microRNA-target Interactions Research Articles

Identification and coregulation pattern analysis of long noncoding RNAs in the mouse brain after Angiostrongylus cantonensis infection

BackgroundAngiostrongyliasis is a highly dangerous infectious disease. Angiostrongylus cantonensis larvae migrate to the mouse brain and cause symptoms, such as brain swelling and bleeding. Noncoding RNAs (ncRNAs) are novel targets for the control of parasitic infections. However, the role of these molecules in A. cantonensis infection has not been fully clarified.MethodsIn total, 32 BALB/c mice were randomly divided into four groups, and the infection groups were inoculated with 40 A. cantonensis larvae by gavage. Hematoxylin and eosin (H&E) staining and RNA library construction were performed on brain tissues from infected mice. Differential expression of long noncoding RNAs (lncRNAs) and mRNAs in brain tissues was identified by high-throughput sequencing. The pathways and functions of the differentially expressed lncRNAs were determined by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. The functions of the differentially expressed lncRNAs were further characterized by lncRNA‒microRNA (miRNA) target interactions. The potential host lncRNAs involved in larval infection of the brain were validated by quantitative real-time polymerase chain reaction (qRT‒PCR).ResultsThe pathological results showed that the degree of brain tissue damage increased with the duration of infection. The transcriptome results showed that 859 lncRNAs and 1895 mRNAs were differentially expressed compared with those in the control group, and several lncRNAs were highly expressed in the middle–late stages of mouse infection. GO and KEGG pathway analyses revealed that the differentially expressed target genes were enriched mainly in immune system processes and inflammatory response, among others, and several potential regulatory networks were constructed.ConclusionsThis study revealed the expression profiles of lncRNAs in the brains of mice after infection with A. cantonensis. The lncRNAs H19, F630028O10Rik, Lockd, AI662270, AU020206, and Mexis were shown to play important roles in the infection of mice with A. cantonensis infection.Graphical

Ocular surface MicroRNA associated with chronic ocular graft versus host disease

Chronic ocular GvHD occurs in 30–60% of patients after stem cell transplantation. Its pathogenesis surmounts to a cycle of chronic inflammation and fibrosis, leading to dry eye that is often severe, and in cases refractive to treatment. There is no gold standard test nor distinct sign or symptom. Instead, a diagnosis is made through a set of criteria that place heavy reliance on clinical findings. This need for subspecialist input can delay diagnosis, treatment and lead to unsatisfactory outcomes and patient suffering.The objective of this study was to identify microRNA associated with disease signature and severity in the conjunctival epithelial cells (CECs) of patients with chronic ocular GvHD. Through an exploration of their predicted gene targets this study sought to develop a deeper understanding of the epigenetic signalling pathways involved in disease as well as identifying urgently needed biomarkers of diagnostic and therapeutic potential.Consecutive patients with a diagnosis of chronic ocular GvHD as per the National Institute of Health and/or the International Chronic Ocular Graft‐versus‐Host Disease diagnostic criteria were recruited along with healthy age and gender matched controls. CECs were collected from the ocular surface via impression cytology. MicroRNA were isolated and then analysed via Affymetrix Genechip miRNA 3.0 microarray. MicroRNA with a fold change of >2 or <−2 and an FDR p value of <0.05 were retained for further analysis. A LASSO regression model was applied in order to identify microRNA associated with disease signature and severity (SAS v. 9.4). Computational predictive methods were then used to identify high confidence microRNA target interactions (MTIs) using R studio (R version 4.1.3).Nine patients, five male (56%) four female (44%) with an average age of 38.6 yrs (±11.3) participated. Differentially expressed microRNAs were identified (n = 94, FDR <0.05, fold change ±2). A panel of microRNAs associated with disease signature (n = 3) and 50 MTIs were identified. Of interest was the upregulation of HDAC4, an inflammation and pain mediator and NOTCH2 which is overexpressed in dry eye models. In addition, SCARF1, which is needed for immunotolerance, PCDH1 needed for epithelial repair, NTS which is a regulator of pain and inflammation, and IHH which is needed for corneal healing were all downregulated. An additional panel of microRNA were identified terms of disease severity (n = 3), along with 27 MTIs. Those downregulated gene targets which were of particular interest included CDH1, which is involved in healing and maintaining the integrity of the corneal epithelium, NFIC, which suppresses the expression of pro‐inflammatory mediators and ZNF148 whose suppressed expression is associated with immune mediated inflammatory pathways.Overall, there is an urgent need for biomarkers in chronic ocular GvHD. The results from this study provide novel microRNA panels and associated target genes which have potential diagnostic and therapeutic value, along with providing an innovative perspective into the molecular mechanisms underpinning disease development and severity.

