Cross-Modal Retrieval With Noisy Correspondence via Consistency Refining and Mining.

The success of existing cross-modal retrieval (CMR) methods heavily rely on the assumption that the annotated cross-modal correspondence is faultless. In practice, however, the correspondence of some pairs would be inevitably contaminated during data collection or annotation, thus leading to the so-called Noisy Correspondence (NC) problem. To alleviate the influence of NC, we propose a novel method termed Consistency REfining And Mining (CREAM) by revealing and exploiting the difference between correspondence and consistency. Specifically, the correspondence and the consistency only be coincident for true positive and true negative pairs, while being distinct for false positive and false negative pairs. Based on the observation, CREAM employs a collaborative learning paradigm to detect and rectify the correspondence of positives, and a negative mining approach to explore and utilize the consistency. Thanks to the consistency refining and mining strategy of CREAM, the overfitting on the false positives could be prevented and the consistency rooted in the false negatives could be exploited, thus leading to a robust CMR method. Extensive experiments verify the effectiveness of our method on three image-text benchmarks including Flickr30K, MS-COCO, and Conceptual Captions. Furthermore, we adopt our method into the graph matching task and the results demonstrate the robustness of our method against fine-grained NC problem. The code is available on https://github.com/XLearning-SCU/2024-TIP-CREAM.

METTL16 suppressed the proliferation and cisplatin-chemoresistance of bladder cancer by degrading PMEPA1 mRNA in a m6A manner through autophagy pathway.

N6-methyladenosine (m6A) is important in the physiological processes of many species. Methyltransferase-like 16 (METTL16) is a novel discovered m6A methylase, regulating various tumors in an m6A-dependent manner. However, its function in bladder cancer (BLCA) remains largely unclear. In the present study, we found that low expression of METTL16 predicted poor survival in BLCA patients. METTL16 inhibited the proliferation and cisplatin-resistance function of bladder cancer cells in vitro and in vivo. In addition, METTL16 reduced the mRNA stability of prostate transmembrane protein androgen induced-1 (PMEPA1) via binding to its m6A site in the 3'-UTR, thereby inhibited the proliferation of bladder cancer cells and increased the sensitivity of cisplatin through PMEPA1-mediated autophagy pathway. Finally, we found that hypoxia-inducible factor 2α (HIF-2α) exerted its tumor-promoting effect by binding the METTL16 promoter region to repress its transcription. Taken together, High expression of METTL16 predicted better survival in BLCA. METTL16 significantly inhibited bladder cancer cell proliferation and sensitized bladder cancer cells to cisplatin via HIF-2α-METTL16-PMEPA1-autophagy axis in a m6A manner. These findings might provide fresh insights into BLCA therapy.

A comprehensive pan-cancer analysis of CDH5 in immunological response.

Background: Cadherin 5 (CDH5) functions critically in maintaining cell adhesion and integrity of endothelial and vascular cells. The expression of CDH5 is abnormal in tumor cells, which may have great potential to serve as a new immune checkpoint. The current pan-cancer analysis was performed to better understand the role of CDH5 in tumor. Methods: The clinical significance and immunological function of CDH5 in pan-cancers were comprehensively analyzed based on the correlations between CDH5 and clinicopathologic features, prognosis values, tumor mutation burden (TMB), microsatellite instability (MSI), immune cells infiltration and immune response genes using 33 datasets from The Cancer Genome Atlas (TCGA). We further confirmed the expression of CDH5 in bladder cancer (BCa) tissues and cell lines. The CD8+ T cells were screened from peripheral blood of healthy controls and activated. BCa cell-CD8+ T cell co-culture assay and ELISA assay were carried out to verify the immunological function of CDH5. Results: The expression of CDH5 was down-regulated in 8 types of tumors including in BCa but up-regulated in 4 types of tumors. CDH5 was significantly correlated with tumor stage in 6 types of tumors. In addition, CDH5 was positively or negatively correlated with tumor prognosis. Furthermore, CDH5 was closely associated with TMB in 15 types of tumors and with MSI in 9 types of tumors. KEGG-GSEA and Hallmarks-GSEA analyses results indicated that CDH5 was positively related to immune response in most tumor types. In many tumors, CDH5 showed a positive correlation with immune cell infiltration. Enrichment analyses demonstrated that CDH5 was significantly related to the expression of many immunomodulators and chemokines. Further experiments showed that CDH5 was low-expressed in BCa tissues and cell lines in comparison to adjacent normal tissues and normal urothelial cell line, but it was positively associated with a better prognosis of BCa patients. The results of in vitro co-culture assay and ELISA assay demonstrated that CDH5 could promote the function of CD8+ T cells in TME of BCa. Conclusion: In summary, CDH5 was positively associated with a favorable prognosis and effective immune response in tumors, showing a great potential to serve as a novel tumor biomarker and immune checkpoint.

Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies.

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

scBridge embraces cell heterogeneity in single-cell RNA-seq and ATAC-seq data integration.

Single-cell multi-omics data integration aims to reduce the omics difference while keeping the cell type difference. However, it is daunting to model and distinguish the two differences due to cell heterogeneity. Namely, even cells of the same omics and type would have various features, making the two differences less significant. In this work, we reveal that instead of being an interference, cell heterogeneity could be exploited to improve data integration. Specifically, we observe that the omics difference varies in cells, and cells with smaller omics differences are easier to be integrated. Hence, unlike most existing works that homogeneously treat and integrate all cells, we propose a multi-omics data integration method (dubbed scBridge) that integrates cells in a heterogeneous manner. In brief, scBridge iterates between i) identifying reliable scATAC-seq cells that have smaller omics differences, and ii) integrating reliable scATAC-seq cells with scRNA-seq data to narrow the omics gap, thus benefiting the integration for the rest cells. Extensive experiments on seven multi-omics datasets demonstrate the superiority of scBridge compared with six representative baselines.

Hsa_circ_0003098 promotes bladder cancer progression via miR-377-5p/ACAT2 axis.

Accumulating evidence has proven that circRNAs play vital roles in tumor progression. Nevertheless, the mechanisms underlying circRNAs in bladder cancer (BCa) remain largely unknown. The purpose of this study was to identify the role and investigate the potential molecular mechanisms of hsa_circ_0003098 in BCa. We confirmed that hsa_circ_0003098 expression was significantly upregulated in BCa tissues, of which expression was remarkably associated with poor prognosis. Functionally, overexpression of hsa_circ_0003098 promoted BCa cell proliferation, migration, and invasion in vitro as well as tumor growth in vivo. Mechanistically, hsa_circ_0003098 promoted upregulation of ACAT2 expression and induced cholesteryl ester accumulation via acting as a sponge for miR-377-5p. Thus, hsa_circ_0003098 plays an oncogenic role in BCa and may serve as a potential biomarker and therapeutic target for BCa.

Lysine N-methyltransferase SETD7 promotes bladder cancer progression and immune escape via STAT3/PD-L1 cascade.

Background: The immunotherapy sensitivity of patients with bladder cancer (BCa) remains low. As the role of protein methylation in tumorigenesis and development becomes clearer, the role of lysine N-methyltransferase SET domain containing 7 (SETD7) in the progression and immune escape of BCa is worth studying. Methods: The correlation between lysine methyltransferase family and prognosis or immunotheray sensitivity of BCa patients were analyzed, and SETD7 was screened out because of the significant correlation between its expression and survival data or immunotherapy sensitivity. The expression of SETD7 in BCa tissues and cell lines were explored. The functions of SETD7 were investigated by proliferation and migration assays. The role of SETD7 in BCa immune escape was validated by analyzing the correlation between SETD7 expression and tumor microenvironment (TME)-related indicators. The results were further confirmed by conducting BCa cell-CD8+ T cell co-culture assays and tumorigenesis experiment in human immune reconstitution NOG mice (HuNOG mice). Bioinformatic prediction, CO-IP, qRT-PCR, and western blot were used to validate the SETD7/STAT3/PD-L1 cascade. Results: SETD7 was highly expressed in BCa, and it was positively associated with high histological grade and worse prognosis. SETD7 promoted the proliferation and migration of BCa cells. The results of bioinformatics, in vitro co-culture, and in vivo tumorigenesis assays showed that SETD7 could inhibit the chemotoxis and cytotoxicity of CD8+ T cells in BCa TME. Mechanistically, bioinformatics analysis, CO-IP assay, qRT-PCR, and western blot results indicated that SETD7 could increase the expression of PD-L1 via binding and promoting STAT3. Conclusions: Taken together, SETD7 indicated poor prognosis and promoted the progression and immune escape of BCa cells. It has great potential to act as a new indicator for BCa diagnosis and treatment, especially immunotherapy.

