Long non-coding RNAs and complex diseases: from experimental results to computational models

Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting

lncRNAs are densely related to many human diseases. Identifying new lncRNA-disease associations (LDAs) conduces to better deciphering mechanisms of diseases, finding new biomarkers, and further promoting their diagnosis and treatment. In this manuscript, we devise an LDA prediction framework called LDA-GARB. LDA-GARB first combines nonnegative matrix factorization to extract linear features of lncRNAs and diseases. Next, it computes lncRNA similarity and disease similarity and adopts a graph autoencoder to extract nonlinear features of lncRNAs and diseases. Subsequently, the extracted features are concatenated as a vector. Finally, it takes the obtained vector as inputs and designs a noise-robust gradient boosting model to uncover potential associations from unknown lncRNA-disease pairs. To investigate the LDA-GARB performance, we used precision, recall, accuracy, F1-score, AUC, and AUPR as measurement metrics and performed multiple comparison experiments. First, it was benchmarked with four representative LDA prediction methods, i.e., SDLDA, LDNFSGB, LDAenDL, and LDA-VGHB, under 5-fold cross validations on lncRNAs, diseases, and lncRNA-disease pairs. Next, it was compared with four representative boosting models, i.e., XGBoost, AdaBoost, CatBoost, and LightGBM, under the above three different cross validations. Subsequently, the performance of LDA-GARB against LDA-LNSUBRW, GAMCLDA, LDA-VGHB, LDAGM, and GANLDA on imbalanced data was evaluated. We also performed parameter sensitivity analysis and ablation experiments. The results demonstrated that LDA-GARB improved LDA prediction. Finally, LDA-GARB was applied to predict potential associated lncRNAs for colorectal cancer and breast cancer. CCDC26 and HAR1A have been inferred to have an association with the two cancers, respectively. As a useful LDA identification tool, LDA-GARB is freely available at https://github.com/smiling199/LDA-GARB .

Construction and validation of a prognostic model for colorectal cancer based on migrasome-related long non-coding RNAs

Background

Colon adenocarcinoma (COAD) is a globally prevalent and deadly malignancy of the digestive system. Recently, migrasomes have gained significant attention as important regulators of tumor cell migration and metastasis. The current research developed a highly accurate prognostic model using migrasome-related long non-coding RNAs (lncRNAs) in COAD, providing new insights for prognostic assessment and immunotherapy of COAD patients.

Methods

RNA sequencing data from COAD patients were acquired from The Cancer Genome Atlas Program (TCGA) database to construct a prognostic lncRNA model based on known migrasome-related genes (MRGs). The model's predictive accuracy was then assessed using concordance index (C-index) analysis, nomograms, principal component analysis, and receiver operating characteristic curves. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify significant differences in biological functions and signaling pathways associated with differentially expressed genes in the high-risk subgroup. A comprehensive evaluation of the model incorporated clinical-pathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognostic genes in COAD patients were validated via quantitative reverse transcription polymerase chain reaction (RT-qPCR). Furthermore, the role of LCMT1-AS1 in colorectal cancer was examined through CCK-8 assays, colony formation assays, and Transwell experiments.

Results

Migrasome-related lncRNAs were identified as robust prognostic predictors for COAD. Multivariate analysis revealed that the risk score derived from these lncRNAs is an independent prognostic factor for COAD. Patients in the low-risk group exhibited significantly longer overall survival (OS) compared to those in the high-risk group. Accordingly, the nomogram prediction model we developed, which integrates clinical features and risk scores, demonstrated excellent prognostic performance. In vitro experiments further showed that LCMT1-AS1 promotes the proliferation and migration of COAD cells.

A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network

Introduction

In recent years, lots of computational models have been proposed to infer potential lncRNA-disease associations.

Methods

In this manuscript, we introduced a novel end-to-end learning framework named CNMCLDA, in which, we first adopted two convolutional neural networks to extract hidden features of diseases and lncRNAs separately. And then, by combining these hidden features of diseases and lncRNAs with known lncRNA-disease associations, we designed five different loss functions. Next, based on errors obtained by these loss functions, we would perform back propagation to fit parameters in CNMCLDA, and complete those missing values in lncRNA-disease relational matrix according to these fitted parameters. In order to demonstrate the prediction performance of CNMCLDA, intensive experiments have been carried out and experimental results show that CNMCLDA can achieve better performances than state-of-the-art competitive predictive models in frameworks of five-fold cross validation, ten-fold cross validation and leave-one-disease-out cross validation respectively.

Results and Discussion

Moreover, in case studies of gastric cancer, glioma and breast cancer, there are 19, 17 and 16 out of top 20 candidate lncRNAs inferred by CNMCLDA having been confirmed by recent relevant literatures separately, which demonstrated the outstanding performance of CNMCLDA as well. Hence, it is obvious that CNMCLDA may be an effective tool for prediction of potential lncRNA-disease associations in the future.

lncRNA-disease association prediction based on optimizing measures of multi-graph regularized matrix factorization

In this paper, we propose a novel lncRNA-disease association prediction algorithm based on optimizing measures of multi-graph regularized matrix factorization (OM-MGRMF). The method first calculates the semantic similarity of diseases, the functional similarity of lncRNAs, and the Gaussian similarity of both. It then constructs a new lncRNA-disease association matrix by using the K-nearest-neighbor (KNN) algorithm. Finally, the objective function is constructed through the utilization of ranking measures and multi-graph regularization constraints. This objective function is iteratively optimized by an adaptive gradient descent algorithm. The experimental results of OM-MGRMF outperform those of classical methods in both K-fold cross-validation.

Potential role of long noncoding RNA maternally expressed gene 3 (MEG3) in the process of neurodegeneration

Neurodegenerative diseases (ND) are complex diseases of still unknown etiology. Lately, long non-coding RNAs (lncRNAs) have become increasingly popular and implicated in several pathologies as they have several roles and appear to be involved in all biological processes such as cell signaling and cycle control as well as translation and transcription. MEG3 is one of these and acts by binding proteins or directly or competitively binding miRNAs. It has a crucial role in controlling cell death, inflammatory process, oxidative stress, endoplasmic reticulum stress, epithelial-mesenchymal transition and other processes. Recent reports showed that MEG3 is a major driving force of the necrosis phenomena in AD, causing the death of neurons, and its upregulation in cancer patients was linked to tumor suppression. Dysregulation of MEG3 affects neuronal cell death, inflammatory process, smooth muscle cell proliferation and consequently leads to the initiation or the acceleration of the disease. This review examines the current state of knowledge concerning the level of expression and the regulatory function of MEG3 in relation to several NDs. In addition, we examined the relation of MEG3 with neurotrophic factors such as Tumor growth factor β (TGFβ) and its possible mechanism of action. A comprehensive and in-depth analysis of the role of MEG3 in ND could give a clearer picture about the initiation of the process of neuronal death and help develop an alternative therapy that targets MEG3.

