CircNet 2.0: an updated database for exploring circular RNA regulatory networks in cancers.

Circular RNAs (circRNAs), which are single-stranded RNA molecules that have individually formed into a covalently closed continuous loop, act as sponges of microRNAs to regulate transcription and translation. CircRNAs are important molecules in the field of cancer diagnosis, as growing evidence suggests that they are closely related to pathological cancer features. Therefore, they have high potential for clinical use as novel cancer biomarkers. In this article, we present our updates to CircNet (version 2.0), into which circRNAs from circAtlas and MiOncoCirc, and novel circRNAs from The Cancer Genome Atlas database have been integrated. In total, 2732 samples from 37 types of cancers were integrated into CircNet 2.0 and analyzed using several of the most reliable circRNA detection algorithms. Furthermore, target miRNAs were predicted from the full-length circRNA sequence using three reliable tools (PITA, miRanda and TargetScan). Additionally, 384 897 experimentally verified miRNA-target interactions from miRTarBase were integrated into our database to facilitate the construction of high-quality circRNA-miRNA-gene regulatory networks. These improvements, along with the user-friendly interactive web interface for data presentation, search, and visualization, showcase the updated CircNet database as a powerful, experimentally validated resource, for providing strong data support in the biomedical fields. CircNet 2.0 is currently accessible at https://awi.cuhk.edu.cn/∼CircNet.

dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data.

The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.

Effects of osteoporosis medications on bone fracture in patients with chronic kidney disease.

PURPOSE OF THE STUDY: The risk of bone fracture is high in patients with chronic kidney disease (CKD), and aggressive treatment to reduce fragility fracture risk is the major strategy. However, the outcomes of osteoporosis medications in patients with CKD remain unclear. STUDY DESIGN: Patients with stage 3-5 CKD during 2011-2019 were enrolled. Patients were divided into two groups based on receiving osteoporosis medications (bisphosphonates, raloxifene, teriparatide or denosumab) or not. Two groups were matched at a 1:1 ratio by using propensity scores. The outcomes of interest were bone fractures, cardiovascular (CV) events and all-cause mortality. Cox proportional hazard regression models were applied to identify the risk factors. Additional stratified analyses by cumulative dose, treatment length and menopause condition were performed. RESULTS AND CONCLUSIONS: 67 650 patients were included. After propensity score matching, 1654 patients were included in the study and control group, respectively. The mean age was 70.2±12.4 years, and 32.0% of patients were men. After a mean follow-up of 3.9 years, the incidence rates of bone fracture, CV events and all-cause mortality were 2.0, 1.7 and 6.5 per 1000 person-months, respectively. Multivariate analysis results showed that osteoporosis medications reduced the risk of CV events (HR, 0.35; 95% CI, 0.18 to 0.71; p = 0.004), but did not alleviate the risks of bone fracture (HR, 1.48; 95% CI, 0.73 to 2.98; p = 0.28) and all-cause mortality (HR, 0.93; 95% CI, 0.67 to 1.28; p = 0.65). Stratified analysis showed that bisphosphonates users have most benefits in the reduction of CV events (HR, 0.26; 95% CI, 0.11 to 0.64; p = 0.003). In conclusion, osteoporosis medications did not reduce the risk of bone fractures, or mortality, but improved CV outcomes in patients with CKD.

Hypermethylation of TMEM240 predicts poor hormone therapy response and disease progression in breast cancer.