Clinical significance, tumor immune landscape and immunotherapy responses of ADAR in pan-cancer and its association with proliferation and metastasis of bladder cancer.

BACKGROUND: ADAR is an enzyme involved in adenosine-inosine RNA editing. However, the role of ADAR in tumorigenesis, progression, and immunotherapy has not been fully elucidated. METHODS: The TCGA, GTEx and GEO databases were extensively utilized to explore the expression level of ADAR across cancers. Combined with the clinical information of patients, the risk profile of ADAR in various cancers was delineated. We identified pathways enriched in ADAR and their related genes and explored the association between ADAR expression and the cancer immune microenvironment score and response to immunotherapy. Finally, we specifically explored the potential value of ADAR in the treatment of the bladder cancer immune response and verified the critical role of ADAR in the development and progression of bladder cancer through experiments. RESULTS: ADAR is highly expressed in most cancers at both the RNA and protein level. ADAR is associated with the aggressiveness of some cancers, especially bladder cancer. In addition, ADAR is associated with immune-related genes, especially immune checkpoint genes, in the tumor immune microenvironment. Moreover, ADAR expression is positively correlated with tumor mutation burden and microsatellite instability in a variety of cancers, indicating that ADAR could be used as a biomarker of immunotherapy. Finally, we demonstrated that ADAR is a key pathogenic factor in bladder cancer. ADAR promoted proliferation and metastasis of bladder cancer cells. CONCLUSION: ADAR regulates the tumor immune microenvironment and can be used as a biomarker of the tumor immunotherapy response, providing a novel strategy for the treatment of tumors, especially bladder cancer.

Shunting at Arbitrary Feature Levels via Spatial Disentanglement: Toward Selective Image Translation.

The past few years have witnessed considerable efforts devoted to translating images from one domain to another, mainly aiming at editing global style. Here, we focus on a more general case, selective image translation (SLIT), under an unsupervised setting. SLIT essentially operates through a shunt mechanism that involves learning gates to manipulate only the contents of interest (CoIs), which can be either local or global, while leaving the irrelevant parts unchanged. Existing methods typically rely on a flawed implicit assumption that CoIs are separable at arbitrary levels, ignoring the entangled nature of DNN representations. This leads to unwanted changes and learning inefficiency. In this work, we revisit SLIT from an information-theoretical perspective and introduce a novel framework, which equips two opposite forces to disentangle the visual features. One force encourages independence between spatial locations on the features, while the other force unites multiple locations to form a "block" that jointly characterizes an instance or attribute that a single location may not independently characterize. Importantly, this disentanglement paradigm can be applied to visual features of any layer, enabling shunting at arbitrary feature levels, which is a significant advantage not explored in existing works. Our approach has undergone extensive evaluation and analysis, confirming its effectiveness in significantly outperforming the state-of-the-art baselines.

Dual-feature Fusion Attention Network for Small Object Segmentation.

