Data_Sheet_1_An Immune-Related Long Non-Coding RNA Signature to Predict the Prognosis of Ewing’s Sarcoma Based on a Machine Learning Iterative Lasso Regression.docx

The aim of this study was to construct a new immune-associated long non-coding RNA (lncRNA) signature to predict the prognosis of Ewing sarcoma (ES) and explore its molecular mechanisms. We downloaded transcriptome and clinical prognosis data from the Gene Expression Omnibus (GSE17679, which included 88 ES samples and 18 matched normal skeletal muscle samples), and used it as a training set to identify immune-related lncRNAs with different expression levels in ES. Univariable Cox regression was used to screen immune-related lncRNAs related to ES prognosis, and an immune-related lncRNA signature was constructed based on machine learning iterative lasso regression. An external verification set was used to confirm the predictive ability of the signature. Clinical feature subgroup analysis was used to explore whether the signature was an independent prognostic factor. In addition, CIBERSORT was used to explore immune cell infiltration in the high- and low-risk groups, and to analyze the correlations between the lncRNA signature and immune cell levels. Gene set enrichment and variation analyses were used to explore the possible regulatory mechanisms of the immune-related lncRNAs in ES. We also analyzed the expression of 17 common immunotherapy targets in the high- and low-risk groups to identify any that may be regulated by immune-related lncRNAs. We screened 35 immune-related lncRNAs by univariate Cox regression. Based on this, an immune-related 11-lncRNA signature was generated by machine learning iterative lasso regression. Analysis of the external validation set confirmed its high predictive ability. DPP10 antisense RNA 3 was negatively correlated with resting dendritic cell, neutrophil, and γδ T cell infiltration, and long intergenic non-protein coding RNA 1398 was positively correlated with resting dendritic cells and M2 macrophages. These lncRNAs may affect ES prognosis by regulating GSE17721_CTRL_VS_PAM3CSK4_12H_BMDC_UP, GSE2770_IL4_ACT_VS_ACT_CD4_TCELL_48H_UP, GSE29615_CTRL_VS_DAY3_ LAIV_IFLU_VACCINE_PBMC_UP, complement signaling, interleukin 2-signal transducer and activator of transcription 5 signaling, and protein secretion. The immune-related 11-lncRNA signature may also have regulatory effects on the immunotherapy targets CD40 molecule, CD70 molecule, and CD276 molecule. In conclusion, we constructed a new immune-related 11-lncRNA signature that can stratify the prognoses of patients with ES.

本研究旨在构建一种新型免疫相关长链非编码RNA（long non-coding RNA, lncRNA）预后特征，以预测尤因肉瘤（Ewing sarcoma, ES）患者的预后，并探究其潜在分子机制。我们从基因表达综合数据库（Gene Expression Omnibus, GEO）下载了转录组与临床预后数据（数据集编号GSE17679，包含88例ES样本及18例匹配的正常骨骼肌样本），将其作为训练集以筛选在ES中存在差异表达的免疫相关lncRNA。采用单因素Cox回归分析筛选与ES预后相关的免疫相关lncRNA，并基于机器学习迭代LASSO回归构建免疫相关lncRNA预后特征。使用外部验证集验证该预后特征的预测效能。通过临床特征亚组分析探究该预后特征是否为独立预后因素。此外，采用CIBERSORT分析高低风险组的免疫细胞浸润情况，并分析该lncRNA预后特征与免疫细胞水平的相关性。通过基因集富集分析与变异分析探究免疫相关lncRNA在ES中的潜在调控机制。我们还分析了17种常见免疫治疗靶点在高低风险组中的表达水平，以筛选可能受免疫相关lncRNA调控的靶点。经单因素Cox回归分析共筛选出35个免疫相关lncRNA，在此基础上通过机器学习迭代LASSO回归构建了包含11个免疫相关lncRNA的预后特征。外部验证集分析证实该预后特征具有良好的预测效能。DPP10反义RNA 3与静息树突状细胞、中性粒细胞及γδ T细胞浸润呈负相关，而基因间长链非编码RNA 1398与静息树突状细胞及M2型巨噬细胞呈正相关。这些lncRNA可能通过调控GSE17721_CTRL_VS_PAM3CSK4_12H_BMDC_UP、GSE2770_IL4_ACT_VS_ACT_CD4_TCELL_48H_UP、GSE29615_CTRL_VS_DAY3_LAIV_IFLU_VACCINE_PBMC_UP、补体信号通路、白细胞介素2-信号转导与转录激活因子5信号通路及蛋白质分泌过程影响ES患者的预后。该11个免疫相关lncRNA组成的预后特征还可对免疫治疗靶点CD40分子、CD70分子及CD276分子产生调控作用。综上，本研究构建了一种新型免疫相关11-lncRNA预后特征，可有效区分ES患者的预后分层。