Cui Yi, Zheng Mi, Chen Jing, Xu Nuo
Department of Ophthalmology, Fujian Medical University Union Hospital, Union Clinical Medical College, Fujian Medical University, Fuzhou, China.
Department of Ophthalmology, Fujian Provincial Hospital, Shengli Clinical Medical College, Fujian Medical University, Fuzhou, China.
Front Genet. 2021 Apr 1;12:625583. doi: 10.3389/fgene.2021.625583. eCollection 2021.
This study aimed to develop an autophagy-associated long non-coding RNA (lncRNA) signature to predict the prognostic outcomes of uveal melanoma (UM). The data of UM from The Cancer Genome Atlas (TCGA) were enrolled to obtain differentially expressed genes (DEGs) between metastasizing and non-metastasizing UM patients. A total of 13 differentially expressed autophagy genes were identified and validated in Gene Expression Omnibus, and 11 autophagy-related lncRNAs were found to be associated with overall survival. Through performing least absolute shrinkage and selection operator regression analyses, a six-autophagy-related lncRNA signature was built, and its efficacy was confirmed by receiver-operating characteristic, Kaplan-Meier analysis, and univariate and multivariate Cox regression analyses. A comprehensive nomogram was established and its clinical net benefit was validated by decision curve analysis. GSEA revealed that several biological processes and signaling pathways including Toll-like receptor signaling pathway, natural killer cell-mediated cytotoxicity, and B- and T-cell receptor signaling pathway were enriched in the high-risk group. CIBERSORT results showed that the signature was related to the immune response especially HLA expression. This signature could be deployed to assist clinicians to identify high-risk UM patients and help scientists to explore the molecular mechanism of autophagy-related lncRNAs in UM pathogenesis.
本研究旨在开发一种自噬相关长链非编码RNA(lncRNA)特征,以预测葡萄膜黑色素瘤(UM)的预后结果。纳入来自癌症基因组图谱(TCGA)的UM数据,以获取转移型和非转移型UM患者之间的差异表达基因(DEG)。共鉴定出13个差异表达的自噬基因,并在基因表达综合数据库中进行了验证,发现11个自噬相关lncRNA与总生存期相关。通过进行最小绝对收缩和选择算子回归分析,构建了一个包含六个自噬相关lncRNA的特征,并通过受试者工作特征曲线、Kaplan-Meier分析以及单因素和多因素Cox回归分析证实了其有效性。建立了一个综合列线图,并通过决策曲线分析验证了其临床净效益。基因集富集分析(GSEA)显示,包括Toll样受体信号通路、自然杀伤细胞介导的细胞毒性以及B细胞和T细胞受体信号通路在内的几个生物学过程和信号通路在高危组中富集。CIBERSORT结果表明,该特征与免疫反应特别是HLA表达相关。该特征可用于协助临床医生识别高危UM患者,并帮助科学家探索自噬相关lncRNA在UM发病机制中的分子机制。