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一个新的与细胞焦亡相关的长链非编码 RNA 特征可预测皮肤黑色素瘤的预后并提示肿瘤免疫微环境。

A novel pyroptosis-related LncRNA signature predicts prognosis and indicates tumor immune microenvironment in skin cutaneous melanoma.

机构信息

Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410000, China.

School of Computer Science, Hunan First Normal University, Changsha 410205, China.

出版信息

Life Sci. 2022 Oct 15;307:120832. doi: 10.1016/j.lfs.2022.120832. Epub 2022 Aug 5.

Abstract

AIMS

To explore the correlation between the pyroptosis-related lncRNAs (PRlncRNAs) and the prognosis of skin cutaneous melanoma (SKCM), and clarify the effects of the PRlncRNAs on the tumor immune infiltration.

MAIN METHODS

In this study, we utilized RNA-seq and clinical characteristics data obtained from TCGA and GEO database to perform co-expression analysis and LASSO Cox regression analysis to construct a 12-PRlncRNA prognostic prediction model. We also performed functional analysis, immune infiltration analysis and drug sensitivity analysis, as well as correlation analysis with autophagy/ferroptosis genes, tumor mutational burden, and PD-1/PD-L1 genes.

KEY FINDING

The model based on the 12-PRlncRNA signature could effectively predict the prognosis of SKCM. Low risk group had a higher anti-tumor immune level generally compared with high-risk group. The signature was correlated with the expression of autophagy/ferroptosis-related genes and PD1/PD-L1 genes and tumor mutational burden. Additionally, drug sensitivity analysis indicated potential therapeutic targets.

SIGNIFICANCE

Our study demonstrated the impact of PRlncRNAs on SKCM. The model established based on the 12-PRlncRNA showed significant prognostic value for SKCM and may be instructive in pyroptosis-related targeted therapy in the clinic.

摘要

目的

探讨与细胞焦亡相关的长非编码 RNA(PRlncRNAs)与皮肤黑色素瘤(SKCM)预后的相关性,并阐明 PRlncRNAs 对肿瘤免疫浸润的影响。

主要方法

本研究利用 TCGA 和 GEO 数据库中的 RNA-seq 和临床特征数据进行共表达分析和 LASSO Cox 回归分析,构建了一个由 12 个 PRlncRNA 组成的预后预测模型。我们还进行了功能分析、免疫浸润分析和药物敏感性分析,并与自噬/铁死亡基因、肿瘤突变负担和 PD-1/PD-L1 基因进行了相关性分析。

主要发现

基于 12 个 PRlncRNA 特征的模型可以有效地预测 SKCM 的预后。低风险组的抗肿瘤免疫水平普遍高于高风险组。该特征与自噬/铁死亡相关基因和 PD1/PD-L1 基因以及肿瘤突变负担的表达相关。此外,药物敏感性分析表明存在潜在的治疗靶点。

意义

本研究表明 PRlncRNAs 对 SKCM 有影响。基于 12 个 PRlncRNA 的模型对 SKCM 具有显著的预后价值,可能对临床中与细胞焦亡相关的靶向治疗具有指导意义。

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