Sulforaphane attenuates EGFR signaling in NSCLC cells.

BACKGROUND: EGFR, a receptor tyrosine kinase (RTK), is frequently overexpressed and mutated in non-small cell lung cancer (NSCLC). Tyrosine kinase inhibitors (TKIs) have been widely used in the treatment of many cancers, including NSCLC. However, intrinsic and acquired resistance to TKI remains a common obstacle. One strategy that may help overcome EGFR-TKI resistance is to target EGFR for degradation. As EGFR is a client protein of heat-shock protein 90 (HSP90) and sulforaphane is known to functionally regulate HSP90, we hypothesized that sulforaphane could attenuate EGFR-related signaling and potentially be used to treat NSCLC. RESULTS: Our study revealed that sulforaphane displayed antitumor activity against NSCLC cells both in vitro and in vivo. The sensitivity of NSCLC cells to sulforaphane appeared to positively correlate with the inhibition of EGFR-related signaling, which was attributed to the increased proteasomal degradation of EGFR. Combined treatment of NSCLC cells with sulforaphane plus another HSP90 inhibitor (17-AAG) enhanced the inhibition of EGFR-related signaling both in vitro and in vivo. CONCLUSIONS: We have shown that sulforaphane is a novel inhibitory modulator of EGFR expression and is effective in inhibiting the tumor growth of EGFR-TKI-resistant NSCLC cells. Our findings suggest that sulforaphane should be further explored for its potential clinical applications against NSCLC.

[Study on nonlinear dynamic characteristic indexes of epileptic electroencephalography and electroencephalography subbands].

Electroencephalogram (EEG) is the primary tool in investigation of the brain science. It is necessary to carry out a deepgoing study into the characteristics and information hidden in EEGs to meet the needs of the clinical research. In this paper, we present a wavelet-nonlinear dynamic methodology for analysis of nonlinear characteristic of EEGs and delta, theta, alpha, and beta sub-bands. We therefore studied the effectiveness of correlation dimension (CD), largest Lyapunov exponen, and approximate entropy (ApEn) in differentiation between the interictal EEG and ictal EEG based on statistical significance of the differences. The results showed that the nonlinear dynamic char acteristic of EEG and EEG subbands could be used as effective identification statistics in detecting seizures.

Approximate entropy and support vector machines for electroencephalogram signal classification.

The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index-approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.

Application of approximate entropy on dynamic characteristics of epileptic absence seizure.

Electroencephalogram signals are time-varying complex electrophysiological signals. Existing studies show that approximate entropy, which is a nonlinear dynamics index, is not an ideal method for electroencephalogram analysis. Clinical electroencephalogram measurements usually contain electrical interference signals, creating additional challenges in terms of maintaining robustness of the analytic methods. There is an urgent need for a novel method of nonlinear dynamical analysis of the electroencephalogram that can characterize seizure-related changes in cerebral dynamics. The aim of this paper was to study the fluctuations of approximate entropy in preictal, ictal, and postictal electroencephalogram signals from a patient with absence seizures, and to improve the algorithm used to calculate the approximate entropy. The approximate entropy algorithm, especially our modified version, could accurately describe the dynamical changes of the brain during absence seizures. We could also demonstrate that the complexity of the brain was greater in the normal state than in the ictal state. The fluctuations of the approximate entropy before epileptic seizures observed in this study can form a good basis for further study on the prediction of seizures with nonlinear dynamics.