microRNAs in breast cancer: regulatory roles governing the hallmarks of cancer

Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations

Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA-disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations.

Analysis of lncRNA-related studies of ivermectin-sensitive and -resistant strains of Haemonchus contortus

In this study, 858 novel long non-coding RNAs (lncRNAs) were predicted as sensitive and resistant strains of Haemonchus contortus to ivermectin. These lncRNAs underwent bioinformatic analysis. In total, 205 lncRNAs significantly differed using log2 (difference multiplicity) > 1 or log2 (difference multiplicity) <  - 1 and FDR < 0.05 as the threshold for significant difference analysis. We selected five lncRNAs based on significant differences in expression, cis-regulation, and their association with the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. These expressions of lncRNAs, namely MSTRG.12610.1, MSTRG.8169.1, MSTRG.6355.1, MSTRG.980.1, and MSTRG.9045.1, were significantly downregulated. These findings were consistent with the results of transcriptomic sequencing. We further investigated the relative expression of target gene mRNAs and the regulation of mRNA and miRNA, starting with lncRNA cis-regulation of mRNA, and constructed a lncRNA-mRNA-miRNA network regulation. After a series of statistical analyses, we finally screened out UGT8, Unc-116, Fer-related kinase-1, GGPP synthase 1, and sart3, which may be involved in developing drug resistance under the regulation of their corresponding lncRNAs. The findings of this study provide a novel direction for future studies on drug resistance targets.

Sample-to-answer salivary miRNA testing: New frontiers in point-of-care diagnostic technologies

MicroRNA (miRNA), crucial non-coding RNAs, have emerged as key biomarkers in molecular diagnostics, prognosis, and personalized medicine due to their significant role in gene expression regulation. Salivary miRNA, in particular, stands out for its non-invasive collection method and ease of accessibility, offering promising avenues for the development of point-of-care diagnostics for a spectrum of diseases, including cancer, neurodegenerative disorders, and infectious diseases. Such development promises rapid and precise diagnosis, enabling timely treatment. Despite significant advancements in salivary miRNA-based testing, challenges persist in the quantification, multiplexing, sensitivity, and specificity, particularly for miRNA at low concentrations in complex biological mixtures. This work delves into these challenges, focusing on the development and application of salivary miRNA tests for point-of-care use. We explore the biogenesis of salivary miRNA and analyze their quantitative expression and their disease relevance in cancer, infection, and neurodegenerative disorders. We also examined recent progress in miRNA extraction, amplification, and multiplexed detection methods. This study offers a comprehensive view of the development of salivary miRNA-based point-of-care testing (POCT). Its successful advancement could revolutionize the early detection, monitoring, and management of various conditions, enhancing healthcare outcomes. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > Diagnostic Nanodevices.

Prediction of MicroRNA-Disease Potential Association Based on Sparse Learning and Multilayer Random Walks

More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained. The disease similarity network, miRNA similarity network, and miRNA-disease association network are used to construct heterogeneous networks, and the stable probability is obtained based on the topological structure features of diseases and miRNAs through a multilayer random walk algorithm to predict miRNA-disease potential association. The experimental results show that the prediction accuracy of this model is significantly improved compared with the previous related models. We evaluated the model using global leave-one-out cross-validation (global LOOCV) and fivefold cross-validation (5-fold CV). The area under the curve (AUC) value for the LOOCV is 0.9368. The mean AUC value for 5-fold CV is 0.9335 and the variance is 0.0004. In the case study, the results show that SLMRWMDA is effective in inferring the potential association of miRNA-disease.

Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases

MicroRNAs (miRNAs) play important roles in the occurrence and development of diseases. However, it is still challenging to identify the effective miRNA biomarkers for improving the disease diagnosis and prognosis. In this study, we proposed the miRNA data analysis method based on multi-view miRNA networks and reinforcement learning, miRMarker, to define the potential miRNA disease biomarkers. miRMarker constructs the cooperative regulation network and functional similarity network based on the expression data and known miRNA-disease relations, respectively. The cooperative regulation of miRNAs was evaluated by measuring the changes of relative expression. Natural language processing was introduced for calculating the miRNA functional similarity. Then, miRMarker integrates the multi-view miRNA networks and defines the informative miRNA modules through a reinforcement learning strategy. We compared miRMarker with eight efficient data analysis methods on nine transcriptomics datasets to show its superiority in disease sample discrimination. The comparison results suggested that miRMarker outperformed other data analysis methods in receiver operating characteristic analysis. Furthermore, the defined miRNA modules of miRMarker on colorectal cancer data not only show the excellent performance of cancer sample discrimination but also play significant roles in the cancer-related pathway disturbances. The experimental results indicate that miRMarker can build the robust miRNA interaction network by integrating the multi-view networks. Besides, exploring the miRNA interaction network using reinforcement learning favors defining the important miRNA modules. In summary, miRMarker can be a hopeful tool in biomarker identification for human diseases.

The Analysis of Selected miRNAs and Target MDM2 Gene Expression in Oral Squamous Cell Carcinoma

MiRNAs could play an important role in tumorigenesis and progression. The oncoprotein MDM2 (murine double minute 2) was identified as a negative regulator of the tumour suppressor p53. This study aims to analyse the expression of the MDM2 target miRNA candidates (miR-3613-3p, miR-371b-5p and miR-3658) and the MDM2 gene in oral squamous cell carcinoma tumour and margin samples and their association with the selected socio-demographic and clinicopathological characteristics. The study group consisted of 50 patients. The miRNAs and MDM2 gene expression levels were assessed by qPCR. The expression analysis of the miRNAs showed the expression of only one of them, i.e., miR-3613-3p. We found no statistically significant differences in the miR-3613-3p expression in tumour samples compared to the margin samples. When analysing the effect of smoking on miR-3613-3p expression, we demonstrated a statistically significant difference between smokers and non-smokers. In addition, we showed an association between the miR-3613-3p expression level and some clinical parameters in tumour samples (T, N and G). Our study demonstrates that miR-3613-3p overexpression is involved in the tumour progression of OSCC. This indicates that miR-3613-3p possesses potential prognostic values.

Identification and verification of a prognostic signature based on a miRNA-mRNA interaction pattern in colon adenocarcinoma

The expression characteristics of non-coding RNA (ncRNA) in colon adenocarcinoma (COAD) are involved in regulating various biological processes. To achieve these functions, ncRNA and a member of the Argonaute protein family form an RNA-induced silencing complex (RISC). The RISC is directed by ncRNA, especially microRNA (miRNA), to bind the target complementary mRNAs and regulate their expression by interfering with mRNA cleavage, degradation, or translation. However, how to identify potential miRNA biomarkers and therapeutic targets remains unclear. Here, we performed differential gene screening based on The Cancer Genome Atlas dataset and annotated meaningful differential genes to enrich related biological processes and regulatory cancer pathways. According to the overlap between the screened differential mRNAs and differential miRNAs, a prognosis model based on a least absolute shrinkage and selection operator-based Cox proportional hazards regression analysis can be established to obtain better prognosis characteristics. To further explore the therapeutic potential of miRNA as a target of mRNA intervention, we conducted an immunohistochemical analysis and evaluated the expression level in the tissue microarray of 100 colorectal cancer patients. The results demonstrated that the expression level of POU4F1, DNASE1L2, and WDR72 in the signature was significantly upregulated in COAD and correlated with poor prognosis. Establishing a prognostic signature based on miRNA target genes will help elucidate the molecular pathogenesis of COAD and provide novel potential targets for RNA therapy.

