Granulocyte colony-stimulating factor mediates bone loss via the activation of IL-1ß/JNK signaling pathway in murine Staphylococcus aureus-induced osteomyelitis.

Staphylococcus aureus (S. aureus)-induced bone loss is a significant challenge in the treatment of osteomyelitis. Our previous study was the first to confirm that granulocyte colony-stimulating factor (G-CSF) mediates S. aureus-induced bone loss. However, the underlying mechanism remains unknown. The objective of this study was to elucidate this. We found G-CSF mediated BMSC senescence and increased IL-1ß concentration of serum and bone marrow in mice after S. aureus infection. Furthermore, we demonstrated that G-CSF promoted the expression of IL1b in murine bone marrow-derived neutrophils. Notably, we identified that IL-1ß mediated BMSC (bone marrow mesenchymal stromal cell) senescence in mice after S. aureus infection. Importantly, IL-1ß neutralizing antibody effectively alleviated BMSC senescence and bone loss caused by S. aureus infection in mice. In terms of molecular mechanism, we found IL-1ß induced BMSC senescence by JNK/P53 and JNK/BCL2 pathways. Collectively, G-CSF promotes IL-1ß production which induces BMSC senescence via JNK/P53 and JNK/BCL2 pathways, leading to S. aureus-induced bone loss. This study identified novel targets for preventing and treating S. aureus-induced bone loss in osteomyelitis.

PLA plastic particles disrupt bile acid metabolism leading to hepatic inflammatory injury in male mice.

Microplastics, such as polylactic acid (PLA), are ubiquitous environmental pollutants with unclear implications for health impact. This study aims to elucidate the mechanisms of PLA-induced inflammatory liver injury, focusing on disturbance of bile acid metabolism. The in vitro PLA exposure experiment was conducted using HepG2 cells to assess cell viability, cytokine secretion, and effects on bile acid metabolism. In vivo, male C57BL/6 J mice were exposed to PLA for ten days continuously, liver function and histopathological assessment were evaluated after the mice sacrificed. Molecular analyses including quantitative reverse transcription polymerase chain reaction (RT-qPCR) and Western blotting, were applied to evaluate the expression of bile acid metabolizing enzymes and transporters. PLA exposure resulted in decreased cell viability in HepG2 cells, increased inflammation and altered bile acid metabolism. In mice, PLA exposure resulted in decreased body weight and food intake, impaired liver function, increased hepatic inflammation, altered bile acid profiles, and dysregulated expression of bile acid metabolic pathways. PLA exposure disrupts bile acid metabolism through inhibition of the CYP7A1 enzyme and activation of the FGF-JNK/ERK signaling pathway, contributing to liver injury. These findings highlight the potential hepatotoxic effects of environmentally friendly plastics PLA and underscore the need for further research on their biological impact.

Tethered cord syndrome from pediatric and adult perspectives: a comprehensive systematic review of 6135 cases.

OBJECTIVE: This study aimed to investigate the differences in clinical features, diagnostic examination, treatment, and pathological results between adult-onset and pediatric-onset tethered cord syndrome (TCS). METHODS: The authors searched the PubMed, Embase, and Cochrane Library databases through January 2023 for reports on TCS, extracting information on clinical features, imaging data, treatment modalities, prognosis, and pathological research results. A total of 6135 cases from 246 articles were included in the analysis. This review was conducted in accordance with the 2020 PRISMA guidelines and registered on PROSPERO. RESULTS: The most common adult clinical manifestations were pain, urinary symptoms, and numbness; in children, they were urinary symptoms, skin lesions, bowel symptoms, and unspecific motor deficits. Surgical treatment was the primary approach for both adults and children, with a higher clinical improvement rate observed in adults. However, adults also had a higher rate of surgical complications than children. TCS pathological studies have not yet identified the differences between adults and children, and the pathogenesis of adult-onset TCS requires further investigation. CONCLUSIONS: Adult-onset and pediatric-onset TCS exhibit certain differences in clinical characteristics, diagnostic examinations, and treatments. However, significant differences have not been found in current pathological studies between adults and children. Systematic review registration no.: CRD42023479450 (www.crd.york.ac.uk/prospero).

