Zhang Rui, Jiang Chunfan, Li Feng, Li Lin, Qin Xiaomin, Yang Jiang, Lv Huabing, Ai Tao, Deng Lei, Huang Chencui, Xing Hui, Wu Feng
Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, No. 136 Jinzhou Road, Xiangyang, 441021, Hubei Province, People's Republic of China.
Department of Pathology, Xiangyang Central Hospital, Affiliated Hospital Of Hubei University of Arts and Science, Xiangyang, Hubei, People's Republic of China.
Sci Rep. 2025 Jul 23;15(1):26754. doi: 10.1038/s41598-025-12162-9.
The study investigates the correlation between CD3 T-cell expression levels and cervical cancer (CC) while developing a magnetic resonance (MR) imaging-based radiomics model for preoperative prediction of CD3 T-cell expression levels. Prognostic correlations between CD3D, CD3E, and CD3G gene expressions and various cancers were analyzed using the Cancer Genome Atlas (TCGA) database. Protein-protein interaction (PPI) analysis via the STRING database identified associations between these genes and T lymphocyte activity. Gene Set Enrichment Analysis (GSEA) revealed immune pathway enrichment by categorizing genes based on CD3D expression levels. Correlations between immune checkpoint molecules and CD3 complex genes were also assessed. The study retrospectively included 202 patients with pathologically confirmed early-stage CC who underwent preoperative MRI, divided into training and test groups. Radiomic features were extracted from the whole-lesion tumor region of interest (ROI) and from peritumoral regions with 3 mm and 5 mm margins (ROI and ROI, respectively). Various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Random Forest, AdaBoost, and Decision Tree, were used to construct radiomics models based on different ROIs, and diagnostic performances were compared to identify the optimal approach. The best-performing algorithm was combined with intra- and peritumoral features and clinically relevant independent risk factors to develop a comprehensive predictive model. Analysis of the TCGA database demonstrated significant associations between CD3D, CD3E, and CD3G expressions and several cancers, including CC (p < 0.05). PPI analysis highlighted connections between these genes and T lymphocyte function, while GSEA indicated enrichment of immune-related pathways linked to CD3D. Immune checkpoint correlations showed positive associations with CD3 complex genes. Radiomics analysis selected 18 features from ROI and ROI across MRI sequences. The SVM algorithm achieved the highest predictive performance for CD3 T-cell expression status, with an area under the curve (AUC) of 0.93 in the training group and 0.92 in the test group. This MR-based radiomics model effectively predicts CD3 expression status in patients with early-stage CC, offering a non-invasive tool for preoperative assessment of CD3 expression, but its clinical utility needs further prospective validation.
本研究调查了CD3 T细胞表达水平与宫颈癌（CC）之间的相关性，同时开发了一种基于磁共振（MR）成像的放射组学模型，用于术前预测CD3 T细胞表达水平。使用癌症基因组图谱（TCGA）数据库分析了CD3D、CD3E和CD3G基因表达与各种癌症之间的预后相关性。通过STRING数据库进行的蛋白质-蛋白质相互作用（PPI）分析确定了这些基因与T淋巴细胞活性之间的关联。基因集富集分析（GSEA）通过根据CD3D表达水平对基因进行分类，揭示了免疫途径的富集。还评估了免疫检查点分子与CD3复合体基因之间的相关性。该研究回顾性纳入了202例经病理证实的早期CC患者，这些患者均接受了术前MRI检查，并分为训练组和测试组。从全病变肿瘤感兴趣区域（ROI）以及边缘为3 mm和5 mm的瘤周区域（分别为ROI和ROI）提取放射组学特征。使用包括支持向量机（SVM）、逻辑回归、随机森林、AdaBoost和决策树在内的各种机器学习算法，基于不同的ROI构建放射组学模型，并比较诊断性能以确定最佳方法。将表现最佳的算法与瘤内和瘤周特征以及临床相关的独立危险因素相结合，开发出一个综合预测模型。对TCGA数据库的分析表明，CD3D、CD3E和CD3G表达与包括CC在内的几种癌症之间存在显著关联（p < 0.05）。PPI分析突出了这些基因与T淋巴细胞功能之间的联系，而GSEA表明与CD3D相关的免疫相关途径富集。免疫检查点相关性显示与CD3复合体基因呈正相关。放射组学分析从整个MRI序列的ROI和ROI中选择了18个特征。SVM算法在预测CD3 T细胞表达状态方面表现出最高的性能，训练组的曲线下面积（AUC）为0.93，测试组为0.92。这种基于MR的放射组学模型有效地预测了早期CC患者的CD3表达状态，为术前评估CD3表达提供了一种非侵入性工具，但其临床实用性需要进一步的前瞻性验证。