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.

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.

Global trends in machine learning applied to clinical research in liver cancer: Bibliometric and visualization analysis (2001-2024)

This study explores the intersection of liver cancer and machine learning through bibliometric analysis. The aim is to identify highly cited papers in the field and examine the current research landscape, highlighting emerging trends and key areas of focus in liver cancer and machine learning. By analyzing citation patterns, this study sheds light on the evolving role of machine learning in liver cancer research and its potential for future advancements.

Long Non-Coding RNA LINC01116 Promotes the Proliferation of Lung Adenocarcinoma by Targeting miR-9-5p/CCNE1 Axis

Long non-coding RNA (lncRNA) LINC01116 is crucial in promoting cell proliferation, invasion and migration in solid tumours, including lung adenocarcinoma (LUAD). LINC01116 acts as a competing endogenous RNAs (ceRNA) that binds competitively to microRNAs and plays a critical role in tumour migration and invasion. However, other mechanisms of action besides the ceRNA theory have been rarely reported and remain to be elucidated further. The differences in RNA and protein levels in cells and tissues were assessed through real-time quantitative PCR and Western blot analysis. In vitro functional assays and in vivo xenograft models were used to analyse the function of LINC01116 in LUAD. Thus, the molecular correlation between miR-9-5p and CCNE1 was investigated through direct and indirect mechanism experiments. LINC01116, miR-9-5p and CCNE1 were upregulated in LUAD cell lines and tissues and were associated with a poor prognosis in patients. LINC01116 depletion inhibited proliferation but facilitated cell apoptosis. AGO2-RNA binding protein immunoprecipitation (AGO2-RIP) experiments confirmed that AGO2 binds to LINC01116 and miR-9-5p, indicating that LINC01116 interacts with miR-9-5p. The overexpression of miR-9-5p and CCNE1 effectively counteracts the biological effects of LINC01116 knockdown on reduced proliferation and cell cycle arrest in LUAD cells. The downregulation of miR-9-5p significantly reduces the CCNE1 level in A549 cells, and the upregulation of LINC01116 counteracts the downregulation of miR-9-5p effect, restoring the expression level of CCNE1. Our data demonstrated that LINC01116 regulates the expression of CCNE1 by positively regulating miR-9-5p, thereby affecting cell cycle, proliferation and participating in the development of LUAD.

The improved de Bruijn graph for multitask learning: predicting functions, subcellular localization, and interactions of noncoding RNAs

Noncoding RNA refers to RNA that does not encode proteins. The lncRNA and miRNA it contains play crucial regulatory roles in organisms, and their aberrant expression is closely related to various diseases. Traditional experimental methods for validating the interactions of these RNAs have limitations, and existing prediction models exhibit relatively limited functionality, relying on isolated feature extraction and performing poorly in handling various types of small sample tasks. This paper proposes an improved de Bruijn graph that can inject RNA structural information into the graph while preserving sequence information. Furthermore, the improved de Bruijn graph enables graph neural networks to learn broader dependencies and correlations among data by introducing richer edge relationships. Meanwhile, the multitask learning model, DVMnet, proposed in this paper can handle multiple related tasks, and we optimize model parameters by integrating the total loss of three tasks. This enables multitask prediction of RNA interactions, disease associations, and subcellular localization. Compared with the best existing models in this field, DVMnet has achieved the best performance with a 3% improvement in the area under the curve value and demonstrates robust results in predicting diseases and subcellular localization. The improved de Bruijn graph is also applicable to various scenarios and can unify the sequence and structural information of various nucleic acids into a single graph.

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.

Prediction of miRNA-disease association based on multisource inductive matrix completion

MicroRNAs (miRNAs) are endogenous non-coding RNAs approximately 23 nucleotides in length, playing significant roles in various cellular processes. Numerous studies have shown that miRNAs are involved in the regulation of many human diseases. Accurate prediction of miRNA-disease associations is crucial for early diagnosis, treatment, and prognosis assessment of diseases. In this paper, we propose the Autoencoder Inductive Matrix Completion (AEIMC) model to identify potential miRNA-disease associations. The model captures interaction features from multiple similarity networks, including miRNA functional similarity, miRNA sequence similarity, disease semantic similarity, disease ontology similarity, and Gaussian interaction kernel similarity between miRNAs and diseases. Autoencoders are used to extract more complex and abstract data representations, which are then input into the inductive matrix completion model for association prediction. The effectiveness of the model is validated through cross-validation, stratified threshold evaluation, and case studies, while ablation experiments further confirm the necessity of introducing sequence and ontology similarities for the first time.

GUCA2A dysregulation as a promising biomarker for accurate diagnosis and prognosis of colorectal cancer

Colorectal cancer is a leading cause of global mortality and presents a significant barrier to improving life expectancy. The primary objective of this study was to discern a unique differentially expressed gene (DEG) that exhibits a strong association with colorectal cancer. By achieving this goal, the research aims to contribute valuable insights to the field of translational medicine. We performed analysis of colorectal cancer microarray and the TCGA colon adenoma carcinoma (COAD) datasets to identify DEGs associated with COAD and common DEGs were selected. Furthermore, a pan-cancer analysis encompassing 33 different cancer types was performed to identify differential genes significantly expressed only in COAD. Then, comprehensively in-silico analysis including gene set enrichment analysis, constructing Protein-Protein interaction, co-expression, and competing endogenous RNA (ceRNA) networks, investigating the correlation between tumor-immune signatures in distinct tumor microenvironment and also the potential interactions between the identified gene and various drugs was executed. Further, the candidate gene was experimentally validated in tumoral colorectal tissues and colorectal adenomatous polyps by qRael-Time PCR. GUCA2A emerged as a significant DEG specific to colorectal cancer (|log2FC|> 1 and adjusted q-value < 0.05). Importantly, GUCA2A exhibited excellent diagnostic performance for COAD, with a 99.6% and 78% area under the curve (AUC) based on TCGA-COAD and colon cancer patients. In addition, GUCA2A expression in adenomatous polyps equal to or larger than 5 mm was significantly lower compared to smaller than 5 mm. Moreover, low expression of GUCA2A significantly impacted overall patient survival. Significant correlations were observed between tumor-immune signatures and GUCA2A expression. The ceRNA constructed included GUCA2A, 8 shared miRNAs, and 61 circRNAs. This study identifies GUCA2A as a promising prognostic and diagnostic biomarker for colorectal cancer. Further investigations are warranted to explore the potential of GUCA2A as a therapeutic biomarker.

