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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.

Simple Summary The recent infectious disease, coronavirus disease 2019, has become the novel pandemic event in the last decade after swine flu, which happened in 2009. While dealing with the pandemic, the challenge of gaining accurate identification results from abundant samples in a timely manner has still persisted. Here, in this study, we show the implementation of an optimized cloud workflow for a robust, yet accurate, identification process from these two latest pandemics events. This is a great example of how we integrate two current available technologies, next-generation sequencing and cloud computing, in practice into an applicable workflow for pandemics to tackle the issue of obtaining satisfactory results in a shorter time, while the abundant samples are available. Hopefully, the methods used in this study will intrigue more healthcare professionals to implement the cloud workflow as a part of the current identification method during the current or future pandemic and other infectious diseases as well. Abstract Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently become a novel pandemic event following the swine flu that occurred in 2009, which was caused by the influenza A virus (H1N1 subtype). The accurate identification of the huge number of samples during a pandemic still remains a challenge. In this study, we integrate two technologies, next-generation sequencing and cloud computing, into an optimized workflow version that uses a specific identification algorithm on the designated cloud platform. We use 182 samples (92 for COVID-19 and 90 for swine flu) with short-read sequencing data from two open-access datasets to represent each pandemic and evaluate our workflow performance based on an index specifically created for SARS-CoV-2 or H1N1. Results show that our workflow could differentiate cases between the two pandemics with a higher accuracy depending on the index used, especially when the index that exclusively represented each dataset was used. Our workflow substantially outperforms the original complete identification workflow available on the same platform in terms of time and cost by preserving essential tools internally. Our workflow can serve as a powerful tool for the robust identification of cases and, thus, aid in controlling the current and future pandemics.

Perfluorooctane sulfonate (PFOS) is among the most abundant organic pollutants and is widely distributed in the environment, wildlife, and humans. Its toxic effects and biological hazards are associated with its long elimination half-life in humans. However, how it affects renal tubular cells (RTCs) remains unclear. In this study, PFOS was observed to mediate the increase in reactive oxygen species (ROS) generation, followed by the activation of the extracellular-signal-regulated kinase 1/2 (ERK1/2) pathway, which induced autophagy in RTCs. Although PFOS treatment induced autophagy after 6 h, prolonged treatment (24 h) reduced the autophagic flux by increasing lysosomal membrane permeability (LMP), leading to increased p62 protein accumulation and subsequent apoptosis. The increase in LMP was visualized through increased green fluorescence with acridine orange staining, and this was attenuated by 3-methyladenine, an autophagy inhibitor. N-acetyl cysteine and an inhibitor of the mitogen-activated protein kinase kinases (U0126) attenuated autophagy and apoptosis. Taken together, these results indicate that ROS activation and ROS-mediated phosphorylated ERK1/2 activation are essential to activate autophagy, resulting in the apoptosis of PFOS-treated RTCs. Our findings provide insight into the mechanism of PFOS-mediated renal toxicity.

Several classification algorithms have been applied into microarray studies for colorectal cancer identification. Algorithms such as naive bayes, random forest, logistic regression, support vector machine, and deep learning have been successfully used in previous studies. The accuracy of these algorithms shown promising result through n-fold validation. However, most of studies are limited to transcript-level that will implicate to biased interpretation of classification result due to different microarray platform entanglement. Therefore, we applied gene-level classification to generalize transcript-level classification result on multiple colorectal cancer microarray studies through different classification algorithms including: naive Bayes, random forest, logistic regression, support vector machine, and deep learning. We evaluated classification performance using several parameters including: accuracy, area under ROC curve, recall and precision. As the result, we found biased classification result in transcript-level from multiple microarray studies can be solved through gene-level classification by applying annotation and merging. In addition, applying batch effect removal method can make gene-level classification performance slightly improved. Furthermore, annotation and merging also can be used to solve another biased result of feature selection in transcript-level.

