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

Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis

Non-coding RNAs (ncRNAs) play a crucial role in breast cancer progression, necessitating advanced computational approaches for precise disease classification. This study introduces a Deep Reinforcement Learning (DRL)-based framework for predicting ncRNA-disease associations in metaplastic breast cancer (MBC) using a multi-dimensional descriptor system (ncRNADS) integrating 550 sequence-based features and 1,150 target gene descriptors (miRDB score ≥ 90). The model achieved 96.20% accuracy, 96.48% precision, 96.10% recall, and a 96.29% F1-score, outperforming traditional classifiers such as support vector machines (SVM) and neural networks. Feature selection and optimization reduced dimensionality by 42.5% (4,430 to 2,545 features) while maintaining high accuracy, demonstrating computational efficiency. External validation confirmed model specificity to breast cancer subtypes (87-96.5% accuracy) and minimal cross-reactivity with unrelated diseases like Alzheimer's (8-9% accuracy), ensuring robustness. SHAP analysis identified key sequence motifs (e.g., "UUG") and structural free energy (ΔG = - 12.3 kcal/mol) as critical predictors, validated by PCA (82% variance) and t-SNE clustering. Survival analysis using TCGA data revealed prognostic significance for MALAT1, HOTAIR, and NEAT1 (associated with poor survival, HR = 1.76-2.71) and GAS5 (protective effect, HR = 0.60). The DRL model demonstrated rapid training (0.08 s/epoch) and cloud deployment compatibility, underscoring its scalability for large-scale applications. These findings establish ncRNA-driven classification as a cornerstone for precision oncology, enabling patient stratification, survival prediction, and therapeutic target identification in MBC.

GMIP: A Novel Prognostic Biomarker Influencing Immune Infiltration and Tumour Dynamics Across Cancer Types

GMIP, a member of the RhoGAP family, plays a critical role in cytoskeletal remodelling, cell migration and immune modulation. Its aberrant expression in cancers suggests a pivotal role in tumour progression. GMIP expression was assessed using transcriptomic datasets from GDC and UCSC XENA, and protein distribution across tissues via HPA and GeneMANIA. The TISCH database identified primary GMIP-expressing cell types in the tumour microenvironment. Univariate Cox regression assessed GMIP's prognostic potential, while cBioPortal and GSCA explored genomic alterations. TIMER 2.0 was used to investigate immune cell infiltration and GMIP's role in immune regulation. GSEA and GSVA unveiled GMIP-related biological pathways, and molecular docking with CellMiner identified potential drug interactions. In vitro assays confirmed GMIP's functional relevance in breast cancer. GMIP exhibits differential expression across multiple cancer types, demonstrating significant prognostic implications. Its expression is inversely correlated with CNV and methylation in several cancers. GMIP is closely linked to immunotherapy biomarkers and immune suppression, influencing therapeutic responses. Functional studies suggest that GMIP inhibition reduces cancer cell proliferation and migration. GMIP is identified as a promising oncological biomarker, particularly in breast cancer, with potential therapeutic implications. GMIP's therapeutic potential is especially pronounced in BRCA-mutated tumours, underscoring its relevance for novel anticancer interventions.

Long Noncoding RNA E2F1 Messenger RNA Stabilizing Factor (EMS) Promotes Sorafenib Resistance in Renal Cell Carcinoma by Regulating miR-363-3p and Dual-Specificity Phosphatase 10 Expression

Renal cell carcinoma (RCC) is a common kidney disease associated with high mortality. Sorafenib is a protein kinase inhibitor that targets multiple kinases and is used for treating different cancers, including RCC. However, sorafenib resistance in patients with RCC hampers its use. Therefore, elucidating the molecular mechanisms underlying sorafenib resistance in RCC and developing novel therapeutic strategies to overcome drug resistance are vital. In this study, we found that the expression level of the long noncoding RNA (lncRNA) E2F1 messenger RNA stabilizing factor (EMS) was significantly higher in sorafenib-resistant RCC tissues and cell lines than in sorafenib-sensitive RCC tissues and cell lines. lncRNA EMS knockdown improved the sensitivity of sorafenib-resistant RCC cells to sorafenib treatment, as evidenced by decreased cell proliferation and increased apoptosis. Additionally, lncRNA EMS silencing combined with sorafenib treatment markedly inhibited RCC tumor development in vivo. Moreover, it was systematically shown that lncRNA EMS sponged miR-363-3p, whose expression was decreased in sorafenib-resistant RCC. Notably, miR-363-3p negatively regulated the expression of dual-specificity phosphatase 10 (DUSP10) by targeting its 3'-UTR. Furthermore, miR-363-3p overexpression restored sorafenib sensitivity, whereas upregulated DUSP10 expression promoted sorafenib resistance in sorafenib-resistant cell lines. In conclusion, the lncRNA EMS/miR-363-3p/DUSP10 axis regulates sorafenib resistance in RCC, and these molecules are promising biomarkers and therapeutic targets for patients with sorafenib-resistant RCC.

