Abstract

Backgrounds

Among tumor microenvironment, the immune components in it have an important influence on gene expression and clinical efficacy. We aim to find out the role of those in skin cutaneous melanoma (SKCM).

Objectives

Gene expression profile and homologous clinical information of SKCM patients were obtained by TCGA (The Cancer Genome Atlas) and UCSC Toil. SsGSEA method was used to evaluate the immune cell infiltration of 468 TCGA-SKCM samples divided into high immune cell infiltration group (HICI) and low immune cell infiltration group (LICI). We used the Edger packet to conduct difference analysis on normal samples (GTEx) and cancer samples (TCGA), and combined it with the difference of the HICI group and LICI group, to find out the common differential expression of lncRNA in both groups. The prognostic value of immune-related lncRNAs was studied by univariate Cox, Lasso-Cox and multivariate Cox regression analysis, and a prognostic model was established. C index and calibration diagram were used to judge the accuracy of the model, and DCA was used to judge the net benefit.

Results

Six prognostic markers of immune-related lncRNA genes were established, which could be used as independent prognostic factors. The net benefit and prediction accuracy are significantly higher than TNM Stage.

Conclusion

The prognostic model identified in this study is a reliable biomarker for SKCM. The Nomogram survival prediction model based on it is a reliable way to predict the median survival time of patients, which may lay the foundation for future treatment of this disease.

中文翻译：

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皮肤黑色素瘤的新型免疫相关 LncRNA 预后特征

摘要

背景

在肿瘤微环境中，其中的免疫成分对基因表达和临床疗效具有重要影响。我们的目标是找出它们在皮肤黑色素瘤 (SKCM) 中的作用。

目标

通过TCGA（癌症基因组图谱）和UCSC Toil获得SKCM患者的基因表达谱和同源临床信息。采用SsGSEA方法评估468份TCGA-SKCM样本的免疫细胞浸润情况，分为高免疫细胞浸润组（HICI）和低免疫细胞浸润组（LICI）。我们利用Edger包对正常样本（GTEx）和癌症样本（TCGA）进行差异分析，并结合HICI组和LICI组的差异，找出两组lncRNA共同的差异表达。通过单变量Cox、Lasso-Cox和多变量Cox回归分析研究免疫相关lncRNA的预后价值，并建立预后模型。使用C指数和校准图来判断模型的准确性，并使用DCA来判断净效益。

结果

建立了免疫相关lncRNA基因的6个预后标志物，可作为独立的预后因素。净效益和预测精度明显高于TNM Stage。

结论

本研究中确定的预后模型是 SKCM 的可靠生物标志物。基于其的诺模图生存预测模型是预测患者中位生存时间的可靠方法，可能为未来治疗该疾病奠定基础。