Neutrophil extracellular trap-mediated impairment of meningeal lymphatic drainage exacerbates secondary hydrocephalus after intraventricular hemorrhage.

Rationale: Hydrocephalus is a substantial complication after intracerebral hemorrhage (ICH) or intraventricular hemorrhage (IVH) that leads to impaired cerebrospinal fluid (CSF) circulation. Recently, brain meningeal lymphatic vessels (mLVs) were shown to serve as critical drainage pathways for CSF. Our previous studies indicated that the degradation of neutrophil extracellular traps (NETs) after ICH/IVH alleviates hydrocephalus. However, the mechanisms by which NET degradation exerts beneficial effects in hydrocephalus remain unclear. Methods: A mouse model of hydrocephalus following IVH was established by infusing autologous blood into both wildtype and Cx3cr1-/- mice. By studying the features and processes of the model, we investigated the contribution of mLVs and NETs to the development and progression of hydrocephalus following secondary IVH. Results: This study observed the widespread presence of neutrophils, fibrin and NETs in mLVs following IVH, and the degradation of NETs alleviated hydrocephalus and brain injury. Importantly, the degradation of NETs improved CSF drainage by enhancing the recovery of lymphatic endothelial cells (LECs). Furthermore, our study showed that NETs activated the membrane protein CX3CR1 on LECs after IVH. In contrast, the repair of mLVs was promoted and the effects of hydrocephalus were ameliorated after CX3CR1 knockdown and in Cx3cr1-/- mice. Conclusion: Our findings indicated that mLVs participate in the development of brain injury and secondary hydrocephalus after IVH and that NETs contribute to acute LEC injury and lymphatic thrombosis. CX3CR1 is a key molecule in NET-induced LEC damage and meningeal lymphatic thrombosis, which leads to mLV dysfunction and exacerbates hydrocephalus and brain injury. NETs may be a critical target for preventing the obstruction of meningeal lymphatic drainage after IVH.

Influential factors on urine EV DNA methylation detection and its diagnostic potential in prostate cancer.

The value of Extracellular vesicles (EVs) diagnostic markers is widely recognized. However, current research on EV DNA remains limited. This study investigates the biological properties, preprocessing factors, and diagnostic potential of EV DNA. We found that DNA positive vesicles account for 23.3% ± 6.7% of the urine total EV, with a large amount of DNA attached to the outside. EV DNA fragments are large, there is no significant effect on uEV DNA when store urine less than 6 h at 4°C. In addition, the influence of different EV extraction methods on methylation detection is also minor. More importantly, RASSF1A methylation in urine total EV DNA can distinguish between PCa and BPH, with an AUC of 0.874. Our results suggest the potential of urine EV DNA as a novel marker for PCa diagnosis. This provides a new idea for the study of urinary tumor markers.

Metabolic profiling in tissues and urine of patients with prostatic lesions and the diagnostic value of urine extracellular vesicles metabolites in prostate cancer.

BACKGROUND: Prostate cancer (PCa) lacks convenient and highly specific diagnostic markers. Although the value of extracellular vesicles (EV) in oncology is widely recognized, the diagnostic value of EV metabolites requires further exploration. This study aimed to explore the diagnostic value of urine EV (u-EV) metabolomics in PCa. METHODS: We first detected metabolites in paired tissues cells (cells), tissue EV (t-EVs), u-EVs, and urine samples in cohort 1 (8 PCa vs. 5 benign prostatic hypertrophy, BPH) to prob the feasibility of EV metabolites as diagnostic markers. We then analyzed the value of u-EVs as markers for PCa diagnosis and typing in the expanded sample cohort (60 PCa vs. 40 BPH). RESULTS: U-EV metabolites were more consistent with those in tissue-derived samples (cells and t-EVs) than those in urine, and more differential metabolites between BPH and PCa were identified in u-EV. Subsequently, we used a random forest model to construct a panel of six metabolites for PCa, which showed an area under the curve (AUC) of 0.833 in training cohort and 0.844 in validation cohort. We also found significantly differentially expressed metabolites between PCa subtypes (Gleason ≤ 7 vs. Gleason > 7 and localized vs. metastasis), demonstrating the value of EV metabolites in PCa typing and prognostic assessment. CONCLUSION: Metabolomic analysis of u-EVs is a promising source of noninvasive markers for PCa diagnosis.

