scholarly journals An Empirical Evaluation of normalization Methods for MicroRNA Arrays in a Liposarcoma Study

2013 ◽  
Vol 12 ◽  
pp. CIN.S11384 ◽  
Author(s):  
Li-Xuan Qin ◽  
Tom Tuschl ◽  
Samuel Singer
Keyword(s):  
Differential Expression ◽  
Deep Sequencing ◽  
Expression Profiles ◽  
Microrna Expression ◽  
Quantile Normalization ◽  
Tissue Samples ◽  
Expression Arrays ◽  
Normalization Methods ◽  
Array Normalization ◽  
Disease Relevance

Background Methods for array normalization, such as median and quantile normalization, were developed for mRNA expression arrays. These methods assume few or symmetric differential expression of genes on the array. However, these assumptions are not necessarily appropriate for microRNA expression arrays because they consist of only a few hundred genes and a reasonable fraction of them are anticipated to have disease relevance. Methods We collected microRNA expression profiles for human tissue samples from a liposarcoma study using the Agilent microRNA arrays. For a subset of the samples, we also profiled their microRNA expression using deep sequencing. We empirically evaluated methods for normalization of microRNA arrays using deep sequencing data derived from the same tissue samples as the benchmark. Results: In this study, we demonstrated array effects in microRNA arrays using data from a liposarcoma study. We found moderately high correlation between Agilent data and sequence data on the same tumors, with the Pearson correlation coefficients ranging from 0.6 to 0.9. Array normalization resulted in some improvement in the accuracy of the differential expression analysis. However, even with normalization, there is still a significant number of false positive and false negative microRNAs, many of which are expressed at moderate to high levels. Conclusions Our study demonstrated the need to develop more efficient normalization methods for microRNA arrays to further improve the detection of genes with disease relevance. Until better methods are developed, an existing normalization method such as quantile normalization should be applied when analyzing microRNA array data.

Abstract WP120: MicroRNA Expression in Patients With Acute Ischemic Stroke

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Janhavi M Modak ◽  
Meaghan A Roy-O’Reilly ◽  
Sarah E Conway ◽  
Liang Zhu ◽  
Louise D McCullough
Keyword(s):  
Ischemic Stroke ◽  
Differential Expression ◽  
Expression Profiles ◽  
Outpatient Setting ◽  
Microrna Expression ◽  
Neurological Deficits ◽  
Stroke Patients ◽  
Significant Differential Expression ◽  
Blood Samples ◽  
Mrna Targets

Background and Purpose: MicroRNAs (miRNAs) are a class of endogenous small non-coding ribonucleic acids that regulate gene expression and can impact cellular function by suppressing or activating downstream mRNA targets. Pre-clinical studies in animal models of stroke have demonstrated specific changes in miRNA expression profiles after ischemic stroke. Methods: Patients admitted to Hartford Hospital from January 2011 - March 2014 were considered for this study. Blood samples were collected within 24 hours of stroke presentation. miRNA profiles from peripheral blood samples of ischemic stroke patients were compared to controls. Patients with acute middle cerebral artery (MCA) cardioembolic strokes (based on TOAST criteria) were included (n=16). Blood collected from patients with no acute neurological deficits in an outpatient setting served as controls (n=8). Individuals with a history of active cancer, neoplastic brain lesions or traumatic brain injury were excluded. Based on literature review, 173 miRNAs were selected to assess for differential expression between cases and controls. miRNA profiling was conducted at Exiqon Services, Denmark, using miRCURY LNA™ microRNA Array. Statistical analysis was performed using SAS. Results: In patients with acute ischemic strokes, a statistically significant differential expression was observed in 14 miRNAs as compared to controls. MicroRNAs miR-1273e, miR-5187-3p were found to be downregulated in stroke patients (p=0.01). Other miRNAs showing a significant downregulation included let 7e-5p (p=0.03); miR-4709-3p, miR-4756-3p, miR-5584-3p, miR-647 (p=0.02); miR-4742-3p (p=0.03); miR-4764-5p, miR-4531 and miR-2116-5p (p=0.04). MicroRNAs miR-664a-3p (p=0.02), miR-943 (p=0.04) and miR-145-5p (p=0.03) were significantly upregulated. Differential expression in males and females was not observed. Conclusion: Ischemic stroke patients show a differential microRNA expression profile as compared to controls. Further studies can help identify microRNA signatures as well as the downstream targets involved in the ischemic stroke molecular cascade.

