State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China.
Sci Rep. 2024 Feb 28;14(1):4926. doi: 10.1038/s41598-024-55086-6.
The peroxisome proliferator-activated receptor (PPAR) signaling pathway plays a crucial role in systemic cell metabolism, energy homeostasis and immune response inhibition. However, its significance in hepatocellular carcinoma (HCC) has not been well documented. In our study, based on the RNA sequencing data of HCC, consensus clustering analyses were performed to identify PPAR signaling pathway-related molecular subtypes, each of which displaying varying survival probabilities and immune infiltration status. Following, a prognostic prediction model of HCC was developed by using the random survival forest method and Cox regression analysis. Significant difference in survival outcome, immune landscape, drug sensitivity and pathological features were observed between patients with different prognosis. Additionally, decision tree and nomogram models were adopted to optimize the prognostic prediction model. Furthermore, the robustness of the model was verified through single-cell RNA-sequencing data. Collectively, this study systematically elucidated that the PPAR signaling pathway-related prognostic model has good predictive efficacy for patients with HCC. These findings provide valuable insights for further research on personalized treatment approaches for HCC.
过氧化物酶体增殖物激活受体 (PPAR) 信号通路在全身细胞代谢、能量稳态和免疫反应抑制中发挥着关键作用。然而，其在肝细胞癌 (HCC) 中的意义尚未得到充分证明。在我们的研究中，基于 HCC 的 RNA 测序数据，进行了共识聚类分析以鉴定与 PPAR 信号通路相关的分子亚型，每个亚型显示出不同的生存概率和免疫浸润状态。随后，采用随机生存森林方法和 Cox 回归分析构建了 HCC 的预后预测模型。不同预后患者之间在生存结果、免疫景观、药物敏感性和病理特征方面存在显著差异。此外，还采用决策树和列线图模型对预后预测模型进行了优化。进一步，通过单细胞 RNA-seq 数据验证了模型的稳健性。总的来说，本研究系统地阐明了与 PPAR 信号通路相关的预后模型对 HCC 患者具有良好的预测效果。这些发现为进一步研究 HCC 的个体化治疗方法提供了有价值的见解。
