Department of Urology, Affiliated Hospital of Guilin Medical College Guilin, Guangxi, China.
Centre for Genomic and Personalized Medicine Guangxi Medical University, Nanning, Guangxi, China.
Cancer Biomark. 2023;37(2):95-107. doi: 10.3233/CBM-210445.
Ferroptosis is a recently discovered type of programmed cell death that plays a crucial role in tumor occurrence and progression. However, no prognostic model has been established yet for clear cell renal cell carcinoma (ccRCC) using ferroptosis-related long non-coding RNAs (lncRNAs).
In the present study, lncRNA expression profiles, sex, age, TMN stage, and other clinical data of ccRCC samples were extracted from The Cancer Genome Atlas database. In addition, ferroptosis-related lncRNAs were identified using co-expression analysis, and the risk model was established using Cox regression and least absolute shrinkage and selection operator regression analyses. Log-rank test and Kaplan-Meier analysis were performed to evaluate the predictive accuracy of the risk model for the overall survival (OS) of patients with ccRCC. Moreover, the functional enrichment of ferroptosis-related lncRNAs was performed and visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes.
Eight prognostic ferroptosis-related lncRNAs were identified, such as LINC01615, AC026401.3, LINC00944, AL590094.1, DLGAP1-AS2, AC016773.1, AC147651.1, and AP000439.2, making up the ferroptosis-related lncRNA risk model. The risk model effectively divided patients with ccRCC into high- and low-risk groups, and their survival time was calculated. The high-risk group showed significantly shorter OS compared to the low-risk group. The nomogram to predict the survival rate of the patients revealed that the risk score was the most critical factor affecting OS in patients with ccRCC. The ferroptosis-related lncRNA risk model was an independent predictor of prognostic risk assessment in patients with ccRCC.
The ferroptosis-related lncRNAs risk model and genomic clinicopathological nomogram have the potential to accurately predict the prognosis of patients with ccRCC and could serve as potential therapeutic targets in the future.
铁死亡是一种新近发现的程序性细胞死亡方式,在肿瘤发生和进展中起着至关重要的作用。然而,目前尚未建立基于铁死亡相关长链非编码 RNA(lncRNA)的 clear cell renal cell carcinoma(ccRCC)预后模型。
本研究从 The Cancer Genome Atlas 数据库中提取了 ccRCC 样本的 lncRNA 表达谱、性别、年龄、TMN 分期和其他临床数据。此外,通过共表达分析确定铁死亡相关 lncRNA,并使用 Cox 回归和最小绝对收缩和选择算子回归分析建立风险模型。对数秩检验和 Kaplan-Meier 分析用于评估 ccRCC 患者总体生存(OS)的风险模型预测准确性。此外,使用基因本体论和京都基因与基因组百科全书对铁死亡相关 lncRNA 的功能进行富集和可视化。
确定了 8 个与铁死亡相关的预后 lncRNA,如 LINC01615、AC026401.3、LINC00944、AL590094.1、DLGAP1-AS2、AC016773.1、AC147651.1 和 AP000439.2,构成了铁死亡相关 lncRNA 风险模型。该风险模型有效地将 ccRCC 患者分为高风险和低风险组,并计算了他们的生存时间。高风险组的 OS 明显短于低风险组。预测患者生存率的列线图显示,风险评分是影响 ccRCC 患者 OS 的最关键因素。铁死亡相关 lncRNA 风险模型是评估 ccRCC 患者预后风险的独立预测因子。
铁死亡相关 lncRNA 风险模型和基因组临床病理列线图有可能准确预测 ccRCC 患者的预后,并可能成为未来的潜在治疗靶点。