IDDI: integrated domain-domain interaction and protein interaction analysis system

Background

Deciphering protein-protein interaction (PPI) in domain level enriches valuable information about binding mechanism and functional role of interacting proteins. The 3D structures of complex proteins are reliable source of domain-domain interaction (DDI) but the number of proven structures is very limited. Several resources for the computationally predicted DDI have been generated but they are scattered in various places and their prediction show erratic performances. A well-organized PPI and DDI analysis system integrating these data with fair scoring system is necessary.

Method

We integrated three structure-based DDI datasets and twenty computationally predicted DDI datasets and constructed an interaction analysis system, named IDDI, which enables to browse protein and domain interactions with their relationships. To integrate heterogeneous DDI information, a novel scoring scheme is introduced to determine the reliability of DDI by considering the prediction scores of each DDI and the confidence levels of each prediction method in the datasets, and independencies between predicted datasets. In addition, we connected this DDI information to the comprehensive PPI information and developed a unified interface for the interaction analysis exploring interaction networks at both protein and domain level.

Result

IDDI provides 204,705 DDIs among total 7,351 Pfam domains in the current version. The result presents that total number of DDIs is increased eight times more than that of previous studies. Due to the increment of data, 50.4% of PPIs could be correlated with DDIs which is more than twice of previous resources. Newly designed scoring scheme outperformed the previous system in its accuracy too. User interface of IDDI system provides interactive investigation of proteins and domains in interactions with interconnected way. A specific example is presented to show the efficiency of the systems to acquire the comprehensive information of target protein with PPI and DDI relationships. IDDI is freely available at http://pcode.kaist.ac.kr/iddi/.

     

A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks

Background

The accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks.

Results

In this paper, we introduce a novel multi-network clustering algorithm to detect protein complexes from multiple heterogeneous networks. Unlike existing protein complex identification algorithms that focus on the analysis of a single PPI network, our model can jointly exploit the information inherent in PPI and DDI data to achieve more reliable prediction results. Extensive experiment results on real-world data sets demonstrate that our method can predict protein complexes more accurately than other state-of-the-art protein complex identification algorithms.

Conclusions

In this work, we demonstrate that the joint analysis of PPI network and DDI network can help to improve the accuracy of protein complex detection.

     

Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites

Background

The invasion of red blood cells (RBCs) by malarial parasites is an essential step in the life cycle of Plasmodium falciparum. Human-parasite surface protein interactions play a critical role in this process. Although several interactions between human and parasite proteins have been discovered, the mechanism related to invasion remains poorly understood because numerous human-parasite protein interactions have not yet been identified. High-throughput screening experiments are not feasible for malarial parasites due to difficulty in expressing the parasite proteins. Here, we performed computational prediction of the PPIs involved in malaria parasite invasion to elucidate the mechanism by which invasion occurs.

Results

In this study, an expectation maximization algorithm was used to estimate the probabilities of domain-domain interactions (DDIs). Estimates of DDI probabilities were then used to infer PPI probabilities. We found that our prediction performance was better than that based on the information of D. melanogaster alone when information related to the six species was used. Prediction performance was assessed using protein interaction data from S. cerevisiae, indicating that the predicted results were reliable. We then used the estimates of DDI probabilities to infer interactions between 490 parasite and 3,787 human membrane proteins. A small-scale dataset was used to illustrate the usability of our method in predicting interactions between human and parasite proteins. The positive predictive value (PPV) was lower than that observed in S. cerevisiae. We integrated gene expression data to improve prediction accuracy and to reduce false positives. We identified 80 membrane proteins highly expressed in the schizont stage by fast Fourier transform method. Approximately 221 erythrocyte membrane proteins were identified using published mass spectral datasets. A network consisting of 205 interactions was predicted. Results of network analysis suggest that SNARE proteins of parasites and APP of humans may function in the invasion of RBCs by parasites.