Predicting microRNA target genes using pan-cancer correlation patterns

The interaction relationship between miRNAs and genes is important as miRNAs play a crucial role in regulating gene expression. In the literature, several databases have been constructed to curate known miRNA target genes, which are valuable resources but likely only represent a small fraction of all miRNA-gene interactions. In this study, we constructed machine learning models to predict miRNA target genes that have not been previously reported. Using the miRNA and gene expression data from TCGA, we performed a correlation analysis between all miRNAs and all genes across multiple cancer types. The correlations served as features to describe each miRNA-gene pair. Using the existing databases of curated miRNA targets, we labeled the miRNA-gene pairs, and trained machine learning models to predict novel miRNA-gene interactions. For the miRNA-gene pairs that were consistently predicted across the models, we called them significant miRNA-gene pairs. Using held-out miRNA target databases and a literature survey, we validated 5.5% of the predicted significant miRNA-gene pairs. The remaining predicted miRNA-gene pairs could serve as hypotheses for experimental validation. Additionally, we explored several additional datasets that provided gene expression data before and after a specific miRNA perturbation and observed consistency between the correlation direction of predicted miRNA-gene pairs and their regulatory patterns. Together, this analysis revealed a novel framework for uncovering previously unidentified miRNA-gene relationships, enhancing the collective comprehension of miRNA-mediated gene regulation.

Expression of some circulating microRNAs as predictive biomarkers for prognosis and treatment response in glioblastoma

Glioblastoma multiforme (GBM) is the most prevalent, treatment-resistant, and fatal form of brain malignancy. It is characterized by genetic heterogeneity, and an infiltrative nature, and GBM treatment is highly challenging. Despite multimodal therapies, clinicians lack efficient prognostic and predictive markers. Therefore, new insights into GBM management are urgently needed to increase the chance of therapeutic success. Circulating miRNAs (miRs) are important regulators of cancer progression and are potentially useful for GBM diagnosis and treatment. This study investigated how miR-29a, miR-106a, and miR-200a affect the prognosis of GBM patients. This study was conducted on 25 GBM patients and 20 healthy volunteers as a control group. The expression levels of target miRs were analyzed pre- and post-treatment using qRT-PCR and evaluated in relation to both clinical GBM criteria and the patient's survival modes. The diagnostic efficacy of target miRs was assessed using the receiver operating characteristic (ROC) curve. MiRs levels showed significant differences among the enrolled participants. All investigated miRs were significantly elevated in GBM patients with non-frontal lesions. Only miR-200a showed a significant difference in GBM patients older than 60 years with a tumor size ≥ 5 mm. Regarding miR-106a, a significant difference was detected based on the surgical strategy and use of an Eastern Cooperative Oncology Group (ECOG) performance status equal to 2. For miR-29a, a significant upregulation was detected according to the surgical strategy. All post-treatment miRs levels in GBM patients were significantly downregulated. In conclusion, circulating miRs revealed a significant role in predicting GBM patient treatment outcomes providing valuable insights for personalized therapeutic strategies.

CLHGNNMDA: Hypergraph Neural Network Model Enhanced by Contrastive Learning for miRNA-Disease Association Prediction

Numerous biological experiments have demonstrated that microRNA (miRNA) is involved in gene regulation within cells, and mutations and abnormal expression of miRNA can cause a myriad of intricate diseases. Forecasting the association between miRNA and diseases can enhance disease prevention and treatment and accelerate drug research, which holds considerable importance for the development of clinical medicine and drug research. This investigation introduces a contrastive learning-augmented hypergraph neural network model, termed CLHGNNMDA, aimed at predicting associations between miRNAs and diseases. Initially, CLHGNNMDA constructs multiple hypergraphs by leveraging diverse similarity metrics related to miRNAs and diseases. Subsequently, hypergraph convolution is applied to each hypergraph to extract feature representations for nodes and hyperedges. Following this, autoencoders are employed to reconstruct information regarding the feature representations of nodes and hyperedges and to integrate various features of miRNAs and diseases extracted from each hypergraph. Finally, a joint contrastive loss function is utilized to refine the model and optimize its parameters. The CLHGNNMDA framework employs multi-hypergraph contrastive learning for the construction of a contrastive loss function. This approach takes into account inter-view interactions and upholds the principle of consistency, thereby augmenting the model's representational efficacy. The results obtained from fivefold cross-validation substantiate that the CLHGNNMDA algorithm achieves a mean area under the receiver operating characteristic curve of 0.9635 and a mean area under the precision-recall curve of 0.9656. These metrics are notably superior to those attained by contemporary state-of-the-art methodologies.

Diagnostic Power of MicroRNAs in Melanoma: Integrating Machine Learning for Enhanced Accuracy and Pathway Analysis

This study identifies microRNAs (miRNAs) with significant discriminatory power in distinguishing melanoma from nevus, notably hsa-miR-26a and hsa-miR-211, which have exhibited diagnostic potential with accuracy of 81% and 78% respectively. To enhance diagnostic accuracy, we integrated miRNAs into various machine-learning (ML) models. Incorporating miRNAs with AUC scores above 0.70 significantly improved diagnostic accuracy to 94%, with a sensitivity of 91%. These findings underscore the potential of ML models to leverage miRNA data for enhanced melanoma diagnosis. Additionally, using the miRNet tool, we constructed a network of miRNA-miRNA interactions, revealing 170 key genes in melanoma pathophysiology. Protein-protein interaction network analysis via Cytoscape identified hub genes including MYC, BRCA1, JUN, AURKB, CDKN2A, DDX5, MAPK14, DDX3X, DDX6, FOXM1 and GSK3B. The identification of hub genes and their interactions with miRNAs enhances our understanding of the molecular mechanisms driving melanoma. Pathway enrichment analyses highlighted key pathways associated with differentially expressed miRNAs, including the PI3K/AKT, TGF-beta signalling pathway and cell cycle regulation. These pathways are implicated in melanoma development and progression, reinforcing the significance of our findings. The functional enrichment of miRNAs suggests their critical role in modulating essential pathways in melanoma, suggesting their potential as therapeutic targets.

