A novel strategy for dynamic modeling of genome-scale interaction networks.

MOTIVATION: The recent availability of omics data allows the construction of holistic maps of interactions between numerous role-playing biomolecules. However, these networks are often static, ignoring the dynamic behavior of biological processes. On the other hand, dynamic models are commonly constructed on small scales. Hence, the construction of large-scale dynamic models that can quantitatively predict the time-course cellular behaviors remains a big challenge. RESULTS: In this study, a pipeline is proposed for the automatic construction of large-scale dynamic models. The pipeline uses a list of biomolecules and their time-course trajectories in a given phenomenon as input. First, the interaction network of the biomolecules is constructed. To state the underlying molecular events of each interaction, it is translated into a map of biochemical reactions. Next, to define the kinetics of the reactions, an ordinary differential equation (ODE) is generated for each involved biomolecule. Finally, the parameters of the ODE system are estimated by a novel large-scale parameter approximation method. The high performance of the pipeline is demonstrated by modeling the response of a colorectal cancer cell line to different chemotherapy regimens. In conclusion, Systematic Protein Association Dynamic ANalyzer constructs genome-scale dynamic models, filling the gap between large-scale static and small-scale dynamic modeling strategies. This simulation approach allows for holistic quantitative predictions which are critical for the simulation of therapeutic interventions in precision medicine. AVAILABILITY AND IMPLEMENTATION: Detailed information about the constructed large-scale model of colorectal cancer is available in supplementary data. The SPADAN toolbox source code is also available on GitHub (https://github.com/PooyaBorzou/SPADAN). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptome meta-analysis and validation to discovery of hub genes and pathways in focal and segmental glomerulosclerosis.

BACKGROUND: Focal segmental glomerulosclerosis (FSGS), a histologic pattern of injury in the glomerulus, is one of the leading glomerular causes of end-stage renal disease (ESRD) worldwide. Despite extensive research, the underlying biological alterations causing FSGS remain poorly understood. Studying variations in gene expression profiles offers a promising approach to gaining a comprehensive understanding of FSGS molecular pathogenicity and identifying key elements as potential therapeutic targets. This work is a meta-analysis of gene expression profiles from glomerular samples of FSGS patients. The main aims of this study are to establish a consensus list of differentially expressed genes in FSGS, validate these findings, understand the disease's pathogenicity, and identify novel therapeutic targets. METHODS: After a thorough search in the GEO database and subsequent quality control assessments, seven gene expression datasets were selected for the meta-analysis: GSE47183 (GPL14663), GSE47183 (GPL11670), GSE99340, GSE108109, GSE121233, GSE129973, and GSE104948. The random effect size method was applied to identify differentially expressed genes (meta-DEGs), which were then used to construct a regulatory network (STRING, MiRTarBase, and TRRUST) and perform various pathway enrichment analyses. The expression levels of several meta-DEGs, specifically ADAMTS1, PF4, EGR1, and EGF, known as angiogenesis regulators, were analyzed using quantitative reverse transcription polymerase chain reaction (RT-qPCR). RESULTS: The identified 2,898 meta-DEGs, including 665 downregulated and 669 upregulated genes, were subjected to various analyses. A co-regulatory network comprising 2,859 DEGs, 2,688 microRNAs (miRNAs), and 374 transcription factors (TFs) was constructed, and the top molecules in the network were identified based on degree centrality. Part of the pathway enrichment analysis revealed significant disruption in the angiogenesis regulatory pathways in the FSGS kidney. The RT-qPCR results confirmed an imbalance in angiogenesis pathways by demonstrating the differential expression levels of ADAMTS1 and EGR1, two key angiogenesis regulators, in the FSGS condition. CONCLUSION: In addition to presenting a consensus list of differentially expressed genes in FSGS, this meta-analysis identified significant distortions in angiogenesis-related pathways and factors in the FSGS kidney. Targeting these factors may offer a viable strategy to impede the progression of FSGS.

Proteomics to Metabolomics: A New Insight into the Pathogenesis of Hypertensive Nephropathy.

