SPMLMI: predicting lncRNA-miRNA interactions in humans using a structural perturbation method

lncRNA TUG1 and kidney diseases

Long noncoding RNAs (lncRNAs) cover a large class of transcribed RNA molecules that are more than 200 nucleotides in length. An increasing number of studies have shown that lncRNAs control gene expression through different mechanisms and play important roles in a range of biological processes including growth, cell differentiation, proliferation, apoptosis, and invasion. TUG1 was originally discovered in a genomic screen of taurine-treated mouse retinal cells. Previous evidences pointed out that lncRNA TUG1 could inhibit apoptosis and the release of inflammatory factors, improve mitochondrial function, thereby protecting cells from damage, and showing a protective role of TUG1 in diseases. Given that TUG1 has multiple targets and can interfere with multiple steps in the oncogenic process, it has been proposed as a therapeutic target. In this review, we summarize the research progress of lncRNA TUG1 in kidney diseases in the past 8 years, and discuss its related molecular mechanisms.

Computational prediction of new therapeutic effects of probiotics

Probiotics are living microorganisms that provide health benefits to their hosts, potentially aiding in the treatment or prevention of various diseases, including diarrhea, irritable bowel syndrome, ulcerative colitis, and Crohn's disease. Motivated by successful applications of link prediction in medical and biological networks, we applied link prediction to the probiotic-disease network to identify unreported relations. Using data from the Probio database and International Classification of Diseases-10th Revision (ICD-10) resources, we constructed a bipartite graph focused on the relationship between probiotics and diseases. We applied customized link prediction algorithms for this bipartite network, including common neighbors, Jaccard coefficient, and Adamic/Adar ranking formulas. We evaluated the results using Area under the Curve (AUC) and precision metrics. Our analysis revealed that common neighbors outperformed the other methods, with an AUC of 0.96 and precision of 0.6, indicating that basic formulas can predict at least six out of ten probable relations correctly. To support our findings, we conducted an exact search of the top 20 predictions and found six confirming papers on Google Scholar and Science Direct. Evidence suggests that Lactobacillus jensenii may provide prophylactic and therapeutic benefits for gastrointestinal diseases and that Lactobacillus acidophilus may have potential activity against urologic and female genital illnesses. Further investigation of other predictions through additional preclinical and clinical studies is recommended. Future research may focus on deploying more powerful link prediction algorithms to achieve better and more accurate results.

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

The long non-coding RNA NEAT1 promotes the progression of human ovarian cancer through targeting miR-214-3p and regulating angiogenesis

BACKGROUND

Angiogenesis and metastasis contributes substantially to the poor outcome of patients with ovarian cancer. We aimed to explore the role and mechanisms of the long non-coding RNA NEAT1 (nuclear enriched abundant transcript 1) in regulating angiogenesis and metastasis of human ovarian cancer. NEAT1 expression in human ovarian cancer tissues and cell lines including SKOV-3 and A2780 was investigated through in situ hybridization. Gene knockdown and overexpressing were achieved through lentivirus infection, transfection of plasmids or microRNA mimics. Cell viability was measured with the cell counting kit-8 assay, while apoptosis was determined by flow cytometry. Cell migration and invasion were evaluated by transwell experiments, and protein expression was determined by western blot assays or immunohistochemistry. Duo-luciferase reporter assay was employed to confirm the interaction between NEAT1 and target microRNA. In vivo tumor growth was evaluated in nude mice with xenografted SKOV-3/A2780 cells, and blood vessel formation in tumor was examined by histological staining.

RESULTS

NEAT1 was highly expressed in ovarian cancer tissues of patients and cell lines. MiR-214-3p was identified as a sponging target of NEAT1, and they antagonizedeach other in a reciprocal manner. NEAT1-overexpressing SKOV-3 and A2780 cells had significantly increased proliferation, reduced apoptosis, and augmented abilities of migration and invasion, while cells with NEAT1-knockdown displayed markedly attenuated traits of malignancies. Additionally, the levels of NEAT1 appeared to be positively correlated with the expression levels of angiogenesis-related molecules, including Semaphorin 4D (Sema4D), Sema4D receptor Plexin B1, T-lymphoma invasion and metastasis-inducing protein-1 (Tiam1), and Rho-like GTPases Rac1/2/3. In the xenograft mouse model, more NEAT1 expression resulted in faster in vivo tumor growth, more blood vessel formation in tumor tissues, as well as higher expression levels of angiogenesis-related molecules and CD31.

