Potential Candidate miRNAs Research Articles

Revealing miRNAs' Janus-Faced Nature: Transforming Cold Tumours into Immunotherapy Hotspots and Overcoming Chemoresistance.

MicroRNA (miRNA) modulation has emerged as a promising strategy in cancer immunotherapy, particularly in converting "cold" tumors with limited immune cell infiltration into "hot" tumors responsive to immunotherapy. miRNAs regulate immune cell recruitment and activation within the tumor microenvironment, influencing tumor behavior targeting specific miRNAs in cold tumors aims to enhance the immune response, potentially improving therapeutic efficacy. Despite ongoing research challenges, such as tumor complexity and treatment resistance, miRNA-based therapies offer personalized approaches with potential ethical considerations. Advances in miRNA profiling may enable early cancer detection and tailored treatments, underscoring its role in future oncology. This review sheds light on the role of miRNA in cold and hot tumor microenvironments, how they modulate depending on the tumor niche and the current research challenges in implementing miRNA-based therapies include the complexity of tumors and their heterogeneity, which makes it difficult to identify the most relevant miRNAs to target. Additionally, treatment resistance can develop over time, reducing the effectiveness of miRNA modulation. Despite these challenges, ongoing research and advancements in miRNA profiling hold promise for overcoming these obstacles and improving the outcomes of cancer immunotherapy. To overcome the challenges of identifying relevant miRNAs to target, researchers can employ high-throughput sequencing techniques to comprehensively profile miRNA expression in different tumor subtypes. They can also utilize bioinformatics tools and databases to analyze the vast amount of miRNA-related data and identify potential candidate miRNAs. Furthermore, collaborations between scientists and clinicians can help validate the functional significance of identified miRNAs and their potential as therapeutic targets.

Target prediction of potential candidate miRNAs from Oryza sativa to silence the Pyricularia oryzae genome in rice blast

Rice (Oryza sativa) is a staple food for billions of people across the globe, that feeds nearly three-quarters of the human population on Earth, particularly in Asian countries. Rice yield has been drastically reduced and severely affected by various biotic and abiotic stresses, especially pathogens. Controlling the attack of such pathogens is a matter of immediate concern as yield losses in rice crops could deprive millions of lives of nourishment worldwide. Pyricularia oryzae is one such pathogen that has been considered the major disease of rice because of its worldwide geographic distribution. P. oryzae belongs to the kingdom fungi, that causes rice blast ultimately adversely affecting the yield of the rice crop. Keeping in view this alarming scenario, the present study was designed so that the identifications of genome-encoded miRNAs of Oryza sativa were employed to target and silence the genome of P. oryzae. This study accomplished the computational analysis of algorithms related to miRNA target prediction. Four computational target prediction algorithms i.e., psRNATarget, RNA22, miRanda, and RNAhybrid were utilized in this investigation. The consensus among target prediction algorithms was created to discover six miRNAs from the O. sativa genome with the conservation of the target site fully evaluated on the genome of P. oryzae. The discovery of these novel six miRNAs in Oryza sativa paved a strong way toward the control of this disease in rice. It will open doors for further research in the field of gene silencing in rice. These miRNAs can be designed and employed in the future as experimentation to create constructs regarding the silencing of P. oryzae in rice crops. In the future, this research would be surely helpful for the development of P. oryzae resistant rice varieties.