Conclusions

We predicted a small-scale PPI network that may be involved in parasite invasion of RBCs by integrating DDI information and expression profiles. Experimental studies should be conducted to validate the predicted interactions. The predicted PPIs help elucidate the mechanism of parasite invasion and provide directions for future experimental investigations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0393-z) contains supplementary material, which is available to authorized users.     

Inferring domain-domain interactions from protein-protein interactions in the complex network conformation

Background

As protein domains are functional and structural units of proteins, a large proportion of protein-protein interactions (PPIs) are achieved by domain-domain interactions (DDIs), many computational efforts have been made to identify DDIs from experimental PPIs since high throughput technologies have produced a large number of PPIs for different species. These methods can be separated into two categories: deterministic and probabilistic. In deterministic methods, parsimony assumption has been utilized. Parsimony principle has been widely used in computational biology as the evolution of the nature is considered as a continuous optimization process. In the context of identifying DDIs, parsimony methods try to find a minimal set of DDIs that can explain the observed PPIs. This category of methods are promising since they can be formulated and solved easily. Besides, researches have shown that they can detect specific DDIs, which is often hard for many probabilistic methods. We notice that existing methods just view PPI networks as simply assembled by single interactions, but there is now ample evidence that PPI networks should be considered in a global (systematic) point of view for it exhibits general properties of complex networks, such as 'scale-free' and 'small-world'.

Results

In this work, we integrate this global point of view into the parsimony-based model. Particularly, prior knowledge is extracted from these global properties by plausible reasoning and then taken as input. We investigate the role of the added information extensively through numerical experiments. Results show that the proposed method has improved performance, which confirms the biological meanings of the extracted prior knowledge.

Conclusions

This work provides us some clues for using these properties of complex networks in computational models and to some extent reveals the biological meanings underlying these general network properties.

     

Evolutionary conservation of domain-domain interactions

Background

Recently, there has been much interest in relating domain-domain interactions (DDIs) to protein-protein interactions (PPIs) and vice versa, in an attempt to understand the molecular basis of PPIs.

Results

Here we map structurally derived DDIs onto the cellular PPI networks of different organisms and demonstrate that there is a catalog of domain pairs that is used to mediate various interactions in the cell. We show that these DDIs occur frequently in protein complexes and that homotypic interactions (of a domain with itself) are abundant. A comparison of the repertoires of DDIs in the networks of Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens shows that many DDIs are evolutionarily conserved.

Conclusion

Our results indicate that different organisms use the same 'building blocks' for PPIs, suggesting that the functionality of many domain pairs in mediating protein interactions is maintained in evolution.     

Are We Heeding the Warning Signs? Examining Providers’ Overrides of Computerized Drug-Drug Interaction Alerts in Primary Care

Background

Health IT can play a major role in improving patient safety. Computerized physician order entry with decision support can alert providers to potential prescribing errors. However, too many alerts can result in providers ignoring and overriding clinically important ones.

Objective

To evaluate the appropriateness of providers’ drug-drug interaction (DDI) alert overrides, the reasons why they chose to override these alerts, and what actions they took as a consequence of the alert.

Design

A cross-sectional, observational study of DDI alerts generated over a three-year period between January 1st, 2009, and December 31st, 2011.

Setting

Primary care practices affiliated with two Harvard teaching hospitals. The DDI alerts were screened to minimize the number of clinically unimportant warnings.

Participants

A total of 24,849 DDI alerts were generated in the study period, with 40% accepted. The top 62 providers with the highest override rate were identified and eight overrides randomly selected for each (a total of 496 alert overrides for 438 patients, 3.3% of the sample).

Results

Overall, 68.2% (338/496) of the DDI alert overrides were considered appropriate. Among inappropriate overrides, the therapeutic combinations put patients at increased risk of several specific conditions including: serotonin syndrome (21.5%, n=34), cardiotoxicity (16.5%, n=26), or sharp falls in blood pressure or significant hypotension (28.5%, n=45). A small number of drugs and DDIs accounted for a disproportionate share of alert overrides. Of the 121 appropriate alert overrides where the provider indicated they would “monitor as recommended”, a detailed chart review revealed that only 35.5% (n=43) actually did. Providers sometimes reported that patients had already taken interacting medications together (15.7%, n=78), despite no evidence to confirm this.