Accurate segmentation of medical images is an important step during radiotherapy planning and clinical diagnosis. However, manually marking organ or lesion boundaries is tedious, time-consuming, and prone to error due to subjective variability of radiologist. Automatic segmentation remains a challenging task owing to the variation (in shape and size) across subjects. Moreover, existing convolutional neural networks based methods perform poorly in small medical objects segmentation due to class imbalance and boundary ambiguity. In this paper, we propose a dual feature fusion attention network (DFF-Net) to improve the segmentation accuracy of small objects. It mainly includes two core modules: the dual-branch feature fusion module (DFFM) and the reverse attention context module (RACM). We first extract multi-resolution features by multi-scale feature extractor, then construct DFFM to aggregate the global and local contextual information to achieve information complementarity among features, which provides sufficient guidance for accurate small objects segmentation. Moreover, to alleviate the degradation of segmentation accuracy caused by blurred medical image boundaries, we propose RACM to enhance the edge texture of features. Experimental results on datasets NPC, ACDC, and Polyp demonstrate that our proposed method has fewer parameters, faster inference, and lower model complexity, and achieves better accuracy than more state-of-the-art methods.

Neural Network With a Preference Sampling Paradigm for Imbalanced Data Classification.

Most data in real life are characterized by imbalance problems. One of the classic models for dealing with imbalanced data is neural networks. However, the data imbalance problem often causes the neural network to display negative class preference behavior. Using an undersampling strategy to reconstruct a balanced dataset is one of the methods to alleviate the data imbalance problem. However, most existing undersampling methods focus more on the data or aim to preserve the overall structural characteristics of the negative class through potential energy estimation, while the problems of gradient inundation and insufficient empirical representation of positive samples have not been well considered. Therefore, a new paradigm for solving the data imbalance problem is proposed. Specifically, to solve the problem of gradient inundation, an informative undersampling strategy is derived from the performance degradation and used to restore the ability of neural networks to work under imbalanced data. In addition, to alleviate the problem of insufficient empirical representation of positive samples, a boundary expansion strategy with linear interpolation and the prediction consistency constraint is considered. We tested the proposed paradigm on 34 imbalanced datasets with imbalance ratios ranging from 16.90 to 100.14. The test results show that our paradigm obtained the best area under the receiver operating characteristic curve (AUC) on 26 datasets.

Identifying spatial agglomeration and driving forces of land use by green industries: a case study of Jiangsu Province, China.

In response to China's aims of becoming "carbon-neutral," the development of green industries such as renewable energy and recycling has flourished. Based on 2015 and 2019 data, this study uses spatial autocorrelation to analyze the evolution of land use by the green industries in Jiangsu Province. The Geodetector model was also applied to identify the driving factors underlying these spatial patterns. The spatial variability of green industrial land use in Jiangsu Province is significant, with the land-use area gradually decreasing from Southern to Northern Jiangsu. In terms of spatial-temporal changes, there is an increase in land use and a trend of expansion in the central and northern regions of Jiangsu. Land use by green industries in the province exhibits a more significant spatial clustering pattern but with a weakened clustering effect. The clustering types are mainly H-H and L-L, with the H-H type distributed mainly in the Su-Xi-Chang region and the L-L type distributed mainly in the Northern Jiangsu region. The levels of technology, economic development, industrialization, and industrial diversification are important individual driving factors, and the interaction among the different factors enhances their driving forces. This study suggests that spatial spillover effects should be focused to promote the coordinated development of regional energy-saving and environmental protection industries. At the same time, joint efforts should be made from the aspects of resources, government, economy, and related industries to promote the agglomeration of land for energy-saving and environmental protection industries.

HNRNPL induced circFAM13B increased bladder cancer immunotherapy sensitivity via inhibiting glycolysis through IGF2BP1/PKM2 pathway.

