Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion

Predicting associations between CircRNA and diseases through structure-aware graph transformer and path-integral convolution

A series of biological experiments has demonstrated that circular RNAs play a crucial regulatory role in cellular processes and may be potentially associated with diseases. Uncovering these connections helps in understanding potential disease mechanisms and advancing the development of treatment strategies. However, in biology, traditional experiments face limitations in terms of efficiency and cost, especially when enumerating possible associations. To address these limitations, several computational methods have been proposed, but existing methods only measure from a nodal perspective and cannot capture structural similarities between edges. In this study, we introduce an advanced computational method called SATPIC2CD for analyzing potential associations between circular RNAs and diseases. Specifically, we first employ an Structure-Aware Graph Transformer (SAT), which extracts five predefined metapath representations before calculating attention. This adaptive network integrates structural information into the original self-attention by aggregating information within and between paths. Subsequently, we use Path Integral Convolutional Networks (PACN) to integrate feature information for all path weights between two nodes. Afterward, we complement the network node features with feature loss and feature smoothing using Gated Recurrent Units (GRU) and node centrality. Finally, a Multi-Layer Perceptron (MLP) is employed to obtain the ultimate prediction scores for each circular RNA-disease pair. SATPIC2CD performs remarkably well, with an accuracy of up to 0.9715 measured by the Area Under the Curve (AUC) in a 5-fold cross-validation, surpassing other comparative models. Case studies further emphasize the high precision of our method in identifying circular RNA-disease associations, laying a solid foundation for guiding future biological research efforts.

PDE1B, a potential biomarker associated with tumor microenvironment and clinical prognostic significance in osteosarcoma

PDE1B had been found to be involved in various diseases, including tumors and non-tumors. However, little was known about the definite role of PDE1B in osteosarcoma. Therefore, we mined public data on osteosarcoma to reveal the prognostic values and immunological roles of the PDE1B gene. Three osteosarcoma-related datasets from online websites were utilized for further data analysis. R 4.3.2 software was utilized to conduct difference analysis, prognostic analysis, gene set enrichment analysis (GSEA), nomogram construction, and immunological evaluations, respectively. Experimental verification of the PDE1B gene in osteosarcoma was conducted by qRT-PCR and western blot, based on the manufacturer's instructions. The PDE1B gene was discovered to be lowly expressed in osteosarcoma, and its low expression was associated with poor OS (all P < 0.05). Experimental verifications by qRT-PCR and western blot results remained consistent (all P < 0.05). Univariate and multivariate Cox regression analyses indicated that the PDE1B gene had independent abilities in predicting OS in the TARGET osteosarcoma dataset (both P < 0.05). GSEA revealed that PDE1B was markedly linked to the calcium, cell cycle, chemokine, JAK STAT, and VEGF pathways. Moreover, PDE1B was found to be markedly associated with immunity (all P < 0.05), and the TIDE algorithm further shed light on that patients with high-PDE1B expression would have a better immune response to immunotherapies than those with low-PDE1B expression, suggesting that the PDE1B gene could prevent immune escape from osteosarcoma. The PDE1B gene was found to be a tumor suppressor gene in osteosarcoma, and its high expression was related to a better OS prognosis, suppressing immune escape from osteosarcoma.

Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma

Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO) Cox combined with seven machine learning algorithms to analyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performance of the nomogram was assessed through the use of receiver operating characteristic (ROC) curves and calibration curves. Cell-type identification was estimated by using the cell-type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) and single sample gene set enrichment analysis methods. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 were investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram performed well in predicting outcomes according to time-dependent ROC and calibration plots. In addition, a high-risk score was significantly related to high expression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death-based prognostic model. Upregulated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associated with poor clinical outcomes and was positively related to CD163, PD-L1 and vimentin expression in the in-horse cohort. The cell death score provides a precise stratification and immune status for glioma. MYD88 was found to be an outstanding representative that might play an important role in glioma.

