microarray data
در نشریات گروه پزشکی-
BackgroundBreast cancer is one of the most prevalent types of cancer in Iranian women and the second cause of death in women worldwide. Gene mutations are the key determinants of the disease; therefore, the genetic study of this disease is of paramount importance. One of the genetic evaluation methods of this disease is microarray technology, which allows the examination of the simultaneous expression of thousands of genes. Clustering is the method for analyzing high-dimension data, which we used in the present research for collecting similar genes in separated clusters.MethodA descriptive and inferential statistical analysis was carried out to evaluate unsupervised learning models of gene expression analysis and five bi-clustering methods (including PLAID (PL), Fabia, Bimax, Cheng & Church (CC), and Xmotif) were compared. For this purpose, we obtained the microarray gene expression data for lapatinib-resistant breast cancer cell lines from previously published research. The enrichment efficacy of the clusters was evaluated with gene ontology, and the results of these five models were compared with the Jaccard index, variance stability, least-square error, and goodness of fit indices. Furthermore, the results of the best model were assessed for building a genes sets network with Bayesian networks.ResultsAfter preprocessing, clustering was performed on the data with the dimension (4710 × 18) of the genes. Four models, except for CC, successfully found bi-clusters in the data set. The data evaluation revealed that the results of the models were almost the same, but the PL model performed better than the others, finding 11 bi-clusters; this model was used to build the network of gene sets.ConclusionAccording to the results, the PL method was suitable for clustering the data. Accordingly, it could be recommended for data analysis. In addition, the gene sets network formed on gene expression data was incompetent.Keywords: Breast cancer, Bi-clustering, Cluster Analysis, Microarray data, Gene expression, Neoplasms, Bayesian network
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Background
Almost all diabetic patients develop diabetic retinopathy. Interpersonal diversity contributes significantly to the susceptibility of serious manifestations of diabetic retinopathy leading to impaired vision. Further, insufficient studies have been performed on the diagnosis of molecular biomarkers for diabetic retinopathy using machine learning. Hence, this study proposed an approach for gene selection in microarray data.
Material and MethodsThe proposed method involves a primary filter approach that uses the results of different gene expression analyzes, thereby reducing the primary genes and thus the complexity of space and search time. A set of genes that improve classification accuracy are then identified using the Ant Colony Optimization (ACO) method based on the heuristic approach. Selected genes in the final phase are evaluated using a ROC curve (receptor function trait) to determine the most effective while the smallest subset of traits. The classifier evaluated in the proposed framework is the K-nearest neighbor. A set of diabetic retinopathy microarrays is used to test the proposed approach.
ResultsThe results of the experiment reveal that the our suggested method obtained a high accuracy rate with 9 highly informative genes. Furthermore, we found four genes including ANKDD1A, ZNF786, SNORA3B and, C14orf2 as novel potential molecular biomarker in diabetic retinopathy.
ConclusionThe results showed that the other heuristic algorithms can be used in eye diseases for gene selection. Also, it is worthwhile evaluating them through biological research and experimentation because of the good discrimination power of the selected genes.
Keywords: K-nearest Neighbor Classification, Gene Selection, Microarray Data, DiabeticRetinopathy, Ant Colony Optimization -
IntroductionCoronaviruses are significant pathogens of both human and animals and are globally distributed. Out of seven CoVs strains, the most lethal coronavirus strains being portrayed is SARS-CoV-2. It can cause bronchial asthma, and severe pneumonia and acute respiratory disease. Due to its contagion in infants, adults, and immunocompromised patients which further results in making this a deadly disease, thus there is an urgent need to develop effective and safe therapeutics against it.Materials and MethodsMeta-analysis of publicly available gene expression datasets belonging to SARS-CoV-2, SARS-CoV, MERS-CoV, and HCoV-229E were carried out to identify the potential differentially expressed genes exclusively associated with SARS-CoV-2, and then a network model was developed to decipher significant drug targets, associated pathways and drug candidates which can be repurposed for this infection.ResultsThe COVID-19 infection mainly targets immune responses and regulatory processes. A novel role of Relaxin signaling pathway was identified in SARS-CoV-2 infection. Anti-inflammatory, anti-tumor, nutraceutical and anthelmintic agents were found to be good prospective candidates for repurposing against COVID-19.ConclusionsThis theoretical study resulted in the identification of approved drug targets that may have the potential to be repurposed for COVID 19 treatment.Keywords: COVID-19, Microarray Data, RNA-Seq Data, Systems biology, drug targets, Pathogenesis
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سابقه و اهداف
هدف از مطالعه حاضر ارزیابی بیوانفورماتیکی و تجربی تاثیر ملاتونین بر بیان ژن های UBE2W و SSX2IP در رده سلولی HepG2 تیمار شده با داروی ملاتونین می باشد. UBE2W پروتئینی را رمزگذاری می کند که باعث افزایش یوبی کویتینه شدن پروتئین های گروه مکمل آنمی فانکونی شده و ممکن است در ترمیم آسیب DNA مهم باشد. SSX2IP متعلق به سیستم اتصال سلولی است که در حرکت سلول دخیل است و به عنوان یک عامل بلوغ سانتروزوم عمل می کند.
