piRDisease v1.0: a manually curated database for piRNA associated diseases

Databases and computational methods for the identification of piRNA-related molecules: A survey

Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs (ncRNAs) that plays important roles in many biological processes and major cancer diagnosis and treatment, thus becoming a hot research topic. This study aims to provide an in-depth review of computational piRNA-related research, including databases and computational models. Herein, we perform literature analysis and use comparative evaluation methods to summarize and analyze three aspects of computational piRNA-related research: (i) computational models for piRNA-related molecular identification tasks, (ii) computational models for piRNA-disease association prediction tasks, and (iii) computational resources and evaluation metrics for these tasks. This study shows that computational piRNA-related research has significantly progressed, exhibiting promising performance in recent years, whereas they also suffer from the emerging challenges of inconsistent naming systems and the lack of data. Different from other reviews on piRNA-related identification tasks that focus on the organization of datasets and computational methods, we pay more attention to the analysis of computational models, algorithms, and performances that aim to provide valuable references for computational piRNA-related identification tasks. This study will benefit the theoretical development and practical application of piRNAs by better understanding computational models and resources to investigate the biological functions and clinical implications of piRNA.

piR-823 tale as emerging cancer-hallmark molecular marker in different cancer types: a step-toward ncRNA-precision

PIWI-interacting RNAs (piRNAs) have received a lot of attention for their functions in cancer research. This class of short non-coding RNAs (ncRNA) has roles in genomic stability, chromatin remodeling, messenger RNA (mRNA) integrity, and genome structure. We summarized the mechanisms underlying the biogenesis and regulatory molecular functions of piRNAs. Among all piRNAs studied in cancer, this review offers a comprehensive analysis of the emerging roles of piR-823 in various types of cancer, including colorectal, gastric, liver, breast, and renal cancers, as well as multiple myeloma. piR-823 has emerged as a crucial modulator of various cancer hallmarks through regulating multiple pathways. In the current review, we analyzed several databases and conducted an extensive literature search to explore the influence of piR-823 in carcinogenesis in addition to describing the potential application of piR-823 as prognostic and diagnostic markers as well as the therapeutic potential toward ncRNA precision.

RDscan: Extracting RNA-disease relationship from the literature based on pre-training model

With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease (RD) relationships from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models (LLM). Initially, we constructed a dedicated RD corpus by manually curating from literature, comprising 2,082 positive and 2,000 negative sentences, alongside an independent test dataset (comprising 500 positive and 500 negative sentences) for training and evaluating RDscan. Subsequently, by fine-tuning the Bioformer and BioBERT pre-trained models, RDscan demonstrated exceptional performance in text classification and named entity recognition (NER) tasks. In 5-fold cross-validation, RDscan significantly outperformed traditional machine learning methods (Support Vector Machine, Logistic Regression and Random Forest). In addition, we have developed an accessible webserver that assists users in extracting RD relationships from text. In summary, RDscan represents the first text mining tool specifically designed for RD relationship extraction, and is poised to emerge as an invaluable tool for researchers dedicated to exploring the intricate interactions between RNA and diseases. Webserver of RDscan is free available at https://cellknowledge.com.cn/RDscan/.

piRNA-disease association prediction based on multi-channel graph variational autoencoder

Piwi-interacting RNA (piRNA) is a type of non-coding small RNA that is highly expressed in mammalian testis. PiRNA has been implicated in various human diseases, but the experimental validation of piRNA-disease associations is costly and time-consuming. In this article, a novel computational method for predicting piRNA-disease associations using a multi-channel graph variational autoencoder (MC-GVAE) is proposed. This method integrates four types of similarity networks for piRNAs and diseases, which are derived from piRNA sequences, disease semantics, piRNA Gaussian Interaction Profile (GIP) kernel, and disease GIP kernel, respectively. These networks are modeled by a graph VAE framework, which can learn low-dimensional and informative feature representations for piRNAs and diseases. Then, a multi-channel method is used to fuse the feature representations from different networks. Finally, a three-layer neural network classifier is applied to predict the potential associations between piRNAs and diseases. The method was evaluated on a benchmark dataset containing 5,002 experimentally validated associations with 4,350 piRNAs and 21 diseases, constructed from the piRDisease v1.0 database. It achieved state-of-the-art performance, with an average AUC value of 0.9310 and an AUPR value of 0.9247 under five-fold cross-validation. This demonstrates the method's effectiveness and superiority in piRNA-disease association prediction.

