Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers

Non-coding RNAs (ncRNAs) act as important modulators of gene expression and they have been confirmed to play critical roles in the physiology and development of malignant tumors. Understanding the synergism of multiple ncRNAs in competing endogenous RNA (ceRNA) regulation can provide important insights into the mechanisms of malignant tumors caused by ncRNA regulation. In this work, we present a framework, SCOM, for identifying ncRNA synergistic competition. We systematically construct the landscape of ncRNA synergistic competition across 31 malignant tumors, and reveal that malignant tumors tend to share hub ncRNAs rather than the ncRNA interactions involved in the synergistic competition. In addition, the synergistic competition ncRNAs (i.e. ncRNAs involved in the synergistic competition) are likely to be involved in drug resistance, contribute to distinguishing molecular subtypes of malignant tumors, and participate in immune regulation. Furthermore, SCOM can help to infer ncRNA synergistic competition across malignant tumors and uncover potential diagnostic and prognostic biomarkers of malignant tumors. Altogether, the SCOM framework (https://github.com/zhangjunpeng411/SCOM/) and the resulting web-based database SCOMdb (https://comblab.cn/SCOMdb/) serve as a useful resource for exploring ncRNA regulation and to accelerate the identification of carcinogenic biomarkers.

Mesenchymal stem cell-derived extracellular vesicles carrying miR-99b-3p restrain microglial activation and neuropathic pain by stimulating autophagy

Neuropathic pain is a complex condition that seriously affects human quality of life. This study aimed to investigate the therapeutic mechanism of mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) and try to discover new targets for alleviating neuropathic pain. Extracellular vesicles were isolated and identified via ultracentrifugation. BV-2 microglial cells were stimulated with lipopolysaccharide (LPS) in the presence or absence of MSC-EVs. Further, microglial activation and neuroinflammation were evaluated by flow cytometry, RT-qPCR, and ELISA. High-throughput sequencing analysis was performed to reveal the differentially expressed (DE) miRNAs in BV-2 microglia. Autophagy-related regulators were assessed by Western blotting and Immunofluorescence staining. Chronic constriction injury (CCI) model was used to induce neuropathic pain in rats, and the mechanical withdrawal threshold (MWT) was measured. High-throughput sequencing analysis identified 17 DE miRNAs, which were mainly enriched in PI3K-AKT and mTOR signaling pathways. MSC-EVs inhibited the activation of PI3K/AKT/mTOR signaling pathway in LPS-stimulated microglia. Moreover, MSC-EVs treatment enhanced the autophagy level in activated microglia, whereas autophagy inhibitor 3-MA reversed the suppressing effects of MSC-EVs on microglial activation and neuroinflammation. The MSC-EV-mediated transfer of miR-99b-3p was verified to promote microglial autophagy, and miR-99b-3p overexpression suppressed the expression of pro-inflammatory factors in activated microglia. During in vivo studies, intrathecal injection of MSC-EVs significantly up-regulated the expression of miR-99b-3p, and alleviated mechanical allodynia caused by activated microglia in the spinal cord dorsal horn of CCI rats. Moreover, MSC-EVs treatment repaired CCI-induced autophagic impairment by stimulating autophagy in the spinal cord. Collectively, our findings demonstrated that MSC-EVs had an analgesic effect on neuropathic pain via promoting autophagy, and these antinociceptive effects were at least in part caused by MSC-EV-mediated transfer of miR-99b-3p, thereby inhibiting microglial activation and pro-inflammatory cytokines expression.

Extraction of microRNA-target interaction sentences from biomedical literature by deep learning approach

MicroRNA (miRNA)-target interaction (MTI) plays a substantial role in various cell activities, molecular regulations and physiological processes. Published biomedical literature is the carrier of high-confidence MTI knowledge. However, digging out this knowledge in an efficient manner from large-scale published articles remains challenging. To address this issue, we were motivated to construct a deep learning-based model. We applied the pre-trained language models to biomedical text to obtain the representation, and subsequently fed them into a deep neural network with gate mechanism layers and a fully connected layer for the extraction of MTI information sentences. Performances of the proposed models were evaluated using two datasets constructed on the basis of text data obtained from miRTarBase. The validation and test results revealed that incorporating both PubMedBERT and SciBERT for sentence level encoding with the long short-term memory (LSTM)-based deep neural network can yield an outstanding performance, with both F1 and accuracy being higher than 80% on validation data and test data. Additionally, the proposed deep learning method outperformed the following machine learning methods: random forest, support vector machine, logistic regression and bidirectional LSTM. This work would greatly facilitate studies on MTI analysis and regulations. It is anticipated that this work can assist in large-scale screening of miRNAs, thereby revealing their functional roles in various diseases, which is important for the development of highly specific drugs with fewer side effects. Source code and corpus are publicly available at https://github.com/qi29.

