model of significant prognostic value was constructed based on GSE72056 and the TCGA cohort. PRRT3-AS1 combined with DANCR was then found to provide significant prognostic value in SKCM. PRRT3-AS1 was filtered for its higher expression in more advanced melanoma and significant prognosis value. Cellular function experiments in vitro revealed that PRRT3-AS1 may be required for cancer cell migration in SKCM. PRRT3-AS1 was found to be related to epithelial-mesenchymal transition (EMT) signaling pathways. DNA methylation of PRRT3-AS1 was negatively related to PRRT3-AS1 expression and showed significant prognosis value. In addition, PRRT3-AS1 may suppress immune infiltration and be involved in immunotherapy resistance.\nConclusion: PRRT3-AS1 may be a diagnostic and prognostic biomarker of SKCM.", "claims": [{"id": "claim-method", "statement": "LASSO Cox regression analysis showed that 5 lncRNAs model including DANCR, NORAD, PRRT3-AS1, NEAT1, and DSCR8 meet the best degree of fitting.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36211371", "locator": "sections[15] \"A 5-marker-lncRNAs model of significant prognostic value was constructed\" paragraph 2", "quote": "LASSO Cox regression analysis showed that 5 lncRNAs model including DANCR, NORAD, PRRT3-AS1, NEAT1, and DSCR8 meet the best degree of fitting."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36211371", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper used LASSO Cox regression analysis to construct a five-lncRNA prognostic model comprising DANCR, NORAD, PRRT3-AS1, NEAT1, and DSCR8.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36211371", "locator": "sections[15] \"A 5-marker-lncRNAs model of significant prognostic value was constructed\" paragraph 2", "quote": "LASSO Cox regression analysis showed that 5 lncRNAs model including DANCR, NORAD, PRRT3-AS1, NEAT1, and DSCR8 meet the best degree of fitting."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The paper used LASSO Cox regression analysis to construct a five-lncRNA prognostic model comprising DANCR, NORAD, PRRT3-AS1, NEAT1, and DSCR8.", "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:36211371"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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Single-cell RNA sequencing (scRNA-seq) enables genome analysis at the s", "mor progression. Single-cell RNA sequencing (scRNA-seq) enables genome analysis at the single-cell", "explored prognostic lncRNAs in SKCM based on scRNA-seq and bulk RNA sequencing data. Materials and", "ration analysis of the model A total of 4645 single-cell RNA-seq data points from 19 patients were included i"], "order_basis": "paper_text_order:436", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["icant role in tumor progression. Single-cell RNA sequencing (scRNA-seq) enables genome analysis at the s", "lncRNAs in SKCM based on scRNA-seq and bulk RNA sequencing data. 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