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations

MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases' biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease.

Size-Controlled DNA Tile Self-Assembly Nanostructures Through Caveolae-Mediated Endocytosis for Signal-Amplified Imaging of MicroRNAs in Living Cells

Signal-amplified imaging of microRNAs (miRNAs) is a promising strategy at the single-cell level because liquid biopsy fails to reflect real-time dynamic miRNA levels. However, the internalization pathways for available conventional vectors predominantly involve endo-lysosomes, showing nonideal cytoplasmic delivery efficiency. In this study, size-controlled 9-tile nanoarrays are designed and constructed by integrating catalytic hairpin assembly (CHA) with DNA tile self-assembly technology to achieve caveolae-mediated endocytosis for the amplified imaging of miRNAs in a complex intracellular environment. Compared with classical CHA, the 9-tile nanoarrays possess high sensitivity and specificity for miRNAs, achieve excellent internalization efficiency by caveolar endocytosis, bypassing lysosomal traps, and exhibit more powerful signal-amplified imaging of intracellular miRNAs. Because of their excellent safety, physiological stability, and highly efficient cytoplasmic delivery, the 9-tile nanoarrays can realize real-time amplified monitoring of miRNAs in various tumor and identical cells of different periods, and imaging effects are consistent with the actual expression levels of miRNAs, ultimately demonstrating their feasibility and capacity. This strategy provides a high-potential delivery pathway for cell imaging and targeted delivery, simultaneously offering a meaningful reference for the application of DNA tile self-assembly technology in relevant fundamental research and medical diagnostics.

Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion

MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.

MEAHNE: miRNA-Disease Association Prediction Based on Semantic Information in a Heterogeneous Network

Correct prediction of potential miRNA-disease pairs can considerably accelerate the experimental process in biomedical research. However, many methods cannot effectively learn the complex information contained in multisource data, limiting the performance of the prediction model. A heterogeneous network prediction model (MEAHNE) is proposed to make full use of the complex information contained in multisource data. To fully mine the potential relationship between miRNA and disease, we collected multisource data and constructed a heterogeneous network. After constructing the network, we mined potential associations in the network through a designed heterogeneous network framework (MEAHNE). MEAHNE first learned the semantic information of the metapath instances, then used the attention mechanism to encode the semantic information as attention weights and aggregated nodes of the same type using the attention weights. The semantic information was also integrated into the node. MEAHNE optimized parameters through end-to-end training. MEAHNE was compared with other state-of-the-art heterogeneous graph neural network methods. The values of the area under the precision-recall curve and the receiver operating characteristic curve demonstrated the superiority of MEAHNE. In addition, MEAHNE predicted 20 miRNAs each for breast cancer and nasopharyngeal cancer and verified 18 miRNAs related to breast cancer and 14 miRNAs related to nasopharyngeal cancer by consulting related databases.

Role of microRNA/lncRNA Intertwined With the Wnt/β-Catenin Axis in Regulating the Pathogenesis of Triple-Negative Breast Cancer

Objective (s): In this mini-review, we aimed to discuss the Wnt/β-catenin signaling pathway modulation in triple-negative breast cancer, particularly the contribution of lncRNAs and miRNAs in its regulation and their possible entwining role in breast cancer pathogenesis, proliferation, migration, or malignancy.

Background: Malignant tumor formation is very high for breast cancer in women and is a leading cause of death all over the globe. Among breast cancer subtypes, triple-negative breast cancer is rife in premenopausal women, most invasive, and prone to metastasis. Complex pathways are involved in this cancer’s pathogenesis, advancement, and malignancy, including the Wnt/β-catenin signaling pathway. This pathway is conserved among vertebrates and is necessary for sustaining cell homeostasis. It is regulated by several elements such as transcription factors, enhancers, non-coding RNAs (lncRNAs and miRNAs), etc.

Methods: We evaluated lncRNAs and miRNAs differentially expressed in triple-negative breast cancer (TNBC) from the cDNA microarray data set literature survey. Using in silico analyses combined with a review of the current literature, we anticipated identifying lncRNAs and miRNAs that might modulate the Wnt/β-catenin signaling pathway.

Result: The miRNAs and lncRNAs specific to triple-negative breast cancer have been identified based on literature and database searches. Tumorigenesis, metastasis, and EMT were all given special attention. Apart from cross-talk being essential for TNBC tumorigenesis and treatment outcomes, our results indicated eight upregulated and seven downregulated miRNAs and 19 upregulated and three downregulated lncRNAs that can be used as predictive or diagnostic markers. This consolidated information could be useful in the clinic and provide a combined literature resource for TNBC researchers working on the Wnt/β-catenin miRNA/lncRNA axis.

Conclusion: In conclusion, because the Wnt pathway and miRNAs/lncRNAs can modulate TNBC, their intertwinement results in a cascade of complex reactions that affect TNBC and related processes. Their function in TNBC pathogenesis has been highlighted in molecular processes underlying the disease progression.

MicroRNA-135 inhibits initiation of epithelial-mesenchymal transition in breast cancer by targeting ZNF217 and promoting m6A modification of NANOG

MicroRNAs play significant roles in various malignancies, with breast cancer (BC) being no exception. Consequently, we explored the functional mechanism of miR-135 in the progression of BC. In total, 55 pairs of BC and matched adjacent normal tissues were clinically collected from patients, followed by quantification of miR-135 and zinc finger protein 217 (ZNF217) expression patterns in BC tissues and cells. Accordingly, high ZNF217 expression and low miR-135 expression levels were identified in BC tissues and cells. Subsequently, the expressions of miR-135 and ZNF217 were altered to evaluate their effects on BC cell migration, invasion and EMT initiation. It was found that when ZNF217 was silenced or miR-135 was elevated, BC cell malignant behaviors were significantly inhibited, which was reproduced in nude mice for in vivo evidence. Furthermore, dual-luciferase reporter gene assay revealed the presence of direct binding between miR-135 and ZNF217. Subsequent co-immunoprecipitation, methylated-RNA binding protein immunoprecipitation and photoactivatable ribonucleoside enhanced-crosslinking and immunoprecipitation assays further revealed that ZNF217 could upregulate NANOG by reducing N6-methyladenosine levels via methyltransferase-like 13 (METTL3). Collectively, our findings highlighted the role of the miR-135/ZNF217/METTL3/NANOG axis in the progression of BC, emphasizing potential therapeutic targets ZNF217 silencing and miR-135 upregulation in preventing or treating BC.

MiR-556-5p modulates migration, invasion, and epithelial-mesenchymal transition in breast cancer cells via targeting PTHrP

Breast cancer bone metastases may block normal bone remodeling and promote bone degradation, during which several signaling pathways and small non-coding miRNAs might all play a role. miRNAs and target mRNAs that might be associated with breast cancer bone metastasis were analyzed and selected using bioinformatics analyses based on online data. The 3' untranslated region of key factors associated with breast cancer metastasis were examined for candidate miRNA binding site using Targetscan. The predicted binding was validated. The specific effects of single miRNA and dynamic effects of the miRNA-mRNA axis on breast cancer cell metastasis were investigated. miR-556-5p was downregulated in breast cancer samples according to online datasets and experimental analyses. In breast cancer cells, miR-556-5p overexpression inhibited, whereas miR-556-5p inhibition promoted cancer cell invasion and migration. Among key factors associated with breast cancer bone metastasis, parathyroid hormone related protein (PTHrP) 3'UTR possessed miR-556-5p binding site. Through direct binding, miR-556-5p negatively regulated PTHrP expression. In breast cancer cell lines, miR-556-5p inhibition promoted, whereas PTHrP silencing suppressed cancer cell migration, invasion, and epithelial-mesenchymal transition; the effects of miR-556-5p inhibition were partially reversed by PTHrP silencing. In summary, miR-556-5p targets PTHrP to modulate the cell migration and invasion of breast cancer.