Construction of a Spatial-Confined Self-Stacking Catalytic Circuit for Rapid and Sensitive Imaging of Piwi-Interacting RNA in Living Cells

Piwi-interacting RNAs (piRNAs) are small noncoding RNAs that repress transposable elements to maintain genome integrity. The canonical catalytic hairpin assembly (CHA) circuit relies on random collisions of free-diffused reactant probes, which substantially slow down reaction efficiency and kinetics. Herein, we demonstrate the construction of a spatial-confined self-stacking catalytic circuit for rapid and sensitive imaging of piRNA in living cells based on intramolecular and intermolecular hybridization-accelerated CHA. We rationally design a 3WJ probe that not only accelerates the reaction kinetics by increasing the local concentration of reactant probes but also eliminates background signal leakage caused by cross-entanglement of preassembled probes. This strategy achieves high sensitivity and good specificity with shortened assay time. It can quantify intracellular piRNA expression at a single-cell level, discriminate piRNA expression in tissues of breast cancer patients and healthy persons, and in situ image piRNA in living cells, offering a new approach for early diagnosis and postoperative monitoring.

Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration

Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.

piRNA associates with immune diseases

PIWI-interacting RNA (piRNA) is the most abundant small non-coding RNA in animal cells, typically 26-31 nucleotides in length and it binds with PIWI proteins, a subfamily of Argonaute proteins. Initially discovered in germ cells, piRNA is well known for its role in silencing transposons and maintaining genome integrity. However, piRNA is also present in somatic cells as well as in extracellular vesicles and exosomes. While piRNA has been extensively studied in various diseases, particular cancer, its function in immune diseases remains unclear. In this review, we summarize current research on piRNA in immune diseases. We first introduce the basic characteristics, biogenesis and functions of piRNA. Then, we review the association of piRNA with different types of immune diseases, including autoimmune diseases, immunodeficiency diseases, infectious diseases, and other immune-related diseases. piRNA is considered a promising biomarker for diseases, highlighting the need for further research into its potential mechanisms in disease pathogenesis.

PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning

Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA-disease interactions. Although there have been computational methods proposed for the detection of piRNA-disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA-disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA-disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA-disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https://github.com/chenqiuhao/PUTransGCN.

iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network

Accurately identifying potential piRNA-disease associations is of great importance in uncovering the pathogenesis of diseases. Recently, several machine-learning-based methods have been proposed for piRNA-disease association detection. However, they are suffering from the high sparsity of piRNA-disease association network and the Boolean representation of piRNA-disease associations ignoring the confidence coefficients. In this study, we propose a supplementarily weighted strategy to solve these disadvantages. Combined with Graph Convolutional Networks (GCNs), a novel predictor called iPiDA-SWGCN is proposed for piRNA-disease association prediction. There are three main contributions of iPiDA-SWGCN: (i) Potential piRNA-disease associations are preliminarily supplemented in the sparse piRNA-disease network by integrating various basic predictors to enrich network structure information. (ii) The original Boolean piRNA-disease associations are assigned with different relevance confidence to learn node representations from neighbour nodes in varying degrees. (iii) The experimental results show that iPiDA-SWGCN achieves the best performance compared with the other state-of-the-art methods, and can predict new piRNA-disease associations.

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network

BACKGROUND

Piwi-interacting RNAs (piRNAs) have been proven to be closely associated with human diseases. The identification of the potential associations between piRNA and disease is of great significance for complex diseases. Traditional "wet experiment" is time-consuming and high-priced, predicting the piRNA-disease associations by computational methods is of great significance.

METHODS

In this paper, a method based on the embedding transformation graph convolution network is proposed to predict the piRNA-disease associations, named ETGPDA. Specifically, a heterogeneous network is constructed based on the similarity information of piRNA and disease, as well as the known piRNA-disease associations, which is applied to extract low-dimensional embeddings of piRNA and disease based on graph convolutional network with an attention mechanism. Furthermore, the embedding transformation module is developed for the problem of embedding space inconsistency, which is lightweighter, stronger learning ability and higher accuracy. Finally, the piRNA-disease association score is calculated by the similarity of the piRNA and disease embedding.

