Objective. To investigate the effect of cantharidin on DNA damage in hepatocellular carcinoma cells and its possible mechanism. Methods. Cell proliferation assay and terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) assay were used to analyze the effects of cantharidin on cell proliferation and apoptosis of hepatocellular carcinoma cells. The expression levels of DNA damage markers H2AX and P21 were analyzed by qRT-PCR. The expression of KDM4A and H3K36me3 was observed by western blot. The expression of KDM4A was regulated by siRNA or plasmid transfection. The effect of KDM4A on DNA damage induced by cantharidin in liver cancer was observed after overexpression and addiction of KDM4A. Results. Cantharidin can significantly inhibit the growth of hepatocellular carcinoma cells and induce apoptosis of hepatocellular carcinoma cells. Cantharidin enhances the chemotherapy sensitivity of liver cancer by targeting the upregulation of KDM4A and the regulation of DNA damage induced by H3K36me3. Overexpression of KDM4A enhances DNA damage induced by cantharidin in HCC. KDM4A silencing attenuated the damage of cantharidin to the DNA of HCC cells. Conclusion. Cantharidin can inhibit the growth and promote apoptosis of hepatocellular carcinoma cells. Meanwhile, cantharidin can induce DNA damage in HCC cells. Mechanism studies have shown that cantharidin induces DNA damage through the demethylation of KDM4A-dependent histone H3K36.

In this paper, in-depth research analysis of anti-hepatocellular carcinoma molecular targets for hepatocellular carcinoma diagnosis was conducted using artificial intelligence. Because BRD4 plays an important role in gene transcription for cell cycle regulation and apoptosis, tumor-targeted therapy by inhibiting the expression or function of BRD4 has received increasing attention in the field of antitumor research. Study subjects in small samples were used as the validation set for validating each diagnostic model constructed based on the training set. The diagnostic effect of each model in the validation set is evaluated by calculating the sensitivity, specificity, and compliance rate, and the model with the best and most stable diagnostic value is selected by combining the results of model construction, validation, and evaluation. The total sample was divided into a training set and test set by using a stratified sampling method in the ratio of 7 : 3. Logistic regression, weighted k -nearest neighbor, decision tree, and BP artificial neural network were used in the training set to construct diagnostic models for early-stage liver cancer, respectively, and the optimal parameters of the corresponding models were obtained, and then, the constructed models were validated in the test set. To evaluate the diagnostic efficacy, stability, and generalization ability of the four classification methods more robustly, a 10-fold crossover test was performed for each classification method. BRD4 is an epigenetic regulator that is associated with the upregulation of expression of various oncogenic drivers in tumors. Targeting BRD4 with pharmacological inhibitors has emerged as a novel approach for tumor treatment. However, before we implemented this topic, there were no detailed studies on whether BRD4 could be used for the treatment of HCC, the role of BRD4 in HCC cell proliferation and apoptosis, and the ability of small molecule BRD4 inhibitors to induce apoptosis in hepatocellular carcinoma cells.

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