Higher-Order Proximity-Based MiRNA-Disease Associations Prediction

MiRNA-disease association prediction plays an important role in identifying human disease-related miRNAs. This approach is helpful not only to formulate individualized diagnosis schemes, but also to understand the pathogenesis of diseases. Many studies have focused on enhancing the prediction performance using explicit side information, such as miRNA functional similarity and disease semantic similarity. The existing approaches, however, often ignore the higher-order implicit proximity among miRNAs and diseases. To this end, in this paper, we first propose a novel approach HOP_MDA (Higher-Order Proximity based MiRNA and Disease Association Prediction) for predicting potential association between miRNA and disease. Both explicit interaction information and implicit higher-order proximity information between miRNA and disease are encoded with different order proximity matrices which are weightily combined into a parameterized prediction matrix. A supervised learning approach based on the known miRNAs-disease associations is proposed to determine the optimal weight parameters. The prediction matrix is then used to achieve effective prediction. Additionally, a higher-order proximity approximation technique (HOPA_MDA) is presented to make more efficient predictions. 5-fold cross validation is used to evaluate the performance of our proposed method. The average AUC values of HOPA_MDA for two real datasets are 0.921+/-0.002 and 0.944+/-0.0015, respectively. Our method can also predict potential miRNAs specific to new diseases with no known related miRNAs.

Exploring the Mechanism of Baicalin Intervention in Breast Cancer Based on MicroRNA Microarrays and Bioinformatics Strategies

Objective

To explore the mechanism of baicalin intervention in breast cancer based on microRNA microarrays.

Methods

The inhibitory rate of baicalin intervention in MCF-7 breast cancer cells was determined by MTT. Then, the miRNA microarrays were used to validate the key microRNAs. After that, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to validate microRNA, hsa-miR-15a, hsa-miR-100, hsa-miR-16, and hsa-miR-7t. Finally, the potential targets of these key microRNAs are predicted by miRWalk, and DAVID was utilized for gene ontology (GO) enrichment analysis and pathway enrichment analysis.

Results

Baicalin may inhibit the proliferation of MCF-7 cells in a dose-dependent and time-dependent manner. The concentration of baicalin 150 μmol/L was determined for the subsequent miRNA chip research. A total of 92 upregulated microRNAs and 35 downregulated microRNAs were obtained. The upregulated miRNAs include hsa-miR-6799-5p, hsa-miR-6126, hsa-miR-4792, hsa-miR-6848-5p, hsa-miR-3197, hsa-miR-6779-5p, and hsa-miR -654-5p. The downregulated miRNAs include hsa-miR-3911, hsa-miR-504-5p, hsa-miR-30a-3p, hsa-miR-193b-3p, and hsa-miR-181b-5p. Then, differentially expressed miRNA was verified by qRT-PCR. The results showed that the expression of hsa-miR-15a, hsa-miR-100, hsa-miR-16, and hsa-let-7c was upregulated (P < 0.05), which was consistent with the results of the miRNA microarray. The enrichment analysis showed that baicalin might regulate the DNA-templated proliferation, DNA-templated transcription, p53 signaling pathway, etc., of MCF-7 breast cancer cells through miRNA.

Conclusion

Baicalin inhibits the proliferation of breast cancer cells. It may achieve antitumor effects through regulating microRNAs so as to affect the DNA replication (such as cellular response to DNA damage stimulus and DNA binding), RNA transcription (such as regulation of transcription, DNA-templated, transcription from RNA polymerase II promoter, and transcription factor binding), protein synthesis (such as mRNA binding, Golgi apparatus, and protein complex), endocytosis, pathways in cancer, p53 signaling pathway, and so on.

Predicting miRNA-disease associations using an ensemble learning framework with resampling method

MOTIVATION

Accumulating evidences have indicated that microRNA (miRNA) plays a crucial role in the pathogenesis and progression of various complex diseases. Inferring disease-associated miRNAs is significant to explore the etiology, diagnosis and treatment of human diseases. As the biological experiments are time-consuming and labor-intensive, developing effective computational methods has become indispensable to identify associations between miRNAs and diseases.

RESULTS

We present an Ensemble learning framework with Resampling method for MiRNA-Disease Association (ERMDA) prediction to discover potential disease-related miRNAs. Firstly, the resampling strategy is proposed for building multiple different balanced training subsets to address the challenge of sample imbalance within the database. Then, ERMDA extracts miRNA and disease feature representations by integrating miRNA-miRNA similarities, disease-disease similarities and experimentally verified miRNA-disease association information. Next, the feature selection approach is applied to reduce the redundant information and increase the diversity among these subsets. Lastly, ERMDA constructs an individual learner on each subset to yield primitive outcomes, and the soft voting method is introduced for making the final decision based on the prediction results of individual learners. A series of experimental results demonstrates that ERMDA outperforms other state-of-the-art methods on both balanced and unbalanced testing sets. Besides, case studies conducted on the three human diseases further confirm the ERMDA's prediction capability for identifying potential disease-related miRNAs. In conclusion, these experimental results demonstrate that our method can serve as an effective and reliable tool for researchers to explore the regulatory role of miRNAs in complex diseases.

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolutional autoencoder (GCAE) was utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performances were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA was beneficial to infer potential associations of miRNA-disease.

Numerous studies have demonstrated that miRNAs are closely related to several common human diseases, so observing unverified associations between miRNAs and diseases is conducive to the diagnose and treatment of complex diseases. Considerable models proposed to infer potential miRNA-disease associations have made the prediction more effective and productive. We constructed GCAEMDA model to acquire more accuracy prediction result by integrating graph convolutional network and autoencoder to make prediction based on multi-source miRNA and disease information. The five-fold cross validation and global leave-one-out cross validation were implemented to evaluate the performance of our model. Consequently, GCAEMDA reached AUCs of 0.9415 and 0.9505 respectively that were distinctly higher than AUCs of other comparative models. Furthermore, we carried out case studies on lung neoplasms and breast neoplasms to demonstrate the practical application of the model, 47 and 47 of top-50 candidate miRNAs were confirmed by experimental reports. In summary, GCAEMDA could be considered as an effective and accuracy model to reveal relationship between miRNAs and diseases.

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

BACKGROUND

A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA-disease associations could provide us a root cause understanding of the underlying pathogenesis in which promotes the progress of drug development. However, traditional biological experiments are very time-consuming and costly. Therefore, we come up with an efficient models to solve this challenge.