Nicotine promotes Staphylococcus aureus-induced osteomyelitis by activating the Nrf2/Slc7a11 signaling axis.

Although smoking is a significant risk factor for osteomyelitis, there is limited experimental evidence that nicotine, a key tobacco constituent, is associated with this condition, leaving its mechanistic implications uncharacterized. This study revealed that nicotine promotes Staphylococcus aureus-induced osteomyelitis by increasing Nrf2 and Slc7a11 expression in vivo and in vitro. Inhibition of Slc7a11 using Erastin augmented bacterial phagocytosis/killing capabilities and fortified antimicrobial responses in an osteomyelitis model. Moreover, untargeted metabolomic analysis demonstrated that Erastin mitigated the effects of nicotine on S. aureus-induced osteomyelitis by altering glutamate/glutathione metabolism. These findings suggest that nicotine aggravates S. aureus-induced osteomyelitis by activating the Nrf2/Slc7a11 signaling pathway and that Slc7a11 inhibition can counteract the detrimental health effects of nicotine.

Differences in the response of normal oral mucosa, oral leukoplakia, oral squamous cell carcinoma-derived mesenchymal stem cells, and epithelial cells to photodynamic therapy.

OBJECTIVE: The objective of this study is to investigate the variances in transcriptome gene expression of normal oral mucosa-derived mesenchymal stem cell (OM-MSC), oral leukoplakia-derived MSC (OLK-MSC) and oral squamous cell carcinoma-derived MSC(OSCC-MSC). as Additionally, the study aims to compare the in vitro proliferation, migration, invasion ability, and response to photodynamic therapy (PDT) of these three MSC, HOK, DOK, leuk1, and Cal27 cell lines. METHODS: HOK, DOK, leuk1, Cal27 cells were cultured in vitro. 3 MSC cells were obtained from OM, OLK, OSCC tissue (n = 3) and identified through flow cytometry. They were also cultured in vitro for osteogenic and lipogenic-induced differentiation. Based on the Illumina HiSeq high-throughput sequencing platform, OM-MSC, OLK-MSC, OSCC-MSC (n = 3) were subjected to transcriptome sequencing, functional annotation, and enrichment analysis of differentially expressed genes and related genes. CCK8 assay, wound healing assay, and transwell assay were performed to compare the proliferation, migration, and invasion of the seven types of cells. The 7 cells were incubated with 0, 0.125 mM, 0.25 mM, 0.5 mM, 1 mM, and 2 mM of the photosensitizer (5-aminolevulinic acid, 5-ALA) in vitro. Subsequently, they were irradiated with a 150 mM, 635 nm laser for 1 min, and the cell activity was detected using the CCK8 assay after 24 h. The mitochondrial changes in the 7 cells before and after the treatment of PDT were detected using the JC-10 probe, and the changes in ATP content were measured before and after the PDT treatment. RESULTS: OM-MSC, OLK-MSC, and OSCC-MSC expressed positive MSC surface markers. After osteogenic and lipogenic-induced differentiation culture, stained calcium nodules and lipid droplets were visible, meeting the identification criteria of MSC. Pathway enrichment analysis revealed that the differentially expressed genes (DEGs) of OSCC-MSC compared to OLK-MSC were primarily associated with the PI3K-Akt signaling pathway and tumor-related pathways. OSCC-MSC exhibited stronger migratory and invasive abilities compared to Cal27. The IC50 values required for OM, OLK, and OSCC-derived MSC were lower than those required for epithelial cells treated with PDT, which were 1.396 mM, 0.9063 mM, and 2.924 mM, respectively. Cell membrane and mitochondrial disruption were observed in seven types of cells after 24 h of PDT treatment. However, HOK, DOK, leuk1, and Cal27 cells had an ATP content increased. CONCLUSIONS: OLK, OSCC epithelial cells require higher concentrations of 5-ALA for PDT treatment than MSC of the same tissue origin. The concentration of 5-ALA required increases with increasing cell malignancy. Differences in the response of epithelial cells and MSC to PDT treatment may have varying impacts on OLK recurrence and malignancy.