RESULTS

Evaluated by fivefold cross-validation, the AUC of ETGPDA achieves 0.9603, which is better than the other five selected computational models. The case studies based on Head and neck squamous cell carcinoma and Alzheimer's disease further prove the superior performance of ETGPDA.

CONCLUSIONS

Hence, the ETGPDA is an effective method for predicting the hidden piRNA-disease associations.

SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs

piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. In this study, a novel computational model based on the structural perturbation method is proposed to predict potential disease-associated piRNAs, called SPRDA. Notably, SPRDA belongs to positive-unlabeled learning, which is unaffected by negative examples in contrast to previous approaches. In the 5-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.

PDA-PRGCN: identification of Piwi-interacting RNA-disease associations through subgraph projection and residual scaling-based feature augmentation

BACKGROUND

Emerging evidences show that Piwi-interacting RNAs (piRNAs) play a pivotal role in numerous complex human diseases. Identifying potential piRNA-disease associations (PDAs) is crucial for understanding disease pathogenesis at molecular level. Compared to the biological wet experiments, the computational methods provide a cost-effective strategy. However, few computational methods have been developed so far.

RESULTS

Here, we proposed an end-to-end model, referred to as PDA-PRGCN (PDA prediction using subgraph Projection and Residual scaling-based feature augmentation through Graph Convolutional Network). Specifically, starting with the known piRNA-disease associations represented as a graph, we applied subgraph projection to construct piRNA-piRNA and disease-disease subgraphs for the first time, followed by a residual scaling-based feature augmentation algorithm for node initial representation. Then, we adopted graph convolutional network (GCN) to learn and identify potential PDAs as a link prediction task on the constructed heterogeneous graph. Comprehensive experiments, including the performance comparison of individual components in PDA-PRGCN, indicated the significant improvement of integrating subgraph projection, node feature augmentation and dual-loss mechanism into GCN for PDA prediction. Compared with state-of-the-art approaches, PDA-PRGCN gave more accurate and robust predictions. Finally, the case studies further corroborated that PDA-PRGCN can reliably detect PDAs.

CONCLUSION

PDA-PRGCN provides a powerful method for PDA prediction, which can also serve as a screening tool for studies of complex diseases.

Line graph attention networks for predicting disease-associated Piwi-interacting RNAs

PIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlate with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. Line graphs can extend many heterogeneous networks to replace dichotomous networks. In this study, we present a new graph neural network framework, line graph attention networks (LGAT). And we apply it to predict PiRNA disease association (GAPDA). In the experiment, GAPDA performs excellently in 5-fold cross-validation with an AUC of 0.9038. Not only that, it still has superior performance compared with methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.

RNADisease v4.0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction

Numerous studies have shown that RNA plays an important role in the occurrence and development of diseases, and RNA-disease associations are not limited to noncoding RNAs in mammals but also exist for protein-coding RNAs. Furthermore, RNA-associated diseases are found across species including plants and nonmammals. To better analyze diseases at the RNA level and facilitate researchers in exploring the pathogenic mechanism of diseases, we decided to update and change MNDR v3.0 to RNADisease v4.0, a repository for RNA-disease association (http://www.rnadisease.org/ or http://www.rna-society.org/mndr/). Compared to the previous version, new features include: (i) expanded data sources and categories of species, RNA types, and diseases; (ii) the addition of a comprehensive analysis of RNAs from thousands of high-throughput sequencing data of cancer samples and normal samples; (iii) the addition of an RNA-disease enrichment tool and (iv) the addition of four RNA-disease prediction tools. In summary, RNADisease v4.0 provides a comprehensive and concise data resource of RNA-disease associations which contains a total of 3 428 058 RNA-disease entries covering 18 RNA types, 117 species and 4090 diseases to meet the needs of biological research and lay the foundation for future therapeutic applications of diseases.

