Glioma, the most common type of primary central nervous system cancers, was progressive with poor survival. MicroRNA, as a novel biomarker, was suspected to be novel biomarkers for glioma diagnosis and prognosis. The study aimed at investigating the diagnostic and predictive value of miR-221/222 family for glioma. In the first phase, we compared plasma miR-221/222 family levels between 50 glioma patients and 51 healthy controls by real-time qRT-PCR amplification. Meanwhile, a meta-analysis based on published studies and presents study was performed to explore the diagnostic performance of miR-221/222 family in human cancers. In the second phase, we correlated the miR-221/222 family expression level with prognosis of glioma using Kaplan-Meier survival curves. The plasma miR-221/222 family levels were found to be significantly upregulated in glioma patients (P = 0.001). The ROC curve analysis yielded an AUC values of 0.84 (95% confidence interval (CI): 0.74-0.93) for miR-221 and 0.92 (95% CI 0.87-0.94) for miR-222. In the meta-analysis, the summary receiver operating characteristic (sROC) was plotted with an AUC of 0.82 (95% CI 0.78-0.85) for miR-221/222 family. It was also demonstrated that high positive plasma miR-221 and miR-222 were both correlated with poor survival rate (miR-221: HR = 2.13; 95% CI, 1.05-4.31; miR-222: HR = 2.09; 95% CI, 1.00-4.37). This study demonstrated that the detection of the miRNA-221/222 family should be considered as a new additional tool to better characterize glioma.

Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi-parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki-67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross-validation, which randomly divided the patients into non-overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 ± 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 ± 0.08, 0.90 ± 0.08, and 0.90 ± 0.07 for grade II, III, and IV gliomas, respectively.

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