A tetrahedral framework nucleic acids-based gene therapeutic nanococktail alleviates cartilage damage and protects against osteoarthritis progression

Gene therapy based on therapeutic microRNAs (miRNAs) shows great potential in Osteoarthritis (OA) treatment. However, individual miRNAs application is limited by the single target, the instability, and the low efficiency of cell internalization. Here, we identified two potential candidate miRNAs, miR-140-5p and miR-455-3p, exhibited combined enhancing effects against OA progression. Furthermore, a nanococktail therapeutic platform, T-(140 + 455) was constructed by employing tetrahedral framework nucleic acids (tFNAs) to codeliver miR-140-5p/miR-455-3p and achieve enhanced gene therapy for OA. Compared with the delivery of individual miRNAs, T-(140 + 455) can effectively reverse the cell phenotype and metabolism of OA chondrocytes. Moreover, the nanococktail T-(140 + 455) reprogrammed macrophage polarization by modulating the crosstalk between chondrocytes and macrophages, combatting the damage caused by inflammation. The OA rat model treated with nanococktail T-(140 + 455) exhibited favorable performance in halting cartilage damage and regulating extracellular matrix (ECM) metabolic homeostasis. Thus, tFNA-based nanococktails enhance the delivery and efficacy of miRNAs, offering a promising strategy for OA gene therapy.

Identification of potential candidate miRNAs related to semen quality in seminal plasma extracellular vesicles and sperms of male duck (Anas Platyrhynchos)

Semen quality is an important indicator that can directly affect fertility. In mammals, miRNAs in seminal plasma extracellular vesicles (SPEVs) and sperms can regulate semen quality. However, relevant regulatory mechanism in duck sperms remains largely unclear. In this study, duck SPEVs were isolated and characterized by transmission electron microscopy (TEM), western blot (WB), and nanoparticle tracking analysis (NTA). To identify the important molecules affecting semen quality, we analysed the miRNA expression in sperms and SPEVs of male ducks in high semen quality group ((DHS, DHSE) and low semen quality group (DLS, DLSE). We identified 94 differentially expressed (DE) miRNAs in the comparison of DHS vs. DLS, and 21 DE miRNAs in DHSE vs. DLSE. Target genes of SPEVs DE miRNAs were enriched in ErbB signaling pathway, glycometabolism, and ECM-receptor interaction pathways (P < 0.05), while the target genes of sperm DE miRNAs were enriched in ribosome (P < 0.05). The miRNA-target-pathway interaction network analyses indicated that 5 DE miRNAs (miR-34c-5p, miR-34b-3p, miR-449a, miR-31-5p, and miR-128-1-5p) targeted the largest number of target genes enriched in MAPK, Wnt and calcium signaling pathways, of which FZD9 and ANAPC11 were involved in multiple biological processes related to sperm functions, indicating their regulatory effects on sperm quality. The comparison of DE miRNAs of SPEVs and sperms found that mir-31-5p and novel-273 could potentially serve as biomarkers for semen quality detection. Our findings enhance the insight into the crucial role of SPEV and sperm miRNAs in regulating semen quality and provide a new perspective for subsequent studies.

MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.

Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes the development of new medicine. Recently, network-based approaches have been widely proposed for inferring the potential associations between miRNAs and diseases. However, these approaches ignore the importance of different relations in meta-paths when learning the embeddings of miRNAs and diseases. Besides, they pay little attention to screening out reliable negative samples which is crucial for improving the prediction accuracy. In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unlabeled sample set with the negative distance-based sample selection strategy. Finally, we train MGCNSS under an unsupervised learning manner and predict the potential associations between miRNAs and diseases. The experimental results fully demonstrate that MGCNSS outperforms all baseline methods on both balanced and imbalanced datasets. More importantly, we conduct case studies on colon neoplasms and esophageal neoplasms, further confirming the ability of MGCNSS to detect potential candidate miRNAs. The source code is publicly available on GitHub https://github.com/15136943622/MGCNSS/tree/master.