Respiratory syncytial virus infection changes the piwi-interacting RNA content of airway epithelial cells

Piwi-interacting RNAs (piRNAs) are small non-coding RNAs (sncRNAs) of about 26–32 nucleotides in length and represent the largest class of sncRNA molecules expressed in animal cells. piRNAs have been shown to play a crucial role to safeguard the genome, maintaining genome complexity and integrity, as they suppress the insertional mutations caused by transposable elements. However, there is growing evidence for the role of piRNAs in controlling gene expression in somatic cells as well. Little is known about changes in piRNA expression and possible function occurring in response to viral infections. In this study, we investigated the piRNA expression profile, using a human piRNA microarray, in human small airway epithelial (SAE) cells infected with respiratory syncytial virus (RSV), a leading cause of acute respiratory tract infections in children. We found a time-dependent increase in piRNAs differentially expressed in RSV-infected SAE cells. We validated the top piRNAs upregulated and downregulated at 24 h post-infection by RT-qPCR and identified potential targets. We then used Gene Ontology (GO) tool to predict the biological processes of the predicted targets of the most represented piRNAs in infected cells over the time course of RSV infection. We found that the most significant groups of targets of regulated piRNAs are related to cytoskeletal or Golgi organization and nucleic acid/nucleotide binding at 15 and 24 h p.i. To identify common patterns of time-dependent responses to infection, we clustered the significantly regulated expression profiles. Each of the clusters of temporal profiles have a distinct set of potential targets of the piRNAs in the cluster Understanding changes in piRNA expression in RSV-infected airway epithelial cells will increase our knowledge of the piRNA role in viral infection and might identify novel therapeutic targets for viral lung-mediated diseases.

iPiDA-LTR: Identifying piwi-interacting RNA-disease associations based on Learning to Rank

Piwi-interacting RNAs (piRNAs) are regarded as drug targets and biomarkers for the diagnosis and therapy of diseases. However, biological experiments cost substantial time and resources, and the existing computational methods only focus on identifying missing associations between known piRNAs and diseases. With the fast development of biological experiments, more and more piRNAs are detected. Therefore, the identification of piRNA-disease associations of newly detected piRNAs has significant theoretical value and practical significance on pathogenesis of diseases. In this study, the iPiDA-LTR predictor is proposed to identify associations between piRNAs and diseases based on Learning to Rank. The iPiDA-LTR predictor not only identifies the missing associations between known piRNAs and diseases, but also detects diseases associated with newly detected piRNAs. Experimental results demonstrate that iPiDA-LTR effectively predicts piRNA-disease associations outperforming the other related methods.

PIWI-interacting RNAs in human diseases: databases and computational models

PIWI-interacting RNAs (piRNAs) are short 21-35 nucleotide molecules that comprise the largest class of non-coding RNAs and found in a large diversity of species including yeast, worms, flies, plants and mammals including humans. The most well-understood function of piRNAs is to monitor and protect the genome from transposons particularly in germline cells. Recent data suggest that piRNAs may have additional functions in somatic cells although they are expressed there in far lower abundance. Compared with microRNAs (miRNAs), piRNAs have more limited bioinformatics resources available. This review collates 39 piRNA specific and non-specific databases and bioinformatics resources, describes and compares their utility and attributes and provides an overview of their place in the field. In addition, we review 33 computational models based upon function: piRNA prediction, transposon element and mRNA-related piRNA prediction, cluster prediction, signature detection, target prediction and disease association. Based on the collection of databases and computational models, we identify trends and potential gaps in tool development. We further analyze the breadth and depth of piRNA data available in public sources, their contribution to specific human diseases, particularly in cancer and neurodegenerative conditions, and highlight a few specific piRNAs that appear to be associated with these diseases. This briefing presents the most recent and comprehensive mapping of piRNA bioinformatics resources including databases, models and tools for disease associations to date. Such a mapping should facilitate and stimulate further research on piRNAs.

