Lung adenocarcinoma-related target gene prediction and drug repositioning.

Lung cancer is the leading cause of cancer deaths globally, and lung adenocarcinoma (LUAD) is the most common type of lung cancer. Gene dysregulation plays an essential role in the development of LUAD. Drug repositioning based on associations between drug target genes and LUAD target genes are useful to discover potential new drugs for the treatment of LUAD, while also reducing the monetary and time costs of new drug discovery and development. Here, we developed a pipeline based on machine learning to predict potential LUAD-related target genes through established graph attention networks (GATs). We then predicted potential drugs for the treatment of LUAD through gene coincidence-based and gene network distance-based methods. Using data from 535 LUAD tissue samples and 59 precancerous tissue samples from The Cancer Genome Atlas, 48,597 genes were identified and used for the prediction model building of the GAT. The GAT model achieved good predictive performance, with an area under the receiver operating characteristic curve of 0.90. 1,597 potential LUAD-related genes were identified from the GAT model. These LUAD-related genes were then used for drug repositioning. The gene overlap and network distance with the target genes were calculated for 3,070 drugs and 672 preclinical compounds approved by the US Food and Drug Administration. At which, bromoethylamine was predicted as a novel potential preclinical compound for the treatment of LUAD, and cimetidine and benzbromarone were predicted as potential therapeutic drugs for LUAD. The pipeline established in this study presents new approach for developing targeted therapies for LUAD.

Identifying Predictors of COVID-19 Mortality Using Machine Learning.

(1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to investigate COVID-19 mortality in patients with pre-existing health conditions and to examine the association between COVID-19 mortality and other morbidities. (2) Methods: De-identified data from 113,882, including 14,877 COVID-19 patients, were collected from the UK Biobank. Different types of data, such as disease history and lifestyle factors, from the COVID-19 patients, were input into the following three machine learning models: Deep Neural Networks (DNN), Random Forest Classifier (RF), eXtreme Gradient Boosting classifier (XGB) and Support Vector Machine (SVM). The Area under the Curve (AUC) was used to measure the experiment result as a performance metric. (3) Results: Data from 14,876 COVID-19 patients were input into the machine learning model for risk-level mortality prediction, with the predicted risk level ranging from 0 to 1. Of the three models used in the experiment, the RF model achieved the best result, with an AUC value of 0.86 (95% CI 0.84-0.88). (4) Conclusions: A risk-level prediction model for COVID-19 mortality was developed. Age, lifestyle, illness, income, and family disease history were identified as important predictors of COVID-19 mortality. The identified factors were related to COVID-19 mortality.

Puerarin 6″-O-xyloside suppressed HCC via regulating proliferation, stemness, and apoptosis with inhibited PI3K/AKT/mTOR.

Puerarin 6″-O-xyloside is a tumor suppressive derivate of Puerarin that is recently characterized as a lysine-specific demethylase 6B inhibitor. Here we investigated the effects of Puerarin 6″-O-xyloside in hepatocellular carcinoma (HCC) cell lines SMMC-7721 and HepG2. Cell viability, proliferation, stemness, protein expression, and autophagy were tested by CCK-8, colony formation, sphere formation, western blotting, and LC3B GFP puncta per cell, respectively. Apoptosis, CD133-positive cells, and JC-1-labeled mitochondrial membrane potential were measured by flow cytometry. The effects of Puerarin 6″-O-xyloside in vivo were explored in HepG2 xenograft mice. Puerarin 6″-O-xyloside inhibited cell viability, proliferation, and stemness, and promoted apoptosis in both SMMC-7721 and HepG2 cells. Further experiments showed promoted autophagy and decreased mitochondrial membrane potential, and decreased expression of p-PI3K, p-AKT, and p-mTOR in HepG2 cells. Co-administration of 3-MA with Puerarin 6″-O-xyloside obviously augmented these effects including inhibited protein expression of p-PI3K, p-AKT, and p-mTOR, and inhibited proliferation, promoted apoptosis, and decreased stemness. In HepG2 xenograft mice, 100 mg/kg/d Puerarin 6″-O-xyloside significantly suppressed tumor growth, stemness, and apoptosis. In conclusion, our study indicated that Puerarin 6″-O-xyloside decreased cell viability, proliferation, and stemness, and promoted autophagy and mitochondria-dependent apoptosis of HCC, at least partly through inhibiting PI3K/AKT/mTOR. These results highlighted Puerarin 6″-O-xyloside as a promising prodrug that could inhibit both PI3K/AKT/mTOR and epigenetic demethylation.

Green tea extract for treatment of cancers: A systematic review protocol.

BACKGROUND: Previous clinical studies suggested that green tea extract (GTE) may benefit patients with a variety of cancers. However, its efficacy is still inconclusive. Thus, the objective of this study will systematically collate the clinical studies testing its efficacy and safety for cancers. METHODS: We will perform a systematic review of clinical studies assessing the efficacy of GTE in variety of cancers. We will search Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, MEDILINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Allied and Complementary Medicine Database (AMED), and Chinese Biomedical Literature Database (CBM) using a comprehensive strategy. We will also screen the reference lists of relevant studies to identify any additional studies for potential inclusion. All databases will be searched up to February 1, 2019. All eligible case-control studies and randomized controlled trials will be included in this study. Two independent authors will review all searched literature. Upon inclusion of trials, we will extract data by using a predefined standardized form. The risk of bias assessment will be evaluated by using Cochrane risk of bias tool. We will use RevMan 5.3 software to pool the data and carry out meta-analysis. RESULTS: The primary outcome includes overall response rate. The secondary outcomes comprise of overall survival, progression-free survival, the disease control rate, and any adverse events. CONCLUSIONS: The results of this study will contribute to the understanding of the efficacy of GTE in the setting of cancers and promote future research of GTE in patients with cancers. DISSEMINATION AND ETHICS: The results of this systematic review are expected to be published through peer-reviewed journals. This study does not need ethic approval, because it does not utilize individual patient data. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019125111.

Effects of RANKL, osteoprotegerin, calcium and phosphorus on survival and activation of Muscovy duck osteoclasts in vitro.

The aim of this study was to determine whether receptor activator of nuclear factor NF-kappaB ligand (RANKL), osteoprotegerin (OPG) and a calcium:phosphorus (Ca:P) ratio of 2:1 could affect survival and activation of Muscovy duck osteoclasts (OCs). Bone marrow cells were obtained from 5-day-old Muscovy ducks and cultured with (Group A) No added factors, (B) 30ng/mL soluble RANKL (sRANKL), (C) 30ng/mL sRANKL and 10ng/mL OPG, (D) 10ng/mL OPG, (E) 50ng/mL OPG, (F) 100ng/mL OPG and (G) 30ng/mL sRANKL, 6mmol/L Ca and 3mmol/L P. sRANKL promoted the survival of OCs on day 2, whereas the number of OCs decreased with addition of OPG in a dose-dependent manner. OPG and Ca:P (2:1) both inhibited OC survival induced by RANKL. RANKL stimulated bone resorption by OCs, whereas OPG, but not Ca:P (2:1), inhibited the activity of OCs induced by RANKL. RANKL promotes the survival and activation of OCs from Muscovy ducks, whereas OPG and, to a lesser extent, Ca:P (2:1) reduce the life span and inhibited the activation of OCs induced by RANKL.