Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A RNA-Sequencing approach for the identification of novel long non-coding RNA biomarkers in colorectal cancer.

ABSTRACT: Long non-coding RNAs (lncRNAs) have been implicated in human pathology, however, their role in colorectal carcinogenesis have not been fully elucidated. In the current study, whole-transcriptome analysis was performed in 3 pairs of colorectal cancer (CRC) and matched normal mucosa (NM) by RNA sequencing (RNA-seq). Followed by confirmation using the Cancer Genome Atlas (TCGA) dataset, we identified 27 up-regulated and 22 down-regulated lncRNAs in CRC. Up-regulation of four lncRNAs, hereby named colorectal cancer associated lncRNA (CRCAL)-1 [AC021218.2], CRCAL-2 [LINC00858], CRCAL-3 [RP11-138J23.1] and CRCAL-4 [RP11-435O5.2], was further validated by real-time RT-PCR in 139 colorectal neoplasms and matched NM tissues. Knockdown of CRCAL-3 and CRCAL-4 in colon cancer cells reduced cell viability and colony formation ability, and induced cell cycle arrest. TCGA dataset supported the associations of CRCAL-3 and CRCAL-4 with cell cycle and revealed a co-expression network comprising dysregulated lncRNAs associated with protein-coding genes. In conclusion, RNA-seq identified numbers of novel lncRNAs dysregulated in CRC. In vitro experiments and GO term enrichment analysis indicated the functional relevance of CRCAL-3 and CRCAL-4 in association with cell cycle. Our data highlight the capability of RNA-seq to discover novel lncRNAs involved in human carcinogenesis, which may serve as alternative biomarkers and/or molecular treatment targets.

SUBMITTER: Yamada A

PROVIDER: S-EPMC5766599 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

RNA-Sequencing approach for the identification of novel long non-coding RNA biomarkers in colorectal cancer

Project description:Long non-coding RNA (lncRNA) have been implicated in human pathology, however, their roles in colorectal carcinogenesis has not been fully elucidated. In the current study, whole-transcriptome was analyzed in 3 pairs of colorectal cancer (CRC) and matched normal mucosa (NM) by RNA sequencing (RNA-seq). Followed by confirmation using the Cancer Genome Atlas (TCGA) dataset, we identified 27 up-regulated and 22 down-regulated lncRNAs in CRC. Up-regulation of four lncRNAs, hereby named colorectal cancer associated lncRNA (CRCAL)-1 [AC021218.2], CRCAL-2 [LINC00858], CRCAL-3 [RP11-138J23.1] and CRCAL-4 [RP11-435O5.2], was further validated by real-time RT-PCR in 139 colorectal neoplasms and matched NM tissues. Knockdown of CRCAL-3 and CRCAL-4 in colon cancer cells reduced cell viability and colony formation ability, and induced cell cycle arrest. TCGA dataset supported the associations of CRCAL-3 and CRCAL-4 with cell cycle and revealed a co-expression network comprising dysregulated lncRNAs associated with protein-coding genes. In conclusion, RNA-seq identified numbers of novel lncRNAs dysregulated in CRC. In vitro experiments and GO term enrichment analysis indicated the functional relevance of CRCAL-3 and CRCAL-4 in association with cell cycle. Our data highlight the capability of RNA-seq to discover novel lncRNAs involved in human carcinogenesis, which may serve as alternative biomarkers and/or molecular treatment targets.

2018-02-14 | GSE104178 | GEO

RNA-Sequencing approach for the identification of novel long non-coding RNA biomarkers in colorectal cancer

Project description:RNA-Sequencing approach for the identification of novel long non-coding RNA biomarkers in colorectal cancer

| PRJNA411984 | ENA

Identification of prognostic biomarkers in colorectal cancer using a long non-coding RNA-mediated competitive endogenous RNA network.

Project description:Colorectal cancer (CRC) is a highly malignant gastrointestinal tumor accompanied by poor prognosis. Long non-coding RNA (lncRNA) plays an important role in the progression and physiology of tumors as it competes with endogenous RNAs, including miRNA and mRNA. In the present study, a multi-step computational method was used to build a CRC-related functional lncRNA-mediated competitive endogenous RNA (ceRNA) network (LMCN). lncRNAs with more degrees and betweenness centrality (BC) were screened out as hub lncRNAs. Then functional enrichment analyses of lncRNAs were carried out from the Gene Ontology (GO) and Reactome pathway databases based on the 'guilt by association' principle. As a result, lncRNAs in the LMCN displayed specific topological characteristics in accordance with the regulatory correlation of coding mRNAs in CRC pathology. HCP5, EPB41L4A-AS1, SNHG12, and LINC00649 were screened out as hub lncRNAs which were more significantly related to the development and prognosis of CRC. The hub lncRNAs in CRC were obviously involved in functions of cell cycle arrest, vacuolar transport, histone modification, and in pathways of GPCR, signaling by Rho GTPases, axon guidance pathways, meaning that they might be potential biomarkers for diagnosis, evaluation and gene-targeted therapy of CRC. Thus, the LMCN construction method could accelerate lncRNA discovery and therapeutic development in CRC.

| S-EPMC6365949 | biostudies-literature

Identification of novel long non-coding RNAs deregulated in hepatocellular carcinoma using RNA-sequencing.