Detection of transcriptome-wide microRNA–target interactions in single cells with agoTRIBE

MicroRNAs (miRNAs) exert their gene regulatory effects on numerous biological processes based on their selection of target transcripts. Current experimental methods available to identify miRNA targets are laborious and require millions of cells. Here we have overcome these limitations by fusing the miRNA effector protein Argonaute2 to the RNA editing domain of ADAR2, allowing the detection of miRNA targets transcriptome-wide in single cells. miRNAs guide the fusion protein to their natural target transcripts, causing them to undergo A>I editing, which can be detected by sensitive single-cell RNA sequencing. We show that agoTRIBE identifies functional miRNA targets, which are supported by evolutionary sequence conservation. In one application of the method we study microRNA interactions in single cells and identify substantial differential targeting across the cell cycle. AgoTRIBE also provides transcriptome-wide measurements of RNA abundance and allows the deconvolution of miRNA targeting in complex tissues at the single-cell level.

TNF-α promotes CXCL-1/8 production in keratinocytes by downregulating galectin-3 through NF-κB and hsa-miR-27a-3p pathway to contribute psoriasis development

ObjectiveTreatment with TNF-α inhibitors improve psoriasis with minimize/minor neutrophils infiltration and CXCL-1/8 expression in psoriatic lesions. However, the fine mechanism of TNF-α initiating psoriatic inflammation by tuning keratinocytes is unclear. Our previous research identified the deficiency of intracellular galectin-3 was sufficient to promote psoriasis inflammation characterized by neutrophil accumulation. This study aims to investigate whether TNF-α participated in psoriasis development through dysregulating galectin-3 expression.MethodsmRNA levels were assessed through quantitative real-time PCR. Flow cytometry was used to detect cell cycle/apoptosis. Western blot was used to evaluate the activation of the NF-κB signaling pathway. HE staining and immunochemistry were used to detect epidermal thickness and MPO expression, respectively. Specific small interfering RNA (siRNA) was used to knock down hsa-miR-27a-3p while plasmids transfection was used to overexpress galectin-3. Further, the multiMiR R package was utilized to predict microRNA-target interaction.Results and discussionWe found that TNF-α stimulation altered cell proliferation and differentiation and promoted the production of psoriasis-related inflammatory mediators along with the inhibition of galectin-3 expression in keratinocytes. Supplement of galectin-3 could counteract the rise of CXCL-1/8 but not the other phenotypes of keratinocytes induced by TNF-α. Mechanistically, inhibition of the NF-κB signaling pathway could counteract the decrease of galectin-3 and the increase of hsa-miR-27a-3p expression whereas silence of hsa-miR-27a-3p could counteract the decrease of galectin-3 expression induced by TNF-α treatment in keratinocytes. Intradermal injection of murine anti-CXCL-2 antibody greatly alleviated imiquimod-induced psoriasis-like dermatitis.ConclusionTNF-α initiates psoriatic inflammation by increasing CXCL-1/8 in keratinocytes mediated by the axis of NF-κB-hsa-miR-27a-3p-galectin-3 pathway.

MicroRNAs - small RNAs with a big influence on brain excitability.

MicroRNAs are non-coding RNAs, approximately 22nt in length, which serve to negatively regulate gene expression through binding to complementary sequences in the 3' untranslated region (3'UTR) of target mRNA. The microRNA-target interaction does not require perfect complementarity, meaning that an individual microRNA often has a pool of hundreds of gene targets. Equally, one 3'UTR can contain target sites for many different microRNAs. This gives rise to a complex web of molecular interactions. An emerging concept is that microRNAs have a role as 'master' regulators of certain cellular properties, simultaneously mediating the subtle repression of multiple related genes within a pathway or system, thereby achieving a common phenotypic output. One such example is regulation of brain excitability. There are numerous examples of microRNAs which can target ion channels, ion transporters and genes associated with synaptic transmission. Often, the expression of the microRNA itself is regulated in an activity-dependent manner, thereby forming homeostatic loops. Limitations in our understanding arise from the sheer complexity of microRNA-target interactions, which are difficult to capture experimentally and computationally. Further, many microRNA studies rely on animal model systems, but many microRNAs (and mRNA targets) have sequences which are either not conserved or are entirely unique in the human brain. This leaves many exciting and challenging opportunities to further progress the field in an attempt to fully understand the roles of microRNAs in brain function.

