基于长非编码RNA（lncRNA）的模型，独立于表达量量化，用于评估皮肤黑色素瘤的预后和对免疫治疗的反应。从癌症基因组学图谱和基因型-组织表达库中提取和下载RNA测序数据和临床信息。我们识别差异表达的免疫相关lncRNA（DEirlncRNAs），将它们匹配，使用最小绝对值收缩和选择算子以及Cox回归构建预测模型。利用接收器操作特征曲线确定模型的最佳截断值，并用于将黑色素瘤病例分为高风险和低风险组。将模型对预后的预测效力与临床数据和ESTIMATE（利用表达数据评估恶性肿瘤组织中基质和免疫细胞的估计）进行比较。然后，我们分析了风险评分与临床特征、免疫细胞入侵、抗肿瘤和促肿瘤活性之间的相关性。还评估了高风险和低风险组的生存差异、免疫细胞浸润程度和抗肿瘤和促肿瘤活性的强度差异。建立了基于21个DEirlncRNA对的模型。与ESTIMATE得分和临床数据相比，该模型可以更好地预测黑色素瘤患者的预后。模型有效性的随访分析表明，高风险组患者的预后较差，并且不太可能从免疫治疗中受益。此外，高风险组和低风险组之间的肿瘤浸润免疫细胞存在差异。通过将DEirlncRNA配对，我们建立了一个模型，独立于特定水平的lncRNA表达量来评估皮肤黑色素瘤的预后。©2023年。作者，独家授权Springer-Verlag GmbH Germany，Springer Nature集团的一部分。

A model based on long non-coding RNA (lncRNA) pairs independent of expression quantification was constructed to evaluate prognosis melanoma and response to immunotherapy in melanoma. RNA sequencing data and clinical information were retrieved and downloaded from The Cancer Genome Atlas and the Genotype-Tissue Expression databases. We identified differentially expressed immune-related lncRNAs (DEirlncRNAs), matched them, and used least absolute shrinkage and selection operator and Cox regression to construct predictive models. The optimal cutoff value of the model was determined using a receiver operating characteristic curve and used to categorize melanoma cases into high-risk and low-risk groups. The predictive efficacy of the model with respect to prognosis was compared with that of clinical data and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data). Then, we analyzed the correlations of risk score with clinical characteristics, immune cell invasion, anti-tumor, and tumor-promoting activities. Differences in survival, degree of immune cell infiltration, and intensity of anti-tumor and tumor-promoting activities were also evaluated in the high- and low-risk groups. A model based on 21 DEirlncRNA pairs was established. Compared with ESTIMATE score and clinical data, this model could better predict outcomes of melanoma patients. Follow-up analysis of the model's effectiveness showed that patients in the high-risk group had poorer prognosis and were less likely to benefit from immunotherapy compared with those in the low-risk group. Moreover, there were differences in tumor-infiltrating immune cells between the high-risk and low-risk groups. By pairing the DEirlncRNA, we constructed a model to evaluate the prognosis of cutaneous melanoma independent of a specific level of lncRNA expression.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.