Identification of piRNA disease associations using deep learning

Piwi-interacting RNAs (piRNAs) play a pivotal role in maintaining genome integrity by repression of transposable elements, gene stability, and association with various disease progressions. Cost-efficient computational methods for the identification of piRNA disease associations promote the efficacy of disease-specific drug development. In this regard, we developed a simple, robust, and efficient deep learning method for identifying the piRNA disease associations known as piRDA. The proposed architecture extracts the most significant and abstract information from raw sequences represented in a simplicated piRNA disease pair without any involvement of features engineering. Two-step positive unlabeled learning and bootstrapping technique are utilized to abstain from the false-negative and biased predictions dealing with positive unlabeled data. The performance of proposed method piRDA is evaluated using k-fold cross-validation. The piRDA is significantly improved in all the performance evaluation measures for the identification of piRNA disease associations in comparison to state-of-the-art method. Moreover, it is thus projected conclusively that the proposed computational method could play a significant role as a supportive and practical tool for primitive disease mechanisms and pharmaceutical research such as in academia and drug design. Eventually, the proposed model can be accessed using publicly available and user-friendly web tool athttp://nsclbio.jbnu.ac.kr/tools/piRDA/.

piRNAs and PIWI Proteins as Diagnostic and Prognostic Markers of Genitourinary Cancers

piRNAs (PIWI-interacting RNAs) are small non-coding RNAs capable of regulation of transposon and gene expression. piRNAs utilise multiple mechanisms to affect gene expression, which makes them potentially more powerful regulators than microRNAs. The mechanisms by which piRNAs regulate transposon and gene expression include DNA methylation, histone modifications, and mRNA degradation. Genitourinary cancers (GC) are a large group of neoplasms that differ by their incidence, clinical course, biology, and prognosis for patients. Regardless of the GC type, metastatic disease remains a key therapeutic challenge, largely affecting patients’ survival rates. Recent studies indicate that piRNAs could serve as potentially useful biomarkers allowing for early cancer detection and therapeutic interventions at the stage of non-advanced tumour, improving patient’s outcomes. Furthermore, studies in prostate cancer show that piRNAs contribute to cancer progression by affecting key oncogenic pathways such as PI3K/AKT. Here, we discuss recent findings on biogenesis, mechanisms of action and the role of piRNAs and the associated PIWI proteins in GC. We also present tools that may be useful for studies on the functioning of piRNAs in cancers.

piRNAQuest V.2: an updated resource for searching through the piRNAome of multiple species

PIWI interacting RNAs (piRNAs) have emerged as important gene regulators in recent times. Since the release of our first version of piRNAQuest in 2014, lots of novel piRNAs have been annotated in different species other than human, mouse and rat. Such new developments in piRNA research have led us to develop an updated database piRNAQuest V.2. It consists of 92,77,689 piRNA entries for 25 new species of different phylum along with human, mouse and rat. Besides providing primary piRNA features which include their genomic location, with further information on piRNAs overlapping with repeat elements, pseudogenes and syntenic regions, etc., the novel features of this version includes (i) density based cluster prediction, (ii) piRNA expression profile across various healthy and disease systems and (iii) piRNA target prediction. The concept of density-based piRNA cluster identification is robust as it does not consider parametric distribution in its model. The piRNA expression profile for 21 disease systems including cancer have been hosted in addition to 32 tissue specific piRNA expression profile for various species. Further, the piRNA target prediction section includes both predicted and curated piRNA targets within eight disease systems and developmental stages of mouse testis. Further, users can visualize the piRNA-target duplex structure and the ping-pong signature pattern for all the ping-pong piRNA partners in different species. Overall, piRNAQuest V.2 is an updated user-friendly database which will serve as a useful resource to survey, search and retrieve information on piRNAs for multiple species. This freely accessible database is available at http://dibresources.jcbose.ac.in/zhumur/pirnaquest2.

A Review of Discovery Profiling of PIWI-Interacting RNAs and Their Diverse Functions in Metazoans

PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs (sncRNAs) that perform crucial biological functions in metazoans and defend against transposable elements (TEs) in germ lines. Recently, ubiquitously expressed piRNAs were discovered in soma and germ lines using small RNA sequencing (sRNA-seq) in humans and animals, providing new insights into the diverse functions of piRNAs. However, the role of piRNAs has not yet been fully elucidated, and sRNA-seq studies continue to reveal different piRNA activities in the genome. In this review, we summarize a set of simplified processes for piRNA analysis in order to provide a useful guide for researchers to perform piRNA research suitable for their study objectives. These processes can help expand the functional research on piRNAs from previously reported sRNA-seq results in metazoans. Ubiquitously expressed piRNAs have been discovered in the soma and germ lines in Annelida, Cnidaria, Echinodermata, Crustacea, Arthropoda, and Mollusca, but they are limited to germ lines in Chordata. The roles of piRNAs in TE silencing, gene expression regulation, epigenetic regulation, embryonic development, immune response, and associated diseases will continue to be discovered via sRNA-seq.