BACKGROUND: Approximately 25% of patients with early-stage breast cancer experience cancer progression throughout the disease course. Alterations in TMEM240 in breast cancer were identified and investigated to monitor treatment response and disease progression. METHODS: Circulating methylated TMEM240 in the plasma of breast cancer patients was used to monitor treatment response and disease progression. The Cancer Genome Atlas (TCGA) data in Western countries and Illumina methylation arrays in Taiwanese breast cancer patients were used to identify novel hypermethylated CpG sites and genes related to poor hormone therapy response. Quantitative methylation-specific PCR (QMSP), real-time reverse transcription PCR, and immunohistochemical analyses were performed to measure DNA methylation and mRNA and protein expression levels in 394 samples from Taiwanese and Korean breast cancer patients. TMEM240 gene manipulation, viability, migration assays, RNA-seq, and MetaCore were performed to determine its biological functions and relationship to hormone drug treatment response in breast cancer cells. RESULTS: Aberrant methylated TMEM240 was identified in breast cancer patients with poor hormone therapy response using genome-wide methylation analysis in the Taiwan and TCGA breast cancer cohorts. A cell model showed that TMEM240, which is localized to the cell membrane and cytoplasm, represses breast cancer cell proliferation and migration and regulates the expression levels of enzymes involved in estrone and estradiol metabolism. TMEM240 protein expression was observed in normal breast tissues but was not detected in 88.2% (67/76) of breast tumors and in 90.0% (9/10) of metastatic tumors from breast cancer patients. QMSP revealed that in 54.5% (55/101) of Taiwanese breast cancer patients, the methylation level of TMEM240 was at least twofold higher in tumor tissues than in matched normal breast tissues. Patients with hypermethylation of TMEM240 had poor 10-year overall survival (p = 0.003) and poor treatment response, especially hormone therapy response (p < 0.001). Circulating methylated TMEM240 dramatically and gradually decreased and then diminished in patients without disease progression, whereas it returned and its levels in plasma rose again in patients with disease progression. Prediction of disease progression based on circulating methylated TMEM240 was found to have 87.5% sensitivity, 93.1% specificity, and 90.2% accuracy. CONCLUSIONS: Hypermethylation of TMEM240 is a potential biomarker for treatment response and disease progression monitoring in breast cancer.

CCL5 promotion of bioenergy metabolism is crucial for hippocampal synapse complex and memory formation.

Glucoregulatory efficiency and ATP production are key regulators for neuronal plasticity and memory formation. Besides its chemotactic and neuroinflammatory functions, the CC chemokine--CCL5 displays neurotrophic activity. We found impaired learning-memory and cognition in CCL5-knockout mice at 4 months of age correlated with reduced hippocampal long-term potentiation and impaired synapse structure. Re-expressing CCL5 in knockout mouse hippocampus restored synaptic protein expression, neuronal connectivity and cognitive function. Using metabolomics coupled with FDG-PET imaging and seahorse analysis, we found that CCL5 participates in hippocampal fructose and mannose degradation, glycolysis, gluconeogenesis as well as glutamate and purine metabolism. CCL5 additionally supports mitochondrial structural integrity, purine synthesis, ATP generation, and subsequent aerobic glucose metabolism. Overexpressing CCL5 in WT mice also enhanced memory-cognition performance as well as hippocampal neuronal activity and connectivity through promotion of de novo purine and glutamate metabolism. Thus, CCL5 actions on glucose aerobic metabolism are critical for mitochondrial function which contribute to hippocampal spine and synapse formation, improving learning and memory.

Effects of osteoporosis medications on bone fracture in patients with chronic kidney disease.

PURPOSE OF THE STUDY: The risk of bone fracture is high in patients with chronic kidney disease (CKD), and aggressive treatment to reduce fragility fracture risk is the major strategy. However, the outcomes of osteoporosis medications in patients with CKD remain unclear. STUDY DESIGN: Patients with stage 3-5 CKD during 2011-2019 were enrolled. Patients were divided into two groups based on receiving osteoporosis medications (bisphosphonates, raloxifene, teriparatide or denosumab) or not. Two groups were matched at a 1:1 ratio by using propensity scores. The outcomes of interest were bone fractures, cardiovascular (CV) events and all-cause mortality. Cox proportional hazard regression models were applied to identify the risk factors. Additional stratified analyses by cumulative dose, treatment length and menopause condition were performed. RESULTS AND CONCLUSIONS: 67 650 patients were included. After propensity score matching, 1654 patients were included in the study and control group, respectively. The mean age was 70.2±12.4 years, and 32.0% of patients were men. After a mean follow-up of 3.9 years, the incidence rates of bone fracture, CV events and all-cause mortality were 2.0, 1.7 and 6.5 per 1000 person-months, respectively. Multivariate analysis results showed that osteoporosis medications reduced the risk of CV events (HR, 0.35; 95% CI, 0.18 to 0.71; p = 0.004), but did not alleviate the risks of bone fracture (HR, 1.48; 95% CI, 0.73 to 2.98; p = 0.28) and all-cause mortality (HR, 0.93; 95% CI, 0.67 to 1.28; p = 0.65). Stratified analysis showed that bisphosphonates users have most benefits in the reduction of CV events (HR, 0.26; 95% CI, 0.11 to 0.64; p = 0.003). In conclusion, osteoporosis medications did not reduce the risk of bone fractures, or mortality, but improved CV outcomes in patients with CKD.

