Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Department of Clinical Medicine, Qingdao University, Qingdao, Shandong, China.
Dis Markers. 2020 Nov 10;2020:8841859. doi: 10.1155/2020/8841859. eCollection 2020.
Autophagy plays an essential role in tumorigenesis. At present, due to the unclear role of autophagy in renal clear cell carcinoma, we studied the potential value of autophagy-related genes (ARGs) in renal clear cell carcinoma (ccRCC).
We obtained all ccRCC data from The Cancer Genome Atlas (TCGA). We extracted the expression data of ARGs for difference analysis and carried out biological function analysis on the different results. The autophagy risk model was constructed. The 5-year survival rate was assessed using the model, and the predictive power of the model was evaluated from multiple perspectives. Cox regression analysis was use to assess whether the model could be an independent prognostic factor. Finally, the correlation between the model and clinical indicators is analyzed.
The patients were divided into the high-risk group and low-risk group according to the median of autophagy risk score, and the results showed that the prognosis of the low-risk group was better than that of a high-risk group. The validation results of external data sets show that our model has good predictive value for ccRCC patients. The model can be an independent prognostic factor. Finally, the results show that our model has a stable predictive ability.
The autophagy gene model we constructed can be used as an excellent prognostic indicator for ccRCC. Our study provides the possibility of individualized and precise treatment for ccRCC patients.
自噬在肿瘤发生中起着至关重要的作用。目前，由于自噬在肾透明细胞癌中的作用尚不清楚，我们研究了自噬相关基因（ARGs）在肾透明细胞癌（ccRCC）中的潜在价值。
我们从癌症基因组图谱（TCGA）中获得了所有 ccRCC 数据。我们提取了 ARGs 的表达数据进行差异分析，并对不同结果进行了生物学功能分析。构建自噬风险模型。使用该模型评估 5 年生存率，并从多个角度评估模型的预测能力。Cox 回归分析用于评估该模型是否可以作为独立的预后因素。最后，分析了模型与临床指标的相关性。
根据自噬风险评分的中位数将患者分为高风险组和低风险组，结果表明低风险组的预后较好。外部数据集的验证结果表明，我们的模型对 ccRCC 患者具有良好的预测价值。该模型可以作为独立的预后因素。最后，结果表明我们的模型具有稳定的预测能力。
我们构建的自噬基因模型可作为 ccRCC 的优秀预后指标。我们的研究为 ccRCC 患者的个体化和精准治疗提供了可能性。