An analysis of AURKB's prognostic and immunological roles across various cancers

Aurora kinase B (AURKB), an essential regulator in the process of mitosis, has been revealed through various studies to have a significant role in cancer development and progression. However, the specific mechanisms remain poorly understood. This study, therefore, seeks to elucidate the multifaceted role of AURKB in diverse cancer types. This study utilized bioinformatics techniques to examine the transcript, protein, promoter methylation and mutation levels of AURKB. The study further analysed associations between AURKB and factors such as prognosis, pathological stage, biological function, immune infiltration, tumour mutational burden (TMB) and microsatellite instability (MSI). In addition, immunohistochemical staining data of 50 cases of renal clear cell carcinoma and its adjacent normal tissues were collected to verify the difference in protein expression of AURKB in the two tissues. The results show that AURKB is highly expressed in most cancers, and the protein level of AURKB and the methylation level of its promoter vary among cancer types. Survival analysis showed that AURKB was associated with overall survival in 12 cancer types and progression-free survival in 11 cancer types. Elevated levels of AURKB were detected in the advanced stages of 10 different cancers. AURKB has a potential impact on cancer progression through its effects on cell cycle regulation as well as inflammatory and immune-related pathways. We observed a strong association between AURKB and immune cell infiltration, immunomodulatory factors, TMB and MSI. Importantly, we confirmed that the AURKB protein is highly expressed in kidney renal clear cell carcinoma (KIRC). Our study reveals that AURKB may be a potential biomarker for pan-cancer and KIRC.

Expression analysis of necroptosis related genes and lncRNAs in patients with pituitary neuroendocrine tumors

Necroptosis can either be the cause of tumorigenesis or it can impede its process. Recently, it has been proved that long non-coding RNAs (lncRNAs) have different crucial roles at cellular level, especially on cell death. Regarding the important role of necroptosis and lncRNAs in the pathogenesis of different cancers, especially pituitary adenomas (PAs), we assessed expression levels of two necroptosis related genes, namely TRADD and BIRC2, in addition to three related lncRNAs, namely FLVCR1-DT, MAGI2-AS3, and NEAT1 in PAs compared with adjacent normal tissues (ANTs). TRADD had no significant difference between two groups; however, BIRC2, FLVCR1-DT, MAGI2-AS3, and NEAT1 were upregulated in PAs compared to ANTs (Expression ratios [95% CI] = 2.3 [1.47-3.6], 2.13 [1.02-4.44], 3.01 [1.76-5.16] and 2.47 [1.37-4.45], respectively). When taking into account different types of PAs, significant upregulation of BIRC2, MAGI2-AS3 and NEAT1 was recorded in non-functioning PAs compared with corresponding ANTs (Expression ratios [95% CI] =1.9 [1.04-3.43], 2.69 [1.26-5.72] and 2.22 [0.98-5.01], respectively). Additionally, higher levels of BIRC2 were associated with higher flow of CSF (P value=0.048). Moreover, higher Knosp classified tumors had lower levels of BIRC2 (P value=0.001). Finally, lower levels of MAGI2-AS3 were associated with larger tumor size (P value=0.006). NEAT1 expression was correlated with FLVCR1-DT and TRADD. TRADD expression was correlated with FLVCR1-DT. Additional correlation was observed between expression of BIRC2 and MAGI2-AS3. In sum, this study provides evidence that dysregulated levels of studied genes could contribute to the pathogenesis of pituitary tumors.

IDMIR: identification of dysregulated miRNAs associated with disease based on a miRNA-miRNA interaction network constructed through gene expression data

Micro ribonucleic acids (miRNAs) play a pivotal role in governing the human transcriptome in various biological phenomena. Hence, the accumulation of miRNA expression dysregulation frequently assumes a noteworthy role in the initiation and progression of complex diseases. However, accurate identification of dysregulated miRNAs still faces challenges at the current stage. Several bioinformatics tools have recently emerged for forecasting the associations between miRNAs and diseases. Nonetheless, the existing reference tools mainly identify the miRNA-disease associations in a general state and fall short of pinpointing dysregulated miRNAs within a specific disease state. Additionally, no studies adequately consider miRNA-miRNA interactions (MMIs) when analyzing the miRNA-disease associations. Here, we introduced a systematic approach, called IDMIR, which enabled the identification of expression dysregulated miRNAs through an MMI network under the gene expression context, where the network's architecture was designed to implicitly connect miRNAs based on their shared biological functions within a particular disease context. The advantage of IDMIR is that it uses gene expression data for the identification of dysregulated miRNAs by analyzing variations in MMIs. We illustrated the excellent predictive power for dysregulated miRNAs of the IDMIR approach through data analysis on breast cancer and bladder urothelial cancer. IDMIR could surpass several existing miRNA-disease association prediction approaches through comparison. We believe the approach complements the deficiencies in predicting miRNA-disease association and may provide new insights and possibilities for diagnosing and treating diseases. The IDMIR approach is now available as a free R package on CRAN (https://CRAN.R-project.org/package=IDMIR).

