Human Splicing Finder: an online bioinformatics tool to predict splicing signals

Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-β Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5′ and 3′ splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project.     

Lifestyle-related biomarkers and endometrial cancer survival: Elevated gamma-glutamyltransferase as an important risk factor

Background: Lifestyle seems to play an important role in endometrial cancer mortality, but it remains unclear which biomarkers are involved. The aim of this study was to assess the extent of the association between lifestyle-related biomarkers and the survival of endometrial cancer patients. Methods: A sub-cohort of 242 endometrial cancer patients, from a population-based study of the more than 90,000 female participants of the Vorarlberg Health Monitoring and Promotion Programme, was followed for a median duration of twelve years. Besides age, tumour staging, and histology, also pre-diagnostic levels of body mass index, blood pressure, triglycerides, total cholesterol, glucose, gamma-glutamyltransferase (GGT), and serum uric acid were analysed in Cox proportional hazards regression models to estimate multivariate mortality risks. Results: During follow-up 89 deaths occurred of which 49 were cancer-related. Survival was associated with age, tumour stage, and histology. Of the biomarkers, log₁₀-transformed GGT showed a large effect on cancer-related mortality (HR = 3.35, 95% CI 1.12–10.03), whereas the other parameters did not appear with significant effects after adjustment for the other factors. Conclusion: Elevated level of GGT, a lifestyle-related marker, was associated with poor survival among endometrial cancer patients.     

Selection of biomarkers by a multivariate statistical processing of composite metabonomic data sets using multiple factor analysis

We introduce a statistical approach for integrating data from several analytical platforms. We illustrate this approach using (1)H-(13)C Heteronuclear Multiple Bond Connectivity nuclear magnetic resonance spectroscopy ((1)H-(13)C HMBC NMR) and Pyrolysis Metastable Atom Bombardment Time-of-Flight mass spectrometry (Py-MAB-TOF-MS) to perform metabolic fingerprinting on cattle treated with anabolic steroids. Multiple factor analysis (MFA) integrates complementary aspects from NMR and MS data into a unique metabolic signature describing the biomarkers related to the dose-response. This work also indicates that, from a practical point of view, metabonomics and other "-omics" biotechnologies can benefit significantly from a generalized multi-platform integrative approach using multiple factor analysis.     

VisANT: an online visualization and analysis tool for biological interaction data

Background  

New techniques for determining relationships between biomolecules of all types – genes, proteins, noncoding DNA, metabolites and small molecules – are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems.     

CASPAR: a hierarchical bayesian approach to predict survival times in cancer from gene expression data

MOTIVATION: DNA microarrays allow the simultaneous measurement of thousands of gene expression levels in any given patient sample. Gene expression data have been shown to correlate with survival in several cancers, however, analysis of the data is difficult, since typically at most a few hundred patients are available, resulting in severely underdetermined regression or classification models. Several approaches exist to classify patients in different risk classes, however, relatively little has been done with respect to the prediction of actual survival times. We introduce CASPAR, a novel method to predict true survival times for the individual patient based on microarray measurements. CASPAR is based on a multivariate Cox regression model that is embedded in a Bayesian framework. A hierarchical prior distribution on the regression parameters is specifically designed to deal with high dimensionality (large number of genes) and low sample size settings, that are typical for microarray measurements. This enables CASPAR to automatically select small, most informative subsets of genes for prediction. RESULTS: Validity of the method is demonstrated on two publicly available datasets on diffuse large B-cell lymphoma (DLBCL) and on adenocarcinoma of the lung. The method successfully identifies long and short survivors, with high sensitivity and specificity. We compare our method with two alternative methods from the literature, demonstrating superior results of our approach. In addition, we show that CASPAR can further refine predictions made using clinical scoring systems such as the International Prognostic Index (IPI) for DLBCL and clinical staging for lung cancer, thus providing an additional tool for the clinician. An analysis of the genes identified confirms previously published results, and furthermore, new candidate genes correlated with survival are identified.     