Blood miRNAs miR-549a, miR-552, and miR-592 serve as potential disease-specific panels to diagnose colorectal cancer

IntroductionmiRNAs originating from colorectal cancer (CRC) tissue receive significant focus in the early diagnosis of CRC due to their stability in body fluids. However, if these miRNAs originated from alternative organs, their prognostic value will diminish. Thus, in this study, we aim to identify disease-specific miRNAs for colorectal cancer (CRC) by employing bioinformatics and experimental methodologies. MethodTo identify CRC-specific miRNAs, we retrieved miRNA profiles of CRC and normal tissues from the Cancer Genome Atlas (TCGA) database. Subsequently, computational strategies were utilized to select potential candidate miRNAs. Following this, the expression levels of the potent miRNAs were assessed through RT-qPCR in both CRC tissue and serum samples from patients (N = 46), as well as healthy individuals (N = 46). Additionally, the associations between clinicopathological characteristics, survival outcomes, and diagnostic accuracy were evaluated. ResultsA total of 8893 RNA-seq expression data were acquired from TCGA, comprising 8250 data from 19 distinct cancer types and 643 corresponding healthy samples. Based on the computational methodology, miR-549a, miR-552, and miR-592 were identified as the principal expressed miRNAs in colorectal cancer (CRC). Within these miRNAs, miR-552 displayed a substantial association with tumors at the N and T stages. miR-549a and miR-592 were observed to be linked exclusively to the invasion of tumor depth and tumor stage (TNM), respectively. The receiver operating characteristic (ROC) analysis conducted on the miRNA expression in serum samples revealed that all miRNAs exhibited an area under the ROC curve (AUC) of up to 0.86, thereby indicating their high diagnostic accuracy. ConclusionConsidering the strong associations of these three identified miRNAs with CRC, they can collectively serve as a panel for specific discrimination of CRC from other types of cancer within the body. Although this study focused solely on CRC, this approach can potentially be applied to other cancer types as well.

An In Vitro Study for the Role of Schizophrenia-Related Potential miRNAs in the Regulation of COMT Gene

This study aimed to analyze the possible association of miR-30a-5p, miR-30e-5p, and miR-34a-5p identified as potential candidate miRNAs in schizophrenia, with the COMT gene. Candidate miRNAs were obtained from the TargetScan database. The SH-SY5Y human neuroblastoma cell line was used as a cellular model for schizophrenia. miR-30a-5p, miR-30e-5p, and miR-34a-5p mimics were transfected into the SH-SY5Y cell line. Total RNA was isolated from transfected cells and RNA-IP samples and reverse transcripted for miRNA and mRNA analysis. RT-qPCR and western blot were performed to observe changes in expression levels of COMT. RNA-ımmunoprecipitation was performed to determine RNA–protein interactions after mimic transfection. In the study, it was observed that COMT gene expression levels decreased significantly after miR-30a-5p and miR-34a-5p expressions, whereas increased significantly as a result of miR-30e-5p transfection. RNA-IP data have shown that the amount of COMT pulled down by Ago2 was increased after miR-30a-5p and miR-34a-5p transfections. RNA-IP results revealed that miR-30a-5p and miR-34a-5p are direct targets for the COMT gene.

A multi‐omics approach to investigate the synapse dysfunction in Alzheimer’s disease

AbstractBackgroundSynapse dysfunction is an early event for Alzheimer’s disease (AD) progression. Synapse dysfunctions are caused by multiple cellular and pathologic factors such as Amyloid beta, p‐tau, inflammation and aging. However, exact molecular mechanism of synapse dysfunction is largely unknown in AD. Therefore, to understand the molecular basis of synapse dysfunction in AD, we conducted a high throughput multi‐omics analysis of synaptosome fraction in AD and cognitively normal postmortem brain samples.MethodSynaptosomes were extracted from healthy control (HC) postmortem brain (n = 14) and AD postmortem brains (n = 27). Next, we performed miRNAs and mRNAs HiSeq analysis on synaptosomal RNAs. We also used HC (n = 10) and AD (n = 10) synaptosome samples for mass spectroscopy proteomic analysis. We conducted the synaptosomal miRNAs and mRNA sequencing and mass spec analysis on the same groups of samples. Further, we conducted a high throughput multi‐comics analysis to understand the molecular interaction of deregulated synapse miRNAs, mRNAs and proteins at AD synapse.ResultMiRNA HiSeq analysis showed the significant deregulation of several miRNAs in synaptosome fraction in AD vs HC. Our study also identified the several novel potential candidate miRNAs those were deregulated in AD vs HC synaptosomes. Similarly, several protein coding genes and novel transcript were also found to be deregulated in AD vs HC synaptosomes. Further, mass spec analysis showed significant deregulation of several synaptic proteins in AD vs HC synaptosomes. Further, to understand the molecular interaction of synapse miRNAs‐mRNA‐proteins and their deregulation in AD, we conducted high throughput multi‐Omics analysis. Study revealed the involvement of omics targets in several biological process and molecular function such as signal transduction, protein binding, GABAergic synapse and synaptic vesicle cycle etc.ConclusionOur study unveiled the synapse centered novel omics candidates those could be potential therapeutic targets for synapse dysfunction in AD.