Conclusions and Relevance

We found that providers continue to override important and useful alerts that are likely to cause serious patient injuries, even when relatively few false positive alerts are displayed.     

The social network around influenza vaccination in health care workers: a cross-sectional study

Background

Influenza vaccination coverage remains low among health care workers (HCWs) in many health facilities. This study describes the social network defined by HCWs’ conversations around an influenza vaccination campaign in order to describe the role played by vaccination behavior and other HCW characteristics in the configuration of the links among subjects.

Methods

This study used cross-sectional data from 235 HCWs interviewed after the 2010/2011 influenza vaccination campaign at the Hospital Clinic of Barcelona (HCB), Spain. The study asked: “Who did you talk to or share some activity with respect to the seasonal vaccination campaign?” Variables studied included sociodemographic characteristics and reported conversations among HCWs during the influenza campaign. Exponential random graph models (ERGM) were used to assess the role of shared characteristics (homophily) and individual characteristics in the social network around the influenza vaccination campaign.

Results

Links were more likely between HCWs who shared the same professional category (OR 3.13, 95% CI?=?2.61–3.75), sex (OR 1.34, 95% CI?=?1.09–1.62), age (OR 0.7, 95% CI?=?0.63–0.78 per decade of difference), and department (OR 11.35, 95% CI?=?8.17–15.64), but not between HCWs who shared the same vaccination behavior (OR 1.02, 95% CI?=?0.86–1.22). Older (OR 1.26, 95% CI?=?1.14–1.39 per extra decade of HCW) and vaccinated (OR 1.32, 95% CI?=?1.09–1.62) HCWs were more likely to be named.

Conclusions

This study finds that there is no homophily by vaccination status in whom HCWs speak to or interact with about a workplace vaccination promotion campaign. This result highlights the relevance of social network analysis in the planning of health promotion interventions.

     

Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

Background

Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF.

Methods

A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF.

Results

After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p?=?0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p?=?0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p?=?0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p?=?0.0005).

Conclusions

The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF.

     

Metabolomic prediction of endometrial cancer

Introduction

Endometrial cancer (EC) is associated with metabolic disturbances including obesity, diabetes and metabolic syndrome. Identifying metabolite biomarkers for EC detection has a crucial role in reducing morbidity and mortality.

Objective

To determine whether metabolomic based biomarkers can detect EC overall and early-stage EC.

Methods

We performed NMR and mass spectrometry based metabolomic analyses of serum in EC cases versus controls. A total of 46 early-stage (FIGO stages I–II) and 10 late-stage (FIGO stages III–IV) EC cases constituted the study group. A total of 60 unaffected control samples were used. Patients and controls were divided randomly into a discovery group (n?=?69) and an independent validation group (n?=?47). Predictive algorithms based on biomarkers and demographic characteristics were generated using logistic regression analysis.

Results

A total of 181 metabolites were evaluated. Extensive changes in metabolite levels were noted in the EC versus the control group. The combination of C14:2, phosphatidylcholine with acyl-alkyl residue sum C38:1 (PCae C38:1) and 3-hydroxybutyric acid had an area under the receiver operating characteristics curve (AUC) (95% CI)?=?0.826 (0.706–0.946) and a sensitivity?=?82.6%, and specificity?=?70.8% for EC overall. For early EC prediction: BMI, C14:2 and PC ae C40:1 had an AUC (95% CI)?=?0.819 (0.689–0.95) and a sensitivity?=?72.2% and specificity?=?79.2% in the validation group.

Conclusions

EC is characterized by significant perturbations in important cellular metabolites. Metabolites accurately detected early-stage EC cases and EC overall which could lead to the development of non-invasive biomarkers for earlier detection of EC and for monitoring disease recurrence.