SGLMDA: A Subgraph Learning-based Method for miRNA-disease Association Prediction.

MicroRNAs (miRNA) are endogenous non-coding RNAs, typically around 23 nucleotides in length. Many miRNAs have been founded to play crucial roles in gene regulation though post-transcriptional repression in animals. Existing studies suggest that the dysregulation of miRNA is closely associated with many human diseases. Discovering novel associations between miRNAs and diseases is essential for advancing our understanding of disease pathogenesis at molecular level. However, experimental validation is time-consuming and expensive. To address this challenge, numerous computational methods have been proposed for predicting miRNA-disease associations. Unfortunately, most existing methods face difficulties when applied to large-scale miRNA-disease complex networks. In this paper, we present a novel subgraph learning method named SGLMDA for predicting miRNA-disease associations. For miRNA-disease pairs, SGLMDA samples K-hop subgraphs from the global heterogeneous miRNA-disease graph. It then introduces a novel subgraph representation algorithm based on Graph Neural Network (GNN) for feature extraction and prediction. Extensive experiments conducted on benchmark datasets demonstrate that SGLMDA can effectively and robustly predict potential miRNA-disease associations. Compared to other state-of-the-art methods, SGLMDA achieves superior prediction performance in terms of Area Under the Curve (AUC) and Average Precision (AP) values during 5-fold Cross-Validation (5CV) on benchmark datasets such as HMDD v2.0 and HMDD v3.2. Additionally, case studies on Colon Neoplasms and Triple-Negative Breast Cancer (TNBC) further underscore the predictive power of SGLMDA. The dataset and source code of SGLMDA are available at https://github.com/cunmeiji/SGLMDA.

Development and validation of an immune-related gene prognostic index for lung adenocarcinoma.

Background: Lung cancer is the most common malignant tumor in the world, and its prognosis is still not optimistic. The aim of this study was to establish an immune-related gene (IRG) prognostic index (IRGPI) for lung adenocarcinoma (LUAD) based on IRGs, and to explore the prognosis, molecular and immune features, and response to immune checkpoint inhibitor (ICI) therapy in IRGPI-classified different subgroups of LUAD. Methods: Based on the LUAD transcriptome RNA-sequencing data in TCGA database, the differentially expressed genes (DEGs) were selected. Subsequently, DEGs were intersected with IRGs to obtain differentially expressed immune-related genes (DEIRGs). Weighted gene co-expression network analysis (WGCNA) identified hub genes in DEIRGs. Finally, univariate and multivariate Cox regression analyses were used to build an IRGPI model. Subsequently, TCGA patients were divided into high- and low-risk groups, and the survival of patients in different groups was further analyzed. Besides, we validated the molecular and immune characteristics, relationship with immune checkpoints, angiogenesis-related genes, and immune subtypes distribution in different subgroups. Meanwhile, we further validated the response to ICI therapy in different subgroups. Results: The IRGPI was constructed based on 13 DEIRGs. Compared with the low-risk group, overall survival (OS) was lower in the high-risk group, and the high-risk score was independently associated with poorer OS. Besides, the high-risk score was associated with cell cycle pathway, high mutation rate of TP53 and KRAS, high infiltration of M0 macrophages, and immunosuppressive state, and these patients had poorer prognosis but the TIDE score of the high-risk group was lower than that of the other group, which means that the high-risk group could benefit more from ICI treatment. In contrast, the low-risk score was related to low mutation rate of TP53 and KRAS, high infiltration of plasma cells, and immunoactive state, and these patients had better prognosis but the low-risk group less benefit from ICI treatment based on the results of TIDE score. Conclusions: IRGPI is a prospective biomarker based on IRGs that can distinguish high- and low-risk groups to predict patient prognosis, help characterize the tumor immune microenvironment, and evaluate the benefit of ICI therapy in LUAD.

