Comprehensive Analysis of Differentially Expressed LncRNAs associated with Lipid Metabolism in Patients with Colorectal Cance
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PROVIDER: PRJNA1047096 | ENA |
REPOSITORIES: ENA
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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.
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Project description:Gastric cancer (GC) is the fourth most common malignant tumor. The mechanism underlying GC occurrence and development remains unclear. Previous studies have indicated that long non-coding RNAs (lncRNAs) are significantly associated with gastric cancer, but a systematic understanding of the role of lncRNAs in gastric cancer is lacking. In recent years, with the development of next-generation sequencing technology, tens of thousands of lncRNAs have been discovered. However, a large number of unannotated lncRNAs remain unidentified in different tissues, including potential gastric cancer-related lncRNAs. In this study, RNA sequencing (RNA-seq) data from 16 samples of eight gastric cancer patients were obtained and analyzed. A total of 1,854 previously unannotated lncRNAs were identified by ab initio assembly, and 520 differentially expressed lncRNAs were validated in the TCGA expression dataset. Methylation and copy number variation (CNV) array data from the same sample were integrated in the analysis. Changes in DNA methylation levels and CNVs may be responsible for the differential expression of 91 lncRNAs. Differentially expressed lncRNAs were enriched in coexpressed clusters of genes related to functions such as cell signaling, cell cycle, immune response, metabolic processes, angiogenesis, and regulation of retinoic acid (RA) receptors. Finally, a differentially expressed lncRNA, AC004510.3, was identified as a potential biomarker for the prediction of the overall survival of gastric cancer patients.
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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
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2020-06-30 | GSE134594 | 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:Long noncoding RNAs (lncRNAs) is already evidently involved in a variety of biological functions and pathophysiological mechanisms underlying the diabetes. However, the role of lncRNAs in the type 1 diabetes (T1D) has not been explored clearly yet. The aim of this study was to determine the circulating lncRNA profile and confirmed the differentially expressed lncRNA between T1D patients and healthy control.
2019-12-31 | GSE130279 | GEO
Project description:Colorectal cancer (CRC) is the third most common type of cancer worldwide. Recent studies had revealed the important roles of long non-coding RNAs (lncRNAs) in a variety of human cancers, including CRC. However, the molecular mechanisms associated with CRC remain largely undetermined. In the current study, the GSE21510 dataset was analyzed to identify differentially expressed mRNAs and lncRNAs in CRC samples. The Database for Annotation, Visualization and Integrated Discovery was used to perform Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway bioinformatics analysis. Furthermore, protein-protein interaction networks were constructed to reveal interactions among differentially expressed proteins. Kaplan-Meier analysis was subsequently performed to determine the association between key lncRNA expression and the overall survival of patients with CRC. A total of 107 upregulated lncRNAs and 43 downregulated lncRNAs were identified in CRC. A lncRNA mediated co-expression network was also constructed in CRC. Bioinformatics analysis indicated that lncRNAs were associated with a series of biological processes, including 'xenobiotic glucuronidation', 'rRNA processing', 'sister chromatid cohesion', 'cell proliferation', 'mitotic nuclear division' and 'cell cycle regulation'. Furthermore, a higher expression of small nucleolar RNA host gene 17, tetratricopeptide repeat domain 2B-antisense RNA (AS) 1, erythrocyte membrane protein band 4.1 like 4A-AS2, deleted in lymphocytic leukemia 2, and a lower expression of muscle blind like splicing regulator 1-AS1 and LOC389332 were associated with shorter overall survival time in CRC samples. The present study provides useful information that can be used in the identification of novel biomarkers for CRC.
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