Application of colony-stimulating factor 3 in determining the prognosis of high-grade gliomas based on magnetic resonance imaging radiomics

Abstract

Rationale and objectives: Radiomics is a promising, non-invasive method for determining the prognosis of high-grade glioma (HGG). The connection between radiomics and the HGG prognostic biomarker is still insufficient.

Materials and methods: In this study, we collected the pathological, clinical, RNA-sequencing, and enhanced MRI data of HGG from TCIA and TCGA databases. We characterized the prognostic value of CSF3. Kaplan-Meier (KM) analysis, univariate and multivariate Cox regression, subgroup analysis, Spearman analysis, and gene set variation analysis enrichment were used to elucidate the prognostic value of the CSF3 gene and the correlation between CSF3 and tumor features. We used CIBERSORT to analyze the correlation between CSF3 and cancer immune infiltrates. Logistic regression (LR) and support vector machine methods (SVM) were used to build the radiomics models for the prognosis prediction of HGG based on the expression of CSF3.

Results: Based on the radiomics score calculated from LR model, 182 patients with HGG from TCGA database were divided into radiomics score (RS) high and low groups. CSF3 expression varied between tumor and normal group tissues. CSF3 expression was found to be a significant risk factor for survival outcomes. A positive association was found between CSF3 expression and immune infiltration. The radiomics model based on both LR and SVM methods showed high clinical practicability.

Conclusion: The results showed that CSF3 has a prognostic value in HGG. The developed radiomics models can predict the expression of CSF3, and further validate the predictions of the radiomics models for HGG.

Keywords: CSF3; High-grade glioma; MRI; Prognosis; Radiomics.

Fig. 1

(A) Data summary. (B) Radiomics workflow.

Fig. 2

Comparison of CSF3 expression between normal and tumor tissues.

Fig. 3

Association of CSF3 expression and the prognosis of high-grade glioma (HGG) patients. (A) Kaplan–Meier survival curves for the effects of CSF3 expression. (B) Forest plot of the univariate Cox regression model. (C) Forest plot of the multivariate Cox regression model. (D) Subgroup analysis of the association of CSF3 with overall survival (OS). (E) Interaction analysis of CSF3 expression and other variables. (F) Relationship of CSF3 expression with the clinical characteristics of HGG patients.

Fig. 4

Heat map showing the correlation of immune cell infiltration with CSF3 expression.

Fig. 5

Heat maps of CSF3 expression in different gene sets. (A) KEGG pathway analysis; (B) Hallmark pathway analysis.

Fig. 6

Evaluation of the logistic regression (LR) model. (A) Receiver operating characteristic (ROC) curve of the training cohort. (B) ROC curve of the validation cohort. (C) Precision–recall (PR) curve. (D) Calibration curve. (E) Decision curve analysis (DCA). (F) Differences in the radiomics scores between the CSF3 high- and low-expression groups.

Fig. 7

Evaluation of the support vector machine (SVM) model. (A) Receiver operating characteristic (ROC) curve of the training cohort. (B) ROC curve of the validation cohort. (C) Precision–recall (PR) curve. (D) Calibration curve. (E) Decision curve analysis (DCA). (F) The radiomics scores between the CSF3 high- and low-expression groups.

Fig. 8

Association of the radiomics score based on the logistic regression (LR) model and the prognosis of high-grade glioma (HGG) patients. (A) Kaplan–Meier curves for the effects of the radiomics score on overall survival (OS). (B) Forest plot of the univariate Cox regression model. (C) Forest plot of the multivariate Cox regression model. (D) Subgroup analysis of the associations of radiomics scores with OS. (E) Interaction analysis between the radiomics score and other variables.