Identification and Validation of a m6A-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer

Accumulating research indicates that N6-methyladenosine (m6A) modification plays a pivotal role in colorectal cancer (CRC). Hence, investigating the m6A-related long noncoding RNAs (lncRNAs) significantly improves therapeutic strategies and prognostic assessments. This study aimed to develop and validate a prognostic model based on m6A-related lncRNAs to improve the prediction of clinical outcomes and identify potential immunological mechanisms in CRC. We obtained high-throughput CRC data from The Cancer Genome Atlas to identify a prognostic model based on m6A-related lncRNAs. Then, the model was constructed and validated through LASSO analysis and Cox regression using R software. The clinical applicability was enhanced by developing a nomogram. We further conducted experiments to reveal the biological function of LINC00543. The prognostic model based on eight m6A-related lncRNAs exhibited impressive accuracy, achieving an area under the receiver-operating curve value of 0.753, 0.682 and 0.706 for predictions after 1, 3 and 5 years, respectively. The Kaplan-Meier analysis confirmed the consistency of the model across different pathological characteristics, with a high-risk group showing a poorer prognosis. Furthermore, the model was linked to immune function, particularly the type I interferon response, through gene set enrichment analysis and experimental validation. Our study presented a m6A-related lncRNA prognostic model for CRC with potential clinical utility. The model not only provided improved accuracy over traditional staging but also offered insights into the immunological mechanisms of CRC, facilitating personalised medicine approaches.

Modeling ncRNA Synergistic Regulation in Cancer

Cancer seriously threatens human life and health, and the structure and function of genes within cancer cells have changed relative to normal cells. Essentially, cancer is a polygenic disorder, and the core of its occurrence and development is caused by polygenic synergy. Non-coding RNAs (ncRNAs) act as regulators to modulate gene expression levels, and they provide theoretical basis and new technology for the diagnosis and preventive treatment of cancer. However, the study of ncRNA regulation and its role as biomarkers in cancer remain largely unearthed. Driven by multi-omics data, an abundance of computational methods, tools, and databases have been developed for predicting ncRNA-cancer association/causality, inferring ncRNA regulation, and modeling ncRNA synergistic regulation. This chapter aims to provide a comprehensive perspective of modeling ncRNA synergistic regulation. Since the ncRNAs involved in cancer contribute to modeling cancer-associated ncRNA synergistic regulation, we first review the databases and tools of cancer-related ncRNAs. Then we investigate the existing tools or methods for modeling ncRNA-directed and ncRNA-mediated regulation. In addition, we survey the available computational tools or methods for modeling ncRNA synergistic regulation, including synergistic interaction and synergistic competition. Finally, we discuss the future directions and challenges in modeling ncRNA synergistic regulation.

Predicting human miRNA disease association with minimize matrix nuclear norm

microRNAs (miRNAs) are non-coding RNA molecules that influence the development and progression of many diseases. Research have documented that miRNAs have a significant role in the prevention, diagnosis, and treatment of complex human diseases. Recently, scientists have devoted extensive resources to attempting to find the connections between miRNAs and diseases. Since the experimental methods used to discover that new miRNA-disease associations are time-consuming and expensive, many computational methods have been developed. In this research, a novel computational method based on matrix decomposition was proposed to predict new associations between miRNAs and diseases. Furthermore, the nuclear norm minimization method was employed to acquire breast cancer-associated miRNAs. We then evaluated the effectiveness of our method by utilizing two different cross-validation techniques and the results were compared to seven different methods. Moreover, a case study on breast cancer further validated our technique, confirming its predictive accuracy. These experimental results demonstrate that our method is a reliable computational model for uncovering potential miRNA-disease relationships.

HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization

Existing matrix factorization methods face challenges, including the cold start problem and global nonlinear data loss during similarity learning, particularly in predicting associations between long noncoding RNAs (LncRNAs) and diseases. To overcome these issues, we introduce HPTRMF, a matrix factorization approach incorporating high-order perturbation and flexible trifactor regularization. HPTRMF constructs a high-order correlation matrix utilizing the known association matrix, leveraging high-order perturbation to effectively address the cold start problem caused by data sparsity. Additionally, HPTRMF incorporates a flexible trifactor regularization term to capture similarity information on LncRNAs and diseases, enabling the effective handling of global nonlinear data loss by capturing such data in the similarity matrix. Experimental results demonstrate the superiority of HPTRMF over nine state-of-the-art algorithms in Leave-One-Out Cross-Validation (LOOCV) and Five-Fold Cross-Validation (5-Fold CV) on three data sets.HPTRMF and data sets are available in https://github.com/Llvvvv/HPTRMF.

Disentangled similarity graph attention heterogeneous biological memory network for predicting disease-associated miRNAs

BACKGROUND

The association between MicroRNAs (miRNAs) and diseases is crucial in treating and exploring many diseases or cancers. Although wet-lab methods for predicting miRNA-disease associations (MDAs) are effective, they are often expensive and time-consuming. Significant advancements have been made using Graph Neural Network-based methods (GNN-MDAs) to address these challenges. However, these methods still face limitations, such as not considering nodes' deep-level similarity associations and hierarchical learning patterns. Additionally, current models do not retain the memory of previously learned heterogeneous historical information about miRNAs or diseases, only focusing on parameter learning without the capability to remember heterogeneous associations.

