基于磁共振成像的低级别胶质瘤中 CCND1 表达水平和预后的放射组学预测。
Radiomic Prediction of CCND1 Expression Levels and Prognosis in Low-grade Glioma Based on Magnetic Resonance Imaging.
机构信息
Department of Neurology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (K.Z., S.X., J.L.); Department of Cell Biology, Institute of Bioengineering, School of Medicine, Soochow University, Suzhou, Jiangsu, China (K.Z., W.W.); Suzhou Niumag Analytical Instrument Corporation, Suzhou, Jiangsu, China (K.Z., D.C., J.Y.).
Fujian Center for Safety Evaluation of New Drug, Fujian Medical University, Fuzhou, Fujian, China (H.Z.).
出版信息
Acad Radiol. 2024 Nov;31(11):4595-4610. doi: 10.1016/j.acra.2024.03.031. Epub 2024 Jun 1.
OJECTIVES
Low-grade glioma (LGG) is associated with increased mortality owing to recrudescence and the tendency for malignant transformation. Therefore, it is imperative to discover novel prognostic biomarkers as existing traditional prognostic biomarkers of glioma, including clinicopathological features and imaging examinations, are unable to meet the clinical demand for precision medicine. Accordingly, we aimed to evaluate the prognostic value of cyclin D1 (CCND1) expression levels and construct radiomic models to predict these levels in patients with LGG MATERIALS AND METHODS: A total of 412 LGG cases from The Cancer Genome Atlas (TCGA) were used for gene-based prognostic analysis. Using magnetic resonance imaging (MRI) images stored in The Cancer Imaging Archive with genomic data from TCGA, 149 cases were selected for radiomics feature extraction and model construction. After feature extraction, the radiomic signature was constructed using logistic regression (LR) and support vector machine (SVM) analyses.
RESULTS
CCND1 was identified as a prognosis-related gene with differential expression in tumor and normal samples and plays a role in regulating both the cell cycle and immune response. Landmark analysis revealed that high-expression levels of CCND1 were beneficial for survival (P < 0.05) in advanced LGG. Four optimal radiomics features were selected to construct radiomics models. The performance of LR and SVM achieved areas under the curve of 0.703 and 0.705, as well as 0.724 and 0.726 in the training and validation sets, respectively.
CONCLUSION
Elevated levels of CCND1 expression could impact the prognosis of patients with LGG. MRI-based radiomics, especially the AUC values, can serve as a novel tool for predicting CCND1 expression and understanding the correlation between elevated CCND1 expression and prognosis.
AVAILABILITY OF DATA AND MATERIALS
The datasets analyzed during the current study are available in the TCGA, TCIA, UCSC XENA and GTEx repository, https://portal.gdc.cancer.gov/, https://www.cancerimagingarchive.net/, https://xenabrowser.net/datapages/, https://www.gtexportal.org/home/.
目的
低级别胶质瘤（LGG）由于复发和恶性转化的趋势，导致死亡率增加。因此，发现新的预后生物标志物是当务之急，因为现有的胶质瘤传统预后生物标志物，包括临床病理特征和影像学检查，无法满足精准医学的临床需求。因此，我们旨在评估细胞周期蛋白 D1（CCND1）表达水平的预后价值，并构建放射组学模型以预测 LGG 患者的这些水平。
材料和方法
从癌症基因组图谱（TCGA）中使用了 412 例 LGG 病例进行基于基因的预后分析。使用从 TCGA 存储在癌症成像档案中的磁共振成像（MRI）图像，从 TCGA 中选择了 149 例进行放射组学特征提取和模型构建。特征提取后，使用逻辑回归（LR）和支持向量机（SVM）分析构建放射组学特征。
结果
CCND1 被鉴定为肿瘤和正常样本中差异表达的与预后相关基因，并且在调节细胞周期和免疫反应方面发挥作用。标志分析显示，高水平的 CCND1 表达有利于晚期 LGG 的生存（P < 0.05）。选择了四个最佳放射组学特征来构建放射组学模型。LR 和 SVM 的性能在训练和验证集中的曲线下面积分别为 0.703 和 0.705，以及 0.724 和 0.726。
结论
CCND1 表达水平升高可能会影响 LGG 患者的预后。基于 MRI 的放射组学，特别是 AUC 值，可以作为一种新的工具，用于预测 CCND1 表达并了解升高的 CCND1 表达与预后之间的相关性。
数据和材料的可用性
本研究中分析的数据集可从 TCGA、TCIA、UCSC XENA 和 GTEx 存储库中获得，网址分别为 https://portal.gdc.cancer.gov/、https://www.cancerimagingarchive.net/、https://xenabrowser.net/datapages/、https://www.gtexportal.org/home/。
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