CASPAR: a hierarchical Bayesian approach to predict survival times in cancer from gene expression data

Lars Kaderali, Thomas Zander, Ulrich Faigle, Jürgen Wolf,Joachim L. Schultze     

Immunologic biomarkers as correlates of clinical response to cancer immunotherapy

Over the last few years, several newly developed immune-based cancer therapies have been shown to induce clinical responses in significant numbers of patients. As a result, there is a need to identify immune biomarkers capable of predicting clinical response. If there were laboratory parameters that could define patients with improved disease outcomes after immunomodulation, product development would accelerate, optimization of existing immune-based treatments would be facilitated and patient selection for specific interventions might be optimized. Although there are no validated cancer immunologic biomarkers that are predictive of clinical response currently in widespread use, there is much published literature that has informed investigators as to which markers may be the most promising. Population-based studies of endogenous tumor immune infiltrates and gene expression analyses have identified specific cell populations and phenotypes of immune cells that are most likely to mediate anti-tumor immunity. Further, clinical trials of cancer vaccines and other cancer directed immunotherapy have identified candidate immunologic biomarkers that are statistically associated with beneficial clinical outcomes after immune-based cancer therapies. Biomarkers that measure the magnitude of the Type I immune response generated with immune therapy, epitope spreading, and autoimmunity are readily detected in the peripheral blood and, in clinical trials of cancer immunotherapy, have been associated with response to treatment.     

SurvJamda: an R package to predict patients' survival and risk assessment using joint analysis of microarray gene expression data

SurvJamda (Survival prediction by joint analysis of microarray data) is an R package that utilizes joint analysis of microarray gene expression data to predict patients' survival and risk assessment. Joint analysis can be performed by merging datasets or meta-analysis to increase the sample size and to improve survival prognosis. The prognosis performance derived from the combined datasets can be assessed to determine which feature selection approach, joint analysis method and bias estimation provide the most robust prognosis for a given set of datasets. AVAILABILITY: The survJamda package is available at the Comprehensive R Archive Network, http://cran.r-project.org. CONTACT: hyasrebi@yahoo.com.     

Five microRNAs in plasma as novel biomarkers for screening of early-stage non-small cell lung cancer

Background

In order to find novel noninvasive biomarkers with high accuracy for the screening of early-stage non-small cell lung cancer (NSCLC), we investigate the predictive power of 5 microRNAs (miR-20a, miR-145, miR-21, miR223 and miR-221) as potential biomarkers in early-stage NSCLC.

Methods

In training set, 25 early-stage NSCLC patients and 25 matched healthy controls are included to assess the miRNA expression profile between early-stage NSCLC patients and healthy controls by real-time RT-PCR. We found that five of these miRNAs (miR-20a, miR-223, miR-21, miR-221 and miR-145) levels in NSCLC patients were significantly dysregulated compared with the healthy groups and thus were selected to validation set. Therefore, a validation experiment was further performed to investigate the potential predictive power of these five miRNAs based on 126 early-stage NSCLC patients, 42 NCPD patients and 60 healthy controls. The receiver operating characteristic (ROC) curves were generated for the five miRNAs.

Results

ROC curve analyses suggested that these five plasma miRNAs could be promising biomarkers for NSCLC, with relatively high AUC values as follows: miR-20a, 0.89 with 95% CI of [0.85-0.93]; miR-223, 0.94 with 95% CI of [0.91-0.96]; miR-21, 0.77 with 95% CI of [0.71-0.83]; miR-155, 0.92 with 95% CI of [0.89-0.96]; miR-145, 0.77 with 95% CI of [0.71-0.83]. Stratified analyses indicated that plasma miR-20a, miR-223, miR-21 and miR-145 showed better predictive value in smokers than in non-smokers, while miR-155 might be more suitable for non-smokers. In addition, all of these five miRNAs could differentiate NSCLC from controls with a higher accuracy in advanced stage and squamous carcinoma subgroups.

Conclusions

In conclusion, our study suggested that five plasma miRNAs (miR-20a, miR-145, miR-21, miR-223 and miR-221) can be used as promising biomarkers in early screening of NSCLC. Nevertheless, further validation and optimizing improvement should be performed on larger sample to confirm our results.     

