Identification of differentially expressed lncRNAs and coding mRNAs in SNAI1/2 overexpression cell line
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA678692 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13067436_1.fastq.gz | Fastqsanger.gz | |||
| SRR13067436_2.fastq.gz | Fastqsanger.gz | |||
| SRR13067437_1.fastq.gz | Fastqsanger.gz | |||
| SRR13067437_2.fastq.gz | Fastqsanger.gz | |||
| SRR13067438_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 6 |
Similar Datasets
Project description:The oncogene SNAI2 is known to be a master transcription factor that regulates a broad range of cellular processes, including epithelial-mesenchymal transition (EMT). As a transcription factor, there is no doubt that SNAI1/2 could directly regulate the expression of thousands of genes, including lncRNAs. In this study, we took SNAI1/2 as a breakthrough point to screen and identify lncRNAs regulated by SNAI2 in GC. Our data showed that overexpression of SNAI2 caused a 1.5-fold change in the expression of about 654 lncRNAs (|Log2FC| >0.5), and a 2-fold change in the expression of about 123 lncRNAs (|Log2FC| >1).
2021-10-01 | GSE161551 | GEO
Project description:Ossification of the ligamentum flava (OLF) is a common spinal disorder among the elderly that causes myelopathy and radiculopathy. Although studies have identified several genes that correlated with OLF, the underlying regulation network is far from clear. To identify transcriptional regulators for OLF, we compared the lncRNAs and mRNAs expression of the ligamentum flava tissues from OLF patients and healthy volunteers through microarray analysis, which revealed a panel of lncRNAs and mRNAs that were specifically regulated in ligament tissues of human undergoing ossification. To identify transcriptional regulators for OLF, we compared the lncRNAs and mRNAs expression of the ligamentum flava tissues from OLF patients and healthy volunteers through microarray analysis.
2017-10-27 | GSE106253 | GEO
Project description:Identification of differentially expressed lncRNAs and mRNAs in OLF
| PRJNA415999 | ENA
Project description:Long non-coding RNAs (lncRNAs) are RNA molecules with a transcript length of more than 200 nucleotides, which do not encode proteins (non-coding), yet they participate in the regulation of gene expression through multiple mechanisms in the form of RNA. A total of 51 lncRNAs and 21 mRNAs whose expression changed significantly, were identified. Five deregulated lncRNAs and five mRNAs were confirmed by quantitative real-time polymerase chain reaction (PCR) in 10 paired samples. Gene Ontology (GO) and KEGG pathway enrichment analyses were performed to explore the principal functions of the deregulated genes. Furthermore, correlated expression networks of coding-noncoding co-expression (CNC) was constructed by using bioinformatics methods. We predicted the potential targets of lncRNAs by using bioinformatics methods. In summary, novel lnRNAs were identified as potential biomarkers and therapeutic targets for POCD treatment.
2017-02-28 | GSE95426 | GEO
Project description:Cancer associated cachexia (CAC) causes white adipose tissue (WAT) lose by inhibiting adipogenesis, and promoting lipolysis, fat oxidation and browning. To uncover the specific lncRNAs and mRNAs involved in these processes, we used RNA microarray to identify the transcriptomes and found numerous lncRNAs and mRNAs differentially expressed in the fat tissue between CAC and normal mice.
2018-08-16 | GSE118611 | GEO
Project description:LncRNA expression profiling for liver tissues of mice fed for NFD, LSF and HSF groups Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.
2022-11-24 | E-MTAB-9167 | biostudies-arrayexpress
Project description:Background: Cryptosporidium baileyi is the most common Cryptosporidium species in birds. However, effective prevention measures and treatment for C. baileyi infection were less understood. Long non-coding RNA (lncRNA) and circular RNA (circRNA) were discovered to play an important role in regulating occurrence and progression of many diseases and identified as effective biomarkers for diagnosis and prognosis of several diseases. In the present study, the expression profiles of mRNA, lncRNA and circRNA associated with C. baileyi infection were investigated for the first time. Results: The tracheal tissues of experimental (C. baileyi infection) and control chickens were collected for deep RNA sequencing, and 545,479,934 clean reads were obtained. Of them, 1376 novel lncRNAs were identified, including 1161 long intergenic non-coding RNAs (lincRNAs) and 215 anti-sense lncRNAs. 124 lncRNAs were found to be significantly differentially expressed between experimental and control groups. Additionally, 14,698 mRNAs and 9085 circRNAs were identified, and significantly different expressions were observed for 1317 mRNAs and 104 circRNAs between two groups. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of their targets and background genes suggested that these dysregulated genes would be involved in the interaction between host and C. baileyi. Conclusions: The present study firstly provides the expression profiles of mRNAs, lncRNAs and circRNAs during C. baileyi infection, and describes a novel perspective on the potential roles of lncRNAs and circRNAs underlying the pathogenesis mechanism of Cryptosporidium infection.