Non-coding transcriptomic profiles in the sheep mammary gland during different lactation periods.

Sheep milk production is a dynamic and multifactorial trait regulated by diverse biological mechanisms. To improve the quality and production of sheep milk, it is necessary to understand the underlying non-coding transcriptomic mechanisms. In this study, ribonucleic acid-sequencing (RNA-seq) was used to profile the expression of microRNAs (miRNAs) and circular RNAs (circRNAs) in the sheep mammary gland at three key lactation time points (perinatal period, PP; early lactation, EL; and peak lactation, PL). A total of 2,369 novel circRNAs and 272 miRNAs were profiled, of which 348, 373, and 36 differentially expressed (DE) circRNAs and 30, 34, and 7 DE miRNAs were detected in the comparison of EL vs. PP, PL vs. PP, and PL vs. EL, respectively. A series of bioinformatics analyses including functional enrichment, machine learning prediction, and competing endogenous RNA (ceRNA) network analyses were conducted to identify subsets of the potential candidate miRNAs (e.g., oar_miR_148a, oar_miR_362, and oar_miR_432) and circRNAs (e.g., novel_circ_0011066, novel_circ_0010460, and novel_circ_0006589) involved in sheep mammary gland development. Taken together, this study offers a window into the dynamics of non-coding transcriptomes that occur during sheep lactation and may provide further insights into miRNA and circRNA that influence sheep mammary gland development.

A knowledge-driven network for fine-grained relationship detection between miRNA and disease.

Increasing biological evidence indicated that microRNAs (miRNAs) play a vital role in exploring the pathogenesis of various human diseases (especially in tumors). Mining disease-related miRNAs is of great significance for the clinical diagnosis and treatment of diseases. Compared with the traditional experimental methods with the significant limitations of high cost, long cycle and small scale, the methods based on computing have the advantages of being cost-effective. However, although the current methods based on computational biology can accurately predict the correlation between miRNAs and disease, they can not predict the detailed association information at a fine level. We propose a knowledge-driven approach to the fine-grained prediction of disease-related miRNAs (KDFGMDA). Different from the previous methods, this method can finely predict the clear associations between miRNA and disease, such as upregulation, downregulation or dysregulation. Specifically, KDFGMDA extracts triple information from massive experimental data and existing datasets to construct a knowledge graph and then trains a depth graph representation learning model based on knowledge graph to complete fine-grained prediction tasks. Experimental results show that KDFGMDA can predict the relationship between miRNA and disease accurately, which is of far-reaching significance for medical clinical research and early diagnosis, prevention and treatment of diseases. Additionally, the results of case studies on three types of cancers, Kaplan-Meier survival analysis and expression difference analysis further provide the effectiveness and feasibility of KDFGMDA to detect potential candidate miRNAs. Availability: Our work can be downloaded from https://github.com/ShengPengYu/KDFGMDA.

In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus.

