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

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.

Fig 1. The radiomics workflow adopted in this study.

Fig 2. The comparison of clinical and survival data.

(A) A comparison of the podoplanin (PDPN) expression level between the high-grade glioma and normal brain tissues. (B) The PDPN expression level Kaplan–Meier curve shows that patients with low expression levels have longer overall survival than patients with high expression; the log-rank test showed a significant difference (p < 0.001). (C) A comparison of the podoplanin (PDPN) expression level in subgroup. (D) Survival analyses according to PDPN expression in the IDH-WT and IDH-MUT subgroups.

Fig 3. Association between podoplanin (PDPN) expression level and clinicopathological characteristics.

(A) Associations between overall survival and clinicopathological characteristics using univariate and multivariable regression: PDPN. (B) Interaction test between PDPN expression level and clinicopathological characteristics. (C) The correlation between PDPN expression level and clinicopathological characteristics (*p < 0.05, **p < 0.01, ***p < 0.001).

Fig 4. Estimation of tumour-infiltrating immune cells in high and low PDPN expression levels based on ImmuCellAI.

Fig 5. Gene Set Variation Analysis (GSVA) estimated differences in pathway podoplanin (PDPN) expression levels.

(A) GSVA of the Hallmark Pathways further uncovered the differences between high and low PDPN expression levels. (B) Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed to explore the potential mechanism between high and low PDPN expression levels using GSVA.

Fig 6. Construction of the radiomics model in high-grade glioma.

(A) Recursive feature elimination analysis on feature radiomics reduction. (B) The model was constructed using the gradient boosting machine and the glcm_Idmn and glcm_Idn were selected. (C) The bar chart of the performance of the model prediction podoplanin expression level.

Fig 7. The evaluation the performance of radiomics model in different methods.

(A) The receiver operating characteristic curve showing the performance in the training and validation cohort. (B) Precision-recall curve illustrating the classifier performance in the training cohort. (C) The Hosmer-Lemeshow test indicated that the calibration curves of the model were a good fit for the data. (D) Decision curve analysis for the model, established using the radiomics features from MRI T1WI sequences.

Fig 8. Evaluation of the performance of the model, combining radiomics score (RS) with clinicopathological characteristics.

(A) The Kaplan–Meier curve based on RS shows that patients with low RS have longer overall survival than patients with high RS; the log-rank test showed a significant difference between the groups (p < 0.001). (B) The nomogram showed that the RS was combined with clinicopathological characteristics to determine the predictive performance of the model in HGG. (C) The calibration plot of this nomogram was developed, showing that the nomogram was well-calibrated for overall survival at 12, 24, and 36 months. (D) Receiver operating characteristic curve showing the performance of the model in the TCGA-HGG cohort.