Cell‐free microRNAs in blood and other body fluids,as cancer biomarkers

The discovery of cell‐free microRNAs (miRNAs) in serum, plasma and other body fluids has yielded an invaluable potential source of non‐invasive biomarkers for cancer and other non‐malignant diseases. miRNAs in the blood and other body fluids are highly stable in biological samples and are resistant to environmental conditions, such as freezing, thawing or enzymatic degradation, which makes them convenient as potential biomarkers. In addition, they are more easily sampled than tissue miRNAs. Altered levels of cell‐free miRNAs have been found in every type of cancer analysed, and increasing evidence indicates that they may participate in carcinogenesis by acting as cell‐to‐cell signalling molecules. This review summarizes the biological characteristics and mechanisms of release of cell‐free miRNAs that make them promising candidates as non‐invasive biomarkers of cancer.     

Circulating microRNAs as potential diagnostic biomarkers and therapeutic targets in prostate cancer: Current status and future perspectives

Prostate cancer (PCa) is one of the most common malignancies among men. Despite advancement in technology and medicine over past decades, late diagnosis remains a critical milestone in effective treatment. Therefore, it is necessary to identify novel and reliable biomarkers which are specifically sensitive and specific for prognosis and prediction of clinical outcomes. MicroRNAs (miRNAs) play important roles in posttranslational regulations of genes. Circulating and exosomal miRNAs can be applied as useful diagnostic markers for a different type of malignancies, including PCa. Herein, we summarized various roles of miRNAs (diagnostic, therapeutic, and prognostic) in PCa. Moreover, we highlighted exosomal miRNAs as a new candidate in diagnosis and monitoring response to therapy in patients with PCa.     

microRNAs: New prognostic,diagnostic, and therapeutic biomarkers in cervical cancer

Cervical cancer is as a kind of cancer beginning from the cervix. Given that cervical cancer could be observed in women who infected with papillomavirus, regular oral contraceptives, and multiple pregnancies. Early detection of cervical cancer is one of the most important aspects of the therapy of this malignancy. Despite several efforts, finding and developing new biomarkers for cervical cancer diagnosis are required. Among various prognostic, diagnostic, and therapeutic biomarkers, miRNA have been emerged as powerful biomarkers for detection, treatment, and monitoring of response to therapy in cervical cancer. Here, we summarized various miRNAs as an employable platform for prognostic, diagnostic, and therapeutic biomarkers in the treatment of cervical cancer.     

Look before planting: using smokewater as an inventory tool to predict the soil seed bank and inform ecological management and restoration

This study tested the efficacy of smokewater to determine the potential germination from soil seed bank in three management sites of the same National Park: a forest site prior to restoration, an ex‐pine plantation site and an ex‐mine site. This will provide further information to land managers so that more accurate planning can occur. Results showed that smokewater significantly increased the germination from the soil seed bank, and significant differences in the level of germination of weed species from the soil seed bank were seen between the three management sites. This use of smokewater may be a useful tool to help predict differences in the soil seed bank compared with predicting soil seed bank based on land‐use history and recent condition.     

Modeling as a tool to manage ecosystems under multiple stresses: an application to Lake Ontario

Present-day ecosystem management involves understanding of the synergistic effect of multiple stressors on multiple and frequently nebulous management end-points. An example is the simultaneous management of nutrient load reductions and salmon stocking in Lake Ontario. In this study, a simple whole-lake annual time scale model was developed to assess the relationship between these two stressors and various ecosystem responses. The model was used to explore the utility of some possible management end-points for ecosystem health. In historical simulations, production per stocked fish and salmon survival appeared to be good indicators, while nutrient recycling rate and average ecosystem-wide food limitation were found to be fairly unresponsive to the two stressors. The model was further used to predict long term averages of salmon biomass and selected health indicators at various sustained loading and stocking rates. Salmon biomass increased with stocking rate at all stocking rates examined, but the rate of increase declined somewhat at high stocking rates. The response of salmon biomass to nutrient loading appeared to be approximately sigmoidal i.e. there was a nutrient threshold below which fish biomass could not be sustained and another nutrient threshold above which salmon biomass either remained constant or even decreased. The response to either stressor was found to be modified by the value of the other stressor, illustrating the importance of ecosystem-level models for aquatic ecosystem management.     