مواد و روش هادر ابتدا، نرم افزار MATLAB 2018a برای ارزیابی میزان بیان ژن های UBE2W و SSX2IP از داده های میکرو آرایه مورد استفاده قرار گرفت. سپس، بعد از طراحی و تهیه پرایمرها، آزمایشات تجربی شامل تست MTT و Real-time PCR انجام گرفت.
یافته ها:
نتیجه تست MTT نشان داد که زنده مانی سلول مورد مطالعه در گروه تیمار شده با ملاتونین بصورت معنی داری کاهش می یابد و میزان IC50 برای 48 ساعت برابر 450 میکرومولار تخمین زده شد. نتایج Real-time PCR ثابت کرد که میزان بیان هر دو ژن مورد مطالعه در سلول های تیمار شده با ملاتونین در مقایسه با گروه شاهد افزایش (غیر معنی دار p≥ 0.05 در 24 ساعت و معنی دار p≤0.05 در 48 ساعت) می یابد که هم راستا با نتایج آنالیز بیوانفورماتیکی می باشد.
استنتاجنتایج این مطالعه نشان می دهد که UBE2W و SSX2IP می توانند به عنوان ژن هدف در درمان هپاتوسلولار کارسینوما به کار گرفته شود، اگر چه برای تایید این نتایج نیاز به انجام مطالعات گسترده می باشد.
کلید واژگان: ملاتونین، کارسینومای هپاتوسلولار، بیوانفورماتیک، داده های میکرو آرایهBackground and purposeThe aim of this study was to bioinformatically and experimentally evaluate the effect of melatonin on the expression levels of UBE2W and SSX2IP genes in melatonin treated Human Hepatocellular carcinoma G2 (HepG2) cancer cell line. UBE2W encodes a protein that promotes ubiquitination of Fanconi anemia complementation group proteins and may be important in the repair of DNA damage. SSX2IP belongs to an adhesion system, involved in cell movement and acts as a centrosome maturation factor.
Materials and methodsAt first, as a bioinformatics tool, MATLAB 2018a software was employed to evaluate the UBE2W and SSX2IP genes expression levels using microarray data. Then, after designing and preparing the primers, MTT and Real-time PCR were carried out in three replicates.
ResultsMTT assay showed that the viability of cancer cell significantly decreased in melatonin treated group (P≤0.01). The IC50 value was estimated to be 2080 µM for 48 hours, but no significant changes were seen in the survival of human normal cells (HUVEC-C) (P≥0.05). Real-time PCR proved that the expression levels of both genes increased (P≥0.05 in 24h and P≤ 0.05 in 48h) in melatonin treated cells compared to un-treated group, which was in accordance with bioinformatics analysis.
ConclusionOur study showed that UBE2W and SSX2IP genes could be used as therapeutic target genes for Hepatocellular carcinoma. However, complementary studies are needed to prove current findings.
Keywords: melatonin, Hepatocellular carcinoma, bioinformatics, microarray data -
شناسایی بیوانفورماتیکی microRNA های با توان بیومارکری در سرطان روده بزرگ از روی داده های ریز آرایهزمینه و هدف
سرطان روده بزرگ یک بیماری شایع در جهان میباشد که باعث مرگ و میر بالایی در افراد مبتلا میشود. نبود مارکر مناسب تشخیصی و پیش آگهی باعث عدم شناسایی بدخیمیهای سرطان کولون در زمان مناسب شده است. ژنهای microRNA با کنترل بیان ژنهای هدف در بروز و پیشروی سرطان کولون نقش مهمی را ایفا میکنند. هدف از مطالعه حاضر، شناسایی بیوانفورماتیکی microRNA های احتمالی موثر با بیان متمایز در نمونه های سرطانی و غیرسرطانی کولون بود.
