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CDGSH iron sulfur domain 2 (CISD2) has been found to be important in carcinogenesis. However, the role of CISD2 in glioma remains to be elucidated. The present study aimed to investigate the role of CISD2 in glioma using the reverse transcription-quantitative polymerase chain reaction, western blotting, co-immunoprecipitation assay, immunofluorescence staining and other methods. The results demonstrated that the mRNA and protein levels of CISD2 were found to be upregulated in glioma tissues, compared with the levels in matched normal tissues. Clinical data analysis showed that the level of CISD2 was negatively correlated with the survival rates of patients with glioma. In addition, high levels of CISD2 were associated with advanced clinical stage, relapse, vascular invasion and increased tumor size. The inhibition of CISD2 suppressed the proliferation and survival of glioma cells in vitro and in vivo. Mechanistically, it was found that small interfering RNA-induced knock down of CISD2 inhibited the proliferation of glioma cells through activating beclin-1-mediated autophagy. The results also revealed that CISD2 was a target of microRNA (miR)-449a. Together, the results of the present study demonstrated that CISD2 was increased in glioma samples and was associated with poor prognosis and aggressive tumor behavior. The miR-449a/CISD2/beclin-1-mediated autophagy regulatory network contributed to the proliferation of glioma cells. Targeting this pathway may be a promising strategy for glioma therapy.

BackgroundThe aim of this study was to detect the expression of cold-inducible RNA-binding protein in pituitary adenoma and to determine its effects on tumor recurrence.Material/MethodsWe collected a total of 60 post-op samples collected from pituitary adenoma patients (including 20 cases of invasive pituitary adenoma, 20 cases of non-invasive adenoma, and 20 cases of non-invasive recurrent adenoma) admitted in our hospital. Both protein and mRNA levels of CIRP in 3 types of pituitary adenoma samples were quantified by Western blotting and real-time PCR, respectively.ResultsWestern blotting revealed significantly elevated CIRP expression levels in invasive pituitary adenoma compared to non-invasive tumors, with statistical significance (p<0.05). Recurrent pituitary adenoma expressed significantly higher CIRP levels compared to non-recurrent tumors (p<0.05). Real-time PCR for CIRP mRNA obtained consistent results: transcript levels were significantly higher in invasive pituitary adenoma compared to non-invasive adenoma (p<0.05); recurrent adenoma also had significantly higher CIRP mRNA levels compared to non-recurrent tumors (p<0.05). Among all 3 types of pituitary adenoma, recurrent tumors had the highest levels of CIRP mRNA and protein.ConclusionsThe expression of CIRP in pituitary adenoma is closely related with tumor proliferation and invasion, and its significantly elevated expression level indicates post-op recurrence.

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Background Infiltration is important for the surgical planning and prognosis of pituitary adenomas. Differences in preoperative diagnosis have been noted. The aim of this article is to assess the accuracy of machine learning analysis of texture-derived parameters of pituitary adenoma obtained from preoperative MRI for the prediction of high infiltration. Methods A total of 196 pituitary adenoma patients (training set: n = 176; validation set: n = 20) were enrolled in this retrospective study. In total, 4120 quantitative imaging features were extracted from CE-T1 MR images. To select the most informative features, the least absolute shrinkage and selection operator (LASSO) and variance threshold method were performed. The linear support vector machine (SVM) was used to fit the predictive model based on infiltration features. Furthermore, the receiver operating characteristic curve (ROC) was generated, and the diagnostic performance of the model was evaluated by calculating the area under the curve (AUC), accuracy, precision, recall, and F1 value. Results A variance threshold of 0.85 was used to exclude 16 features with small differences using the LASSO algorithm, and 19 optimal features were finally selected. The SVM models for predicting high infiltration yielded an AUC of 0.86 (sensitivity: 0.81, specificity 0.79) in the training set and 0.73 (sensitivity: 0.87, specificity: 0.80) in the validation set. The four evaluation indicators of the predictive model achieved good diagnostic capabilities in the training set (accuracy: 0.80, precision: 0.82, recall: 0.81, F1 score: 0.81) and independent verification set (accuracy: 0.85, precision: 0.93, recall: 0.87, F1 score: 0.90). Conclusions The radiomics model developed in this study demonstrates efficacy for the prediction of pituitary adenoma infiltration. This model could potentially aid neurosurgeons in the preoperative prediction of infiltration in PAs and contribute to the selection of ideal surgical strategies.