     

Integrating node embeddings and biological annotations for genes to predict disease-gene associations

Background

Predicting disease causative genes (or simply, disease genes) has played critical roles in understanding the genetic basis of human diseases and further providing disease treatment guidelines. While various computational methods have been proposed for disease gene prediction, with the recent increasing availability of biological information for genes, it is highly motivated to leverage these valuable data sources and extract useful information for accurately predicting disease genes.

Results

We present an integrative framework called N2VKO to predict disease genes. Firstly, we learn the node embeddings from protein-protein interaction (PPI) network for genes by adapting the well-known representation learning method node2vec. Secondly, we combine the learned node embeddings with various biological annotations as rich feature representation for genes, and subsequently build binary classification models for disease gene prediction. Finally, as the data for disease gene prediction is usually imbalanced (i.e. the number of the causative genes for a specific disease is much less than that of its non-causative genes), we further address this serious data imbalance issue by applying oversampling techniques for imbalance data correction to improve the prediction performance. Comprehensive experiments demonstrate that our proposed N2VKO significantly outperforms four state-of-the-art methods for disease gene prediction across seven diseases.

Conclusions

In this study, we show that node embeddings learned from PPI networks work well for disease gene prediction, while integrating node embeddings with other biological annotations further improves the performance of classification models. Moreover, oversampling techniques for imbalance correction further enhances the prediction performance. In addition, the literature search of predicted disease genes also shows the effectiveness of our proposed N2VKO framework for disease gene prediction.

     

Toward the precision breast cancer survival prediction utilizing combined whole genome-wide expression and somatic mutation analysis

Background

Breast cancer is the most common type of invasive cancer in woman. It accounts for approximately 18% of all cancer deaths worldwide. It is well known that somatic mutation plays an essential role in cancer development. Hence, we propose that a prognostic prediction model that integrates somatic mutations with gene expression can improve survival prediction for cancer patients and also be able to reveal the genetic mutations associated with survival.

Method

Differential expression analysis was used to identify breast cancer related genes. Genetic algorithm (GA) and univariate Cox regression analysis were applied to filter out survival related genes. DAVID was used for enrichment analysis on somatic mutated gene set. The performance of survival predictors were assessed by Cox regression model and concordance index(C-index).

Results

We investigated the genome-wide gene expression profile and somatic mutations of 1091 breast invasive carcinoma cases from The Cancer Genome Atlas (TCGA). We identified 118 genes with high hazard ratios as breast cancer survival risk gene candidates (log rank p?<? 0.0001 and c-index?=?0.636). Multiple breast cancer survival related genes were found in this gene set, including FOXR2, FOXD1, MTNR1B and SDC1. Further genetic algorithm (GA) revealed an optimal gene set consisted of 88 genes with higher c-index (log rank p?<? 0.0001 and c-index?=?0.656). We validated this gene set on an independent breast cancer data set and achieved a similar performance (log rank p?<? 0.0001 and c-index?=?0.614). Moreover, we revealed 25 functional annotations, 15 gene ontology terms and 14 pathways that were significantly enriched in the genes that showed distinct mutation patterns in the different survival risk groups. These functional gene sets were used as new features for the survival prediction model. In particular, our results suggested that the Fanconi anemia pathway had an important role in breast cancer prognosis.

Conclusions

Our study indicated that the expression levels of the gene signatures remain the effective indicators for breast cancer survival prediction. Combining the gene expression information with other types of features derived from somatic mutations can further improve the performance of survival prediction. The pathways that were associated with survival risk suggested by our study can be further investigated for improving cancer patient survival.

     

Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer

Introduction

Colorectal cancer (CRC) is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. Only some CRC patients will benefit from neoadjuvant chemotherapy (NACT).

Objectives

An accurate prediction of response to NACT in CRC patients would greatly facilitate optimal personalized management, which could improve their long-term survival and clinical outcomes.

Methods

In this study, plasma metabolite profiling was performed to identify potential biomarker candidates that can predict response to NACT for CRC. Metabolic profiles of plasma from non-response (n?=?30) and response (n?=?27) patients to NACT were studied using UHPLC–quadruple time-of-flight)/mass spectrometry analyses and statistical analysis methods.

Results

The concentrations of nine metabolites were significantly different when comparing response to NACT. The area under the receiver operating characteristic curve value of the potential biomarkers was up to 0.83 discriminating the non-response and response group to NACT, superior to the clinical parameters (carcinoembryonic antigen and carbohydrate antigen 199).

Conclusion

These results show promise for larger studies that could result in more personalized treatment protocols for CRC patients.

     

CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features

Background

Long noncoding RNAs (lncRNAs) are widely involved in the initiation and development of cancer. Although some computational methods have been proposed to identify cancer-related lncRNAs, there is still a demanding to improve the prediction accuracy and efficiency. In addition, the quick-update data of cancer, as well as the discovery of new mechanism, also underlay the possibility of improvement of cancer-related lncRNA prediction algorithm. In this study, we introduced CRlncRC, a novel Cancer-Related lncRNA Classifier by integrating manifold features with five machine-learning techniques.

Results

CRlncRC was built on the integration of genomic, expression, epigenetic and network, totally in four categories of features. Five learning techniques were exploited to develop the effective classification model including Random Forest (RF), Naïve bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbors (KNN). Using ten-fold cross-validation, we showed that RF is the best model for classifying cancer-related lncRNAs (AUC?=?0.82). The feature importance analysis indicated that epigenetic and network features play key roles in the classification. In addition, compared with other existing classifiers, CRlncRC exhibited a better performance both in sensitivity and specificity. We further applied CRlncRC to lncRNAs from the TANRIC (The Atlas of non-coding RNA in Cancer) dataset, and identified 121 cancer-related lncRNA candidates. These potential cancer-related lncRNAs showed a certain kind of cancer-related indications, and many of them could find convincing literature supports.

Conclusions

Our results indicate that CRlncRC is a powerful method for identifying cancer-related lncRNAs. Machine-learning-based integration of multiple features, especially epigenetic and network features, had a great contribution to the cancer-related lncRNA prediction. RF outperforms other learning techniques on measurement of model sensitivity and specificity. In addition, using CRlncRC method, we predicted a set of cancer-related lncRNAs, all of which displayed a strong relevance to cancer as a valuable conception for the further cancer-related lncRNA function studies.

     

<Emphasis Type="Italic">ABI3</Emphasis> and <Emphasis Type="Italic">PLCG2</Emphasis> missense variants as risk factors for neurodegenerative diseases in Caucasians and African Americans

Background

Rare coding variants ABI3_rs616338-T and PLCG2_rs72824905-G were identified as risk or protective factors, respectively, for Alzheimer’s disease (AD).

Methods

We tested the association of these variants with five neurodegenerative diseases in Caucasian case-control cohorts: 2742 AD, 231 progressive supranuclear palsy (PSP), 838 Parkinson’s disease (PD), 306 dementia with Lewy bodies (DLB) and 150 multiple system atrophy (MSA) vs. 3351 controls; and in an African-American AD case-control cohort (181 AD, 331 controls). 1479 AD and 1491 controls were non-overlapping with a prior report.

Results

Using Fisher’s exact test, there was significant association of both ABI3_rs616338-T (OR?=?1.41, p?=?0.044) and PLCG2_rs72824905-G (OR?=?0.56, p?=?0.008) with AD. These OR estimates were maintained in the non-overlapping replication AD-control analysis, albeit at reduced significance (ABI3_rs616338-T OR?=?1.44, p?=?0.12; PLCG2_rs72824905-G OR?=?0.66, p?=?0.19). None of the other cohorts showed significant associations that were concordant with those for AD, although the DLB cohort had suggestive findings (Fisher’s test: ABI3_rs616338-T OR?=?1.79, p?=?0.097; PLCG2_rs72824905-G OR?=?0.32, p?=?0.124). PLCG2_rs72824905-G showed suggestive association with pathologically-confirmed MSA (OR?=?2.39, p?=?0.050) and PSP (OR?=?1.97, p?=?0.061), although in the opposite direction of that for AD. We assessed RNA sequencing data from 238 temporal cortex (TCX) and 224 cerebellum (CER) samples from AD, PSP and control patients and identified co-expression networks, enriched in microglial genes and immune response GO terms, and which harbor PLCG2 and/or ABI3. These networks had higher expression in AD, but not in PSP TCX, compared to controls. This expression association did not survive adjustment for brain cell type population changes.

Conclusions

We validated the associations previously reported with ABI3_rs616338-T and PLCG2_rs72824905-G in a Caucasian AD case-control cohort, and observed a similar direction of effect in DLB. Conversely, PLCG2_rs72824905-G showed suggestive associations with PSP and MSA in the opposite direction. We identified microglial gene-enriched co-expression networks with significantly higher levels in AD TCX, but not in PSP, a primary tauopathy. This co-expression network association appears to be driven by microglial cell population changes in a brain region affected by AD pathology. Although these findings require replication in larger cohorts, they suggest distinct effects of the microglial genes, ABI3 and PLCG2 in neurodegenerative diseases that harbor significant vs. low/no amyloid ß pathology.

     

Measuring disease similarity and predicting disease-related ncRNAs by a novel method

Background

Similar diseases are always caused by similar molecular origins, such as diasease-related protein-coding genes (PCGs). And the molecular associations reflect their similarity. Therefore, current methods for calculating disease similarity often utilized functional interactions of PCGs. Besides, the existing methods have neglected a fact that genes could also be associated in the gene functional network (GFN) based on intermediate nodes.

Methods

Here we presented a novel method, InfDisSim, to deduce the similarity of diseases. InfDisSim utilized the whole network based on random walk with damping to model the information flow. A benchmark set of similar disease pairs was employed to evaluate the performance of InfDisSim.

Results

The region beneath the receiver operating characteristic curve (AUC) was calculated to assess the performance. As a result, InfDisSim reaches a high AUC (0.9786) which indicates a very good performance. Furthermore, after calculating the disease similarity by the InfDisSim, we reconfirmed that similar diseases tend to have common therapeutic drugs (Pearson correlation γ²?=?0.1315, p?=?2.2e-16). Finally, the disease similarity computed by infDisSim was employed to construct a miRNA similarity network (MSN) and lncRNA similarity network (LSN), which were further exploited to predict potential associations of lncRNA-disease pairs and miRNA-disease pairs, respectively. High AUC (0.9893, 0.9007) based on leave-one-out cross validation shows that the LSN and MSN is very appropriate for predicting novel disease-related lncRNAs and miRNAs, respectively.

Conclusions

The high AUC based on benchmark data indicates the method performs well. The method is valuable in the prediction of disease-related lncRNAs and miRNAs.

     

Predictors of thrombolysis in the telestroke and non telestroke settings for hypertensive acute ischemic stroke patients

Background

In acute ischemic stroke patients, telestroke technology provides sustainable approaches to improve the use of thrombolysis therapy. How this is achieved as it relates to inclusion or exclusion of clinical risk factors for thrombolysis is not fully understood. We investigated this in a population of hypertensive stroke patients.

Methods

Structured data from a regional stroke registry that contained telestroke and non telestroke patients with a primary diagnosis of acute ischemic stroke with history of hypertension were collected between January 2014 and June 2016. Clinical risk factors associated with inclusion or exclusion for recombinant tissue plasminogen activator (rtPA) in the telestroke and non telestroke were identified using multiple regression analysis. Associations between variables and rtPA in the regression models were determined using variance inflation factors while the fitness of each model was determined using the ROC curve to predict the power of each logistic regression model.