Construction of a HOXA11-AS-Interact Ed Network in Keloid Fibroblasts Using Integrated Bioinformatic Analysis and in Vitro Validation

Background: Expression of the long noncoding RNA (lncRNA) HOXA11-AS significantly increased in keloids by unclarified molecular regulation mechanisms.

Methods: Using successfully primary cultured keloid-derived fibroblasts from central region of chronic keloid tissues (sample 0), small interfering RNAs were designed and transfected into two keloid fibroblast samples (samples 1 and 2) to knockdown HOXA11-AS. One nonspecific transfection control (sample 3) and one blank control (sample 4) were used to remove nonspecific overlap from the studied group. The lncRNAs, messenger RNAs (mRNAs), and microRNAs (miRNAs) of five samples were sequenced to identify differentially expressed (DE) profiles in HOXA11-AS-knockdown keloid fibroblasts in samples 1 and 2 (by intersection), which facilitated removal of overlap with the nonspecific controls (samples 3 and 4, by union). Using stepwise bioinformatic analysis, a HOXA11-AS-interacted competing endogenous network (ceRNA) was screened based on three DE profiles.

Results: Keloid fibroblasts with or without HOXA11-AS as well as with or without nonspecific interferences were successfully constructed respectively. A total of 1,396 mRNAs and 39 lncRNAs were significantly changed in keloid fibroblast with HOXA11-AS knockdown. Simultaneously, 1,626 mRNAs and 99 lncRNAs were significantly changed in keloid fibroblast with nonspecific interference. With removal of nonspecific overlap, a lncRNA–mRNA interactive network characterized by close natural/intronic antisense relationship was initially constructed in keloid fibroblast with HOXA11-AS knockdown. Based on this network, a lncRNA–mRNA–protein interaction network was extended by integration of the human protein–protein interaction network. Significant functional genes were screened using PageRank algorithm in the extended network. Three genes, including SNED1, NIPAL3, and VTN, were validated by real-time PCR in HOXA11-AS-knockdown keloid fibroblasts. Only NIPAL3 was predicted to be a target gene for HOXA11-AS via three competing endogenous miRNAs (hsa-miRNA-19a-3p, hsa-miR-141-3p, and hsa-miR-140-5p).

Conclusion: An interactive network of HOXA11-AS–three miRNAs–NIPAL3 was predicted in keloid fibroblasts by integrative bioinformatic analysis and in vitro validation.

Illuminating lncRNA Function Through Target Prediction

Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.

Construction of a microRNA-mRNA Regulatory Network in De Novo Cytogenetically Normal Acute Myeloid Leukemia Patients

Background: The association between dysregulated microRNAs (miRNAs) and acute myeloid leukemia (AML) is well known. However, our understanding of the regulatory role of miRNAs in the cytogenetically normal AML (CN-AML) subtype pathway is still poor. The current study integrated miRNA and mRNA profiles to explore novel miRNA-mRNA interactions that affect the regulatory patterns of de novo CN-AML. Methods: We utilized a multiplexed nanoString nCounter platform to profile both miRNAs and mRNAs using similar sets of patient samples (n = 24). Correlations were assessed, and an miRNA-mRNA network was constructed. The underlying biological functions of the mRNAs were predicted by gene enrichment. Finally, the interacting pairs were assessed using TargetScan and microT-CDS. We identified 637 significant negative correlations (false discovery rate <0.05). Results: Network analysis revealed a cluster of 12 miRNAs representing the majority of mRNA targets. Within the cluster, five miRNAs (miR-495-3p, miR-185-5p, let-7i-5p, miR-409-3p, and miR-127-3p) were posited to play a pivotal role in the regulation of CN-AML, as they are associated with the negative regulation of myeloid leukocyte differentiation, negative regulation of myeloid cell differentiation, and positive regulation of hematopoiesis. Conclusion: Three novel interactions in CN-AML were predicted as let-7i-5p:HOXA9, miR-495-3p:PIK3R1, and miR-495-3p:CDK6 may be responsible for regulating myeloid cell differentiation in CN-AML.

