LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations.

Previous studies have proven that circular RNAs (circRNAs) are inextricably connected to the etiology and pathophysiology of complicated diseases. Since conventional biological research are frequently small-scale, expensive, and time-consuming, it is essential to establish an efficient and reasonable computation-based method to identify disease-related circRNAs. In this article, we proposed a novel ensemble model for predicting probable circRNA-disease associations based on multi-source similarity information(LMGATCDA). In particular, LMGATCDA first incorporates information on circRNA functional similarity, disease semantic similarity, and the Gaussian interaction profile (GIP) kernel similarity as explicit features, along with node-labeling of the three-hop subgraphs extracted from each linked target node as graph structural features. After that, the fused features are used as input, and further implied features are extracted by graph sampling aggregation (GraphSAGE) and multi-hop attention graph neural network (MAGNA). Finally, the prediction scores are obtained through a fully connected layer. With five-fold cross-validation, LMGATCDA demonstrated excellent competitiveness against gold standard data, reaching 95.37% accuracy and 91.31% recall with an AUC of 94.25% on the circR2Disease benchmark dataset. Collectively, the noteworthy findings from these case studies support our conclusion that the LMGATCDA model can provide reliable circRNA-disease associations for clinical research while helping to mitigate experimental uncertainties in wet-lab investigations.

Adaptive deep propagation graph neural network for predicting miRNA-disease associations.

BACKGROUND: A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the development of more efficient and accurate deep learning methods for predicting miRNA-disease associations. RESULTS: In this paper, we propose a novel model on the basis of adaptive deep propagation graph neural network to predict miRNA-disease associations (ADPMDA). We first construct the miRNA-disease heterogeneous graph based on known miRNA-disease pairs, miRNA integrated similarity information, miRNA sequence information and disease similarity information. Then, we project the features of miRNAs and diseases into a low-dimensional space. After that, attention mechanism is utilized to aggregate the local features of central nodes. In particular, an adaptive deep propagation graph neural network is employed to learn the embedding of nodes, which can adaptively adjust the local and global information of nodes. Finally, the multi-layer perceptron is leveraged to score miRNA-disease pairs. CONCLUSION: Experiments on human microRNA disease database v3.0 dataset show that ADPMDA achieves the mean AUC value of 94.75% under 5-fold cross-validation. We further conduct case studies on the esophageal neoplasm, lung neoplasms and lymphoma to confirm the effectiveness of our proposed model, and 49, 49, 47 of the top 50 predicted miRNAs associated with these diseases are confirmed, respectively. These results demonstrate the effectiveness and superiority of our model in predicting miRNA-disease associations.

Efficacy of oblique lumbar interbody fusion versus transforaminal lumbar interbody fusion in the treatment of lumbar degenerative diseases: a systematic review and meta-analysis.

INTRODUCTION: This meta-analysis aimed to compare the differences in postoperative efficacy between oblique lumbar interbody fusion (OLIF) and transforaminal lumbar interbody fusion (TLIF) in the treatment of lumbar degenerative diseases. MATERIALS AND METHODS: Strictly based on the search strategy, we searched the published papers on OLIF and TLIF for the treatment of lumbar degenerative diseases in PubMed, Embase, CINAHL, and Cochrane Library. A total of 607 related papers were retrieved, and 15 articles were finally included. The quality of the papers was evaluated according to the Cochrane systematic review methodology, and the data were extracted and meta-analyzed using Review manager 5.4 software. RESULTS: Through comparison, it was found that in the treatment of lumbar degenerative diseases, the OLIF group had certain advantages over the TLIF group in terms of intraoperative blood loss, hospital stay, visual analog scale (VAS) for leg pain (VAS-LP), Oswestry disability index (ODI), disc height (DH), foraminal height (FH), fused segmental lordosis (FSL), and cage height, and the differences were statistically significant. The results were similar in terms of surgery time, complications, fusion rate, VAS for back pain (VAS-BP) and various sagittal imaging indicators, and there was no significant difference. CONCLUSIONS: OLIF and TLIF can relieve low back pain symptoms in the treatment of lumbar degenerative diseases, but OLIF has certain advantages in terms of ODI and VAS-LP. In addition, OLIF has the advantages of minor intraoperative trauma and quick postoperative recovery.

The association between ADAM12 gene polymorphisms and osteoarthritis: an updated meta-analysis.

