Enrichment Analysis Identifies Functional MicroRNA-Disease Associations in Humans

Comparative Analysis of Tumor-Associated microRNAs and Tetraspanines from Exosomes of Plasma and Ascitic Fluids of Ovarian Cancer Patients

Ovarian cancer (OC) is one of the most common and fatal types of gynecological cancer. In the early phase of OC detection, the current treatment and diagnostic methods are not efficient and sensitive enough. Therefore, it is crucial to explore the mechanisms of OC metastasis and discover valuable factors for early diagnosis of female cancers and novel therapeutic strategies for metastasis. Exosomes are known to be involved in the development, migration, and invasion of cancer cells, and their cargo could be useful for the non-invasive biopsy development. CD151- and Tspan8-positive exosomes are known to support the degradation of the extracellular matrix, and are involved in stroma remodeling, angiogenesis and cell motility, as well as the association of miR-24 and miR-101 with these processes. The objective of this study was to explore the relationship of these components of exosomal cargo, in patients with OC, to clarify the clinical significance of these markers in liquid biopsies. The levels of tetraspanins Tspan8+ and CD151+ exosomes were significantly higher in plasma exosomes of OC patients compared with healthy females (HFs). The relative levels of miR-24 and miR-101 in plasma exosomes of HFs were significantly higher than in plasma exosomes of OC patients, while the levels of these microRNAs in exosomes from plasma and ascites of ill females showed no difference. Our study revealed a strong direct correlation between the change in the ascites exosomes CD151+Tspan8+ subpopulation level and the expression levels of the ascites (R = 0.81, p < 0.05) and plasma exosomal miR-24 (R = 0.74, p < 0.05) in OC patients, which confirms the assumption that exosomal cargo act synergistically to increase cellular motility, affecting cellular processes and signaling. Bioinformatics analysis confirmed the involvement of CD151 and Tspan8 tetraspanins and genes controlled by miR-24-3p and miR-101 in signaling pathways, which are crucial for carcinogenesis, demonstrating that these tetraspanins and microRNAs are potential biomarkers for OC screening, and predictors of poor clinicopathological behavior in tumors.

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.

FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association

BACKGROUND

In the process of post-transcription, microRNAs (miRNAs) are closely related to various complex human diseases. Traditional verification methods for miRNA-disease associations take a lot of time and expense, so it is especially important to design computational methods for detecting potential associations. Considering the restrictions of previous computational methods for predicting potential miRNAs-disease associations, we develop the model of FKL-Spa-LapRLS (Fast Kernel Learning Sparse kernel Laplacian Regularized Least Squares) to break through the limitations.

RESULT

First, we extract three miRNA similarity kernels and three disease similarity kernels. Then, we combine these kernels into a single kernel through the Fast Kernel Learning (FKL) model, and use sparse kernel (Spa) to eliminate noise in the integrated similarity kernel. Finally, we find the associations via Laplacian Regularized Least Squares (LapRLS). Based on three evaluation methods, global and local leave-one-out cross validation (LOOCV), and 5-fold cross validation, the AUCs of our method achieve 0.9563, 0.8398 and 0.9535, thus it can be seen that our method is reliable. Then, we use case studies of eight neoplasms to further analyze the performance of our method. We find that most of the predicted miRNA-disease associations are confirmed by previous traditional experiments, and some important miRNAs should be paid more attention, which uncover more associations of various neoplasms than other miRNAs.

CONCLUSIONS

Our proposed model can reveal miRNA-disease associations and improve the accuracy of correlation prediction for various diseases. Our method can be also easily extended with more similarity kernels.

Medical examination powers miR-194-5p as a biomarker for postmenopausal osteoporosis

An important attribute of microRNAs is their potential use as disease biomarkers. However, such applications may be restricted because of unsatisfactory performance of the microRNA of interest. Owing to moderate correlation with spine T-score, miR-194-5p was identified as a potential biomarker for postmenopausal osteoporosis. Here, we determined whether medical examination could improve its characteristic as a biomarker for postmenopausal osteoporosis. We recruited 230 postmenopausal Chinese women to measure circulating levels of miR-194-5p, determine the spine bone status, and perform a 42-item medical examination. No obvious information redundancy was observed between miR-194-5p and any one item. However, on examining miR-194-5p alone, the sensitivity at fixed specificity of 0.9 (SE_SP=0.9) was 0.27, implying poor identification of at-risk individuals. Model integration of the microRNA and multiple medical items strengthened this property; in addition, model complexity greatly contributed to performance improvement. Using a model composed of two artificial neural networks, the ability of miR-194-5p to identify at-risk individuals significantly improved (SE_SP=0.9 = 0.54) when correlated with five medical items: weight, age, left ventricular end systolic diameter, alanine aminotransferase, and urine epithelial cell count. We present a feasible way to achieve a more accurate microRNA-based biomarker for a disease of interest.