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

MOTIVATION

Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming.

RESULTS

We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966.

AVAILABILITY

Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git.

DGAMDA: Predicting miRNA-disease association based on dynamic graph attention network

MiRNA (microRNA)-disease association prediction has essential applications for early disease screening. The process of traditional biological experimental validation is both time-consuming and expensive. However, as artificial intelligence technology continues to advance, computational methods have become efficient tools for predicting miRNA-disease associations. These methods often rely on the combination of multiple sources of association data and require improved feature mining. This study proposes a dynamic graph attention-based association prediction model, DGAMDA, which combines feature mapping and dynamic graph attention mechanisms through feature mining on a single miRNA-disease association network. DGAMDA effectively solves the problems of feature heterogeneity and inadequate feature mining by previous static graph attention mechanisms and achieves high-precision feature mining and association scoring prediction. We conducted a five-fold cross-validation experiment and obtained the mean values of Accuracy, Precision, Recall, and F1-score, which were .8986, .8869, .9115, and .8984, respectively. Our proposed model outperforms other advanced models in terms of experimental results, demonstrating its effectiveness in feature mining and association prediction based on a single association network. In addition, our model can also be used to predict miRNAs associated with unknown diseases.

A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions

MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD.

Coupled scRNA-seq and Bulk-seq reveal the role of HMMR in hepatocellular carcinoma

Background

Hyaluronan-mediated motility receptor (HMMR) is overexpressed in multiple carcinomas and influences the development and treatment of several cancers. However, its role in hepatocellular carcinoma (HCC) remains unclear.

Methods

The "limma" and "GSVA" packages in R were used to perform differential expression analysis and to assess the activity of signalling pathways, respectively. InferCNV was used to infer copy number variation (CNV) for each hepatocyte and "CellChat" was used to analyse intercellular communication networks. Recursive partitioning analysis (RPA) was used to re-stage HCC patients. The IC50 values of various drugs were evaluated using the "pRRophetic" package. In addition, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to confirm HMMR expression in an HCC tissue microarray. Flow cytometry (FCM) and cloning, Edu and wound healing assays were used to explore the capacity of HMMR to regulate HCC tumour.

Results

Multiple cohort studies and qRT-PCR demonstrated that HMMR was overexpressed in HCC tissue compared with normal tissue. In addition, HMMR had excellent diagnostic performance. HMMR knockdown inhibited the proliferation and migration of HCC cells in vitro. Moreover, high HMMR expression was associated with "G2M checkpoint" and "E2F targets" in bulk RNA and scRNA-seq, and FCM confirmed that HMMR could regulate the cell cycle. In addition, HMMR was involved in the regulation of the tumour immune microenvironment via immune cell infiltration and intercellular interactions. Furthermore, HMMR was positively associated with genomic heterogeneity with patients with high HMMR expression potentially benefitting more from immunotherapy. Moreover, HMMR was associated with poor prognosis in patients with HCC and the re-staging by recursive partitioning analysis (RPA) gave a good prognosis prediction value and could guide chemotherapy and targeted therapy.

Conclusion

The results of the present study show that HMMR could play a role in the diagnosis, prognosis, and treatments of patients with HCC based on bulk RNA-seq and scRAN-seq analyses and is a promising molecular marker for HCC.

Development of m6A/m5C/m1A regulated lncRNA signature for prognostic prediction, personalized immune intervention and drug selection in LUAD