Network Regulation of microRNA Biogenesis and Target Interaction.

MicroRNAs (miRNAs) are versatile, post-transcriptional regulators of gene expression. Canonical miRNAs are generated through the two-step DROSHA- and DICER-mediated processing of primary miRNA (pri-miRNA) transcripts with optimal or suboptimal features for DROSHA and DICER cleavage and loading into Argonaute (AGO) proteins, whereas multiple hairpin-structured RNAs are encoded in the genome and could be a source of non-canonical miRNAs. Recent advances in miRNA biogenesis research have revealed details of the structural basis of miRNA processing and cluster assistance mechanisms that facilitate the processing of suboptimal hairpins encoded together with optimal hairpins in polycistronic pri-miRNAs. In addition, a deeper investigation of miRNA-target interaction has provided insights into the complexity of target recognition with distinct outcomes, including target-mediated miRNA degradation (TDMD) and cooperation in target regulation by multiple miRNAs. Therefore, the coordinated or network regulation of both miRNA biogenesis and miRNA-target interaction is prevalent in miRNA biology. Alongside recent advances in the mechanistic investigation of miRNA functions, this review summarizes recent findings regarding the ordered regulation of miRNA biogenesis and miRNA-target interaction.

In silico screening and analysis of candidate microRNA-target interaction involved in progression of prediabetes to Type II Diabetes Mellitus

In silico screening and analysis of candidate microRNA-target interaction involved in progression of prediabetes to Type II Diabetes Mellitus

Targeting Class I Histone Deacetylases in Human Uterine Leiomyosarcoma.

Uterine leiomyosarcoma (uLMS) is the most frequent subtype of uterine sarcoma that presents a poor prognosis, high rates of recurrence, and metastasis. Currently, the molecular mechanism of the origin and development of uLMS is unknown. Class I histone deacetylases (including HDAC1, 2, 3, and 8) are one of the major classes of the HDAC family and catalyze the removal of acetyl groups from lysine residues in histones and cellular proteins. Class I HDACs exhibit distinct cellular and subcellular expression patterns and are involved in many biological processes and diseases through diverse signaling pathways. However, the link between class I HDACs and uLMS is still being determined. In this study, we assessed the expression panel of Class I HDACs in uLMS and characterized the role and mechanism of class I HDACs in the pathogenesis of uLMS. Immunohistochemistry analysis revealed that HDAC1, 2, and 3 are aberrantly upregulated in uLMS tissues compared to adjacent myometrium. Immunoblot analysis demonstrated that the expression levels of HDAC 1, 2, and 3 exhibited a graded increase from normal and benign to malignant uterine tumor cells. Furthermore, inhibition of HDACs with Class I HDACs inhibitor (Tucidinostat) decreased the uLMS proliferation in a dose-dependent manner. Notably, gene set enrichment analysis of differentially expressed genes (DEGs) revealed that inhibition of HDACs with Tucidinostat altered several critical pathways. Moreover, multiple epigenetic analyses suggested that Tucidinostat may alter the transcriptome via reprogramming the oncogenic epigenome and inducing the changes in microRNA-target interaction in uLMS cells. In the parallel study, we also determined the effect of DL-sulforaphane on the uLMS. Our study demonstrated the relevance of class I HDACs proteins in the pathogenesis of malignant uLMS. Further understanding the role and mechanism of HDACs in uLMS may provide a promising and novel strategy for treating patients with this aggressive uterine cancer.

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

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

MicroRNA biomarkers of type 2 diabetes: evidence synthesis from meta-analyses and pathway modelling

