Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs

Non-Coding RNAs and Innate Immune Responses in Cancer

Non-coding RNAs (ncRNAs) and the innate immune system are closely related, acting as defense mechanisms and regulating gene expression and innate immunity. Both are modulators in the initiation, development and progression of cancer. We aimed to review the major types of ncRNAs, including small interfering RNAs (siRNAs), microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and long non-coding RNAs (lncRNAs), with a focus on cancer, innate immunity, and inflammation. We found that ncRNAs are closely related to innate immunity, epigenetics, chronic inflammation, and cancer and share properties such as inducibility, specificity, memory, and transfer. These similarities and interrelationships suggest that ncRNAs and modulators of trained immunity, together with the control of chronic inflammation, can be combined to develop novel therapeutic approaches for personalized cancer treatment. In conclusion, the close relationship between ncRNAs, the innate immune system, and inflammation highlights their importance in cancer pathways and their potential as targets for novel therapeutic strategies.

The role of long noncoding RNAs in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with a poor prognosis leading to death. The diagnosis and treatment of ALS are inherently challenging due to its complex pathomechanism. Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nucleotides involved in different cellular processes, incisively gene expression. In recent years, more studies have been conducted on lncRNA classes and interference in different disease pathologies, showing their promising contribution to diagnosing and treating neurodegenerative diseases. In this review, we discussed the role of lncRNAs like NEAT1 and C9orf72-as in ALS pathogenesis mechanisms caused by mutations in different genes, including TAR DNA-binding protein-43 (TDP-43), fused in sarcoma (FUS), superoxide dismutase type 1 (SOD1). NEAT1 is a well-established lncRNA in ALS pathogenesis; hence, we elaborate on its involvement in forming paraspeckles, stress response, inflammatory response, and apoptosis. Furthermore, antisense lncRNAs (as-lncRNAs), a key group of transcripts from the opposite strand of genes, including ZEB1-AS1 and ATXN2-AS, are discussed as newly identified components in the pathology of ALS. Ultimately, we review the current standing of using lncRNAs as biomarkers and therapeutic agents and the future vision of further studies on lncRNA applications.

Research progress and potential application of microRNA and other non-coding RNAs in forensic medicine

At present, epigenetic markers have been extensively studied in various fields and have a high value in forensic medicine due to their unique mode of inheritance, which does not involve DNA sequence alterations. As an epigenetic phenomenon that plays an important role in gene expression, non-coding RNAs (ncRNAs) act as key factors mediating gene silencing, participating in cell division, and regulating immune response and other important biological processes. With the development of molecular biology, genetics, bioinformatics, and next-generation sequencing (NGS) technology, ncRNAs such as microRNA (miRNA), circular RNA (circRNA), long non-coding RNA (lncRNA), and P-element induced wimpy testis (PIWI)-interacting RNA (piRNA) are increasingly been shown to have potential in the practice of forensic medicine. NcRNAs, mainly miRNA, may provide new strategies and methods for the identification of tissues and body fluids, cause-of-death analysis, time-related estimation, age estimation, and the identification of monozygotic twins. In this review, we describe the research progress and application status of ncRNAs, mainly miRNA, and other ncRNAs such as circRNA, lncRNA, and piRNA, in forensic practice, including the identification of tissues and body fluids, cause-of-death analysis, time-related estimation, age estimation, and the identification of monozygotic twins. The close links between ncRNAs and forensic medicine are presented, and their research values and application prospects in forensic medicine are also discussed.

Utilizing non-coding RNA-mediated regulation of ATP binding cassette (ABC) transporters to overcome multidrug resistance to cancer chemotherapy

Multidrug resistance (MDR) is one of the primary factors that produces treatment failure in patients receiving cancer chemotherapy. MDR is a complex multifactorial phenomenon, characterized by a decrease or abrogation of the efficacy of a wide spectrum of anticancer drugs that are structurally and mechanistically distinct. The overexpression of the ATP-binding cassette (ABC) transporters, notably ABCG2 and ABCB1, are one of the primary mediators of MDR in cancer cells, which promotes the efflux of certain chemotherapeutic drugs from cancer cells, thereby decreasing or abolishing their therapeutic efficacy. A number of studies have suggested that non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), play a pivotal role in mediating the upregulation of ABC transporters in certain MDR cancer cells. This review will provide updated information about the induction of ABC transporters due to the aberrant regulation of ncRNAs in cancer cells. We will also discuss the measurement and biological profile of circulating ncRNAs in various body fluids as potential biomarkers for predicting the response of cancer patients to chemotherapy. Sequence variations, such as alternative polyadenylation of mRNA and single nucleotide polymorphism (SNPs) at miRNA target sites, which may indicate the interaction of miRNA-mediated gene regulation with genetic variations to modulate the MDR phenotype, will be reviewed. Finally, we will highlight novel strategies that could be used to modulate ncRNAs and circumvent ABC transporter-mediated MDR.