2018-04-28 | GSE108147 | GEO
Project description:Lung cancer is a heterogeneous disease with high mortality rate globally. Lung adenocarcinoma (ADC) is the most common histological subtype of lung cancer. Multiple studies indicate that lncRNAs emerge as a novel class of critical regulators of cancer. However, the expression of lncRNAs has not been comprehensively surveyed in ADC at a genome-wide scale. Herein we examined landscapes of the lncRNAs and their potential target genes in an integrative fashion in ADC. The differences expression level of lncRNAs and mRNA were determined by microarray. qRT-PCR was performed to validate the lncRNAs expression level in a cohort of 42 tumor tissues and adjacent normal lung tissues. R and Bioconductor were used for data analysis. TF-lncRNAs-gene networks were generated by Cytoscape software. A total of 3045 lncRNAs were differentially expressed between tumor and normal tissues (1048 up and 1997 down). The qRT-PCR of 9 lncRNAs results showed that the expression patterns of these lncRNAs were consistent with the microarray data. Meanwhile, we find NONHSAT077036 expression was associated with N classification and clinical stage. Further, we analyzed the potential co-regulatory relationship between the lncRNAs and target genes using the ‘cis’ and ‘trans’ model. In the 25 related transcription factors (TFs), our analysis in TCGA database found that patients with lower expression of POU2F2 and higher expression of TRIM28 had a shorter overall survival time, suggesting that POU2F2 and TRIM28 could be biomarkers for poor prognosis of ADC. Hence, “POU2F2-lncRNAs” “TRIM28-lncRNAs” two element networks and “POU2F2-lncRNAs-mRNAs” “TRIM28-lncRNAs-mRNAs” there element relationship tables were generated.
2017-02-01 | GSE85716 | GEO
Project description:Chemoresistance is a major cause of poor prognosis of breast cancer.More and more mRNAs and lncRNAs are reported to upregulate chemoresistance in breast cancer.To explore the how mRNAs and lncRNAs involved in chemoresistance of breast cancer,we sceened upregulated mRNAs and lncRNA from parental MCF-7 , chemoresistant MCF-7 cells as well as 4 breast cancer tissue sensitive to chemotherapy and 4 resistant to chemotherapy . Total RNA was extracted using Trizol reagent. Agilent Human lncRNA Microarray V6 (4*180K) was used to analyze the global profiling of human lncRNAs and protein-coding transcripts in these samples. The microarray contains 83,835 lncRNAs and 27,233 coding genes.
2020-02-25 | E-MTAB-8787 | biostudies-arrayexpress
Project description:Background: Luminal B cancers show much worse outcomes compared to luminal A. This present study aims to screen key lncRNAs and mRNAs correlated with luminal-B breast cancer. Methods: Luminal-B breast cancer tissue samples and adjacent tissue samples were obtained from 4 patients with luminal-B breast cancer. To obtain differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) between luminal-B breast cancer tumor tissues and adjacent tissues, RNA-sequencing and bioinformatics analysis were performed. Functional annotation of DEmRNAs and protein-protein interaction networks (PPI) construction were performed. DEmRNAs transcribed within a 100kb window up- or down-stream of DElncRNAs were searched, which were defined as cis nearby-targeted DEmRNAs of DElncRNAs. DElncRNA-DEmRNA co-expression networks were performed. Results: A total of 1178 DEmRNAs and 273 DElncRNAs between luminal-B breast cancer tumor tissues and adjacent tissues were obtained. Hematopoietic cell lineage, Cytokine-cytokine receptor interaction, Cell adhesion molecules (CAMs) and Primary immunodeficiency were significantly enriched KEGG pathways in luminal-B breast cancer. FN1, EGFR, JAK3, TUBB3 and PTPRC were five hub proteins of the PPI networks. A total of 99 DElncRNAs-nearby-targeted DEmRNA pairs and 1878 DElncRNA-DEmRNA co-expression pairs were obtained. Conclusion: This study determined key genes and lncRNAs involved in luminal-B breast cancer, which expected to present a new avenue for the diagnosis and treatment of luminal-B breast cancer.
2020-04-14 | GSE139274 | GEO