目的:本研究旨在利用二硫键下垂相关的长链非编码RNA(lncRNAs)构建乳头状肾细胞癌(pRCC)的预后模型。此外,它研究了这些lncRNAs在预测pRCC的免疫反应和药物敏感性方面的潜力。
背景:LncRNAs与pRCC的进展和预后有关。最近,二硫化物下垂,一种新兴的受调节的细胞死亡形式,已经显示出作为癌症治疗方法的潜力。然而,二硫键下垂相关lncRNAs与pRCC之间的潜在关联尚不清楚.
方法:我们分析了来自癌症基因组图谱数据库的pRCC患者的转录组谱和临床数据。采用皮尔逊相关分析,我们鉴定了与二硫键凋亡相关的lncRNAs.基于确定的与总生存期(OS)相关的二硫细胞凋亡相关的lncRNAs,我们使用最小绝对收缩和选择算子构建了一个新的预测模型,单变量Cox回归,和多变量Cox回归分析。通过Kaplan-Meier生存率评估模型的效用,接收机工作特性,和主成分分析。此外,功能分析有助于确定潜在的预后机制,并对pRCC的化学药物进行了预测。最后,qRT-PCR验证了pRCC细胞和患者样品中预后性lncRNAs的表达。
结果:我们的预测模型基于9个与二硫细胞凋亡相关的lncRNAs。评估和验证分析表明,该模型具有良好的性能,一致,和pRCC患者的独立预后价值,曲线下面积(AUC)值为0.954,0.910和0.830,3-,和5年操作系统,分别。通过功能分析,我们发现确定的预后特征与免疫之间存在显著相关性.此外,就化疗敏感性而言,我们的分析表明,低危组对舒尼替尼和帕唑帕尼的敏感性更高.此外,在从pRCC细胞和患者获得的样本中验证了鉴定的lncRNAs的表达模式.
结论:本研究成功建立并验证了一种新的与二硫沉积相关的预测模型。研究结果表明,免疫相关途径可能参与lncRNA签名相关的存活。该模型有望在临床实践中区分预后并改善pRCC的个性化治疗策略。
OBJECTIVE: This study aimed to construct a prognostic model for papillary renal cell carcinoma (pRCC) utilizing disulfidptosis-associated long non-coding RNAs (lncRNAs). Additionally, it investigated the potential of these lncRNAs in predicting immune responses and drug sensitivity in pRCC.
BACKGROUND: LncRNAs have been implicated in the progression and prognosis of pRCC. Recently, disulfidptosis, an emerging form of regulated cell death, has shown potential as a therapeutic approach for cancer. However, the potential association between disulfidptosis-related lncRNAs and pRCC remains unclear.
METHODS: We analyzed transcriptome profiling and clinical data of pRCC patients from The Cancer Genome Atlas database. Using Pearson correlation analysis, we identified lncRNAs associated with disulfidptosis. Based on the identified disulfidptosis-related lncRNAs that were correlated with overall survival (OS), we constructed a novel prediction model using the least absolute shrinkage and selection operator, univariable Cox regression, and multivariable Cox regression analyses. The model\'s utility was assessed through Kaplan-Meier survival, receiver operating characteristics, and principal component analyses. Moreover, functional analysis helped identify potential prognostic mechanisms, and the prediction of chemical drugs for pRCC was also performed. Finally, qRT-PCR validated the expression of prognostic lncRNAs in pRCC cells and patient samples.
RESULTS: Our prediction model was based on nine disulfidptosis-related lncRNAs. Evaluation and validation analyses demonstrated that the model had excellent, consistent, and independent prognostic value for pRCC patients, with area under the curve (AUC) values of 0.954, 0.910, and 0.830 for 1-, 3-, and 5-year OS, respectively. Through functional analysis, we discovered a significant correlation between the identified prognostic signature and immunity. Additionally, in terms of chemotherapy sensitivity, our analysis indicated that the low-risk group exhibited higher sensitivity to sunitinib and
pazopanib. Furthermore, the expression patterns of the identified lncRNAs were validated in samples obtained from pRCC cells and patients.
CONCLUSIONS: This study successfully established and validated a novel disulfidptosis-related prediction model. The findings suggest the potential involvement of immune-related pathways in lncRNA signature-associated survival. This model holds promise for differentiating prognosis and improving personalized therapeutic strategies for pRCC in clinical practice.