Glycyrrhetinic Acid Receptor-Mediated Zeolitic Imidazolate Framework-8 Loaded Doxorubicin as a Nanotherapeutic System for Liver Cancer Treatment.

In this study, we designed and developed a DOX nanodrug delivery system (PEG-GA@ZIF-8@DOX) using ZIF-8 as the carrier and glycyrrhetinic acid (GA) as the targeting ligand. We confirmed that DOX was loaded and PEG-GA was successfully modified on the surface of the nanoparticles. The in vitro release profile of the system was investigated at pH 5.0 and 7.4. The cellular uptake, in vitro cytotoxicity, and lysosomal escape characteristics were examined using HepG2 cells. We established an H22 tumor-bearing mouse model and evaluated the in vivo antitumor activity. The results showed that the system had a uniform nanomorphology. The drug loading capacity was 11.22 ± 0.87%. In acidic conditions (pH 5.0), the final release rate of DOX was 57.73%, while at pH 7.4, it was 25.12%. GA-mediated targeting facilitated the uptake of DOX by the HepG2 cells. PEG-GA@ZIF-8@DOX could escape from the lysosomes and release the drug in the cytoplasm, thus exerting its antitumor effect. When the in vivo efficacy was analyzed, we found that the tumor inhibition rate of PEG-GA@ZIF-8@DOX was 67.64%; it also alleviated the loss of the body weight of the treated mice. This drug delivery system significantly enhanced the antitumor effect of doxorubicin in vitro and in vivo, while mitigating its toxic side effects.

Molecular subtyping based on immune cell marker genes predicts prognosis and therapeutic response in patients with lung adenocarcinoma.

OBJECTIVE: Lung adenocarcinoma (LA) is one of the most common malignancies and is responsible for the greatest number of tumor-related deaths. Our research aimed to explore the molecular subtype signatures of LA to clarify the correlation among the immune microenvironment, clinical outcomes, and therapeutic response. METHODS: The LA immune cell marker genes (LICMGs) identified by single-cell RNA sequencing (scRNA-seq) analysis were used to discriminate the molecular subtypes and homologous immune and metabolic traits of GSE72094 LA cases. In addition, the model-building genes were identified from 1441 LICMGs by Cox-regression analysis, and a LA immune difference score (LIDscore) was developed to quantify individual differences in each patient, thereby predicting prognosis and susceptibility to immunotherapy and chemotherapy of LA patients. RESULTS: Patients of the GSE72094 cohort were divided into two distinct molecular subtypes based on LICMGs: immune activating subtype (Cluster-C1) and metabolically activating subtype (cluster-C2). The two molecular subtypes have distinct characteristics regarding prognosis, clinicopathology, genomics, immune microenvironment, and response to immunotherapy. Among the LICMGs, LGR4, GOLM1, CYP24A1, SFTPB, COL1A1, HLA-DQA1, MS4A7, PPARG, and IL7R were enrolled to construct a LIDscore model. Low-LIDscore patients had a higher survival rate due to abundant immune cell infiltration, activated immunity, and lower genetic variation, but probably the higher levels of Treg cells in the immune microenvironment lead to immune cell dysfunction and promote tumor immune escape, thus decreasing the responsiveness to immunotherapy compared with that of the high-LIDscore patients. Overall, high-LIDscore patients had a higher responsiveness to immunotherapy and a higher sensitivity to chemotherapy than the low-LIDscore group. CONCLUSIONS: Molecular subtypes based on LICMGs provided a promising strategy for predicting patient prognosis, biological characteristics, and immune microenvironment features. In addition, they helped identify the patients most likely to benefit from immunotherapy and chemotherapy.