BACKGROUND: Osteoarthritis of the knee is an irreversible disease that causes great pain, and genetic factors play an important role in its occurrence and development. There have been many studies on the correlation between ADAM12 polymorphisms and genetic susceptibility to osteoarthritis, but the results remain inconclusive. METHODS: Papers from PubMed, Web of Science, EMbase, Springer, SCOPUS, Google Scholar and other databases were systematically retrieved with a cut-off of January 2022. All case-control studies on ADAM12 rs3740199, rs1871054, rs1044122, and rs1278279 polymorphisms and osteoarthritis were searched. Fixed or random effects models were used for pooled analysis with OR values and 95% confidence intervals (CI), and publication bias was assessed. In addition, the false-positive reporting probability test was used to assess the confidence of a statistically significant association. RESULTS: Eleven articles were included, which included 3332 patients with osteoarthritis and 5108 healthy controls. Meta-analysis showed that the rs1871054 polymorphism of ADAM12 was associated with osteoarthritis in dominant, recessive, allelic, and homozygote genetic models [C vs. T: OR = 1.34 95% CI (1.05, 1.71), P < 0.001]. Our subgroup analysis revealed an association between the ADAM12 polymorphism rs1871054 in Asians and osteoarthritis [C vs. T: OR = 1.61, 95% CI (1.25, 2.08), P < 0.001], albeit this was only for three studies. In addition, the ADAM12 polymorphism rs1871054 is associated with osteoarthritis in patients younger than 60 years of age [C vs. T: OR = 1.39, 95% CI (1.01, 1.92), P = 0.289]; however, the ADAM12 gene rs3740199, rs1044122, and rs1278279 site polymorphisms were not significantly. Furthermore, when assessing the confidence of the positive results, the positive results were found to be credible (except for Age < 60). CONCLUSION: Polymorphism at the rs1871054 site of ADAM12 is associated with genetic susceptibility to osteoarthritis, but rs3740199, rs1044122, and rs1278279 site polymorphisms are not.

Adversarial dense graph convolutional networks for single-cell classification.

MOTIVATION: In single-cell transcriptomics applications, effective identification of cell types in multicellular organisms and in-depth study of the relationships between genes has become one of the main goals of bioinformatics research. However, data heterogeneity and random noise pose significant difficulties for scRNA-seq data analysis. RESULTS: We have proposed an adversarial dense graph convolutional network architecture for single-cell classification. Specifically, to enhance the representation of higher-order features and the organic combination between features, dense connectivity mechanism and attention-based feature aggregation are introduced for feature learning in convolutional neural networks. To preserve the features of the original data, we use a feature reconstruction module to assist the goal of single-cell classification. In addition, HNNVAT uses virtual adversarial training to improve the generalization and robustness. Experimental results show that our model outperforms the existing classical methods in terms of classification accuracy on benchmark datasets. AVAILABILITY AND IMPLEMENTATION: The source code of HNNVAT is available at https://github.com/DisscLab/HNNVAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

circPTPN22 attenuates immune microenvironment of pancreatic cancer via STAT3 acetylation.

Accumulating research implicated that circular RNAs exhibited significant roles in cancer development. Nonetheless, the role regarding circPTPN22 in pancreatic cancer remains unclear. Expression of circPTPN22 in pancreatic cancer cell lines and normal cells was determined with quantitative real-time PCR (qRT-PCR). Cell counting kit-8 assay and colony formation assay were used to measure the proliferation of pancreatic cancer cells. RNA immunoprecipitation and Western blot were employed for investigation the binding between circPTPN22 and STAT3. circPTPN22 expression was highly upregulated in pancreatic cancer tissues and cell lines. Knockdown of circPTPN22 inhibited cell proliferation and attenuates pancreatic cancer immune microenvironment. Furthermore, STAT3 acetylation was involved in these effects. circPTPN22 promoted STAT3 acetylation via inhibiting STAT3/SIRT1 interaction. circPTPN22 attenuates pancreatic cancer immune microenvironment by promoting STAT3 acetylation via inhibiting STAT3/SIRT1 interaction.

Predicting MiRNA-Disease Associations by Graph Representation Learning Based on Jumping Knowledge Networks.

Growing studies have shown that miRNAs are inextricably linked with many human diseases, and a great deal of effort has been spent on identifying their potential associations. Compared with traditional experimental methods, computational approaches have achieved promising results. In this article, we propose a graph representation learning method to predict miRNA-disease associations. Specifically, we first integrate the verified miRNA-disease associations with the similarity information of miRNA and disease to construct a miRNA-disease heterogeneous graph. Then, we apply a graph attention network to aggregate the neighbor information of nodes in each layer, and then feed the representation of the hidden layer into the structure-aware jumping knowledge network to obtain the global features of nodes. The output features of miRNAs and diseases are then concatenated and fed into a fully connected layer to score the potential associations. Through five-fold cross-validation, the average AUC, accuracy and precision values of our model are 93.30%, 85.18% and 88.90%, respectively. In addition, for three case studies of the esophageal tumor, lymphoma and prostate tumor, 46, 45 and 45 of the top 50 miRNAs predicted by our model were confirmed by relevant databases. Overall, our method could provide a reliable alternative for miRNA-disease association prediction.

