Long ncRNA in HCC cells
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Tushar Patel
PROVIDER: E-GEOD-25859 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Normal human hepatocytes and 3 different HCC cell lines have been analyzed for the global expression of long non coding RNA (lncRNA). Total RNA was extracted from each sample using RNeasy Kit (Qiagen) according to the manufacturer’s instructions. RNA quality was assessed by Nanodrop ND-1000 and Bioanalyser2100. DNA microarray The Human Long Non-coding RNA Array was manufactured by Roche NimbleGen. Each array represents all long transcripts, both protein coding mRNAs and lncRNAs (long non-coding RNAs) in the human genome. More than 22000 lncRNAs are collected from the authoritative data sources including NCBI RefSeq, UCSC, RNAdb, lncRNAs from literatures and UCRs. Fluorescent cDNA targets were prepared using NimbleGen One-Color DNA Labeling Kit. Microarray hybridization was performed at 42°C for 16-20 hours in NimbleGen Hybridization System. After being washed in an ozone-free environment, the slides were scanned using the Axon GenePix 4000B microarray scanner. Data Analysis Raw data were extracted as pair files by NimbleScan software (version 2.5). NimbleScan software’s implementation of RMA offers quantile normalization and background correction. The Probe level (*_norm_RMA.pair) files and Gene summary (*_RMA.calls) files were produced. The gene summary files were imported into Agilent GeneSpring Software (version 10.0) for further analysis. Differentially expressed genes and non-coding RNA were identified through Fold-change screening. Unsupervised hierarchical clustering, GO analysis and Pathway analysis was performed using the Agilent GeneSpring GX software (version 10.0).
2013-12-01 | GSE25859 | GEO
Project description:Total RNA from each sample was quantified by the NanoDrop ND-1000 and RNA integrity was assessed by standard denaturing agarose gel electrophoresis. Total RNA of each sample was used for labeling and array hybridization as the following steps: 1) Reverse transcription with by Invitrogen Superscript ds-cDNA synthesis kit; 2) ds-cDNA labeling with NimbleGen one-color DNA labeling kit; 3) Array hybridization using the NimbleGen Hybridization System and followed by washing with the NimbleGen wash buffer kit; 4) Array scanning using the Axon GenePix 4000B microarray scanner (Molecular Devices Corporation). Scanned images (TIFF format) were then imported into NimbleScan software (version 2.5) for grid alignment and expression data analysis. Expression data were normalized through quantile normalization and the Robust Multichip Average (RMA) algorithm included in the NimbleScan software. The Probe level (*_norm_RMA.pair) files and Gene level (*_RMA.calls) files were generated after normalization. The 2 gene level files were imported into Agilent GeneSpring GX software (version 11.5.1). Genes that have values greater than or equal to lower cut-off: 100.0 in that at least 1 out of 2 samples (“All Targets Value”) were chosen for data analysis.
2022-07-20 | GSE208583 | GEO
Project description:We have completed the Human lncRNA microarray analysis of the 4 samples that you submitted. Total RNA from each sample was quantified using the Nanodrop 1000 and the RNA integrity was assessed by Agilent 2100 Bioanalyzer. About 5 μg total RNA of each sample was used for labeling and array hybridization as the following steps: 1) Reverse transcription with invitrogen Superscript ds-cDNA synthesis kit; 2) ds-cDNA labeling with Nimblegen one-color DNA labeling kit; 3) Array hybridization using the NimbleGen Hybridization System and followed by washing with the Nimblegen wash buffer kit. 4) Array scanning using the Axon GenePix 4000B microarray scanner (Molecular Devices Corporation). Scanned images (TIFF format) were then imported into NimbleScan software (version 2.5) for grid alignment and expression data analysis. Expression data were normalized through quantile normalization and the Robust Multichip Average (RMA) algorithm included in the NimbleScan software. The Probe level (*_norm_RMA.pair) files and mRNA level (*_RMA.calls) files were generated after normalization. The 4 mRNA level files were imported into Agilent GeneSpring Software (version 11.0) for further analysis. lncRNAs and mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 (“All Targets Value”) were chosen for data analysis. Differentially expressed lncRNAs and mRNAs were identified through Fold Change filtering. Pathway Analysis and GO analysis were applied to determine the roles of these differentially expressed mRNAs played in these biological pathways or GO terms. Finally, Hierarchical Clustering was performed to show distinguishable lncRNA and mRNA expression profiling among samples. Array Information: Human 4x44K lncRNA expression array information. Data Analysis for lncRNAs. Data Analysis for lncRNAs. Data Analysis for mRNAs. 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity mRNAs were filtered. (mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis) 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering. 3. Differentially expressed mRNAs screening: Contains significant differentially expressed mRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the mRNAs after filtering. 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. 1. Raw lncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity lncRNAs were filtered. (lncRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis). 2. Quality assessment of lncRNA data after filtering: Contains Box Plot and Scatter Plot for lncRNAs after filtering. 3. Differentially expressed lncRNAs screening: Contains significant differentially expressed lncRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the lncRNAs after filtering. Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity mRNAs were filtered. (mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis) 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering. 3. Differentially expressed mRNAs screening: Contains significant differentially expressed mRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the mRNAs after filtering. 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. 1. Raw lncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity lncRNAs were filtered. (lncRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis). 2. Quality assessment of lncRNA data after filtering: Contains Box Plot and Scatter Plot for lncRNAs after filtering. 3. Differentially expressed lncRNAs screening: Contains significant differentially expressed lncRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the lncRNAs after filtering. Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity mRNAs were filtered. (mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis) 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering. 3. Differentially expressed mRNAs screening: Contains significant differentially expressed mRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the mRNAs after filtering. 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs.
