Comparison of microRNA expression levels in patients with schizophrenia before and after electroconvulsive therapy.

OBJECTIVE: Exploring the role of microRNAs in the antipsychotic efficacy of electroconvulsive therapy (ECT) will contribute to understanding the underlying mechanism through which ECT exerts its therapeutic effects. The primary objective of this study was to identify microRNA alterations before and after ECT in patients with schizophrenia. METHODS: We compared microarray-based microRNA profiles in peripheral blood from eight patients with schizophrenia before and after ECT and eight healthy controls. Then, we aimed to validate selected differentially expressed microRNAs in 30 patients with schizophrenia following a course of ECT, alongside 30 healthy controls by using quantitative reverse-transcription PCR. RESULTS: Microarray-based expression profiling revealed alterations in 681 microRNAs when comparing pre- and post-ECT samples. Subsequent quantitative reverse-transcription PCR analysis of the selected microRNAs (miR-20a-5p and miR-598) did not reveal any statistical differences between pre- and post-ECT samples nor between pre-ECT samples and those of healthy controls. CONCLUSION: As neuroepigenetic studies on ECT are still in their infancy, the results reported in this study are best interpreted as exploratory outcomes. Additional studies are required to explore the potential epigenetic mechanisms underlying the therapeutic efficacy of ECT.

Signal execution modes emerge in biochemical reaction networks calibrated to experimental data.

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells and provide previously unrecognized mechanistic explanation for the survival responses of CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses.

Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways.

Small cell lung cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

Hyperpolarized 13C metabolic imaging detects long-lasting metabolic alterations following mild repetitive traumatic brain injury.

Career athletes, active military, and head trauma victims are at increased risk for mild repetitive traumatic brain injury (rTBI), a condition that contributes to the development of epilepsy and neurodegenerative diseases. Standard clinical imaging fails to identify rTBI-induced lesions, and novel non-invasive methods are needed. Here, we evaluated if hyperpolarized 13C magnetic resonance spectroscopic imaging (HP 13C MRSI) could detect long-lasting changes in brain metabolism 3.5 months post-injury in a rTBI mouse model. Our results show that this metabolic imaging approach can detect changes in cortical metabolism at that timepoint, whereas multimodal MR imaging did not detect any structural or contrast alterations. Using Machine Learning, we further show that HP 13C MRSI parameters can help classify rTBI vs. Sham and predict long-term rTBI-induced behavioral outcomes. Altogether, our study demonstrates the potential of metabolic imaging to improve detection, classification and outcome prediction of previously undetected rTBI.

Nano-gelcoat application of glass fiber reinforced polymer composites for marine application: Structural, mechanical, and thermal analysis.

To address the critical issue of environmental sustainability, the application of nano-gelcoat has emerged as a promising approach to extend the service life and enhance the durability of glass fiber-reinforced polymer composites (GFRPs), thereby protecting the marine environment. Despite the extensive use of GFRPs in marine structures, their performance is significantly influenced by marine environmental factors. However, despite its potential, there is a lack of research on the experimental application of nano-gelcoat. To fully understand the underlying mechanisms and optimize the performance of nano-gelcoat application, more experimental investigations are required. In the present study, the hydrothermal aging of nano-gelcoat-coated glass fiber-reinforced polymer composites was investigated. Nano TiO2 and nanoclay were used to enhance the performance of gelcoat. A three-month hydrothermal aging experiment was carried out in artificial seawater heated to 80 °C. Mechanical and water absorption properties of GFRPs and thermal, morphological and FTIR (Fourier transform infrared spectroscopy) properties of nano-gelcoat were investigated. An ANOVA analysis was performed to establish if the experimental results were statistically significant. The long-term aging tests showed that the addition of nano-TiO2 improved water resistance and maintained the mechanical properties of the composite. It also improved the melting point.

Data-driven structural analysis of Small Cell Lung Cancer transcription factor network suggests potential subtype regulators and transition pathways.

Small Cell Lung Cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

Learning feedback molecular network models using integer linear programming.

Analysis of intracellular molecular networks has many applications in understanding of the molecular bases of some complex diseases and finding effective therapeutic targets for drug development. To perform such analyses, the molecular networks need to be converted into computational models. In general, network models constructed using literature and pathway databases may not accurately predict experimental network data. This can be due to the incompleteness of literature on molecular pathways, the resources used to construct the networks, or some conflicting information in the resources. In this paper, we propose a network learning approach via an integer linear programming formulation that can systematically incorporate biological dynamics and regulatory mechanisms of molecular networks in the learning process. Moreover, we present a method to properly consider the feedback paths, while learning the network from data. Examples are also provided to show how one can apply the proposed learning approach to a network of interest. In particular, we apply the framework to the ERBB signaling network, to learn it from some experimental data. Overall, the proposed methods are useful for reducing the gap between the curated networks and experimental data, and result in calibrated networks that are more reliable for making biologically meaningful predictions.