Emerging functions of piwi-interacting RNAs in diseases

PIWI‐interacting RNAs (piRNAs) are recently discovered small non‐coding RNAs consisting of 24‐35 nucleotides, usually including a characteristic 5‐terminal uridine and an adenosine at position 10. PIWI proteins can specifically bind to the unique structure of the 3′ end of piRNAs. In the past, it was thought that piRNAs existed only in the reproductive system, but recently, it was reported that piRNAs are also expressed in several other human tissues with tissue specificity. Growing evidence shows that piRNAs and PIWI proteins are abnormally expressed in various diseases, including cancers, neurodegenerative diseases and ageing, and may be potential biomarkers and therapeutic targets. This review aims to discuss the current research status regarding piRNA biogenetic processes, functions, mechanisms and emerging roles in various diseases.

Computational Methods and Online Resources for Identification of piRNA-Related Molecules

piRNAs are a class of small non-coding RNA molecules, which interact with the PIWI family and have many important and diverse biological functions. The present review is aimed to provide guidelines and contribute to piRNA research. We focused on the four types of identification models on piRNA-related molecules, including piRNA, piRNA cluster, piRNA target, and disease-related piRNA. We evaluated the types of tools for the identification of piRNAs based on five aspects: datasets, features, classifiers, performance, and usability. We found the precision of 2lpiRNApred was the highest in datasets of model organisms, piRNN had a better performance of datasets of non-model organisms, and 2L-piRNA had the fastest recognition speed of all tools. In addition, we presented an overview of piRNA databases. The databases were divided into six categories: basic annotation, comprehensive annotation, isoform, cluster, target, and disease. We found that piRNA data of non-model organisms, piRNA target data, and piRNA-disease-associated data should be strengthened. Our review might assist researchers in selecting appropriate tools or datasets for their studies, reveal potential problems and shed light on future bioinformatics studies.

The Implications of ncRNAs in the Development of Human Diseases

The mammalian genome comprehends a small minority of genes that encode for proteins (barely 2% of the total genome in humans) and an immense majority of genes that are transcribed into RNA but not encoded for proteins (ncRNAs). These non-coding genes are intimately related to the expression regulation of protein-coding genes. The ncRNAs subtypes differ in their size, so there are long non-coding genes (lncRNAs) and other smaller ones, like microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs). Due to their important role in the maintenance of cellular functioning, any deregulation of the expression profiles of these ncRNAs can dissemble in the development of different types of diseases. Among them, we can highlight some of high incidence in the population, such as cancer, neurodegenerative, or cardiovascular disorders. In addition, thanks to the enormous advances in the field of medical genomics, these same ncRNAs are starting to be used as possible drugs, approved by the FDA, as an effective treatment for diseases.

MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation

Many studies have indicated that non-coding RNA (ncRNA) dysfunction is closely related to numerous diseases. Recently, accumulated ncRNA-disease associations have made related databases insufficient to meet the demands of biomedical research. The constant updating of ncRNA-disease resources has become essential. Here, we have updated the mammal ncRNA-disease repository (MNDR, http://www.rna-society.org/mndr/) to version 3.0, containing more than one million entries, four-fold increment in data compared to the previous version. Experimental and predicted circRNA-disease associations have been integrated, increasing the number of categories of ncRNAs to five, and the number of mammalian species to 11. Moreover, ncRNA-disease related drug annotations and associations, as well as ncRNA subcellular localizations and interactions, were added. In addition, three ncRNA-disease (miRNA/lncRNA/circRNA) prediction tools were provided, and the website was also optimized, making it more practical and user-friendly. In summary, MNDR v3.0 will be a valuable resource for the investigation of disease mechanisms and clinical treatment strategies.