Hypergraph convolutional network for longitudinal data analysis in Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disease. Longitudinal structural magnetic resonance imaging (sMRI) data have been widely used for tracking AD pathogenesis and diagnosis. However, existing methods tend to treat each time point equally without considering the temporal characteristics of longitudinal data. In this paper, we propose a weighted hypergraph convolution network (WHGCN) to use the internal correlations among different time points and leverage high-order relationships between subjects for AD detection. Specifically, we construct hypergraphs for sMRI data at each time point using the K-nearest neighbor (KNN) method to represent relationships between subjects, and then fuse the hypergraphs according to the importance of the data at each time point to obtain the final hypergraph. Subsequently, we use hypergraph convolution to learn high-order information between subjects while performing feature dimensionality reduction. Finally, we conduct experiments on 518 subjects selected from the Alzheimer's disease neuroimaging initiative (ADNI) database, and the results show that the WHGCN can get higher AD detection performance and has the potential to improve our understanding of the pathogenesis of AD.

Glibenclamide pretreatment attenuates early hematoma expansion of warfarin-associated intracerebral hemorrhage in rats by alleviating perihematomal blood-brain barrier dysfunction.

BACKGROUND: Hematoma expansion is a determinant of poor outcome of intracerebral hemorrhage but occurs frequently, especially in warfarin-associated intracerebral hemorrhage (W-ICH). In the present study, we employ the warfarin-associated intracerebral hemorrhage (W-ICH) rat model, to explore the efficacy and potential mechanism of glibenclamide pretreatment on hematoma expansion after intracerebral hemorrhage, hoping to provide proof of concept that glibenclamide in stroke primary and secondary prevention is also potentially beneficial for intracerebral hemorrhage patients at early stage. METHODS: In the present study, we tested whether glibenclamide, a common hypoglycemic drug, could attenuate hematoma expansion in a rat model of W-ICH. Hematoma expansion was evaluated using magnetic resonance imaging; brain injury was evaluated by brain edema and neuronal death; and functional outcome was evaluated by neurological scores. Then blood-brain barrier integrity was assessed using Evans blue extravasation and tight junction-related protein. RESULTS: The data indicated that glibenclamide pretreatment significantly attenuated hematoma expansion at 24 h after W-ICH, thus mitigating brain edema and neuronal death and promoting neurological function recovery, which may benefit from alleviating blood-brain barrier disruption by suppressing matrix metallopeptidase-9. CONCLUSIONS: The results indicate that glibenclamide pretreatment in stroke primary and secondary prevention might be a promising therapy for hematoma expansion at the early stage of W-ICH.

Identification and discovery of imaging genetic patterns using fusion self-expressive network in major depressive disorder.

Introduction: Major depressive disorder (MDD) is a prevalent mental illness, with severe symptoms that can significantly impair daily routines, social interactions, and professional pursuits. Recently, imaging genetics has received considerable attention for understanding the pathogenesis of human brain disorders. However, identifying and discovering the imaging genetic patterns between genetic variations, such as single nucleotide polymorphisms (SNPs), and brain imaging data still present an arduous challenge. Most of the existing MDD research focuses on single-modality brain imaging data and neglects the complex structure of brain imaging data. Methods: In this study, we present a novel association analysis model based on a self-expressive network to identify and discover imaging genetics patterns between SNPs and multi-modality imaging data. Specifically, we first build the multi-modality phenotype network, which comprises voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. Then, we apply intra-class similarity information to construct self-expressive networks of multi-modality phenotype features via sparse representation. Subsequently, we design a fusion method guided by diagnosis information, which iteratively fuses the self-expressive networks of multi-modality phenotype features into a single new network. Finally, we propose an association analysis between MDD risk SNPs and the multi-modality phenotype network based on a fusion self-expressive network. Results: Experimental results show that our method not only enhances the association between MDD risk SNP rs1799913 and the multi-modality phenotype network but also identifies some consistent and stable regions of interest (ROIs) multi-modality biological markers to guide the interpretation of MDD pathogenesis. Moreover, 15 new potential risk SNPs highly associated with MDD are discovered, which can further help interpret the MDD genetic mechanism. Discussion: In this study, we discussed the discriminant and convergence performance of the fusion self-expressive network, parameters, and atlas selection.