A hybrid model to study how late long-term potentiation is affected by faulty molecules in an intraneuronal signaling network regulating transcription factor CREB.

Systems biology analysis of intracellular signaling networks has tremendously expanded our understanding of normal and diseased cell behaviors and has revealed paths to finding proper therapeutic molecular targets. When it comes to neurons in the human brain, analysis of intraneuronal signaling networks provides invaluable information on learning, memory and cognition-related disorders, as well as potential therapeutic targets. However, neurons in the human brain form a highly complex neural network that, among its many roles, is also responsible for learning, memory formation and cognition. Given the impairment of these processes in mental and psychiatric disorders, one can envision that analyzing interneuronal processes, together with analyzing intraneuronal signaling networks, can result in a better understanding of the pathology and, subsequently, more effective target discovery. In this paper, a hybrid model is introduced, composed of the long-term potentiation (LTP) interneuronal process and an intraneuronal signaling network regulating CREB. LTP refers to an increased synaptic strength over a long period of time among neurons, typically induced upon occurring an activity that generates high-frequency stimulations (HFS) in the brain, and CREB is a transcription factor known to be highly involved in important functions of the cognitive and executive human brain such as learning and memory. The hybrid LTP-signaling model is analyzed using a proposed molecular fault diagnosis method. It allows to study the importance of various signaling molecules according to how much they affect an intercellular phenomenon when they are faulty, i.e. dysfunctional. This paper is intended to suggest another angle for understanding the pathology and therapeutic target discovery by classifying and ranking various intraneuronal signaling molecules based on how much their faulty behaviors affect an interneuronal process. Possible relations between the introduced hybrid analysis and the previous purely intracellular analysis are investigated in the paper as well.

Exploring extreme signaling failures in intracellular molecular networks.

Developing novel methods for the analysis of intracellular signaling networks is essential for understanding interconnected biological processes that underlie complex human disorders. A fundamental goal of this research is to quantify the vulnerability of a signaling network to the dysfunction of one or multiple molecules, when the dysfunction is defined as an incorrect response to the input signals. In this study, we propose an efficient algorithm to identify the extreme signaling failures that can induce the most detrimental impact on the physiological function of a molecular network. The algorithm finds the molecules, or groups of molecules, with the maximum vulnerability, i.e., the highest probability of causing the network failure, when they are dysfunctional. We propose another algorithm that efficiently accounts for signaling feedbacks. The algorithms are tested on experimentally verified ERBB and T-cell signaling networks. Surprisingly, results reveal that as the number of concurrently dysfunctional molecules increases, the maximum vulnerability values quickly reach to a plateau following an initial increase. This suggests the specificity of vulnerable molecule(s) involved, as a specific number of faulty molecules cause the most detrimental damage to the function of the network. Increasing the number of simultaneously faulty molecules does not further deteriorate the network function. Such a group of specific molecules whose dysfunction causes the extreme signaling failures can better elucidate the molecular mechanisms underlying the pathogenesis of complex trait disorders, and can offer new insights for the development of novel therapeutics.

Kallikrein gene family as biomarkers for recurrent prostate cancer.

AIM: To assess kallikrein (KLK) expression in recurrent and non-recurrent prostate tumors and adjacent healthy prostate tissues. METHODS: The expression levels of 15 KLK genes in 34 recurrent and 36 non-recurrent prostate cancer samples and 19 adjacent healthy prostate tissue samples was assessed with quantitative reverse-transcription polymerase chain reaction. The samples were obtained from Baylor College of Medicine, Houston, TX, USA between 2013 and 2016. RESULTS: Compared with controls, prostate cancer samples showed a strong decrease in KLK1, KLK4, KLK9, and KLK14. Recurrent samples were negative for KLK1, KLK2, and KLK14 but demonstrated higher levels of KLK3, KLK4, and KLK9 than controls. Other KLKs were not significantly expressed. CONCLUSION: This study for the first time showed a difference in the expression levels of the KLK gene family in recurrent prostate cancer. KLKs could be used as recurrence markers for prostate cancer.

Oxidative stress and apoptosis in electromagnetic waves exposed Zebrafish embryos and protective effects of conductive nonwoven fabric.