RESULTS

In this work, we propose a deep learning model called EOESGC to predict potential miRNA-disease associations based on embedding of embedding and simplified convolutional network. Firstly, integrated disease similarity, integrated miRNA similarity, and miRNA-disease association network are used to construct a coupled heterogeneous graph, and the edges with low similarity are removed to simplify the graph structure and ensure the effectiveness of edges. Secondly, the Embedding of embedding model (EOE) is used to learn edge information in the coupled heterogeneous graph. The training rule of the model is that the associated nodes are close to each other and the unassociated nodes are far away from each other. Based on this rule, edge information learned is added into node embedding as supplementary information to enrich node information. Then, node embedding of EOE model training as a new feature of miRNA and disease, and information aggregation is performed by simplified graph convolution model, in which each level of convolution can aggregate multi-hop neighbor information. In this step, we only use the miRNA-disease association network to further simplify the graph structure, thus reducing the computational complexity. Finally, feature embeddings of both miRNA and disease are spliced into the MLP for prediction. On the EOESGC evaluation part, the AUC, AUPR, and F1-score of our model are 0.9658, 0.8543 and 0.8644 by 5-fold cross-validation respectively. Compared with the latest published models, our model shows better results. In addition, we predict the top 20 potential miRNAs for breast cancer and lung cancer, most of which are validated in the dbDEMC and HMDD3.2 databases.

CONCLUSION

The comprehensive experimental results show that EOESGC can effectively identify the potential miRNA-disease associations.

Small molecules with huge impacts: the role of miRNA-regulated PI3K pathway in human malignancies

Along with evolution, a considerable number of signaling cascades have evolved within cells to meet their multifaceted needs. Among transmitting molecules, phosphoinositide 3-kinase (PI3K), Akt, and mammalian target of rapamycin (mTOR) have teamed up to build a signaling axis that effectively regulates various cellular processes including cell proliferation and migration. Given the extensive output of the PI3K/Akt/mTOR signaling axis, its aberrancy could subsequently lead to the formation of a wide range of human cancers spanning from hematologic malignancies to different types of solid tumors. Despite the high frequency of the PI3K pathway over-activation in most malignancies, mutations in the DNA sequence are not equally common. Such incompatibility sheds light on the possible effects of post-translational modification mechanisms that may take control of this pathway, some of the most important ones of which are through microRNAs (miRNAs or miRs). The present review is designed to take off the veil from the regulatory role of these small non-coding RNAs on the PI3K/Akt/mTOR signaling axis in carcinogenesis.

MicroRNA expression profile and identification of novel microRNA biomarkers for metabolic syndrome

The lack of efficient biomarkers is the main reason for the inaccurate early diagnosis and poor treatment outcomes of patients with metabolic syndrome (MetS). The current study aimed to identify several novel microRNA (miRNA) biomarkers for metabolic syndrome via high-throughput sequencing and comprehensive bioinformatics analysis. Through high-throughput sequencing and differentially expressed miRNA (DEM) analysis, we first identified two upregulated and 36 downregulated DEMs in the plasma samples of patients with MetS compared to the healthy volunteers. Additionally, we also predicted 379 potential target genes and subsequently carried out enrichment analysis and protein–protein interaction network analysis to investigate the signaling pathways and functions of the identified DEMs as well as the interactions between their target genes. Furthermore, we selected two upregulated and top 10 downregulated DEMs with the highest |log2FC| values as the key microRNAs, which may serve as potential biomarkers for MetS. RT-qPCR was performed to validated these result. Finally, hsa-miR-526b-5p, hsa-miR-6516-5p was identified as the novel biomarkers for MetS.

miR-142-3p Modulates Cell Invasion and Migration via PKM2-Mediated Aerobic Glycolysis in Colorectal Cancer

Decreased expression of miR-142-3p was observed in human cancers. However, the function and mechanism of miR-142-3p in human colorectal cancer remain obscure. The expressions of miR-142-3p in human colorectal cancer tissues and cell lines were measured by RT-qPCR. The effects of miR-142-3p on cell invasion and migration were detected by transwell assays. The efficiency of aerobic glycolysis was determined by glucose consumption and lactate production. Dual-luciferase reporter assays were performed to confirm the correlation between miR-142-3p and pyruvate kinase isozyme M2 (PKM2). The level of PKM2 was assessed by western blotting. Our results showed that the expression of miR-142-3p was decreased both in human colorectal cancer tissues and in cells. Overexpression of miR-142-3p in cell line attenuated colorectal cancer cell invasion and migration. About the underlying mechanism, we found that miR-142-3p modulated aerobic glycolysis via targeting pyruvate kinase M2 (PKM2). In addition, we demonstrated PKM2 and PKM2-mediated aerobic glycolysis contributes to miR-142-3p-mediated colorectal cancer cell invasion and migration. Hence, these data suggested that miR-142-3p was a potential therapeutic target for the treatment of human colorectal cancer.

The Role of microRNA Let-7d in Female Malignancies and Diseases of the Female Reproductive Tract

microRNAs are small noncoding RNAs that regulate gene expression at the posttranscriptional level. Let-7d is a microRNA of the conserved let-7 family that is dysregulated in female malignancies including breast cancer, ovarian cancer, endometrial cancer, and cervical cancer. Moreover, a dysregulation is observed in endometriosis and pregnancy-associated diseases such as preeclampsia and fetal growth restriction. Let-7d expression is regulated by cytokines and steroids, involving transcriptional regulation by OCT4, MYC and p53, as well as posttranscriptional regulation via LIN28 and ADAR. By downregulating a wide range of relevant mRNA targets, let-7d affects cellular processes that drive disease progression such as cell proliferation, apoptosis (resistance), angiogenesis and immune cell function. In an oncological context, let-7d has a tumor-suppressive function, although some of its functions are context-dependent. Notably, its expression is associated with improved therapeutic responses to chemotherapy in breast and ovarian cancer. Studies in mouse models have furthermore revealed important roles in uterine development and function, with implications for obstetric diseases. Apart from a possible utility as a diagnostic blood-based biomarker, pharmacological modulation of let-7d emerges as a promising therapeutic concept in a variety of female disease conditions.

New insight towards development of paclitaxel and docetaxel resistance in cancer cells: EMT as a novel molecular mechanism and therapeutic possibilities

Epithelial-to-mesenchymal transition (EMT) mechanism is responsible for metastasis and migration of cancer cells to neighboring cells and tissues. Morphologically, epithelial cells are transformed to mesenchymal cells, and at molecular level, E-cadherin undergoes down-regulation, while an increase occurs in N-cadherin and vimentin levels. Increasing evidence demonstrates role of EMT in mediating drug resistance of cancer cells. On the other hand, paclitaxel (PTX) and docetaxel (DTX) are two chemotherapeutic agents belonging to taxene family, capable of inducing cell cycle arrest in cancer cells via preventing microtubule depolymerization. Aggressive behavior of cancer cells resulted from EMT-mediated metastasis can lead to PTX and DTX resistance. Upstream mediators of EMT such as ZEB1/2, TGF-β, microRNAs, and so on are involved in regulating response of cancer cells to PTX and DTX. Tumor-suppressing factors inhibit EMT to promote PTX and DTX sensitivity of cancer cells. Furthermore, three different strategies including using anti-tumor compounds, gene therapy and delivery systems have been developed for suppressing EMT, and enhancing cytotoxicity of PTX and DTX against cancer cells that are mechanistically discussed in the current review.