Tumor-derived extracellular vesicle nucleic acids as promising diagnostic biomarkers for prostate cancer.

Liquid biopsy as a non-invasive method has a bright future in cancer diagnosis. Tumor-related extracellular vesicles (EVs) and their components (nucleic acids, proteins, and lipids) in biofluids may exert multiple functions in tumor growth, metastasis, immune escape, and angiogenesis. Among all the components, nucleic acids have attracted the most interest due to their simplicity of extraction and detection. In this review, the biological functions of EVs in prostate cancer (PCa) genesis and progression were summarized. Moreover, the diagnostic value of EV RNA markers found in clinical body fluid samples was reviewed, including their trends, challenging isolation methods, and diagnostic efficacy. Lastly, because relatively much progress has been made in PCa, studies on EV DNA markers are also discussed.

miR-92a-1-5p enriched prostate cancer extracellular vesicles regulate osteoclast function via MAPK1 and FoxO1.

BACKGROUND: We have previously reported that extracellular vesicles (EVs) derived from osteoblastic, osteoclastic and mixed prostate cancer cells promote osteoclast differentiation and inhibit osteoblast differentiation via transferring miR-92a-1-5p. In the present study, we focused on engineering miR-92a-1-5p into EVs and determining any therapeutic roles and mechanisms of the engineered EVs. METHODS: A stable prostate cancer cell line (MDA PCa 2b) overexpressing miR-92a-1-5p was constructed by lentivirus, and EVs were isolated by ultracentrifugation. The overexpression of miR-92a-1-5p in both cells and EVs was tested using qPCR. Osteoclast function was evaluated by Trap staining, mRNA expression of osteoclastic markers ctsk and trap, immunolabeling of CTSK and TRAP and microCT using either in vitro and in vivo assays. Target gene of miR-92a-1-5p was proved by a dual-luciferase reporter assay system. siRNAs were designed and used for transient expression in order to determine the role of downstream genes on osteoclast differentiation. RESULTS: Stable overexpression cells of miRNA-92a-5p was associated with EVs upregulating this microRNA, as confirmed by qPCR. Further, miR-92a-1-5p enriched EVs promote osteoclast differentiation in vitro by reducing MAPK1 and FoxO1 expression, associated with increased osteoclast function as shown by TRAP staining and mRNA expression of osteoclast functional genes. siRNA targeting MAPK1 or FoxO1 resulted in similar increase in osteoclast function. In vivo, the miR-92a-1-5p enriched EVs given via i.v. injection promote osteolysis, which was associated with reduction of MAPK1 and FoxO1 expression in bone marrow. CONCLUSION: These experiments suggest that miR-92a-1-5p enriched EVs regulate osteoclast function via reduction of MAPK1 and FoxO1.

Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder.