BACKGROUND: The response rate to immunotherapy in patients with bladder cancer (BCa) remains relatively low. Considering the stable existence and important functions in tumour metabolism, the role of circRNAs in regulating immune escape and immunotherapy sensitivity is receiving increasing attention. METHODS: Circular RNA (circRNA) sequencing was performed on five pairs of BCa samples, and circFAM13B (hsa_circ_0001535) was screened out because of its remarkably low expression in BCa. Further mRNA sequencing was conducted, and the association of circFAM13B with glycolysis process and CD8+ T cell activation was confirmed. The functions of circFAM13B were verified by proliferation assays, glycolysis assays, BCa cells-CD8+ T cell co-culture assays and tumorigenesis experiment among human immune reconstitution NOG mice. Bioinformatic analysis, RNA-protein pull down, mass spectrometry, RNA immunoprecipitation, luciferase reporter assay and fluorescence in situ hybridization were performed to validate the HNRNPL/circFAM13B/IGF2BP1/PKM2 cascade. RESULTS: Low expression of circFAM13B was observed in BCa, and it was positively associated with lower tumour stage and better prognosis among patients with BCa. The function of CD8+ T cells was promoted by circFAM13B, and it could attenuate the glycolysis of BCa cells and reverse the acidic tumour microenvironment (TME). The production of granzyme B and IFN-γ was improved, and the immunotherapy (PD-1 antibodies) sensitivity was facilitated by the inhibition of acidic TME. Mechanistically, circFAM13B was competitively bound to the KH3-4 domains of IGF2BP1 and subsequently reduced the binding of IGF2BP1 and PKM2 3'UTR. Thus, the stability of the PKM2 mRNA decreased, and glycolysis-induced acidic TME was inhibited. The generation of circFAM13B was explored by confirming whether heterogeneous nuclear ribonucleoprotein L (HNRNPL) could promote circFAM13B formation via pre-mRNA back-splicing. CONCLUSIONS: HNRNPL-induced circFAM13B could repress immune evasion and enhance immunotherapy sensitivity by inhibiting glycolysis and acidic TME in BCa through the novel circFAM13B/IGF2BP1/PKM2 cascade. Therefore, circFAM13B can be used as a biomarker for guiding the immunotherapy among patients with BCa.

Robust Multi-View Clustering With Incomplete Information.

The success of existing multi-view clustering methods heavily relies on the assumption of view consistency and instance completeness, referred to as the complete information. However, these two assumptions would be inevitably violated in data collection and transmission, thus leading to the so-called Partially View-unaligned Problem (PVP) and Partially Sample-missing Problem (PSP). To overcome such incomplete information challenges, we propose a novel method, termed robuSt mUlti-view clusteRing with incomplEte information (SURE), which solves PVP and PSP under a unified framework. In brief, SURE is a novel contrastive learning paradigm which uses the available pairs as positives and randomly chooses some cross-view samples as negatives. To reduce the influence of the false negatives caused by random sampling, SURE is with a noise-robust contrastive loss that theoretically and empirically mitigates or even eliminates the influence of the false negatives. To the best of our knowledge, this could be the first successful attempt that simultaneously handles PVP and PSP using a unified solution. In addition, this could be one of the first studies on the noisy correspondence problem (i.e., the false negatives) which is a novel paradigm of noisy labels. Extensive experiments demonstrate the effectiveness and efficiency of SURE comparing with 10 state-of-the-art approaches on the multi-view clustering task.

Large-Scale Meta-Heuristic Feature Selection Based on BPSO Assisted Rough Hypercuboid Approach.

The selection of prominent features for building more compact and efficient models is an important data preprocessing task in the field of data mining. The rough hypercuboid approach is an emerging technique that can be applied to eliminate irrelevant and redundant features, especially for the inexactness problem in approximate numerical classification. By integrating the meta-heuristic-based evolutionary search technique, a novel global search method for numerical feature selection is proposed in this article based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm, namely RH-BPSO. To further alleviate the issue of high computational cost when processing large-scale datasets, parallelization approaches for calculating the hybrid feature evaluation criteria are presented by decomposing and recombining hypercuboid equivalence partition matrix via horizontal data partitioning. A distributed meta-heuristic optimized rough hypercuboid feature selection (DiRH-BPSO) algorithm is thus developed and embedded in the Apache Spark cloud computing model. Extensive experimental results indicate that RH-BPSO is promising and can significantly outperform the other representative feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, experiments on distributed-memory multicore clusters show that DiRH-BPSO is significantly faster than its sequential counterpart and is perfectly capable of completing large-scale feature selection tasks that fail on a single node due to memory constraints. Parallel scalability and extensibility analysis also demonstrate that DiRH-BPSO could scale out and extend well with the growth of computational nodes and the volume of data.