BACKGROUND: Hypertensive nephropathy (HN) is a high-burden disorder and a leading cause of end-stage renal disease. Despite huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to providing a comprehensive insight into this complex disorder. METHODS: Proteome profiles of kidney tubulointerstitium and outer and inner cortex from a rat model of HN were retrieved from the proteomics identification database, and the quality of the datasets was assessed. Proteins that exhibited differential expression were detected and their interactions were analyzed in the kidney sub-compartments. Furthermore, enzymes were linked to the attributed metabolites. Functional enrichment analyses were performed to identify key pathways and processes based on the differentially expressed proteins and predicted metabolites. RESULTS: Proteasome-mediated protein degradation, actin cytoskeleton organization, and Rho GTPase signaling pathway are involved in the pathogenesis of HN. Furthermore, tissue hypoxia and dysregulated energy homeostasis are among the key underlying events. The metabolism of purine and amino acids is also affected in HN. CONCLUSION: Although the proposed pathogenic mechanisms remain to be further validated in experimental studies, this study contributes to the understanding of the molecular mechanisms of HN through a systematic unsupervised approach. Considering the significant alterations of metabolic pathways, HN can be viewed as an "acquired error of metabolism."

miR-802-5p is a key regulator in diabetic kidney disease.

Background: Diabetic kidney disease has substantial burden and limited therapeutic options. An inadequate understanding of the complex gene regulatory circuits underlying this disorder contributes to the insufficiency of current treatment strategies. MicroRNAs (miRNAs) play a crucial role as regulators of functionally related gene networks. Previously, mmu-mir-802-5p was identified as the sole dysregulated miRNA in both the kidney cortex and medulla of diabetic mice. This study aims to investigate the role of miR-802-5p in diabetic kidney disease. Materials and Methods: The validated and predicted targets of miR-802-5p were identified using miRTarBase and TargetScan databases, respectively. The functional role of this miRNA was inferred using gene ontology enrichment analysis. The expression of miR-802-5p and its selected targets were assessed by qPCR. The expression of the angiotensin receptor (Agtr1a) was measured by ELISA. Results: miR-802-5p exhibited dysregulation in both the kidney cortex and medulla of diabetic mice, with two- and four-fold over-expressions, respectively. Functional enrichment analysis of the validated and predicted targets of miR-802-5p revealed its involvement in the renin-angiotensin pathway, inflammation, and kidney development. Differential expression was observed in the Pten transcript and Agtr1a protein among the examined gene targets. Conclusion: These findings suggest that miR-802-5p is a critical regulator of diabetic nephropathy in the cortex and medulla compartments, contributing to disease pathogenesis through the renin-angiotensin axis and inflammatory pathways.

A deep learning approach to predict inter-omics interactions in multi-layer networks.

BACKGROUND: Despite enormous achievements in the production of high-throughput datasets, constructing comprehensive maps of interactions remains a major challenge. Lack of sufficient experimental evidence on interactions is more significant for heterogeneous molecular types. Hence, developing strategies to predict inter-omics connections is essential to construct holistic maps of disease. RESULTS: Here, as a novel nonlinear deep learning method, Data Integration with Deep Learning (DIDL) was proposed to predict inter-omics interactions. It consisted of an encoder that performs automatic feature extraction for biomolecules according to existing interactions coupled with a predictor that predicts unforeseen interactions. Applicability of DIDL was assessed on different networks, namely drug-target protein, transcription factor-DNA element, and miRNA-mRNA. Also, validity of the novel predictions was evaluated by literature surveys. According to the results, the DIDL outperformed state-of-the-art methods. For all three networks, the areas under the curve and the precision-recall curve exceeded 0.85 and 0.83, respectively. CONCLUSIONS: DIDL offers several advantages like automatic feature extraction from raw data, end-to-end training, and robustness to network sparsity. In addition, reliance solely on existing inter-layer interactions and independence of biochemical features of interacting molecules make this algorithm applicable for a wide variety of networks. DIDL paves the way to understand the underlying mechanisms of complex disorders through constructing integrative networks.

Transmembrane signaling molecules play a key role in the pathogenesis of IgA nephropathy: a weighted gene co-expression network analysis study.

