Discovery and Validation of a Metastasis-Related Prognostic and Diagnostic Biomarker for Melanoma Based on Single Cell and Gene Expression Datasets.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33324561", "kind": "science-record", "revision": 8, "content_hash": "sha256:b70558134301d249faca2e94c440dcd7b6a9deaf05c3732c66ce5a22f2a1e307", "created_at": "2026-11-01T15:41:19Z", "updated_at": "2026-11-01T22:45:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Discovery and Validation of a Metastasis-Related Prognostic and Diagnostic Biomarker for Melanoma Based on Single Cell and Gene Expression Datasets.", "doi": "10.3389/fonc.2020.585980", "pmid": "33324561", "authors": ["Qi Wan", "Chengxiu Liu", "Chang Liu", "Weiqin Liu", "Xiaoran Wang", "Zhichong Wang"], "journal": "Frontiers in oncology", "year": 2020, "abstract": "BACKGROUND: Single cell sequencing can provide comprehensive information about gene expression in individual tumor cells, which can allow exploration of heterogeneity of malignant melanoma cells and identification of new anticancer therapeutic targets.\nMETHODS: Single cell sequencing of 31 melanoma patients in GSE115978 was downloaded from the Gene Expression Omniniub (GEO) database. First, the limma package in R software was used to identify the differentially expressed metastasis related genes (MRGs). Next, we developed a prognostic MRGs biomarker in the cancer genome atlas (TCGA) by combining univariate cox analysis and the least absolute shrinkage and selection operator (LASSO) method and was further validated in another two independent datasets. The efficiency of MRGs biomarker in diagnosis of melanoma was also evaluated in multiple datasets. The pattern of somatic tumor mutation, immune infiltration, and underlying pathways were further explored. Furthermore, nomograms were constructed and decision curve analyses were also performed to evaluate the clinical usefulness of the nomograms.\nRESULTS: In total, 41 MRGs were screened out from 1958 malignant melanoma cell samples in GSE115978. Next, a 5-MRGs prognostic marker was constructed and validated, which show more effective performance for the diagnosis and prognosis of melanoma patients. The nomogram showed good accuracies in predicting 3 and 5 years survival, and the decision curve of nomogram model manifested a higher net benefit than tumor stage and clark level. In addition, melanoma patients can be divided into high and low risk subgroups, which owned differential mutation, immune infiltration, and clinical features. The low risk subgroup suffered from a higher tumor mutation burden (TMB), and higher levels of T cells infiltrating have a significantly longer survival time than the high risk subgroup. Gene Set Enrichment Analysis (GSEA) revealed that the extracellular matrix (ECM) receptor interaction and epithelial mesenchymal transition (EMT) were the most significant upregulated pathways in the high risk group.\nCONCLUSIONS: We identified a robust MRGs marker based on single cell sequencing and validated in multiple independent cohort studies. Our finding provides a new clinical application for prognostic and diagnostic prediction and finds some potential targets against metastasis of melanoma.", "claims": [{"id": "claim-method", "statement": "Finally, the cell samples were divided into metastasis and primary tumor group. The MRGs were screened by conducting Limma package of Bioconductor.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33324561", "locator": "sections[4] \"Single-Cell Data Processing and MRGs Screening\" paragraph 2", "quote": "Finally, the cell samples were divided into metastasis and primary tumor group. 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NA means not available.", "structure": {"pmcid": "PMC7722782", "section_index": 3, "section_title": "Data Collection", "row_count": 8, "column_count": 9}, "table": {"label": "Table 1", "caption": "Summary of data sets used in this research. NA means not available.", "rows": [["Data set", "Platform", "Sample size (n)", "Median age (year)", "Sex (male%)", "Metastasis (%)", "Survival terms", "Purpose", "References"], ["GSE115978", "Illumina NextSeq 500", "31", "67", "70.96", "90.32", "NA", "Exploration", "(14)"], ["TCGA-SKCM", "Illumina HiSeqV2", "456", "58.15", "62.05", "77.78", "OS, DSS", "Construction", "(15)"], ["GSE65904", "Illumina HumanHT-12 V4.0", "214", "62.35", "57.94", "NA", "DSS", "Validation", "(16)"], ["GSE46517", "Affymetrix Human Genome U133A Array", "104", "58.19", "72.54", "70.19", "NA", "Validation", "(17)"], ["GSE8401", "Affymetrix Human Genome U133A Array", "83", "NA", "NA", "62.66", "NA", "Validation", "(18)"], ["GSE15605", "Affymetrix Human Genome U133 Plus 2.0 Array", "58", "59.27", "65.51", "20.69", "NA", "Validation", "(19)"], ["COHORT", "NA", "112", "NA", "50", "NA", "PFS", "Validation", "( 15 )"]], "row_count": 8, "column_count": 9, "numeric_cells": 40, "header_rows": 1, "locator": "section[3]/table-wrap[1]#T1"}}, {"id": "asset:table:pmid:33324561:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:5a0a00ef084a7dece4120c335b6d3574e178af9d57f85bfed45a397ab1cf196a", "bytes": 1085, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[13]/table-wrap[1]#T2", "version": null, "note": "The subgroups analysis of clinical characteristics between low and high risk groups.", "structure": {"pmcid": "PMC7722782", "section_index": 13, "section_title": "Subgroups analysis Somatic Mutation, Immune Microenvironment, and Clinical Characteristics", "row_count": 18, "column_count": 4}, "table": {"label": "Table 2", "caption": "The subgroups analysis of clinical characteristics between low and high risk groups.", "rows": [["", "High_risk", "Low_risk", "p-value"], ["n", "227", "226", ""], ["melanoma_clark_level_value (%)", "", "", "0.011"], ["I", "1 (0.7)", "0 (0.0)", ""], ["II", "6 (3.9)", "12 (7.5)", ""], ["III", "27 (17.6)", "49 (30.8)", ""], ["IV", "87 (56.9)", "79 (49.7)", ""], ["V", "32 (20.9)", "19 (11.9)", ""], ["pathologic_M = M1 (%)", "12 (5.7)", "11 (5.1)", "0.972"], ["pathologic_N = N2-3 (%)", "54 (24.8)", "49 (22.7)", "0.691"], ["pathologic_T = T3-4 (%)", "131 (60.9)", "109 (51.7)", "0.067"], ["gender = male (%)", "147 (64.8)", "133 (58.8)", "0.231"], ["race = white (%)", "212 (95.9)", "219 (98.6)", "0.141"], ["tumor_stage = StageIII-IV (%)", "94 (44.5)", "97 (47.1)", "0.673"], ["metastasis_type = Primary Tumor (%)", "58 (25.7)", "42 (18.6)", "0.089"], ["OS = dead (%)", "127 (55.9)", "86 (38.1)", "<0.001"], ["DSS = dead (%)", "110 (49.8)", "77 (34.1)", "0.001"], ["AGE = >60 (%)", "115 (50.7)", "94 (41.6)", "0.066"]], "row_count": 18, "column_count": 4, "numeric_cells": 46, "header_rows": 1, "locator": "section[13]/table-wrap[1]#T2"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Finally, the cell samples were divided into metastasis and primary tumor group. 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