2024-11-27 | GSE282877 | GEO
Project description:Investigation of whole genome gene expression level changes of LncRNAs in tumor tissues and paired non-tumor tissues in HBV-positive hapatocellular carcinoma. The different expression genes were further analysised. The human LncRNA microarray analysis of the 10 samples (5 non-tumor tissues and 5 paired tumor tissues) were completed. Total RNA from each sample was quantified using the NanoDrop ND-1000 and RNA integrity was assessed using standard denaturing agarose gel electrophoresis. Total RNA of each sample was used for labeling and array hybridization as the following steps: 1) Reverse transcription with by Invitrogen Superscript ds-cDNA synthesis kit; 2) ds-cDNA labeling with NimbleGen one-color DNA labeling kit; 3) Array hybridization using the NimbleGen Hybridization System and followed by washing with the NimbleGen wash buffer kit; 4) Array scanning using the Agilent Scanner G2505C. Scanned images (TIFF format) were then imported into NimbleScan software (version 2.5) for grid alignment and expression data analysis. Expression data were normalized through quantile normalization and the Robust Multichip Average (RMA) algorithm included in the NimbleScan software. The Probe level (*_norm_RMA.pair) files and mRNA level (*_RMA.calls) files were generated after normalization. All mRNAs level files were imported into Agilent GeneSpring GX software (version 11.5.1) for further analysis.mRNAs that at least 3 out of 6 samples have values greater than or equal to lower cut-off: 50.0 (“All Targets Value”) were chosen for further data analysis. Differentially expressed mRNAs were identified through Volcano Plot filtering. Pathway analysis and GO analysis were applied to determine the roles of these differentially expressed mRNAs played in these biological pathways or GO terms. Finally, Hierarchical Clustering was performed to show the distinguishable mRNAs expression pattern among samples.
2013-08-10 | E-GEOD-49713 | biostudies-arrayexpress
Project description:Long non-coding RNAs (lncRNAs) comprise a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins. Recent genome-wide studies in human and mouse have annotated lncRNAs expressed in cell lines and adult tissues, but a systematic analysis of lncRNAs expressed during vertebrate embryogenesis has been elusive. To identify lncRNAs with potential functions in vertebrate embryogenesis, we performed a time series of RNA-Seq experiments at eight stages during early zebrafish development. We reconstructed 56,535 high-confidence transcripts in 28,912 loci, recovering the vast majority of expressed RefSeq transcripts, while identifying thousands of novel isoforms and expressed loci. We defined a stringent set of 1,133 non-coding multi-exonic transcripts expressed during embryogenesis. These include long intergenic ncRNAs (lincRNAs), intronic overlapping lncRNAs, exonic antisense overlapping lncRNAs, and precursors for small RNAs (sRNAs). Zebrafish lncRNAs share many of the characteristics of their mammalian counterparts: relatively short length, low exon number, low expression, and conservation levels comparable to introns. Subsets of lncRNAs carry chromatin signatures characteristic of genes with developmental functions. The temporal expression profile of lncRNAs revealed two novel properties: lncRNAs are expressed in narrower time windows than protein-coding genes and are specifically enriched in early-stage embryos. In addition, several lncRNAs show tissue-specific expression and distinct subcellular localization patterns. Integrative computational analyses associated individual lncRNAs with specific pathways and functions, ranging from cell cycle regulation to morphogenesis. Our study provides the first comprehensive identification of lncRNAs in a vertebrate embryo and forms the foundation for future genetic, genomic and evolutionary studies. RNA-Seq for 8 zebrafish developmental stages, 2 lanes for each stage (3 for shield).