Advancements in long non-coding RNA-based therapies for cancer: targeting, delivery, and clinical implications

Long non-coding RNAs (lncRNAs) have been in the spotlight for the past two decades due to their extensive role in regulating a wide range of cellular processes. Development, differentiation, regulation, and modulation are some of the vital cellular cascades coordinated by these molecules. Despite their importance, there has been limited literature on their practical implications in cancer prevention. Advancements in lncRNA biology have enabled the characterization of numerous secondary structures and sequence motifs, which could serve as potential targets for cellular therapies. Several studies have highlighted the involvement of lncRNAs in human pathologies, where they can be targeted by small molecules or antisense oligonucleotides to prevent diseases. However, progress has been hindered by the challenge of developing specific delivery vehicles for targeted delivery. Recent improvements in sequence optimization and nucleotide modification have enhanced drug stability and reduced the immunogenicity of lncRNA-based therapies, yet further advances are needed to fully realize their potential in treating complex diseases like cancer. This review aims to explore current lncRNA biology, their mechanisms of action, nanoformulation strategies, and the clinical trials focused on lncRNA delivery systems.

Adversarial regularized autoencoder graph neural network for microbe-disease associations prediction

BACKGROUND

Microorganisms inhabit various regions of the human body and significantly contribute to numerous diseases. Predicting the associations between microbes and diseases is crucial for understanding pathogenic mechanisms and informing prevention and treatment strategies. Biological experiments to determine these associations are time-consuming and costly. Therefore, integrating deep learning with biological networks can efficiently identify potential microbe-disease associations on a large scale.

METHODS

We propose an adversarial regularized autoencoder graph neural network algorithm, named Stacked Adversarial Regularization for Microbe-Disease Associations Prediction (SARMDA), for predicting associations between microbes and diseases. First, we integrate topological structural similarity and functional similarity metrics of microbes and diseases to construct a heterogeneous network. Then, utilizing an autoencoder based on GraphSAGE, we learn both the topological and attribute representations of nodes within the constructed network. Finally, we introduce an adversarial regularized autoencoder graph neural network embedding model to address the inherent limitations of traditional GraphSAGE autoencoders in capturing global information.

RESULTS

Under the five-fold cross-validation on microbe-disease pairs, SARMDA was compared with eight advanced methods using the Human Microbe-Disease Association Database (HMDAD) and Disbiome databases. The best area under the ROC curve (AUC) achieved by SARMDA on HMDAD was 0.9891$\pm$0.0057, and the best area under the precision-recall curve (AUPR) was 0.9902$\pm$0.0128. On the Disbiome dataset, the AUC was 0.9328$\pm$0.0072, and the best AUPR was 0.9233$\pm$0.0089, outperforming the other eight MDAs prediction methods. Furthermore, the effectiveness of our model was demonstrated through a detailed analysis of asthma and inflammatory bowel disease cases.

Exploring ncRNA-Drug Sensitivity Associations via Graph Contrastive Learning

Increasing evidence has shown that noncoding RNAs (ncRNAs) can affect drug efficiency by modulating drug sensitivity genes. Exploring the association between ncRNAs and drug sensitivity is essential for drug discovery and disease prevention. However, traditional biological experiments for identifying ncRNA-drug sensitivity associations are time-consuming and laborious. In this study, we develop a novel graph contrastive learning approach named NDSGCL to predict ncRNA-drug sensitivity. NDSGCL uses graph convolutional networks to learn feature representations of ncRNAs and drugs in ncRNA-drug bipartite graphs. It integrates local structural neighbours and global semantic neighbours to learn a more comprehensive representation by contrastive learning. Specifically, the local structural neighbours aim to capture the higher-order relationship in the ncRNA-drug graph, while the global semantic neighbours are defined based on semantic clusters of the graph that can alleviate the impact of data sparsity. The experimental results show that NDSGCL outperforms basic graph convolutional network methods, existing contrastive learning methods, and state-of-the-art prediction methods. Visualization experiments show that the contrastive objectives of local structural neighbours and global semantic neighbours play a significant role in contrastive learning. Case studies on two drugs show that NDSGCL is an effective tool for predicting ncRNA-drug sensitivity associations.

Development of a disulfidptosis-related lncRNA prognostic signature for enhanced prognostic assessment and therapeutic strategies in lung squamous cell carcinoma

Limited treatment options and poor prognosis present significant challenges in the treatment of lung squamous cell carcinoma (LUSC). Disulfidptosis impacts cancer progression and prognosis. We developed a prognostic signature using disulfidptosis-related long non-coding RNAs (lncRNAs) to predict the prognosis of LUSC patients. Gene expression matrices and clinical information for LUSC were downloaded from the TCGA database. Co-expression analysis identified 209 disulfidptosis-related lncRNAs. LASSO-Cox regression analysis identified nine key lncRNAs, forming the basis for establishing a prognostic model. The model's validity was confirmed by Kaplan-Meier and ROC curves. Cox regression analysis identified the risk score (RS) as an independent prognostic factor inversely correlated with overall survival. A nomogram based on the RS demonstrated good predictive performance for LUSC patient prognosis. The relationship between RS and immune function was explored using ESTIMATE, CIBERSORT, and ssGSEA algorithms. According to the TIDE database, a negative correlation was found between RS and immune therapy responsiveness. The GDSC database revealed that 49 drugs were beneficial for the low-risk group and 25 drugs for the high-risk group. Silencing C10orf55 expression in SW900 cells reduced invasiveness and migration potential. In summary, this lncRNA model based on TCGA-LUSC data effectively predicts prognosis and assists clinical decision-making.

A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation

BACKGROUND

The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechnology have generated a wealth of data linking diseases to specific genes. While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance.

RESULTS

Addressing these limitations, here we introduce a deep learning framework called ModulePred for predicting disease-gene associations. ModulePred performs graph augmentation on the protein interaction network using L3 link prediction algorithms. It builds a heterogeneous module network by integrating disease-gene associations, protein complexes and augmented protein interactions, and develops a novel graph embedding for the heterogeneous module network. Subsequently, a graph neural network is constructed to learn node representations by collectively aggregating information from topological structure, and gene prioritization is carried out by the disease and gene embeddings obtained from the graph neural network. Experimental results underscore the superiority of ModulePred, showcasing the effectiveness of incorporating functional modules and graph augmentation in predicting disease-gene associations. This research introduces innovative ideas and directions, enhancing the understanding and prediction of gene-disease relationships.