Tissue microbiota in nasopharyngeal adenoid and its association with pneumococcal carriage.

The microbial colonization in the nasopharynx is a prerequisite for the onset of infectious diseases. For successful infection, pathogens should overcome host defenses as well as compete effectively with the resident microbiota. Hence, elucidating the richness and diversity of the microbiome at the site of pathogen colonization is pivotal. Here, we investigated the adenoidal tissue microbiota collected through adenoidectomy to evaluate the impact of Streptococcus pneumoniae. Prospectively, children with sleep-disordered breathing (SDB) and otitis media with effusion (OME) were enrolled. During adenoidectomy, the nasopharyngeal swab and adenoid tissues were collected to determine the pneumococcal carriage and tissue microbiota, using multiplex PCR and 16S ribosomal RNA (16S rRNA) pyrosequencing. A total of 66 pediatric patients comprising 38 children with SDB and 28 children with OME were enrolled. There was no difference between the bacterial cultures from the surface of the nasopharyngeal adenoid in the SDB and OME groups. Thirty-four samples (17 SDB and 17 OME) underwent 16S rRNA pyrosequencing and fulfilled the criteria for further analysis. The Shannon diversity index for the samples from the SDB patients was found to be higher than that observed for the samples from OME patients, although the difference was not significant (p = 0.095). The Shannon diversity index for the samples negative for the pneumococcal carriage was significantly higher than that for the samples positive for pneumococcal carriage (p = 0.038). Alloprevotella, Staphylococcus, Moraxella, and Neisseriaceae were significantly dominant in the samples positive for the pneumococcal carriage. Dialister was significantly less present in the adenoid tissue positive for the pneumococcal carriage. Streptococcus pneumoniae, one of the most common pathogens of the airway, significantly influences the composition and diversity of the microbiota in the nasopharyngeal adenoid. Thus, bacterial community analysis based on 16S rRNA pyrosequencing allows for better understanding of the relationship between the adenoidal microbial communities.

Correction: A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.

[This corrects the article DOI: 10.2196/27806.].

A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.

BACKGROUND: More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country's policy measures. OBJECTIVE: We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries. METHODS: The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set. RESULTS: A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea. CONCLUSIONS: The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning-based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

Untargeted metabolomics predicts the functional outcome of ischemic stroke.

BACKGROUND/PURPOSE: Metabolites in blood have been found associated with the occurrence of vascular diseases, but its role in the functional recovery of stroke is unclear. The aim of this study is to investigate whether the untargeted metabolomics at the acute stage of ischemic stroke is able to predict functional recovery. METHODS: One hundred and fifty patients with acute ischemic stroke were recruited and followed up for 3 months. Fasting blood samples within 7 days of stroke were obtained, liquid chromatography and mass spectrometry were applied to identify outcome-associated metabolites. The patients' clinical characteristics and identified metabolites were included for constructing the outcome prediction model using machine learning approaches. RESULTS: By using multivariate analysis, 220 differentially expressed metabolites (DEMs) were discovered between patients with favorable outcomes (modified Rankin Scale, mRS ≤ 2 at 3 months, n = 77) and unfavorable outcomes (mRS ≥ 3 at 3 months, n = 73). After feature selection, 63 DEMs were chosen for constructing the outcome prediction model. The predictive accuracy was below 0.65 when including patients' clinical characteristics, and could reach 0.80 when including patients' clinical characteristics and 63 selected DEMs. The functional enrichment analysis identified platelet activating factor (PAF) as the strongest outcome-associated metabolite, which involved in proinflammatory mediators release, arachidonic acid metabolism, eosinophil degranulation, and production of reactive oxygen species. CONCLUSION: Metabolomics is a potential method to explore the blood biomarkers of acute ischemic stroke. The patients with unfavorable outcomes had a lower PAF level compared to those with favorable outcomes.

Sarcomeres Morphology and Z-Line Arrangement Disarray Induced by Ventricular Premature Contractions through the Rac2/Cofilin Pathway.