Depression (major depressive disorder, MDD) is a common and serious medical illness. Globally, it is estimated that 5% of adults suffer from depression. Recently, imaging genetics receives growing attention and become a powerful strategy for discoverying the associations between genetic variants (e.g., single-nucleotide polymorphisms, SNPs) and multi-modality brain imaging data. However, most of the existing MDD imaging genetic research studies conducted by clinicians usually utilize simple statistical analysis methods and only consider single-modality brain imaging, which are limited in the deeper discovery of the mechanistic understanding of MDD. It is therefore imperative to utilize a powerful and efficient technology to fully explore associations between genetic variants and multi-modality brain imaging. In this study, we developed a novel imaging genetic association framework to mine the multi-modality phenotype network between genetic risk variants and multi-stage diagnosis status. Specifically, the multi-modality phenotype network consists of voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI). Thereafter, an association model based on multi-task learning strategy was adopted to fully explore the relationship between the MDD risk SNP and the multi-modality phenotype network. The multi-stage diagnosis status was introduced to further mine the relation among the multiple modalities of different subjects. A multi-modality brain imaging data and genotype data were collected by us from two hospitals. The experimental results not only demonstrate the effectiveness of our proposed method but also identify some consistent and stable brain regions of interest (ROIs) biomarkers from the node and edge features of multi-modality phenotype network. Moreover, four new and potential risk SNPs associated with MDD were discovered.

Neutrophil Extracellular Traps (NETs): A New Therapeutic Target for Neuroinflammation and Microthrombosis After Subarachnoid Hemorrhage?

Neutrophil extracellular traps (NETs) play a major role in intrinsic immunity by limiting and killing pathogens. Recently, a series of studies have confirmed that NETs are closely associated with vascular injury and microthrombosis. Furthermore, NETs play an important role in neuroinflammation after ischemic and hemorrhagic stroke. Neuroinflammation and microthrombosis after subarachnoid hemorrhage are key pathophysiological processes associated with poor prognosis, but their crucial formation mechanisms and interventions remain to be elucidated. Could NETs, as an emerging and important pathogenesis, be a new therapeutic target after subarachnoid hemorrhage?

The Role of Neutrophil Extracellular Traps in Early Microthrombosis and Brain Injury After Subarachnoid Hemorrhage in Mice.

Microthrombosis plays an important role in secondary brain injury after experimental subarachnoid hemorrhage (SAH), but the specific mechanism of microthrombosis remains unclear. The purpose of this study was to investigate the role of neutrophil extracellular traps (NETs) in microthrombosis after SAH. SAH was induced in male C57BL/6 mice using an endovascular perforation technique. The marker protein of NETs, citrullinated histone H3 (CitH3), was significantly elevated in the cerebral cortex after SAH, and was co-labeled with microthrombi. Both depletion of neutrophils by anti-Ly6G antibody and DNase I treatment significantly reduced the formation of NETs and microthrombi, and ameliorated neurological deficits, brain edema, BBB disruption, and neuronal injury at 24 h after SAH induction. Cerebral hypoperfusion in the first hours after SAH is a major determinant of poor neurological outcome; in this study, we found that DNase I treatment significantly improved the restoration of early cortical perfusion after SAH. In addition, DNase I treatment also significantly attenuated cerebrospinal fluid (CSF) flow after SAH, which was associated with the diffusion barrier caused by microthrombi in the paravascular space after SAH. In conclusion, NETs are associated with early microthrombosis after SAH; they may be a novel therapeutic target for early brain injury (EBI) after SAH.

Identifying Modality-Consistent and Modality-Specific Features via Label-Guided Multi-Task Sparse Canonical Correlation Analysis for Neuroimaging Genetics.

Brain imaging genetics provides the foundation for further revealing brain disorder, which combines genetic variation with brain structure or functions. Recently, sparse canonical correlation analysis (SCCA) and multimodality analysis have been widely utilized for imaging genetics. However, SCCA is an unsupervised learning method which ignores the diagnostic information related to the disease. Traditional multimodality analysis cannot distinguish the consistent and specific information from different neuroimaging that are correlated to the genotypic variances. In this paper, we propose the Label-Guided Multi-task Sparse Canonical Correlation Analysis (LGMTSCCA) method to identify the informative features from the single nucleotide polymorphisms (SNPs) and brain regions related to the pathogenesis of Alzheimer's disease (AD). Specifically, LGMTSCCA uses label constraint via inducing diagnostic information to guide the imaging genetic correlation learning. Considering multi-modal imaging genetic correlations, we use the weight decomposition strategy to calculate the correlation weights in consistency and specificity with different parameters. We evaluate the effectiveness of the LGMTSCCA on synthetic and real data sets. The experimental results show LGMTSCCA can achieve superior performances than the existing methods, which has more flexible ability for identifying modality-consistent and modality-specific features.