The amount of technological products including television, radio transmitters, and mobile phone that have entered our daily life has increased in recent years. But these devices may cause adverse effects on human health. Electromagnetic shielding fabrics may limit and inhibit electromagnetic waves. Aim of our study was to evaluate electromagnetic wave blocking performance of nonwoven textile surfaces on zebrafish embryos that were exposed to electromagnetic waves at specific frequencies. Oxidant-antioxidant system parameters were evaluated spectrophotometrically. The expressions of tp53 and casp3a were evaluated by RT-PCR. Results showed that electromagnetic shielding fabrics produced as conductive nonwoven textile surfaces improved oxidant-antioxidant status and tp53 expression that were impaired in electromagnetic waves exposed zebrafish embryos. Also, electromagnetic shielding fabrics decreased casp3a expression responsible for the execution phase of apoptosis that increased in electromagnetic waves exposed zebrafish embryos.

Modeling and measurement of signaling outcomes affecting decision making in noisy intracellular networks using machine learning methods.

Characterization of decision-making in cells in response to received signals is of importance for understanding how cell fate is determined. The problem becomes multi-faceted and complex when we consider cellular heterogeneity and dynamics of biochemical processes. In this paper, we present a unified set of decision-theoretic, machine learning and statistical signal processing methods and metrics to model the precision of signaling decisions, in the presence of uncertainty, using single cell data. First, we introduce erroneous decisions that may result from signaling processes and identify false alarms and miss events associated with such decisions. Then, we present an optimal decision strategy which minimizes the total decision error probability. Additionally, we demonstrate how graphing receiver operating characteristic curves conveniently reveals the trade-off between false alarm and miss probabilities associated with different cell responses. Furthermore, we extend the introduced framework to incorporate the dynamics of biochemical processes and reactions in a cell, using multi-time point measurements and multi-dimensional outcome analysis and decision-making algorithms. The introduced multivariate signaling outcome modeling framework can be used to analyze several molecular species measured at the same or different time instants. We also show how the developed binary outcome analysis and decision-making approach can be extended to more than two possible outcomes. As an example and to show how the introduced methods can be used in practice, we apply them to single cell data of PTEN, an important intracellular regulatory molecule in a p53 system, in wild-type and abnormal cells. The unified signaling outcome modeling framework presented here can be applied to various organisms ranging from viruses, bacteria, yeast and lower metazoans to more complex organisms such as mammalian cells. Ultimately, this signaling outcome modeling approach can be utilized to better understand the transition from physiological to pathological conditions such as inflammation, various cancers and autoimmune diseases.

Revealing the functions of novel mutations in RAB3GAP1 in Martsolf and Warburg micro syndromes.

PURPOSE: Martsolf (MS) and Warburg micro syndromes (WARBM) are rare autosomal recessive inherited allelic disorders, which share similar clinical features including microcephaly, intellectual disability, brain malformations, ocular abnormalities, and spasticity. Here, we revealed the functions of novel mutations in RAB3GAP1 in a Turkish female patient with MS and two siblings with WARBM. We also present a review of MS patients as well as all reported RAB3GAP1 pathogenic mutations in the literature. METHODS: We present a female with MS phenotype and two siblings with WARBM having more severe phenotypes. We utilized whole-exome sequencing to identify the molecular basis of these syndromes and confirmed suspected variants by Sanger sequencing. Quantitative (q) RT-PCR analysis was carried out to reveal the functions of novel splice site mutation detected in MS patient. RESULTS: We found a novel homozygous c.2607-1G>C splice site mutation in intron 22 of RAB3GAP1 in MS patient and a novel homozygous c.2187_2188delinsCT, p.(Met729_Lys730delinsIleTer) mutation in exon 19 of RAB3GAP1 in the WARBM patients. We showed exon skipping in MS patient by Sanger sequencing and gel electrophoresis. qRT-PCR analysis demonstrated the reduced expression of RAB3GAP1 in the patient with the c.2607-1G>C splice site mutation compared to a healthy control individual. CONCLUSION: Here, we have studied two novel RAB3GAP1 mutations in two different phenotypes; a MS associated novel splice site mutation, and a WARBM1 associated novel deletion-insertion mutation. Our findings suggest that this splice site mutation is responsible for milder phenotype and the deletion-insertion mutation presented here is associated with severe phenotype.

MicroRNAs as prognostic markers in prostate cancer.

