Circulating miRNA microarray data from breast cancer patients
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Landoni Elena
PROVIDER: S-ECPF-GEOD-59993 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Independent studies have reported that circulating miRNAs have the potential as biomarkers; however, no consolidated guidelines for the discovery process are available. We developed a pipeline using innovative applications of existing bioinformatics methods to (1) face the inapplicability of the classical normalization methods, (2) detect global differences of miRNA distributions between the comparison classes and (3) develop a 'robust’ classifier.
2014-08-01 | GSE59993 | GEO
Project description:Independent studies have reported that circulating miRNAs have the potential as biomarkers; however, no consolidated guidelines for the discovery process are available. We developed a pipeline using innovative applications of existing bioinformatics methods to (1) face the inapplicability of the classical normalization methods, (2) detect global differences of miRNA distributions between the comparison classes and (3) develop a 'robustM-bM-^@M-^Y classifier. 78 samples (26 hemolyzed and 52 non hemolyzed) were 1:2 paired with caliper matching according to disease status, age at drawing and drawing year, starting from a total of 208 hybridized samples collected between 1987 and 2004 (including 94 paired samples according to tumor ER status, age at drawing, drawing year, time from surgery, 7 additional samples from 'no evidence of disease' (NED) patients, 10 references, 1 healthy donor and 2 replicated samples)
2014-08-01 | E-GEOD-59993 | biostudies-arrayexpress
Project description:Chemotherapy resistance frequently drives tumor progression. However, the underlying molecular mechanisms are poorly characterized. Epithelial-to-mesenchymal transition (EMT) has been shown to correlate with therapy resistance, but the functional link and signaling pathways remain to be elucidated. We report here that miR-30c, a human breast tumor prognostic marker, plays a pivotal role in chemo-resistance by a direct targeting of the actintransporter TWF1, which promotes EMT. An IL-6 family member, IL-11 was identified as a secondary target of TWF1 in the miR-30c signaling pathway. Expression of miR-30c inversely correlated with IL-11 expression in clinical tumors and IL-11 correlated with relapse-free survival in breast cancer patients. Identification of a novel miRNA-mediated pathway that regulates chemo-resistance in breast cancer will facilitate the development of new management strategies. For the primary breast tumor and normal breast samples, both mRNAs (Agilent array data deposited in GSE22049) and miRNAs (Exiqon array data deposited here) profiles have been examined. Patient information include age, tumor subtype and outcome etc. When clustering samples and genes 152 of 757 miRNA passed the filtering criteria on variation across samples; standard deviation > 0.50. Hence, the upper 152 miRNAs in the expression matrix were used in the two-way hierarchical clustering of genes. The matrix numbers are all log2(Hy3/Hy5) ratios, meaning sample/pool. The percentages above the matrix indicates the present call in each sample/slide. When calling of a particular miRNA failed on an array this is indicated as a blank in the row containing this miRNA and in the column corresponding to this array. The criteria for deciding that the calling of a miRNA had failed on a particular array, was that 2 or more of the 4 replicated measures of this miRNA were flagged 1 or 2 (i.e. the signal is below background) by the image analysis software. Further in the expression matrix all capture probes with Hy3 and Hy5 signals lower than 1.5x of the median signal intensity of the given slide is indicated as a blank.
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Project description:Oral drugs stimulating endogenous insulin production (insulin secretagogues) may have detrimental effects on breast cancer outcomes. The data presented shows the relationship between pre-existing insulin secretagogues use, adipokine profiles at the time of breast cancer (BC) diagnosis and subsequent cancer outcomes in women diagnosed with BC and type 2 diabetes mellitus (T2DM). The Pearson correlation analysis evaluating the relationship between adipokines stratified by T2DM pharmacotherapy and controls is also provided. This information is the extension of the data presented and discussed in "Insulin use, adipokine profiles and breast cancer prognosis" (Wintrob et al., in press) [1].
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Project description:Analysis of hybridisation performance and utility in identifying differentially expressed miRNAs of six miRNA microarray platforms using three biological samples in quadruplicate.