GCMCDTI: Graph convolutional autoencoder framework for predicting drug-target interactions based on matrix completion.

Identification of potential drug-target interactions (DTIs) plays a pivotal role in the development of drug and target discovery in the public healthcare sector. However, biological experiments for predicting interactions between drugs and targets are still expensive, complicated, and time-consuming. Thus, computational methods are widely applied for aiding drug-target interaction prediction. In this paper, we propose a novel model, named GCMCDTI, for DTIs prediction which adopts a graph convolutional network based on matrix completion. We regard the association prediction between drugs and targets as link prediction and treat the process as matrix completion, and then a graph convolutional auto-encoder framework is employed to construct the drug and target embeddings. Then, a bilinear decoder is applied to reconstruct the DTI matrix. We conduct our experiments on four benchmark datasets consisting of enzymes, G protein-coupled receptors (GPCRs), ion channels, and nuclear receptors. The five-fold cross-validation results achieve the high average AUC values of 95.78%, 95.31%, 93.90%, and 91.77%, respectively. To further evaluate our method, we compare our proposed method with other state-of-the-art approaches. The comparison results illustrate that our proposed method obtains improvement in performance on DTI prediction. The proposed method will be a good choice in the field of DTI prediction.

Identification of feature genes and pathways for Alzheimer's disease via WGCNA and LASSO regression.

While Alzheimer's disease (AD) can cause a severe economic burden, the specific pathogenesis involved is yet to be elucidated. To identify feature genes associated with AD, we downloaded data from three GEO databases: GSE122063, GSE15222, and GSE138260. In the filtering, we used AD for search keywords, Homo sapiens for species selection, and established a sample size of > 20 for each data set, and each data set contains Including the normal group and AD group. The datasets GSE15222 and GSE138260 were combined as a training group to build a model, and GSE122063 was used as a test group to verify the model's accuracy. The genes with differential expression found in the combined datasets were used for analysis through Gene Ontology (GO) and The Kyoto Encyclopedia of Genes and Genome Pathways (KEGG). Then, AD-related module genes were identified using the combined dataset through a weighted gene co-expression network analysis (WGCNA). Both the differential and AD-related module genes were intersected to obtain AD key genes. These genes were first filtered through LASSO regression and then AD-related feature genes were obtained for subsequent immune-related analysis. A comprehensive analysis of three AD-related datasets in the GEO database revealed 111 common differential AD genes. In the GO analysis, the more prominent terms were cognition and learning or memory. The KEGG analysis showed that these differential genes were enriched not only in In the KEGG analysis, but also in three other pathways: neuroactive ligand-receptor interaction, cAMP signaling pathway, and Calcium signaling pathway. Three AD-related feature genes (SST, MLIP, HSPB3) were finally identified. The area under the ROC curve of these AD-related feature genes was greater than 0.7 in both the training and the test groups. Finally, an immune-related analysis of these genes was performed. The finding of AD-related feature genes (SST, MLIP, HSPB3) could help predict the onset and progression of the disease. Overall, our study may provide significant guidance for further exploration of potential biomarkers for the diagnosis and prediction of AD.

Aldh1a1 and Scl25a30 in diaphragmatic dysfunction.