روش کاراین مطالعه از نوع تیوریکال بیوانفورماتیکی بوده و داده های ریزآرایه مربوط به 1513 نمونه سرطان روده با شماره دستیابی GSE115513 از سایت GEO انتخاب شد و با استفاده از برنامه R، ژنهای مارکر انتخاب گردید. ژنهای هدف microRNA های شناسایی شده با برنامه TARGETSCAN مشخص شدند و در نهایت، شبکه گرافیکی با برنامه Cytoscape رسم گردید.
یافته ها:
آنالیز داده های ریزآرایه نشان داد که has- miR-663b، has-miR-650، has- miR-17-5p، has-miR-4539 و has-miR-501-3p دارای توان بیومارکری در نمونه های سرطانی میباشند. آنالیز آماری و بررسی ژنهای هدف این microRNA نشان داد که ژنهای has-miR-663b (p=0.001 و ROCAUC=0.8965) و has-miR-650 (p=0.001 و ROCAUC=0.9104) دارای بیان متمایز معنی دار بین نمونه های سرطانی و حاشیه غیرتوموری بوده و توان بیومارکری دارند.
نتیجه گیریژنهای has- miR-663b و has-miR-650 میتوانند به عنوان مارکر تشخیصی برای تفکیک نمونه های سرطانی روده بزرگ از نمونه های غیرسرطانی بکار گرفته شوند.
کلید واژگان: بیوانفورماتیک، microRNA، سرطان روده بزرگ، داده های میکرواریBackground & objectivesColon cancer is a common disease in the world that causes high mortality in affected people. The lack of appropriate diagnostic and prognostic markers has led to the failure in early diagnosis of colorectal malignancies. MicroRNAs play an important role in controlling the expression of target genes involved in the development and progression of colon cancer. The aim of the present study was the bioinformatics identification of microRNAs with distinct expression in cancerous and non-cancerous colon samples.
MethodsThis type of study was theoretical bioinformatics and microarray data of 1513 colon cancer samples with the accession number of GSE115513 were obtained from the GEO site and marker genes were selected by using R program. Target genes of the identified microRNAs were provided by TARGETSCAN software and finally, the graphical network was plotted in Cytoscape software.
ResultsAnalysis of microarray data showed that has-miR-663b, has-miR-650, has-miR-17-5p, has-miR-4539 and has-miR-501-3p have biomarker potential in cancer samples. Statistical analysis and investigation of the target genes indicated that miR-663b (ROCAUC=0.8965, p=0.001) and has-miR-650 (ROCAUC=0.9104, p=0.001) had significant distinct expression between cancerous and non-tumor margins with biomarker potential.