CircZNF609 promotes bladder cancer progression and inhibits cisplatin sensitivity via miR-1200/CDC25B pathway.

Circular RNAs (circRNAs) have been extensively studied in tumor development and treatment. CircZNF609 (hsa_circ_0000615) has been shown to serve as an oncogene in all kinds of solid tumors and may act as the novel biomarker in tumor diagnosis and therapy in tumor early diagnosis and therapy. However, the underlying character and mechanism of circZNF609 in cisplatin chemosensitivity and bladder cancer (BCa) development were unknown. The expression level of cell division cycle 25B (CDC25B), microRNA 1200 (miR-1200), and circZNF609 in BCa cells and tissues depended on quantitative real-time PCR (qRT-PCR). CDC25B protein level was assayed with Western blot. Functional assays in vitro and in vivo had been conducted to inspect the important role of circZNF609 on BCa progression and cisplatin chemosensitivity in BCa. RNA sequencing and online databases were used to predict the interactions among circZNF609, miR-1200, and CDC25B. Mechanistic exploration was confirmed by RNA pull-down assay, RNA fluorescence in situ hybridization (FISH) and Dual luciferase reporter assay. CircZNF609 expression was increased significantly in BCa cell lines and tissues. For BCa patients, increased expression of circZNF609 was correlated with a worse survival. In vitro and in vivo, enforced expression of circZNF609 enhanced BCa cells proliferation, migration, and cisplatin chemoresistance. Mechanistically, circZNF609 alleviated the inhibition effect on target CDC25B expression by sponging miR-1200. CircZNF609 promoted tumor growth through novel circZNF609/miR-1200/CDC25B axis, implying that circZNF609 has significant potential to act as a new diagnostic biomarker and therapeutic target in BCa. Enhancing cisplatin sensitivity is an important direction for bladder cancer management. 1. This research reveals that circZNF609 improves bladder cancer progression and inhibits cisplatin sensitivity by inducing G1/S cell cycle arrest via a novel miR-1200/CDC25B cascades. 2. CircZNF609 was confirmed associated with worse survival of bladder cancer patients. 3. CircZNF609 act as a prognostic biomarker for bladder cancer treatment.

Dual Contrastive Prediction for Incomplete Multi-View Representation Learning.

In this article, we propose a unified framework to solve the following two challenging problems in incomplete multi-view representation learning: i) how to learn a consistent representation unifying different views, and ii) how to recover the missing views. To address the challenges, we provide an information theoretical framework under which the consistency learning and data recovery are treated as a whole. With the theoretical framework, we propose a novel objective function which jointly solves the aforementioned two problems and achieves a provable sufficient and minimal representation. In detail, the consistency learning is performed by maximizing the mutual information of different views through contrastive learning, and the missing views are recovered by minimizing the conditional entropy through dual prediction. To the best of our knowledge, this is one of the first works to theoretically unify the cross-view consistency learning and data recovery for representation learning. Extensive experimental results show that the proposed method remarkably outperforms 20 competitive multi-view learning methods on six datasets in terms of clustering, classification, and human action recognition. The code could be accessed from https://pengxi.me.

N6-methyladenosine-related single-nucleotide polymorphism analyses identify oncogene RNFT2 in bladder cancer.