The most common ventricular premature contractions (VPCs) originate from the right ventricular outflow tract (RVOT), but the molecular mechanisms of altered cytoskeletons of VPC-induced cardiomyopathy remain unexplored. We created a RVOT bigeminy VPC pig model (n = 6 in each group). Echocardiography was performed. The histopathological alternations in the LV myocardium were analyzed, and next generation sequencing (NGS) and functional enrichment analyses were employed to identify the differentially expressed genes (DEGs) responsible for the histopathological alternations. Finally, a cell silencing model was used to confirm the key regulatory gene and pathway. VPC pigs had increased LV diameters in the 6-month follow-up period. A histological study showed more actin cytoskeleton disorganization and actin accumulation over intercalated disc, Z-line arrangement disarray, increased ß-catenin expression, and cardiomyocyte enlargement in the LV myocardium of the VPC pigs compared to the control pigs. The NGS study showed actin cytoskeleton signaling, RhoGDI signaling, and signaling by Rho Family GTPases and ILK Signaling presented z-scores with same activation states. The expressions of Rac family small GTPase 2 (Rac2), the p-cofilin/cofilin ratio, and the F-actin/G-actin ratio were downregulated in the VPC group compared to the control group. Moreover, the intensity and number of actin filaments per cardiomyocyte were significantly decreased by Rac2 siRNA in the cell silencing model. Therefore, the Rac2/cofilin pathway was found to play a crucial role in the sarcomere morphology and Z-line arrangement disarray induced by RVOT bigeminy VPCs.

Genome-wide discovery of viral microRNAs based on phylogenetic analysis and structural evolution of various human papillomavirus subtypes.

In mammals, microRNAs (miRNAs) play key roles in controlling posttranscriptional regulation through binding to the mRNAs of target genes. Recently, it was discovered that viral miRNAs may be involved in human cancers and diseases. It is likely that viral miRNAs help viruses enter the latent phase of their life cycle and become undetected by the host's immune system, while increasing the host's risk for cancer development. Cervical cancer is typically related to the infection of human papillomavirus (HPV) through sexual transmission. To further understand the molecular mechanisms underlying the associations of HPV infection with genital diseases, we developed a systematic method for viral miRNA identification and viral miRNA-mediated regulatory network construction based on genome-wide sequence analysis. The complete genomes of certain high-risk HPV subtypes were used to predict putative viral pre-miRNAs by bioinformatics approaches. In addition, small RNA libraries in human cervical lesions from existing publications were collected to validate the predicted HPV pre-miRNAs. For the construction of virally encoded miRNA-mediated regulatory network of HPV infection, cervical squamous epithelial carcinoma gene expression data were extracted from the RNA sequencing platform in The Cancer Genome Atlas; the differentially expressed genes were used to identify the putative targets of viral miRNAs. Predicted cellular target genes of HPV-encoded miRNAs provide an overview of these viral miRNA's putative functions. Finally, a large-scale genome analysis was carried out to examine the phylogenetic relationship and structural evolution among genital HPV types that have the potential to cause genital cancer. In this study, we discovered putative HPV-encoded miRNAs, which were validated against the small RNA libraries in human cervical lesions. Furthermore, as indicated by their biological functions, host genes targeted by HPV-encoded miRNAs may play significant roles in virus infection and carcinogenesis. These viral miRNAs pose as promising candidates for the development of antiviral drugs. More importantly, the identified subtype-specific miRNAs have the potential to be used as biomarkers for HPV subtype determination.

Characterization of Fecal Microbiota with Clinical Specimen Using Long-Read and Short-Read Sequencing Platform.

Accurate and rapid identification of microbiotic communities using 16S ribosomal (r)RNA sequencing is a critical task for expanding medical and clinical applications. Next-generation sequencing (NGS) is widely considered a practical approach for direct application to communities without the need for in vitro culturing. In this report, a comparative evaluation of short-read (Illumina) and long-read (Oxford Nanopore Technologies (ONT)) platforms toward 16S rRNA sequencing with the same batch of total genomic DNA extracted from fecal samples is presented. Different 16S gene regions were amplified, bar-coded, and sequenced using the Illumina MiSeq and ONT MinION sequencers and corresponding kits. Mapping of the sequenced amplicon using MinION to the entire 16S rRNA gene was analyzed with the cloud-based EPI2ME algorithm. V3-V4 reads generated using MiSeq were aligned by applying the CLC genomics workbench. More than 90% of sequenced reads generated using distinct sequencers were accurately classified at the genus or species level. The misclassification of sequenced reads at the species level between the two approaches was less substantial as expected. Taken together, the comparative results demonstrate that MinION sequencing platform coupled with the corresponding algorithm could function as a practicable strategy in classifying bacterial community to the species level.