Gas Evolution Principle during Gas Drainage from a Drill Hole along the Coal Seam and Reasonable Borehole Layout Spacing.

The efficiency of coal seam gas drainage can be further improved by the accurate mastery of the gas evolution principle during gas drainage from the drill hole along the coal seam and reasonable optimization of borehole layout spacing. Based on the actual geological conditions of the no. 2 coal seam in a coal mine in Guizhou, China, and relevant control equations, a fluid-structure interaction model of gas drainage from a drill hole along the coal seam was established in this paper. Besides, numerical simulation research on the gas evolution principle during gas drainage along the coal seam and optimization of the borehole layout spacing was carried out with the COMSOL simulation software; and these were verified in combination with the project site. The results showed that in the early stage of gas drainage the gas pressure in the area near the drill holes decreased significantly. As the gas drainage went on, the degree of influence decreased gradually. During the gas drainage from adjacent drill holes, the gas pressure in the coal body between the holes decreased rapidly, and the migration was obvious. With the increase of the spacing between the drill holes, the drainage superposition effect between these holes gradually decreased until the influence area around a single hole is independently distributed in a circle-like shape, indicating that the optimization and the reasonable spacing of the drill holes are directly related to the effect of the gas drainage. With the increase of drilling spacing, the superposition effect of extraction between the holes gradually decreased until the influence area around a single hole is independently distributed in the shape of a circle, indicating that the optimization of drilling spacing is directly related to the effect of gas drainage. The results of numerical simulation were verified by analyzing the three-dimensional map of the gas pressure during the period of gas drainage at the project site and by comparing and examining the rational borehole layout spacing of the drill hole along the coal seam. The results of this study can be used to determine the spacing of gas extraction boreholes and improve the efficiency of gas extraction in the no. 2 coal seam of a coal mine in Guizhou, China, as well as to provide a reference for the gas pressure evolution, velocity field distribution, the prediction of effective drainage area, and the selection of rational borehole layout spacing during gas drainage.

Potential circRNA-Disease Association Prediction Using DeepWalk and Nonnegative Matrix Factorization.

Circular RNAs (circRNAs) are a category of noncoding RNAs that exist in great numbers in eukaryotes. They have recently been discovered to be crucial in the growth of tumors. Therefore, it is important to explore the association of circRNAs with disease. This paper proposes a new method based on DeepWalk and nonnegative matrix factorization (DWNMF) to predict circRNA-disease association. Based on the known circRNA-disease association, we calculate the topological similarity of circRNA and disease via the DeepWalk-based method to learn the node features on the association network. Next, the functional similarity of the circRNAs and the semantic similarity of the diseases are fused with their respective topological similarities at different scales. Then, we use the improved weighted K-nearest neighbor (IWKNN) method to preprocess the circRNA-disease association network and correct nonnegative associations by setting different parameters K1 and K2 in the circRNA and disease matrices. Finally, the L2,1-norm, dual-graph regularization term and Frobenius norm regularization term are introduced into the nonnegative matrix factorization model to predict the circRNA-disease correlation. We perform cross-validation on circR2Disease, circRNADisease, and MNDR. The numerical results show that DWNMF is an efficient tool for forecasting potential circRNA-disease relationships, outperforming other state-of-the-art approaches in terms of predictive performance.

PD-1/PD-L1 blockade is a potent adjuvant in treatment of Staphylococcus aureus osteomyelitis in mice.