MotifCluster: an interactive online tool for clustering and visualizing sequences using shared motifs

MotifCluster finds related motifs in a set of sequences, and clusters the sequences into families using the motifs they contain. MotifCluster, at , lets users test whether proteins are related, cluster sequences by shared conserved motifs, and visualize motifs mapped onto trees, sequences and three-dimensional structures. We demonstrate MotifCluster's accuracy using gold-standard protein superfamilies; using recommended settings, families were assigned to the correct superfamilies with 0.17% false positive and no false negative assignments.     

Process‐based functions for seed retention on animals: a test of improved descriptions of dispersal using multiple data sets

Studies of external seed transport on animals usually assume that the probability of detachment is constant, so that seed retention should show a simple exponential relationship with time. This assumption has not been tested explicitly, and may lead to inaccurate representation of long distance seed dispersal by animals. We test the assumption by comparing the fit to empirical data of simple, two‐parameter functions. Fifty‐two data sets were obtained from five published studies, describing seed retention of 32 plant species on sheep, cattle, deer, goats and mice. Model selection suggested a simple exponential function was adequate for data sets in which seed retention was followed for short periods ( <48 h). The data gathered over longer periods (49–219 days) were best described by the power exponential function, a form of the stretched exponential which allows a changing dropping rate. In these cases the power exponential showed that seed dropping rate decreased with time, suggesting that seeds vary in attachment, with some seeds becoming deeply buried or wound up in the animal's coat. Comparison of fitted parameters across all the data sets also confirmed that seeds with adhesive structures have lower dropping rates than those without. We conclude that the seed dropping rate often changes with time during external transport on animals and that the power exponential is an effective function to describe this change. We advise that, to analyse seed dropping rates adequately, retention should be measured over reasonable time periods – until most seeds are dropped – and both the simple and power exponential functions should be fitted to the resulting data. To increase its utility, we provide functions describing the seed dropping rate and the dispersal kernel resulting from the power exponential relationship.     

The potential of microRNAs as human prostate cancer biomarkers: A meta‐analysis of related studies

Prostate cancer (PC) is a very important kind of male malignancies. When PC evolves into a stage of hormone resistance or metastasis, the fatality rate is very high. Currently, discoveries and advances in miRNAs as biomarkers have opened the potential for the diagnosis of PC, especially early diagnosis. miRNAs not only can noninvasively or minimally invasively identify PC, but also can provide the data for optimization and personalization of therapy. Moreover, miRNAs have been shown to play an important role to predict prognosis of PC. The purpose of this meta‐analysis is to integrate the currently published expression profile data of miRNAs in PC, and evaluate the value of miRNAs as biomarkers for PC. All of relevant records were selected via electronic databases: Pubmed, Embase, Cochrane, and CNKI based on the assessment of title, abstract, and full text. we extracted mean ± SD or fold change of miRNAs expression levels in PC versus BPH or normal controls. Pooled hazard ratios (HRs) with 95% confidence intervals (CI) for overall survival (OS) and recurrence‐free survival (RFS), were also calculated to detect the relationship between high miRNAs expression and PC prognosis. Selected 104 articles were published in 2007‐2017. According to the inclusion criteria, 104 records were included for this meta‐analysis. The pooled or stratified analyze showed 10 up‐regulated miRNAs (miR‐18a, miR‐34a, miR‐106b, miR‐141, miR‐182, miR‐183, miR‐200a/b, miR‐301a, and miR‐375) and 14 down‐regulated miRNAs (miR‐1, miR‐23b/27b, miR‐30c, miR‐99b, miR‐139‐5p, miR‐152, miR‐187, miR‐204, miR‐205, miR‐224, miR‐452, miR‐505, and let‐7c) had relatively good diagnostic and predictive potential to discriminate PC from BPH/normal controls. Furthermore, high expression of miR‐32 and low expression of let‐7c could be used to differentiate metastatic PC from local/primary PC. Additional interesting findings were that the expression profiles of five miRNAs (miR‐21, miR‐30c, miR‐129, miR‐145, and let‐7c) could predict poor RFS of PC, while the evaluation of miR‐375 was associated with worse OS. miRNAs are important regulators in PC progression. Our results indicate that miRNAs are suitable for predicting the different stages of PC. The detection of miRNAs is an effective way to control patient's prognosis and evaluate therapeutic efficacy. However, large‐scale detections based on common clinical guidelines are still necessary to further validate our conclusions, due to the bias induced by molecular heterogeneity and differences in study design and detection methods.