Aims/hypothesisMicroRNAs are being sought as biomarkers for the early identification of type 2 diabetes. This study aimed to synthesise the evidence from microRNA–type 2 diabetes association studies and microRNA-regulated type 2 diabetes pathway delineation studies that met stringent quality criteria to identify and validate microRNAs of both statistical and biological significance as type 2 diabetes biomarkers.MethodsEligible controlled studies on microRNA expression profiling of type 2 diabetes were retrieved from PubMed, ScienceDirect and Web of Science. MicroRNA-regulated type 2 diabetes pathway delineation studies were conducted by integrating and cross-verifying the data from miRTarBase, TransmiR, miRecords, TargetScanHuman, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Retraction Watch database. Before meta-analysis, quality assessment was performed according to the corresponding reporting guidelines for evidence-based medicine. To select the most statistically significant microRNAs, we conducted extensive meta-analyses according to the latest methodology. Subgroup and sensitivity analyses were carried out to further examine the microRNA candidates for their tissue specificity and blood fraction specificity and the robustness of the evidence. Signalling pathway impact analysis of dysregulated microRNAs identified from meta-analyses was performed to select biologically significant microRNAs that were enriched in our newly built microRNA-regulated pathways.ResultsOf the 404 differentially expressed microRNAs identified in the 156 controlled profiling studies with a combined sample size of >15,000, only 60 were both consistently and significantly dysregulated in human type 2 diabetes. No microRNAs were both consistently and significantly dysregulated in multiple tissues according to subgroup analyses. In total, 58 microRNAs were found to be robust in sensitivity analyses. A total of 1966 pathway delineation studies were identified, including 3290 microRNA–target interactions, which were further combined with KEGG pathways, producing 225 microRNA-regulated pathways. Impact analysis found that 16 dysregulated microRNAs identified from extensive meta-analyses were statistically significantly enriched in the augmented KEGG type 2 diabetes pathway.Conclusions/interpretationSixteen microRNAs met the criteria for biomarker selection. In terms of both significance and relevance, the order of priority for verification of these microRNAs is as follows: miR-29a-3p, miR-221-3p, miR-126-3p, miR-26a-5p, miR-503-5p, miR-100-5p, miR-101-3p, mIR-103a-3p, miR-122-5p, miR-199a-3p, miR-30b-5p, miR-130a-3p, miR-143-3p, miR-145-5p, miR-19a-3p and miR-311-3p.RegistrationPROSPERO registration number CRD42017081659.Graphical abstract

Construction of MicroRNA-Target Interaction Networks Based on MicroRNA Expression Profiles of HRV16-infected H1-HeLa Cells

In the present study we investigated the changes in miRNA levels inhuman rhinovirus 16 (HRV16)-infected cells. A small RNA deep sequencing experiment was performed through next-generation sequencing. In total, 53 differentially expressed miRNAs were confirmed by RT-qPCR, including 37 known miRNAs and 16 novel miRNAs. Interaction networks between differentially expressed miRNAs and their targets were established by mirDIP and Navigator. The prediction results showed that QKI, NFAT5, BNC2, CELF2, LCOR, MBNL2, MTMR3, NFIB, PPARGC1A, RSBN1, TRPS1, WDR26, and ZNF148, which are associated with cellular differentiation and transcriptional regulation, were recognized by 12, 11, or 9 miRNAs. Many correlations were observed between transcriptional or post-transcriptional regulation of an miRNA and the expression levels of its target genes in HRV16-infected H1-HeLa cells.

Multi-genomic analysis of 260 adrenocortical cancer patient tumors identifies novel network BIRC5-hsa-miR-335-5p-PAX8-AS1 strongly associated with poor survival

BackgroundAdrenocortical carcinoma is a rare endocrine cancer with poor overall survival. Linking survival outcomes to a common target across multiple genomic datasets incorporating microRNA-long non-coding RNA dysregulation have not been well described. We hypothesized that a multi-database analysis of microRNA-long noncoding RNA-messenger RNA regulatory networks associated with survival will identify novel biomarkers. MethodsSignificantly dysregulated genes or microRNA in adrenocortical carcinoma compared to normal adrenal was identified from sequencing data for 260 human adrenocortical carcinomas using GEO2R. The miRnet identified hub microRNA and genes and long noncoding RNA and microRNA associated with survival genes. The R2 generated Kaplan-Meier curves. The database miRTarBase linked genes associated with poor survival and dysregulated microRNA. ResultsAnalysis of genes and microRNAs differentially regulated in >50% of datasets revealed 75 genes and 12 microRNAs were upregulated, and 167 genes and 12 microRNAs were downregulated (bonf. P < .05). Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed cell cycle, P53 signaling, arachidonic acid and innate immune response, and PI3/Akt are altered in adrenocortical carcinoma. A microRNA-target interaction network of differentially regulated microRNAs identified upregulated miRNA107, 103a-3p and 27a-3p, 16-5p, and downregulated 335-5p to have the highest degree of interaction with upregulated (ie, TPX2, CDK1, BIRC5, PRC1, CCNB1, GINS1) and downregulated (ie, RSPO3, NR2F1, TLR4, HOXA5, USP53, SLC16A9) hub genes as well as hub long noncoding RNAs XIST, NEAT1, KCNQ1OT1, and PAX8-AS1. Survival analysis revealed that the hub genes are associated with poor overall survival (P < .05) of adrenocortical carcinoma in the Cancer Genome Atlas data. ConclusionA messenger RNA–microRNA–long noncoding RNA network analysis identified the BIRC5-miR335-5p-PAX8-AS1 network as one that was associated with poor overall survival in adrenocortical carcinoma, warranting further validation as a potential therapeutic target.

Abstract 5718: Rab34 is downregulated in human prostate cancer tissue

Abstract Background: Rab34 encodes a protein belonging to the Rab family of proteins, which are small GTPases important in regulating signal transduction and cellular processes. A potential role of Rab34 in cancer has been suggested, but few studies have reported its function in human epithelial cancers. It was reported that prostate cancer (PCa) patients were more prone to biochemical recurrence when Rab34 was downregulated in PCa tissue compared with benign prostate tissue (BPT), being negatively associated with miR-148, a miRNA that increased the growth in cell lines and was up-regulated in PCa samples. Rab34 is a putative target gene for miR-9. We aimed to explore the Rab34 and miR-9 expression in PCa using a bioinformatic analysis, however, the mere presence of miRNA-binding sites is insufficient for predicting target regulation, and therefore, we also analyzed Rab34 expression in BPT and PCa tissues. Design: The expression of miR9-5p and Rab34 mRNA was explored in the tumor tissues of the Cancer Genome Atlas (TCGA) through the bioinformatics tools of the dbDEMC2 and GEPIA2 web servers, and additional evidence of miR-9/Rab34 interaction was sought in the miRTarBase of experimentally validated microRNA-target interactions. Two pathologists evaluated Rab34 immunohistochemical expression in localized PCa and BPT in a prostate tissue microarray. For each spot an H-score was obtained by microscopic assessment of epithelial cells with positive staining. Differences in Rab34 expression were found by comparing H-Scores between BPT and PCa tissues using Mann-Whitney test, a p value less than 0.05 was considered statistically significant. Results: The analysis of miR-9 and Rab34 expression in the TCGA showed that Rab34 has a diverse expression in tumor tissues, being downregulated in colon, rectum and PCa, and upregulated in lymphoma, leukemia and pancreatic adenocarcinoma. On the other hand, miR-9 was upregulated in PCa and downregulated in pancreatic adenocarcinoma. There were no experimental evidence supporting the miR-9/Rab34 interaction in PCa tissues from the miRTarBase. However, most of the breast cancer datasets showed a negative correlation in the expression of both genes. Rab34 expression at protein level was assessed in 123 tissue cores representing PCa, and 86 tissue cores representing BPT. In cores with PCa mean Rab34 expression was significantly lower than the mean Rab34 expression in cores with BPT (p less than 0.0001). There was a lower mean staining score in PCa (87.3, 95% CI: 73.78 - 100.9), compared to BPT (156.3, 95% CI: 144 - 168.5). Conclusions: Our study shows that Rab34 is downregulated in PCa tissue. These findings are in accordance to a previous report that associated its lower expression to worse prognosis and tumor recurrence. The downregulation of Rab34 in PCa tissue might be related to overexpression of its putative microRNA, miR-9. Additional studies to detect the in situ expression of miR-9 in PCa are in progress to validate these results. Citation Format: Ines Benedetti, Lia Barrios, Juan Rebollo. Rab34 is downregulated in human prostate cancer tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5718.

Identification of novel biomarkers in prostate cancer diagnosis and prognosis.