Prostate cancer small extracellular vesicles participate in androgen-independent transformation of prostate cancer by transferring let-7a-5p.

Objectives: Androgen deprivation therapy (ADT) is a standard treatment for advanced prostate cancer (PCa). However, after 2-3 years ADT treatment, prostate cancer inevitably transits from androgen-dependent PCa (ADPC) to androgen-independent PCa (AIPC), which has a poor prognosis owing to its unclear mechanism and lack of effective therapeutic targets. Small extracellular vesicles (sEVs) play a vital role in the development of cancer. However, the role of PCa sEVs in the transformation of AIPC remains poorly understood. Materials and methods: Two different cell models were employed and compared. sEVs from ADPC cells (LNCaP) and AIPC cells (LNCaP-AI + F cells) were isolated and characterized. After co-culture of LNCaP-AI + F sEVs with LNCaP cells and of LNCaP sEVs with LNCaP-AI + F cells, androgen-independent transformation was determined respectively. Mechanically, small RNA sequencing was performed. Androgen-independent transformation was examined by the upregulation and downregulation of miRNA and downstream pathways were analyzed. Results: LNCaP-AI + F sEVs promoted the androgen-independent transformation of LNCaP cells. Interestingly, LNCaP sEVs exhibited a capacity to reverse the process.Let-7a-5p transfer was demonstrated. Furthermore, let-7a-5p overexpression promotes the androgen-independent transformation and let-7a-5p down-regulation reverses the process. Androgen receptor (AR) and PI3K/Akt pathways were identified and demonstrated by both let-7a-5p regulation and PCa sEVs coculture. Conclusions: PCa sEVs are intimately involved in the regulation of androgen-independent transformation of prostate cancer by transferring the key sEVs molecular let-7a-5p and then activating the AR and PI3K/Akt signaling pathways. Our results provide new perspectives for the development of sEVs and sEVs molecular targeted treatment approaches for AIPC patients.

Exploring high-order correlations with deep-broad learning for autism spectrum disorder diagnosis.

Recently, a lot of research has been conducted on diagnosing neurological disorders, such as autism spectrum disorder (ASD). Functional magnetic resonance imaging (fMRI) is the commonly used technique to assist in the diagnosis of ASD. In the past years, some conventional methods have been proposed to extract the low-order functional connectivity network features for ASD diagnosis, which ignore the complexity and global features of the brain network. Most deep learning-based methods generally have a large number of parameters that need to be adjusted during the learning process. To overcome the limitations mentioned above, we propose a novel deep-broad learning method for learning the higher-order brain functional connectivity network features to assist in ASD diagnosis. Specifically, we first construct the high-order functional connectivity network that describes global correlations of the brain regions based on hypergraph, and then we use the deep-broad learning method to extract the high-dimensional feature representations for brain networks sequentially. The evaluation of the proposed method is conducted on Autism Brain Imaging Data Exchange (ABIDE) dataset. The results show that our proposed method can achieve 71.8% accuracy on the multi-center dataset and 70.6% average accuracy on 17 single-center datasets, which are the best results compared with the state-of-the-art methods. Experimental results demonstrate that our method can describe the global features of the brain regions and get rich discriminative information for the classification task.

Identify connectome between genotypes and brain network phenotypes via deep self-reconstruction sparse canonical correlation analysis.