MicroRNAs tend to synergistically control expression of genes encoding extensively-expressed proteins in humans

Considering complicated microRNA (miRNA) biogenesis and action mechanisms, it was thought so high energy-consuming for a cell to afford simultaneous over-expression of many miRNAs. Thus it prompts that an alternative miRNA regulation pattern on protein-encoding genes must exist, which has characteristics of energy-saving and precise protein output. In this study, expression tendency of proteins encoded by miRNAs’ target genes was evaluated in human organ scale, followed by quantitative assessment of miRNA synergism. Expression tendency analysis suggests that universally expressed proteins (UEPs) tend to physically interact in clusters and participate in fundamental biological activities whereas disorderly expressed proteins (DEPs) are inclined to relatively independently execute organ-specific functions. Consistent with this, miRNAs that mainly target UEP-encoding mRNAs, such as miR-21, tend to collaboratively or even synergistically act with other miRNAs in fine-tuning protein output. Synergistic gene regulation may maximize miRNAs’ efficiency with less dependence on miRNAs’ abundance and overcome the deficiency that targeting plenty of genes by single miRNA makes miRNA-mediated regulation high-throughput but insufficient due to target gene dilution effect. Furthermore, our in vitro experiment verified that merely 25 nM transfection of miR-21 be sufficient to influence the overall state of various human cells. Thus miR-21 was identified as a hub in synergistic miRNA–miRNA interaction network. Our findings suggest that synergistic miRNA–miRNA interaction is an important endogenous miRNA regulation mode, which ensures adequate potency of miRNAs at low abundance, especially those implicated in fundamental biological regulation.

miRsig: a consensus-based network inference methodology to identify pan-cancer miRNA-miRNA interaction signatures

Decoding the patterns of miRNA regulation in diseases are important to properly realize its potential in diagnostic, prog- nostic, and therapeutic applications. Only a handful of studies computationally predict possible miRNA-miRNA interactions; hence, such interactions require a thorough investigation to understand their role in disease progression. In this paper, we design a novel computational pipeline to predict the common signature/core sets of miRNA-miRNA interactions for different diseases using network inference algorithms on the miRNA-disease expression profiles; the individual predictions of these algorithms were then merged using a consensus-based approach to predict miRNA-miRNA associations. We next selected the miRNA-miRNA associations across particular diseases to generate the corresponding disease-specific miRNA-interaction networks. Next, graph intersection analysis was performed on these networks for multiple diseases to identify the common signature/core sets of miRNA interactions. We applied this pipeline to identify the common signature of miRNA-miRNA inter- actions for cancers. The identified signatures when validated using a manual literature search from PubMed Central and the PhenomiR database, show strong relevance with the respective cancers, providing an indirect proof of the high accuracy of our methodology. We developed miRsig, an online tool for analysis and visualization of the disease-specific signature/core miRNA-miRNA interactions, available at: http://bnet.egr.vcu.edu/miRsig.

Prioritizing cancer-related microRNAs by integrating microRNA and mRNA datasets

MicroRNAs (miRNAs) are small non-coding RNAs regulating the expression of target genes, and they are involved in cancer initiation and progression. Even though many cancer-related miRNAs were identified, their functional impact may vary, depending on their effects on the regulation of other miRNAs and genes. In this study, we propose a novel method for the prioritization of candidate cancer-related miRNAs that may affect the expression of other miRNAs and genes across the entire biological network. For this, we propose three important features: the average expression of a miRNA in multiple cancer samples, the average of the absolute correlation values between the expression of a miRNA and expression of all genes, and the number of predicted miRNA target genes. These three features were integrated using order statistics. By applying the proposed approach to four cancer types, glioblastoma, ovarian cancer, prostate cancer, and breast cancer, we prioritized candidate cancer-related miRNAs and determined their functional roles in cancer-related pathways. The proposed approach can be used to identify miRNAs that play crucial roles in driving cancer development, and the elucidation of novel potential therapeutic targets for cancer treatment.

Genome-wide miR-155 and miR-802 target gene identification in the hippocampus of Ts65Dn Down syndrome mouse model by miRNA sponges

BACKGROUND

Down syndrome (DS) or trisomy 21 is the result of a genetic dosage imbalance that translates in a broad clinical spectrum. A major challenge in the study of DS is the identification of functional genetic elements with wide impact on phenotypic alterations. Recently, miRNAs have been recognized as major contributors to several disease conditions by acting as post-transcriptional regulators of a plethora of genes. Five chromosome 21 (HSA21) miRNAs have been found overexpressed in DS individuals and could function as key elements in the pathophysiology. Interestingly, in the trisomic Ts65Dn DS mouse model two of these miRNAs (miR-155 and miR-802) are also triplicated and overexpressed in brain.

RESULTS

In the current work, we interrogated the impact of miR-155 and miR-802 upregulation on the transcriptome of Ts65Dn brains. We developed a lentiviral miRNA-sponge strategy (Lv-miR155-802T) to identify in vivo relevant miR-155 and miR-802 target mRNAs. Hippocampal injections of lentiviral sponges in Ts65Dn mice normalized the expression of miR-155 and miR-802 and rescued the levels of their targets methyl-CpG-binding protein 2 gene (Mecp2), SH2 (Src homology 2)-containing inositol phosphatase-1 (Ship1) and Forkhead box protein M1 (FoxM1). Transcriptomic data of Lv-miR155-802T miRNA-sponge treated hippocampi correlated with candidate targets highlighting miRNA dosage-sensitive genes. Significant associations were found in a subset of genes (Rufy2, Nova1, Nav1, Thoc1 and Sumo3) that could be experimentally validated.

CONCLUSIONS

The lentiviral miRNA-sponge strategy demonstrated the genome-wide regulatory effects of miR-155 and miR-802. Furthermore, the analysis combining predicted candidates and experimental transcriptomic data proved to retrieve genes with potential significance in DS-hippocampal phenotype bridging with DS other neurological-associated diseases such as Alzheimer's disease.