Prostate cancer (PCa) is a common urinary malignancy. The lack of specific and sensitive biomarkers for the early diagnosis and prognosis of PCa makes it important to seek alternatives.R software was used to analyze the PCa expression profile from data sets in Gene Expression Omnibus. Core differential genes were identified by String and Cytoscape and further validated by Gene Expression Profiling Interactive Analysis (GEPIA) and The Human Protein Atlas(HPA). Gene Ontologyanalysis was done in the DIVID database and visualization analysis was conducted by Hiplot. Pathway enrichment was analyzed by IPA. To identify potential competitive endogenous RNAs(ceRNA) networks, the experimentally validated microRNA-target interactions database(miRTarBase), The Encyclopedia of RNA Interactomes (StarBase), lncBase, and GEPIA were used. The lncLocator was utilized to perform subcellular localization of longnoncoding RNAs(lncRNAs). Both miRTarBase and StarBase were used to find the binding site of mRNAs-miRNAs and miRNAs-lncRNAs. Visualization of the ceRNA network was performed with Cytoscape.Nine genes closely related to the diagnosis and prognosis of PCa were obtained, including four identified biomarkers by HPA, CENPF, TPX2, TK1, and CCNB1, and five novel PCa biomarkers, RRM2, UBE2C, TOP2A, BIRC5, and ZWINT. Pathway analysis indicated that PCa carcinogenesis was highly correlated with liver fibrosis pathways, ILK signaling, and NRF2-mediated oxidative stress response. Two sets of ceRNA networks,BIRC5/hsa-miR-218-5p/NEAT1and UBE2C/hsa-miR-483-3p/NEAT1 were found to be novel biomarkers for the identification of PCa. The quantitative real-time polymerase chain reactionresults verified that UBE2C, BIRC5, and NEAT1 were upregulated and hsa-miR-218-5p and hsa-miR-483-3p were downregulated in human PCa cells compared with normal prostate epithelial cells.The novel identified biomarkers in this study would be valuable for the diagnosis and prognosis of PCa.

Computing microRNA-gene interaction networks in pan-cancer using miRDriver

DNA copy number aberrated regions in cancer are known to harbor cancer driver genes and the short non-coding RNA molecules, i.e., microRNAs. In this study, we integrated the multi-omics datasets such as copy number aberration, DNA methylation, gene and microRNA expression to identify the signature microRNA-gene associations from frequently aberrated DNA regions across pan-cancer utilizing a LASSO-based regression approach. We studied 7294 patient samples associated with eighteen different cancer types from The Cancer Genome Atlas (TCGA) database and identified several cancer-specific and common microRNA-gene interactions enriched in experimentally validated microRNA-target interactions. We highlighted several oncogenic and tumor suppressor microRNAs that were cancer-specific and common in several cancer types. Our method substantially outperformed the five state-of-art methods in selecting significantly known microRNA-gene interactions in multiple cancer types. Several microRNAs and genes were found to be associated with tumor survival and progression. Selected target genes were found to be significantly enriched in cancer-related pathways, cancer hallmark and Gene Ontology (GO) terms. Furthermore, subtype-specific potential gene signatures were discovered in multiple cancer types.

Exploring potential genes and pathways related to calcific aortic valve disease.

Calcific aortic valve disease (CAVD) is currently the most prevalent valvular disease. However, the pathological mechanism of CAVD has not yet been fully elucidated, and no drugs can delay or halt the progression of CAVD. This study aimed to screen for potential biomarkers and pathways of CAVD through bioinformatics analysis. The identification of differentially expressed genes (DEGs) between calcific aortic valves and the control group was performed based on four microarray datasets: GSE12644, GSE51472, GSE77287 and GSE83453. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted. Furthermore, the protein-protein interaction network, and microRNA-target interaction was performed, and hub genes were obtained by using twelve cytoHubba algorithms. As a result, 327 DEGs were identified, including 206 up-regulated and 121 down-regulated genes. KEGG analysis showed that these DEGs were mainly enriched in the PI3K-AKT signaling pathway, ECM-receptor interaction, cytokine-cytokine receptor interaction, and chemokine signaling pathway etc. Moreover, we identified 19 hub genes: CXCL8, CXCL12, CSF1R, HCK, PLEK, CCL5, TLR8, VCAM1, CCR1, CCR7, FPR1, TYROBP, CX3CR1, KIT, PPBP, SPP1, SYK, TLR7, and VWF. And multiple potential miRNAs, including miR-141, miR-34a, miR-155, and miR-486, were identified. And western blot was performed to validate the expression level of hub genes. In conclusion, this study identified several promising biomarkers and pathways for CAVD, which may provide novel molecular markers for diagnosis and targeted therapy.

MicroRNA-Target Interaction Regulatory Network in Alzheimer's Disease.