Research indicates that there are links between m6A, m5C and m1A modifications and the development of different types of tumours. However, it is not yet clear if these modifications are involved in the prognosis of LUAD. The TCGA-LUAD dataset was used as for signature training, while the validation cohort was created by amalgamating publicly accessible GEO datasets including GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081. The study focused on 33 genes that are regulated by m6A, m5C or m1A (mRG), which were used to form mRGs clusters and clusters of mRG differentially expressed genes clusters (mRG-DEG clusters). Our subsequent LASSO regression analysis trained the signature of m6A/m5C/m1A-related lncRNA (mRLncSig) using lncRNAs that exhibited differential expression among mRG-DEG clusters and had prognostic value. The model's accuracy underwent validation via Kaplan-Meier analysis, Cox regression, ROC analysis, tAUC evaluation, PCA examination and nomogram predictor validation. In evaluating the immunotherapeutic potential of the signature, we employed multiple bioinformatics algorithms and concepts through various analyses. These included seven newly developed immunoinformatic algorithms, as well as evaluations of TMB, TIDE and immune checkpoints. Additionally, we identified and validated promising agents that target the high-risk mRLncSig in LUAD. To validate the real-world expression pattern of mRLncSig, real-time PCR was carried out on human LUAD tissues. The signature's ability to perform in pan-cancer settings was also evaluated. The study created a 10-lncRNA signature, mRLncSig, which was validated to have prognostic power in the validation cohort. Real-time PCR was applied to verify the actual manifestation of each gene in the signature in the real world. Our immunotherapy analysis revealed an association between mRLncSig and immune status. mRLncSig was found to be closely linked to several checkpoints, such as IL10, IL2, CD40LG, SELP, BTLA and CD28, which could be appropriate immunotherapy targets for LUAD. Among the high-risk patients, our study identified 12 candidate drugs and verified gemcitabine as the most significant one that could target our signature and be effective in treating LUAD. Additionally, we discovered that some of the lncRNAs in mRLncSig could play a crucial role in certain cancer types, and thus, may require further attention in future studies. According to the findings of this study, the use of mRLncSig has the potential to aid in forecasting the prognosis of LUAD and could serve as a potential target for immunotherapy. Moreover, our signature may assist in identifying targets and therapeutic agents more effectively.

RFEM: A framework for essential microRNA identification in mice based on rotation forest and multiple feature fusion

With the increasing number of microRNAs (miRNAs), identifying essential miRNAs has become an important task that needs to be solved urgently. However, there are few computational methods for essential miRNA identification. Here, we proposed a novel framework called Rotation Forest for Essential MicroRNA identification (RFEM) to predict the essentiality of miRNAs in mice. We first constructed 1,264 miRNA features of all miRNA samples by fusing 38 miRNA features obtained from the PESM paper and 1,226 miRNA functional features calculated based on miRNA-target gene interactions. Then, we employed 182 training samples with 1,264 features to train the rotation forest model, which was applied to compute the essentiality scores of the candidate samples. The main innovations of RFEM were as follows: 1) miRNA functional features were introduced to enrich the diversity of miRNA features; 2) the rotation forest model used decision tree as the base classifier and could increase the difference among base classifiers through feature transformation to achieve better ensemble results. Experimental results show that RFEM significantly outperformed two previous models with the AUC (AUPR) of 0.942 (0.944) in three comparison experiments under 5-fold cross validation, which proved the model's reliable performance. Moreover, ablation study was further conducted to demonstrate the effectiveness of the novel miRNA functional features. Additionally, in the case studies of assessing the essentiality of unlabeled miRNAs, experimental literature confirmed that 7 of the top 10 predicted miRNAs have crucial biological functions in mice. Therefore, RFEM would be a reliable tool for identifying essential miRNAs.

Prediction of MicroRNA-Disease Potential Association Based on Sparse Learning and Multilayer Random Walks

More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained. The disease similarity network, miRNA similarity network, and miRNA-disease association network are used to construct heterogeneous networks, and the stable probability is obtained based on the topological structure features of diseases and miRNAs through a multilayer random walk algorithm to predict miRNA-disease potential association. The experimental results show that the prediction accuracy of this model is significantly improved compared with the previous related models. We evaluated the model using global leave-one-out cross-validation (global LOOCV) and fivefold cross-validation (5-fold CV). The area under the curve (AUC) value for the LOOCV is 0.9368. The mean AUC value for 5-fold CV is 0.9335 and the variance is 0.0004. In the case study, the results show that SLMRWMDA is effective in inferring the potential association of miRNA-disease.

