Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China.
J Immunol Res. 2022 Jun 2;2022:9412119. doi: 10.1155/2022/9412119. eCollection 2022.
This study is aimed at exploring the potential mechanism of the PPAR signaling pathway in breast cancer (BRCA) and constructing a novel prognostic-related risk model. We used various bioinformatics methods and databases to complete our exploration in this research. Based on TCGA database, we use multiple extension packages based on the R language for data conversion, processing, and statistics. We use LASSO regression analysis to establish a prognostic-related risk model in BRCA. And we combined the data of multiple online websites, including GEPIA, ImmuCellAI, TIMER, GDSC, and the Human Protein Atlas database to conduct a more in-depth exploration of the risk model. Based on the mRNA data in TCGA database, we conducted a preliminary screening of genes related to the PPAR signaling pathway through univariate Cox analysis, then used LASSO regression analysis to conduct a second screening, and successfully established a risk model consisting of ten genes in BRCA. The results of ROC curve analysis show that the risk model has good prediction accuracy. We can successfully divide breast cancer patients into high- and low-risk groups with significant prognostic differences ( = 1.92 - 05) based on this risk model. Combined with the clinical data in TCGA database, there is a correlation between the risk model and the patient's N, T, gender, and fustat. The results of multivariate Cox regression show that the risk score of this risk model can be used as an independent risk factor for BRCA patients. In particular, we draw a nomogram that can predict the 5-, 7-, and 10-year survival rates of BRCA patients. Subsequently, we conducted a series of pancancer analyses of CNV, SNV, OS, methylation, and immune infiltration for this risk model gene and used GDSC data to investigate drug sensitivity. Finally, to gain insight into the predictive value and protein expression of these risk model genes in breast cancer, we used GEO and HPA databases for validation. This study provides valuable clues for future research on the PPAR signaling pathway in BRCA.
本研究旨在探讨 PPAR 信号通路在乳腺癌（BRCA）中的潜在机制，并构建一个新的预后相关风险模型。我们使用各种生物信息学方法和数据库来完成本研究的探索。基于 TCGA 数据库，我们使用基于 R 语言的多个扩展包进行数据转换、处理和统计。我们使用 LASSO 回归分析在 BRCA 中建立一个预后相关的风险模型。我们结合了多个在线网站的数据，包括 GEPIA、ImmuCellAI、TIMER、GDSC 和 Human Protein Atlas 数据库，对风险模型进行了更深入的探索。基于 TCGA 数据库中的 mRNA 数据，我们通过单变量 Cox 分析初步筛选与 PPAR 信号通路相关的基因，然后使用 LASSO 回归分析进行二次筛选，成功建立了一个由 BRCA 中十个基因组成的风险模型。ROC 曲线分析的结果表明，该风险模型具有良好的预测准确性。我们可以根据该风险模型成功地将乳腺癌患者分为高风险和低风险组，两组患者的预后差异具有统计学意义（ = 1.92 - 05）。结合 TCGA 数据库中的临床数据，发现风险模型与患者的 N、T、性别和 fustat 之间存在相关性。多变量 Cox 回归分析的结果表明，该风险模型的风险评分可作为 BRCA 患者的独立危险因素。特别是，我们绘制了一个列线图，可以预测 BRCA 患者的 5 年、7 年和 10 年生存率。随后，我们对该风险模型基因进行了一系列泛癌分析，包括 CNV、SNV、OS、甲基化和免疫浸润，并使用 GDSC 数据进行了药物敏感性研究。最后，为了深入了解这些风险模型基因在乳腺癌中的预测价值和蛋白表达，我们使用了 GEO 和 HPA 数据库进行验证。本研究为进一步研究 BRCA 中的 PPAR 信号通路提供了有价值的线索。
