列线图预测前列腺癌患者的预后：基于蛋白组学的研究

A Nomogram for Predicting the Prognosis of Patients in Prostate Cancer: Research Based on Proteomics

摘要: 前列腺癌是男性最常见且致命的恶性肿瘤之一，具有较高的发病率和死亡率。本研究利用TCGA数据库蛋白质组学数据，采用生物信息学方法利用蛋白组学构建前列腺癌预后模型。通过COX回归分析确定了关键的蛋白质，并利用它们构建了风险评分模型。通过Kaplan-Mier曲线和ROC曲线等多种方法验证了该模型的预测性能。结果表明，模型能够有效地将前列腺癌患者分为高风险组和低风险组，具有很强的预后准确性。本研究为前列腺癌的精准医疗提供了理论依据，并为临床个性化治疗提供了新的方向。

Abstract: Prostate cancer is one of the most common and lethal malignant tumors in men, which has a high incidence and mortality rate. This study leverages proteomics data from the TCGA database and applies bioinformatics methods to build a protein expression prognostic model for prostate cancer. Key protein markers were identified COX regression analysis, and a risk scoring model was constructed based on these key proteins. The model’s predictive performance was verified by various methods, including Kaplan-Mier Curve and ROC Curve. The results demonstrate that this model can effectively distinguish between high-risk and low-risk groups of patients about prostate cancer with strong prognostic accuracy. This study provides a theoretical basis for precision medicine in prostate cancer and offers a new direction for clinical personalized treatment.