BACKGROUND: Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using the weighted gene co-expression network analysis (WGCNA) algorithm. RESULTS: GSE104948 dataset (the expression data from glomerular tissue of IgAN patients) was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease's most correlated module were mainly enriched in the immune system, cell-cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potentially key players in the pathogenesis of IgAN. In the constructed regulatory network, hsa-miR-129-2-3p, hsa-miR-34a-5p and hsa-miR-27a-3p, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes. CONCLUSIONS: The excavated hub genes from the hearts of co-expressed modules and the PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: Meta-analysis of profiling metabolomics studies.

AIM: Diabetic nephropathy (DN) is one of the worst complications of diabetes. Despite a growing number of DN metabolite profiling studies, most studies are suffering from inconsistency in their findings. The main goal of this meta-analysis was to reach to a consensus panel of significantly dysregulated metabolites as potential biomarkers in DN. DATA SYNTHESIS: To identify the significant dysregulated metabolites, meta-analysis was performed by "vote-counting rank" and "robust rank aggregation" strategies. Bioinformatics analyses were performed to identify the most affected genes and pathways. Among 44 selected studies consisting of 98 metabolite profiles, 17 metabolites (9 up-regulated and 8 down-regulated metabolites), were identified as significant ones by both the meta-analysis strategies (p-value<0.05 and OR>2 or <0.5) and selected as DN metabolite meta-signature. Furthermore, enrichment analyses confirmed the involvement of various effective biological pathways in DN pathogenesis, such as urea cycle, TCA cycle, glycolysis, and amino acid metabolisms. Finally, by performing a meta-analysis over existing time-course studies in DN, the results indicated that lactic acid, hippuric acid, allantoin (in urine), and glutamine (in blood), are the topmost non-invasive early diagnostic biomarkers. CONCLUSION: The identified metabolites are potentially involved in diabetic nephropathy pathogenesis and could be considered as biomarkers or drug targets in the disease. PROSPERO REGISTRATION NUMBER: CRD42020197697.

Comprehensive analysis of IgA nephropathy expression profiles: identification of potential biomarkers and therapeutic agents.

BACKGROUND: IgA nephropathy (IgAN) is a kidney disease recognized by the presence of IgA antibody depositions in kidneys. The underlying mechanisms of this complicated disease are remained to be explored and still, there is an urgent need for the discovery of noninvasive biomarkers for its diagnosis. In this investigation, an integrative approach was applied to mRNA and miRNA expression profiles in PBMCs to discover a gene signature and novel potential targets/biomarkers in IgAN. METHODS: Datasets were selected from gene expression omnibus database. After quality control checking, two datasets were analyzed by Limma to identify differentially expressed genes/miRNAs (DEGs and DEmiRs). Following identification of DEmiR-target genes and data integration, intersecting mRNAs were subjected to different bioinformatic analyses. The intersecting mRNAs, DEmiRs, related transcription factors (from TRRUST database), and long-non coding RNAs (from LncTarD database) were used for the construction of a multilayer regulatory network via Cytoscape. RESULT: "GSE25590" (miRNA) and "GSE73953" (mRNA) datasets were analyzed and after integration, 628 intersecting mRNAs were identified. The mRNAs were mainly associated with "Innate immune system", "Apoptosis", as well as "NGF signaling" pathways. A multilayer regulatory network was constructed and several hub-DEGs (Tp53, STAT3, Jun, etc.), DEmiRs (miR-124, let-7b, etc.), TFs (NF-kB, etc.), and lncRNAs (HOTAIR, etc.) were introduced as potential factors in the pathogenesis of IgAN. CONCLUSION: Integration of two different expression datasets and construction of a multilayer regulatory network not only provided a deeper insight into the pathogenesis of IgAN, but also introduced several key molecules as potential therapeutic target/non-invasive biomarkers.

Analysis of time-course microarray data: Comparison of common tools.

High-throughput time-series data have a special value for studying the dynamism of biological systems. However, the interpretation of such complex data can be challenging. The aim of this study was to compare common algorithms recently developed for the detection of differentially expressed genes in time-course microarray data. Using different measures such as sensitivity, specificity, predictive values, and related signaling pathways, we found that limma, timecourse, and gprege have reasonably good performance for the analysis of datasets in which only test group is followed over time. However, limma has the additional advantage of being able to report significance cut off, making it a more practical tool. In addition, limma and TTCA can be satisfactorily used for datasets with time-series data for all experimental groups. These findings may assist investigators to select appropriate tools for the detection of differentially expressed genes as an initial step in the interpretation of time-course big data.