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs

As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.

Integrative analysis of the lncRNA-miRNA-mRNA interactions in smooth muscle cell phenotypic transitions

Objectives: We previously found that the pluripotency factor OCT4 is reactivated in smooth muscle cells (SMC) in human and mouse atherosclerotic plaques and plays an atheroprotective role. Loss of OCT4 in SMC in vitro was associated with decreases in SMC migration. However, molecular mechanisms responsible for atheroprotective SMC-OCT4-dependent effects remain unknown. Methods: Since studies in embryonic stem cells demonstrated that OCT4 regulates long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), making them candidates for OCT4 effect mediators, we applied an in vitro approach to investigate the interactions between OCT4-regulated lncRNAs, mRNAs, and miRNAs in SMC. We used OCT4 deficient mouse aortic SMC (MASMC) treated with the pro-atherogenic oxidized phospholipid POVPC, which, as we previously demonstrated, suppresses SMC contractile markers and induces SMC migration. Differential expression of lncRNAs, mRNAs, and miRNAs was obtained by lncRNA/mRNA expression array and small-RNA microarray. Long non-coding RNA to mRNA associations were predicted based on their genomic proximity and association with vascular diseases. Given a recently discovered crosstalk between miRNA and lncRNA, we also investigated the association of miRNAs with upregulated/downregulated lncRNA-mRNA pairs. Results: POVPC treatment in SMC resulted in upregulating genes related to the axon guidance and focal adhesion pathways. Knockdown of Oct4 resulted in differential regulation of pathways associated with phagocytosis. Importantly, these results were consistent with our data showing that OCT4 deficiency attenuated POVPC-induced SMC migration and led to increased phagocytosis. Next, we identified several up- or downregulated lncRNA associated with upregulation of the specific mRNA unique for the OCT4 deficient SMC, including upregulation of ENSMUST00000140952-Hoxb5/6 and ENSMUST00000155531-Zfp652 along with downregulation of ENSMUST00000173605-Parp9 and, ENSMUST00000137236-Zmym1. Finally, we found that many of the downregulated miRNAs were associated with cell migration, including miR-196a-1 and miR-10a, targets of upregulated ENSMUST00000140952, and miR-155 and miR-122, targets of upregulated ENSMUST00000155531. Oppositely, the upregulated miRNAs were anti-migratory and pro-phagocytic, such as miR-10a/b and miR-15a/b, targets of downregulated ENSMUST00000173605, and miR-146a/b and miR-15b targets of ENSMUST00000137236. Conclusion: Our integrative analyses of the lncRNA-miRNA-mRNA interactions in SMC indicated novel potential OCT4-dependent mechanisms that may play a role in SMC phenotypic transitions.

RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction

Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods for predicting ncRPIs have been developed, the problem of predicting ncRPIs remains challenging. It has always been the focus of ncRPIs research to select suitable feature extraction methods and develop a deep learning architecture with better recognition performance. In this work, we proposed an ensemble deep learning framework, RPI-EDLCN, based on a capsule network (CapsuleNet) to predict ncRPIs. In terms of feature input, we extracted the sequence features, secondary structure sequence features, motif information, and physicochemical properties of ncRNA/protein. The sequence and secondary structure sequence features of ncRNA/protein are encoded by the conjoint k-mer method and then input into an ensemble deep learning model based on CapsuleNet by combining the motif information and physicochemical properties. In this model, the encoding features are processed by convolution neural network (CNN), deep neural network (DNN), and stacked autoencoder (SAE). Then the advanced features obtained from the processing are input into the CapsuleNet for further feature learning. Compared with other state-of-the-art methods under 5-fold cross-validation, the performance of RPI-EDLCN is the best, and the accuracy of RPI-EDLCN on RPI1807, RPI2241, and NPInter v2.0 data sets was 93.8%, 88.2%, and 91.9%, respectively. The results of the independent test indicated that RPI-EDLCN can effectively predict potential ncRPIs in different organisms. In addition, RPI-EDLCN successfully predicted hub ncRNAs and proteins in Mus musculus ncRNA-protein networks. Overall, our model can be used as an effective tool to predict ncRPIs and provides some useful guidance for future biological studies.

HOPEXGB: A Consensual Model for Predicting miRNA/lncRNA-Disease Associations Using a Heterogeneous Disease-miRNA-lncRNA Information Network

Predicting disease-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) is crucial to find new biomarkers for the prevention, diagnosis, and treatment of complex human diseases. Computational predictions for miRNA/lncRNA-disease associations are of great practical significance, since traditional experimental detection is expensive and time-consuming. In this paper, we proposed a consensual machine-learning technique-based prediction approach to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding (HOPE) and eXtreme Gradient Boosting (XGB), named HOPEXGB. By connecting lncRNA, miRNA, and disease nodes based on their correlations and relationships, we first created a heterogeneous disease-miRNA-lncRNA (DML) information network to achieve an effective fusion of information on similarities, correlations, and interactions among miRNAs, lncRNAs, and diseases. In addition, a more rational negative data set was generated based on the similarities of unknown associations with the known ones, so as to effectively reduce the false negative rate in the data set for model construction. By 10-fold cross-validation, HOPE shows better performance than other graph embedding methods. The final consensual HOPEXGB model yields robust performance with a mean prediction accuracy of 0.9569 and also demonstrates high sensitivity and specificity advantages compared to lncRNA/miRNA-specific predictions. Moreover, it is superior to other existing methods and gives promising performance on the external testing data, indicating that integrating the information on lncRNA-miRNA interactions and the similarities of lncRNAs/miRNAs is beneficial for improving the prediction performance of the model. Finally, case studies on lung, stomach, and breast cancers indicate that HOPEXGB could be a powerful tool for preclinical biomarker detection and bioexperiment preliminary screening for the diagnosis and prognosis of cancers. HOPEXGB is publicly available at https://github.com/airpamper/HOPEXGB.

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.

Identification and validation of tryptophan metabolism-related lncRNAs in lung adenocarcinoma prognosis and immune response

BACKGROUND

Tryptophan (Trp) is an essential amino acid. Increasing evidence suggests that tryptophan metabolism plays a complex role in immune escape from Lung adenocarcinoma (LUAD). However, the role of long non-coding RNAs (lncRNAs) in tryptophan metabolism remains to be investigated.