RESULTS

This study introduces the K-means disentangled high-level biological similarity to utilize potential hierarchical relationships fully and proposes a Graph Attention Heterogeneous Biological Memory Network architecture (DiGAMN) with memory capabilities. Extensive experiments were conducted across four datasets, comparing the DiGAMN model and its disentangling method against ten state-of-the-art non-disentangled methods and six traditional GNNs. DiGAMN excelled, achieving AUC scores of 96.35%, 96.10%, 96.01%, and 95.89% on the Data1 to Data4 datasets, respectively, surpassing all other models. These results confirm the superior performance of DiGAMN and its disentangling method. Additionally, various ablation studies were conducted to validate the contributions of different modules within the framework, and's encoding statuses and memory units of DiGAMN were visualized to explore the utility and functionality of its modules. Case studies confirmed the effectiveness of DiGAMN's predictions, identifying several new disease-associated miRNAs.

CONCLUSIONS

DiGAMN introduces the use of a disentangled biological similarity approach for the first time and successfully constructs a Disentangled Graph Attention Heterogeneous Biological Memory Network model. This network can learn disentangled representations of similarity information and effectively store the potential biological entanglement information of miRNAs and diseases. By integrating disentangled similarity information with a heterogeneous attention memory network, DiGAMN enhances the model's ability to capture and utilize complex underlying biological data, significantly outperforming many existing models. The concepts used in this method also provide new perspectives for predicting miRNAs associated with diseases.

microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites

microRNAs (miRNAs) are central post-transcriptional gene expression regulators in healthy and diseased states. Despite decades of effort, deciphering miRNA targets remains challenging, leading to an incomplete miRNA interactome and partially elucidated miRNA functions. Here, we introduce microT-CNN, an avant-garde deep convolutional neural network model that moves the needle by integrating hundreds of tissue-matched (in-)direct experiments from 26 distinct cell types, corresponding to a unique training and evaluation set of >60 000 miRNA binding events and ~30 000 unique miRNA-gene target pairs. The multilayer sequence-based design enables the prediction of both host and virus-encoded miRNA interactions, providing for the first time up to 67% of direct genuine Epstein-Barr virus- and Kaposi's sarcoma-associated herpesvirus-derived miRNA-target pairs corresponding to one out of four binding events of virus-encoded miRNAs. microT-CNN fills the existing gap of the miRNA-target prediction by providing functional targets beyond the canonical sites, including 3' compensatory miRNA pairings, prompting 1.4-fold more validated miRNA binding events compared to other implementations and shedding light on previously unexplored facets of the miRNA interactome.

A redox-related lncRNA signature in bladder cancer

The redox status is intricately linked to the development and progression of cancer, a process that can be modulated by long non-coding RNAs (lncRNAs). Previous studies have demonstrated that redox regulation can be considered a potential therapeutic approach for cancer. However, the redox-related lncRNA predictive signature specific to bladder cancer (BCa) has yet to be fully elucidated. The purpose of our study is to establish a redox-related lncRNA signature to improve the prognostic prediction for BCa patients. To achieve this, we downloaded transcriptome and clinical data from the Cancer Genome Atlas (TCGA) database. Prognostic redox-related lncRNAs were identified through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis, resulting in the establishment of two risk groups. A comprehensive analysis corresponding to clinical features between high-risk and low-risk groups was conducted. Eight redox-related lncRNAs (AC018653.3, AC090229.1, AL357033.4, AL662844.4, AP003352.1, LINC00649, LINC01138, and MAFG-DT) were selected to construct the risk model. The overall survival (OS) in the high-risk group was worse than that in the low-risk group (p < 0.001). The redox-related lncRNA signature exhibits superior predictive accuracy compared to traditional clinicopathological characteristics. Gene Set Enrichment Analysis (GSEA) showed that the MAPK signaling pathway and Wnt signaling pathway were enriched in the high-risk group. Compared with the low-risk group, patients in the high-risk group demonstrated increased sensitivity to cisplatin, docetaxel, and paclitaxel. Furthermore, IGF2BP2, a potential target gene of MAFG-DT, was found to be overexpressed in tumor tissues and correlated with overall survival (OS). Our study demonstrated that the predictive signature based on eight redox-related lncRNAs can independently and accurately predict the prognosis of BCa patients.

Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma

The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17-gene prognosis-related gene signature (PRS) predictive model was constructed using the random survival forest algorithm as the optimal algorithm among 99 machine-learning algorithm combinations based on data from 260 patients obtained from TCGA and GEO. The PRS model consistently outperformed other clinicopathological features and previously published signatures with superior prognostic accuracy, as evidenced by the receiver operating characteristic curve, C-index and decision curve analysis in both training and validation cohorts. In the Cox regression analysis, PRS score was an independent adverse prognostic factor. The 17 genes of PRS were predominantly expressed in malignant cells by single-cell RNA-seq analysis via the TISCH2 database. They were involved in immunological and metabolic pathways according to GSEA and GSVA. The high-risk group exhibited increased immune cell infiltration based on seven immunological algorithms, accompanied by a complex immune function status and elevated immune factor expression. Overall, the PRS model can serve as an excellent tool for overall survival prediction in ESCC and may facilitate individualized treatment strategies and predction of immunotherapy for patients with ESCC.