Optimal consistency in microRNA expression analysis using reference-gene-based normalization

2015 ◽  
Vol 11 (5) ◽  
pp. 1235-1240 ◽  
Author(s):  
Xi Wang ◽  
Erin J. Gardiner ◽  
Murray J. Cairns
Keyword(s):  
Differential Expression ◽  
Reference Gene ◽  
Expression Analysis ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Microrna Expression

Reference gene-based normalization of expression profiles secures consistent differential expression analysis between samples of different phenotypes or biological conditions, and facilitates comparison between experimental batches.

scholarly journals Deep Sequencing of Guinea Pig (Cavia Porcellus) Lung Small RNAs Reveals Differential Expression of microRNAs between Non-infected and BCG-Infected Lungs

2021 ◽  
Author(s):  
Xiaoqi jing ◽  
Biqiong Jiang ◽  
Long Cheng ◽  
Yong Li
Keyword(s):  
Immune Responses ◽  
Guinea Pig ◽  
Differential Expression ◽  
Deep Sequencing ◽  
Target Genes ◽  
Transforming Growth Factor ◽  
Expression Profiles ◽  
Mitogen Activated Protein Kinase ◽  
Public Health Burden ◽  
Small Noncoding Rnas

Abstract Background: Pulmonary tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) infection remains a major public health burden worldwide. It has been well documented that a group of small noncoding RNAs, microRNAs (miRNAs) are involved in the development and pathogenesis many diseases, including the TB. Guinea pigs are considered as one of the best animal models for biomedical research in TB, the potential roles of miRNAs in the innate immune regulation of guinea pig lung against Mtb infection are not well understood. Methods: In this study, we investigated the differential expression of miRNA profiles between the un-infected lungs and Mycobacterium bovis bacillus Calmette-Guérin (BCG)-infected lungs of guinea pigs via deep sequencing and bioinformatics analysis. Results: A total of 2508 miRNAs were identified, among them 1187 were conserved miRNAs and 56 were novel miRNAs in the uninfected lungs, and 1202 were identified as conserved miRNAs and 63 were novel miRNAs in the BCG-infected lungs. Interestingly, comparison analysis further identified 902 co-expressed miRNAs and 585 distinct miRNAs between these two groups. Of the 15 most abundantly conserved miRNAs in guinea pig lungs, which belong to 7 miRNA families, including miR23, miR29, miR145, miR320, miR378, miR451, and miR423. 13 of these 15 most abundant miRNAs were significantly downregulated and 2 of them were significantly upregulated in the BCG-infected lungs. Individually, miRNA Let-7f-5p, let-7f, let-7-5p and let-7b-5p were the most abundant in both profiles of the non-infected and BCG-infected guinea pig lungs. The predicted target genes of specific miRNAs found in guinea pig lungs were involved in regulation signaling pathways related to immune responses, including Toll-like receptors (TLRs), nuclear factor (NF)-kappa B, Wnt, mitogen-activated protein kinase (MAPK), and transforming growth factor (TGF)-beta signaling, as well as related to autophagy signaling mTOR and apoptotic molecule p53. Conclusions: These data of comprehensive analysis of miRNA transcriptome demonstrated the differential expression profiles of miRNAs during M. tuberculosis infection of guinea pig lungs. These results could facilitate the future exploitation of the roles of miRNAs in regulation of immune responses to M. tuberculosis infection using the guinea pig model.

scholarly journals Characterization of microRNA expression profiles by deep sequencing of small RNA libraries in leukemia patients from Naxi ethnic

2019 ◽  
Vol 8 (1) ◽  
pp. 160-169
Author(s):  
Zhenglei Shen ◽  
Xuezhong Gu ◽  
Wenwen Mao ◽  
Honghua Cao ◽  
Rui Zhang ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Small Rna ◽  
Expression Profiles ◽  
Microrna Expression

scholarly journals Long Noncoding RNA ASB16-AS1 Promotes Proliferation, Migration, and Invasion in Glioma Cells

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Delong Zhang ◽  
Huanggui Zhou ◽  
Jun Liu ◽  
Jie Mao
Keyword(s):  
Differential Expression ◽  
Noncoding Rna ◽  
Expression Profiles ◽  
Tumor Staging ◽  
Quantitative Polymerase Chain Reaction ◽  
Differential Expression Analysis ◽  
Glioma Cells ◽  
The Cancer Genome Atlas ◽  
Migration And Invasion ◽  
Tissue Samples

