鉴定 PI3K-AKT 通路相关基因并构建 ccRCC 的预后预测模型。

BACKGROUND: Clear cell renal cell carcinoma (ccRCC), the predominate histological type of renal cell carcinoma (RCC), has been extensively studied, with poor prognosis as the stage increases. Research findings consistently indicated that the PI3K-Akt pathway is commonly dysregulated across various cancer types, including ccRCC. Targeting the PI3K-Akt pathway held promise as a potential therapeutic approach for treating ccRCC. Development and validation of PI3K-Akt pathway-related genes related biomarkers can enhance healthcare management of patients with ccRCC. PURPOSE: This study aimed to identify the key genes in the PI3K-Akt pathway associated with the diagnosis and prognosis of CCRCC using data mining from the Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO) datasets. METHODS: The purpose of this study is to use bioinformatics methods to screen data sets and clinicopathological characteristics associated with ccRCC patients. The exhibited significantly differential expressed genes (DEGs) associated with the PI3K-Akt pathway were examined by KEGG. In addition, Kaplan-Meier (KM) analysis used to estimate the survival function of the differential genes by using the UALCAN database and graphPad Prism 9.0. And exploring the association between the expression levels of the selected genes and the survival status and time of patients with ccRCC based on SPSS22.0. Finally, a multigene prognostic model was constructed to assess the prognostic risk of ccRCC patients. RESULTS: A total of 911 genes with common highly expressed were selected based on the GEO and TCGA databases. According to the KEGG pathway analysis, there were 42 genes enriched in PI3K-Akt signalling pathway. And seven of highly expressed genes were linked to a poor prognosis in ccRCC. And a multigene prognostic model was established based on IL2RG, EFNA3, and MTCP1 synergistic expression might be utilized to predict the survival of ccRCC patients. CONCLUSIONS: Three PI3K-Akt pathway-related genes may be helpful to identify the prognosis and molecular characteristics of ccRCC patients and to improve therapeutic regimens, and these risk characteristics might be further applied in the clinic.

背景：透明细胞肾细胞癌（ccRCC）是肾细胞癌（RCC）的主要组织学类型，随着分期的增加，其预后较差。研究结果一致表明，PI3K-Akt 通路在包括 ccRCC 在内的各种癌症类型中经常失调。靶向 PI3K-Akt 通路有望成为治疗 ccRCC 的一种潜在治疗方法。开发和验证与 PI3K-Akt 通路相关的基因相关生物标志物可以增强对 ccRCC 患者的医疗保健管理。

目的：本研究旨在使用癌症基因组图谱（TCGA）和基因表达综合（GEO）数据集的数据挖掘，鉴定与 ccRCC 诊断和预后相关的 PI3K-Akt 通路中的关键基因。

方法：本研究旨在使用生物信息学方法筛选与 ccRCC 患者相关的数据组和临床病理特征。通过 KEGG 检查与 PI3K-Akt 通路相关的表现出显著差异表达基因（DEGs）。此外，使用 UALCAN 数据库和 graphPad Prism 9.0 进行 Kaplan-Meier（KM）分析，以估计差异基因的生存功能。并基于 SPSS22.0 探索所选基因的表达水平与 ccRCC 患者的生存状况和时间之间的关系。最后，构建多基因预后模型，以评估 ccRCC 患者的预后风险。

结果：基于 GEO 和 TCGA 数据库，共选择了 911 个具有共同高表达的基因。根据 KEGG 通路分析，有 42 个基因富集在 PI3K-Akt 信号通路中。并且，有 7 个高表达基因与 ccRCC 的不良预后相关。基于 IL2RG、EFNA3 和 MTCP1 的协同表达建立了一个多基因预后模型，可能用于预测 ccRCC 患者的生存。

结论：三个 PI3K-Akt 通路相关基因可能有助于识别 ccRCC 患者的预后和分子特征，并改善治疗方案，这些风险特征可能进一步应用于临床。