Up-regulation of HOXA-AS2 and MEG3 long non-coding RNAs acts as a potential peripheral biomarker for bipolar disorder

Bipolar disorder (BD) is a psychiatric condition that is frequently misdiagnosed and linked to inadequate treatment. Long non-coding RNAs (lncRNAs) have lately gained recognition as crucial genetic elements and are now regarded as regulatory mechanisms in the neurological system. Our objective was to measure the quantities of HOXA-AS2 and MEG3 ncRNA transcripts. HOXA-AS2 and MEG3 ncRNA levels were checked in the peripheral blood of 50 type I BD and 50 control samples by real-time PCR. Furthermore, we conducted ROC curve analysis and correlation analysis to examine the association between gene expression and specific clinical characteristics in instances with BD. Additionally, a computational study was performed to investigate the binding sites of miRNAs on the HOXA-AS2 and MEG3 lncRNAs. BD subjects showed a significant increase in the expression of HOXA-AS2 and MEG3 compared to controls. The lncRNAs HOXA-AS2 and MEG3 have an area under the ROC curve (AUC) values of 0.70 and 0.71, respectively. There was a significant correlation between the expression levels of ncRNAs HOXA-AS2 and MEG3 in the peripheral blood of patients with BD and occupation scores. The data presented indicate a potential correlation between the expression of HOXA-AS2 and MEG3 lncRNAs with an elevated risk of BD. Furthermore, these lncRNAs may be linked to several molecular pathways. Our findings indicate that the amounts of lncRNAs HOXA-AS2 and MEG3 in transcripts might be a promising potential biomarker for patients with BD.

LINC01929 Is a Prognostic Biomarker for Multiple Tumours and Promotes Cell Proliferation in Breast Cancer Through the TNF/STAT3 Axis

The aim of this study was to investigate whether long intergenic non-coding RNA 1929 (LINC01929), a novel long non-coding RNA, could serve as a prognostic biomarker for various tumours and explore its function. The expression and prognosis of LINC01929 across 33 different tumour types in patients in the Cancer Genome Atlas (TCGA) database were analysed. Also, the correlation between LINC01929 expression, tumour mutational burden (TMB), microsatellite instability (MSI), immune checkpoint status and immune cell infiltration was examined. Moreover, the function of LINC01929 in the breast cancer cell lines was explored via CCK-8, colony formation and cell cycle assays. In addition, the downstream mechanisms of LINC01929 were analysed via transcriptome sequencing, RT-qPCR, and western blotting. Our analysis revealed that LINC01929 was weakly expressed in 3 tumour types and highly expressed in 14 tumour types, and low expression of LINC01929 was correlated with better clinical outcomes in 15 tumour types. Furthermore, LINC01929 expression was correlated significantly with the TMB, MSI, immune checkpoint and immune cell infiltration across multiple tumour types. The knockdown of LINC01929 inhibited cell cycle progression, cell proliferation, and tumorigenesis and downregulated the TNF pathway and STAT3 expression. The treatment with exogenous TNF-α partially reversed the cell cycle progression and proliferation inhibition caused by LINC01929 knockdown, and these effects were accompanied by changes in STAT3 expression. LINC01929 may serve as an effective biomarker affecting the TMB, MSI, immune cell infiltration and immune checkpoint status. Mechanistically, LINC01929 affects cell cycle progression and cell proliferation through the TNF/STAT3 axis. These findings offer valuable insights into the potential applications of LINC01929 in tumour therapy, which may yield novel targets and strategies for the diagnosis and treatment of patients.

Prediction of Potential miRNA-Disease Associations Based on a Masked Graph Autoencoder

Biomedical evidence has demonstrated the relevance of microRNA (miRNA) dysregulation in complex human diseases, and determining the relationship between miRNAs and diseases can aid in the early detection and prevention of diseases. Traditional biological experimental methods have the disadvantages of high cost and low efficiency, which are well compensated by computational methods. However, many computational methods have the challenge of excessively focusing on the neighbor relationship, ignoring the structural information of the graph, and belittling the redundant information of the graph structure. This study proposed a computational model based on a graph-masking autoencoder named MGAEMDA. MGAEMDA is an asymmetric framework in which the encoder maps partially observed graphs into latent representations. The decoder reconstructs the masked structural information based on the edge and node levels and combines it with linear matrices to obtain the result. The empirical results on the two datasets reveal that the MGAEMDA model performs better than its counterparts. We also demonstrated the predictive performance of MGAEMDA using a case study of four diseases, and all the top 30 predicted miRNAs were validated in the database, providing further evidence of the excellent performance of the model.

A multi-task prediction method based on neighborhood structure embedding and signed graph representation learning to infer the relationship between circRNA, miRNA, and cancer

MOTIVATION

Research shows that competing endogenous RNA is widely involved in gene regulation in cells, and identifying the association between circular RNA (circRNA), microRNA (miRNA), and cancer can provide new hope for disease diagnosis, treatment, and prognosis. However, affected by reductionism, previous studies regarded the prediction of circRNA-miRNA interaction, circRNA-cancer association, and miRNA-cancer association as separate studies. Currently, few models are capable of simultaneously predicting these three associations.

RESULTS

Inspired by holism, we propose a multi-task prediction method based on neighborhood structure embedding and signed graph representation learning, CMCSG, to infer the relationship between circRNA, miRNA, and cancer. Our method aims to extract feature descriptors of all molecules from the circRNA-miRNA-cancer regulatory network using known types of association information to predict unknown types of molecular associations. Specifically, we first constructed the circRNA-miRNA-cancer association network (CMCN), which is constructed based on the experimentally verified biomedical entity regulatory network; next, we combine topological structure embedding methods to extract feature representations in CMCN from local and global perspectives, and use denoising autoencoder for enhancement; then, combined with balance theory and state theory, molecular features are extracted from the point of social relations through the propagation and aggregation of signed graph attention network; finally, the GBDT classifier is used to predict the association of molecules. The results show that CMCSG can effectively predict the relationship between circRNA, miRNA, and cancer. Additionally, the case studies also demonstrate that CMCSG is capable of accurately identifying biomarkers across various types of cancer. The data and source code can be found at https://github.com/1axin/CMCSG.

Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks

The discovery of diagnostic and therapeutic biomarkers for complex diseases, especially cancer, has always been a central and long-term challenge in molecular association prediction research, offering promising avenues for advancing the understanding of complex diseases. To this end, researchers have developed various network-based prediction techniques targeting specific molecular associations. However, limitations imposed by reductionism and network representation learning have led existing studies to narrowly focus on high prediction efficiency within single association type, thereby glossing over the discovery of unknown types of associations. Additionally, effectively utilizing network structure to fit the interaction properties of regulatory networks and combining specific case biomarker validations remains an unresolved issue in cancer biomarker prediction methods. To overcome these limitations, we propose a multi-view learning framework, CeRVE, based on directed graph neural networks (DGNN) for predicting unknown type cancer biomarkers. CeRVE effectively extracts and integrates subgraph information through multi-view feature learning. Subsequently, CeRVE utilizes DGNN to simulate the entire regulatory network, propagating node attribute features and extracting various interaction relationships between molecules. Furthermore, CeRVE constructed a comparative analysis matrix of three cancers and adjacent normal tissues through The Cancer Genome Atlas and identified multiple types of potential cancer biomarkers through differential expression analysis of mRNA, microRNA, and long noncoding RNA. Computational testing of multiple types of biomarkers for 72 cancers demonstrates that CeRVE exhibits superior performance in cancer biomarker prediction, providing a powerful tool and insightful approach for AI-assisted disease biomarker discovery.

Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, effective and non-invasive diagnostic tests are greatly needed. Recently, circulating miRNAs have been reported to be altered in PDAC. They are promising biomarkers because of stability in the blood, ease of non-invasive detection, and convenient screening methods. This study aimed to use blood-based miRNA biomarkers and various analysis methods in the development of a machine-learning (ML) model for PDAC.

METHODS

Blood-based miRNAs associated with PDAC were collected from open sources. miRNA sequences, targeted genes, and involved pathways were used to construct a set of descriptors for an ML model.

RESULTS

Bioinformatics analysis revealed that most genes in pancreatic cancer and insulin signaling pathways were targeted by the PDAC-related miRNAs. The best-performing ML model with the Random Forest classifier was able to achieve an accuracy of 88.4%. Model evaluations of an independent PDAC-associated miRNAs test set had 100% accuracy while non-cancer miRNAs had 52.4% accuracy, indicating specificity to PDAC.

CONCLUSIONS

Our results suggest an ML model developed using blood-based miRNA biomarkers' target gene, pathway, and sequence features could be potentially implicated in PDAC diagnostics.

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.

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.

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.

RDGAN: Prediction of circRNA-Disease Associations via Resistance Distance and Graph Attention Network

As a series of single-stranded RNAs, circRNAs have been implicated in numerous diseases and can serve as valuable biomarkers for disease therapy and prevention. However, traditional biological experiments demand significant time and effort. Therefore, various computational methods have been proposed to address this limitation, but how to extract features more comprehensively remains a challenge that needs further attention in the future. In this study, we propose a unique approach to predict circRNA-disease associations based on resistance distance and graph attention network (RDGAN). First, the associations of circRNA and disease are obtained by fusing multiple databases, and resistance distance as a similarity matrix is used to further deal with the sparse of the similarity matrices. Then the circRNA-disease heterogeneous network is constructed based on the similiarity of circRNA-circRNA, disease-disease and the known circRNA-disease adjacency matric. Second, leveraging the three neural network modules-ResGatedGraphConv, GAT and MFConv-we gather node feature embeddings collected from the heterogeneous network. Subsequently, all the characteristics are supplied to the self-attention mechanism to predict new potential connections. Finally, our model obtains a remarkable AUC value of 0.9630 through five-fold cross-validation, surpassing the predictive performance of the other eight state-of-the-art models.

HTFSMMA: Higher-Order Topological Guided Small Molecule-MicroRNA Associations Prediction

Small molecules (SMs) play a pivotal role in regulating microRNAs (miRNAs). Existing prediction methods for associations between SM-miRNA have overlooked crucial aspects: the incorporation of local topological features between nodes, which represent either SMs or miRNAs, and the effective fusion of node features with topological features. This study introduces a novel approach, termed high-order topological features for SM-miRNA association prediction (HTFSMMA), which specifically addresses these limitations. Initially, an association graph is formed by integrating SM-miRNA association data, SM similarity, and miRNA similarity. Subsequently, we focus on the local information of links and propose target neighborhood graph convolutional network for extracting local topological features. Then, HTFSMMA employs graph attention networks to amalgamate these local features, thereby establishing a platform for the acquisition of high-order features through random walks. Finally, the extracted features are integrated into the multilayer perceptron to derive the association prediction scores. To demonstrate the performance of HTFSMMA, we conducted comprehensive evaluations including five-fold cross-validation, leave-one-out cross-validation (LOOCV), SM-fixed local LOOCV, and miRNA-fixed local LOOCV. The area under receiver operating characteristic curve values were 0.9958 ± 0.0024 (0.8722 ± 0.0021), 0.9986 (0.9504), 0.9974 (0.9111), and 0.9977 (0.9074), respectively. Our findings demonstrate the superior performance of HTFSMMA over existing approaches. In addition, three case studies and the DeLong test have confirmed the effectiveness of the proposed method. These results collectively underscore the significance of HTFSMMA in facilitating the inference of associations between SMs and miRNAs.

MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations

PIWI-interacting RNAs (piRNAs) are a typical class of small non-coding RNAs, which are essential for gene regulation, genome stability and so on. Accumulating studies have revealed that piRNAs have significant potential as biomarkers and therapeutic targets for a variety of diseases. However current computational methods face the challenge in effectively capturing piRNA-disease associations (PDAs) from limited data. In this study, we propose a novel method, MRDPDA, for predicting PDAs based on limited data from multiple sources. Specifically, MRDPDA integrates a deep factorization machine (deepFM) model with regularizations derived from multiple yet limited datasets, utilizing separate Laplacians instead of a simple average similarity network. Moreover, a unified objective function to combine embedding loss about similarities is proposed to ensure that the embedding is suitable for the prediction task. In addition, a balanced benchmark dataset based on piRPheno is constructed and a deep autoencoder is applied for creating reliable negative set from the unlabeled dataset. Compared with three latest methods, MRDPDA achieves the best performance on the pirpheno dataset in terms of the five-fold cross validation test and independent test set, and case studies further demonstrate the effectiveness of MRDPDA.

TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network

The identification of miRNA-disease associations is crucial for early disease prevention and treatment. However, it is still a computational challenge to accurately predict such associations due to improper information encoding. Previous methods characterize miRNA-disease associations only from single levels, causing the loss of multi-level association information. In this study, we propose TriFusion, a powerful and interpretable deep learning framework for miRNA-disease association prediction. It develops a tri-channel architecture to encode the association features of miRNAs and diseases from different levels and designs a feature fusion encoder to smoothly fuse these features. After training and testing, TriFusion outperforms other leading methods and offers strong interpretability through its learned representations. Furthermore, TriFusion is applied to three high-risk sexually associated cancers (ovarian, breast, and prostate cancers) and exhibits remarkable ability in the identification of miRNAs associated with the three diseases.

Identification of prostate cancer bone metastasis related genes and potential therapy targets by bioinformatics and in vitro experiments

The aetiology of bone metastasis in prostate cancer (PCa) remains unclear. This study aims to identify hub genes involved in this process. We utilized machine learning, GO, KEGG, GSEA, Single-cell analysis, ROC methods to identify hub genes for bone metastasis in PCa using the TCGA and GEO databases. Potential drugs targeting these genes were identified. We validated these results using 16 specimens from patients with PCa and analysed the relationship between the hub genes and clinical features. The impact of APOC1 on PCa was assessed through in vitro experiments. Seven hub genes with AUC values of 0.727-0.926 were identified. APOC1, CFH, NUSAP1 and LGALS1 were highly expressed in bone metastasis tissues, while NR4A2, ADRB2 and ZNF331 exhibited an opposite trend. Immunohistochemistry further confirmed these results. The oxidative phosphorylation pathway was significantly enriched by the identified genes. Aflatoxin B1, benzo(a)pyrene, cyclosporine were identified as potential drugs. APOC1 expression was correlated with clinical features of PCa metastasis. Silencing APOC1 significantly inhibited PCa cell proliferation, clonality, and migration in vitro. This study identified 7 hub genes that potentially facilitate bone metastasis in PCa through mitochondrial metabolic reprogramming. APOC1 emerged as a promising therapeutic target and prognostic marker for PCa with bone metastasis.

CXCL9, IL2RB, and SPP1, potential diagnostic biomarkers in the co-morbidity pattern of atherosclerosis and non-alcoholic steatohepatitis

Non-alcoholic steatohepatitis (NASH) is a hepatocyte inflammation based on hepatocellular steatosis, yet there is no effective drug treatment. Atherosclerosis (AS) is caused by lipid deposition in the endothelium, which can lead to various cardiovascular diseases. NASH and AS share common risk factors, and NASH can also elevate the risk of AS, causing a higher morbidity and mortality rate for atherosclerotic heart disease. Therefore, timely detection and diagnosis of NASH and AS are particularly important. In this study, differential gene expression analysis and weighted gene co-expression network analysis were performed on the AS (GSE100927) and NASH (GSE89632) datasets to obtain common crosstalk genes, respectively. Then, candidate Hub genes were screened using four topological algorithms and externally validated in the GSE43292 and GSE63067 datasets to obtain Hub genes. Furthermore, immune infiltration analysis and gene set variation analysis were performed on the Hub genes to explore the underlying mechanisms. The DGIbd database was used to screen candidate drugs for AS and NASH. Finally, a NASH model was constructed using free fatty acid-induced human L02 cells, an AS model was constructed using lipopolysaccharide-induced HUVECs, and a co-morbidity model was constructed using L02 cells and HUVECs to verify Hub gene expression. The result showed that a total of 113 genes common to both AS and NASH were identified as crosstalk genes, and enrichment analysis indicated that these genes were mainly involved in the regulation of immune and metabolism-related pathways. 28 candidate Hub genes were screened according to four topological algorithms, and CXCL9, IL2RB, and SPP1 were identified as Hub genes after in vitro experiments and external dataset validation. The ROC curves and SVM modeling demonstrated the good diagnostic efficacy of these three Hub genes. In addition, the Hub genes are strongly associated with immune cell infiltration, especially macrophages and γ-δ T cell infiltration. Finally, five potential therapeutic drugs were identified. has-miR-185 and hsa-miR-335 were closely related to AS and NASH. This study demonstrates that CXCL9, IL2RB, and SPP1 may serve as potential biomarkers for the diagnosis of the co-morbidity patterns of AS and NASH and as potential targets for drug therapy.