A systematic integrative approach reveals novel microRNAs in diabetic nephropathy.

BACKGROUND: Despite huge efforts, the underlying molecular mechanisms of diabetic nephropathy (DN) are yet elusive, and holistic views have rarely been generated. Considering the complexity of DN pathogenesis, the integration of datasets from different molecular types to construct a multilayer map of DN can provide a comprehensive insight toward the disease mechanisms and also can generate new knowledge. Here, we have re-analyzed two mRNA microarray datasets related to glomerular and tubulointerstitial compartments of human diabetic kidneys. MATERIALS AND METHODS: The quality of the datasets was confirmed by unsupervised hierarchical clustering and principal component analysis. For each dataset, differentially expressed (DE) genes were identified, and transcription factors (TFs) regulating these genes and kinases phosphorylating the TFs were enriched. Furthermore, microRNAs (miRNAs) targeting the DE genes, TFs, and kinases were detected. Based on the harvested genes for glomeruli and tubulointerstitium, key signaling pathways and biological processes involved in diseases pathogenesis were recognized. In addition, the interaction of different elements in each kidney compartment was depicted in multilayer networks, and topology analysis was performed to identify key nodes. Central miRNAs whose target genes were most likely to be related to DN were selected, and their expressions were quantitatively measured in a streptozotocin-induced DN mouse model. RESULTS: Among the examined miRNAs, miR-208a-3p and miR-496a-3p are, for the first time, found to be significantly overexpressed in the cortex of diabetic kidneys compared to controls. CONCLUSION: We predict that miR-208 is involved in oxygen metabolism and regulation of cellular energy balance. Furthermore, miR-496 potentially regulates protein metabolism and ion transport. However, their exact functions remain to be investigated in future studies. Taken together, starting from transcriptomics data, we have generated multilayer interaction networks and introduced novel players in DN.

Evaluating the effect of remote ischemic preconditioning on kidney ischemia-reperfusion injury.

BACKGROUND: Acute kidney injury is a high-risk complication in a variety of clinical situations mostly due to ischemia-reperfusion (IR) injuries. The novel idea of remote ischemic preconditioning (rIPC) was proposed to prevent serious ischemia sequels. To address the controversy of previous reports, the current study was performed to assess the effect of rIPC on kidney IR injury. MATERIALS AND METHODS: Male BALB/c mice were exposed to either rIPC or sham intervention, 24 h before kidney IR. In two independent sets of experiments, rIPC was accomplished by inducing three cycles of 5 min ischemia with 5 min reperfusion intervals through the ligation of the left external iliac artery or infrarenal abdominal aorta. Kidney IR injury was performed by left renal pedicle occlusion for 35 min and simultaneous right nephrectomy. After 48 h, mice were sacrificed for the assessment of kidney function and structure. RESULTS: According to the serum urea and creatinine, as well as histopathological measures, none of the exploited rIPC procedures could significantly protect against kidney IR injury. CONCLUSION: Based on our findings and the divergent results of previous animal and human studies, it can be concluded that the renoprotective effects of rIPC are minimal, if any, and are not robustly detectable.

In vitro versus in vivo models of kidney fibrosis: Time-course experimental design is crucial to avoid misinterpretations of gene expression data.

BACKGROUND: In vitro models are common tools in nephrology research. However, their validity has rarely been scrutinized. MATERIALS AND METHODS: Considering the critical role of transforming growth factor (TGF)-ß and hypoxia pathways in kidney fibrosis, kidney-derived cells were exposed to TGF-ß and/or hypoxic conditions and the expression levels of some genes related to these two signaling pathways were quantified in a time-course manner. Furthermore, a unilateral ureteral obstruction mouse model was generated, and the expressions of the same genes were assessed. RESULTS: In all in vitro experimental groups, the expression of the genes was noisy with no consistent pattern. However, in the animal model, TGF-ß pathway-related genes demonstrated considerable overexpression in the ureteral obstruction group compared with the sham controls. Interestingly, hypoxia pathway genes had prominent fluctuations with very similar patterns in both animal groups, suggesting a periodical pattern not affected by the intervention. CONCLUSION: The findings of this study suggest that in vitro findings should be interpreted cautiously and if possible are substituted or supported by animal models that are more consistent and reliable. Furthermore, we underscore the importance of time-course evaluation of both case and control groups in gene expression studies to avoid misconceptions caused by gene expression noise or intrinsic rhythms.

