Liu J W, Supandi F, Dhillon S K
Institute of Biological Sciences, Faculty of Science, University Malaya, Kuala Lumpur, Malaysia.
Folia Biol (Praha). 2022;68(1):1-15. doi: 10.14712/fb2022068010001.
Clear cell renal cell carcinoma (ccRCC) is very common and accounts for most kidney cancer deaths. While many studies are being conducted in finding the prognostic signatures of ccRCC, we believe that ferroptosis, which involves programmed cell death dependent on iron accumulation, has therapeutic potential in ccRCC. Recent research has shown that long noncoding RNAs (lncRNAs) are involved in ferroptosis-related tumour processes and are closely related to survival in patients with ccRCC. Hence, in this study we aim to further explore the role of ferroptosis-related lncRNAs (FRLs) in ccRCC, hoping to establish a signature to predict the survival outcome of ccRCC. We analysed transcriptome data from The Cancer Genome Atlas database (TCGA) and ferroptosis-related genes (FRGs) from FerrDb to identify FRLs using Pearson's correlation. Lasso Cox regression analysis and multivariate Cox proportional hazards models screened seventeen optimal FRLs for developing prognostic signatures. Kaplan-Meier survival curves and ROC curves were then plotted for validating the sensitivity, specificity, and accuracy of the identified signatures. Gene Set Enrichment Analysis and CIBERSORT algorithm were deployed to explore the role of these FRLs in the tumour microenvironment. It was concluded that these models demonstrate excellent performance in predicting prognosis among patients with ccRCC, also indicating association with the clinicopathologic parameters such as tumour grade, tumour stage and tumour immune infiltration. In conclusion, our findings provide novel insights into ferroptosis-related lncRNAs in ccRCC, which are important targets for investigating the tumorigenesis of ccRCC.
透明细胞肾细胞癌(ccRCC)非常常见,占肾癌死亡病例的大多数。虽然目前正在进行许多研究以寻找ccRCC的预后特征,但我们认为,铁死亡(一种依赖铁积累的程序性细胞死亡)在ccRCC中具有治疗潜力。最近的研究表明,长链非编码RNA(lncRNA)参与铁死亡相关的肿瘤过程,并且与ccRCC患者的生存密切相关。因此,在本研究中,我们旨在进一步探索铁死亡相关lncRNA(FRL)在ccRCC中的作用,希望建立一个预测ccRCC生存结果的特征。我们分析了来自癌症基因组图谱数据库(TCGA)的转录组数据和来自FerrDb的铁死亡相关基因(FRG),使用Pearson相关性来识别FRL。Lasso Cox回归分析和多变量Cox比例风险模型筛选出17个最佳FRL以建立预后特征。然后绘制Kaplan-Meier生存曲线和ROC曲线,以验证所识别特征的敏感性、特异性和准确性。采用基因集富集分析和CIBERSORT算法来探索这些FRL在肿瘤微环境中的作用。得出的结论是,这些模型在预测ccRCC患者的预后方面表现出色,同时也表明与肿瘤分级、肿瘤分期和肿瘤免疫浸润等临床病理参数相关。总之,我们的研究结果为ccRCC中铁死亡相关lncRNA提供了新的见解,这些lncRNA是研究ccRCC肿瘤发生的重要靶点。