Dataset Information

Differentially RNA expressions of brain metastases from lung adenocarcinoma and paired paracancerous tissue

ABSTRACT: 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 three cases of BMs from LADC and the paired paracancerous tissue samples, it was found that there were a large number of differentially expressed lncRNAs and mRNAs in BMs tissues and paracancerous tissues.

ORGANISM(S): Homo sapiens

PROVIDER: GSE146460 | GEO | 2020/03/06

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

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

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

Dataset Information

Differentially RNA expressions of brain metastases from lung adenocarcinoma and paired paracancerous tissue

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets