Impact of obesity-related indicators on first-pass effect in patients with ischemic stroke receiving mechanical thrombectomy.

PURPOSE: The first-pass effect (FPE), defined as complete revascularization after a single thrombectomy pass in large vessel occlusion, is a predictor of good prognosis in patients with acute ischemic stroke (AIS) receiving mechanical thrombectomy (MT). We aimed to evaluate obesity-related indicators if possible be predictors of FPE. METHODS: We consecutively enrolled patients with AIS who were treated with MT between January 2019 and December 2021 at our institution. Baseline characteristics, procedure-related data, and laboratory test results were retrospectively analyzed. A multivariable logistic regression analysis was performed to evaluate the independent predictors of FPE. RESULTS: A total of 151 patients were included in this study, of whom 47 (31.1%) had FPE. After adjusting for confounding factors, the independent predictors of achieving FPE were low levels of body mass index (BMI) (OR 0.85, 95% CI 0.748 to 0.971), non-intracranial atherosclerotic stenosis (OR 4.038, 95% CI 1.46 to 11.14), and non-internal carotid artery occlusion (OR 13.14, 95% CI 2.394 to 72.11). Patients with lower total cholesterol (TC) (< 3.11 mmol/L) were more likely to develop FPE than those with higher TC (≥ 4.63 mmol/L) (OR 4.280; 95% CI 1.24 to 14.74) CONCLUSION: Lower BMI, non-intracranial atherosclerotic stenosis, non-internal carotid artery occlusion, and lower TC levels were independently associated with increased rates of FPE in patients with AIS who received MT therapy. FPE was correlated with better clinical outcomes after MT.

Sex Differences in Outcomes after Endovascular Thrombectomy for Patients with Acute Ischemic Stroke.

INTRODUCTION: Endovascular thrombectomy (EVT) is the standard of care for patients with large-vessel occlusion acute ischemic stroke (AIS). There may be differing recanalization effectiveness based on patients' sex, and understanding such variations can improve patient outcomes by adjusting for differences. We aimed to assess the sex differences in outcome after EVT for patients with AIS. METHODS: We retrospectively analyzed 250 consecutive AIS patients who underwent EVT from July 2019 to February 2022 across two large comprehensive tertiary care stroke centers in China. Outcomes of male patients were compared to females, where poor outcome was defined as a modified Rankin score (mRS) of 3-6 at 90 days. RESULTS: Male patients had higher rates of symptomatic intracranial hemorrhage (sICH) (12.50% vs. 4.05%, p = 0.042) and higher hospitalization costs (114,541.08 vs. 105,790.27 RMB, p = 0.024). Male patients also had a longer median onset-to-needle time (ONT) (146.00 [104.00, 202.00] versus 120.00 [99.25, 144.75], p = 0.026). However, there were no differences in hospitalization length (p = 0.251), 90-day favorable outcome (p = 0.952), and 90-day mortality (p = 0.931) between the sexes. CONCLUSION: Female patients had lower hospitalization costs and sICH rates than males after EVT for AIS. Identifying such differences and implementing measures, including adaptations to workflow optimization, would help to reduce the ONT and last known normal-to-puncture time seen in males to improve patient outcomes. Despite such variations, favorable outcomes and mortality are similar in female and male AIS patients.

Comparative Analysis of the Mitochondrial Genomes of Chloropidae and Their Implications for the Phylogeny of the Family.

Chloropidae, commonly known as grass flies, represent the most taxonomically diverse family of Diptera Carnoidea, comprising over 3000 described species worldwide. Previous phylogenetic studies of this family have predominantly relied on morphological characters, with mitochondrial genomes being reported in a few species. This study presents 11 newly sequenced mitochondrial genomes (10 Chloropidae and 1 Milichiidae) and provides the first comprehensive comparative analysis of mitochondrial genomes for Chloropidae. Apart from 37 standard genes and the control region, three conserved intergenic sequences across Diptera Cyclorrhapha were identified in all available chloropid mitochondrial genomes. Evolutionary rates within Chloropidae exhibit significant variation across subfamilies, with Chloropinae displaying higher rates than the other three subfamilies. Phylogenetic relationships based on mitochondrial genomes were inferred using maximum likelihood and Bayesian methods. The monophyly of Chloropidae and all four subfamilies is consistently strongly supported, while subfamily relationships within Chloropidae remain poorly resolved, possibly due to rapid evolution.

Skin precursor-derived Schwann cells accelerate in vivo prevascularization of tissue-engineered nerves to promote peripheral nerve regeneration.

Prevascularization strategies have become a hot spot in tissue engineering. As one of the potential candidates for seed cells, skin precursor-derived Schwann cells (SKP-SCs) were endowed with a new role to more efficiently construct prevascularized tissue-engineered peripheral nerves. The silk fibroin scaffolds seeded with SKP-SCs were prevascularized through subcutaneously implantation, which was further assembled with the SKP-SC-containing chitosan conduit. SKP-SCs expressed pro-angiogenic factors in vitro and in vivo. SKP-SCs significantly accelerated the satisfied prevascularization in vivo of silk fibroin scaffolds compared with VEGF. Moreover, the NGF expression revealed that pregenerated blood vessels adapted to the nerve regeneration microenvironment through reeducation. The short-term nerve regeneration of SKP-SCs-prevascularization was obviously superior to that of non-prevascularization. At 12 weeks postinjury, both SKP-SCs-prevascularization and VEGF-prevascularization significantly improved nerve regeneration with a comparable degree. Our figures provide a new enlightenment for the optimization of prevascularization strategies and how to further utilize tissue engineering for better repair.

Complete chloroplast genomes provide insights into evolution and phylogeny of Zingiber (Zingiberaceae).

BACKGROUND: The genus Zingiber of the Zingiberaceae is distributed in tropical, subtropical, and in Far East Asia. This genus contains about 100-150 species, with many species valued as important agricultural, medicinal and horticultural resources. However, genomic resources and suitable molecular markers for species identification are currently sparse. RESULTS: We conducted comparative genomics and phylogenetic analyses on Zingiber species. The Zingiber chloroplast genome (size range 162,507-163,711 bp) possess typical quadripartite structures that consist of a large single copy (LSC, 86,986-88,200 bp), a small single copy (SSC, 15,498-15,891 bp) and a pair of inverted repeats (IRs, 29,765-29,934 bp). The genomes contain 113 unique genes, including 79 protein coding genes, 30 tRNA and 4 rRNA genes. The genome structures, gene contents, amino acid frequencies, codon usage patterns, RNA editing sites, simple sequence repeats and long repeats are conservative in the genomes of Zingiber. The analysis of sequence divergence indicates that the following genes undergo positive selection (ccsA, ndhA, ndhB, petD, psbA, psbB, psbC, rbcL, rpl12, rpl20, rpl23, rpl33, rpoC2, rps7, rps12 and ycf3). Eight highly variable regions are identified including seven intergenic regions (petA-pabJ, rbcL-accD, rpl32-trnL-UAG, rps16-trnQ-UUG, trnC-GCA-psbM, psbC-trnS-UGA and ndhF-rpl32) and one genic regions (ycf1). The phylogenetic analysis revealed that the sect. Zingiber was sister to sect. Cryptanthium rather than sect. Pleuranthesis. CONCLUSIONS: This study reports 14 complete chloroplast genomes of Zingiber species. Overall, this study provided a solid backbone phylogeny of Zingiber. The polymorphisms we have uncovered in the sequencing of the genome offer a rare possibility (for Zingiber) of the generation of DNA markers. These results provide a foundation for future studies that seek to understand the molecular evolutionary dynamics or individual population variation in the genus Zingiber.

Nonlinear connection between remnant cholesterol and stroke risk: evidence from the China health and retirement longitudinal study.

OBJECTIVE: The evidence on the relationship between remnant cholesterol (RC) and stroke remains controversial. Therefore, this study aimed to explore the relationship between RC and stroke risk in a Chinese population of middle-aged and elderly individuals. METHODS: The present study included 10067 Chinese subjects of middle-aged and elderly individuals. The connection between RC and incident stroke was investigated using the multivariate Cox proportional hazards regression model, several sensitivity analyses, generalized additive models, and smoothed curve fitting. RESULTS: A total of 1180 participants with stroke were recorded during the follow-up period. The multivariate Cox proportional hazards regression model identified a positive connection between RC and stroke risk (hazard ratio (HR) = 1.087, 95% confidence interval (CI): 1.001-1.180). In addition, the current study discovered a nonlinear connection between RC and incident stroke, and the point of inflection for RC was 1.78 mmol/L. The risk of stroke increased by 25.1% with each unit increase in RC level when RC was < 1.78 mmol/L (HR:1.251, 95%CI: 1.089-1.437, P = 0.0015). The results were not affected by sensitivity tests. CONCLUSION: The current study showed a positive and nonlinear connection between RC and stroke risk in a middle-aged and elderly Chinese population. These findings provided new information to help researchers better understand the relationship between RC levels and incident stroke.

Joint eQTL mapping and inference of gene regulatory network improves power of detecting both cis- and trans-eQTLs.

MOTIVATION: Genetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: (i) relatively small size of samples available, and (ii) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identify trans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identify trans-eQTLs without increasing multiple testing burden. RESULTS: In this article, we use the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM for eQTL mapping (SSEMQ), to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determine trans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms nine existing eQTL mapping methods. SSEMQ is further used to analyze two real datasets of human breast and whole blood tissues, yielding a number of cis- and trans-eQTLs. AVAILABILITY AND IMPLEMENTATION: R package ssemQr is available at https://github.com/Ivis4ml/ssemQr.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation.

BACKGROUND: With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. RESULTS: A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. CONCLUSIONS: Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

Inference of differential gene regulatory networks based on gene expression and genetic perturbation data.

MOTIVATION: Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. RESULTS: In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. AVAILABILITY AND IMPLEMENTATION: The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Brain connectivity markers in advanced Parkinson's disease for predicting mild cognitive impairment.

OBJECTIVES: Mild cognitive impairment (MCI) is a well-defined non-motor manifestation and a harbinger of dementia in Parkinson's disease. This study is to investigate brain connectivity markers of MCI using diffusion tensor imaging and resting-state functional MRI, and help MCI diagnosis in PD patients. METHODS: We evaluated 131 advanced PD patients (disease duration > 5 years; 59 patients with MCI) and 48 healthy control subjects who underwent a diffusion-weighted and resting-state functional MRI scanning. The patients were randomly assigned to training (n = 100) and testing (n = 31) groups. According to the Brainnetome Atlas, ROI-based structural and functional connectivity analysis was employed to extract connectivity features. To identify features with significant discriminative power for patient classification, all features were put into an all-relevant feature selection procedure within cross-validation loops. RESULTS: Nine features were identified to be significantly relevant to patient classification. They showed significant differences between PD patients with and without MCI and positively correlated with the MoCA score. Five of them did not differ between general MCI subjects and healthy controls from the ADNI database, which suggested that they could uniquely play a part in the MCI diagnosis of PD. On basis of these relevant features, the random forest model constructed from the training group achieved an accuracy of 83.9% in the testing group, to discriminate patients with and without MCI. CONCLUSIONS: The results of our study provide preliminary evidence that structural and functional connectivity abnormalities may contribute to cognitive impairment and allow to predict the outcome of MCI diagnosis in PD. KEY POINTS: • Nine MCI markers were identified using an all-relevant feature selection procedure. • Five of nine markers differed between MCI and NC in PD, but not in general persons. • A random forest model achieved an accuracy of 83.9% for MCI diagnosis in PD.

Brain connectivity markers for the identification of effective contacts in subthalamic nucleus deep brain stimulation.

The clinical benefit of deep brain stimulation (DBS) for Parkinson's disease (PD) is relevant to the tracts adjacent to the stimulation site, but it remains unclear what connectivity pattern is associated with effective DBS. The aim of this study was to identify clinically effective electrode contacts on the basis of brain connectivity markers derived from diffusion tensor tractography. We reviewed 77 PD patients who underwent bilateral subthalamic nucleus DBS surgery. The patients were assigned into the training (n = 58) and validation (n = 19) groups. According to the therapeutic window size, all contacts were classified into effective and ineffective groups. The whole-brain connectivity of each contact's volume of tissue activated was estimated using tractography with preoperative diffusion tensor data. Extracted connectivity features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for contact classification. A total of 616 contacts on 154 DBS leads were discriminated, with 388 and 228 contacts being classified as effective and ineffective ones, respectively. After the feature selection, the connectivity of contacts with the thalamus, pallidum, hippocampus, primary motor area, supplementary motor area and superior frontal gyrus was identified to significantly contribute to contact classification. Based on these relevant features, the random forest model constructed from the training group achieved an accuracy of 84.9% in the validation group, to discriminate effective contacts from the ineffective. Our findings advanced the understanding of the specific brain connectivity patterns associated with clinical effective electrode contacts, which potentially guided postoperative DBS programming.

Associations between seizures and MRI in patients with anti-NMDAR encephalitis.

OBJECTIVES: Seizures are a prominent feature of anti-N-methyl-d-aspartate receptor (anti-NMDAR) encephalitis. Nearly half of brain magnetic resonance image (MRI) results are abnormal. The aim of our study was to evaluate the associations between seizures and brain MRI results in patients with anti-NMDAR encephalitis. METHODS: Patients with anti-NMDAR encephalitis were enrolled between January 2015 and December 2018. The patients included were divided into normal and abnormal MRI groups. Seizure outcomes and modified Rankin Scale scores at the 1-year follow-up were assessed. Seizure characteristics and outcomes were compared between groups. RESULTS: Of 35 patients with anti-NMDAR encephalitis, 28 patients (80%) had reported seizures in the acute phase. Patients with abnormal MRI findings more frequently had focal seizures than patients with normal MRI findings (72.7% vs 17.6%, P < .01). The incidence of patients treated with 2 or more antiepileptic drugs was higher in the normal MRI group than in the abnormal MRI group (100% vs 45.4%, P < .01). The onset-immunotherapy time was shorter in the abnormal MRI group than in the normal MRI group (P < .05). There were no statistically significant differences in seizure outcomes between the normal and abnormal MRI groups (P > .05). CONCLUSIONS: Focal seizures were most common in patients with abnormal MRI lesions. In the acute stage of the disease, the abnormal MRI group was more likely than the normal MRI group to achieve seizure control. Abnormal MRI findings did not affect the overall good prognosis of patients with anti-NMDAR encephalitis with seizures.

Sleep spindles, K-complexes, limb movements and sleep stage proportions may be biomarkers for amnestic mild cognitive impairment and Alzheimer's disease.

PURPOSE: Sleep disturbances are common in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients. Non-rapid eye movement stage 3 (N3), rapid eye movement stage (REM), spindle density, and K-complex (KC) density are decreased in MCI and AD patients. Periodic limb movements in sleep (PLMS) are increased in other neurodegenerative diseases. We aimed to distinguish amnestic mild cognitive impairment (aMCI) patients from the overall population of MCI patients by comparing the N3 and REM proportions, the morphological characteristics of spindles and KCs and the periodic limb movement index (PLMI) among control, aMCI and AD subjects. METHODS: In 92 subjects (30 controls, 32 aMCI and 30 AD), sleep stages, spindles, KCs and PLMS were recorded during the second of two nights of polysomnography (PSG). We compared the above parameters among the three groups. RESULTS: AD and aMCI subjects had lower proportions of N3 and REM, poorer spindle and KC activities and more frequent PLMS than controls. These alterations were associated with decreased Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. We determined cut-off values for distinguishing aMCI and AD using logistic regression and receiver operating characteristic (ROC) analyses. CONCLUSIONS: AD and aMCI patients have abnormal sleep stage proportions, spindles, KCs and PLMS. The combination of the above alterations may distinguish aMCI and AD patients from controls with high specificity and sensitivity.

Efficient proximal gradient algorithm for inference of differential gene networks.

BACKGROUND: Gene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very important to understand molecular basis of various biological process. While existing algorithms can be used to infer two gene networks separately from gene expression data under two different conditions, and then to identify network changes, such an approach does not exploit the similarity between two gene networks, and it is thus suboptimal. A desirable approach would be clearly to infer two gene networks jointly, which can yield improved estimates of network changes. RESULTS: In this paper, we developed a proximal gradient algorithm for differential network (ProGAdNet) inference, that jointly infers two gene networks under different conditions and then identifies changes in the network structure. Computer simulations demonstrated that our ProGAdNet outperformed existing algorithms in terms of inference accuracy, and was much faster than a similar approach for joint inference of gene networks. Gene expression data of breast tumors and normal tissues in the TCGA database were analyzed with our ProGAdNet, and revealed that 268 genes were involved in the changed network edges. Gene set enrichment analysis identified a significant number of gene sets related to breast cancer or other types of cancer that are enriched in this set of 268 genes. Network analysis of the kidney cancer data in the TCGA database with ProGAdNet also identified a set of genes involved in network changes, and the majority of the top genes identified have been reported in the literature to be implicated in kidney cancer. These results corroborated that the gene sets identified by ProGAdNet were very informative about the cancer disease status. A software package implementing the ProGAdNet, computer simulations, and real data analysis is available as Additional file 1. CONCLUSION: With its superior performance over existing algorithms, ProGAdNet provides a valuable tool for finding changes in gene networks, which may aid the discovery of gene-gene interactions changed under different conditions.

CD36 promotes the epithelial-mesenchymal transition and metastasis in cervical cancer by interacting with TGF-ß.

BACKGROUND: Accumulating evidence indicates that CD36 initiates metastasis and correlates with an unfavorable prognosis in cancers. However, there are few reports regarding the roles of CD36 in initiation and metastasis of cervical cancer. METHODS: Using immunohistochemistry, we analyzed 133 cervical cancer samples for CD36 protein expression levels, and then investigated the correlation between changes in its expression and clinicopathologic parameters. The effect of CD36 expression on the epithelial-mesenchymal transition (EMT) in cervical cancer cells was evaluated by Western immunoblotting analysis. In vitro invasion and in vivo metastasis assays were also used to evaluate the role of CD36 in cervical cancer metastasis. RESULTS: In the present study, we confirmed that CD36 was highly expressed in cervical cancer samples relative to normal cervical tissues. Moreover, overexpression of CD36 promoted invasiveness and metastasis of cervical cancer cells in vitro and in vivo, while CD36 knockdown suppressed proliferation, migration, and invasiveness. We demonstrated that TGF-ß treatment attenuated E-cadherin expression and enhanced the expression levels of CD36, vimentin, slug, snail, and twist in si-SiHa, si-HeLa, and C33a-CD36 cells, suggesting that TGF-ß synergized with CD36 on EMT via active CD36 expression. We also observed that the expression levels of TGF-ß in si-SiHa cells and si-HeLa cells were down-regulated, whereas the expression levels of TGF-ß were up-regulated in C33a-CD36 cells. These results imply that CD36 and TGF-ß interact with each other to promote the EMT in cervical cancer. CONCLUSIONS: Our findings suggest that CD36 is likely to be an effective target for guiding individualized clinical therapy of cervical cancer.

Treatment of Parkinson's disease in rats by Nrf2 transfection using MRI-guided focused ultrasound delivery of nanomicrobubbles.

Parkinson's disease (PD) is a very common neurological disorder. However, effective therapy is lacking. Although the blood-brain-barrier (BBB) protects the brain, it prevents the delivery of about 90% of drugs and nucleotides into the brain, thereby hindering the development of gene therapy for PD. Magnetic resonance imaging (MRI)-guided focused ultrasound delivery of microbubbles enhances the delivery of gene therapy vectors across the BBB and improves transfection efficiency. In the present study, we delivered nuclear factor E2-related factor 2 (Nrf2, NFE2L2) contained in nanomicrobubbles into the substantia nigra of PD rats by MRI-guided focused ultrasound, and we examined the effect of Nrf2 over-expression in this animal model of PD. The rat model of PD was established by injecting 6-OHDA in the right substantia nigra stereotactically. Plasmids (pDC315 or pDC315/Nrf2) were loaded onto nanomicrobubbles, and then injected through the tail vein with the assistance of MRI-guided focused ultrasound. MRI-guided focused ultrasound delivery of nanomicrobubbles increased gene transfection efficiency. Furthermore, Nrf2 gene transfection reduced reactive oxygen species levels, thereby protecting neurons in the target region.

A family with hereditary cerebellar ataxia finally confirmed as Gerstmann-Straussler-Scheinker syndrome with P102L mutation in PRNP gene.

Gerstmann-Straussler-Scheinker syndrome (GSS) is an exceedingly rare prion disease. There are only 3 case reports of GSS in China. Here we report the first GSS family in southern China. A 47-year-old female complained of unsteady gait and dysarthria. Seven other individuals presented similar symptoms in 3 generations of her family, and all died 4-6 years after onset. To detect causative mutations, we employed a gene analysis panel of hereditary diseases. This revealed a P102L mutation in the prion protein gene (PRNP) gene, which is commonly found in GSS featuring cerebellar ataxia. However, GSS is an uncommon cause of hereditary cerebellar ataxia that might be overlooked because many neurologists are unfamiliar with it. To avoid misdiagnosis in the patients with hereditary cerebellar ataxia, GSS should be taken into account if other causes are absent, especially in patients that have accompanying psychiatric symptoms and a short survival time.

Prognostic Value of MicroRNA-196a in Asian Cancer Patients: a Meta-Analysis.

BACKGROUND: Growing evidence from studies has shown that microRNA-196a (miR-196a) is correlated with treatment response and prognosis in Asian cancer patients. However, the studies reveal that the role of miR-196a is not totally consistent, making it rational to perform a meta-analysis to assess the prognostic value of miR-196a in cancers. METHODS: This meta-analysis was conducted by searching PubMed, Embase, the Cochrane library, China National Knowledge Infrastructure (CNKI), and Web of Science. Baseline characteristics and key statistics such as hazard ratio (HR), 95% confidence interval (CI), and p-value were extracted from studies investigating the association between clinical outcomes in Asian patients with cancers and the expression of miR-196a. The pooled HRs and CIs were calculated. RESULTS: 13 studies were included to assess the prognostic role of miR-196a in cancer patients. The pooled HR of higher miR-196a expression for overall survival (OS) was 3.08 (95% CI: 2.32 - 4.10, p < 0.001). For disease free survival (DFS) and recurrence free survival (RFS), the pooled HR is 3.83 (95% CI: 2.39 - 6.12, p < 0.001). No obvious between-study heterogeneity was shown among included studies. Hence, a fixed model was utilized. In our subgroup analysis, the results remain consistent. It shows that higher expression of miR-196a was both associated with poor OS and RFS/DFS in different kinds of cancers. CONCLUSIONS: The present meta-analysis suggested that higher expression of miR-196a might predict poor prognosis in Asian cancer patients.

Detecting genetic interactions in pathway-based genome-wide association studies.

Pathway-based genome-wide association studies (GWAS) can exploit collective effects of causal variants in a pathway to increase power of detection. However, current methods for pathway-based GWAS do not consider epistatic effects of genetic variants, although interactions between genetic variants may play an important role in influencing complex traits. In this paper, we employed a Bayesian Lasso logistic regression model for pathway-based GWAS to include all possible main effects and a large number of pairwise interactions of single nucleotide polymorphisms (SNPs) in a pathway, and then inferred the model with an efficient group empirical Bayesian Lasso (EBLasso) method. Using the inferred model, the statistical significance of a pathway was tested with the Wald statistics. Reliable effects in a significant pathway were selected using the stability selection technique. Extensive computer simulations demonstrated that our group EBlasso method significantly outperformed two competitive methods in most simulation setups and offered similar performance in other simulation setups. When applying to a GWAS dataset for Parkinson disease, EBLasso identified three significant pathways including the primary bile acid biosynthesis pathway, the neuroactive ligand-receptor interaction, and the MAPK signaling pathway. All effects identified in the primary bile acid biosynthesis pathway and many of effects in the other two pathways were epistatic effects. The group EBLasso method provides a valuable tool for pathway-based GWAS to identify main and epistatic effects of genetic variants.