Noninvasive prediction of CCL2 expression level in high‐grade glioma patients

Abstract

Abstract Background Gliomas are recognized as the most frequent type of malignancies in the central nervous system, and efficacious prognostic indicators are essential to treat patients with gliomas and improve their clinical outcomes. The chemokine (C‐C motif) ligand 2 (CCL2) is a promising predictor for glioma malignancy and progression. However, at present, the methods to evaluate CCL2 expression level are invasive and operator‐dependent. Objective It was expected to noninvasively predict CCL2 expression levels in malignant glioma tissues by magnetic resonance imaging (MRI)‐based radiomics and assess the association between the developed radiomics model and prognostic indicators and related genes. Methods MRI‐based radiomics was used to predict CCL2 expression level using data obtained from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) databases. A support vector machine (SVM)‐based radiomics model and a logistic regression (LR)‐based radiomics model were used to predict the radiomics score, and its correlation with CCL2 expression level was analyzed. Results The results revealed that there was an association between CCL2 expression level and the overall survival of cases with gliomas, and bioinformatics correlation analysis showed that CCL2 expression level was highly correlated with disease‐related pathways, such as mTOR signaling pathway, cGMP‐PKG signaling pathway, and MAPK signaling pathway. Both SVM‐ and LR‐based radiomics data robustly predicted CCL2 expression level, and radiomics scores could also be used to predict the overall survival of patients. Moreover, the high/low radiomics scores were highly correlated with the known glioma‐related genes, including CD70, CD27, and PDCD1. Conclusion An MRI‐based radiomics model was successfully developed, and its clinical benefits were confirmed, including the prediction of CCL2 expression level and patients' prognosis.

Keywords

• CCL2 expression level
• gliomas
• machine learning
• MRI‐based radiomics
• noninvasive prediction