New methods to prevent ventilator-induced diaphragmatic dysfunction (VIDD) are urgently needed, and the cellular basis of VIDD is poorly understood. This study evaluated whether transvenous phrenic nerve stimulation (PNS) could prevent VIDD in rabbits undergoing mechanical ventilation (MV) and explored whether oxidative stress-related genes might be candidate molecular markers for VIDD. Twenty-four adult male New Zealand white rabbits were allocated to control, MV, and PNS groups (n = 8 in each group). Rabbits in the MV and PNS groups underwent MV for 24 h. Intermittent bilateral transvenous PNS was performed in rabbits in the PNS group. Transdiaphragmatic pressure was recorded using balloon catheters. The diameters and cross-sectional areas (CSAs) of types I and II diaphragmatic fibers were measured using immunohistochemistry (IHC) techniques. Genes associated with VIDD were identified by RNA sequencing (RNA-seq), differentially expressed gene (DEG) analysis, and weighted gene co-expression network analysis (WGCNA). Reverse transcription polymerase chain reaction (RT-PCR), Western blotting, and IHC analyses were carried out to verify the transcriptome profile. Pdi60Hz, Pdi80Hz, and Pdi100Hz were significantly higher in the PNS group than in the MV group at 12 and 24 h (P < 0.05 at both time points). The diameters and CSAs of types I (slow-twitch) and II (fast-twitch) fibers were significantly larger in the PNS group than in the MV group (P < 0.05). RNA-seq, RT-PCR, Western blotting, and IHC experiments identified two candidate genes associated with VIDD: Aldh1a1 and Scl25a30. The MV group had significantly higher mRNA and protein expressions of Aldh1a1/ALDH1A1 and significantly lower mRNA and protein expressions of Scl25a30/SCL25A30 than the control or PNS groups (P < 0.05). We have identified two candidate genes involved in the prevention of VIDD by transvenous PNS. These two key genes may provide a theoretical basis for targeted therapy against VIDD.

eTumorMetastasis: A Network-based Algorithm Predicts Clinical Outcomes Using Whole-exome Sequencing Data of Cancer Patients.

Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here, we develop a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles and identifies network operational gene (NOG) signatures. NOG signatures model the tipping point at which a tumor cell shifts from a state that doesn't favor recurrence to one that does. We show that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the 'most recent common ancestor' of the cells within a tumor) significantly distinguish the recurred and non-recurred breast tumors as well as outperform the most popular genomic test (i.e., Oncotype DX). These results imply that mutations of the tumor founding clones are associated with tumor recurrence and can be used to predict clinical outcomes. As such, predictive tools could be used in clinics to guide treatment routes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases. eTumorMetastasis pseudocode and related data used in this study are available at https://github.com/WangEdwinLab/eTumorMetastasis.

Gene Regulatory Network Rewiring in the Immune Cells Associated with Cancer.

The gene regulatory networks (GRNs) of immune cells not only indicate cell identity but also reveal the dynamic changes of immune cells when comparing their GRNs. Cancer immunotherapy has advanced in the past few years. Immune-checkpoint blockades (i.e., blocking PD-1, PD-L1, or CTLA-4) have shown durable clinical effects on some patients with various advanced cancers. However, major gaps in our knowledge of immunotherapy have been recognized. To fill these gaps, we conducted a systematic analysis of the GRNs of key immune cell subsets (i.e., B cell, CD4, CD8, CD8 naïve, CD8 Effector memory, CD8 Central Memory, regulatory T, Thelper1, Thelper2, Thelp17, and NK (Nature killer) and DC (Dendritic cell) cells associated with cancer immunologic therapies. We showed that most of the GRNs of these cells in blood share key important hub regulators, but their subnetworks for controlling cell type-specific receptors are different, suggesting that transformation between these immune cell subsets could be fast so that they can rapidly respond to environmental cues. To understand how cancer cells send molecular signals to immune cells to make them more cancer-cell friendly, we compared the GRNs of the tumor-infiltrating immune T cells and their corresponding immune cells in blood. We showed that the network size of the tumor-infiltrating immune T cells' GRNs was reduced when compared to the GRNs of their corresponding immune cells in blood. These results suggest that the shutting down certain cellular activities of the immune cells by cancer cells is one of the key molecular mechanisms for helping cancer cells to escape the defense of the host immune system. These results highlight the possibility of genetic engineering of T cells for turning on the identified subnetworks that have been shut down by cancer cells to combat tumors.

In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences.

Analysis of drug-target interactions (DTIs) is of great importance in developing new drug candidates for known protein targets or discovering new targets for old drugs. However, the experimental approaches for identifying DTIs are expensive, laborious and challenging. In this study, we report a novel computational method for predicting DTIs using the highly discriminative information of drug-target interactions and our newly developed discriminative vector machine (DVM) classifier. More specifically, each target protein sequence is transformed as the position-specific scoring matrix (PSSM), in which the evolutionary information is retained; then the local binary pattern (LBP) operator is used to calculate the LBP histogram descriptor. For a drug molecule, a novel fingerprint representation is utilized to describe its chemical structure information representing existence of certain functional groups or fragments. When applying the proposed method to the four datasets (Enzyme, GPCR, Ion Channel and Nuclear Receptor) for predicting DTIs, we obtained good average accuracies of 93.16%, 89.37%, 91.73% and 92.22%, respectively. Furthermore, we compared the performance of the proposed model with that of the state-of-the-art SVM model and other previous methods. The achieved results demonstrate that our method is effective and robust and can be taken as a useful tool for predicting DTIs.

