Xu Yingkun, Li Xiunan, Han Yuqing, Wang Zilong, Han Chenglin, Ruan Ningke, Li Jianyi, Yu Xiao, Xia Qinghua, Wu Guangzhen
Department of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China.
Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, China.
PPAR Res. 2020 Sep 22;2020:6937475. doi: 10.1155/2020/6937475. eCollection 2020.
This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).
For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.
We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.
To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.
本研究旨在利用过氧化物酶体增殖物激活受体（PPAR）途径相关基因建立肾透明细胞癌（KIRC）的预后风险模型。
在本研究中，我们首先在基因集富集分析（GSEA）网站上查找PPAR途径相关基因，并通过TCGA数据库获取KIRC的mRNA表达数据和临床数据。随后，我们使用R语言和多个R语言扩展包对KIRC中PPAR途径相关基因的表达、风险比分析和共表达分析进行研究。之后，利用搜索互作基因/蛋白的工具（STRING）网站，我们构建了PPAR途径相关基因的蛋白质-蛋白质相互作用（PPI）网络。在此之后，我们使用套索回归曲线分析在KIRC中建立预后生存模型。最后，基于该模型，我们进行了临床病理特征的相关性分析、单因素分析和多因素分析。
我们发现，大多数与PPAR途径相关的基因在KIRC中存在不同程度的表达差异。其中，27个基因的高表达与KIRC患者的低生存率相关，另外13个基因的高表达与患者的高生存率相关。最重要的是，我们成功地利用其中13个基因建立了一个能够准确预测患者预后的风险模型。该模型与转移、肿瘤、分期、分级和富斯他特之间存在明显的相关性。
据我们所知，这是第一项详细分析KIRC中整个PPAR途径并成功建立患者预后风险模型的研究。我们相信我们的研究可以为未来的研究人员和临床医生提供有价值的数据。
