Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China.
Department of Cardiovascular Thoracic Surgery, Huashan Hospital, Fudan University, Shanghai, China.
Cancer Sci. 2024 Sep;115(9):3127-3142. doi: 10.1111/cas.16279. Epub 2024 Jul 11.
The incomplete prediction of prognosis in esophageal squamous cell carcinoma (ESCC) patients is attributed to various therapeutic interventions and complex prognostic factors. Consequently, there is a pressing demand for enhanced predictive biomarkers that can facilitate clinical management and treatment decisions. This study recruited 491 ESCC patients who underwent surgical treatment at Huashan Hospital, Fudan University. We incorporated 14 blood metabolic indicators and identified independent prognostic indicators for overall survival through univariate and multivariate analyses. Subsequently, a metabolism score formula was established based on the biochemical markers. We constructed a nomogram and machine learning models utilizing the metabolism score and clinically significant prognostic features, followed by an evaluation of their predictive accuracy and performance. We identified alkaline phosphatase, free fatty acids, homocysteine, lactate dehydrogenase, and triglycerides as independent prognostic indicators for ESCC. Subsequently, based on these five indicators, we established a metabolism score that serves as an independent prognostic factor in ESCC patients. By utilizing this metabolism score in conjunction with clinical features, a nomogram can precisely predict the prognosis of ESCC patients, achieving an area under the curve (AUC) of 0.89. The random forest (RF) model showed superior predictive ability (AUC = 0.90, accuracy = 86%, Matthews correlation coefficient = 0.55). Finally, we used an RF model with optimal performance to establish an online predictive tool. The metabolism score developed in this study serves as an independent prognostic indicator for ESCC patients.
食管鳞癌（ESCC）患者预后预测不完整归因于各种治疗干预和复杂的预后因素。因此，迫切需要增强预测生物标志物，以促进临床管理和治疗决策。本研究招募了 491 名在复旦大学华山医院接受手术治疗的 ESCC 患者。我们纳入了 14 个血液代谢指标，并通过单因素和多因素分析确定了总生存的独立预后指标。随后，基于生化标志物建立了代谢评分公式。我们构建了一个列线图和机器学习模型，利用代谢评分和临床上有意义的预后特征，并评估它们的预测准确性和性能。我们确定碱性磷酸酶、游离脂肪酸、同型半胱氨酸、乳酸脱氢酶和甘油三酯是 ESCC 的独立预后指标。随后，基于这五个指标，我们建立了一个代谢评分，作为 ESCC 患者的独立预后因素。通过将代谢评分与临床特征结合使用，列线图可以精确预测 ESCC 患者的预后，曲线下面积（AUC）为 0.89。随机森林（RF）模型显示出更好的预测能力（AUC=0.90，准确率=86%，马修斯相关系数=0.55）。最后，我们使用具有最佳性能的 RF 模型建立了一个在线预测工具。该研究建立的代谢评分可作为 ESCC 患者的独立预后指标。