Big data to knowledge: common pitfalls in transcriptomics data analysis and representation.

The omics technologies provide an invaluable opportunity to employ a global view towards human diseases. However, the appropriate translation of big data to knowledge remains a major challenge. In this study, we have performed quality control assessments for 91 transcriptomics datasets deposited in gene expression omnibus database and also have evaluated the publications derived from these datasets. This survey shows that drawbacks in the analyses and reports of transcriptomics studies are more common than one may assume. This report is concluded with some suggestions for researchers and reviewers to enhance the minimal requirements for gene expression data generation, analysis and report.

Retraction Note to: Inhibition of microRNA miR-92a induces apoptosis and inhibits cell proliferation in human acute promyelocytic leukemia through modulation of p63 expression.

The Editor-in-Chief has decided to retract this article [1]. The article shows significant overlap with two prior publications by the same authors [2, 3], without providing the due citation and attribution.

Retraction Note to: Inhibition of microRNA miR-92a induces apoptosis and inhibits cell proliferation in human acute promyelocytic leukemia through modulation of p63 expression.

The Editor-in-Chief has decided to retract this article [1]. The article shows significant overlap with two prior publications by the same authors [2, 3], without providing the due citation and attribution.

The analysis of a time-course transcriptome profile by systems biology approaches reveals key molecular processes in acute kidney injury.

BACKGROUND: Acute kidney injury is a common debilitating disease with no curative treatment. The recent development of big biological data is expected to expand our understanding of the disorder if appropriately analyzed to generate translational knowledge. We have here re-analyzed a time-course microarray data on mRNA expression of rat kidneys exposed to ischemia-reperfusion to identify key underlying biological processes. MATERIALS AND METHODS: The dataset was quality controlled by principal component analysis and hierarchical clustering. Using limma R package, differentially expressed (DE) genes were detected which were then clustered according to their expression trajectories. The biological processes related to each cluster were harvested using gene ontology enrichment analysis. In addition, the interaction map of proteins encoded by the DE genes was constructed, and the functions related to network central genes were determined. Furthermore, signaling pathways related to the DE genes were harvested using pathway enrichment analysis. RESULTS: We found 8139 DE genes that drive critical processes such as the control of blood circulation, reactive species metabolism, mitochondrial respiration, apoptosis, cell proliferation, as well as inflammatory and immunological reactions. The role of less recognized pathways such as olfactory signaling in acute kidney injury is also proposed that remains to be investigated in future studies. CONCLUSION: Using systems biology top-down approach, we have suggested novel potential genes and pathways to be intervened toward kidney regeneration.

Transcriptional noise in intact and TGF-beta treated human kidney cells; the importance of time-series designs.

The transforming growth factor (TGF)-ß signaling pathway plays a key role in various cellular processes. However, insufficient knowledge about the complex and sometimes paradoxical functions of this pathway hinders its therapeutic targeting. In this study, the transcriptional profile of seven mediators and downstream elements of the TGF-ß pathway were assessed in TGF-ß treated and untreated human kidney derived cells for 2 weeks in a time course manner. As expected the up-regulation of ACTA2 and COL1A2 was evident in the treated cells. However, we observed remarkable fluctuations in gene expression, even in the supposedly steady states. The magnitude of noise was diverse in the examined genes. Our findings underscore the significance of time-course designs for gene expression analyses and clearly show that misleading data can be obtained in single point measurements. Furthermore, we propose specific considerations in the interpretation of time-course data in the context of noisy gene expression.