BACKGROUND: Single-nucleotide polymorphisms (SNPs) in N6-methyladenosine (m6A) related genetic locus play significant roles in tumorigenesis and development. The expression level of many oncogenes and tumour suppressor genes changed because of m6A-associated SNPs. In addition, the relationship between m6A-SNP and bladder cancer (BCa) has not been well studied. METHODS: We screened m6A-SNPs in BCa by combining m6A-SNPs data and GWAS-SNPs data. Expression quantitative trait loci (eQTL) and differential expression gene (DEGs) analyses were performed. In ring finger protein, transmembrane 2 (RNFT2), rs3088107 (C > G) was found to have significant eQTL signals and make RNFT2 gene differentially-regulated mostly in BCa. We validated the expression level of RNFT2 in 32 pairs of BCa tissues and eight BCa cell lines by quantitative real-time PCR (qRT-PCR). Functional assays were performed to investigate the role of rs3088107 and RNFT2 in BCa in vitro. RESULTS: We identified 673 m6A-SNPs, which were associated with BCa. Of these m6A-SNPs, 221 showed eQTL signals, amongst which, rs3088107 in RNFT2 showed significant eQTL signals. Results of bioinformatic analyses showed that 11 genes with m6A-SNPs had a differential expression level in BCa. RNFT2 was predicted to be significantly up-regulated in BCa. The qRT-PCR results validated that RNFT2 was highly expressed in our own BCa tissues and cell lines. High expression of RNFT2 also indicated a worse overall survival. We also revealed that rs3088107 (C > G) could inhibit the expression and m6A modification of RNFT2 by qRT-PCR, western-blot and m6A-RIP assays. Moreover, the results of functional assays indicated that RNFT2 promoted BCa cell proliferation and migration. CONCLUSION: This research found that m6A-SNPs were associated with oncogene RNFT2 in BCa. Furthermore, m6A-SNPs showed great application potential as a new BCa diagnostic biomarker and prognostic indicator.

CGDD: Multiview Graph Clustering via Cross-Graph Diversity Detection.

Multiview graph clustering has emerged as an important yet challenging technique due to the difficulty of exploiting the similarity relationships among multiple views. Typically, the similarity graph for each view learned by these methods is easily corrupted because of the unavoidable noise or diversity among views. To recover a clean graph, existing methods mainly focus on the diverse part within each graph yet overlook the diversity across multiple graphs. In this article, instead of merely considering the sparsity of diversity within a graph as previous methods do, we incline to a more suitable consideration that the diversity should be sparse across graphs. It is intuitive that the divergent parts are supposed to be inconsistent with each other, otherwise it would contradict the definition of diversity. By simultaneously and explicitly detecting the multiview consistency and cross-graph diversity, a pure graph for each view can be expected. The multiple pure graphs are further fused to the structured consensus graph with exactly r connected components where r is the number of clusters. Once the consensus graph is obtained, the cluster label to each instance can be directly allocated as each connected component precisely corresponds to an individual cluster. An alternating iterative algorithm is designed to optimize the subtasks of learning the similarity graphs adaptively, detecting the consistency as well as cross-graph diversity, fusing the multiple pure graphs, and assigning cluster label to each instance in a mutual reinforcement manner. Extensive experimental results on several benchmark multiview datasets demonstrate the effectiveness of our model, in comparison to several state-of-the-art algorithms.

Exosomes in urological diseases - Biological functions and clinical applications.

Exosomes are extracellular vesicles with a variety of biological functions that exist in various biological body fluids and exert their functions through proteins, nucleic acids, lipids, and metabolites. Recent discoveries have revealed the functional and biomarker roles of miRNAs in urological diseases, including benign diseases and malignancies. Exosomes have several uses in the diagnosis, treatment, and monitoring of urological diseases, especially cancer. Proteins and nucleic acids can be used as alternative biomarkers for detecting urological diseases. Additionally, exosomes can be detected in most body fluids, thereby avoiding pathogenesis. More importantly, for urological tumors, exosomes display a higher sensitivity than circulating tumor cells and tumor-derived DNA in body fluid biopsies because of their low immunogenicity and high stability. These advantages have made it a research hotspot in recent years. In this review, we focus on the biological characteristics and functions of exosomes and summarize their advantages and the latest progress in the diagnosis and treatment of urological diseases.