Sugarcane bacilliform virus (SCBV) is considered one of the most economically damaging pathogens for sugarcane production worldwide. Three open reading frames (ORFs) are characterized in the circular, ds-DNA genome of the SCBV; these encode for a hypothetical protein (ORF1), a DNA binding protein (ORF2), and a polyprotein (ORF3). A comprehensive evaluation of sugarcane (Saccharum officinarum L.) miRNAs for the silencing of the SCBV genome using in silico algorithms were carried out in the present study using mature sugarcane miRNAs. miRNAs of sugarcane are retrieved from the miRBase database and assessed in terms of hybridization with the SCBV genome. A total of 14 potential candidate miRNAs from sugarcane were screened out by all used algorithms used for the silencing of SCBV. The consensus of three algorithms predicted the hybridization site of sof-miR159e at common locus 5534. miRNA-mRNA interactions were estimated by computing the free-energy of the miRNA-mRNA duplex using the RNAcofold algorithm. A regulatory network of predicted candidate miRNAs of sugarcane with SCBV-ORFs, generated using Circos-is used to identify novel targets. The predicted data provide useful information for the development of SCBV-resistant sugarcane plants.

NFKB1 Signalling Activation Contributes to TRPV1 Over-expression via Repressing MiR-375 and MiR-455: a Study on Neuropathic Low Back Pain.

Transient receptor potential cation channel subfamily V member 1 (TRPV1) has been found over-expressed in low back pain (LBP) patients with neuropathic pain (NP), but the underlying mechanism is still unclear. In the present study, the up-regulation of the TRPV1 protein level in sinuvertebral nerve biopsies from patients with NP was verified by immunoblotting, but the TRPV1 mRNA level was not significantly changed. MiRNAs targeting TRPV1 mRNA were predicted by a bioinformatic tool, and the interactions between the miRNAs and TRPV1 were confirmed by dual luciferase assay. The correlation between NFKB1 signalling and TRPV1 expression was analysed and confirmed by using sNF96.2 cells after lipopolysaccharide stimulation. We found that five out of 18 miRNAs repressed TRPV1 expression, and the levels of miR-375 and miR-455 were negatively correlated with the protein level of TRPV1 in patients with NP. MiR-375 and miR-455 were identified to repress TRPV1 expression via targeting the 3'UTR of TRPV1 mRNA. NFKB1 signalling activation down-regulated the expression of miR-375 and miR-455, and thus up-regulated the TRPV1 protein level. In conclusion, we partially unveiled the mechanism of how TRPV1 is over-expressed in chronic LBP patients with NP and provided two potential candidate miRNAs for NP treatment.

Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA-disease association prediction.

Identifying disease-related microRNAs (miRNAs) assists the understanding of disease pathogenesis. Existing research methods integrate multiple kinds of data related to miRNAs and diseases to infer candidate disease-related miRNAs. The attributes of miRNA nodes including their family and cluster belonging information, however, have not been deeply integrated. Besides, the learning of neighbor topology representation of a pair of miRNA and disease is a challenging issue. We present a disease-related miRNA prediction method by encoding and integrating multiple representations of miRNA and disease nodes learnt from the generative and adversarial perspective. We firstly construct a bilayer heterogeneous network of miRNA and disease nodes, and it contains multiple types of connections among these nodes, which reflect neighbor topology of miRNA-disease pairs, and the attributes of miRNA nodes, especially miRNA-related families and clusters. To learn enhanced pairwise neighbor topology, we propose a generative and adversarial model with a convolutional autoencoder-based generator to encode the low-dimensional topological representation of the miRNA-disease pair and multi-layer convolutional neural network-based discriminator to discriminate between the true and false neighbor topology embeddings. Besides, we design a novel feature category-level attention mechanism to learn the various importance of different features for final adaptive fusion and prediction. Comparison results with five miRNA-disease association methods demonstrated the superior performance of our model and technical contributions in terms of area under the receiver operating characteristic curve and area under the precision-recall curve. The results of recall rates confirmed that our model can find more actual miRNA-disease associations among top-ranked candidates. Case studies on three cancers further proved the ability to detect potential candidate miRNAs.