ConclusionThe has-miR-663b and has-miR-650 genes can be used as diagnostic markers to distinguish colon cancer from non-cancerous samples
Keywords: Bioinformatic, microRNAs, Colon Cancer, Microarray Data -
زمینه و هدفدر سال های اخیر، فن آوری های جدید منجر به تولید حجم انبوهی از داده ها شده و در حوزه زیستی، فناوری ریزآرایه نیز به صورت چشمگیری توسعه یافته است. در این میان، جهت مقایسه گروه کنترل با دو یا چند گروه آزمایشی، همچنین یافتن ژن هایی با بیان متفاوت، از آزمون فیشر استفاده می شود. در این مطالعه نرخ کشف کاذب در آزمون فرض جایگشتی فیشر و جایگشتی تعدیل یافته همزمان بر داده های ریزآرایه بررسی گردید.روش بررسیدر این مطالعه، ابتدا با شبیه سازی و انتخاب سه حالت مختلف برای نمونه های کنترل و آزمایشی ، نرخ کشف کاذب با استفاده از دو روش آزمون جایگشتی فیشر و جایگشتی تعدیل یافته محاسبه گردید، سپس این دو روش بر روی 8799 ژن مربوط به سلول مغز 29 موش (در سه گروه سنی جوان، میانسال و پیر) اعمال گردید و تاثیر فرآیند سن مغز بر افزایش ایجاد بیماری آلزایمر مورد بررسی قرار گرفت.یافته هانتایج نشان داد استفاده از روش آزمون جایگشتی فیشر، نرخ کشف کاذب را نمی تواند کنترل کند، ولی روش جایگشتی تعدیل یافته بهتر عمل کرده و اختلافات واقعی معنی دار را درست تر تشخیص می دهد؛ لذا مقدار FP در روش دوم کاهش خواهد یافت.نتیجه گیریبا توجه به نتایج این مطالعه، استفاده از روش های مرسوم ازجمله آزمون جایگشتی فیشر که مبنای تحلیل داده های زیستی در بسیاری از نرم افزارها می باشد، در داده های بزرگ مقیاس، ازجمله داده های ریزآرایه کارایی مطلوب را ندارد و نرخ کشف کاذب را نمی تواند کنترل کند؛ درحالی که روش جایگشتی تعدیل یافته با عملکرد بهتر در کنترل نرخ کشف کاذب، نتایج قابل اعتمادتری در پی دارد.کلید واژگان: ریزآرایه، بیان ژن، بیماری آلزایمر، نرخ کشف کاذب، روش هاBackground and ObjectivesIn recent years, new technologies have led to produce a large amount of data and in the field of biology, microarray technology has also dramatically developed. Meanwhile, the Fisher test is used to compare the control group with two or more experimental groups and also to detect the differentially expressed genes. In this study, the false discovery rate was investigated in the simultaneous Fisher and adjusted permutation hypothesis testing on microarray data.
MethodsIn this study, first, false discovery rate was computed through the simulation study and selection of three different modes for the samples of control group and experimental groups, then, these two methods, were applied to 8799 genes related to brain cell of the 29 rats (in three age groups of young, middle-aged, and aged), and the effect of the process of brain aging, was investigated on increased development of the Alzheimer disease.
ResultsThe results showed that the Fisher permutation methods cannot control the false discovery rate, but the adjusted permutation method works better and detects the significant differences more accurately, therefore, the number of false positives decrease in the second method.
ConclusionConsidering the results of this study, use of the customary methods such as the Fisher permutation test, which is the base of analyzing biological data in many of the software, do not have suitable efficiency in the large-scale data, including microarray data, and cannot control the false discovery rate, whereas the justified permutation method with better performance in false discovery rate leads to more reliable results.Keywords: Microarray data, Gene expression, Alzheimer disease, False discovery rate, Methods -
Background
A great number of data mining methods have been widely made such as gene regulatory networks and gene set analyses to connect genes that reveal similar expression patterns. These methods generally fail to unveil gene-gene interactions in the same cluster. The aim of this study is to use several nonparametric correlation coefficient methods to transform the linear rank statistics into distance metrics on a Saccharomyces cerevisiae data set.
MethodsThese nonparametric correlation coefficients, Kendall’s tau index and Gini rank correlation, were compared with common Pearson correlation method. The reliability and advantages of our proposed is satisfied using genetic website, http://www.yeast genome .org/. To address the interactions and characterize the gene–gene biological processes explicitly, the gene relationships are shown as a Pajek graph topology.
ResultThe results of biological interactions and characteristics demonstrated that the proposed nonparametric correlation coefficient methods have a strong capability to identify interaction genes. Moreover, suggested techniques could accurately detect the main genes and functional interactions in comparison to generally used Pearson correlation coefficient.
ConclusionThe two non-linear correlation coefficient techniques are proposed to measure the gene interactions more precisely.