Systematics for types and effects of RNA variations

Systematics is described for annotation of variations in RNA molecules. The conceptual framework is part of Variation Ontology (VariO) and facilitates depiction of types of variations, their functional and structural effects and other consequences in any RNA molecule in any organism. There are more than 150 RNA related VariO terms in seven levels, which can be further combined to generate even more complicated and detailed annotations. The terms are described together with examples, usually for variations and effects in human and in diseases. RNA variation type has two subcategories: variation classification and origin with subterms. Altogether six terms are available for function description. Several terms are available for affected RNA properties. The ontology contains also terms for structural description for affected RNA type, post-transcriptional RNA modifications, secondary and tertiary structure effects and RNA sugar variations. Together with the DNA and protein concepts and annotations, RNA terms allow comprehensive description of variations of genetic and non-genetic origin at all possible levels. The VariO annotations are readable both for humans and computer programs for advanced data integration and mining.

iPiDA-sHN: Identification of Piwi-interacting RNA-disease associations by selecting high quality negative samples

As a large group of small non-coding RNAs (ncRNAs), Piwi-interacting RNAs (piRNAs) have been detected to be associated with various diseases. Identifying disease associated piRNAs can provide promising candidate molecular targets to promote the drug design. Although, a few computational ensemble methods have been developed for identifying piRNA-disease associations, the low-quality negative associations even with positive associations used during the training process prevent the predictive performance improvement. In this study, we proposed a new computational predictor named iPiDA-sHN to predict potential piRNA-disease associations. iPiDA-sHN presented the piRNA-disease pairs by incorporating piRNA sequence information, the known piRNA-disease association network, and the disease semantic graph. High-level features of piRNA-disease associations were extracted by the Convolutional Neural Network (CNN). Two-step positive-unlabeled learning strategy based on Support Vector Machine (SVM) was employed to select the high quality negative samples from the unknown piRNA-disease pairs. Finally, the SVM predictor trained with the known piRNA-disease associations and the high quality negative associations was used to predict new piRNA-disease associations. The experimental results showed that iPiDA-sHN achieved superior predictive ability compared with other state-of-the-art predictors.

PIWI-interacting RNAs in human cancer

P-element-induced wimpy testis (PIWI) interacting RNAs (piRNAs) are a class of small regulatory RNAs mechanistically similar to but much less studied than microRNAs and small interfering RNAs. Today the best understood function of piRNAs is transposon control in animal germ cells, which has earned them the name 'guardians of the germline'. Several molecular/cellular characteristics of piRNAs, including high sequence diversity, lack of secondary structures, and target-oriented generation seem to serve this purpose. Recently, aberrant expressions of piRNAs and PIWI proteins have been implicated in a variety of malignant tumors and associated with cancer hallmarks such as cell proliferation, inhibited apoptosis, invasion, metastasis and increased stemness. Researchers have also demonstrated multiple mechanisms of piRNA-mediated target deregulation associated with cancer initiation, progression or dissemination. We review current research findings on the biogenesis, normal functions and cancer associations of piRNAs, highlighting their potentials as cancer diagnostic/prognostic biomarkers and therapeutic tools. Whenever applicable, we draw connections with other research fields to encourage intercommunity conversations. We also offer recommendations and cautions regarding the general process of cancer-related piRNA studies and the methods/tools used at each step. Finally, we call attention to some issues that, if left unsolved, might impede the future development of this field.

PIWI family proteins as prognostic markers in cancer: a systematic review and meta-analysis

BACKGROUND

P-element-induced-wimpy-testis-(PIWI)-like proteins are implicated in germ cells' regulation and detected in numerous cancer types. In this meta-analysis, we aimed to associate, for the first time, the prognosis in cancer patients with intratumoral expression of PIWI family proteins.

METHODS

PubMed, Embase, and Web of Knowledge databases were searched, and studies investigating the association between intratumoral mRNA or protein expression of different PIWI family proteins and survival, metastasis, or recurrence of various cancer types were reviewed. Study qualities were assessed using the REMARK criteria. Studies' heterogeneity was evaluated using I2 index and Cochran Q test. Publication bias was assessed by funnel plots and Egger's regression. Pooled hazard ratios (HR) with 95% confidence intervals (95% CIs) were calculated for different PIWI family proteins separately. Specifically, log of calculated HR was pooled using random-effects model.