Feasibility of using probabilistic methods to analyse microRNA quantitative data in forensically relevant body fluids: a proof-of-principle study.

Several studies have confirmed that microRNAs (miRNAs) are promising markers for body fluid identification since they were introduced to this field. However, there is no consensus on the choice of reference genes and identification strategies. In this study, 13 potential candidate miRNAs were screened from three forensically relevant body fluid datasets, and the expression of 12 markers in five body fluids was determined using a real-time quantitative method. Two probabilistic approaches, Naive Bayes (NB) and partial least squares discriminant analysis (PLS-DA), were then applied to predict the origin of the samples to determine whether probabilistic methods are helpful in body fluid identification using miRNA quantitative data. Furthermore, 14 reference combinations were used to validate the influence of different reference choices on the predicted results simultaneously. Our results showed that in the NB model, leave-one-out cross-validation (LOOCV) achieved 100% accuracy and the prediction accuracy of the test set was 100% in most reference combinations. In the PLS-DA model, the first two components could interpret about 80% expression variance and LOOCV achieved 100% accuracy when miR-92a-3p was used as the reference. This study preliminarily proved that probabilistic approaches hold huge potential in miRNA-based body fluid identification, and the choice of references influences the prediction results to a certain extent.

MicroRNA Sequencing of CD34+ Sorted Adipose Stem Cells Undergoing Endotheliogenesis.

While several microRNAs (miRNAs) that regulate the endotheliogenesis and further promote angiogenesis have been identified in various cancers, the identification of miRNAs that can drive the differentiation of adipose derived stromal/stem cells (ASCs) into the endothelial lineage has been largely unexplored. In this study, CD34+ ASCs sorted using magnetic bead separation were induced to differentiate along the endothelial pathway. miRNA sequencing of ASCs at day 3, 9, and 14 of endothelial differentiation was performed on Ion Proton sequencing system. The data obtained by this high-throughput method were aligned to the human genome HG38, and the differentially expressed miRNAs during endothelial differentiation at various time points (day 3, 9, and 14) were identified. The gene targets of the identified miRNAs were obtained through miRWalk database. The network-pathway analysis of miRNAs and their targets was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatic tools to determine the potential candidate miRNAs that promote endothelial differentiation. Based on these analyses, six upregulated miRNAs (miR-181a-5p, miR-330-5p, miR-335-3p, miR-15b-5p, miR-99a-5p, and miR-199a-5p) and six downregulated miRNAs (miR-145-5p, miR-155-5p, miR-193a-3p, miR-125a-5p, miR-221-5p, and miR-222-3p) were chosen for further studies. In vitro evaluation of these miRNAs to induce endothelial differentiation when transfected into CD34+ sorted ASCs was studied using Von Willebrand Factor (VWF) staining and quantitative real time-polymerase chain reaction (qRT-PCR). Our results suggest that miRNAs: 335-5p, 330-5p, 181a-5p and anti-miRNAs: 125a-5p, 145-5p can likely induce endothelial differentiation in CD34+ sorted ASCs. Further studies are clearly required to elucidate the specific mechanisms on how miRNAs or anti-miRNAs identified through bioinformatics approach can induce the endotheliogenesis in ASCs.

Tumor-associated exosomal miRNA biomarkers to differentiate metastatic vs. nonmetastatic non-small cell lung cancer.