BACKGROUND: Prostate cancer (PCa) is the most commonly diagnosed malignancy in men who are especially over the age of 50 years in the western countries. Currently used therapeutic modalities mostly fail to give positive clinical outcomes and nearly 30% of the PCa patients eventually develop clinical recurrence. Therefore, understanding the underlying mechanisms of PCa progression is of paramount importance to help determining the course of disease. In this study, we aimed at profiling the differentially expressed microRNAs in recurrent PCa samples. METHODS: We profiled the microRNA expression of 20 recurrent and 20 non-recurrent PCa patients with microRNA microarray, and validated the differential expression of significantly deregulated microRNAs in 40 recurrent and 39 non-recurrent PCa specimens using quantitative reverse-transcription PCR (qRT-PCR). Data were statistically analyzed using two-sided Student's t-test, Pearson Correlation test, Receiver operating characteristic (ROC) analysis. RESULTS: Our results demonstrated that a total of 682 probes were significantly deregulated in recurrent versus non-recurrent PCa specimen comparison. Among those, we confirmed the significant downregulation of miR-424 and upregulation of miR-572 with further qRT-PCR analysis in a larger sample set. Further ROC analysis showed that these microRNAs have enough power to distinguish recurrent specimens from non-recurrent ones on their own. CONCLUSIONS: Here, we report that differential expression of miR-424 and miR-572 in recurrent PCa specimens can serve as novel biomarkers for prediction of PCa progression.

Assessing posterior ocular structures in ß-thalassemia minor.

PURPOSE: The aim of this study was to investigate the effect of ß-thalassemia minor on choroidal, macular, and peripapillary retinal nerve fiber layer thickness. METHODS: To form the sample, we recruited 40 patients with ß-thalassemia minor and 44 healthy participants. We used spectral-domain optical coherence tomography to take all measurements of ocular thickness, as well as measured intraocular pressure, axial length, and central corneal thickness. We later analyzed correlations of hemoglobin levels with ocular parameters. RESULTS: A statistically significant difference emerged between patients with ß-thalassemia minor and the healthy controls in terms of mean values of subfoveal, nasal, and temporal choroidal thickness (p = 0.001, p = 0.016, and p = 0.010, respectively). Except for central macular thickness, differences in paracentral macular thicknesses between the groups were also significant (superior: p < 0.001, inferior: p = 0.007, temporal: p = 0.001, and nasal: p = 0.005). Also, no statistically significant differences were noted for retinal nerve fiber layer thickness between two groups. CONCLUSION: Mean values of subfoveal, nasal, temporal choroidal, and macular thickness for the four quadrants were significantly lower in patients with ß-thalassemia minor than in healthy controls.

miR-33a is a tumor suppressor microRNA that is decreased in prostate cancer.

Prostate cancer is one of the most frequently diagnosed neoplasms among men worldwide. MicroRNAs (miRNAs) are involved in numerous important cellular processes including proliferation, differentiation and apoptosis. They have been found to be aberrantly expressed in many types of human cancers. They can act as either tumor suppressors or oncogenes, and changes in their levels are associated with tumor initiation, progression and metastasis. miR-33a is an intronic miRNA embedded within SREBF2 that has been reported to have tumor suppressive properties in some cancers but has not been examined in prostate cancer. SREBF2 increases cholesterol and lipid levels both directly and via miR-33a action. The levels of SREBF2 and miR-33a are correlated in normal tissues by co-transcription from the same gene locus. Paradoxically, SREBF2 has been reported to be increased in prostate cancer, which would be predicted to increase miR-33a levels potentially leading to tumor suppression. We show here that miR-33a has tumor suppressive activities and is decreased in prostate cancer. The decreased miR-33a increases mRNA for the PIM1 oncogene and multiple genes in the lipid ß-oxidation pathway. Levels of miR-33a are not correlated with SREBF2 levels, implying posttranscriptional regulation of its expression in prostate cancer.

Meta-analysis of miRNA expression profiles for prostate cancer recurrence following radical prostatectomy.