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia; however, early diagnosis of the disease is challenging. Research suggests that biomarkers found in blood, such as microRNAs (miRNA), may be promising for AD diagnostics. Experimental data on miRNA–target interactions (MTI) associated with AD are scattered across databases and publications, thus making the identification of promising miRNA biomarkers for AD difficult. In response to this, a list of experimentally validated AD-associated MTIs was obtained from miRTarBase. Cytoscape was used to create a visual MTI network. STRING software was used for protein–protein interaction analysis and mirPath was used for pathway enrichment analysis. Several targets regulated by multiple miRNAs were identified, including: BACE1, APP, NCSTN, SP1, SIRT1, and PTEN. The miRNA with the highest numbers of interactions in the network were: miR-9, miR-16, miR-34a, miR-106a, miR-107, miR-125b, miR-146, and miR-181c. The analysis revealed seven subnetworks, representing disease modules which have a potential for further biomarker development. The obtained MTI network is not yet complete, and additional studies are needed for the comprehensive understanding of the AD-associated miRNA targetome.

Systems biology and machine learning approaches identify drug targets in diabetic nephropathy

Diabetic nephropathy (DN), the leading cause of end-stage renal disease, has become a massive global health burden. Despite considerable efforts, the underlying mechanisms have not yet been comprehensively understood. In this study, a systematic approach was utilized to identify the microRNA signature in DN and to introduce novel drug targets (DTs) in DN. Using microarray profiling followed by qPCR confirmation, 13 and 6 differentially expressed (DE) microRNAs were identified in the kidney cortex and medulla, respectively. The microRNA-target interaction networks for each anatomical compartment were constructed and central nodes were identified. Moreover, enrichment analysis was performed to identify key signaling pathways. To develop a strategy for DT prediction, the human proteome was annotated with 65 biochemical characteristics and 23 network topology parameters. Furthermore, all proteins targeted by at least one FDA-approved drug were identified. Next, mGMDH-AFS, a high-performance machine learning algorithm capable of tolerating massive imbalanced size of the classes, was developed to classify DT and non-DT proteins. The sensitivity, specificity, accuracy, and precision of the proposed method were 90%, 86%, 88%, and 89%, respectively. Moreover, it significantly outperformed the state-of-the-art (P-value ≤ 0.05) and showed very good diagnostic accuracy and high agreement between predicted and observed class labels. The cortex and medulla networks were then analyzed with this validated machine to identify potential DTs. Among the high-rank DT candidates are Egfr, Prkce, clic5, Kit, and Agtr1a which is a current well-known target in DN. In conclusion, a combination of experimental and computational approaches was exploited to provide a holistic insight into the disorder for introducing novel therapeutic targets.