MOTIVATION: As a rising research topic, brain imaging genetics aims to investigate the potential genetic architecture of both brain structure and function. It should be noted that in the brain, not all variations are deservedly caused by genetic effect, and it is generally unknown which imaging phenotypes are promising for genetic analysis. RESULTS: In this work, genetic variants (i.e. the single nucleotide polymorphism, SNP) can be correlated with brain networks (i.e. quantitative trait, QT), so that the connectome (including the brain regions and connectivity features) of functional brain networks from the functional magnetic resonance imaging data is identified. Specifically, a connection matrix is firstly constructed, whose upper triangle elements are selected to be connectivity features. Then, the PageRank algorithm is exploited for estimating the importance of different brain regions as the brain region features. Finally, a deep self-reconstruction sparse canonical correlation analysis (DS-SCCA) method is developed for the identification of genetic associations with functional connectivity phenotypic markers. This approach is a regularized, deep extension, scalable multi-SNP-multi-QT method, which is well-suited for applying imaging genetic association analysis to the Alzheimer's Disease Neuroimaging Initiative datasets. It is further optimized by adopting a parametric approach, augmented Lagrange and stochastic gradient descent. Extensive experiments are provided to validate that the DS-SCCA approach realizes strong associations and discovers functional connectivity and brain region phenotypic biomarkers to guide disease interpretation. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at https://github.com/meimeiling/DS-SCCA/tree/main. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Loading of "cocktail siRNAs" into extracellular vesicles via TAT-DRBD peptide for the treatment of castration-resistant prostate cancer.

Extracellular vesicles (EVs) are cell-derived, membranous nanoparticles that mediate intercellular communication by transferring biomolecules between cells. As natural vehicles, EVs may exhibit higher delivery efficiency, lower immunogenicity, and better compatibility than existing RNA carriers. A major limitation of their therapeutic use is the shortage of efficient, robust, and scalable methods to load siRNA of interest. Here, we report a novel strategy using polycationic membrane-penetrating peptide TAT to encapsulate siRNAs into EVs. Three TAT peptides were co-expressed with DRBD as 3TD fusion protein. The sequence-independent binding of DRBD facilitates multiplex genes targeting of mixed siRNAs. Functional assays for siRNA-mediated gene silencing of CRPC were performed after engineered EVs treatment. EVs were isolated using differential centrifugation from WPMY-1 cell culture medium. The increase of merged yellow fluorescence in the engineered EVs showed by TIRFM and the decrease in zeta potential absolute values certified the co-localization of siRNA with EVs, which indicated that siRNA had been successfully delivered into WPMY-1 EVs. qRT-PCR analysis revealed that the mRNA level of FLOH1, NKX3, and DHRS7 was dramatically decreased when cells were treated with engineered EVs loaded with siRNAs mixtures relative to the level of untreated cells. Western and flow cytometry results indicate that delivery of siRNA mixtures by engineered EVs can effectively downregulate AR expression and induce LNCaP-AI cell apoptosis. The uptake efficiency of the EVs and the significantly downregulated expression of three genes suggested the potential of TAT as efficient siRNA carriers by keeping the function of the cargoes.

Combined strategy of knowledge-based rule selection and historical data percentile-based range determination to improve an autoverification system for clinical chemistry test results.

BACKGROUND: Current autoverification, which is only knowledge-based, has low efficiency. Regular historical data analysis may improve autoverification range determination. We attempted to enhance autoverification by selecting autoverification rules by knowledge and ranges from historical data. This new system was compared with the original knowledge-based system. METHODS: New types of rules, extreme values, and consistency checks were added and the autoverification workflow was rearranged to construct a framework. Criteria for creating rules for extreme value ranges, limit checks, consistency checks, and delta checks were determined by analyzing historical Zhongshan laboratory data. The new system's effectiveness was evaluated using pooled data from 20 centers. Efficiency improvement was assessed by a multicenter process. RESULTS: Effectiveness was evaluated by the true positive rate, true negative rate, and overall consistency rate, as compared to manual verification, which were 77.55%, 78.53%, and 78.3%, respectively for the new system. The original overall consistency rate was 56.2%. The new pass rates, indicating efficiency, were increased by 19%-51% among hospitals. Further customization using individualized data increased this rate. CONCLUSIONS: The improved system showed a comparable effectiveness and markedly increased efficiency. This transferable system could be further improved and popularized by utilizing historical data from each hospital.

Circulating tumour DNA methylation in hepatocellular carcinoma diagnosis using digital droplet PCR.