Time to infer miRNA sponge modules

Inferring competing endogenous RNA (ceRNA) or microRNA (miRNA) sponge modules is a challenging and meaningful task for revealing ceRNA regulation mechanism at the module level. Modules in this context refer to groups of miRNA sponges which have mutual competitions and act as functional units for achieving biological processes. The recent development of computational methods based on heterogeneous data provides a novel way to discern the competitive effects of miRNA sponges on human complex diseases. This article aims to provide a comprehensive perspective of miRNA sponge module discovery methods. We first review the publicly available databases of cancer-related miRNA sponges, as the miRNA sponges involved in human cancers contribute to the discovery of cancer-associated modules. Then we review the existing computational methods for inferring miRNA sponge modules. Furthermore, we conduct an assessment on the performance of the module discovery methods with the pan-cancer dataset, and the comparison study indicates that it is useful to infer biologically meaningful miRNA sponge modules by directly mapping heterogeneous data to the competitive modules. Finally, we discuss the future directions and associated challenges in developing in silico methods to infer miRNA sponge modules. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

An Overview of the Computational Models Dealing with the Regulatory ceRNA Mechanism and ceRNA Deregulation in Cancer

Pools of RNA molecules can act as competing endogenous RNAs (ceRNAs) and indirectly alter their expression levels by competitively binding shared microRNAs. This ceRNA cross talk yields an additional posttranscriptional regulatory layer, which plays key roles in both physiological and pathological processes. MicroRNAs can act as decoys by binding multiple RNAs, as well as RNAs can act as ceRNAs by competing for binding multiple microRNAs, leading to many cross talk interactions that could favor significant large-scale effects in spite of the weakness of single interactions. Identifying and studying these extended ceRNA interaction networks could provide a global view of the fine-tuning gene regulation in a wide range of biological processes and tumor progressions. In this chapter, we review current progress of predicting ceRNA cross talk, by summarizing the most up-to-date databases, which collect computationally predicted and/or experimentally validated miRNA-target and ceRNA-ceRNA interactions, as well as the widespread computational methods for discovering and modeling possible evidences of ceRNA-ceRNA interaction networks. These methods can be grouped in two categories: statistics-based methods exploit multivariate analysis to build ceRNA networks, by considering the miRNA expression levels when evaluating miRNA sponging relationships; mathematical methods build deterministic or stochastic models to analyze and predict the behavior of ceRNA cross talk.

HumiR: Web Services, Tools and Databases for Exploring Human microRNA Data

For many research aspects on small non-coding RNAs, especially microRNAs, computational tools and databases are developed. This includes quantification of miRNAs, piRNAs, tRNAs and tRNA fragments, circRNAs and others. Furthermore, the prediction of new miRNAs, isomiRs, arm switch events, target and target pathway prediction and miRNA pathway enrichment are common tasks. Additionally, databases and resources containing expression profiles, e.g., from different tissues, organs or cell types, are generated. This information in turn leads to improved miRNA repositories. While most of the respective tools are implemented in a species-independent manner, we focused on tools for human small non-coding RNAs. This includes four aspects: (1) miRNA analysis tools (2) databases on miRNAs and variations thereof (3) databases on expression profiles (4) miRNA helper tools facilitating frequent tasks such as naming conversion or reporter assay design. Although dependencies between the tools exist and several tools are jointly used in studies, the interoperability is limited. We present HumiR, a joint web presence for our tools. HumiR facilitates an entry in the world of miRNA research, supports the selection of the right tool for a research task and represents the very first step towards a fully integrated knowledge-base for human small non-coding RNA research. We demonstrate the utility of HumiR by performing a very comprehensive analysis of Alzheimer's miRNAs.

CeRNASeek: an R package for identification and analysis of ceRNA regulation

Competitive endogenous RNA (ceRNA) represents a novel layer of gene regulation that controls both physiological and pathological processes. However, there is still lack of computational tools for quickly identifying ceRNA regulation. To address this problem, we presented an R-package, CeRNASeek, which allows identifying and analyzing ceRNA-ceRNA interactions by integration of multiple-omics data. CeRNASeek integrates six widely used computational methods to identify ceRNA-ceRNA interactions, including two global and four context-specific ceRNA regulation prediction methods. In addition, it provides several downstream analyses for predicted ceRNA-ceRNA pairs, including regulatory network analysis, functional annotation and survival analysis. With examples of cancer-related ceRNA prioritization and cancer subtyping, we demonstrate that CeRNASeek is a valuable tool for investigating the function of ceRNAs in complex diseases. In summary, CeRNASeek provides a comprehensive and efficient tool for identifying and analysis of ceRNA regulation. The package is available on the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=CeRNASeek.

