Background: The complement component C5a receptor 1 (C5aR1) regulates cancer immunity. This retrospective study aimed to assess its prognostic value in high-grade glioma (HGG) and predict C5aR1 expression using a radiomics approach. Methods: Among 298 patients with HGG, 182 with MRI data were randomly divided into training and test groups for radiomics analysis. We examined the association between C5aR1 expression and prognosis through Kaplan–Meier and Cox regression analyses. We used maximum relevance–minimum redundancy and recursive feature elimination algorithms for radiomics feature selection. We then built a support vector machine (SVM) and a logistic regression model, investigating their performances using receiver operating characteristic, calibration curves, and decision curves. Results: C5aR1 expression was elevated in HGG and was an independent prognostic factor (hazard ratio = 3.984, 95% CI: 2.834–5.607). Both models presented with >0.8 area under the curve values in the training and test datasets, indicating efficient discriminatory ability, with SVM performing marginally better. The radiomics score calculated using the SVM model correlated significantly with overall survival (p< 0.01). Conclusions: Our results highlight C5aR1’s role in HGG development and prognosis, supporting its potential as a prognostic biomarker. Our radiomics model can noninvasively and effectively predict C5aR1 expression and patient prognosis in HGG.
背景：补体成分C5a受体1（C5aR1）在癌症免疫调控中发挥重要作用。本研究旨在通过回顾性分析评估C5aR1在高级别胶质瘤（HGG）中的预后价值，并探索基于影像组学方法预测其表达的可行性。方法：在298例HGG患者中，筛选182例具有完整MRI数据的病例，随机分为训练组与测试组进行影像组学分析。通过Kaplan-Meier生存分析与Cox回归模型评估C5aR1表达与预后的关联性。采用最大相关-最小冗余算法与递归特征消除法进行影像组学特征筛选，分别构建支持向量机（SVM）和逻辑回归预测模型，并通过受试者工作特征曲线、校准曲线及决策曲线评估模型性能。结果：C5aR1在HGG组织中表达显著升高，且为独立预后因素（风险比=3.984，95%置信区间：2.834-5.607）。两种模型在训练集与测试集中均表现出良好的判别效能（曲线下面积>0.8），其中SVM模型性能略优。基于SVM模型计算的影像组学评分与患者总生存期显著相关（p<0.01）。结论：本研究证实C5aR1在HGG进展及预后评估中的重要价值，支持其作为预后生物标志物的潜力。所构建的影像组学模型能够无创、有效地预测HGG患者的C5aR1表达水平及临床预后。
肿瘤(癌症)患者之家