Prediction of miRNA-disease associations based on strengthened hypergraph convolutional autoencoder

Most existing graph neural network-based methods for predicting miRNA-disease associations rely on initial association matrices to pass messages, but the sparsity of these matrices greatly limits performance. To address this issue and predict potential associations between miRNAs and diseases, we propose a method called strengthened hypergraph convolutional autoencoder (SHGAE). SHGAE leverages multiple layers of strengthened hypergraph neural networks (SHGNN) to obtain robust node embeddings. Within SHGNN, we design a strengthened hypergraph convolutional network module (SHGCN) that enhances original graph associations and reduces matrix sparsity. Additionally, SHGCN expands node receptive fields by utilizing hyperedge features as intermediaries to obtain high-order neighbor embeddings. To improve performance, we also incorporate attention-based fusion of self-embeddings and SHGCN embeddings. SHGAE predicts potential miRNA-disease associations using a multilayer perceptron as the decoder. Across multiple metrics, SHGAE outperforms other state-of-the-art methods in five-fold cross-validation. Furthermore, we evaluate SHGAE on colon and lung neoplasms cases to demonstrate its ability to predict potential associations. Notably, SHGAE also performs well in the analysis of gastric neoplasms without miRNA associations.

microRNA-486-5p Regulates DNA Damage Inhibition and Cisplatin Resistance in Lung Adenocarcinoma by Targeting AURKB

Lung adenocarcinoma (LUAD) severely affects human health, and cisplatin (DDP) resistance is the main obstacle in LUAD treatment, the mechanism of which is unknown. Bioinformatics methods were utilized to predict expression and related pathways of AURKB in LUAD tissues, as well as the upstream regulated microRNAs. qRT-PCR assayed expression of AURKB and microRNA-486-5p. RIP and dual-luciferase experiments verified the binding and interaction between the two genes. CCK-8 was used to detect cell proliferation ability and IC50 values. Flow cytometry was utilized to assess the cell cycle. Comet assay and western blot tested DNA damage and γ-H2AX protein expression, respectively. In LUAD, AURKB was upregulated, but microRNA-486-5p was downregulated. The targeted relationship between the two was confirmed by RIP and dual-luciferase experiments. Cell experiments showed that AURKB knock-down inhibited cell proliferation, reduced IC50 values, induced cell cycle arrest, and caused DNA damage. The rescue experiment presented that high expression of microRNA-486-5p could weaken the impact of AURKB overexpression on LUAD cell behavior and DDP resistance. microRNA-486-5p regulated DNA damage to inhibit DDP resistance in LUAD by targeting AURKB, implying that microRNA-486-5p/AURKB axis may be a possible therapeutic target for DDP resistance in LUAD patients.

Current Updates on the Role of MicroRNA in the Diagnosis and Treatment of Neurodegenerative Diseases

BACKGROUND

MicroRNAs (miRNA) are small noncoding RNAs that play a significant role in the regulation of gene expression. The literature has explored the key involvement of miRNAs in the diagnosis, prognosis, and treatment of various neurodegenerative diseases (NDD), such as Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD). The miRNA regulates various signalling pathways; its dysregulation is involved in the pathogenesis of NDD.

OBJECTIVE

The present review is focused on the involvement of miRNAs in the pathogenesis of NDD and their role in the treatment or management of NDD. The literature provides comprehensive and cutting-edge knowledge for students studying neurology, researchers, clinical psychologists, practitioners, pathologists, and drug development agencies to comprehend the role of miRNAs in the NDD's pathogenesis, regulation of various genes/signalling pathways, such as α-synuclein, P53, amyloid-β, high mobility group protein (HMGB1), and IL-1β, NMDA receptor signalling, cholinergic signalling, etc. Methods: The issues associated with using anti-miRNA therapy are also summarized in this review. The data for this literature were extracted and summarized using various search engines, such as Google Scholar, Pubmed, Scopus, and NCBI using different terms, such as NDD, PD, AD, HD, nanoformulations of mRNA, and role of miRNA in diagnosis and treatment.

RESULTS

The miRNAs control various biological actions, such as neuronal differentiation, synaptic plasticity, cytoprotection, neuroinflammation, oxidative stress, apoptosis and chaperone-mediated autophagy, and neurite growth in the central nervous system and diagnosis. Various miRNAs are involved in the regulation of protein aggregation in PD and modulating β-secretase activity in AD. In HD, mutation in the huntingtin (Htt) protein interferes with Ago1 and Ago2, thus affecting the miRNA biogenesis. Currently, many anti-sense technologies are in the research phase for either inhibiting or promoting the activity of miRNA.

CONCLUSION

This review provides new therapeutic approaches and novel biomarkers for the diagnosis and prognosis of NDDs by using miRNA.