CircRNA identification and feature interpretability analysis

BACKGROUND

Circular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining insight into their functions. Although several circRNA prediction models have been developed, their prediction accuracy is still unsatisfactory. Therefore, providing a more accurate computational framework to predict circRNAs and analyse their looping characteristics is crucial for systematic annotation.

RESULTS

We developed a novel framework, CircDC, for classifying circRNAs from other lncRNAs. CircDC uses four different feature encoding schemes and adopts a multilayer convolutional neural network and bidirectional long short-term memory network to learn high-order feature representation and make circRNA predictions. The results demonstrate that the proposed CircDC model is more accurate than existing models. In addition, an interpretable analysis of the features affecting the model is performed, and the computational framework is applied to the extended application of circRNA identification.

CONCLUSIONS

CircDC is suitable for the prediction of circRNA. The identification of circRNA helps to understand and delve into the related biological processes and functions. Feature importance analysis increases model interpretability and uncovers significant biological properties. The relevant code and data in this article can be accessed for free at https://github.com/nmt315320/CircDC.git .

IncRna: The R Package for Optimizing lncRNA Identification Processes

In silico identification of long noncoding RNAs (lncRNAs) is a multistage process including filtering of transcripts according to their physical characteristics (e.g., length, exon-intron structure) and determination of the coding potential of the sequence. A common issue within this process is the choice of the most suitable method of coding potential analysis for the conducted research. Selection of tools on the sole basis of their single performance may not provide the most effective choice for a specific problem. To overcome these limitations, we developed the R library lncRna, which provides functions to easily carry out the entire lncRNA identification process. For example, the package prepares the data files for coding potential analysis to perform error analysis. Moreover, the package gives the opportunity to analyze the effectiveness of various combinations of the lncRNA prediction methods to select the optimal configuration of the entire process.

ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning

Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing number of biological researchers are focusing on computational approaches to identify subcellular localizations of ncRNAs. However, the performance of the existing computational methods is low and needs to be further studied. First, most prediction models are trained with outdated databases. Second, only a few predictors can identify multiple subcellular localizations simultaneously. In this work, we establish three human ncRNA subcellular datasets based on the latest RNALocate, including lncRNA, miRNA and snoRNA, and then we propose a novel multi-label classification model based on ensemble learning called ncRNALocate-EL to identify multi-label subcellular localizations of three ncRNAs. The results show that the ncRNALocate-EL outperforms previous methods. Our method achieved an average precision of 0.709,0.977 and 0.730 on three human ncRNA datasets. The web server of ncRNALocate-EL has been established, which can be accessed at https://bliulab.net/ncRNALocate-EL.

Long Non-Coding RNAs and Their "Discrete" Contribution to IBD and Johne's Disease-What Stands out in the Current Picture? A Comprehensive Review

Non-coding RNAs (ncRNA) have paved the way to new perspectives on the regulation of gene expression, not only in biology and medicine, but also in associated fields and technologies, ensuring advances in diagnostic means and therapeutic modalities. Critical in this multistep approach are the associations of long non-coding RNA (lncRNA) with diseases and their causal genes in their networks of interactions, gene enrichment and expression analysis, associated pathways, the monitoring of the involved genes and their functional roles during disease progression from one stage to another. Studies have shown that Johne's Disease (JD), caused by Mycobacterium avium subspecies partuberculosis (MAP), shares common lncRNAs, clinical findings, and other molecular entities with Crohn's Disease (CD). This has been a subject of vigorous investigation owing to the zoonotic nature of this condition, although results are still inconclusive. In this review, on one hand, the current knowledge of lncRNAs in cells is presented, focusing on the pathogenesis of gastrointestinal-related pathologies and MAP-related infections and, on the other hand, we attempt to dissect the associated genes and pathways involved. Furthermore, the recently characterized and novel lncRNAs share common pathologies with IBD and JD, including the expression, molecular networks, and dataset analysis results. These are also presented in an attempt to identify potential biomarkers pertinent to cattle and human disease phenotypes.