There is no effective therapy for implant-associated Staphylococcus aureus osteomyelitis, a devastating complication after orthopedic surgery. An immune-suppressive profile with up-regulated programmed cell death 1/programmed death ligand 1 (PD-1/PD-L1) was identified based on our transcriptional data (GEO: GSE166522) from a mouse model of S. aureus osteomyelitis. PD-1/PD-L1 expression was up-regulated mainly in F4/80+ macrophages surrounding the abscess in S. aureus-infected bone. Mechanistically, PD-1/PD-L1 activated mitophagy to suppress production of mitochondrial reactive oxygen species (ROS), suppressing the bactericidal function of macrophages. Using neutralizing antibodies for PD-L1 or PD-1, or knockout of PD-L1 adjuvant to gentamicin markedly reduced mitophagy in bone marrow F4/80+ cells, enhanced bacterial clearance in bone tissue and implants, and reduced bone destruction in mice. PD-1/PD-L1 expression was also increased in the bone marrow from individuals with S. aureus osteomyelitis. These findings uncover a so far unknown function of PD-1/PD-L1-mediated mitophagy in suppressing the bactericidal function of bone marrow macrophages.

Nanomaterials Respond to Lysosomal Function for Tumor Treatment.

The safety and efficacy of tumor treatment are difficult problems to address. Recently, lysosomes have become an important target for tumor treatment because of their special environment and function. Nanoparticles have unique physicochemical properties which have great advantages in tumor research. Therefore, in recent years, researchers have designed various types of nanoparticles to treat tumors based on lysosomal function and environment. In this review, we summarize and analyze different perspectives of tumor treatment, including direct destruction of lysosomes or lysosomal escape, drug delivery by nanoparticles, response to endogenous or exogenous stimuli, and the targeting of tumor cells or other cells. We describe the advantages and disadvantages of these approaches as well as the developmental prospects in this field. We hope to provide new ideas for better tumor treatment.

Crosstalk among m6A RNA methylation, hypoxia and metabolic reprogramming in TME: from immunosuppressive microenvironment to clinical application.

The tumor microenvironment (TME), which is regulated by intrinsic oncogenic mechanisms and epigenetic modifications, has become a research hotspot in recent years. Characteristic features of TME include hypoxia, metabolic dysregulation, and immunosuppression. One of the most common RNA modifications, N6-methyladenosine (m6A) methylation, is widely involved in the regulation of physiological and pathological processes, including tumor development. Compelling evidence indicates that m6A methylation regulates transcription and protein expression through shearing, export, translation, and processing, thereby participating in the dynamic evolution of TME. Specifically, m6A methylation-mediated adaptation to hypoxia, metabolic dysregulation, and phenotypic shift of immune cells synergistically promote the formation of an immunosuppressive TME that supports tumor proliferation and metastasis. In this review, we have focused on the involvement of m6A methylation in the dynamic evolution of tumor-adaptive TME and described the detailed mechanisms linking m6A methylation to change in tumor cell biological functions. In view of the collective data, we advocate treating TME as a complete ecosystem in which components crosstalk with each other to synergistically achieve tumor adaptive changes. Finally, we describe the potential utility of m6A methylation-targeted therapies and tumor immunotherapy in clinical applications and the challenges faced, with the aim of advancing m6A methylation research.

TA-MSCs, TA-MSCs-EVs, MIF: their crosstalk in immunosuppressive tumor microenvironment.

As an important component of the immunosuppressive tumor microenvironment (TME), it has been established that mesenchymal stem cells (MSCs) promote the progression of tumor cells. MSCs can directly promote the proliferation, migration, and invasion of tumor cells via cytokines and chemokines, as well as promote tumor progression by regulating the functions of anti-tumor immune and immunosuppressive cells. MSCs-derived extracellular vesicles (MSCs-EVs) contain part of the plasma membrane and signaling factors from MSCs; therefore, they display similar effects on tumors in the immunosuppressive TME. The tumor-promoting role of macrophage migration inhibitory factor (MIF) in the immunosuppressive TME has also been revealed. Interestingly, MIF exerts similar effects to those of MSCs in the immunosuppressive TME. In this review, we summarized the main effects and related mechanisms of tumor-associated MSCs (TA-MSCs), TA-MSCs-EVs, and MIF on tumors, and described their relationships. On this basis, we hypothesized that TA-MSCs-EVs, the MIF axis, and TA-MSCs form a positive feedback loop with tumor cells, influencing the occurrence and development of tumors. The functions of these three factors in the TME may undergo dynamic changes with tumor growth and continuously affect tumor development. This provides a new idea for the targeted treatment of tumors with EVs carrying MIF inhibitors.

