The polymerase 1 and transcript release factor (PTRF)-cytoplasmic phospholipase A2 (cPLA2) phospholipid remodeling pathway facilitates tumor proliferation in glioma. Nevertheless, blockade of this pathway leads to the excessive activation of oncogenic receptors on the plasma membrane and subsequent drug resistance. Here, CD26/dipeptidyl peptidase 4 (DPP4) was identified through screening of CRISPR/Cas9 libraries. Suppressing PTRF-cPLA2 signaling resulted in the activation of the epidermal growth factor receptor (EGFR) pathway through phosphatidylcholine and lysophosphatidylcholine remodeling, which ultimately increased DPP4 transcription. In turn, DPP4 interacted with EGFR and prevented its ubiquitination. Linagliptin, a DPP4 inhibitor, facilitated the degradation of EGFR by blocking its interaction with DPP4. When combined with the cPLA2 inhibitor AACOCF3, it exhibited synergistic effects and led to a decrease in energy metabolism in glioblastoma cells. Subsequent in vivo investigations provided further evidence of a synergistic impact of linagliptin by augmenting the sensitivity of AACOCF3 and strengthening the efficacy of temozolomide. DPP4 serves as a novel target and establishes a constructive feedback loop with EGFR. Linagliptin is a potent inhibitor that promotes EGFR degradation by blocking the DPP4-EGFR interaction. This study presents innovative approaches for treating glioma by combining linagliptin with AACOCF3 and temozolomide.

Objective To construct a radiomics model for predicting hematoma enlargement in early hypertensive intracerebral hemorrhage and explore its predictive value.Methods A retrospective collection of 212 patients with hypertensive intracerebral hemorrhage within 6 h of onset,admitted to our hospital from February 2010 to August 2018,was performed.CT examination was performed within half an hour of admission.CT re-examination was performed 24 h after admission to determine whether there was hematoma enlargement.The regions of interest were delineated on the first CT,and 431 image indicators were extracted from the Matlab software.The LASSO regression model was used to screen out the most predictive imaging features,and the selected features and support vector machine classifier (SVM) were used to build the prediction model.Receiver operating characteristic (ROC) curve was used to evaluate the predicted effect of the model.Results After 24 h of admission,the incidence of hematoma enlargement was 18.9％ (40/212).Eighteen imaging ensemble features (including 4 first-order statistics features:standard deviation,kurtosis,uniformity,and variance;one shape-and size-based feature:surface to volume ratio;7 textual features:long run low grey level emphasis,inertia,correlation-angle 90,short run emphasis,correlation-all direction,long run emphasis,and inverse difference moment;6 wavelet features:autocorrelation-3,informational measure of correlation2-3,long run high gray level emphasis-4,short run high gray level emphasis-4,short run low gray level emphasis-7,and sum variance-3) were combined with SVM to establish a prediction model by LASSO regression model.The area under ROC curve was 0.928,enjoying sensitivity and specificity of 92.5％ and 83.5％,respectively.Conclusion The constructed radiomics model is helpful in predicting the expansion of hypertensive cerebral hemorrhage.