Drug-drug interaction prediction: databases, web servers and computational models

In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.

LUNCRW: Prediction of potential lncRNA-disease associations based on unbalanced neighborhood constraint random walk

Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are associated with various complex human diseases. They can serve as disease biomarkers and hold considerable promise for the prevention and treatment of various diseases. The traditional random walk algorithms generally exclude the effect of non-neighboring nodes on random walking. In order to overcome the issue, the neighborhood constraint (NC) approach is proposed in this study for regulating the direction of the random walk by computing the effects of both neighboring nodes and non-neighboring nodes. Then the association matrix is updated by matrix multiplication for minimizing the effect of the false negative data. The heterogeneous lncRNA-disease network is finally analyzed using an unbalanced random walk method for predicting the potential lncRNA-disease associations. The LUNCRW model is therefore developed for predicting potential lncRNA-disease associations. The area under the curve (AUC) values of the LUNCRW model in leave-one-out cross-validation and five-fold cross-validation were 0.951 and 0.9486 ± 0.0011, respectively. Data from published case studies on three diseases, including squamous cell carcinoma, hepatocellular carcinoma, and renal cell carcinoma, confirmed the predictive potential of the LUNCRW model. Altogether, the findings indicated that the performance of the LUNCRW method is superior to that of existing methods in predicting potential lncRNA-disease associations.

LRBmat: A novel gut microbial interaction and individual heterogeneity inference method for colorectal cancer

The gut microbial community has been shown to play a significant role in various diseases, including colorectal cancer (CRC), which is a major public health concern worldwide. The accurate diagnosis and etiological analysis of CRC are crucial issues. Numerous methods have utilized gut microbiota to address these challenges; however, few have considered the complex interactions and individual heterogeneity of the gut microbiota, which are important issues in genetics and intestinal microbiology, particularly in high-dimensional cases. This paper presents a novel method called Binary matrix based on Logistic Regression (LRBmat) to address these concerns. The binary matrix in LRBmat can directly mitigate or eliminate the influence of heterogeneity, while also capturing information on gut microbial interactions with any order. LRBmat is highly adaptable and can be combined with any machine learning method to enhance its capabilities. The proposed method was evaluated using real CRC data and demonstrated superior classification performance compared to state-of-the-art methods. Furthermore, the association rules extracted from the binary matrix of the real data align well with biological properties and existing literature, thereby aiding in the etiological analysis of CRC.

Predicting potential small molecule-miRNA associations utilizing truncated schatten p-norm

MicroRNAs (miRNAs) have significant implications in diverse human diseases and have proven to be effectively targeted by small molecules (SMs) for therapeutic interventions. However, current SM-miRNA association prediction models do not adequately capture SM/miRNA similarity. Matrix completion is an effective method for association prediction, but existing models use nuclear norm instead of rank function, which has some drawbacks. Therefore, we proposed a new approach for predicting SM-miRNA associations by utilizing the truncated schatten p-norm (TSPN). First, the SM/miRNA similarity was preprocessed by incorporating the Gaussian interaction profile kernel similarity method. This identified more SM/miRNA similarities and significantly improved the SM-miRNA prediction accuracy. Next, we constructed a heterogeneous SM-miRNA network by combining biological information from three matrices and represented the network with its adjacency matrix. Finally, we constructed the prediction model by minimizing the truncated schatten p-norm of this adjacency matrix and we developed an efficient iterative algorithmic framework to solve the model. In this framework, we also used a weighted singular value shrinkage algorithm to avoid the problem of excessive singular value shrinkage. The truncated schatten p-norm approximates the rank function more closely than the nuclear norm, so the predictions are more accurate. We performed four different cross-validation experiments on two separate datasets, and TSPN outperformed various most advanced methods. In addition, public literature confirms a large number of predictive associations of TSPN in four case studies. Therefore, TSPN is a reliable model for SM-miRNA association prediction.