MicroRNA-4491 enhances cell proliferation and inhibits cell apoptosis in non-small cell lung cancer via targeting TRIM7

MicroRNAs (miRNAs) are involved in the development of non-small cell lung cancer (NSCLC). However, the biological roles of several aberrantly expressed miRNAs have not been explored yet. In the present study, miR-4491 was identified as a novel upregulated miRNA in NSCLC tissues and cell lines. Downregulation of miR-4491 by a miR-4491 inhibitor inhibited the proliferation and triggered the apoptosis of NSCLC cells. Tripartite motif containing 7 (TRIM7), a tumor suppressor gene expressed in NSCLC, was demonstrated in the present study to be directly targeted by miR-4491. This finding was verified by bioinformatics analysis, reverse transcription-quantitative PCR, western blotting and dual luciferase reporter assays. Furthermore, downregulation of miR-4491 inactivated nuclear factor-κB signaling via induction of TRIM7. In addition, TRIM7 silencing attenuated the effect of miR-4491 inhibitor in NSCLC cells. The decreased TRIM7 level in NSCLC tissues was negatively correlated with miR-4491 expression in NSCLC tissues. In conclusion, the findings from this study demonstrated that miR-4491 expression was upregulated in NSCLC tissues and cells and that miR-4491 may promote NSCLC progression via targeting TRIM7.

The synergistic protection of EGCG and quercetin against streptozotocin (STZ)-induced NIT-1 pancreatic β cell damage via upregulation of BCL-2 expression by miR-16-5p

EGCG and quercetin are flavonoids which usually co-exist in edible plants and they exhibit anti-diabetes effects. This study aimed to explore the mechanisms by which quercetin and EGCG synergistically protected pancreatic β-cells from streptozotocin-induced apoptosis. EGCG, quercetin, and their combinations (both 15 μM) all reversed STZ-induced cells damage and enhanced glucose-stimulated insulin secretion, with the combination being more effective than a single compound. At the molecular level, the EGCG-quercetin combination upregulated BCL-2 expression and caused a greater reduction in miR-16-5p level than EGCG alone or quercetin alone. Overexpression of miR-16-5p could offset the down-regulated apoptotic genes caused by the synergistic action of the combination. These findings suggest that EGCG and quercetin exert synergistic anti-diabetes effect, possibly via decreasing the expression of miR-16-5p that targets directly BCL-2. This is the first report on a miRNA-based mechanism underlying the synergistic protective effect of EGCG and quercetin against pancreatic cell damage.

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.

NMCMDA: neural multicategory MiRNA-disease association prediction

MOTIVATION

There is growing evidence showing that the dysregulations of miRNAs cause diseases through various kinds of the underlying mechanism. Thus, predicting the multiple-category associations between microRNAs (miRNAs) and diseases plays an important role in investigating the roles of miRNAs in diseases. Moreover, in contrast with traditional biological experiments which are time-consuming and expensive, computational approaches for the prediction of multicategory miRNA-disease associations are time-saving and cost-effective that are highly desired for us.

RESULTS

We present a novel data-driven end-to-end learning-based method of neural multiple-category miRNA-disease association prediction (NMCMDA) for predicting multiple-category miRNA-disease associations. The NMCMDA has two main components: (i) encoder operates directly on the miRNA-disease heterogeneous network and leverages Graph Neural Network to learn miRNA and disease latent representations, respectively. (ii) Decoder yields miRNA-disease association scores with the learned latent representations as input. Various kinds of encoders and decoders are proposed for NMCMDA. Finally, the NMCMDA with the encoder of Relational Graph Convolutional Network and the neural multirelational decoder (NMR-RGCN) achieves the best prediction performance. We compared the NMCMDA with other baselines on three experimental datasets. The experimental results show that the NMR-RGCN is significantly superior to the state-of-the-art method TDRC in terms of Top-1 precision, Top-1 Recall, and Top-1 F1. Additionally, case studies are provided for two high-risk human diseases (namely, breast cancer and lung cancer) and we also provide the prediction and validation of top-10 miRNA-disease-category associations based on all known data of HMDD v3.2, which further validate the effectiveness and feasibility of the proposed method.

Small in Size, but Large in Action: microRNAs as Potential Modulators of PTEN in Breast and Lung Cancers

MicroRNAs (miRNAs) are well-known regulators of biological mechanisms with a small size of 19–24 nucleotides and a single-stranded structure. miRNA dysregulation occurs in cancer progression. miRNAs can function as tumor-suppressing or tumor-promoting factors in cancer via regulating molecular pathways. Breast and lung cancers are two malignant thoracic tumors in which the abnormal expression of miRNAs plays a significant role in their development. Phosphatase and tensin homolog (PTEN) is a tumor-suppressor factor that is capable of suppressing the growth, viability, and metastasis of cancer cells via downregulating phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) signaling. PTEN downregulation occurs in lung and breast cancers to promote PI3K/Akt expression, leading to uncontrolled proliferation, metastasis, and their resistance to chemotherapy and radiotherapy. miRNAs as upstream mediators of PTEN can dually induce/inhibit PTEN signaling in affecting the malignant behavior of lung and breast cancer cells. Furthermore, long non-coding RNAs and circular RNAs can regulate the miRNA/PTEN axis in lung and breast cancer cells. It seems that anti-tumor compounds such as baicalein, propofol, and curcumin can induce PTEN upregulation by affecting miRNAs in suppressing breast and lung cancer progression. These topics are discussed in the current review with a focus on molecular pathways.

Stiff stroma increases breast cancer risk by inducing the oncogene ZNF217

Women with dense breasts have an increased lifetime risk of malignancy that has been attributed to a higher epithelial density. Quantitative proteomics, collagen analysis, and mechanical measurements in normal tissue revealed that stroma in the high-density breast contains more oriented, fibrillar collagen that is stiffer and correlates with higher epithelial cell density. microRNA (miR) profiling of breast tissue identified miR-203 as a matrix stiffness-repressed transcript that is downregulated by collagen density and reduced in the breast epithelium of women with high mammographic density. Culture studies demonstrated that ZNF217 mediates a matrix stiffness- and collagen density-induced increase in Akt activity and mammary epithelial cell proliferation. Manipulation of the epithelium in a mouse model of mammographic density supported a causal relationship between stromal stiffness, reduced miR-203, higher levels of the murine homolog Zfp217, and increased Akt activity and mammary epithelial proliferation. ZNF217 was also increased in the normal breast epithelium of women with high mammographic density, correlated positively with epithelial proliferation and density, and inversely with miR-203. The findings identify ZNF217 as a potential target toward which preexisting therapies, such as the Akt inhibitor triciribine, could be used as a chemopreventive agent to reduce cancer risk in women with high mammographic density.

MicroRNA-3613-3p functions as a tumor suppressor and represents a novel therapeutic target in breast cancer

BACKGROUND

MicroRNAs have been reported to participate in tumorigenesis, treatment resistance, and tumor metastasis. Novel microRNAs need to be identified and investigated to guide the clinical prognosis or therapy for breast cancer.

METHOD

The copy number variations (CNVs) of MIR3613 from Cancer Genome Atlas (TCGA) or Cancer Cell Line Encyclopedia (CCLE) were analyzed, and its correlation with breast cancer subtypes or prognosis was investigated. The expression level of miR-3613-3p in tumor tissues or serum of breast cancer patients was detected using in situ hybridization and qPCR. Gain-of-function studies were performed to determine the regulatory role of miR-3613-3p on proliferation, apoptosis, and tumor sphere formation of human breast cancer cells MDA-MB-231 or MCF-7. The effects of miR-3613-3p on tumor growth or metastasis in an immunocompromised mouse model of MDA-MB-231-luciferase were explored by intratumor injection of miR-3613-3p analogue. The target genes, interactive lncRNAs, and related signaling pathways of miR-3613-3p were identified by bioinformatic prediction and 3'-UTR assays.