Exosomal microRNAs (miRNAs) are proposed to be excellent candidate biomarkers for clinical applications. However, little is known about their potential value as diagnostic biomarkers for metastatic non-small cell lung cancer (NSCLC). In this study, microarrays were used to determine distinct miRNA profiles of plasma exosomes in a discovery cohort of healthy donors, metastatic NSCLC and nonmetastatic NSCLC patients. Three potential candidate miRNAs were selected based on the differential expression profiles. The discovery set data were validated by quantitative real-time polymerase chain reaction using a validation cohort. NSCLC patients (n = 80) and healthy controls (n = 30) had different exosome-related miRNA profiles in plasma. Results demonstrated that the level of let-7f-5p was decreased in plasma exosomes of NSCLC patients (p < 0.0001). Further analysis of three differentially expressed miRNAs revealed that miR-320a, miR-622 and let-7f-5p levels could significantly segregate patients with metastatic NSCLC from patients with nonmetastatic NSCLC (p < 0.0001, p < 0.0001 and p = 0.023, respectively). In addition, the combination of let-7f-5p, CEA and Cyfra21-1 generated an area under the curve (AUC) of 0.981 for the diagnosis of NSCLC patients, and the combination of miR-320a, miR-622, CEA and Cyfra21-1 had an AUC of 0.900 for the diagnosis of patients with metastatic NSCLC. This novel study demonstrated that plasma exosomal miRNAs are promising noninvasive diagnostic biomarkers for metastatic NSCLC.

Novel MicroRNA Biomarkers for Colorectal Cancer Early Diagnosis and 5-Fluorouracil Chemotherapy Resistance but Not Prognosis: A Study from Databases to AI-Assisted Verifications.

Colorectal cancer (CRC) is one of the major causes of cancer death worldwide. In general, early diagnosis for CRC and individual therapy have led to better survival for the cancer patients. Accumulating studies concerning biomarkers have provided positive evidence to improve cancer early diagnosis and better therapy. It is, however, still necessary to further investigate the precise biomarkers for cancer early diagnosis and precision therapy and predicting prognosis. In this study, AI-assisted systems with bioinformatics algorithm integrated with microarray and RNA sequencing (RNA-seq) gene expression (GE) data has been approached to predict microRNA (miRNA) biomarkers for early diagnosis of CRC based on the miRNA-messenger RNA (mRNA) interaction network. The relationships between the predicted miRNA biomarkers and other biological components were further analyzed on biological networks. Bayesian meta-analysis of diagnostic test was utilized to verify the diagnostic value of the miRNA candidate biomarkers and the combined multiple biomarkers. Biological function analysis was performed to detect the relationship of candidate miRNA biomarkers and identified biomarkers in pathways. Text mining was used to analyze the relationships of predicted miRNAs and their target genes with 5-fluorouracil (5-FU). Survival analyses were conducted to evaluate the prognostic values of these miRNAs in CRC. According to the number of miRNAs single regulated mRNAs (NSR) and the number of their regulated transcription factor gene percentage (TFP) on the miRNA-mRNA network, there were 12 promising miRNA biomarkers were selected. There were five potential candidate miRNAs (miRNA-186-5p, miRNA-10b-5, miRNA-30e-5p, miRNA-21 and miRNA-30e) were confirmed as CRC diagnostic biomarkers, and two of them (miRNA-21 and miRNA-30e) were previously reported. Furthermore, the combinations of the five candidate miRNAs biomarkers showed better prediction accuracy for CRC early diagnosis than the single miRNA biomarkers. miRNA-10b-5p and miRNA-30e-5p were associated with the 5-FU therapy resistance by targeting the related genes. These miRNAs biomarkers were not statistically associated with CRC prognosis.

