Li Wenshuai, Zhan Yingxuan, Peng Chong, Wang Zhan, Xu Tiantian, Liu Mingjun
Department of Clinical Laboratory, Key Laboratory of Laboratory Medicine, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, China.
Funct Integr Genomics. 2023 Mar 20;23(2):91. doi: 10.1007/s10142-023-01029-9.
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.
构建了一种基于长链非编码RNA（lncRNA）对且独立于表达定量的模型，以评估黑色素瘤的预后及黑色素瘤对免疫疗法的反应。从癌症基因组图谱（The Cancer Genome Atlas）和基因型-组织表达（Genotype-Tissue Expression）数据库中检索并下载了RNA测序数据和临床信息。我们鉴定了差异表达的免疫相关lncRNA（DEirlncRNA），将它们进行配对，并使用最小绝对收缩和选择算子以及Cox回归构建预测模型。使用受试者工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。将该模型对预后的预测效能与临床数据和ESTIMATE（利用表达数据估计恶性肿瘤组织中的基质和免疫细胞）进行比较。然后，我们分析了风险评分与临床特征、免疫细胞浸润、抗肿瘤和促肿瘤活性之间的相关性。还评估了高风险组和低风险组在生存率、免疫细胞浸润程度以及抗肿瘤和促肿瘤活性强度方面的差异。建立了一个基于21对DEirlncRNA的模型。与ESTIMATE评分和临床数据相比，该模型能够更好地预测黑色素瘤患者的预后。对该模型有效性的随访分析表明，与低风险组患者相比，高风险组患者的预后较差，且从免疫疗法中获益的可能性较小。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。通过对DEirlncRNA进行配对，我们构建了一个模型来评估皮肤黑色素瘤的预后，而不依赖于lncRNA的特定表达水平。