RESULTS

We found that MIR3613 was frequently deleted in breast cancer genome and its deletion was correlated with the molecular typing, and an unfavorable prognosis in estrogen receptor-positive patients. MiR-3613-3p level was also dramatically lower in tumor tissues or serum of breast cancer patients. Gain-of-function studies revealed that miR-3613-3p could suppress proliferation and sphere formation and promote apoptosis in vitro and impeded tumor growth and metastasis in vivo. Additionally, miR-3613-3p might regulate cell cycle by targeting SMS, PAFAH1B2, or PDK3 to restrain tumor progression.

CONCLUSION

Our findings indicate a suppressive role of miR-3613-3p in breast cancer progression, which may provide an innovative marker or treatment for breast cancer patients.

Prediction of Potential Associations Between miRNAs and Diseases Based on Matrix Decomposition

It is known that miRNA plays an increasingly important role in many physiological processes. Disease-related miRNAs could be potential biomarkers for clinical diagnosis, prognosis, and treatment. Therefore, accurately inferring potential miRNAs related to diseases has become a hot topic in the bioinformatics community recently. In this study, we proposed a mathematical model based on matrix decomposition, named MFMDA, to identify potential miRNA-disease associations by integrating known miRNA and disease-related data, similarities between miRNAs and between diseases. We also compared MFMDA with some of the latest algorithms in several established miRNA disease databases. MFMDA reached an AUC of 0.9061 in the fivefold cross-validation. The experimental results show that MFMDA effectively infers novel miRNA-disease associations. In addition, we conducted case studies by applying MFMDA to three types of high-risk human cancers. While most predicted miRNAs are confirmed by external databases of experimental literature, we also identified a few novel disease-related miRNAs for further experimental validation.

MiR-27a Facilitates Breast Cancer Progression via GSK-3β

Breast cancer remains one of the leading causes of cancer-associated death in women. MiR-27a is highly expressed in breast cancer tissue. However, the underlying mechanisms that promote breast cancer progression are unknown. In this study, we investigated the regulatory mechanisms of miR-27a and its target glycogen Synthase Kinase 3-β (GSK-3β) in breast cancer cells. We found that miR-27a was highly expressed in breast cancer tissues, which downregulated GSK-3β expression. We further identified GSK-3β as a direct target of miR-27a, and found that the miR-27a mediated suppression of GSK-3β activated Wnt/β-catenin-associated proliferative and invasive factor in breast cancer. The cell transfection assay demonstrated the overexpression of miR-27a also enhanced cell proliferation and invasion, and reduced cell apoptosis through GSK-3β. Finally, we demonstrated that the overexpression of miR-27a facilitated breast cancer progression through its ability to down-regulate the phosphorylation of GSK-3β both in vivo and vitro. These findings highlighted miR-27a as a novel therapeutic target in breast cancer.

miR-497 induces apoptosis by the IRAK2/NF-κB axis in the canine mammary tumour

Since companion dogs have the same living environment as humans, they are a good animal model for the study of human diseases; this is especially true of canine spontaneous mammary tumours models. A better understanding of the natural history and molecular mechanisms of canine mammary tumour is of great significance in comparative medicine. Here, we collected canine mammary tumour cases and then assayed the clinical cases by pathological examination and classification by HE staining and IHC. miRNA-497 family members (miR-497, miR-16, miR-195 and miR-15) were positively correlated with the breast cancer marker genes p63 and PTEN. Modulation of the expression of miR-497 in the canine mammary tumour cell lines CMT1211 and CMT 7364 induced apoptosis and inhibited cell proliferation. Mechanistically, IRAK2 was shown to be a functional target of miR-497 that affects the characteristics of cancer cells by inhibiting the activity of the NF-κB pathway. Overall, our work reveals the miR-497/IRAK2/NF-κB axis as a vital mechanism of canine mammary tumour progression and suggests this axis as a target in breast cancer.

MiR-135-5p inhibits TGF-β-induced epithelial-mesenchymal transition and metastasis by targeting SMAD3 in breast cancer

Breast cancer (BC) is the most frequently diagnosed malignant tumors and the leading cause of death due to cancer in women around the world. A growing body of studies have documented that microRNA (miR)-135-5p is associated with the development and progression of BC. Considering that sekelsky mothers against dpp3 (SMAD3) plays a crucial role in transforming growth factor (TGF)-β/SMAD pathway and epithelial-mesenchymal transition (EMT) process, it is critical to elucidate the crosstalk and underlying regulatory mechanisms between miR-135-5p and SMAD3 in controlling TGF-β-mediated EMT in BC metastasis. Our results revealed a reciprocal expression pattern between miR-135-5p and SMAD3 mRNA in BC tissues and cell lines. Moreover, miR-135-5p was decreased in BC tissues compared to adjacent breast tissues; more interesting, miR-135-5p mRNA levels (Tumor/Normal, T/N) was further decreased in BC patients with lymph node metastasis, while SMAD3 mRNA levels were increased. Gain- and loss-of-function assays indicated that overexpression of miR-135-5p inhibited TGF-β-mediated EMT and BC metastasis in vitro and in vivo. Furthermore, knockdown of SMAD3 produced a consistent phenotype of miR-135-5p overexpression in breast cancer cells. Mechanistically, SMAD3, a pivotal transcriptional modulator of TGF-β/SMAD pathway, for the first time, was analyzed and identified as a target gene of miR-135-5p by bioinformatic algorithms and dual-luciferase reporter assays. Taken together, we clarified that miR-135-5p suppressed TGF-β-mediated EMT and BC metastasis by negatively regulating SMAD3 and TGF-β/SMAD signaling. Our findings supported that miR-135-5p may serve as a tumor suppressor, and be a valuable diagnostic biomarker for the treatment of BC.

MicroRNAs and Their Influence on the ZEB Family: Mechanistic Aspects and Therapeutic Applications in Cancer Therapy

Molecular signaling pathways involved in cancer have been intensively studied due to their crucial role in cancer cell growth and dissemination. Among them, zinc finger E-box binding homeobox-1 (ZEB1) and -2 (ZEB2) are molecules that play vital roles in signaling pathways to ensure the survival of tumor cells, particularly through enhancing cell proliferation, promoting cell migration and invasion, and triggering drug resistance. Importantly, ZEB proteins are regulated by microRNAs (miRs). In this review, we demonstrate the impact that miRs have on cancer therapy, through their targeting of ZEB proteins. MiRs are able to act as onco-suppressor factors and inhibit the malignancy of tumor cells through ZEB1/2 down-regulation. This can lead to an inhibition of epithelial-mesenchymal transition (EMT) mechanism, therefore reducing metastasis. Additionally, miRs are able to inhibit ZEB1/2-mediated drug resistance and immunosuppression. Additionally, we explore the upstream modulators of miRs such as long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), as these regulators can influence the inhibitory effect of miRs on ZEB proteins and cancer progression.