Heterozygosity analysis of polycystic kidney disease 1 gene microsatellite markers for linkage analysis of autosomal dominant polycystic kidney disease type 1 in the Iranian population.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic cause of end-stage renal disease. Although imaging techniques are a means of accurate diagnosis when the cysts appear in the third or fourth decades of the patient's life, they are of little value for early diagnosis. Genetic tests are required for preimplantation genetic diagnosis, decision-making for kidney donation to an affected relative. Although mutation of the polycystic kidney disease (PKD1) gene is solely responsible for the most cases of ADPKD, direct genetic testing is limited by the large size of this gene and the presence of many mutations without hot spots. Therefore, indirect diagnosis with linkage analysis using informative microsatellite markers has been suggested. MATERIALS AND METHODS: In this study, we assessed the informativeness of the PKD1 gene markers D16S475, D16S291, and D16S3252 in Iranian population. Using specific primers, fluorescent polymerase chain reaction (PCR) was performed on genomic DNA extracted from fifty unrelated individuals. PCR products were analyzed by the ALFexpress DNA sequencer system, and the number and frequency of alleles were determined to calculate the heterozygosity (HET) and polymorphism information content (PIC) values. RESULTS: We found that the HET and PIC values for the D16S475 marker are 0.92 and 0.91, respectively. These two values are 0.82 and 0.80 for D16S291 and 0.50 and 0.47 for D16S3252, respectively. CONCLUSION: Based on this data, D16S475 and D16S291 are highly and D16S3252 is moderately informative for indirect genetic diagnosis of PKD1 mutations in this population.

Intra-renal arterial injection of autologous bone marrow mesenchymal stromal cells ameliorates cisplatin-induced acute kidney injury in a rhesus Macaque mulatta monkey model.

BACKGROUND: Clinically, acute kidney injury (AKI) is a potentially devastating condition for which no specific therapy improves efficacy of the repair process. Bone marrow mesenchymal stromal cells (BM-MSCs) are proven to be beneficial for the renal repair process after AKI in different experimental rodent models, but their efficacy in large animals and humans remains unknown. This study aims to assess the effect of autologous rhesus Macaque mulatta monkey BM-MSC transplantation in cisplatin-induced AKI. METHODS: We chose a model of AKI induced by intravenous administration of 5 mg/kg cisplatin. BM-MSCs were transplanted through intra-arterial injection. The animals were followed for survival, biochemistry analysis and pathology. RESULTS: Transplantation of 5 × 10(6) cells/kg ameliorated renal function during the first week, as shown by significantly lower serum creatinine and urea values and higher urine creatinine and urea clearance without hyponatremia, hyperkalemia, proteinuria and polyuria up to 84 d compared with the vehicle and control groups. The superparamagnetic iron oxide nanoparticle-labeled cells were found in both the glomeruli and tubules. BM-MSCs markedly accelerated Foxp3+ T-regulatory cells in response to cisplatin-induced damage, as revealed by higher numbers of Foxp3+ cells within the tubuli of these monkeys compared with cisplatin-treated monkeys in the control and vehicle groups. CONCLUSIONS: These data demonstrate that BM-MSCs in this unique large-animal model of cisplatin-induced AKI exhibited recovery and protective properties.

Inhibition of microRNA miR-92a induces apoptosis and inhibits cell proliferation in human acute promyelocytic leukemia through modulation of p63 expression.

MicroRNAs (miRNAs) are endogenous non-coding RNAs, 19-25 nucleotides in length involved in post-transcriptional regulation of gene expression of great majority of the human protein coding genes. Different aspects of cellular activities like cell growth, proliferation, and differentiation are regulated by miRNAs through their interaction with particular RNA species. In many tumors up or down-regulation of different miRNAs has been reported. Human miR-17-92 gene cluster is located on 13q31.3, rooming several miRNAs including miR-17-5p, miR-17-3p, miR-18, miR-19a, miR-20a and miR-92a. Amplification or overexpression of this cluster has been reported in acute myeloid leukemia, acute lymphoblastic leukemia and several other cancer types. Here, we performed inhibition of miR-92a in an acute promyelocytic leukemia (APL) cell line (HL-60) using locked nucleic acid (LNA) antagomir. In different time points after LNA-anti-miR92a transfection, MTT assay and annexin/propidium iodide staining were performed. These assessments indicate that miR-92a inhibition can extensively decrease the viability of these cells which is mainly due to induction of apoptosis. Western blot analysis of p63 protein also revealed that miR-92a inhibition resulted in p63 expression, hence activation of cellular pathways which are normally controlled by p63 protein are retrieved. These findings could open up a path to the miRNAs based therapeutic approach for treatment of APL.