BackgroundThis study aimed to establish an effective predictive nomogram for non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection.MethodsThe nomogram was based on a retrospective study of 230 NSCLC patients with chronic HBV infection. The predictive accuracy and discriminative ability of the nomogram were determined by a concordance index (C-index), calibration plot and decision curve analysis and were compared with the current tumor, node, and metastasis (TNM) staging system.ResultsIndependent factors derived from Kaplan–Meier analysis of the primary cohort to predict overall survival (OS) were all assembled into a Cox proportional hazards regression model to build the nomogram model. The final model included age, tumor size, TNM stage, treatment, apolipoprotein A-I, apolipoprotein B, glutamyl transpeptidase and lactate dehydrogenase. The calibration curve for the probability of OS showed that the nomogram-based predictions were in good agreement with the actual observations. The C-index of the model for predicting OS had a superior discrimination power compared with the TNM staging system [0.780 (95% CI 0.733–0.827) vs. 0.693 (95% CI 0.640–0.746), P < 0.01], and the decision curve analyses showed that the nomogram model had a higher overall net benefit than did the TNM stage. Based on the total prognostic scores (TPS) of the nomogram, we further subdivided the study cohort into three groups: low risk (TPS ≤ 13.5), intermediate risk (13.5 < TPS ≤ 20.0) and high risk (TPS > 20.0).ConclusionThe proposed nomogram model resulted in more accurate prognostic prediction for NSCLC patients with chronic HBV infection.
Chronic hepatitis B (CHB) virus infection is one of the leading causes of cirrhosis and liver cancer. Although the major drugs against CHB including nucleos(t)ide analogs and PEG-interferon can effectively control human hepatitis B virus (HBV) infection, complete cure of HBV infection is quite rare. Targeting host factors involved in the viral life cycle contributes to developing innovative therapeutic strategies to improve HBV clearance. In this study, we found that the mRNA and protein levels of SIRT2, a class III histone deacetylase, were significantly upregulated in CHB patients, and that SIRT2 protein level was positively correlated with HBV viral load, HBsAg/HBeAg levels, HBcrAg, and ALT/AST levels. Functional analysis confirmed that ectopic SIRT2 overexpression markedly increased total HBV RNAs, 3.5-kb RNA and HBV core DNA in HBV-infected HepG2-Na+/taurocholate cotransporting polypeptide cells and primary human hepatocytes. In contrast, SIRT2 silencing inhibited HBV transcription and replication. In addition, we found a positive correlation between SIRT2 expression and HBV RNAs synthesis as well as HBV covalently closed circular DNA transcriptional activity. A mechanistic study suggested that SIRT2 enhances the activities of HBV enhancer I/HBx promoter (EnI/Xp) and enhancer II/HBc promoter (EnII/Cp) by targeting the transcription factor p53. The levels of HBV EnI/Xp and EnII/Cp-bound p53 were modulated by SIRT2. Both the mutation of p53 binding sites in EnI/Xp and EnII/Cp as well as overexpression of p53 abolished the effect of SIRT2 on HBV transcription and replication. In conclusion, our study reveals that, in terms of host factors, a SIRT2-targeted program might be a more effective therapeutic strategy for HBV infection.
Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we develop a machine learning method to comprehensively predict natural products responses against a panel of cancer cell lines based on both the gene expression and the chemical properties of natural products. The results on two datasets, training set and independent test set, show that this proposed method yields significantly better prediction accuracy. In addition, we also demonstrate the predictive power of our proposed method by modeling the cancer cell sensitivity to two natural products, Curcumin and Resveratrol, which indicate that our method can effectively predict the response of cancer cell lines to these two natural products. Taken together, the method will facilitate the identification of natural products as cancer therapies and the development of precision medicine by linking the features of patient genomes to natural product sensitivity.
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