2011-11-17 | E-GEOD-32898 | biostudies-arrayexpress
Project description:Investigation of whole genome gene expression level changes of mRNAs in portal vein tumor thrombus tissues and paired tumor tissues in hapatocellular carcinoma. The different expression genes were further analyzed. The human mRNA microarray analysis of the 6 samples (3 portal vein tumor thrombus tissues and 3 paired tumor tissues) were completed. Total RNA from each sample was quantified using the NanoDrop ND-1000 and RNA integrity was assessed using standard denaturing agarose gel electrophoresis. Total RNA of each sample was used for labeling and array hybridization as the following steps: 1) Reverse transcription with by Invitrogen Superscript ds-cDNA synthesis kit; 2) ds-cDNA labeling with NimbleGen one-color DNA labeling kit; 3) Array hybridization using the NimbleGen Hybridization System and followed by washing with the NimbleGen wash buffer kit; 4) Array scanning using the Agilent Scanner G2505C. Scanned images (TIFF format) were then imported into NimbleScan software (version 2.5) for grid alignment and expression data analysis. Expression data were normalized through quantile normalization and the Robust Multichip Average (RMA) algorithm included in the NimbleScan software. The Probe level (*_norm_RMA.pair) files and mRNA level (*_RMA.calls) files were generated after normalization. All mRNAs level files were imported into Agilent GeneSpring GX software (version 11.5.1) for further analysis.mRNAs that at least 3 out of 6 samples have values greater than or equal to lower cut-off: 50.0 (“All Targets Value”) were chosen for further data analysis. Differentially expressed mRNAs were identified through Volcano Plot filtering. Pathway analysis and GO analysis were applied to determine the roles of these differentially expressed mRNAs played in these biological pathways or GO terms. Finally, Hierarchical Clustering was performed to show the distinguishable mRNAs expression pattern among samples. .
2013-01-19 | E-GEOD-43630 | biostudies-arrayexpress
Project description:The expression of the long non-coding RNAs (lncRNAs) in the active tuberculosis patients,multidrug-resistant tuberculosis patients,cured tuberculosis subjects and healthy controls were scanned using lncRNA microarray.The Arraystar Human LncRNA Microarray version 4.0 (Agilent Technologies) was used,containing 40173 lncRNA probes and 20730 mRNA probes for the detection of lncRNAs and mRNAs in the sample. Non-coding RNAs and mRNAs profiling by array
2022-12-22 | GSE163292 | GEO
Project description:We used NimbleGen Mouse ChIP-chip 3 × 720K RefSeq Promoter Arrays (NimbleGen Roche, Madison, WI). Briefly, we isolated three independent samples of gender and age matched HDAC6KO and WT control CD4+CD25+ Treg, isolated, cross-linked and processed genomic DNA as above. DNA-protein complexes were bound with Foxp3 mAb, and enriched through immunoprecipitation, which a control sample is retained without immunoprecipitation (input). Both input and Foxp3-IP enriched samples underwent whole-genome amplification or ligation mediated-PCR to obtain adequate amounts of DNA for labeling. Foxp3-IP enriched samples were labeled with Cy5, and input samples with Cy3, and the arrays submitted to NimbleGen for analysis. Data were analyzed using Partek GS (Partek Inc., St Louis, MO) and the Broad Institutes Interactive Genome Viewer (Version 2.3.32) (Robinson et al., 2011). Raw data underwent Loess normalization, and Student t-testing with a false discovery rate of 0.1 (p-value cutoff 0.0029). For visualization, we generated IGV files using NimbleGen mapping information from the SignalMap GFF files.
2016-12-17 | GSE92520 | GEO
Project description:Although many protein-coding genes have been identified to be aberrantly expressed in hepatocellular carcinoma (HCC), the mechanisms that account for development and progression of HCC remain unclear. In recent years, long noncoding RNAs (lncRNAs) have been shown to have critical regulatory roles in mammalian cell biology. Many lncRNAs can result in aberrant expression of gene products that may contribute to cancer biology. In this study, we first identified non-overlapping signatures of a small number of lncRNAs that are aberrantly expressed in human HCC compared with paired peritumoral tissues. Then we used real-time PCR to validate five lncRNAs whose expression was altered in HCC compared with paired peritumoral tissues. Using loss-of-function and gain-of-function approaches, we found that an lncRNA (termed lncRNA-HEIH) plays a key role in cell cycle regulation. We further demonstrated that lncRNA-HEIH bound to enhancer of zeste homolog 2 (EZH2) and that this interaction was required for the repression of EZH2 target genes. Together, these results reveal insights into the molecular regulation mechanisms of HCC cell cycle regulation and lead us to propose that lncRNAs may serve as key regulatory hubs in cancer biology. A ten chip study using total RNA recovered from five separate HCC tissues and five corresponding paired non-tumor samples.
2011-09-04 | E-GEOD-27462 | biostudies-arrayexpress
Project description:This is a Random Forest algorithm-based machine learning model to predict lncRNAs from coding mRNAs in plant transcriptomic data. The model assigns 1 for coding sequences and 2 for long non-coding sequences. The prediction is performed using a combination of Open Reading Frame (ORF) based, Sequence-based and Codon-bias features. Users need to download the curated ONNX model and also need to convert the sequences into feature matrix as mentioned in PLIT paper (Deshpande et al. 2019) to make predictions on sequences from Zea Mays sequence data.
2023-05-22 | BIOMD0000001067 | BioModels