DataSheet1_Identification of Transcriptional Heterogeneity and Construction of a Prognostic Model for Melanoma Based on Single-Cell and Bulk Transcriptome Analysis.docx

Melanoma is one of the most aggressive and heterogeneous life-threatening cancers. However, the heterogeneity of melanoma and its impact on clinical outcomes are largely unknown. In the present study, intra-tumoral heterogeneity of melanoma cell subpopulations was explored using public single-cell RNA sequencing data. Marker genes, transcription factor regulatory networks, and gene set enrichment analysis were further analyzed. Marker genes of each malignant cluster were screened to create a prognostic risk score, and a nomogram tool was further generated to predict the prognosis of melanoma patients. It was found that malignant cells were divided into six clusters by different marker genes and biological characteristics in which the cell cycling subset was significantly correlated with unfavorable clinical outcomes, and the Wnt signaling pathway-enriched subset may be correlated with the resistance to immunotherapy. Based on the malignant marker genes, melanoma patients in TCGA datasets were divided into three groups which had different survival rates and immune infiltration states. Five malignant cell markers (PSME2, ARID5A, SERPINE2, GPC3, and S100A11) were selected to generate a prognostic risk score. The risk score was associated with overall survival independent of routine clinicopathologic characteristics. The nomogram tool showed good performance with an area under the curve value of 0.802.

黑色素瘤(Melanoma)是侵袭性最强、异质性最高的致死性恶性肿瘤之一。然而，黑色素瘤的异质性及其对临床结局的影响目前仍未得到充分阐明。本研究利用公开的单细胞RNA测序(single-cell RNA sequencing)数据，探究了黑色素瘤细胞亚群的瘤内异质性。研究人员进一步对标记基因、转录因子调控网络及基因集富集分析(gene set enrichment analysis)结果展开了深入解析。研究人员筛选各恶性细胞簇的标记基因以构建预后风险评分模型，并进一步生成列线图(nomogram)工具，用于预测黑色素瘤患者的预后情况。研究发现，基于不同标记基因与生物学特征，恶性细胞可被分为6个细胞簇：其中细胞周期亚群与不良临床结局显著相关，而富集Wnt信号通路(Wnt signaling pathway)的亚群可能与免疫治疗(immunotherapy)耐药性存在关联。基于上述恶性标记基因，TCGA(The Cancer Genome Atlas)数据集中的黑色素瘤患者被分为3组，各组具有不同的生存率与免疫浸润状态。研究筛选出5个恶性细胞标记基因（PSME2、ARID5A、SERPINE2、GPC3及S100A11）以构建预后风险评分模型。该风险评分与患者总生存期显著相关，且不受常规临床病理特征的影响。该列线图工具表现优异，其曲线下面积(area under the curve, AUC)达0.802。