Vaginal microbiome variances in sample groups categorized by clinical criteria of bacterial vaginosis.

BACKGROUND: One of the most common and recurrent vaginal infections is bacterial vaginosis (BV). The diagnosis is based on changes to the "normal" vaginal microbiome; however, the normal microbiome appears to differ according to reproductive status and ethnicity, and even among individuals within these groups. The Amsel criteria and Nugent score test are widely used for diagnosing BV; however, these tests are based on different criteria, and so may indicate distinct changes in the vaginal microbial community. Nevertheless, few studies have compared the results of these test against metagenomics analysis. METHODS: Vaginal flora samples from 77 participants were classified according to the Amsel criteria and Nugent score test. The microbiota composition was analyzed using 16S ribosome RNA gene amplicon sequencing. Bioinformatics analysis and multivariate statistical analysis were used to evaluate the microbial diversity and function. RESULTS: Only 3 % of the participants diagnosed BV negative using the Amsel criteria (A-) were BV-positive according to the Nugent score test (N+), while over half of the BV-positive patients using the Amsel criteria (A+) were BV-negative according to the Nugent score test (N-). Thirteen genera showed significant differences in distribution among BV status defined by BV tests (e.g., A - N-, A + N- and A + N+). Variations in the four most abundant taxa, Lactobacillus, Gardnerella, Prevotella, and Escherichia, were responsible for most of this dissimilarity. Furthermore, vaginal microbial diversity differed significantly among the three groups classified by the Nugent score test (N-, N+, and intermediate flora), but not between the Amsel criteria groups. Numerous predictive microbial functions, such as bacterial chemotaxis and bacterial invasion of epithelial cells, differed significantly among multiple BV test, but not between the A- and A+ groups. CONCLUSIONS: Metagenomics analysis can greatly expand our current understanding of vaginal microbial diversity in health and disease. Metagenomics profiling may also provide more reliable diagnostic criteria for BV testing.

Idi1 and Hmgcs2 Are Affected by Stretch in HL-1 Atrial Myocytes.

BACKGROUND: Lipid expression is increased in the atrial myocytes of mitral regurgitation (MR) patients. This study aimed to investigate key regulatory genes and mechanisms of atrial lipotoxic myopathy in MR. METHODS: The HL-1 atrial myocytes were subjected to uniaxial cyclic stretching for eight hours. Fatty acid metabolism, lipoprotein signaling, and cholesterol metabolism were analyzed by PCR assay (168 genes). RESULTS: The stretched myocytes had significantly larger cell size and higher lipid expression than non-stretched myocytes (all p < 0.001). Fatty acid metabolism, lipoprotein signaling, and cholesterol metabolism in the myocytes were analyzed by PCR assay (168 genes). In comparison with their counterparts in non-stretched myocytes, seven genes in stretched monocytes (Idi1, Olr1, Nr1h4, Fabp2, Prkag3, Slc27a5, Fabp6) revealed differential upregulation with an altered fold change >1.5. Nine genes in stretched monocytes (Apoa4, Hmgcs2, Apol8, Srebf1, Acsm4, Fabp1, Acox2, Acsl6, Gk) revealed differential downregulation with an altered fold change <0.67. Canonical pathway analysis, using Ingenuity Pathway Analysis software, revealed that the only genes in the "superpathway of cholesterol biosynthesis" were Idi1 (upregulated) and Hmgcs2 (downregulated). The fraction of stretched myocytes expressing Nile red was significantly decreased by RNA interference of Idi1 (p < 0.05) and was significantly decreased by plasmid transfection of Hmgcs2 (p = 0.004). CONCLUSIONS: The Idi1 and Hmgcs2 genes have regulatory roles in atrial lipotoxic myopathy associated with atrial enlargement.

A Three-MicroRNA Signature as a Potential Biomarker for the Early Detection of Oral Cancer.