Histone methyltransferase SMYD3 regulates the expression of transcriptional factors during bovine oocyte maturation and early embryonic development.

Mammalian early embryonic development is controlled by a unique program of gene expression, and involves epigenetic reprogramming of histone modifications and DNA methylation. SET and MYND domain-containing protein 3 (SMYD3) is a histone H3 lysine 4 methyltransferase that plays important roles in transcription regulation. The expression of SMYD3 has been studied in some cancer cell lines. However, its expression in oocytes and embryos has not previously been reported. Here, we detected the SMYD3 mRNA and found that it was expressed throughout bovine oocyte in vitro maturation and early embryonic development. Microinjection of SMYD3 siRNA at germinal vesicle stage decreased the transcription level of NANOG, and blocked the development of in vitro fertilization embryos at 4-8 cell stage. Conversely, Microinjection of SMYD3 siRNA at pronuclear stage did not affect early embryonic development. Our findings suggest that SMYD3 regulates the expression of NANOG, and plays an essential role in bovine early embryonic development.

Epigenetic reprogramming of human lung cancer cells with the extract of bovine parthenogenetic oocytes.

The tumour suppressor gene silencing and proto-oncogene activation caused by epigenetic alterations plays an important role in the initiation and progression of cancer. Re-establishing the balance between the expression of tumour suppressor genes and proto-oncogenes by epigenetic modulation is a promising strategy for cancer treatment. In this study, we investigated whether cancer cells can be epigenetically reprogrammed by oocyte extract. H460 human lung cancer cells were reversibly permeabilized and incubated with the extract of bovine parthenogenetic oocytes. Bisulphite sequencing showed that bovine parthenogenetic oocyte extract induced significant demethylation at the promoters of the tumour suppressor genes RUNX3 and CDH1, but not at the promoter of the oncogenic pluripotency gene SOX2. Chromatin immunoprecipitation showed that the histone modifications at RUNX3 and CDH1 promoters were modulated towards a transcriptionally activating state, while those at SOX2 promoter towards a transcriptionally repressive state. Correspondingly, bovine parthenogenetic oocyte extract reversed the epigenetic silencing of RUNX3 and CDH1, and repressed the expression of SOX2. At the functional level, proliferation, anchorage-independent growth, migration and invasion of H460 cells was strongly inhibited. These results indicate that bovine parthenogenetic oocyte extract changes the expression patterns of tumour suppressor and oncogenic genes in cancer cells by remodelling the epigenetic modifications at their promoters. Bovine parthenogenetic oocyte extract may provide a useful tool for epigenetically reprogramming cancer cells and for dissecting the epigenetic mechanisms involved in tumorigenesis.

Remodeling epigenetic modifications at tumor suppressor gene promoters with bovine oocyte extract.

BACKGROUND AIMS: Epigenetic silencing of tumor suppressor genes by aberrant DNA methylation and histone modifications at their promoter regions plays an important role in the initiation and progression of cancer. The therapeutic effect of the widely used epigenetic drugs, including DNA methyltransferase inhibitors and histone deacetylase inhibitors, remains unsatisfactory. One important underlying factor in the ineffectiveness of these drugs is that their actions lack specificity. METHODS: To investigate whether oocyte extract can be used for epigenetic re-programming of cancer cells, H460 human lung cancer cells were reversibly permeabilized and incubated with bovine oocyte extract. RESULTS: Bisulfite sequencing showed that bovine oocyte extract induced significant demethylation at hypermethylated promoter CpG islands of the tumor suppressor genes RUNX3 and CDH1; however, the DNA methylation levels of repetitive sequences were not affected. Chromatin immunoprecipitation showed that bovine oocyte extract significantly reduced transcriptionally repressive histone modifications and increased transcriptionally activating histone modifications at the promoter regions of RUNX3 and CDH1. Bovine oocyte extract reactivated the expression of RUNX3 and CDH1 at both the messenger RNA and the protein levels without up-regulating the transcription of pluripotency-associated genes. At the functional level, anchorage-independent proliferation, migration and invasion of H460 cells was strongly inhibited. CONCLUSIONS: These results demonstrate that bovine oocyte extract reactivates epigenetically silenced tumor suppressor genes by remodeling the epigenetic modifications at their promoter regions. Bovine oocyte extract may provide a useful tool for investigating epigenetic mechanisms in cancer and a valuable source for developing novel safe therapeutic approaches that target epigenetic alterations.