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Project description:One of the main disadvantages of using DNA microarrays for miRNA expression profiling is the inability of adequate comparison of expression values across different miRNAs. This leads to a large amount of miRNAs with high scores which are actually not expressed in examined samples, i.e., false positives. We propose a post-processing algorithm which performs scoring of miRNAs in the results of microarray analysis based on expression values, time of discovery of miRNA, and correlation level between the expressions of miRNA and corresponding pre-miRNA in considered samples. The algorithm was successfully validated by the comparison of the results of its application to miRNA microarray breast tumor samples with publicly available miRNA-seq breast tumor data. Additionally, we obtained possible reasons why miRNA can appear as a false positive in microarray study using paired miRNA sequencing and array data. The use of DNA microarrays for estimating miRNA expression profile is limited by several factors. One of them consists of problems with comparing expression values of different miRNAs. In this work, we show that situation can be significantly improved if some additional information is taken into consideration in a comparison.
| S-EPMC7072892 | biostudies-literature
Project description:Ovarian cancer is one of the most common cancer types in women characterized by a high mortality rate due to lack of early diagnosis. Circulating miRNAs besides being important regulators of cancer development could be potential biomarkers to aid diagnosis. We performed the circulating miRNA expression analysis in plasma samples obtained from ovarian cancer patients stratified into FIGO I, FIGO III, and FIGO IV stages and from healthy females using the NanoString quantitative assay. Forty-five miRNAs were differentially expressed, out of these 17 miRNAs showed significantly different expression between controls and patients, 28 were expressed only in patients, among them 19 were expressed only in FIGO I patients. Differentially expressed miRNAs were ranked by the network-based analysis to assess their importance. Target genes of the differentially expressed miRNAs were identified then functional annotation of the target genes by the GO and KEGG-based enrichment analysis was carried out. A general and an ovary-specific protein-protein interaction network was constructed from target genes. Results of our network and the functional enrichment analysis suggest that besides HSP90AA1, MYC, SP1, BRCA1, RB1, CFTR, STAT3, E2F1, ERBB2, EZH2, and MET genes, additional genes which are enriched in cell cycle regulation, FOXO, TP53, PI-3AKT, AMPK, TGFβ, ERBB signaling pathways and in the regulation of gene expression, proliferation, cellular response to hypoxia, and negative regulation of the apoptotic process, the GO terms have central importance in ovarian cancer development. The aberrantly expressed miRNAs might be considered as potential biomarkers for the diagnosis of ovarian cancer after validation of these results in a larger cohort of ovarian cancer patients.
| S-EPMC6769773 | biostudies-literature
Project description:To identify improved molecular markers to support histological examination we used microarray analysis of formalin-fixed and paraffin-embedded samples from different stages of melanomagenesis to identify differentially expressed microRNAs. Total RNA were obtained from isolated 19 human cell lines and 52 FFPE human samples. Cells include 2 immortalised melanocytes, 4 ovarian cancer cell lines and 13 melanoma cell lines. 61 FFPE samples include 11 benign naevi, 20 primary and 21 metastatic melanoma
| S-ECPF-GEOD-35579 | biostudies-other
Project description:BackgroundNowadays, many public repositories containing large microarray gene expression datasets are available. However, the problem lies in the fact that microarray technology are less powerful and accurate than more recent Next Generation Sequencing technologies, such as RNA-Seq. In any case, information from microarrays is truthful and robust, thus it can be exploited through the integration of microarray data with RNA-Seq data. Additionally, information extraction and acquisition of large number of samples in RNA-Seq still entails very high costs in terms of time and computational resources.This paper proposes a new model to find the gene signature of breast cancer cell lines through the integration of heterogeneous data from different breast cancer datasets, obtained from microarray and RNA-Seq technologies. Consequently, data integration is expected to provide a more robust statistical significance to the results obtained. Finally, a classification method is proposed in order to test the robustness of the Differentially Expressed Genes when unseen data is presented for diagnosis.ResultsThe proposed data integration allows analyzing gene expression samples coming from different technologies. The most significant genes of the whole integrated data were obtained through the intersection of the three gene sets, corresponding to the identified expressed genes within the microarray data itself, within the RNA-Seq data itself, and within the integrated data from both technologies. This intersection reveals 98 possible technology-independent biomarkers. Two different heterogeneous datasets were distinguished for the classification tasks: a training dataset for gene expression identification and classifier validation, and a test dataset with unseen data for testing the classifier. Both of them achieved great classification accuracies, therefore confirming the validity of the obtained set of genes as possible biomarkers for breast cancer. Through a feature selection process, a final small subset made up by six genes was considered for breast cancer diagnosis.ConclusionsThis work proposes a novel data integration stage in the traditional gene expression analysis pipeline through the combination of heterogeneous data from microarrays and RNA-Seq technologies. Available samples have been successfully classified using a subset of six genes obtained by a feature selection method. Consequently, a new classification and diagnosis tool was built and its performance was validated using previously unseen samples.
| S-EPMC5697344 | biostudies-literature
Project description:Microarray data from patients samples with triple negative breast cancer
| PRJNA770267 | ENA