Oral squamous cell carcinoma (OSCC) is often diagnosed at a late stage and may be malignantly transformed from oral leukoplakia (OL). This study aimed to identify potential plasma microRNAs (miRNAs) for the early detection of oral cancer. Plasma from normal, OL, and OSCC patients were evaluated. Small RNA sequencing was used to screen the differently expressed miRNAs among the groups. Next, these miRNAs were validated with individual samples by quantitative real-time polymerase chain reaction (qRT-PCR) assays in the training phase (n = 72) and validation phase (n = 178). The possible physiological roles of the identified miRNAs were further investigated using bioinformatics analysis. Three miRNAs (miR-222-3p, miR-150-5p, and miR-423-5p) were identified as differentially expressed among groups; miR-222-3p and miR-423-5p negatively correlated with T stage, lymph node metastasis status, and clinical stage. A high diagnostic accuracy (Area under curve = 0.88) was demonstrated for discriminating OL from OSCC. Bioinformatics analysis reveals that miR-423-5p and miR-222-3p are significantly over-expressed in oral cancer tissues and involved in various cancer pathways. The three-plasma miRNA panel may be useful to monitor malignant progression from OL to OSCC and as potential biomarkers for early detection of oral cancer.

Investigation and identification of protein carbonylation sites based on position-specific amino acid composition and physicochemical features.

BACKGROUND: Protein carbonylation, an irreversible and non-enzymatic post-translational modification (PTM), is often used as a marker of oxidative stress. When reactive oxygen species (ROS) oxidized the amino acid side chains, carbonyl (CO) groups are produced especially on Lysine (K), Arginine (R), Threonine (T), and Proline (P). Nevertheless, due to the lack of information about the carbonylated substrate specificity, we were encouraged to develop a systematic method for a comprehensive investigation of protein carbonylation sites. RESULTS: After the removal of redundant data from multipe carbonylation-related articles, totally 226 carbonylated proteins in human are regarded as training dataset, which consisted of 307, 126, 128, and 129 carbonylation sites for K, R, T and P residues, respectively. To identify the useful features in predicting carbonylation sites, the linear amino acid sequence was adopted not only to build up the predictive model from training dataset, but also to compare the effectiveness of prediction with other types of features including amino acid composition (AAC), amino acid pair composition (AAPC), position-specific scoring matrix (PSSM), positional weighted matrix (PWM), solvent-accessible surface area (ASA), and physicochemical properties. The investigation of position-specific amino acid composition revealed that the positively charged amino acids (K and R) are remarkably enriched surrounding the carbonylated sites, which may play a functional role in discriminating between carbonylation and non-carbonylation sites. A variety of predictive models were built using various features and three different machine learning methods. Based on the evaluation by five-fold cross-validation, the models trained with PWM feature could provide better sensitivity in the positive training dataset, while the models trained with AAindex feature achieved higher specificity in the negative training dataset. Additionally, the model trained using hybrid features, including PWM, AAC and AAindex, obtained best MCC values of 0.432, 0.472, 0.443 and 0.467 on K, R, T and P residues, respectively. CONCLUSION: When comparing to an existing prediction tool, the selected models trained with hybrid features provided a promising accuracy on an independent testing dataset. In short, this work not only characterized the carbonylated substrate preference, but also demonstrated that the proposed method could provide a feasible means for accelerating preliminary discovery of protein carbonylation.

Diversity and enterotype in gut bacterial community of adults in Taiwan.