METHODS

This study uses The Cancer Genome Atlas (TCGA)-LUAD dataset as the training cohort, and several datasets from the Gene Expression Omnibus (GEO) database are merged into the validation cohort. Genes related to tryptophan metabolism were identified from the Molecular Signatures Database (MSigDB) database and further screened for lncRNAs with Trp-related expression. Subsequently, a prognostic signature of lncRNAs related to tryptophan metabolism was constructed using Cox regression analysis, (Least absolute shrinkage and selection operator regression) and LASSO analysis. The predictive performance of this risk score was validated by Kaplan-Meier (KM) survival analysis, (receiver operating characteristic) ROC curves, and nomograms. We also explored the differences in immune cell infiltration, immune cell function, tumor mutational load (TMB), tumor immune dysfunction and exclusion (TIDE), and anticancer drug sensitivity between high- and low-risk groups. Finally, we used real-time fluorescence quantitative PCR, CCK-8, colony formation, wound healing, transwell, flow cytometry, and nude mouse xenotransplantation models to elucidate the role of ZNF8-ERVK3-1 in LUAD.

RESULTS

We constructed 16 tryptophan metabolism-associated lncRNA prognostic models in LUAD patients. The risk score could be used as an independent prognostic indicator for the prognosis of LUAD patients. Kaplan-Meier survival analysis, ROC curves, and risk maps validated the prognostic value of the risk score. The high-risk and low-risk groups showed significant differences in phenotypes, such as the percentage of immune cell infiltration, immune cell function, gene mutation frequency, and anticancer drug sensitivity. In addition, patients with high-risk scores had higher TMB and TIDE scores compared to patients with low-risk scores. Finally, we found that ZNF8-ERVK3-1 was highly expressed in LUAD tissues and cell lines. A series of in vitro experiments showed that knockdown of ZNF8-ERVK3-1 inhibited cell proliferation, migration, and invasion, leading to cell cycle arrest in the G0/G1 phase and increased apoptosis. In vivo experiments with xenografts have shown that knocking down ZNF8-ERVK3-1 can significantly inhibit tumor size and tumor proliferation.

CONCLUSION

We constructed a new prognostic model for tryptophan metabolism-related lncRNA. The risk score was closely associated with common clinical features such as immune cell infiltration, immune-related function, TMB, and anticancer drug sensitivity. Knockdown of ZNF8-ERVK3-1 inhibited LUAD cell proliferation, migration, invasion, and G0/G1 phase blockade and promoted apoptosis.

Finding potential lncRNA-disease associations using a boosting-based ensemble learning model

Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations

BACKGROUND

A growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective prediction of lncRNA-disease association (LDA) can not only suggest solutions to diagnose a condition but also save significant time and labor costs.

METHOD

In this work, we proposed a novel LDA predicting algorithm based on graph convolutional network and transformer, named GCNFORMER. Firstly, we integrated the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, and built a graph adjacency matrix. Secondly, to completely obtain the features between various nodes, we employed a graph convolutional network for feature extraction. Finally, to obtain the global dependencies between inputs and outputs, we used a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations.

RESULTS

The results of fivefold cross-validation experiment on the public dataset revealed that the AUC and AUPR of GCNFORMER achieved 0.9739 and 0.9812, respectively. We compared GCNFORMER with six advanced LDA prediction models, and the results indicated its superiority over the other six models. Furthermore, GCNFORMER's effectiveness in predicting potential LDAs is underscored by case studies on breast cancer, colon cancer and lung cancer.

CONCLUSIONS

The combination of graph convolutional network and transformer can effectively improve the performance of LDA prediction model and promote the in-depth development of this research filed.

Identification of Important Genes Associated with the Development of Atherosclerosis

Atherosclerosis is one of the most important medical problems due to its prevalence and significant contribution to the structure of temporary and permanent disability and mortality. Atherosclerosis is a complex chain of events occurring in the vascular wall over many years. Disorders of lipid metabolism, inflammation, and impaired hemodynamics are important mechanisms of atherogenesis. A growing body of evidence strengthens the understanding of the role of genetic and epigenetic factors in individual predisposition and development of atherosclerosis and its clinical outcomes. In addition, hemodynamic changes, lipid metabolism abnormalities, and inflammation are closely related and have many overlapping links in regulation. A better study of these mechanisms may improve the quality of diagnosis and management of such patients.

LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms

By soaking microRNAs (miRNAs), long non-coding RNAs (lncRNAs) have the potential to regulate gene expression. Few methods have been created based on this mechanism to anticipate the lncRNA-gene relationship prediction. Hence, we present lncRNA-Top to forecast potential lncRNA-gene regulation relationships. Specifically, we constructed controlled deep-learning methods using 12417 lncRNAs and 16127 genes. We have provided retrospective and innovative views among negative sampling, random seeds, cross-validation, metrics, and independent datasets. The AUC, AUPR, and our defined precision@k were leveraged to evaluate performance. In-depth case studies demonstrate that 47 out of 100 projected top unknown pairings were recorded in publications, supporting the predictive power. Our additional software can annotate the scores with target candidates. The lncRNA-Top will be a helpful tool to uncover prospective lncRNA targets and better comprehend the regulatory processes of lncRNAs.

LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier

Numerous scientific evidences have revealed that long non-coding RNAs (lncRNAs) are involved in the progression of human complex diseases and biological life activities. Therefore, identifying novel and potential disease-related lncRNAs is helpful to diagnosis, prognosis and therapy of many human complex diseases. Since traditional laboratory experiments are cost and time-consuming, a great quantity of computer algorithms have been proposed for predicting the relationships between lncRNAs and diseases. However, there are still much room for the improvement. In this paper, we introduce an accurate framework named LDAEXC to infer LncRNA-Disease Associations with deep autoencoder and XGBoost Classifier. LDAEXC utilizes different similarity views of lncRNAs and human diseases to construct features for each data sources. Then, the reduced features are obtained by feeding the constructed feature vectors into a deep autoencoder, and at last an XGBoost classifier is leveraged to calculate the latent lncRNA-disease-associated scores using reduced features. The fivefold cross-validation experiments on four datasets showed that LDAEXC reached AUC scores of 0.9676 ± 0.0043, 0.9449 ± 0.022, 0.9375 ± 0.0331 and 0.9556 ± 0.0134, respectively, significantly higher than other advanced similar computer methods. Extensive experiment results and case studies of two complex diseases (colon and breast cancers) further indicated the practicability and excellent prediction performance of LDAEXC in inferring unknown lncRNA-disease associations. TLDAEXC utilizes disease semantic similarity, lncRNA expression similarity, and Gaussian interaction profile kernel similarity of lncRNAs and diseases for feature construction. The constructed features are fed to a deep autoencoder to extract reduced features, and an XGBoost classifier is used to predict the lncRNA-disease associations based on the reduced features. The fivefold and tenfold cross-validation experiments on a benchmark dataset showed that LDAEXC could achieve AUC scores of 0.9676 and 0.9682, respectively, significantly higher than other state-of-the-art similar methods.