OBJECTIVE: To evaluate the performance of a DNA methylation-based digital droplet polymerase chain reaction (ddPCR) assay to detect aberrant DNA methylation in cell-free DNA (cfDNA) and to determine its application in the detection of hepatocellular carcinoma (HCC). METHODS: The present study recruited patients with liver-related diseases and healthy control subjects. Blood samples were used for the extraction of cfDNA, which was then bisulfite converted and the extent of DNA methylation quantified using a ddPCR platform. RESULTS: A total of 97 patients with HCC, 80 healthy control subjects and 46 patients with chronic hepatitis B/C virus infection were enrolled in the study. The level of cfDNA in the HCC group was significantly higher than that in the healthy control group. For the detection of HCC, based on a cut-off value of 15.7% for the cfDNA methylation ratio, the sensitivity and specificity were 78.57% and 89.38%, respectively. The diagnostic accuracy was 85.27%, the positive predictive value was 81.91% and the negative predictive value was 87.20%. The positive likelihood ratio of 15.7% in HCC diagnosis was 7.40, while the negative likelihood ratio was 0.24. CONCLUSIONS: A sensitive methylation-based assay might serve as a liquid biopsy test for diagnosing HCC.

Identify Complex Imaging Genetic Patterns via Fusion Self-Expressive Network Analysis.

In the brain imaging genetic studies, it is a challenging task to estimate the association between quantitative traits (QTs) extracted from neuroimaging data and genetic markers such as single-nucleotide polymorphisms (SNPs). Most of the existing association studies are based on the extensions of sparse canonical correlation analysis (SCCA) for the identification of complex bi-multivariate associations, which can take the specific structure and group information into consideration. However, they often take the original data as input without considering its underlying complex multi-subspace structure, which will deteriorate the performance of the following integrative analysis. Accordingly, in this paper, the self-expressive property is exploited for the reconstruction of the original data before the association analysis, which can well describe the similarity structure. Specifically, we first apply the within-class similarity information to construct self-expressive networks by sparse representation. Then, we use the fusion method to iteratively fuse the self-expressive networks from multi-modality brain phenotypes into one network. Finally, we calculate the imaging genetic association based on the fused self-expressive network. We conduct the experiments on both single-modality and multi-modality phenotype data. Related experimental results validate that our method can not only better estimate the potential association between genetic markers and quantitative traits but also identify consistent multi-modality imaging genetic biomarkers to guide the interpretation of Alzheimer's disease.

Exosomes derived from osteogenic tumor activate osteoclast differentiation and concurrently inhibit osteogenesis by transferring COL1A1-targeting miRNA-92a-1-5p.

In patients with prostate cancer (PCa), bone lesions appear osteoblastic in radiographs; however, pathological fractures frequently occur in PCa patients, and bone resorption is observed in all metastatic lesions under histopathologic assessment. The mechanisms that balance the activities of osteoblasts and osteoclasts in PCa patients remain unclear. We unexpectedly discovered that PCa exosomes are critical mediators in the regulation of bone homeostasis that results in osteoclastic lesions and thereby promotes tumor growth in bone. We evaluated how exosomes derived from osteoblastic, osteoclastic, and mixed PCa cell lines affect osteoblast and osteoclast differentiation, revealing that all three types of PCa exosomes promoted osteoclastogenesis in vitro and induced osteolysis in vivo. Mechanistically, microRNAs (miRNAs) delivered by PCa exosomes were found to play several key roles in bone homeostasis. Among the delivered miRNAs, miR-92a-1-5p, the most abundant miRNA, downregulated type I collagen expression by directly targeting COL1A1, and thus promoting osteoclast differentiation and inhibiting osteoblastogenesis. Furthermore, PCa exosomes also markedly reduced type I collagen expression in vivo. Our findings not only offer a novel perspective on tumor bone metastasis, where-contrary to our initial hypothesis-exosomes derived from an osteoblastic tumor induce osteoclast differentiation, but also suggest potential therapeutic targets for PCa bone metastasis.