BACKGROUND: Gastrointestinal microbiota, particularly gut microbiota, is associated with human health. The biodiversity of gut microbiota is affected by ethnicities and environmental factors such as dietary habits or medicine intake, and three enterotypes of the human gut microbiome were announced in 2011. These enterotypes are not significantly correlated with gender, age, or body weight but are influenced by long-term dietary habits. However, to date, only two enterotypes (predominantly consisting of Bacteroides and Prevotella) have shown these characteristics in previous research; the third enterotype remains ambiguous. Understanding the enterotypes can improve the knowledge of the relationship between microbiota and human health. RESULTS: We obtained 181 human fecal samples from adults in Taiwan. Microbiota compositions were analyzed using next-generation sequencing (NGS) technology, which is a culture-independent method of constructing microbial community profiles by sequencing 16S ribosomal DNA (rDNA). In these samples, 17,675,898 sequencing reads were sequenced, and on average, 215 operational taxonomic units (OTUs) were identified for each sample. In this study, the major bacteria in the enterotypes identified from the fecal samples were Bacteroides, Prevotella, and Enterobacteriaceae, and their correlation with dietary habits was confirmed. A microbial interaction network in the gut was observed on the basis of the amount of short-chain fatty acids, pH value of the intestine, and composition of the bacterial community (enterotypes). Finally, a decision tree was derived to provide a predictive model for the three enterotypes. The accuracies of this model in training and independent testing sets were 97.2 and 84.0%, respectively. CONCLUSIONS: We used NGS technology to characterize the microbiota and constructed a predictive model. The most significant finding was that Enterobacteriaceae, the predominant subtype, could be a new subtype of enterotypes in the Asian population.

Activation of TWIST1 by COL11A1 promotes chemoresistance and inhibits apoptosis in ovarian cancer cells by modulating NF-κB-mediated IKKß expression.

We have shown that collagen type XI alpha 1 (COL11A1) promotes ovarian cancer progression and is associated with chemoresistance to cisplatin and paclitaxel in ovarian cancer cells. Here, we demonstrate how COL11A1 regulates twist family basic helix-loop-helix transcription factor 1-related protein 1 (TWIST1) to induce chemoresistance and inhibit apoptosis in ovarian cancer cells. Small interfering RNA-mediated reduction in COL11A1 protein levels increased the chemosensitivity to cisplatin and paclitaxel via downregulated TWIST1 expression. TWIST1 messenger RNA levels positively associated with COL11A1 messenger RNA expression levels in ovarian tumors. High TWIST1 expression levels were significantly associated with a progression-free interval of ≤ 6 months (p = 0.001) and death (p = 0.040). In addition, patients with high TWIST1 mRNA levels had significantly shorter 5-year overall-survival (p = 0.004) and progression-free survival (p = 0.009) rates, compared to patients with low TWIST1 levels. Increased TWIST1 expression caused by COL11A1-induced transcription of the inhibitor of nuclear factor kappa B kinase subunit beta (IKKß) gene occurred via increased SP1 phosphorylation and binding to the IKKß promoter. COL11A1-mediated nuclear factor-kappa B activation, via transcriptional activation of IKKß, promoted TWIST1, Mcl-1, and GAS6 expression, which were associated with chemoresistance and anti-apoptosis in ovarian cancer cells. We suggest that IKKß and TWIST1 can potentially be targeted in patients with COL11A1-positive ovarian cancer.

Benzodiazepines use and breast cancer risk: A population-based study and gene expression profiling evidence.

The aim of this study was to investigate whether long-term use of Benzodiazepines (BZDs) is associated with breast cancer risk through the combination of population-based observational and gene expression profiling evidence. We conducted a population-based case-control study by using 1998 to 2009year Taiwan National Health Insurance Research Database and investigated the association between BZDs use and breast cancer risk. We selected subjects age of >20years old and six eligible controls matched for age, sex and the index date (i.e., free of any cancer at the case diagnosis date) by using propensity scores. A bioinformatics analysis approach was also performed for the identification of oncogenesis effects of BZDs on breast cancer. We used breast cancer gene expression data from the Cancer Genome Atlas and perturbagen signatures of BZDs from the Library of Integrated Cellular Signatures database in order to identify the oncogenesis effects of BZDs on breast cancer. We found evidence of increased breast cancer risk for diazepam (OR, 1.16; 95%CI, 0.95-1.42; connectivity score [CS], 0.3016), zolpidem (OR, 1.11; 95%CI, 0.95-1.30; CS, 0.2738), but not for lorazepam (OR, 1.04; 95%CI, 0.89-1.23; CS, -0.2952) consistently in both methods. The finding for alparazolam was contradictory from the two methods. Diazepam and zolpidem trends showed association, although not statistically significant, with breast cancer risk in both epidemiological and bioinformatics analyses outcomes. The methodological value of our study is in introducing the way of combining epidemiological and bioinformatics approaches in order to answer a common scientific question. Combining the two approaches would be a substantial step towards uncovering, validation and further application of previously unknown scientific knowledge to the emerging field of precision medicine informatics.