[A two-site combined prediction model based on HOXA9 DNA methylation for early screening of risks of meningioma progression].

OBJECTIVE

To establish a recurrence risk prediction model for meningioma based on HOXA9 DNA methylation.

METHODS

Meningioma-related datasets were downloaded from GEO database for screening homeobox genes (HOXs) with prognostic values using differential methylation and ROC curve analysis and Cox regression analysis. The differentially methylated CpG sites with high predictive efficacy were selected to establish the risk prediction model using Lasso-Cox regression analysis, based on which the patients were divided into high- and low-risk groups by the cutoff value. The methylation levels of CpG sites were verified at the cell and tissue levels using methylation-specific PCR (MS-PCR). Clinical meningioma tissue samples were used to validate the predictive efficacy of the model.

RESULTS

HOXA9 methylation level was significantly up-regulated in meningiomas (< 0.001) and showed a high diagnostic efficiency (AUC=0.884) as an independent risk factor for overall survival (< 0.01) positively correlated with the degree of malignancy and poor prognosis of meningioma (< 0.05). Risk stratification by HOXA9 methylation was more accurate than WHO grading for predicting recurrence and patient survival time. The AUCs of the sites cg03217995 and cg21001184 were both above 0.8 for meningioma diagnosis and above 0.6 for predicting recurrence. The patients' clinical characteristics differed significantly between the high- and low-risk groups (< 0.001), and the prediction score of the model was an independent prognostic factor for meningioma (< 0.05). MSPCR results showed that the methylation levels of the two sites increased significantly in meningioma cells. In clinical samples, the combined model showed a high prediction efficiency (AUC=0.857), and the predicted risk of progression was highly consistent with the patients' actual condition.

CONCLUSION

High HOXA9 methylation level is a predictor for poor prognosis of meningiomas, and the combined prediction model based on its CpG sites provides a new approach to early screening of meningioma patients at risk of progression.