Identifying Candidate Gene-Disease Associations via Graph Neural Networks

Real-world objects are usually defined in terms of their own relationships or connections. A graph (or network) naturally expresses this model though nodes and edges. In biology, depending on what the nodes and edges represent, we may classify several types of networks, gene-disease associations (GDAs) included. In this paper, we presented a solution based on a graph neural network (GNN) for the identification of candidate GDAs. We trained our model with an initial set of well-known and curated inter- and intra-relationships between genes and diseases. It was based on graph convolutions, making use of multiple convolutional layers and a point-wise non-linearity function following each layer. The embeddings were computed for the input network built on a set of GDAs to map each node into a vector of real numbers in a multidimensional space. Results showed an AUC of 95% for training, validation, and testing, that in the real case translated into a positive response for 93% of the Top-15 (highest dot product) candidate GDAs identified by our solution. The experimentation was conducted on the DisGeNET dataset, while the DiseaseGene Association Miner (DG-AssocMiner) dataset by Stanford's BioSNAP was also processed for performance evaluation only.

KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection

BACKGROUND

Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA-disease associations predicted by computational methods are the best complement to biological experiments.

RESULTS

In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA-disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA-disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA-disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP.

CONCLUSION

A new computational model KATZNCP was proposed for predicting potential miRNA-drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA-disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.

Inferring pseudogene-MiRNA associations based on an ensemble learning framework with similarity kernel fusion

Accumulating evidence shows that pseudogenes can function as microRNAs (miRNAs) sponges and regulate gene expression. Mining potential interactions between pseudogenes and miRNAs will facilitate the clinical diagnosis and treatment of complex diseases. However, identifying their interactions through biological experiments is time-consuming and labor intensive. In this study, an ensemble learning framework with similarity kernel fusion is proposed to predict pseudogene-miRNA associations, named ELPMA. First, four pseudogene similarity profiles and five miRNA similarity profiles are measured based on the biological and topology properties. Subsequently, similarity kernel fusion method is used to integrate the similarity profiles. Then, the feature representation for pseudogenes and miRNAs is obtained by combining the pseudogene-pseudogene similarities, miRNA-miRNA similarities. Lastly, individual learners are performed on each training subset, and the soft voting is used to yield final decision based on the prediction results of individual learners. The k-fold cross validation is implemented to evaluate the prediction performance of ELPMA method. Besides, case studies are conducted on three investigated pseudogenes to validate the predict performance of ELPMA method for predicting pseudogene-miRNA interactions. Therefore, all experiment results show that ELPMA model is a feasible and effective tool to predict interactions between pseudogenes and miRNAs.

Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

SCCPMD: Probability matrix decomposition method subject to corrected similarity constraints for inferring long non-coding RNA-disease associations

Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA-disease associations is essential for disease diagnosis, treatment, and prevention. In recent years, many matrix decomposition methods have also been used to predict potential lncRNA-disease associations. However, these methods do not consider the use of microbe-disease association information to enrich disease similarity, and also do not make more use of similarity information in the decomposition process. To address these issues, we here propose a correction-based similarity-constrained probability matrix decomposition method (SCCPMD) to predict lncRNA-disease associations. The microbe-disease associations are first used to enrich the disease semantic similarity matrix, and then the logistic function is used to correct the lncRNA and disease similarity matrix, and then these two corrected similarity matrices are added to the probability matrix decomposition as constraints to finally predict the potential lncRNA-disease associations. The experimental results show that SCCPMD outperforms the five advanced comparison algorithms. In addition, SCCPMD demonstrated excellent prediction performance in a case study for breast cancer, lung cancer, and renal cell carcinoma, with prediction accuracy reaching 80, 100, and 100%, respectively. Therefore, SCCPMD shows excellent predictive performance in identifying unknown lncRNA-disease associations.

Machine learning in the development of targeting microRNAs in human disease

A microRNA is a small, single-stranded, non-coding ribonucleic acid that plays a crucial role in RNA silencing and can regulate gene expression. With the in-depth study of miRNA in development and disease, miRNA has become an attractive target for novel therapeutic strategies. Exploring miRNA targeting therapy only through experiments is expensive and laborious, so it is essential to develop novel and efficient computational methods to narrow down the search. Recent advances in machine learning applied in biomedical informatics provide opportunities to explore miRNA-targeting drugs, thus promoting miRNA therapeutics. This review provides an overview of recent advancements in miRNA targeting therapeutic using machine learning. First, we mainly describe the basics of predicting miRNA targeting drugs, including pharmacogenomic data resources and data preprocessing. Then we present primary machine learning algorithms and elaborate their application in discovering relationships among miRNAs, drugs, and diseases. Along with the progress of miRNA targeting therapeutics, we finally analyze and discuss the current challenges and opportunities that machine learning confronts.