Keywords: Gene-Gene Interaction, Gini Index, Kendall’s tau, Microarray Data -
BackgroundOocyte maturity includes nuclear and cytoplasmic maturity, both of which are important for embryo fertilization. The development of oocyte is not limited to the period of follicular growth, and starts from the embryonic period and continues throughout life. In this study, for the purpose of evaluating the effect of the FSH hormone on the expression of genes, GEO access codes for this data set, GSE38345, were used.Materials and methodsThe data are microarray and contain the gene expression information for cow's oocyte cells, that their maturation is influenced by the FSH hormone under laboratory conditions. Data analysis was performed by using the GEO2R software link. After identifying the genes and examining the different genes expressed, two gene groups included Increased and decreased expression genes are formed. The interaction of each of the gene groups was examined by using a string database, based on the co-expression information. The meaningful sub networks were explored using the clusterone software. Gene ontology was performed using the comparative GO database. The miRNA-mRNA interaction network was also studied based on the miRWalk database. Finally, meaningful networks and subnets obtained by cytoscape software were drawn.ResultsIn a comparison between oocyte gene expression data in the pre-maturation and the post- maturation stage after treatment with FSH, 5958 increased genes and 4275 decreased genes expression were found. By examining the protein interaction network in the set of increased and decreased expression genes, based on string information, 262 increased and 147 decreased genes (high confidence (0.7) data) were found. In network of Increased expression genes in oocyte maturation, the RPS3, NUSAP1, TBL3 and ATP5H genes which are effective in the biological pathways of positive regulation of rRNA processing, cell division, mitochondrial ATP synthesis coupled proton, also in functional pathways, oxidative phosphorylation and progesterone-mediated were effective, also in decreased expression genes, WDR46 and MRPL22 genes are the most important that were effective in the biological pathways of SRP-dependent cotranslational proteins in targeting to membrane, RNA secondary structure, unwinding and functional pathways of ribosomal and RNA polymerase. The most important microarray gene in the protein network of increased and decreased expression genes bta-miR-10b-5p and miR-29b-2-5p gene were reported.ConclusionIn examining the genes expressed in the pathway of oocyte maturation, three groups of nuclear, mitochondria, microarray genes were determined. Increasing and decreasing gene expression helps maintain balance, which can be considered as a marker.Keywords: Oocyte maturation, Microarray data, expressed genes, FSH hormone
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BackgroundOne substantial part of microarray studies is to predict patients survival based on their gene expression profile. Variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. However, these techniques have not been investigated in competing risks setting. This study aimed to investigate the performance of four sparse variable selection methods in estimating the survival time.MethodsThe data included 1381 gene expression measurements and clinical information from 301 patients with bladder cancer operated in the years 1987 to 2000 in hospitals in Denmark, Sweden, Spain, France, and England. Four methods of the least absolute shrinkage and selection operator, smoothly clipped absolute deviation, the smooth integration of counting and absolute deviation and elastic net were utilized for simultaneous variable selection and estimation under an additive hazards model. The criteria of area under ROC curve, Brier score and c-index were used to compare the methods.ResultsThe median follow-up time for all patients was 47 months. The elastic net approach was indicated to outperform other methods. The elastic net had the lowest integrated Brier score (0.137±0.07) and the greatest median of the over-time AUC and C-index (0.803±0.06 and 0.779±0.13, respectively). Five out of 19 selected genes by the elastic net were significant (PConclusionThe elastic net had higher capability than the other methods for the prediction of survival time in patients with bladder cancer in the presence of competing risks base on additive hazards model.Keywords: Survival analysis, Microarray data, Additive hazards model, Variable selection, Bladder cancer
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BackgroundAn important aspect of microarray studies includes the prediction of patient survival based on their gene expression profile. To deal with the high dimensionality of this data, use of a dimension reduction procedure along with the survival prediction model is necessary. This study aimed to present a new method based on wavelet transform for survival relevant gene selection.MethodsThe data included 2042 gene expression measurements from 40 patients with Diffuse Large B-Cell Lymphomas (DLBCL). The pre-processing gene expression data is decomposed using third level of the 1D discrete wavelet transform. The detail coefficients at levels 1 and 2 are filtered out and expression data reconstructed using the approximation and detailed coefficients at the third level. All the genes are then scored based on the t score. Then genes with the highest scores are selected. By using forward selection method in Cox regression model, significant genes were identified.ResultsThe results showed wavelet-based gene selection method presents acceptable survival prediction. Using this method, six significant genes were selected. It was indicated the expression of GENE3359X and GENE3968X decreased the survival time, whereas the expression of GENE967X, GENE3980X, GENE3405X and GENE1813X increased the survival time.ConclusionWavelet-based gene selection method is a potentially useful tool for the gene selection from microarray data in the context of survival analysis.Keywords: Survival analysis, One dimensional wavelet transform, Microarray data, DLBCL
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