Measuring heavy metal ions in water using nature existed microalgae as medium based on terahertz technology.

Heavy metal pollution in water seriously affects human health. The disadvantages of traditional metal ion detection methods involve long and cumbersome chemical pretreatment in the early stage, and large volume of samples. In this study, microalgae were used as the medium, and terahertz spectroscopy technology was employed to collect the changes of material components in it, so as to deduce the types and concentrations of heavy metal pollution in water. Through the partial least square(PLS), we establish the prediction model of heavy metal concentration, and the results show that the best detection time for Pb2+ is 6 h and Ni2+ is 18 h. The principal component analysis(PCA) shows that ß-carotene is the most affected substance. Afterward we collect five real surface waters in East China and verify that the judgment accuracy of Pb2+ and Ni2+ are 100% and 93.2% respectively. The results indicate that the time is shorter than the traditional pretreatment time from more than 20-6 h, the sample volume is reduced from 50 mL to 10 mL, the detection accuracy is improved from 10 ng/mL to 1 ng/mL. In a word, we provide a new fast and real-time method for biological monitoring of heavy metal pollution in water.

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolutional autoencoder (GCAE) was utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performances were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA was beneficial to infer potential associations of miRNA-disease.

Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks.

In recent years, more and more evidence has shown that microRNAs (miRNAs) play an important role in the regulation of post-transcriptional gene expression, and are closely related to human diseases. Many studies have also revealed that miRNAs can be served as promising biomarkers for the potential diagnosis and treatment of human diseases. The interactions between miRNA and human disease have rarely been demonstrated, and the underlying mechanism of miRNA is not clear. Therefore, computational approaches has attracted the attention of researchers, which can not only save time and money, but also improve the efficiency and accuracy of biological experiments. In this work, we proposed a Heterogeneous Graph Attention Networks (GAT) based method for miRNA-disease associations prediction, named HGATMDA. We constructed a heterogeneous graph for miRNAs and diseases, introduced weighted DeepWalk and GAT methods to extract features of miRNAs and diseases from the graph. Moreover, a fully-connected neural networks is used to predict correlation scores between miRNA-disease pairs. Experimental results under five-fold cross validation (five-fold CV) showed that HGATMDA achieved better prediction performance than other state-of-the-art methods. In addition, we performed three case studies on breast neoplasms, lung neoplasms and kidney neoplasms. The results showed that for the three diseases mentioned above, 50 out of top 50 candidates were confirmed by the validation datasets. Therefore, HGATMDA is suitable as an effective tool to identity potential diseases-related miRNAs.

GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations.

Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circRNA and disease is time-consuming and expensive, effective computational methods are an urgent need for predicting potential circRNA-disease associations. In this study, we proposed a novel computational method named GATNNCDA, which combines Graph Attention Network (GAT) and multi-layer neural network (NN) to infer disease-related circRNAs. Specially, GATNNCDA first integrates disease semantic similarity, circRNA functional similarity and the respective Gaussian Interaction Profile (GIP) kernel similarities. The integrated similarities are used as initial node features, and then GAT is applied for further feature extraction in the heterogeneous circRNA-disease graph. Finally, the NN-based classifier is introduced for prediction. The results of fivefold cross validation demonstrated that GATNNCDA achieved an average AUC of 0.9613 and AUPR of 0.9433 on the CircR2Disease dataset, and outperformed other state-of-the-art methods. In addition, case studies on breast cancer and hepatocellular carcinoma showed that 20 and 18 of the top 20 candidates were respectively confirmed in the validation datasets or published literature. Therefore, GATNNCDA is an effective and reliable tool for discovering circRNA-disease associations.

MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.

BACKGROUND: MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time-consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. METHODS: This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. RESULT: Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. CONCLUSION: MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA-disease association predication.