RESULTS

Twenty-six studies (4299 participants) were included. The pooled HR of mortality in high versus low expression of PIWIL1, PIWIL2, and PIWIL4 was 1.87 (95% CI: 1.31-2.66, p < 0.05), 1.09 (95% CI: 0.58-2.07, p = 0.79), and 0.44 (95% CI: 0.25-0.76, p < 0.05), respectively. The pooled HR of recurrence in high versus low expression of PIWIL1 and PIWIL2 was 1.72 (95% CI: 1.20-2.49, p < 0.05) and 1.98 (95% CI: 0.65-5.98, p = 0.23), respectively.

CONCLUSIONS

Highly variable results were observed for different cancer types. Higher PIWIL1 and lower piwil4 and PIWIL4 expression levels could potentially indicate worse prognosis in cancer. These proteins' expressions could be used for personalized prognosis and treatment in the future.

iPiDi-PUL: identifying Piwi-interacting RNA-disease associations based on positive unlabeled learning

Accumulated researches have revealed that Piwi-interacting RNAs (piRNAs) are regulating the development of germ and stem cells, and they are closely associated with the progression of many diseases. As the number of the detected piRNAs is increasing rapidly, it is important to computationally identify new piRNA-disease associations with low cost and provide candidate piRNA targets for disease treatment. However, it is a challenging problem to learn effective association patterns from the positive piRNA-disease associations and the large amount of unknown piRNA-disease pairs. In this study, we proposed a computational predictor called iPiDi-PUL to identify the piRNA-disease associations. iPiDi-PUL extracted the features of piRNA-disease associations from three biological data sources, including piRNA sequence information, disease semantic terms and the available piRNA-disease association network. Principal component analysis (PCA) was then performed on these features to extract the key features. The training datasets were constructed based on known positive associations and the negative associations selected from the unknown pairs. Various random forest classifiers trained with these different training sets were merged to give the predictive results via an ensemble learning approach. Finally, the web server of iPiDi-PUL was established at http://bliulab.net/iPiDi-PUL to help the researchers to explore the associated diseases for newly discovered piRNAs.

Small RNA-sequence analysis of plasma-derived extracellular vesicle miRNAs in smokers and patients with chronic obstructive pulmonary disease as circulating biomarkers

Extracellular vesicles (EVs) play a vital role in normal lung physiology to maintain homeostasis in the airways via intercellular communication. EVs include exosomes and microvesicles, and are characterized by their phospholipid bilayers. EVs have been recognized as novel circulating biomarkers of disease, which are released by different cell types. In this study, we used different EV isolation and purification methods to characterize the plasma-derived EV miRNAs from non-smokers, smokers and patients with COPD. A small RNA sequencing (RNA-seq) approach was adapted to identify novel circulating EV miRNAs. We found that plasma-derived EVs from non-smokers, smokers and patients with COPD vary in their size, concentration, distribution and phenotypic characteristics as confirmed by nanoparticle tracking analysis, transmission electron microscopy, and immunoblot analysis of EV surface markers. RNA-seq analysis confirmed the most abundant types of small RNAs, such as miRNAs, tRNAs, piRNAs snRNAs, snoRNAs and other biotypes in plasma-derived EVs. We mainly focused on miRNAs as novel biomarkers in smokers and patients with COPD for further analysis. Differential expression by DESeq2 identified distinct miRNA profiles (up-regulated: miR-22-3p, miR-99a-5p, miR-151a-5p, miR-320b, miR-320d; and down-regulated: miR-335-5p, miR-628-3p, miR-887-5p and miR-937-3p) in COPD versus smokers or non-smokers in a pairwise comparison. Gene set enrichment analysis (GSEA) of differentially expressed miRNAs revealed the top pathways, gene ontology and diseases associated with smokers and patients with COPD. We selectively validated miRNAs in EVs isolated from BEAS-2B cells treated with cigarette smoke extract by quantitative PCR analysis. For the first time, we report that plasma-derived EV miRNAs are novel circulating pulmonary disease biomarkers. Thus, molecular profiling of EV miRNAs has great translational potential for the development of biomarkers that may be used in the diagnosis, prognosis, and therapeutics of COPD.