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Among kidney cancers, clear cell renal cell carcinoma (ccRCC) has the highest incidence rate in adults. The survival rate of patients diagnosed as having metastatic ccRCC drastically declines even with intensive treatment. We examined the efficacy of simvastatin, a lipid-lowering drug with reduced mevalonate synthesis, in ccRCC treatment. Simvastatin was found to reduce cell viability and increase autophagy induction and apoptosis. In addition, it reduced cell metastasis and lipid accumulation, the target proteins of which can be reversed through mevalonate supplementation. Moreover, simvastatin suppressed cholesterol synthesis and protein prenylation that is essential for RhoA activation. Simvastatin might also reduce cancer metastasis by suppressing the RhoA pathway. A gene set enrichment analysis (GSEA) of the human ccRCC GSE53757 data set revealed that the RhoA and lipogenesis pathways are activated. In simvastatin-treated ccRCC cells, although RhoA was upregulated, it was mainly restrained in the cytosolic fraction and concomitantly reduced Rho-associated protein kinase activity. RhoA upregulation might be a negative feedback effect owing to the loss of RhoA activity caused by simvastatin, which can be restored by mevalonate. RhoA inactivation by simvastatin was correlated with decreased cell metastasis in the transwell assay, which was mimicked in dominantly negative RhoA-overexpressing cells. Thus, owing to the increased RhoA activation and cell metastasis in the human ccRCC dataset analysis, simvastatin-mediated Rho inactivation might serve as a therapeutic target for ccRCC patients. Altogether, simvastatin suppressed the cell viability and metastasis of ccRCC cells; thus, it is a potentially effective ccRCC adjunct therapy after clinical validation for ccRCC treatment. [ABSTRACT FROM AUTHOR]

Patients with intracranial artery stenosis show high incidence of stroke. Angiography reports contain rich but underutilized information that can enable the detection of cerebrovascular diseases. This study evaluated various natural language processing (NLP) techniques to accurately identify eleven intracranial artery stenosis from angiography reports. Three NLP models, including a rule-based model, a recurrent neural network (RNN), and a contextualized language model, XLNet, were developed and evaluated by internal–external cross-validation. In this study, angiography reports from two independent medical centers (9614 for training and internal validation testing and 315 as external validation) were assessed. The internal testing results showed that XLNet had the best performance, with a receiver operating characteristic curve (AUROC) ranging from 0.97 to 0.99 using eleven targeted arteries. The rule-based model attained an AUROC from 0.92 to 0.96, and the RNN long short-term memory model attained an AUROC from 0.95 to 0.97. The study showed the potential application of NLP techniques such as the XLNet model for the routine and automatic screening of patients with high risk of intracranial artery stenosis using angiography reports. However, the NLP models were investigated based on relatively small sample sizes with very different report writing styles and a prevalence of stenosis case distributions, revealing challenges for model generalization. [ABSTRACT FROM AUTHOR]

Several variants of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are emerging all over the world. Variant surveillance from genome sequencing has become crucial to determine if mutations in these variants are rendering the virus more infectious, potent, or resistant to existing vaccines and therapeutics. Meanwhile, analyzing many raw sequencing data repeatedly with currently available code-based bioinformatics tools is tremendously challenging to be implemented in this unprecedented pandemic time due to the fact of limited experts and computational resources. Therefore, in order to hasten variant surveillance efforts, we developed an installation-free cloud workflow for robust mutation profiling of SARS-CoV-2 variants from multiple Illumina sequencing data. Herein, 55 raw sequencing data representing four early SARS-CoV-2 variants of concern (Alpha, Beta, Gamma, and Delta) from an open-access database were used to test our workflow performance. As a result, our workflow could automatically identify mutated sites of the variants along with reliable annotation of the protein-coding genes at cost-effective and timely manner for all by harnessing parallel cloud computing in one execution under resource-limitation settings. In addition, our workflow can also generate a consensus genome sequence which can be shared with others in public data repositories to support global variant surveillance efforts. [ABSTRACT FROM AUTHOR]

Methylprednisolone (MP) is an anti-inflammatory drug approved for the treatment of acute spinal cord injuries (SCIs). However, MP administration for SCIs has become a controversial issue while the molecular effects of MP remain unexplored to date. Therefore, delineating the benefits and side effects of MP and determining what MP cannot cure in SCIs at the molecular level are urgent issues. Here, genomic profiles of the spinal cord in rats with and without injury insults, and those with and without MP treatment, were generated at 0, 2, 4, 6, 8, 12, 24, and 48 h post-injury. A comprehensive analysis was applied to obtain three distinct classes: side effect of MP (SEMP), competence of MP (CPMP), and incapability of MP (ICMP). Functional analysis using these genes suggested that MP exerts its greatest effect at 8~12 h, and the CPMP was reflected in the immune response, while SEMP suggested aspects of metabolism, such as glycolysis, and ICMP was on neurological system processes in acute SCIs. For the first time, we are able to precisely reveal responsive functions of MP in SCIs at the molecular level and provide useful solutions to avoid complications of MP in SCIs before better therapeutic drugs are available. [ABSTRACT FROM AUTHOR]