A novel immune-related LncRNA prognostic signature for cutaneous melanoma

• Published in: Molecular & cellular toxicology 2024, 20(2), , pp.377-387
• Main Authors: Hu, Nan, Huang, Cancan, He, Yancheng, Li, Shuyang, Yuan, Jingyi, Zhong, Guishu, Chen, Yan
• Format: Article
• Language: English
• Subjects: Biomedical and Life Sciences; Cell Biology; Gene expression; Genomes; Infiltration; Life Sciences; Medical prognosis; Melanoma; Metastases; Non-coding RNA; Original Article; Patients; Pharmacology/Toxicology; Prediction models; Tumor microenvironment; 생물학
• ISSN: 1738-642X; 2092-8467
• DOI: 10.1007/s13273-023-00351-4

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.