Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA142971 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE29173: MicroRNA sequence and expression analysis in breast tumors by deep sequencing [miRNA sequence data] GSE29174: MicroRNA sequence and expression analysis in breast tumors by deep sequencing [mRNA expression array data] MicroRNAs (miRNAs) regulate many genes critical for tumorigenesis. We profiled miRNAs from 11 normal breast tissues, 17 non-invasive, 151 invasive breast carcinomas, and 6 cell lines by in-house-developed barcoded Solexa sequencing. miRNAs were organized in genomic clusters representing promoter-controlled miRNA expression and sequence families representing seed-sequence-dependent miRNA-target regulation. Unsupervised clustering of samples by miRNA sequence families best reflected the clustering based on mRNA expression available for this sample set. Clustering and comparative analysis of miRNA read frequencies showed that normal breast samples were separated from most non-invasive ductal carcinoma in situ and invasive carcinomas by increased miR-21 (the most abundant miRNA in carcinomas) and multiple decreased miRNA families (including mir-98/let-7), with most miRNA changes apparent already in the non-invasive carcinomas. In addition, patients that went on to develop metastasis demonstrated increased expression of mir-423, and triple negative breast carcinomas were most distinct from other tumor subtypes due to up-regulation of the mir-17~92 cluster. However, absolute miRNA levels between normal breast and carcinomas did not reveal any significant differences. We also discovered two polymorphic nucleotide variations among the more abundant miRNAs miR-181a (T19G) and miR-185 (T16G), but we did not identify nucleotide variations expected for classical tumor suppressor function associated with miRNAs. The differentiation of tumor subtypes and prediction of metastasis based on miRNA levels is statistically possible, but is not driven by deregulation of abundant miRNAs, implicating far fewer miRNAs in tumorigenic processes than previously suggested. Refer to individual Series
2011-06-07 | E-GEOD-28884 | biostudies-arrayexpress
Project description:MicroRNA sequence and expression analysis in breast tumors by deep sequencing [miRNA sequence data]
| PRJNA142969 | ENA
Project description:Purpose: We tested global gene transcriptome changes by RNA-sequencing analysis in the offspring breast tumors of SV40 transgenic mice to further identify key epigenetic-controlled genes in regulation of the prenatal/maternal BSp diet-mediated early breast cancer prevention. Method: Mouse offspring mammary tumor mRNA from control and maternal BSp treatment were generated by deep sequencing, in duplicate or triplicate, using Illumina NextSeq500 platform (GPL19057). The sequence reads that passed quality filters were analyzed. We utilized the R/Bioconductor package DESeq to evaluate differential gene expression for sequence count data by the use of negative binomial distributio. qRT–PCR validation was performed using TaqMan and SYBR Green assays. Conclusions: Our data showed differential transcriptome distribution in the breast tumors of mouse offspring between the control and prenatal/maternal BSp treatment groups.
2018-05-02 | GSE113900 | GEO
Project description:MicroRNA (miRNA/miR) miR526b and miR655 overexpressed tumor cell-free secretions promote breast cancer phenotypes in the tumor microenvironment (TME). However, the mechanisms of miRNA regulating TME have never been investigated. With mass spectrometry analysis of MCF7-miRNA-overexpressed versus miRNA-low MCF7-Mock tumor cell secretomes, we identified 34 novel secretory proteins coded by eight genes YWHAB, TXNDC12, MYL6B, SFN, FN1, PSMB6, PRDX4, and PEA15 those are differentially regulated. We used bioinformatic tools and systems biology approaches to identify these markers’ role in breast cancer. Gene ontology analysis showed that the top functions are related to apoptosis, oxidative stress, membrane transport, and motility, supporting miRNA-induced phenotypes. These secretory markers expression is high in breast tumors, and a strong positive correlation exists between upregulated markers’ mRNA expressions with miRNA cluster expression in luminal A breast tumors. Gene expression of secretome markers is higher in tumor tissues compared to normal samples, and immunohistochemistry data supported gene expression data. Moreover, both up and downregulated marker expressions are associated with breast cancer patient survival. miRNA regulates these marker protein expressions by targeting transcription factors of these genes. Premature miRNA (pri-miR526b and pri-miR655) are established breast cancer blood biomarkers. Here we report novel secretory markers upregulated by miR526b and miR655 (YWHAB, MYL6B, PSMB6, and PEA15) are significantly upregulated in breast cancer patients’ plasma, and are potential breast cancer biomarkers.
2023-05-10 | PXD031771 | Pride
Project description:BACKGROUND: The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets. METHODS: Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs. RESULTS: An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line. CONCLUSIONS: A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.
2015-02-02 | GSE58212 | GEO
Project description:BACKGROUND: The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets. METHODS: Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs. RESULTS: An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line. CONCLUSIONS: A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.
2015-02-02 | GSE58210 | GEO
Project description:Intervention type:DRUG. Intervention1:Huaier, Dose form:GRANULES, Route of administration:ORAL, intended dose regimen:20 to 60/day by either bulk or split for 3 months to extended term if necessary. Control intervention1:None.
Primary outcome(s): For mRNA libraries, focus on mRNA studies. Data analysis includes sequencing data processing and basic sequencing data quality control, prediction of new transcripts, differential expression analysis of genes. Gene Ontology (GO) and the KEGG pathway database are used for annotation and enrichment analysis of up-regulated genes and down-regulated genes.
For small RNA libraries, data analysis includes sequencing data process and sequencing data process QC, small RNA distribution across the genome, rRNA, tRNA, alignment with snRNA and snoRNA, construction of known miRNA expression pattern, prediction New miRNA and Study of their secondary structure Based on the expression pattern of miRNA, we perform not only GO / KEGG annotation and enrichment, but also different expression analysis.. Timepoint:RNA sequencing of 240 blood samples of 80 cases and its analysis, scheduled from June 30, 2022..
| 2612481 | ecrin-mdr-crc
Project description:Systems-wide profiling of breast cancer has so far built on RNA and DNA analysis by microarray and sequencing techniques. Dramatic developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analyzed 40 estrogen receptor positive (luminal), Her2 positive and triple negative breast tumors and reached a quantitative depth of more than 10,000 proteins. Comparison to mRNA classifiers revealed multiple discrepancies between proteins and mRNA markers of breast cancer subtypes. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a 19-protein predictive signature, which discriminates between the breast cancer subtypes, through Support Vector Machine (SVM)-based classification and feature selection. The deep proteome profiles also revealed novel features of breast cancer subtypes, which may be the basis for future development of subtype specific therapeutics.
2017-03-29 | MSV000080757 | MassIVE
Project description:Systems-wide profiling of breast cancer has so far built on RNA and DNA analysis by microarray and sequencing techniques. Dramatic developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analyzed 40 estrogen receptor positive (luminal), Her2 positive and triple negative breast tumors and reached a quantitative depth of more than 10,000 proteins. Comparison to mRNA classifiers revealed multiple discrepancies between proteins and mRNA markers of breast cancer subtypes. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a 19-protein predictive signature, which discriminates between the breast cancer subtypes, through Support Vector Machine (SVM)-based classification and feature selection. The deep proteome profiles also revealed novel features of breast cancer subtypes, which may be the basis for future development of subtype specific therapeutics.
2016-01-05 | PXD002619 | Pride