DAmiRLocGNet: miRNA subcellular localization prediction by combining miRNA-disease associations and graph convolutional networks

MicroRNAs (miRNAs) are human post-transcriptional regulators in humans, which are involved in regulating various physiological processes by regulating the gene expression. The subcellular localization of miRNAs plays a crucial role in the discovery of their biological functions. Although several computational methods based on miRNA functional similarity networks have been presented to identify the subcellular localization of miRNAs, it remains difficult for these approaches to effectively extract well-referenced miRNA functional representations due to insufficient miRNA-disease association representation and disease semantic representation. Currently, there has been a significant amount of research on miRNA-disease associations, making it possible to address the issue of insufficient miRNA functional representation. In this work, a novel model is established, named DAmiRLocGNet, based on graph convolutional network (GCN) and autoencoder (AE) for identifying the subcellular localizations of miRNA. The DAmiRLocGNet constructs the features based on miRNA sequence information, miRNA-disease association information and disease semantic information. GCN is utilized to gather the information of neighboring nodes and capture the implicit information of network structures from miRNA-disease association information and disease semantic information. AE is employed to capture sequence semantics from sequence similarity networks. The evaluation demonstrates that the performance of DAmiRLocGNet is superior to other competing computational approaches, benefiting from implicit features captured by using GCNs. The DAmiRLocGNet has the potential to be applied to the identification of subcellular localization of other non-coding RNAs. Moreover, it can facilitate further investigation into the functional mechanisms underlying miRNA localization. The source code and datasets are accessed at http://bliulab.net/DAmiRLocGNet.

CircNTNG1 inhibits renal cell carcinoma progression via HOXA5-mediated epigenetic silencing of Slug

BACKGROUND

Recent studies have identified that circular RNAs (circRNAs) have an important role in cancer via their well-recognized sponge effect on miRNAs, which regulates a large variety of cancer-related genes. However, only a few circRNAs have been well-studied in renal cell carcinoma (RCC) and their regulatory function remains largely elusive.

METHODS

Bioinformatics approaches were used to characterize the differentially expressed circRNAs in our own circRNA-sequencing dataset, as well as two public circRNA microarray datasets. CircNTNG1 (hsa_circ_0002286) was identified as a potential tumor-suppressing circRNA. Transwell assay and CCK-8 assay were used to assess phenotypic changes. RNA pull-down, luciferase reporter assays and FISH experiment were used to confirm the interactions among circNTNG1, miR-19b-3p, and HOXA5 mRNA. GSEA was performed to explore the downstream pathway regulated by HOXA5. Immunoblotting, chromatin immunoprecipitation, and methylated DNA immunoprecipitation were used to study the mechanism of HOXA5.

RESULTS

In all three circRNA datasets, circNTNG1, which was frequently deleted in RCC, showed significantly low expression in the tumor group. The basic properties of circNTNG1 were characterized, and phenotype studies also demonstrated the inhibitory effect of circNTNG1 on RCC cell aggressiveness. Clinically, circNTNG1 expression was associated with RCC stage and Fuhrman grade, and it also served as an independent predictive factor for both OS and RFS of RCC patients. Next, the sponge effect of circNTNG1 on miR-19b-3p and the inhibition of HOXA5 by miR-19b-3p were validated. GSEA analysis indicated that HOXA5 could inactivate the epithelial-mesenchymal transition (EMT) process, and this inactivation was mediated by HOXA5-induced SNAI2 (Slug) downregulation. Finally, it was confirmed that the Slug downregulation was caused by HOXA5, along with the DNA methyltransferase DNMT3A, binding to its promoter region and increasing the methylation level.

CONCLUSIONS

Based on the experimental data, in RCC, circNTNG1/miR-19b-3p/HOXA5 axis can regulate the epigenetic silencing of Slug, thus interfering EMT and metastasis of RCC. Together, our findings provide potential biomarkers and novel therapeutic targets for future study in RCC.

Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction

Circular ribonucleic acids (circRNAs) are novel non-coding RNAs that emanate from alternative splicing of precursor mRNA in reversed order across exons. Despite the abundant presence of circRNAs in human genes and their involvement in diverse physiological processes, the functionality of most circRNAs remains a mystery. Like other non-coding RNAs, sub-cellular localization knowledge of circRNAs has the aptitude to demystify the influence of circRNAs on protein synthesis, degradation, destination, their association with different diseases, and potential for drug development. To date, wet experimental approaches are being used to detect sub-cellular locations of circular RNAs. These approaches help to elucidate the role of circRNAs as protein scaffolds, RNA-binding protein (RBP) sponges, micro-RNA (miRNA) sponges, parental gene expression modifiers, alternative splicing regulators, and transcription regulators. To complement wet-lab experiments, considering the progress made by machine learning approaches for the determination of sub-cellular localization of other non-coding RNAs, the paper in hand develops a computational framework, Circ-LocNet, to precisely detect circRNA sub-cellular localization. Circ-LocNet performs comprehensive extrinsic evaluation of 7 residue frequency-based, residue order and frequency-based, and physio-chemical property-based sequence descriptors using the five most widely used machine learning classifiers. Further, it explores the performance impact of K-order sequence descriptor fusion where it ensembles similar as well dissimilar genres of statistical representation learning approaches to reap the combined benefits. Considering the diversity of statistical representation learning schemes, it assesses the performance of second-order, third-order, and going all the way up to seventh-order sequence descriptor fusion. A comprehensive empirical evaluation of Circ-LocNet over a newly developed benchmark dataset using different settings reveals that standalone residue frequency-based sequence descriptors and tree-based classifiers are more suitable to predict sub-cellular localization of circular RNAs. Further, K-order heterogeneous sequence descriptors fusion in combination with tree-based classifiers most accurately predict sub-cellular localization of circular RNAs. We anticipate this study will act as a rich baseline and push the development of robust computational methodologies for the accurate sub-cellular localization determination of novel circRNAs.

EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction

Subcellular localization of Ribonucleic Acid (RNA) molecules provide significant insights into the functionality of RNAs and helps to explore their association with various diseases. Predominantly developed single-compartment localization predictors (SCLPs) lack to demystify RNA association with diverse biochemical and pathological processes mainly happen through RNA co-localization in multiple compartments. Limited multi-compartment localization predictors (MCLPs) manage to produce decent performance only for target RNA class of particular sub-type. Further, existing computational approaches have limited practical significance and potential to optimize therapeutics due to the poor degree of model explainability. The paper in hand presents an explainable Long Short-Term Memory (LSTM) network "EL-RMLocNet", predictive performance and interpretability of which are optimized using a novel GeneticSeq2Vec statistical representation learning scheme and attention mechanism for accurate multi-compartment localization prediction of different RNAs solely using raw RNA sequences. GeneticSeq2Vec generates optimized statistical vectors of raw RNA sequences by capturing short and long range relations of nucleotide k-mers. Using sequence vectors generated by GeneticSeq2Vec scheme, Long Short Term Memory layers extract most informative features, weighting of which on the basis of discriminative potential for accurate multi-compartment localization prediction is performed using attention layer. Through reverse engineering, weights of statistical feature space are mapped to nucleotide k-mers patterns to make multi-compartment localization prediction decision making transparent and explainable for different RNA classes and species. Empirical evaluation indicates that EL-RMLocNet outperforms state-of-the-art predictor for subcellular localization prediction of 4 different RNA classes by an average accuracy figure of 8% for Homo Sapiens species and 6% for Mus Musculus species. EL-RMLocNet is freely available as a web server at (https://sds_genetic_analysis.opendfki.de/subcellular_loc/).

Circular RNAs as novel biomarkers in triple-negative breast cancer: a systematic review

More effective prognostic and diagnostic tools are urgently required for early detecting and treating triple-negative breast cancer, which is the most acute type of breast cancer because of its lower survival rate, aggressiveness, and non-response to various common treatments. So, it remains the most harmful malignancy for women worldwide. Recently, circular RNAs, as a group of non-coding RNAs, with covalently closed loop and high stability have been discovered, which can modulate gene expression through competing with endogenous microRNA sponges. This finding provided further insight into novel approaches for controlling genes affected in many disorders and malignancies. This review concentrates on the dysregulated expression of circRNAs like their diagnostic and prognostic values in TNBC. This review aims to focus on the abnormal expression of circRNAs and their diagnostic and prognostic values in TNBC. We used PubMed, Embase, and Web of Science databases and ClinicalTrials.gov to systematically search for all relevant clinical studies. This review is based on articles published in databases up to April 2022 with the following keywords: "Circular RNA", "CircRNA", "Triple-Negative Breast Cancer" and "TNBC". We conducted a review of published CircRNA profiled-research articles to identify candidate CircRNA biomarkers for TNBC. The review is registered on JBI at https://jbi.global/systematic-review-register . Accumulating evidence has shown that several circRNAs are downregulated and some are upregulated in TNBC. The results of these studies confirm that circRNAs might be potential biomarkers with the diagnostic, prognostic, and therapeutic target value for TNBC. We also consider the connection between circRNAs and TNBC cell proliferation, apoptosis, metastasis, and chemotherapy resistance and sensitivity.

ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab-based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.