Pervasive Small RNAs in Cardiometabolic Research: Great Potential Accompanied by Biological and Technical Barriers

Advances in small RNA sequencing have revealed the enormous diversity of small noncoding RNA (sRNA) classes in mammalian cells. At this point, most investigators in diabetes are aware of the success of microRNA (miRNA) research and appreciate the importance of posttranscriptional gene regulation in glycemic control. Nevertheless, miRNAs are just one of multiple classes of sRNAs and likely represent only a minor fraction of sRNA sequences in a given cell. Despite the widespread appreciation of sRNAs, very little research into non-miRNA sRNA function has been completed, likely due to some major barriers that present unique challenges for study. To emphasize the importance of sRNA research in cardiometabolic diseases, we highlight the success of miRNAs and competitive endogenous RNAs in cholesterol and glucose metabolism. Moreover, we argue that sequencing studies have demonstrated that miRNAs are just the tip of the iceberg for sRNAs. We are likely standing at the precipice of immense discovery for novel sRNA-mediated gene regulation in cardiometabolic diseases. To realize this potential, we must first address critical barriers with an open mind and refrain from viewing non-miRNA sRNA function through the lens of miRNAs, as they likely have their own set of distinct regulatory factors and functional mechanisms.

Current insight into the roles of microRNA in vitiligo

Vitiligo is a common chronic depigmented skin disease characterized by melanocyte loss or dysfunction in the lesion. The pathogenesis of vitiligo has not been fully clarified. Most studies have suggested that the occurrence and progression of vitiligo are due to multiple factors and gene interactions in which noncoding RNAs contribute to an individual's susceptibility to vitiligo. Noncoding RNAs, including microRNAs (miRNAs), are a hot topic in posttranscriptional regulatory mechanism research. miRNAs are noncoding RNAs with a length of approximately 22 nucleotides and play a negative regulatory role by binding to the 3'-UTR or 5'-UTR of the target mRNA to inhibit translation or initiate mRNA degradation. Previous studies have screened the differential expression profiles of miRNAs in the skin lesions, melanocytes, peripheral blood mononuclear cells (PBMCs) and sera of patients and mouse models with vitiligo. Moreover, several studies have focused on miRNA-25, miRNA-155 and other miRNAs involved in melanin metabolism, oxidative stress, and melanocyte proliferation and apoptosis. These miRNAs and regulatory processes further illuminate the pathogenesis of vitiligo and provide hope for the application of small molecules in the treatment of vitiligo. In this review, we summarize miRNA expression profiles in different tissues of vitiligo patients and the mechanisms by which key miRNAs mediate vitiligo development.

Mesenchymal glioblastoma constitutes a major ceRNA signature in the TGF-β pathway

Rationale: Competitive endogenous RNA (ceRNA) networks play important roles in posttranscriptional regulation. Their dysregulation is common in cancer. However, ceRNA signatures have been poorly examined in the invasive and aggressive phenotypes of mesenchymal glioblastoma (GBM). This study aims to characterize mesenchymal glioblastoma at the mRNA-miRNA level and identify the mRNAs in ceRNA networks (micNET) markers and their mechanisms in tumorigenesis. Methods: The mRNAs in ceRNA networks (micNETs) of glioblastoma were investigated by constructing a GBM ceRNA network followed by integration with a STRING protein interaction network. The prognostic micNET markers of mesenchymal GBM were identified and validated across multiple datasets. ceRNA interactions were identified between micNETs and miR181 family members. LY2109761, an inhibitor of TGFBR2, demonstrated tumor-suppressive effects on both primary cultured cells and a patient-derived xenograft intracranial model. Results: We characterized mesenchymal glioblastoma at the mRNA-miRNA level and reported a ceRNA network that could separate the mesenchymal subtype from other subtypes. Six genes (TGFBR2, RUNX1, PPARG, ACSL1, GIT2 and RAP1B) that interacted with each other in both a ceRNA-related manner and in terms of their protein functions were identified as markers of the mesenchymal subtype. The coding sequence (CDS) and 3'-untranslated region (UTR) of TGFBR2 upregulated the expression of these genes, whereas TGFBR2 inhibition by siRNA or miR-181a/d suppressed their expression levels. Furthermore, mesenchymal subtype-related genes and the invasion phenotype could be reversed by suppressing the six mesenchymal marker genes. Conclusions: This study suggests that the micNETs may have translational significance in the diagnosis of mesenchymal GBM and may be novel therapeutic targets.