Glioma is a lethal, malignant intracranial tumor that becomes progressively common. It has been shown that long noncoding RNAs (lncRNAs) serve important roles in numerous diseases such as gliomas. lncRNAs can regulate the expression of targeted genes through various mechanisms. To identify a novel lncRNA that may be critical in glioma, the present study downloaded the RNA expression profiles of 171 glioma tissues and 5 normal tissues from The Cancer Genome Atlas (TCGA) database using the TCGAbiolinks package in R. Then, lncRNAs in the downloaded TCGA data were identified using the HUGO Gene Nomenclature Committee (HGNC). Based on the fragments per kilobase million value, differential expression analysis was conducted using the limma package in R. In addition, receiver operating characteristic (ROC) analysis was performed, and the area under the curve (AUC) was evaluated using the ROCR package in R. A total of 178 lncRNAs corresponding to differentially expressed genes with an AUC >0.85 were selected. Upon identifying the differential lncRNAs, ceRNA networks were constructed with these differential lncRNAs using the starbase database. From these networks, the top 10% hub genes were selected. In addition, the present study randomly selected 4 lncRNAs for quantitative polymerase chain reaction validation in tissue samples. The results revealed that lncRNA ASB16-AS1 exhibited significantly differential expression in tissue samples and was significantly associated with tumor staging and grading. Furthermore, the proliferation, invasion, and migration of U87MG and U251 glioblastoma stem-like cells (U87GS, U251GS) were significantly inhibited upon inhibition of ASB16-AS1, and the expression of key proteins in the EMT signaling pathway was affected by knocking down ASB16-AS1. Overall, the present study revealed that lncRNA ASB16-AS1 improves the proliferation, migration, and invasion of glioma cells.

scholarly journals Hox dosage contributes to flight appendage morphology in Drosophila

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rachel Paul ◽  
Guillaume Giraud ◽  
Katrin Domsch ◽  
Marilyne Duffraisse ◽  
Frédéric Marmigère ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Differential Expression ◽  
Hox Genes ◽  
Expression Profiles ◽  
Fruit Fly ◽  
Insect Species ◽  
Wing Morphology ◽  
Hox Proteins ◽  
Spatial Expression ◽  
Flying Insects

AbstractFlying insects have invaded all the aerial space on Earth and this astonishing radiation could not have been possible without a remarkable morphological diversification of their flight appendages. Here, we show that characteristic spatial expression profiles and levels of the Hox genes Antennapedia (Antp) and Ultrabithorax (Ubx) underlie the formation of two different flight organs in the fruit fly Drosophila melanogaster. We further demonstrate that flight appendage morphology is dependent on specific Hox doses. Interestingly, we find that wing morphology from evolutionary distant four-winged insect species is also associated with a differential expression of Antp and Ubx. We propose that variation in the spatial expression profile and dosage of Hox proteins is a major determinant of flight appendage diversification in Drosophila and possibly in other insect species during evolution.

scholarly journals Tentacle Morphological Variation Coincides with Differential Expression of Toxins in Sea Anemones

Toxins ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 452
Author(s):  
Lauren M. Ashwood ◽  
Michela L. Mitchell ◽  
Bruno Madio ◽  
David A. Hurwood ◽  
Glenn F. King ◽  
...  
Keyword(s):  
Differential Expression ◽  
Morphological Variation ◽  
Expression Profiles ◽  
The Body ◽  
Sea Anemones ◽  
Tissue Specific ◽  
Venom Composition ◽  
Specific Manner ◽  
Branched Structures ◽  
Morphological Variants

Phylum Cnidaria is an ancient venomous group defined by the presence of cnidae, specialised organelles that serve as venom delivery systems. The distribution of cnidae across the body plan is linked to regionalisation of venom production, with tissue-specific venom composition observed in multiple actiniarian species. In this study, we assess whether morphological variants of tentacles are associated with distinct toxin expression profiles and investigate the functional significance of specialised tentacular structures. Using five sea anemone species, we analysed differential expression of toxin-like transcripts and found that expression levels differ significantly across tentacular structures when substantial morphological variation is present. Therefore, the differential expression of toxin genes is associated with morphological variation of tentacular structures in a tissue-specific manner. Furthermore, the unique toxin profile of spherical tentacular structures in families Aliciidae and Thalassianthidae indicate that vesicles and nematospheres may function to protect branched structures that host a large number of photosynthetic symbionts. Thus, hosting zooxanthellae may account for the tentacle-specific toxin expression profiles observed in the current study. Overall, specialised tentacular structures serve unique ecological roles and, in order to fulfil their functions, they possess distinct venom cocktails.

scholarly journals Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ping Yan ◽  
Zuotian Huang ◽  
Tong Mou ◽  
Yunhai Luo ◽  
Yanyao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Competing Endogenous Rna ◽  
Tissue Samples ◽  
Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

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