BACKGROUND: Prostate cancer (PCa) is a leading reason of death in men and the most diagnosed malignancies in the western countries at the present time. After radical prostatectomy (RP), nearly 30% of men develop clinical recurrence with high serum prostate-specific antigen levels. An important challenge in PCa research is to identify effective predictors of tumor recurrence. The molecular alterations in microRNAs are associated with PCa initiation and progression. Several miRNA microarray studies have been conducted in recurrence PCa, but the results vary among different studies. METHODS: We conducted a meta-analysis of 6 available miRNA expression datasets to identify a panel of co-deregulated miRNA genes and overlapping biological processes. The meta-analysis was performed using the 'MetaDE' package, based on combined P-value approaches (adaptive weight and Fisher's methods), in R version 3.3.1. RESULTS: Meta-analysis of six miRNA datasets revealed miR-125A, miR-199A-3P, miR-28-5P, miR-301B, miR-324-5P, miR-361-5P, miR-363*, miR-449A, miR-484, miR-498, miR-579, miR-637, miR-720, miR-874 and miR-98 are commonly upregulated miRNA genes, while miR-1, miR-133A, miR-133B, miR-137, miR-221, miR-340, miR-370, miR-449B, miR-489, miR-492, miR-496, miR-541, miR-572, miR-583, miR-606, miR-624, miR-636, miR-639, miR-661, miR-760, miR-890, and miR-939 are commonly downregulated miRNA genes in recurrent PCa samples in comparison to non-recurrent PCa samples. The network-based analysis showed that some of these miRNAs have an established prognostic significance in other cancers and can be actively involved in tumor growth. Gene ontology enrichment revealed many target genes of co-deregulated miRNAs are involved in "regulation of epithelial cell proliferation" and "tissue morphogenesis". Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that these miRNAs regulate cancer pathways. The PPI hub proteins analysis identified CTNNB1 as the most highly ranked hub protein. Besides, common pathway analysis showed that TCF3, MAX, MYC, CYP26A1, and SREBF1 significantly interact with those DE miRNA genes. The identified genes have been known as tumor suppressors and biomarkers which are closely related to several cancer types, such as colorectal cancer, breast cancer, PCa, gastric, and hepatocellular carcinomas. Additionally, it was shown that the combination of DE miRNAs can assist in the more specific detection of the PCa and prediction of biochemical recurrence (BCR). CONCLUSION: We found that the identified miRNAs through meta-analysis are candidate predictive markers for recurrent PCa after radical prostatectomy.

Critical flicker frequency test for diagnosing minimal hepatic encephalopathy in patients with cirrhosis.

BACKGROUND/AIMS: The critical flicker frequency (CFF) and psychometric hepatic encephalopathy score (PHES) are commonly proposed tests for detecting minimal hepatic encephalopathy (MHE); however, no studies have examined their value for detecting MHE in Turkey. MATERIALS AND METHODS: A total of 70 patients with cirrhosis without overt HE, 205 controls for PHES, and 100 controls for the CFF test were included. All the patients underwent the PHES and CFF tests during the same session. Psychometric tests comprising number connection test A and B, digit symbol test, serial dotting test, and line drawing test were used. Tests were considered abnormal when test score was more than mean ± 2 standard deviations in comparison with that of the age- and education-matched controls. MHE was diagnosed when ≥2 PHES test were abnormal, and CFF was <39 Hz. RESULTS: The prevalence of MHE among the 70 patients with cirrhosis, as measured by the CFF and PHES tests, was 41.4% (29) and 30.7% (25), respectively. The mean CFF was significantly lower in patients with cirrhosis having MHE (38.3±1.2 Hz) than in patients with cirrhosis not having MHE (42.6±2.3 Hz; p=0.001) and in controls (44.84 ± 3.7 Hz; p=0.001). With a cutoff value of <39, CFF had a sensitivity of 39%, specificity of 82%, and diagnostic accuracy of 70.6% for detecting MHE. CONCLUSION: The CFF test is also a useful method for detecting MHE in xxx patients with cirrhosis. However, the CFF test should be used as an adjunct to the PHES test because of its low sensitivity for detecting MHE.

Identification of microRNA profile specific to cancer stem-like cells directly isolated from human larynx cancer specimens.

BACKGROUND: Emerging evidences proposed that microRNAs are associated with regulation of distinct physio-pathological processes including development of normal stem cells and carcinogenesis. In this study we aimed to investigate microRNA profile of cancer stem-like cells (CSLCs) isolated form freshly resected larynx cancer (LCa) tissue samples. METHODS: CD133 positive (CD133+) stem-like cells were isolated from freshly resected LCa tumor specimens. MicroRNA profile of 12 pair of CD133+ and CD133- cells was determined using microRNA microarray and differential expressions of selvected microRNAs were validated by quantitative real time PCR (qRT-PCR). RESULTS: MicroRNA profiling of CD133+ and CD133- LCa samples with microarray revealed that miR-26b, miR-203, miR-200c, and miR-363-3p were significantly downregulated and miR-1825 was upregulated in CD133+ larynx CSLCs. qRT-PCR analysis in a total of 25 CD133+/CD133- sample pairs confirmed the altered expressions of these five microRNAs. Expressions of miR-26b, miR-200c, and miR-203 were significantly correlated with miR-363-3p, miR-203, and miR-363-3p expressions, respectively. Furthermore, in silico analysis revealed that these microRNAs target both cancer and stem-cell associated signaling pathways. CONCLUSIONS: Our results showed that certain microRNAs in CD133+ cells could be used as cancer stem cell markers. Based on these results, we propose that this panel of microRNAs might carry crucial roles in LCa pathogenesis through regulating stem cell properties of tumor cells.