The novel microRNAs hsa-miR-nov7 and hsa-miR-nov3 are over-expressed in locally advanced breast cancer

miRNAs are an important class of small non-coding RNAs, which play a versatile role in gene regulation at the post-transcriptional level. Expression of miRNAs is often deregulated in human cancers. We analyzed small RNA massive parallel sequencing data from 50 locally advanced breast cancers aiming to identify novel breast cancer related miRNAs. We successfully predicted 10 novel miRNAs, out of which 2 (hsa-miR-nov3 and hsa-miR-nov7) were recurrent. Applying high sensitivity qPCR, we detected these two microRNAs in 206 and 214 out of 223 patients in the study from which the initial cohort of 50 samples were drawn. We found hsa-miR-nov3 and hsa-miR-nov7 both to be overexpressed in tumor versus normal breast tissue in a separate set of 13 patients (p = 0.009 and p = 0.016, respectively) from whom both tumor tissue and normal tissue were available. We observed hsa-miR-nov3 to be expressed at higher levels in ER-positive compared to ER-negative tumors (p = 0.037). Further stratifications revealed particularly low levels in the her2-like and basal-like cancers compared to other subtypes (p = 0.009 and 0.040, respectively). We predicted target genes for the 2 microRNAs and identified inversely correlated genes in mRNA expression array data available from 203 out of the 223 patients. Applying the KEGG and GO annotations to target genes revealed pathways essential to cell development, communication and homeostasis. Although a weak association between high expression levels of hsa-miR-nov7 and poor survival was observed, this did not reach statistical significance. hsa-miR-nov3 expression levels had no impact on patient survival.

Identification of key microRNAs involved in tumorigenesis and prognostic microRNAs in breast cancer

Breast cancer is a commonly diagnosed cancer in women, and one of the leading causes of cancer-related death among female patients However, the key microRNAs involved in its tumorigenesis and microRNAs of prognostic values have not been fully understood. In the present study, we aimed to perform a systematic analysis of microRNA expression profiles to identify some key microRNAs associated with tumor initiation and prognosis. Using TCGA breast cancer datasets, we identified 110 differentially expressed microRNAs. The functional enrichment analysis of the upregulated microRNAs revealed signaling transduction pathways, such as Notch and Wnt signaling pathway, and metabolism-related pathways such as sugar and nucleotide sugar metabolism, and oxidative stress response. Moreover, multivariable Cox model based on three variables of hsa-mir-130a, hsa-mir-3677, and hsa-mir-1247 stratified patients into high-risk and low-risk groups, which showed significant prognostic difference. In addition, we also tested the performance of this model in patient cohorts of any specific breast cancer subtypes or different TNM stages. The high performance in risk prediction was also observed in all of breast cancer subtypes and TNM stages. We also observed that there were highly possible interactions between hsa-mir-130a and seven target genes. Among these target genes, VAV3 and ESR1 were predicted as the target genes of hsa-mir-130a, suggesting that hsa-mir-130a may function by regulating the expression of VAV3 and ESR1 in breast cancer. In conclusion, the stratification based on the multivariable Cox model showed high performance in risk prediction. The dysregulated microRNAs and prognostic microRNAs greatly improved our understanding of the microRNA-related molecular mechanism underlying breast cancer.

Indirect regulation of TFPI-2 expression by miR-494 in breast cancer cells

TFPI-2 has been shown to be involved in breast cancer pathogenesis by inhibiting extracellular matrix degradation, and low levels are associated with disease progression. As microRNA-494 (miR-494) protects against breast cancer progression, we investigated whether miR-494 is involved in the regulation of TFPI-2 in MCF-7 breast cancer cells. TFPI-2 mRNA and protein levels increased after transfection with miR-494 mimic, and TFPI-2 mRNA and miR-494 levels correlated positively in tumors from breast cancer patients. No specific binding sites for miR-494 in the 3'-untranslated region (UTR) of TFPI2 were identified; however, miR-494 was predicted in silico to bind 3'-UTR of the transcription factors AHR and ELF-1, which have potential binding sites in the TFPI2 promoter. ELF-1 mRNA was downregulated whereas AHR mRNA levels were upregulated after transfection with miR-494 mimic. Knockdown of ELF-1 and AHR increased and reduced TFPI-2 mRNA levels, respectively. Increased luciferase activity was seen when TFPI-2 promoter constructs containing the potential AHR or ELF-1 binding sites were co-transfected with miR-494 mimic. In conclusion, TFPI-2 mRNA levels were upregulated by miR-494 in MCF-7 breast cancer cells most likely by an indirect association where miR-494 targeted the transcription factors AHR and ELF-1. This association was supported in a breast cancer cohort.

Integrative analysis of breast cancer profiles in TCGA by TNBC subgrouping reveals novel microRNA-specific clusters, including miR-17-92a, distinguishing basal-like 1 and basal-like 2 TNBC subtypes

Background

The term triple-negative breast cancer (TNBC) is used to describe breast cancers without expression of estrogen receptor, progesterone receptor or HER2 amplification. To advance targeted treatment options for TNBC, it is critical that the subtypes within this classification be described in regard to their characteristic biology and gene expression. The Cancer Genome Atlas (TCGA) dataset provides not only clinical and mRNA expression data but also expression data for microRNAs.

Results

In this study, we applied the Lehmann classifier to TCGA-derived TNBC cases which also contained microRNA expression data and derived subtype-specific microRNA expression patterns. Subsequent analyses integrated known and predicted microRNA-mRNA regulatory nodes as well as patient survival data to identify key networks. Notably, basal-like 1 (BL1) TNBCs were distinguished from basal-like 2 TNBCs through up-regulation of members of the miR-17-92 cluster of microRNAs and suppression of several known miR-17-92 targets including inositol polyphosphate 4-phosphatase type II, INPP4B.

Conclusions

These data demonstrate TNBC subtype-specific microRNA and target mRNA expression which may be applied to future biomarker and therapeutic development studies.

miRNA-5119 regulates immune checkpoints in dendritic cells to enhance breast cancer immunotherapy

Dendritic cell (DC) based immunotherapy is a promising approach to clinical cancer treatment. miRNAs are a class of small non-coding RNA molecules that bind to RNAs to mediate multiple events which are important in diverse biological processes. miRNA mimics and antagomirs may be potent agents to enhance DC-based immunotherapy against cancers. miRNA array analysis was used to identify a representative miR-5119 potentially regulating PD-L1 in DCs. We evaluated levels of ligands of immune cell inhibitory receptors (IRs) and miR-5119 in DCs from immunocompetent mouse breast tumor-bearing mice, and examined the molecular targets of miR-5119. We report that miRNA-5119 was downregulated in spleen DCs from mouse breast cancer-bearing mice. In silico analysis and qPCR data showed that miRNA-5119 targeted mRNAs encoding multiple negative immune regulatory molecules, including ligands of IRs such as PD-L1 and IDO2. DCs engineered to express a miR-5119 mimic downregulated PD-L1 and prevented T cell exhaustion in mice with breast cancer homografts. Moreover, miR-5119 mimic-engineered DCs effectively restored function to exhausted CD8⁺ T cells in vitro and in vivo, resulting in robust anti-tumor cell immune response, upregulated cytokine production, reduced T cell apoptosis, and exhaustion. Treatment of 4T1 breast tumor-bearing mice with miR-5119 mimic-engineered DC vaccine reduced T cell exhaustion and suppressed mouse breast tumor homograft growth. This study provides evidence supporting a novel therapeutic approach using miRNA-5119 mimic-engineered DC vaccines to regulate inhibitory receptors and enhance anti-tumor immune response in a mouse model of breast cancer. miRNA/DC-based immunotherapy has potential for advancement to the clinic as a new strategy for DC-based anti-breast cancer immunotherapy.

miR-106b-5p contributes to the lung metastasis of breast cancer via targeting CNN1 and regulating Rho/ROCK1 pathway

OBJECTIVES

Breast cancer has been the second most prevalent and fatal malignancy due to its frequent metastasis to other organs. We aim to study the effects of a key miRNA-mRNA signaling in breast cancer.

