Ping Shuai, Wang Siyuan, He Jinbing, Chen Jianghai
Department of Orthopaedics, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430077, People's Republic of China.
Department of Hand Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.
Pharmgenomics Pers Med. 2021 Jun 3;14:667-681. doi: 10.2147/PGPM.S310299. eCollection 2021.
Skin cutaneous melanoma (SKCM) is the most aggressive skin cancer that results in high morbidity and mortality rate worldwide. Immune-related long non-coding RNAs (IRlncRs) play an important role in regulating gene expression in tumors. Therefore, in this study, we aimed to identify IRlncRs signature that could predict prognosis and therapeutic targets for melanoma irrespective of the gene expression levels.
RNA-sequencing data were obtained from The Cancer Genome Atlas (TCGA). IRlncRs were identified using co-expression analysis and recognized using univariate analysis. The impact of IRlncRs on survival was analyzed using a modified least absolute shrinkage and selection operator (Lasso) regression model. A 1-year survival receiver operating characteristic curve was constructed, and the area under the curve was calculated to identify the optimal cut-off point to distinguish between high and low-risk groups in patients with SKCM. Furthermore, integrative analysis was performed to identify the impact of clinicopathological features, chemotherapeutic treatment, tumor-infiltrating immune cells, and mutant genes on survival.
A total of 28 IRlncRs significantly associated with survival were identified. Seventeen IRlncRs pairs were used to build a survival risk model that could be used to distinguish between low and high-risk groups. The high-risk group was negatively associated with tumor-infiltrating immune cells and had a higher half inhibitory centration for chemotherapeutic agents such as cisplatin and vinblastine. Additionally, the high-risk group had a positive correlation with the expression of specific mutant genes such as BRAF and KIT.
Our findings demonstrate that some IRlncRs have a significant correlation with survival and therapeutic targets for SKCM patients and may provide new insight into the clinical diagnosis and treatment strategies for SKCM patients.
皮肤黑色素瘤(SKCM)是最具侵袭性的皮肤癌,在全球范围内导致高发病率和死亡率。免疫相关长链非编码RNA(IRlncRs)在调节肿瘤基因表达中起重要作用。因此,在本研究中,我们旨在识别可预测黑色素瘤预后和治疗靶点的IRlncRs特征,而不考虑基因表达水平。
从癌症基因组图谱(TCGA)获得RNA测序数据。使用共表达分析鉴定IRlncRs,并通过单变量分析识别。使用改良的最小绝对收缩和选择算子(Lasso)回归模型分析IRlncRs对生存的影响。构建1年生存受试者工作特征曲线,并计算曲线下面积以确定区分SKCM患者高风险和低风险组的最佳截断点。此外,进行综合分析以确定临床病理特征、化疗治疗、肿瘤浸润免疫细胞和突变基因对生存的影响。
共鉴定出28个与生存显著相关的IRlncRs。使用17对IRlncRs构建了一个生存风险模型,可用于区分低风险和高风险组。高风险组与肿瘤浸润免疫细胞呈负相关,对顺铂和长春碱等化疗药物的半数抑制浓度较高。此外,高风险组与BRAF和KIT等特定突变基因的表达呈正相关。
我们的研究结果表明,一些IRlncRs与SKCM患者的生存和治疗靶点显著相关,可能为SKCM患者的临床诊断和治疗策略提供新的见解。