MP33-20 ADAR1 IS HIGHLY EXPRESSED IN PRIMARY PROSTATE CANCER AND CORRELATED WITH CD8+ T-LYMPHOCYTES DENSITY

You have accessJournal of UrologyProstate Cancer: Basic Research & Pathophysiology I (MP33)1 Sep 2021MP33-20 ADAR1 IS HIGHLY EXPRESSED IN PRIMARY PROSTATE CANCER AND CORRELATED WITH CD8+ T-LYMPHOCYTES DENSITY Paolo Vota, Fabio Grizzi, Pieve Emanuele, Matteo Zanoni, Giovanni Toia, Mattia Nicola Sangalli, Cinzia Mazzieri, Alberto Mandressi, Davide Maffei, Nicolò Buffi, Giovanni Lughezzani, Pieve Emanuele, Massimo Lazzeri Rozzano, Giorgio Guazzoni, Pieve Emanuele, and Gian Luigi Taverna Paolo VotaPaolo Vota More articles by this author , Fabio GrizziFabio Grizzi More articles by this author , Pieve EmanuelePieve Emanuele More articles by this author , Matteo ZanoniMatteo Zanoni More articles by this author , Giovanni ToiaGiovanni Toia More articles by this author , Mattia Nicola SangalliMattia Nicola Sangalli More articles by this author , Cinzia MazzieriCinzia Mazzieri More articles by this author , Alberto MandressiAlberto Mandressi More articles by this author , Davide MaffeiDavide Maffei More articles by this author , Nicolò BuffiNicolò Buffi More articles by this author , Giovanni LughezzaniGiovanni Lughezzani More articles by this author , Pieve EmanuelePieve Emanuele More articles by this author , Massimo Lazzeri RozzanoMassimo Lazzeri Rozzano More articles by this author , Giorgio GuazzoniGiorgio Guazzoni More articles by this author , Pieve EmanuelePieve Emanuele More articles by this author , and Gian Luigi TavernaGian Luigi Taverna More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002042.20AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: It is now recognized that the evolution of cancer cells is dependent by genetic or epigenetic alterations. However, this concept has recently been challenged by another mode of nucleotide alteration, RNA editing, which is frequently upregulated in cancer(1) RNA editing is a biochemical process in which either Adenosine or Cytosine is deaminated by a group of RNA editing enzymes including ADAR (Adenosine deaminase; RNA specific) The result of RNA editing is usually adenosine to inosine (A-to-I) or cytidine to uridine (C-to-U) transition, which can affect protein coding, RNA stability, splicing and microRNA-target interactions(2). The aim of this study was to preliminarily investigate the expression of ADAR1 in a series of prostate cancer specimens and benign prostatic hyperplasia (BPH) following transurethral resection of the prostate (TURP). METHODS: Sixty prostate specimens were investigated. Fifty specimens were diagnosed as prostate carcinoma and 15 as benign prostate hyperplasia. The samples were fixed in 10% formaldehyde and paraffin-embedded. Two-micrometer thick sections were cut and processed for immunohistochemistry with primary antibodies raised against ADAR1 (SantaCruz Biotechnology, Dallas, TX, USA) or CD8+ T-lymphocytes (Dako, Milan, Italy). 3,3’-Diaminobenzidine tetrahydrochloride was used as a chromogen to yield brown reaction products. To quantify the surface covered by infiltrating CTLs each histological section was digitized using an automated image analysis system with incorporated ad hoc constructed image analysis software. The system automatically selected the surface covered by the CTL on the basis of red, green and blue (RGB) color segmentation. RESULTS: ADAR1 up-regulation was heterogeneously detected in a high percentage of prostate cancer tissues, but to a much lesser extent in adjacent non-malignant tissues or tissue affected by BPH (p<0.001). Prostate cancers with high ADAR1 expression exhibited high tumor-infiltrating CD8 + T lymphocyte. ADAR expression is associated with several diseases including cancer, neurological disorders, metabolic diseases, viral infections, and autoimmune disorders. CONCLUSIONS: This study first shows that ADAR1 is highly expressed in a high percentage of prostate cancer tissues, but to a much lesser extent in adjacent non-malignant tissues or tissue affected by benign prostatic hyperplasia. Additionally, prostate cancers with high ADAR1 expression exhibited high tumor-infiltrating CD8 + T lymphocyte. Our findings indicated that ADAR1 might play an important role in the occurrence, progression, and prognosis of prostate cancer, and open new ways for the development of new and more effective immunological therapeutic strategies. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e614-e615 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Paolo Vota More articles by this author Fabio Grizzi More articles by this author Pieve Emanuele More articles by this author Matteo Zanoni More articles by this author Giovanni Toia More articles by this author Mattia Nicola Sangalli More articles by this author Cinzia Mazzieri More articles by this author Alberto Mandressi More articles by this author Davide Maffei More articles by this author Nicolò Buffi More articles by this author Giovanni Lughezzani More articles by this author Pieve Emanuele More articles by this author Massimo Lazzeri Rozzano More articles by this author Giorgio Guazzoni More articles by this author Pieve Emanuele More articles by this author Gian Luigi Taverna More articles by this author Expand All Advertisement Loading ...

Circular RNAs in kidney disease and cancer.

Circular RNAs (circRNAs) are a class of endogenously expressed regulatory RNAs with a single-stranded circular structure. They are generated by back splicing and their expression can be tightly regulated by RNA binding proteins. Cytoplasmic circRNAs can function as molecular sponges that inhibit microRNA-target interactions and protein function or as templates for the efficient generation of peptides via rolling circle amplification. They can also act as molecular scaffolds that enhance the reaction kinetics of enzyme-substrate interactions. In the nucleus, circRNAs might facilitate chromatin modifications and promote gene expression. CircRNAs are resistant to degradation and can be packaged in extracellular vesicles and transported in the circulation. Initial studies suggest that circRNAs have roles in kidney disease and associated cardiovascular complications. They have been implicated in hypertensive nephropathy, diabetic kidney disease, glomerular disease, acute kidney injury and kidney allograft rejection, as well as in microvascular and macrovascular complications of chronic kidney disease, including atherosclerotic vascular disease. In addition, several circRNAs have been reported to have oncogenic or tumour suppressor roles or to regulate drug resistance in kidney cancer. The available data suggest that circRNAs could be promising diagnostic and/or prognostic biomarkers and potential therapeutic targets for kidney disease, cardiovascular disease and kidney cancer.