Molecular Portrait of Potential Attention Deficit/ Hyperactivity Disorder Candidate Genes and Regulating Micrornas Expression in Normal Human Developing Brain Tissues

Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in childhood affecting 5-6% of children, and is a major global health concern, which seems to increase in magnitude. The etiology of ADHD is still poorly understood, however; there are indications of genetic as well as environmental and epigenetic factors contributing to the development of the disorder. The objectives of this study was i) to identify potential ADHD candidate genes; ii) to explore spatial and temporal transcriptional fluctuation of the identified ADHD candidate genes in normal developing human brain tissues, and iii) to identify miRNAs regulating the identified ADHD candidate genes and explore how these miRNAs are expressed in normal developing human brain tissues. From search in literature and publicly available databases, we identified 103 shared potential ADHD candidate genes. These genes were expressed and enriched in several human brain regions and developmental stages. Clustering analysis of these genes based on their expression levels showed a clear difference between fetal stage and the other developmental stages. There was no clear gender or brain region differences between samples. Further, functional analysis of these genes revealed that they participate in a variety of different and widely distributed functional pathways implicated with ADHD. From miRNA-target prediction analysis, we identified twenty miRNAs regulating the identified 103 genes, and the expression pattern of these miRNAs was developmental stage dependent. These miRNAs were enriched in functional pathways and disease ontologies relevant to neurodevelopment. The knowledge of the expression pattern of potential ADHD candidate genes and miRNAs, which regulate these genes across different stages of brain development, is essential for understanding normal brain development and subsequent disease development of the brain. In addition, identification of miRNA-regulated ADHD candidate genes can be used to develop blood-based molecular markers to be investigated in future studies of ADHD patients.

Down-regulation of miR-3068-3p enhances kcnip4-regulated A-type potassium current to protect against glutamate-induced excitotoxicity.

The main cause of excitotoxic neuronal death in ischemic stroke is the massive release of glutamate. Recently, microRNAs (miRNAs) have been found to play an essential role in stroke pathology, although the molecular mechanisms remain to be investigated. Here, to identify potential candidate miRNAs involved in excitotoxicity, we treated rat primary cortical neurons with glutamate and found that miR-3068-3p, a novel miRNA, was up-regulated. We hypothesized that restoring miR-3068-3p expression might influence the neuronal injury outcomes. The inhibition of miR-3068-3p, using tough decoy lentiviruses, significantly attenuated the effects of glutamate on neuronal viability and intracellular calcium overload. To unravel the mechanisms, we employed bioinformatics analysis and RNA sequencing to identify downstream target genes. Additional luciferase assays and western blots validated kcnip4, a Kv4-mediated A-type potassium current (IA ) regulator, as a direct target of miR-3068-3p. The inhibition of miR-3068-3p increased kcnip4 expression and vice versa. In addition, the knockdown of kcnip4 by shRNA abolished the protective effect of miR-3068-3p, and over-expressing kcnip4 alone was sufficient to play a neuroprotective role in excitotoxicity. Moreover the inhibition of miR-3068-3p enhanced the IA density, and the pharmacological inhibition of IA abrogated the protective role of miR-3068-3p inhibition and kcnip4 over-expression. Therefore, we conclude that inhibition of miR-3068-3p protects against excitotoxicity via its target gene, kcnip4, and kcnip4-regulated IA . Our data suggest that the miR-3068-3p/kcnip4 axis may serve as a novel target for the treatment of ischemic stroke.

Expression profile analysis of microRNAs during hair follicle development in the sheep foetus.

MicroRNAs (miRNAs) regulate the development and growth cycle of hair follicles (HFs). The molecular mechanism by which miRNAs determine the development of HFs in the sheep foetus remains elusive. In this study, the expression profiles of miRNAs at 11 development periods (45, 55, 65, 75, 85, 95, 105, 115, 125, 135 and 145 d) in sheep foetus skin were analysed by high-throughput sequencing and bioinformatics analysis. A total of 72 conserved miRNAs, 44 novel miRNAs and 32 known miRNAs were significantly differentially expressed. qRT-PCR results for 18 miRNAs were consistent with the sequencing data. 85 d of foetal development was the starting point for secondary hair follicle (SF) development according to tissue morphology and cluster analysis. In SF development, the prolactin signalling pathway and platelet activation played important roles, and 10 miRNAs were potential candidate miRNAs in SF initiation.