Expression profiles and potential functions of long non-coding RNAs and mRNAs in autoimmune pulmonary alveolar proteinosis patients
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE153957 | GEO | 2021/04/28
REPOSITORIES: GEO
Similar Datasets
Project description:In addition to the well-known short noncoding RNAs as miRNAs, increasing evidence suggests that long noncoding RNAs (lncRNAs) act as key regulators in a wide aspect of biologic processes. Dysregulated expression of lncRNAs has been demonstrated being implicated in a variety of human diseases. However, there is relative paucity of information regarding the role of lncRNAs in intervertebral disc degeneration (IDD) hitherto. We have addressed the expression profiles of miRNAs in IDD previously. To determine whether lncRNAs is differentially expressed in IDD, we employed a lncRNA-mRNA microarray analysis of human nucleus pulposus (five degenerative as IDD and five normal specimens as control). With abundant probes accounting for 33,045 lncRNAs and 30,215 coding transcripts in our microarray, microarray data profiling indicated that 18995 lncRNAs and 14635 mRNAs were detected in all samples with 2309 lncRNAs and 3017 mRNAs differentially expressed in degenerated samples. Amongst them, 164 lncRNAs (97 up and 67 down) and 265 mRNAs were highly differentially expressed with an absolute fold change greater than ten. The upregulated mRNAs included the extracellular matrix components as COL1A2, LUM, FN1ACAN, DCN and ITFG2. 1100 lncRNAs and 1379 mRNAs were altered in the same direction in at least 4 of the 5 degenerative samples with fold change greater than 2 respectively. Three lncRNAs were chosen for the real-time quantitative PCR (qRT-PCR) analysis. qRT-PCR results showed a high consistency with the microarray data. In this study, to explore the potential involvement of lncRNAs in IDD, we conducted lncRNA and mRNA profiling in 5 human control nucleus pulposus tissue and 5 degenerative nucleus pulposus tissue by microarray.Biological replicates: 5 control, 5 degenerated, independently harvested.
2014-11-17 | E-GEOD-56081 | biostudies-arrayexpress
Project description:Recent studies show that long non-coding RNAs (lncRNAs) play crucial roles in human cancers. However, functional lncRNAs and their downstream mechanisms are largely unknown in the molecular pathogenesis of intrahepatic cholangiocarcinoma (ICC) and its progression. In the present study, we performed transcriptomic profiling of five ICC and paired adjacent noncancerous tissues (N) using lncRNA and mRNA microarrays to identify relevant biomarkers in ICC. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the microarray results. We sought correlations between the expression levels of lncRNAs and those of target genes. Clinicopathological characteristics and overall survival were compared using the t-test and the Kaplan–Meier method, respectively. A total of 3,054 and 2,111 lncRNAs were significantly up- and down-regulated(fold change?2, p?0.05) in ICC tissues compared to the adjacent NT samples. Bioinformatic analysis indicated that most such genes were related to carcinogenesis, hepatic system disease, and signal transduction. Positive correlations were evident between four pairs of lncRNAs and target mRNAs (RNA43085 and SULF1, RNA47504 and KDM8, RNA58630 and PCSK6, and RNA40057 and CYP2D6). In addition, some lncRNAs and mRNAs were significantly associated with clinicopathological characteristics. The cumulative overall survival rate was significantly associated with low-level expression of CYP2D6 (p=0.005) and PCSK6 (p=0.038). And patients with high expression levels of CYP2D6 and RNA40057 have significant better prognosis (p=0.014). Our results suggested that lncRNA expression profile in ICC tissues is profoundly different from that in NT samples. The lncRNA signature could be used as a biomarker for the prognosis of patients with ICC. Furthermore, the combination of lncRNA and mRNA can reliably predict the survival. The lncRNA expression profiles of cancer and adjacent normal tissues form 5 ICC patients were studied by microarray and an combination of lncRNA and mRNA could be used as a biomarker for the prognosis of patients with ICC
2014-09-30 | E-GEOD-61850 | biostudies-arrayexpress
Project description:To determine the LncRNA and mRNA expression profile in periparasitic liver tissues and matched distal-liver tissues, we used LncRNA microarray analysis from Arraystar to examine the expression of LncRNAs and mRNAs in periparasitic liver tissues and matched distal-liver tissues.
2018-12-27 | GSE124362 | GEO
Project description:This study aims to find the specific lncRNA molecule relating BMs from LADC in situ tumor to BM tumor tissue using gene chip technology and, combined with biological information database, through the competitive endogenous RNA (ceRNA) mechanism for microRNA (miRNA) related to specific lncRNA, to verify the possible target genes and therefore to explore the role and mechanism of specific lncRNA in BMs from LADC. By detecting the gene expression profiles of lncRNA and mRNA in cancer tissues of five cases of LADC and the paired paracancerous tissue samples, it was found that there were a large number of differentially expressed lncRNAs and mRNAs in LADC tissues and paracancerous tissues. By detecting the gene expression profiles of lncRNA and mRNA in three cases of BMs from LADC and matched paracancerous tissues, it was found that there were significant differences in the expression of lncRNAs and mRNAs between BM tumors of LADC and paracancerous tissues. These findings will provide a scientific basis for the early diagnosis and targeted therapy of BMs from LADC for application in further studies. Differential expression of lncRNAs and mRNAs in cancer tissues of five cases of LADC and the paired paracancerous tissue samples, it was found that there were a large number of differentially expressed lncRNAs and mRNAs in LADC tissues and paracancerous tissues.