RESULTS

CNN1 was identified as the key gene in breast cancer by the bioinformatics analysis, and the downregulation of CNN1 in breast cancer tissues and cell lines was observed. Upregulating CNN1 inhibited cell survival, migration, invasion, and adhesion, but enhanced cell apoptosis. miR-106b-5p not only bound to CNN1 mRNA 3'UTR, but also promoted lung metastasis in vivo. Besides, the miR-106b-5p mimic enhanced breast cancer canceration by targeting CNN1 and activating Rho/ROCK1 signaling pathway.

CONCLUSION

Overall, our results proved that miR-106b-5p promoted the metastasis of breast cancer by suppressing CNN1 and activating Rho/ROCK1 pathway.

METHODS

Bioinformatics analysis was performed to select the key gene in breast cancer. The overexpression and knockdown of Calponin 1 (CNN1) in breast cancer cell lines were performed to conduct cell viability, migrating, invasion, proliferation, adhesion, and apoptosis experiments. To identify the role of miR-106b-5p and Rho/ROCK1 in CNN1-induced breast cancer, a dual-luciferase assay, tumor lung metastasis assay, transcript half-life assay, and Rho/ROCK1 inhibition assay were performed.

Prognostic role of microRNAs in breast cancer: A systematic review

MicroRNAs (miRNAs) have been found to play an important role in breast cancer, functioning either as potential oncogenes or tumor suppressor genes, but their role in the prognosis of patients remains unclear. The aim of the present review study is to highlight recent preclinical and clinical studies performed on both circulating and tissue-specific miRNAs and their potential role as prognostic markers in breast cancer. We systematically searched the PubMed database to explore the prognostic value of miRNAs in breast cancer. After performing the literature search and review, 117 eligible studies were identified. We found that 110 aberrantly expressed miRNAs have been associated with prognosis in breast cancer. In conclusion, the collective data presented in this review indicate that miRNAs could serve as novel prognostic tools in breast cancer, while the clinical application of these findings has yet to be verified.

Diagnostic Value Investigation and Bioinformatics Analysis of miR-31 in Patients with Lymph Node Metastasis of Colorectal Cancer

Colorectal cancer (CRC) is one of the most frequent cancers occurring in developed countries. Distant CRC metastasis causes more than 90% of CRC-associated mortality. MicroRNAs (miRNAs) play a key role in regulating tumor metastasis and could be potential diagnostic biomarkers in CRC patients. This study is aimed at identifying miRNAs that can be used as diagnostic biomarkers for CRC metastasis. Towards this goal, we compared the expression of five miRNAs commonly associated with metastasis (i.e., miR-10b, miR-200c, miR-155, miR-21, and miR-31) between primary CRC (pCRC) tissues and corresponding metastatic lymph nodes (mCRC). Further, bioinformatics analysis of miR-31 was performed to predict target genes and related signaling pathways. Results showed that miR-31, miR-21, miR-10b, and miR-155 expression was increased to different extents, while miR-200c expression was lower in mCRC than that in pCRC. Moreover, we found that the level of both miR-31 and miR-21 was notably increased in pCRC when lymph node metastasis (LNM) was present, and the increase of miR-31 expression was more profound. Hence, upregulated miR-31 and miR-21 expression might be a miRNA signature in CRC metastasis. Moreover, we detected a higher miR-31 level in the plasma of CRC patients with LNM compared to patients without LNM or healthy individuals. With the bioinformatics analysis of miR-31, 121 putative target genes and transition of mitotic cell cycle and Wnt signaling pathway were identified to possibly play a role in CRC progression. We next identified seven hub genes via module analysis; of these, TNS1 was most likely to be the target of miR-31 and had significant prognostic value for CRC patients. In conclusion, miR-31 is significantly increased in the cancer tissues and plasma of CRC patients with LNM; thus, a high level of miR-31 in the plasma is a potential biomarker for the diagnosis of LNM of CRC.

ZMYND10, an epigenetically regulated tumor suppressor, exerts tumor-suppressive functions via miR145-5p/NEDD9 axis in breast cancer

Background

Recent studies suggested that ZMYND10 is a potential tumor suppressor gene in multiple tumor types. However, the mechanism by which ZMYND10 inhibits breast cancer remains unclear. Here, we investigated the role and mechanism of ZMYND10 in breast cancer inhibition.

Results

ZMYND10 was dramatically reduced in multiple breast cancer cell lines and tissues, which was associated with promoter hypermethylation. Ectopic expression of ZMYND10 in silenced breast cancer cells induced cell apoptosis while suppressed cell growth, cell migration and invasion in vitro, and xenograft tumor growth in vivo. Furthermore, molecular mechanism studies indicated that ZMYND10 enhances expression of miR145-5p, which suppresses the expression of NEDD9 protein through directly targeting the 3'-untranslated region of NEDD9 mRNA.

Conclusions

Results from this study show that ZMYND10 suppresses breast cancer tumorigenicity by inhibiting the miR145-5p/NEDD9 signaling pathway. This novel discovered signaling pathway may be a valid target for small molecules that might help to develop new therapies to better inhibit the breast cancer metastasis.

MiRNA therapeutics based on logic circuits of biological pathways

BACKGROUND

In silico experiments, with the aid of computer simulation, speed up the process of in vitro or in vivo experiments. Cancer therapy design is often based on signalling pathway. MicroRNAs (miRNA) are small non-coding RNA molecules. In several kinds of diseases, including cancer, hepatitis and cardiovascular diseases, they are often deregulated, acting as oncogenes or tumor suppressors. miRNA therapeutics is based on two main kinds of molecules injection: miRNA mimics, which consists of injection of molecules that mimic the targeted miRNA, and antagomiR, which consists of injection of molecules inhibiting the targeted miRNA. Nowadays, the research is focused on miRNA therapeutics. This paper addresses cancer related signalling pathways to investigate miRNA therapeutics.

RESULTS

In order to prove our approach, we present two different case studies: non-small cell lung cancer and melanoma. KEGG signalling pathways are modelled by a digital circuit. A logic value of 1 is linked to the expression of the corresponding gene. A logic value of 0 is linked to the absence (not expressed) gene. All possible relationships provided by a signalling pathway are modelled by logic gates. Mutations, derived according to the literature, are introduced and modelled as well. The modelling approach and analysis are widely discussed within the paper. MiRNA therapeutics is investigated by the digital circuit analysis. The most effective miRNA and combination of miRNAs, in terms of reduction of pathogenic conditions, are obtained. A discussion of obtained results in comparison with literature data is provided. Results are confirmed by existing data.

CONCLUSIONS

The proposed study is based on drug discovery and miRNA therapeutics and uses a digital circuit simulation of a cancer pathway. Using this simulation, the most effective combination of drugs and miRNAs for mutated cancer therapy design are obtained and these results were validated by the literature. The proposed modelling and analysis approach can be applied to each human disease, starting from the corresponding signalling pathway.