Long noncoding RNA HIKER regulates erythropoiesis in Monge's disease via CSNK2B

Excessive erythrocytosis (EE) is a major hallmark of patients suffering from chronic mountain sickness (CMS, also known as Monge's disease) and is responsible for major morbidity and even mortality in early adulthood. We took advantage of unique populations, one living at high altitude (Peru) showing EE, with another population, at the same altitude and region, showing no evidence of EE (non-CMS). Through RNA-Seq, we identified and validated the function of a group of long noncoding RNAs (lncRNAs) that regulate erythropoiesis in Monge's disease, but not in the non-CMS population. Among these lncRNAs is hypoxia induced kinase-mediated erythropoietic regulator (HIKER)/LINC02228, which we showed plays a critical role in erythropoiesis in CMS cells. Under hypoxia, HIKER modulated CSNK2B (the regulatory subunit of casein kinase 2). A downregulation of HIKER downregulated CSNK2B, remarkably reducing erythropoiesis; furthermore, an upregulation of CSNK2B on the background of HIKER downregulation rescued erythropoiesis defects. Pharmacologic inhibition of CSNK2B drastically reduced erythroid colonies, and knockdown of CSNK2B in zebrafish led to a defect in hemoglobinization. We conclude that HIKER regulates erythropoiesis in Monge's disease and acts through at least one specific target, CSNK2B, a casein kinase.

miR-802-5p is a key regulator in diabetic kidney disease

Background

Diabetic kidney disease has substantial burden and limited therapeutic options. An inadequate understanding of the complex gene regulatory circuits underlying this disorder contributes to the insufficiency of current treatment strategies. MicroRNAs (miRNAs) play a crucial role as regulators of functionally related gene networks. Previously, mmu-mir-802-5p was identified as the sole dysregulated miRNA in both the kidney cortex and medulla of diabetic mice. This study aims to investigate the role of miR-802-5p in diabetic kidney disease.

Materials and Methods

The validated and predicted targets of miR-802-5p were identified using miRTarBase and TargetScan databases, respectively. The functional role of this miRNA was inferred using gene ontology enrichment analysis. The expression of miR-802-5p and its selected targets were assessed by qPCR. The expression of the angiotensin receptor (Agtr1a) was measured by ELISA.

Results

miR-802-5p exhibited dysregulation in both the kidney cortex and medulla of diabetic mice, with two- and four-fold over-expressions, respectively. Functional enrichment analysis of the validated and predicted targets of miR-802-5p revealed its involvement in the renin-angiotensin pathway, inflammation, and kidney development. Differential expression was observed in the Pten transcript and Agtr1a protein among the examined gene targets.

Conclusion

These findings suggest that miR-802-5p is a critical regulator of diabetic nephropathy in the cortex and medulla compartments, contributing to disease pathogenesis through the renin-angiotensin axis and inflammatory pathways.

Role of RUNX2 in breast cancer development and drug resistance (Review)

Breast cancer is the most common malignancy and ranks second among the causes of tumor-associated death in females. The recurrence and drug resistance of breast cancer are intractable due to the presence of breast cancer stem cells (BCSCs), which are adequate to initiate tumor formation and refractory to conventional remedies. Runt-related transcription factor 2 (RUNX2), a pivotal transcription factor in mammary gland and bone development, has also been related to metastatic cancer and BCSCs. State-of-the-art research has indicated the retention of RUNX2 expression in a more invasive subtype of breast cancer, and in particular, triple-negative breast cancer development and drug resistance are associated with estrogen receptor signaling pathways. The present review mainly focused on the latest updates on RUNX2 in BCSCs and their roles in breast cancer progression and drug resistance, providing insight that may aid the development of RUNX2-based diagnostics and treatments for breast cancer in clinical practice.

The emerging roles of long non-coding RNA in host immune response and intracellular bacterial infections

The long non-coding RNAs (lncRNAs) are evolutionarily conserved classes of non-coding regulatory transcripts of > 200 nucleotides in length. They modulate several transcriptional and post-transcriptional events in the organism. Depending on their cellular localization and interactions, they regulate chromatin function and assembly; and alter the stability and translation of cytoplasmic mRNAs. Although their proposed range of functionality remains controversial, there is increasing research evidence that lncRNAs play a regulatory role in the activation, differentiation and development of immune signaling cascades; microbiome development; and in diseases such as neuronal and cardiovascular disorders; cancer; and pathogenic infections. This review discusses the functional roles of different lncRNAs in regulation of host immune responses, signaling pathways during host-microbe interaction and infection caused by obligate intracellular bacterial pathogens. The study of lncRNAs is assuming significance as it could be exploited for development of alternative therapeutic strategies for the treatment of severe and chronic pathogenic infections caused by Mycobacterium, Chlamydia and Rickettsia infections, as well as commensal colonization. Finally, this review summarizes the translational potential of lncRNA research in development of diagnostic and prognostic tools for human diseases.

Identification of potential LncRNAs as papillary thyroid carcinoma biomarkers based on integrated bioinformatics analysis using TCGA and RNA sequencing data

The roles and mechanisms of long non-coding RNAs (lncRNAs) in papillary thyroid cancer (PTC) remain elusive. We obtained RNA sequencing (RNA-seq) data of surgical PTC specimens from patients with thyroid cancer (THCA; n = 20) and identified differentially expressed genes (DEGs) between cancer and cancer-adjacent tissue samples. We identified 2309 DEGs (1372 significantly upregulated and 937 significantly downregulated). We performed Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment, and protein-protein interaction network analyses and screened for hub lncRNAs. Using the same methods, we analyzed the RNA-seq data from THCA dataset in The Cancer Genome Atlas (TCGA) database to identify differentially expressed lncRNAs. We identified 15 key differentially expressed lncRNAs and pathways that were closely related to PTC. Subsequently, by intersecting the differentially expressed lncRNAs with hub lncRNAs, we identified LINC02407 as the key lncRNA. Assessment of the associated clinical characteristics and prognostic correlations revealed a close correlation between LINC02407 expression and N stage of patients. Furthermore, receiver operating characteristic curve analysis showed that LINC02407 could better distinguish between cancerous and cancer-adjacent tissues in THCA patients. In conclusion, our findings suggest that LINC02407 is a potential biomarker for PTC diagnosis and the prediction of lymph node metastasis.

Establishment of a prognostic signature for lung adenocarcinoma using cuproptosis-related lncRNAs

OBJECTIVE

To establish a prognostic signature for lung adenocarcinoma (LUAD) based on cuproptosis-related long non-coding RNAs (lncRNAs), and to study the immune-related functions of LUAD.