2020-03-06 | GSE146459 | GEO
Project description:A few reports have implicated specific lncRNAs in cardiac development or failure, but precise details of lncRNAs expressed in hearts and how their expression may be altered during embryonic heart development or by adult heart disease is unknown. By comparing lncRNA profiles of normal embryonic (~E14), normal adult, and hypertrophied adult hearts we defined a distinct fetal lncRNA abundance signature that includes 157 lncRNAs differentially expressed compared to adults (fold-change ≥ 50%, FDR=0.02), and which was only poorly recapitulated in hypertrophied hearts (17 differentially expressed lncRNAs; 13 of these observed in embryonic hearts). Analysis of protein-coding mRNAs from the same samples identified 22 concordantly and 11 reciprocally regulated mRNAs within 10 kb of dynamically expressed lncRNAs, reciprocal relationships of lncRNA and mRNA levels was validated for the Mccc1 and Relb genes using in vitro lncRNA knockdown in C2C12 cells. Network analysis suggested a central role for lncRNAs in modulating NFkappaB- and CREB1-regulated genes during embryonic heart growth and identified multiple mRNAs within these pathways that are also regulated, but independently of lncRNAs. Cardiac polyadenylated RNA (mRNA and lncRNA) profiles were generated from C57BL/6J mouse hearts were generated on Illumina HiSeq 2000 instruments. 7 independent E13.5 hearts, 12 adult hearts (6 at 6 weeks of age, 6 at 16 weeks of age), 4 sham-operated hearts at 12 weeks of age, and 4 hearts after 4 weeks of pressure overload (TAC) at 12 weeks of age.
2014-06-14 | E-GEOD-58455 | biostudies-arrayexpress
Project description:Long noncoding RNAs (lncRNAs) are important regulators of cell fate, and their mis-expression has been implicated in many diseases. While distinct polymerases generate messenger vs. noncoding ribosomal or tRNAs3, little is known about distinct mechanisms controlling lncRNA expression. Here we show that transcription of lncRNAs is quantitatively different from that of messenger RNAs (mRNAs)--as revealed by deficiency of Dicer (Dcr), a key ribonuclease that generates microRNAs (miRNAs). Loss of Dcr in mouse embryonic stem cells (mESCs) led surprisingly to decreased level of the majority of lncRNAs. The canonical Dgcr8-Dcr-miRNA pathway is required for robust lncRNA expression, at the level of transcriptional initiation and elongation of lncRNA genes rather than at the level of their stability. cMyc, an oncogenic transcription factor, whose expression is indirectly regulated by Dcr-miRNA in mESCs, is partly responsible for lncRNA transcription. Loss of cMyc led to a more dramatic decrease of lncRNAs than mRNAs, and cMyc overexpression rescues lncRNA expression in Dcr KO cells. A quantitative metric of “mRNA-lncRNA decoupling” revealed that Dcr and cMyc differentially regulate lncRNAs vs. mRNAs in diverse cell types and in vivo, as evidenced by hundreds of microarray experiments. Thus, Dcr and cMyc may allow numerous lncRNAs to be activated or deactivated as a class, implicating lncRNAs to potential regulatory roles in development and disease states where Dcr and cMyc have been associated with. RNA was sequenced from WT and Dcr KO mESCs and the expression of lncRNAs and mRNAs are compared between WT and Dcr KO mESCs.
2014-06-15 | E-GEOD-55338 | biostudies-arrayexpress
Project description:Background: Long non-coding RNAs (lncRNAs) are increasingly implicated as gene regulators and may ultimately be more numerous than protein-coding genes in the human genome. Despite large numbers of reported lncRNAs, reference annotations are likely incomplete due to their lower and tighter tissue-specific expression compared to mRNAs. An unexplored factor potentially confounding lncRNA identification is inter-individual expression variability. Here, we characterize lncRNA natural expression variability in human primary granulocytes. Results: We annotate granulocyte lncRNAs and mRNAs in RNA-seq data from ten healthy individuals, identifying multiple lncRNAs absent from reference annotations, and use this to investigate three known features (higher tissue-specificity, lower expression, and reduced splicing efficiency) of lncRNAs relative to mRNAs. Expression variability was examined in seven individuals sampled three times at one or more than one month intervals. We show that lncRNAs display significantly more inter-individual expression variability compared to mRNAs. We confirm this finding in 2 independent human datasets by analyzing multiple tissues from the GTEx project and lymphoblastoid cell lines from the GEUVADIS project. Using the latter dataset we also show that including more human donors into the transcriptome annotation pipeline allows identification of an increasing number of lncRNAs, but minimally affects mRNA gene number. Conclusions: A comprehensive annotation of lncRNAs is known to require an approach that is sensitive to low and tight tissue-specific expression. Here we show that increased inter-individual expression variability is an additional general lncRNA feature to consider when creating a comprehensive annotation of human lncRNAs or proposing their use as prognostic or disease markers. We used PolyA+ RNA-seq data from human primary granulocytes of 10 healthy individuals to de novo annotate lncRNAs and mRNAs in this cell type and ribosomal depleted (total) RNA-seq data from seven of these individuals sampled three times to analyze lncRNA amd mRNA expression variability
2016-01-05 | E-GEOD-70390 | biostudies-arrayexpress
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:LncRNA and mRNA microarrays were performed to identify differentially expressed lncRNAs and mRNAs in fibroblast-like synoviocytes from rheumatoid arthritis patients compared with fibroblast-like synoviocytes from trauma patients. Fibroblast-like synoviocytes were isolated from synovial tissues. LncRNA and mRNA microarrays were performed using fibroblast-like synoviocytes at passage 3.
2016-06-10 | E-GEOD-83147 | biostudies-arrayexpress
Project description:LncRNA expression profiling for liver tissues of mice fed for a normal diet (NFD, 3mice) and a high-fat diet (HFD, 3mice) 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.
2022-11-23 | E-MTAB-8730 | biostudies-arrayexpress