High cholesterol inhibits EGFR internalization by promoting the competitive binding of APMAP to EPS15R, which in turn activates ERK1/2 to promote epithelial-to-mesenchymal transition. Cholesterol increases the risk of aggressive prostate cancer and has emerged as a potential therapeutic target for prostate cancer. The functional roles of cholesterol in prostate cancer metastasis are not fully understood. Here, we found that cholesterol induces the epithelial-to-mesenchymal transition (EMT) through extracellular-regulated protein kinases 1/2 pathway activation, which is mediated by EGFR and adipocyte plasma membrane-associated protein (APMAP) accumulation in cholesterol-induced lipid rafts. Mechanistically, APMAP increases the interaction with EGFR substrate 15-related protein (EPS15R) to inhibit the endocytosis of EGFR by cholesterol, thus promoting cholesterol-induced EMT. Both the mRNA and protein levels of APMAP are upregulated in clinical prostate cancer samples. Together, these findings shed light onto an APMAP/EPS15R/EGFR axis that mediates cholesterol-induced EMT of prostate cancer cells. Significance: This study delineates the molecular mechanisms by which cholesterol increases prostate cancer progression and demonstrates that the binding of cholesterol-induced APMAP with EPS15R inhibits EGFR internalization and activates ERK1/2 to promote EMT.

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BackgroundMicroRNAs are often abnormally expressed in human non‐small cell lung cancer (NSCLC) and are thought to play a critical role in the emergence or maintenance of NSCLC by binding to its target messenger RNA. We assessed the effects of miR‐155 on cell proliferation and invasion to elucidate the role played by miR‐155/PDCD 4 in NSCLC.MethodsQuantitative reverse transcription‐PCR, Western blotting, and cell counting kit‐8, luciferase, and transwell invasion assays were conducted on a normal human bronchial epithelial cell line (BEAS‐2B) and three NSCLC cell lines (SPC‐A‐1, A549, and H2170).ResultsWe confirmed that miR‐155 was upregulated, while PDCD 4 messenger RNA and protein levels were downregulated in NSCLC cell lines. miR‐155 negatively regulated PDCD 4 at both transcriptional and post‐transcriptional levels. Moreover, PDCD 4 was forecast as an assumed target of miR‐155 using bioinformatic methods and we demonstrated that PDCD 4 was a direct target of miR‐155 using luciferase reporter assays. Furthermore, PDCD 4 overexpression could restrain NSCLC proliferation and invasion induced by miR‐155.ConclusionOur results collectively demonstrate that miR‐155 exerts an oncogenic role in NSCLC by directly targeting PDCD 4.

Summary What is known and objective Drug‐drug interactions (DDI) are frequent causes of adverse clinical drug reactions. Efforts have been directed at the early stage to achieve accurate identification of DDI for drug safety assessments, including the development of in silico predictive methods. In particular, similarity‐based in silico methods have been developed to assess DDI with good accuracies, and machine learning methods have been employed to further extend the predictive range of similarity‐based approaches. However, the performance of a developed machine learning method is lower than expectations partly because of the use of less diverse DDI training data sets and a less optimal set of similarity measures. Method In this work, we developed a machine learning model using support vector machines (SVMs) based on the literature‐reported established set of similarity measures and comprehensive training data sets. The established similarity measures include the 2D molecular structure similarity, 3D pharmacophoric similarity, interaction profile fingerprint (IPF) similarity, target similarity and adverse drug effect (ADE) similarity, which were extracted from well‐known databases, such as DrugBank and Side Effect Resource (SIDER). A pairwise kernel was constructed for the known and possible drug pairs based on the five established similarity measures and then used as the input vector of the SVM. Result The 10‐fold cross‐validation studies showed a predictive performance of AUROC >0.97, which is significantly improved compared with the AUROC of 0.67 of an analogously developed machine learning model. Our study suggested that a similarity‐based SVM prediction is highly useful for identifying DDI. Conclusion in silico methods based on multifarious drug similarities have been suggested to be feasible for DDI prediction in various studies. In this way, our pairwise kernel SVM model had better accuracies than some previous works, which can be used as a pharmacovigilance tool to detect potential DDI.

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Oxalate and calcium are the major risk factors for calcium oxalate (CaOx) stone formation. However, the exact mechanism remains unclear. This study was designed to confirm the potential function of miR-155-5p in the formation of CaOx induced by oxalate and calcium oxalate monohydrate (COM). The HK-2 cells were treated by the different concentrations of oxalate and COM for 48 h. We found that oxalate and COM treatment significantly increased ROS generation, LDH release, cellular MDA levels, and H2O2 concentration in HK-2 cells. The results of qRT-PCR and western blot showed that expression of NOX2 was upregulated, while that of SOD-2 was downregulated following the treatment with oxalate and COM in HK-2 cells. Moreover, the results of miRNA microarray analysis showed that miR-155-5p was significantly upregulated after oxalate and COM treated in HK-2 cells, but miR-155-5p inhibitor treatment significantly decreased ROS generation, LDH release, cellular MDA levels, and H2O2 concentration in HK-2 cells incubated with oxalate and COM. miR-155-5p negatively regulated the expression level of MGP via directly targeting its 3′-UTR, verified by the Dual-Luciferase Reporter System. In vivo, polarized light optical microphotography showed that CaOx crystal significantly increased in the high-dose oxalate and Ca2+ groups compared to the control group. Furthermore, IHC analyses showed strong positive staining intensity for the NOX-2 protein in the high-dose oxalate and Ca2+-treated mouse kidneys, and miR-155-5p overexpression can further enhance its expression. However, the expression of SOD-2 protein was weakly stained. In conclusion, our study indicates that miR-155-5p promotes oxalate- and COM-induced kidney oxidative stress injury by suppressing MGP expression.

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