METHODS

First, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate COX analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate COX analysis were performed to analyze the cuproptosis-related lncRNAs, and a prognostic signature was established. Second, univariate COX analysis and multivariate COX analysis were performed for independent prognostic analyses. Receiver operating characteristic (ROC) curves, C index, survival curve, nomogram, and principal component analysis (PCA) were performed to evaluate the results of the independent prognostic analyses. Finally, gene enrichment analyses and immune-related function analyses were also carried out.

RESULTS

(1) A total of 1,297 cuproptosis-related lncRNAs were screened. (2) A LUAD prognostic signature containing 13 cuproptosis-related lncRNAs was constructed (NIFK-AS1, AC026355.2, SEPSECS-AS1, AL360270.1, AC010999.2, ABCA9-AS1, AC032011.1, AL162632.3, LINC02518, LINC0059, AL031600.2, AP000346.1, AC012409.4). (3) The area under the multi-indicator ROC curves at 1, 3, and 5 years were AUC1 = 0.742, AUC2 = 0.708, and AUC3 = 0.762, respectively. The risk score of the prognostic signature could be used as an independent prognostic factor that was independent of other clinical indicators. (4) The results of gene enrichment analyses showed that 13 biomarkers were primarily related to amoebiasis, the wnt signaling pathway, hematopoietic cell lineage. The ssGSEA volcano map showed significant differences between high- and low-risk groups in immune-related functions, such as human leukocyte antigen (HLA), Type_II_IFN_Reponse, MHC_class_I, and Parainflammation (P < 0.001).

CONCLUSIONS

Thirteen cuproptosis-related lncRNAs may be clinical molecular biomarkers for the prognosis of LUAD.

Circular RNAs: implications of signaling pathways and bioinformatics in human cancer

Circular RNAs (circRNAs) form a class of endogenous single-stranded RNA transcripts that are widely expressed in eukaryotic cells. These RNAs mediate post-transcriptional control of gene expression and have multiple functions in biological processes, such as transcriptional regulation and splicing. They serve predominantly as microRNA sponges, RNA-binding proteins, and templates for translation. More importantly, circRNAs are involved in cancer progression, and may serve as promising biomarkers for tumor diagnosis and therapy. Although traditional experimental methods are usually time-consuming and laborious, substantial progress has been made in exploring potential circRNA-disease associations by using computational models, summarized signaling pathway data, and other databases. Here, we review the biological characteristics and functions of circRNAs, including their roles in cancer. Specifically, we focus on the signaling pathways associated with carcinogenesis, and the status of circRNA-associated bioinformatics databases. Finally, we explore the potential roles of circRNAs as prognostic biomarkers in cancer.

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

BACKGROUND

Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence.

METHODS

Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted.

RESULTS

A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC.

CONCLUSION

Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model

LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA-protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are extracted in a heterogeneous network using the HIN2Vec method of network embedding. The results showed that LPIH2V obtains an AUC of 0.97 and ACC of 0.95 in the 5-fold cross-validation test. The model successfully showed superiority and good generalization ability. Compared to other models, LPIH2V not only extracts attribute characteristics by similarity, but also acquires behavior properties by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein.

Identification of diagnostic biomarkers for idiopathic pulmonary hypertension with metabolic syndrome by bioinformatics and machine learning

Idiopathic pulmonary hypertension (IPAH) is a condition that affects various tissues and organs and the metabolic and inflammatory systems. The most prevalent metabolic condition is metabolic syndrome (MS), which involves insulin resistance, dyslipidemia, and obesity. There may be a connection between IPAH and MS, based on a plethora of studies, although the underlying pathogenesis remains unclear. Through various bioinformatics analyses and machine learning algorithms, we identified 11 immune- and metabolism-related potential diagnostic genes (EVI5L, RNASE2, PARP10, TMEM131, TNFRSF1B, BSDC1, ACOT2, SAC3D1, SLA2, P4HB, and PHF1) for the diagnosis of IPAH and MS, and we herein supply a nomogram for the diagnosis of IPAH in MS patients. Additionally, we discovered IPAH's aberrant immune cells and discuss them here.

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.

Signaling landscape of mitochondrial non-coding RNAs

Human mitochondria are the vital cell organelle acting as a storehouse of energy generation and diverse regulatory functions. Mitochondrial DNA comprises 93% coding region and 7% non-coding regions, in which the non-coding region hypothesized as responsible for signaling is our specific interest. Here, we explored the unknown functions of mitochondrial non-coding RNAs by studying their respective signaling pathways. We retrieved conserved motifs of interactions from known experimental protein-RNA complexes to model unknown mitochondrial ncRNA sequences. Our results provide the ncRNAs list and show their involvement in four crucial pathways, such as (i) Processing of Capped Intron-Containing Pre-mRNA, (ii) Spliceosome, (iii) Spliceosomal assembly, and (iv) RNA Polymerase II Transcription, respectively. The interactome analysis revealed that the SRSF2 and U2AF2 proteins interact with ncRNAs. Further, we have simulated the selected ncRNA-protein complexes in cell-like environmental conditions and found them stable in terms of energetics. Through our study, we have identified an apparent interaction of mitochondrial ncRNAs with proteins and their role in critical signaling pathways, providing new insights into mitochondrial ncRNA-dependent gene regulation.Communicated by Ramaswamy H. Sarma.

MSF-LRR: Multi-Similarity Information Fusion Through Low-Rank Representation to Predict Disease-Associated Microbes

An Increase in microbial activity is shown to be intimately connected with the pathogenesis of diseases. Considering the expense of traditional verification methods, researchers are working to develop high-efficiency methods for detecting potential disease-related microbes. In this article, a new prediction method, MSF-LRR, is established, which uses Low-Rank Representation (LRR) to perform multi-similarity information fusion to predict disease-related microbes. Considering that most existing methods only use one class of similarity, three classes of microbe and disease similarity are added. Then, LRR is used to obtain low-rank structural similarity information. Additionally, the method adaptively extracts the local low-rank structure of the data from a global perspective, to make the information used for the prediction more effective. Finally, a neighbor-based prediction method that utilizes the concept of collaborative filtering is applied to predict unknown microbe-disease pairs. As a result, the AUC value of MSF-LRR is superior to other existing algorithms under 5-fold cross-validation. Furthermore, in case studies, excluding originally known associations, 16 and 19 of the top 20 microbes associated with Bacterial Vaginosis and Irritable Bowel Syndrome, respectively, have been confirmed by the recent literature. In summary, MSF-LRR is a good predictor of potential microbe-disease associations and can contribute to drug discovery and biological research.