CONCLUSIONS

NEAT1 promotes angiogenesis and metastasis in human ovarian cancer. NEAT1 and miR-214-3p are promising targets for developing therapeutics to treat human ovarian cancer.

Inferring pseudogene-MiRNA associations based on an ensemble learning framework with similarity kernel fusion

Accumulating evidence shows that pseudogenes can function as microRNAs (miRNAs) sponges and regulate gene expression. Mining potential interactions between pseudogenes and miRNAs will facilitate the clinical diagnosis and treatment of complex diseases. However, identifying their interactions through biological experiments is time-consuming and labor intensive. In this study, an ensemble learning framework with similarity kernel fusion is proposed to predict pseudogene-miRNA associations, named ELPMA. First, four pseudogene similarity profiles and five miRNA similarity profiles are measured based on the biological and topology properties. Subsequently, similarity kernel fusion method is used to integrate the similarity profiles. Then, the feature representation for pseudogenes and miRNAs is obtained by combining the pseudogene-pseudogene similarities, miRNA-miRNA similarities. Lastly, individual learners are performed on each training subset, and the soft voting is used to yield final decision based on the prediction results of individual learners. The k-fold cross validation is implemented to evaluate the prediction performance of ELPMA method. Besides, case studies are conducted on three investigated pseudogenes to validate the predict performance of ELPMA method for predicting pseudogene-miRNA interactions. Therefore, all experiment results show that ELPMA model is a feasible and effective tool to predict interactions between pseudogenes and miRNAs.

SPCMLMI: A structural perturbation-based matrix completion method to predict lncRNA–miRNA interactions

Accumulating evidence indicated that the interaction between lncRNA and miRNA is crucial for gene regulation, which can regulate gene transcription, further affecting the occurrence and development of many complex diseases. Accurate identification of interactions between lncRNAs and miRNAs is helpful for the diagnosis and therapeutics of complex diseases. However, the number of known interactions of lncRNA with miRNA is still very limited, and identifying their interactions through biological experiments is time-consuming and expensive. There is an urgent need to develop more accurate and efficient computational methods to infer lncRNA–miRNA interactions. In this work, we developed a matrix completion approach based on structural perturbation to infer lncRNA–miRNA interactions (SPCMLMI). Specifically, we first calculated the similarities of lncRNA and miRNA, including the lncRNA expression profile similarity, miRNA expression profile similarity, lncRNA sequence similarity, and miRNA sequence similarity. Second, a bilayer network was constructed by integrating the known interaction network, lncRNA similarity network, and miRNA similarity network. Finally, a structural perturbation-based matrix completion method was used to predict potential interactions of lncRNA with miRNA. To evaluate the prediction performance of SPCMLMI, five-fold cross validation and a series of comparison experiments were implemented. SPCMLMI achieved AUCs of 0.8984 and 0.9891 on two different datasets, which is superior to other compared methods. Case studies for lncRNA XIST and miRNA hsa-mir-195–5-p further confirmed the effectiveness of our method in inferring lncRNA–miRNA interactions. Furthermore, we found that the structural consistency of the bilayer network was higher than that of other related networks. The results suggest that SPCMLMI can be used as a useful tool to predict interactions between lncRNAs and miRNAs.

SVMMDR: Prediction of miRNAs-drug resistance using support vector machines based on heterogeneous network

In recent years, the miRNA is considered as a potential high-value therapeutic target because of its complex and delicate mechanism of gene regulation. The abnormal expression of miRNA can cause drug resistance, affecting the therapeutic effect of the disease. Revealing the associations between miRNAs-drug resistance can help in the design of effective drugs or possible drug combinations. However, current conventional experiments for identification of miRNAs-drug resistance are time-consuming and high-cost. Therefore, it’s of pretty realistic value to develop an accurate and efficient computational method to predicting miRNAs-drug resistance. In this paper, a method based on the Support Vector Machines (SVM) to predict the association between MiRNA and Drug Resistance (SVMMDR) is proposed. The SVMMDR integrates miRNAs-drug resistance association, miRNAs sequence similarity, drug chemical structure similarity and other similarities, extracts path-based Hetesim features, and obtains inclined diffusion feature through restart random walk. By combining the multiple feature, the prediction score between miRNAs and drug resistance is obtained based on the SVM. The innovation of the SVMMDR is that the inclined diffusion feature is obtained by inclined restart random walk, the node information and path information in heterogeneous network are integrated, and the SVM is used to predict potential miRNAs-drug resistance associations. The average AUC of SVMMDR obtained is 0.978 in 10-fold cross-validation.