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基于TCGA和TCIA放射组学预测高级别胶质瘤中血小板源性生长因子受体配体的表达及预后意义

Predicting podoplanin expression and prognostic significance in high-grade glioma based on TCGA TCIA radiomics.

作者信息

Long Shengrong, Xu Hongyu, Li Mingdong, Wang Lesheng, Jiang Jiazhi, Wei Wei, Li Xiang

机构信息

Brain Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China.

Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China.

出版信息

PLoS One. 2025 Jun 24;20(6):e0325964. doi: 10.1371/journal.pone.0325964. eCollection 2025.

DOI:10.1371/journal.pone.0325964
PMID:40554484
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12186906/
Abstract

BACKGROUND

Podoplanin (PDPN) is a membrane glycoprotein implicated in tumor invasion and immune modulation in high-grade gliomas (HGGs). However, the non-invasive prediction of PDPN expression and its prognostic significance using radiomics remains unexplored.

MATERIALS AND METHODS

This study used preoperative contrast-enhanced MRI T1WI data analyzed by gradient boosting machine to predict podoplanin (PDPN) expression and overall survival (OS) in HGG patients.

RESULTS

We retrospectively analyzed 89 HGG patients' clinical data, MRI images, and RNA-seq profiles from TCIA. For each patient, 107 radiomics features were extracted from HGG subregions. The radiomics prognostic model was built using two selected features, glcm_Idmn and glcn_Idn. Through validation with external the REMBRANDT dataset (n=39), the model demonstrated great predictive performance for the PDPN expression levels and OS in HGG. The area under the curve of the ROC in the radiomics signature combined with clinical risk factors for the 1-year, 2-year, and 3-year OS rates in the TCIA-HGG were 0.799, 0.883, and 0.923, respectively. Gradient boosting machine using preoperative MRI T1WI and extracted radiomics features performed well in predicting the expression of PDPN and OS in HGG.

CONCLUSIONS

Radiomics features extracted from MRI images can non-invasively predict PDPN expression and prognosis in HGG, offering a potential imaging biomarker for individualized clinical management.

摘要

背景

血小板源性生长因子结合蛋白（PDPN）是一种膜糖蛋白，与高级别胶质瘤（HGG）的肿瘤侵袭和免疫调节有关。然而，利用放射组学对PDPN表达进行无创预测及其预后意义尚未得到探索。

材料与方法

本研究使用梯度提升机分析术前对比增强MRI T1WI数据，以预测HGG患者的血小板源性生长因子结合蛋白（PDPN）表达和总生存期（OS）。

结果

我们回顾性分析了来自TCIA的89例HGG患者的临床数据、MRI图像和RNA测序谱。对每位患者，从HGG亚区域提取了107个放射组学特征。使用两个选定特征（灰度共生矩阵逆差矩和灰度共生矩阵逆熵）构建了放射组学预后模型。通过使用外部REMBRANDT数据集（n = 39）进行验证，该模型在预测HGG中PDPN表达水平和OS方面表现出良好的预测性能。在TCIA-HGG中，放射组学特征联合临床危险因素对1年、2年和3年OS率的ROC曲线下面积分别为0.799、0.883和0.923。利用术前MRI T1WI和提取的放射组学特征的梯度提升机在预测HGG中PDPN表达和OS方面表现良好。

结论

从MRI图像中提取的放射组学特征可以无创地预测HGG中PDPN的表达和预后，为个体化临床管理提供了一种潜在的影像生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9f9f/12186906/0e5ed299c53c/pone.0325964.g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9f9f/12186906/0e5ed299c53c/pone.0325964.g008.jpg

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