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.

Effects of HAR1 on cognitive function in mice and the regulatory network of HAR1 determined by RNA sequencing and applied bioinformatics analysis

Background: HAR1 is a 118-bp segment that lies in a pair of novel non-coding RNA genes. It shows a dramatic accelerated change with an estimated 18 substitutions in the human lineage since the human-chimpanzee ancestor, compared with the expected 0.27 substitutions based on the slow rate of change in this region in other amniotes. Mutations of HAR1 lead to a different HAR1 secondary structure in humans compared to that in chimpanzees. Methods: We cloned HAR1 into the EF-1α promoter vector to generate transgenic mice. Morris water maze tests and step-down passive avoidance tests were conducted to observe the changes in memory and cognitive abilities of mice. RNA-seq analysis was performed to identify differentially expressed genes (DEGs) between the experimental and control groups. Systematic bioinformatics analysis was used to confirm the pathways and functions that the DEGs were involved in. Results: Memory and cognitive abilities of the transgenic mice were significantly improved. The results of Gene Ontology (GO) analysis showed that Neuron differentiation, Dentate gyrus development, Nervous system development, Cerebral cortex neuron differentiation, Cerebral cortex development, Cerebral cortex development and Neurogenesis are all significant GO terms related to brain development. The DEGs enriched in these terms included Lhx2, Emx2, Foxg1, Nr2e1 and Emx1. All these genes play an important role in regulating the functioning of Cajal-Retzius cells (CRs). The DEGs were also enriched in glutamatergic synapses, synapses, memory, and the positive regulation of long-term synaptic potentiation. In addition, "cellular response to calcium ions" exhibited the second highest rich factor in the GO analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the DEGs showed that the neuroactive ligand-receptor interaction pathway was the most significantly enriched pathway, and DEGs also notably enriched in neuroactive ligand-receptor interaction, axon guidance, and cholinergic synapses. Conclusion: HAR1 overexpression led to improvements in memory and cognitive abilities of the transgenic mice. The possible mechanism for this was that the long non-coding RNA (lncRNA) HAR1A affected brain development by regulating the function of CRs. Moreover, HAR1A may be involved in ligand-receptor interaction, axon guidance, and synapse formation, all of which are important in brain development and evolution. Furthermore, cellular response to calcium may play an important role in those processes.

lncRNA-disease association prediction method based on the nearest neighbor matrix completion model

State-of-the-art medical studies proved that long noncoding ribonucleic acids (lncRNAs) are closely related to various diseases. However, their large-scale detection in biological experiments is problematic and expensive. To aid screening and improve the efficiency of biological experiments, this study introduced a prediction model based on the nearest neighbor concept for lncRNA-disease association prediction. We used a new similarity algorithm in the model that fused potential associations. The experimental validation of the proposed algorithm proved its superiority over the available Cosine, Pearson, and Jaccard similarity algorithms. Satisfactory results in the comparative leave-one-out cross-validation test (with AUC = 0.96) confirmed its excellent predictive performance. Finally, the proposed model's reliability was confirmed by performing predictions using a new dataset, yielding AUC = 0.92.

Hypoxia-associated prognostic markers and competing endogenous RNA coexpression networks in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common form of non-small cell lung cancer (NSCLC). Hypoxia has been found in 50-60% of locally advanced solid tumors and is associated with poor prognosis in various tumors, including NSCLC. This study focused on hypoxia-associated molecular hallmarks in LUAD. Fifteen hypoxia-related genes were selected to define the hypoxia status of LUAD by ConsensusClusterPlus based on data from The Cancer Genome Atlas (TCGA). Then, we investigated the immune status under different hypoxia statuses. Subsequently, we constructed prognostic models based on hypoxia-related differentially expressed genes (DEGs), identified hypoxia-related microRNAs, lncRNAs and mRNAs, and built a network based on the competing endogenous RNA (ceRNA) theory. Two clusters (Cluster 1 and Cluster 2) were identified with different hypoxia statuses. Cluster 1 was defined as the hypoxia subgroup, in which all 15 hypoxia-associated genes were upregulated. The infiltration of CD4+ T cells and Tfh cells was lower, while the infiltration of regulatory T (Treg) cells, the expression of PD-1/PD-L1 and TMB scores were higher in Cluster 1, indicating an immunosuppressive status. Based on the DEGs, a risk signature containing 7 genes was established. Furthermore, three differentially expressed microRNAs (hsa-miR-9, hsa-miR-31, hsa-miR-196b) associated with prognosis under different hypoxia clusters and their related mRNAs and lncRNAs were identified, and a ceRNA network was built. This study showed that hypoxia was associated with poor prognosis in LUAD and explored the potential mechanism from the perspective of the gene signature and ceRNA theory.

Electrochemical DNA Biosensor Based on Immobilization of a Non-Modified ssDNA Using Phosphoramidate-Bonding Strategy and Pencil Graphite Electrode Modified with AuNPs/CB and Self-Assembled Cysteamine Monolayer

The present paper describes an alternative approach to the traditionally used covalent immobilization methods that require cost-intensive and complicated chemistry modification of a single-stranded DNA (ssDNA) capture probe. The low-cost pencil graphite electrode (PGE) modified with carbon black (CB) and gold nanoparticles (AuNPs) was used as an electrochemical platform and the non-modified ssDNA was immobilized on a self-assembled cysteamine modified AuNPs/CB-PGE through a phosphoramidate bond between the 5'-terminal phosphate group of ssDNA and the primary amine group of cysteamine. The microRNA-21 was used as a target model in the fabrication of this electrochemical DNA biosensor and the hybridization process with the complementary probe was monitored by differential pulse voltammetry using methylene blue (MB) as an electrochemical hybridization indicator. The decreased reduction peak current of MB shows a good linear correlation with the increased concentration of microRNA-21 target sequences because the MB signal is determined by the amount of exposed guanine bases. The linear range of the fabricated DNA biosensor was from 1.0 × 10^-8 to 5.0 × 10^-7 M with a detection limit of 1.0 × 10^-9 M. These results show that the covalent immobilization of a non-modified ssDNA capture probe through a phosphoramidate-bonding strategy could serve as a cost-effective and versatile approach for the fabrication of DNA biosensors related to a wide range of applications that cover the fields of medical diagnostic and environmental monitoring. The fabricated electrochemical DNA biosensor was used to analyze microRNA-21 in a (spiked) human serum sample and it showed satisfactory and encouraging results as an electrochemical DNA biosensor platform.

Recent advances in machine learning methods for predicting LncRNA and disease associations

Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists' understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.