Results

The non telestroke admitted more patients (62% vs 38%), when compared with the telestroke. Although the telestroke admitted fewer patients, it excluded 11% and administered thrombolysis therapy to 89% of stroke patients with hypertension. In the non telestroke group, adjusted odd ratios showed significant associations of NIH stroke scale score (OR?=?1.059, 95% CI, 1.025–1.093, P <?0.001) and coronary artery disease (OR?=?2.003, 95% CI, 1.16–3.457, P?=?0.013) with inclusion, while increasing age (OR?=?0.979, 95% CI, 0.961–0.996, P?=?0.017), higher INR (OR?=?0.146, 95% CI, 0.032–0.665, P?=?0.013), history of previous stroke (OR?=?0.39, 95% CI, 0.223–0.68, P?=?0.001), and renal insufficiency (OR?=?0.153, 95% CI, 0.046–0.508, P?=?0.002) were associated with rtPA exclusion. In the telestroke, only direct admission to the telestroke was associated with rtPA administration, (OR?=?4.083, 95% CI, 1.322–12.611, P?=?0.014).

Conclusion

The direct admission of hypertensive stroke patients to the telestroke network was the only factor associated with inclusion for thrombolysis therapy even after adjustment for baseline variables. The telestroke technology provides less restrictive criteria for clinical risk factors associated with the inclusion of hypertensive stroke patients for thrombolysis.

     

Association between <Emphasis Type="Italic">ATM</Emphasis> gene polymorphisms,lung cancer susceptibility and radiation-induced pneumonitis: a meta-analysis

Background

Previous studies have suggested that DNA double-strand break (DSB) repair is an important protective pathway after damage. The ataxia telangiectasia mutated (ATM) gene plays an important role in the DNA DSB repair pathway. DNA damage is a major cytotoxic effect that can be caused by radiation, and the ability to repair DNA after damage varies among different tissues. Impaired DNA repair pathways are associated with high sensitivity to radiation exposure. Hence, ATM gene polymorphisms are thought to influence the risk of cancer and radiation-induced pneumonitis (RP) risk in cancer patients treated with radiotherapy. However, the results of previous studies are inconsistent. We therefore conducted this comprehensive meta-analysis.

Methods

A systematic literature search was performed in the PubMed, Embase, China National Knowledge Internet (CNKI) and Wanfang databases to identify studies that investigated the association between the ATM gene polymorphisms and both lung cancer and RP radiotherapy-treated lung cancer (the last search was conducted on Dec.10, 2015). The odds ratio (OR) and 95% confidence interval (CI) were used to investigate the strength of these relationships. Funnel plots and Begg’s and Egger’s tests were conducted to assess the publication bias. All analyses were performed in STATA 13.0 software.

Results

Ten eligible case-control studies (4731 cases and 5142 controls) on lung cancer susceptibility and four (192 cases and 772 controls) on RP risk were included. The results of the overall and subgroup analyses indicated that in the ATM gene, the rs189037 (?111G?>?A, ?4519G?>?A), rs664677 (44831C?>?T, 49238C?>?T) and rs664143 (131,717 T?>?G) polymorphisms were significantly associated with lung cancer susceptibility (OR?=?1.21, 95% CI?=?1.04–1.39, P?=?0.01; OR?=?1.26, 95% CI?=?1.06–1.49, P?=?0.01; OR?=?1.43, 95% CI?=?1.15–1.78, P?<?0.01). Additionally, the rs189037 variant was significantly associated with RP risk (OR?=?1.74, 95% CI?=?1.02–2.97, P?=?0.04). No publication bias was found in the funnel plots, Begg’s tests or Egger’s tests.

Conclusions

The results indicate that the ATM rs189037, rs664677 and rs664143 gene polymorphisms are risk factors for lung cancer, while the ATM rs189037 variant was significantly associated with RP risk.