Constructing diagnostic signature of serum microRNAs using machine learning for early pan-cancer detection

BACKGROUND

Cancer is a major public health concern and the second leading cause of death worldwide. Various studies have reported the use of serum microRNAs (miRNAs) as non-invasive biomarkers for cancer detection. However, large-scale pan-cancer studies based on serum miRNAs have been relatively scarce.

METHODS

An optimized machine learning workflow, combining least absolute shrinkage and selection operator (LASSO) analyses, recursive feature elimination (RFE), and fourteen kinds of machine learning algorithms, was use to screen out candidate miRNAs from 2540 serum miRNAs and constructed a potent diagnostic signature (Cancer-related Serum miRNA Signatures) for pan-cancer detection, based on a serum miRNA expression dataset of 38,223 samples.

RESULT

Cancer-related Serum miRNA Signatures performed well in pan-cancer detection with an area under curve (AUC) of 0.999, 94.51% sensitivity, and 99.49% specificity in the external validation cohort, and represented an acceptable diagnostic performance for identifying early-stage tumors. Furthermore, the ability of multi-classification of tumors by serum miRNAs in pancreatic, colorectal, and biliary tract cancers was lower than that in other cancers, which showed accuracies of 59%, 58.5%, and 28.9%, respectively, indicating that the difference in serum miRNA expression profiles among a small number of tumor subtypes was not as significant as that between cancer samples and non-cancer controls.

CONCLUSION

We have developed a serum miRNA signature using machine learning that may be a cost-effective risk tool for pan-cancer detection. Our findings will benefit not only the predictive diagnosis of cancer but also a preventive and more personalized screening plan.

Identification of necroptosis-related genes in ankylosing spondylitis by bioinformatics and experimental validation

The pathogenesis of ankylosing spondylitis (AS) remains unclear, and while recent studies have implicated necroptosis in various autoimmune diseases, an investigation of its relationship with AS has not been reported. In this study, we utilized the Gene Expression Omnibus database to compare gene expressions between AS patients and healthy controls, identifying 18 differentially expressed necroptosis-related genes (DENRGs), with 8 upregulated and 10 downregulated. Through the application of three machine learning algorithms-least absolute shrinkage and selection operation, support vector machine-recursive feature elimination and random forest-two hub genes, FASLG and TARDBP, were pinpointed. These genes demonstrated high specificity and sensitivity for AS diagnosis, as evidenced by receiver operating characteristic curve analysis. These findings were further supported by external datasets and cellular experiments, which confirmed the downregulation of FASLG and upregulation of TARDBP in AS patients. Immune cell infiltration analysis suggested that CD4+ T cells, CD8+ T cells, NK cells and neutrophils may be associated with the development of AS. Notably, in the group with high FASLG expression, there was a significant infiltration of CD8+ T cells, memory-activated CD4+ T cells and resting NK cells, with relatively less infiltration of memory-resting CD4+ T cells and neutrophils. Conversely, in the group with high TARDBP expression, there was enhanced infiltration of naïve CD4+ T cells and M0 macrophages, with a reduced presence of memory-resting CD4+ T cells. In summary, FASLG and TARDBP may contribute to AS pathogenesis by regulating the immune microenvironment and immune-related signalling pathways. These findings offer new insights into the molecular mechanisms of AS and suggest potential new targets for therapeutic strategies.

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.

Small non-coding RNA signatures in atrial appendages of patients with atrial fibrillation

The development of high-throughput technologies has enhanced our understanding of small non-coding RNAs (sncRNAs) and their crucial roles in various diseases, including atrial fibrillation (AF). This study aimed to systematically delineate sncRNA profiles in AF patients. PANDORA-sequencing was used to examine the sncRNA profiles of atrial appendage tissues from AF and non-AF patients. Differentially expressed sncRNAs were identified using the R package DEGseq 2 with a fold change >2 and p < 0.05. The target genes of the differentially expressed sncRNAs were predicted using MiRanda and RNAhybrid. Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. In AF patients, the most abundant sncRNAs were ribosomal RNA-derived small RNAs (rsRNAs), followed by transfer RNA-derived small RNAs (tsRNAs), and microRNAs (miRNAs). Compared with non-AF patients, 656 rsRNAs, 45 miRNAs, 191 tsRNAs and 51 small nucleolar RNAs (snoRNAs) were differentially expressed in AF patients, whereas no significantly differentially expressed piwi-interacting RNAs were identified. Two out of three tsRNAs were confirmed to be upregulated in AF patients by quantitative reverse transcriptase polymerase chain reaction, and higher plasma levels of tsRNA 5006c-LysCTT were associated with a 2.55-fold increased risk of all-cause death in AF patients (hazard ratio: 2.55; 95% confidence interval, 1.56-4.17; p < 0.001). Combined with our previous transcriptome sequencing results, 32 miRNA, 31 snoRNA, 110 nucleus-encoded tsRNA, and 33 mitochondria-encoded tsRNA target genes were dysregulated in AF patients. GO and KEGG analyses revealed enrichment of differentially expressed sncRNA target genes in AF-related pathways, including the 'calcium signaling pathway' and 'adrenergic signaling in cardiomyocytes.' The dysregulated sncRNA profiles in AF patients suggest their potential regulatory roles in AF pathogenesis. Further research is needed to investigate the specific mechanisms of sncRNAs in the development of AF and to explore potential biomarkers for AF treatment and prognosis.

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.

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.