Regulation of LncRNAs and microRNAs in neuronal development and disease

Non-coding RNAs (ncRNAs) are RNAs that do not encode proteins but play important roles in regulating cellular processes. Multiple studies over the past decade have demonstrated the role of microRNAs (miRNAs) in cancer, in which some miRNAs can act as biomarkers or provide therapy target. Accumulating evidence also points to the importance of long non-coding RNAs (lncRNAs) in regulating miRNA-mRNA networks. An increasing number of ncRNAs have been shown to be involved in the regulation of cellular processes, and dysregulation of ncRNAs often heralds disease. As the population ages, the incidence of neurodegenerative diseases is increasing, placing enormous pressure on global health systems. Given the excellent performance of ncRNAs in early cancer screening and treatment, here we attempted to aggregate and analyze the regulatory functions of ncRNAs in neuronal development and disease. In this review, we summarize current knowledge on ncRNA taxonomy, biogenesis, and function, and discuss current research progress on ncRNAs in relation to neuronal development, differentiation, and neurodegenerative diseases.

Recent Updates on the Role of the MicroRNA-10 Family in Gynecological Malignancies

The ever-increasing morbidity associated with gynecological malignancies constantly endangers the physical and psychological health of women. Since a long time, there has been an urgent need for a deeper understanding of the tumorigenesis and the development of gynecological cancer to identify new molecular markers for early diagnosis and metastatic disease prognosis and for the development of therapeutic targets. MicroRNAs are crucial cellular regulators. The microRNA-10 (miR-10) family has been found to play an integral role in the evolution of numerous cancer types. A comprehensive understanding of current studies on miR-10 could provide better insights into future research and clinical applications in related fields. This article reviews the latest research on the role of the miR-10 family in gynecological malignancies and the relevant molecular mechanism, mainly focusing on endometrial, cervical, and ovarian cancers.

Circular RNAs Regulate Vascular Remodelling in Pulmonary Hypertension

Circular RNAs (circRNAs) are a newly identified type of noncoding RNA molecule with a unique closed-loop structure. circRNAs are widely expressed in different tissues and developmental stages of many species, participating in many important pathophysiological processes and playing an important role in the occurrence and development of diseases. This article reviews the discovery, characteristics, formation, and biological function of circRNAs. The relationship between circRNAs and vascular remodelling, as well as the current status of research and potential application value in pulmonary hypertension (PH), is discussed to promote a better understanding of the role of circRNAs in PH. circRNAs are closely related to the remodelling of vascular endothelial cells and vascular smooth muscle cells. circRNAs have potential application prospects for in-depth research on the possible pathogenesis and mechanism of PH. Future research on the role of circRNAs in the pathogenesis and mechanism of PH will provide new insights and promote screening, diagnosis, prevention, and treatment of this disease.

Identifying lncRNA–disease association based on GAT multiple-operator aggregation and inductive matrix completion

Computable models as a fundamental candidate for traditional biological experiments have been applied in inferring lncRNA–disease association (LDA) for many years, without time-consuming and laborious limitations. However, sparsity inherently existing in known heterogeneous bio-data is an obstacle to computable models to improve prediction accuracy further. Therefore, a new computational model composed of multiple mechanisms for lncRNA–disease association (MM-LDA) prediction was proposed, based on the fusion of the graph attention network (GAT) and inductive matrix completion (IMC). MM-LDA has two key steps to improve prediction accuracy: first, a multiple-operator aggregation was designed in the n-heads attention mechanism of the GAT. With this step, features of lncRNA nodes and disease nodes were enhanced. Second, IMC was introduced into the enhanced node features obtained in the first step, and then the LDA network was reconstructed to solve the cold start problem when data deficiency of the entire row or column happened in a known association matrix. Our MM-LDA achieved the following progress: first, using the Adam optimizer that adaptively adjusted the model learning rate could increase the convergent speed and not fall into local optima as well. Second, more excellent predictive ability was achieved against other similar models (with an AUC value of 0.9395 and an AUPR value of 0.8057 obtained from 5-fold cross-validation). Third, a 6.45% lower time cost was consumed against the advanced model GAMCLDA. In short, our MM-LDA achieved a more comprehensive prediction performance in terms of prediction accuracy and time cost.