Colorectal cancer is one of the most common cancers with the second highest mortality rate in the world. The microarray can be used to collect gene expression alteration information from many tissue samples that will be useful to understand colorectal cancer from the molecular level. However, the mechanism behind the progression from normal to cancer is not fully understood. Here, a cross-platform comparison among three common microarray platforms (Affymetrix, Agilent, and Illumina) was applied. As results, we found a significant correlation of purine metabolism and p53 signaling pathway role in colorectal cancer progression. Purine metabolism can control the regulation of cell proliferation which involve hydro-lyase activity on organelle lumen. Meanwhile, genetic alterations in p53 signaling pathways could control some hallmarks of cancer. These two terms might play important roles in inducing normal colorectal cells into cancer.

Endothelial nitric oxide synthase (eNOS) modulates vascular blood pressure and is predominantly expressed in endothelial cells and activated through the protein kinase B (Akt/PKB)-dependent pathway. We previously reported that 3-methylcholanthrene (3MC) activates the aryl hydrocarbon receptor (AhR) and reduces PI3K/Akt phosphorylation. This study investigated the mechanism underlying the downregulatory effects of 3-MC on nitric oxide (NO) production occurring through the AhR/RhoA/Akt-mediated mechanism. The mechanism underlying the effects of 3-MC on eNOS activity and blood pressure was examined in vitro and in vivo through genetic and pharmacological approaches. Results indicated that 3-MC modified heat shock protein 90 (HSP90), caveolin-1, dynein, and eNOS mRNA and protein expression through the AhR/RhoA-dependent mechanism in mouse cerebral vascular endothelial cells (MCVECs) and that 3-MC reduced eNOS phosphorylation through the AhR/RhoA-mediated inactivation of Akt1. The upregulation of dynein expression was associated with decreased eNOS dimer formation (eNOS dimer; an activated form of the enzyme). Coimmunoprecipitation assay results indicated that 3-MC significantly reduced the interaction between eNOS and its regulatory proteins, including Akt1 and HSP90, but increased the interaction between eNOS and caveolin-1. Immunofluorescence and Western blot analysis revealed that 3-MC reduced the amount of membrane-bound activated eNOS, and a modified Griess assay revealed that 3-MC concomitantly reduced NO production. However, simvastatin reduced 3-MC-mediated murine hypertension. Our study results indicate that AhR, RhoA, and eNOS have major roles in blood pressure regulation. Statin intervention might provide a potential therapeutic approach for reducing hypertension caused by 3-MC. J. Cell. Physiol. 232: 1020-1029, 2017. © 2016 Wiley Periodicals, Inc.

Objective Toll-like receptors (TLRs) are important molecules for detecting both pathogen invasion and tissue damage. The expression of TLR4 is upregulated in ischemic stroke, at least in the subacute stage. However, the TLR downstream pathways in the context of stroke have not been well studied in previous research. The purpose of this study is to elucidate the TLR4 downstream pathways following ischemic stroke. Design and methods In this study, 12 ischemic stroke patients and 12 controls were selected from among 89 ischemic stroke patients and 166 controls. The chosen subjects had the highest levels of TLR4 mRNA in the peripheral blood. The differences in the TLR downstream signaling pathways, which were studied by using an RT2 Profiler TM PCR array system (Qiagen), were analyzed. The differentially expressed genes were analyzed by using GeneSpring GX and visualized based on the TLR pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG). Results The genes upregulated in stroke patients were found to be involved in the MyD88-independent pathway and in UBE2V1–TRAF6 ubiquitin-mediated proteolysis. The genes were more expressed in extracellular space, receptor binding, and cytokine receptor binding by use of gene ontology (GO) terms than in control patients. Conclusions We found that the MyD88-independent pathway and the ubiquitin-mediated proteolysis pathway, especially TRAF6, may be the most vital molecules among TLR downstream pathways in incidences of ischemic stroke.

The explosion of microarray studies has promised to shed light on the identification of disease markers. To find novel prognostic factors, we used the expression profile of a poor prognostic factor of lung cancer, survivin (BIRC5), as a template to search for and compare transcriptome expression profiles in a lung adenocarcinoma microarray dataset. Trophinin (TRO) was identified as one of the best-correlated genes. The trophinin expression in lung cancer specimens was examined by immunohistochemical staining. The role of trophinin in cancer metastasis was further investigated by approaches of overexpression and knock down with small interfering RNA (siRNA). For stage I lung adenocarcinoma, the patients without trophinin expression had a better overall and disease-free survival. Overexpression of trophinin increases cell invasion ability and knock down with siRNA inhibits cell invasion. Through a combination of data mining and biochemical assays, we identified trophinin, which could enhance cell invasion, as a novel prognostic factor for early stage lung cancer.

