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Abstract：
The Purinergic pathway is involved in a variety of important physiological processes in living organisms, and previous studies have shown that aberrant expression of the Purinergic pathway may contribute to the development of a variety of cancers, including kidney renal clear cell carcinoma (KIRC). The aim of this study was to delve into the Purinergic pathway in KIRC and to investigate its potential significance in prognostic assessment and clinical treatment. 33 genes associated with the Purinergic pathway were selected for pan-cancer analysis. Cluster analysis, targeted drug sensitivity analysis and immune cell infiltration analysis were applied to explore the mechanism of Purinergic pathway in KIRC. Using the machine learning process, we found that combining the Lasso+survivalSVM algorithm worked well for predicting survival accuracy in KIRC. We used LASSO regression to pinpoint nine Purinergic genes closely linked to KIRC, using them to create a survival model for KIRC. ROC survival curve was analyzed, and this survival model could effectively predict the survival rate of KIRC patients in the next 5, 7 and 10 years. Further univariate and multivariate Cox regression analyses revealed that age, grading, staging, and risk scores of KIRC patients were significantly associated with their prognostic survival and were identified as independent risk factors for prognosis. The nomogram tool developed through this study can help physicians accurately assess patient prognosis and provide guidance for developing treatment plans. The results of this study may bring new ideas for optimizing the prognostic assessment and therapeutic approaches for KIRC patients.
摘要：
嘌呤能通路参与生物体内各种重要的生理过程,和以前的研究表明，嘌呤能途径的异常表达可能有助于多种癌症的发展，包括肾透明细胞癌（KIRC）。本研究的目的是深入研究KIRC中的嘌呤能途径，并探讨其在预后评估和临床治疗中的潜在意义。选择与嘌呤能途径相关的33个基因用于泛癌症分析。聚类分析,应用靶向药敏分析和免疫细胞浸润分析探讨嘌呤能通路在KIRC中的作用机制。使用机器学习过程，我们发现，结合Lasso+survivalSVM算法可以很好地预测KIRC中的生存精度。我们使用LASSO回归来确定与KIRC密切相关的9个嘌呤能基因，使用它们为KIRC创建生存模型。分析ROC生存曲线,该生存模型能有效预测KIRC患者未来5年、7年和10年的生存率。进一步的单变量和多变量Cox回归分析显示，年龄，分级,分期,KIRC患者的风险评分与其预后生存显著相关,并被确定为预后的独立危险因素。通过这项研究开发的列线图工具可以帮助医生准确评估患者预后并为制定治疗计划提供指导。本研究结果可能为KIRC患者的预后评估和治疗方法的优化提供新的思路。