Introduction: In an attempt to search for genes with abnormal expression in cancers, Suppressed in Lung Cancer (SLAN, also known as KIAA0256) is found underexpressed in human lung cancer tissues by quantitative real-time PCR (Q-RT-PCR). The study set out to characterize SLAN protein and explore its cellular functions. Methods: SLAN or its specific short hairpin RNA, full length or various deletion mutants were overexpressed in 293T or lung cancer cell lines, and cell proliferation, cell cycle, mitosis progression, and spindle configuration were surveyed. Results: SLAN and its deletion mutants are localized to many subcellular locations such as endoplasmic reticulum (ER), nucleus, nucleolus, spindle pole and midbody, suggesting SLAN may function as a multifunctional protein. Overexpression of SLAN per se or its short hairpin RNAs (shRNAs) inhibits or accelerates cell proliferation through prolonging or shortening mitosis. Time-lapse microscopic recording reveals that cells overexpressing exogenous SLAN are arrested in mitosis or cannot undergo cytokinesis. SLAN 2‐551 mutants drastically arrest cells in mitosis, where - and -tubulin are disorganized. SLAN employs C-terminal to interact with Aurora-A, a key mitosis regulator and an oncogenic kinase associated with a wide range of human cancers. SLAN negatively regulates the activity of Aurora-A by directly inhibiting kinase activity in vitro or reducing the level of active Aurora-A in cells. SLAN is frequently reduced in lung cancer tissues overexpressing Aurora-A, arguing for the necessity to suppress SLAN during the Aurora-A-associated cancer formation. Conclusions: Taken together, we have identified a novel protein SLAN downregulated in lung caner, having multiple subcellular localization including spindle matrix and midbody, inhibiting cell proliferation and Aurora-A.

The continuous quality improvement has become a trend in the contemporary medical society, and that can be achieved by the specialty database implement. Decision-support system in the academic and clinical aspects are included in the process such continuous quality improvement. The database has its limitation in the decision-support due to deficiency of on-line analytic function. The data warehouse offers the sophisticated function for decision-support processes. However, the implement of data warehouse may face a lot of obstacles, included expensive cost and large personnel. We had previously established a database of orthopedics, which collected the patients’ data since 2002. The new system was constructed based on this specialty database, the knowledge architectures was build up via specialists committee and accreditation indicators. The major function was to generate sufficient information for decision-support process in the academic and clinical aspects. The execution efficiency of this system is more effective than database. The unique knowledge architecture can form a distinguishing feature of the department. The cost that saved from personnel and time reduced from reports generation for accreditation is remarkable. The stratification of web-based interface application can be assessed through questionnaires; the outcome is satisfactory as what we previously expected. The sophisticate function of the data warehouse is hard to express in a solitary department of the hospital, especially when they had already owned traditional database. The experience of this system construction can be useful as one option for upgrade of specialty database and a step forward to the goal of the continuous quality improvement.

It was proposed that Epstein-Barr virus (EBV) is closely associated with nasopharyngeal carcinoma (NPC); however, the molecular mechanisms involved in the effect of EBV on NPC host genes have not yet been well defined. For this study, two sets of microarray experiments, NPC (EBV-free) vs normal epithelial cells and EBV(+) vs EBV(-) NPC arrays, were analyzed and the datasets were cross-compared to identify any correlation between gene clusters involved in EBV targeting and the NPC host gene expression profiles. Statistical analysis revealed that EBV seems to have a preference for targeting more genes from the differentially expressed group in NPC cells than those from the ubiquitously expressed group. Furthermore, this trend is also reflected in log ratios where the EBV target genes of the differentially expressed group origin showed greater log ratios than genes with an origin from the ubiquitously expressed NPC group. Taken together, the genome-wide comparative scanning of EBV and NPC transcriptomes has successfully demonstrated that EBV infection has an intensifying effect on the signals involved in NPC gene expression both in breadth (the majority of the genes) and in depth (greater log ratios).