Project description:Functional characterization of long non-coding RNAs (lncRNAs) and their pathological relevance is still a challenging task. Abnormal expression of a few long non-coding RNAs have been found associated with hepatocellular carcinoma, with potential implications to both improve our understanding of molecular mechanism of liver carcinogenesis and to discover biomarkers for early diagnosis or therapy. However, the understanding of the global role of lncRNAs during HCC development is still in its infancy. In this study, we produced RNA-Seq data from 23 liver tissues (controls, cirrhotic and HCCs) and applied statistical and gene network analysis approaches to identify and characterize expressed lncRNAs. We detected 5,525 lncRNAs across different tissue types and identified 57 differentially expressed lncRNAs in HCC compared with adjacent non-tumour tissues using stringent criteria (FDR<0.05, Fold Change>2). Using weighted gene co-expression network analysis (WGCNA), we found that differentially expressed lncRNAs are co-expressed with genes involved in cell cycle regulation, TGF-β signalling and liver metabolism. Furthermore, we found that more than 20% of differentially expressed lncRNAs are associated to actively transcribed enhancers and that the co-expression patterns with their closest genes change dramatically during HCC development. Our study provides the most comprehensive compendium of lncRNAs expressed in HCC, as well as in control or cirrhotic livers. Our results identified both known oncogenic lncRNAs (such as H19 and CRNDE) and novel lncRNAs involved in cell cycle deregulation and liver metabolism deficits occurring during HCC development.

| S-EPMC5077982 | biostudies-literature

Identification of novel long non-coding RNA biomarkers for prognosis prediction of papillary thyroid cancer.

Project description:Papillary thyroid carcinoma (PTC) is the most frequent type of malignant thyroid tumor. Several lncRNA signatures have been established for prognosis prediction in some cancers. However, the prognostic value of lncRNAs has not been investigated in PTC yet. In this study, we performed genome-wide analysis of lncRNA expression profiles in a large cohort of PTC patients from The Cancer Genome Atlas and identified 111 differentially expressed lncRNAs between tumor and non-tumor samples and between recurrent and recurrence-free samples. From the 111 differentially expressed lncRNAs, four independent lncRNA biomarkers associated with prognosis were identified and were integrated into a four-lncRNA signature which classified the patients of training dataset into the high-risk group and low-risk group with significantly different overall survival (p=0.016, log-rank test). The prognostic value of the four-lncRNA signature was validated in the independent testing dataset. Multivariate analysis indicated that the four-lncRNA signature was an independent prognostic predictor. Moreover, identified four lncRNA biomarkers demonstrates good performance in predicting disease recurrence with AUC of 0.833 using leave one out cross-validation. Our study not only highlighted the potential role for lncRNAs to improve clinical prognosis prediction in patients with PTC and but also provided alternative biomarkers and therapeutic targets for PTC patients.

| S-EPMC5542255 | biostudies-literature

Identification of long non-coding RNA SCARNA9L as a novel molecular target for colorectal cancer.

Project description:The aim of the present study was to analyze the microarray data of human colorectal cancer (CRC) tissues and identify novel therapeutic targets for CRC. Microarray analysis from the GSE73360 and GSE84984 datasets was performed to identify novel long non-coding RNAs (lncRNAs) that were differentially expressed in human CRC tissues. Additionally, small interfering RNAs were used to deplete the expression of the indicated lncRNAs in cells. Colony-formation, wound-closure, and transwell assays were performed on CRC cells to assess their proliferation and migration capacities. Through microarray analysis, SCARNA9L, SLMO2-ATP5E and LOC100132062 were identified as differentially expressed lncRNAs in CRC tissues. The present study demonstrated that the ablation of SCARNA9L inhibited cell proliferation and arrested the cell cycle of SW480 and SW620 CRC cells. Additionally, depletion of SCARNA9L restrained the migration of CRC cells in vitro. Overall, the present study investigated the potential involvement of SCARNA9L in CRC and suggests SCARNA9L as a potential biomarker.

| S-EPMC7377086 | biostudies-literature

Identification and Validation of Novel Long Non-coding RNA Biomarkers for Early Diagnosis of Oral Squamous Cell Carcinoma.

Project description:Long non-coding RNAs (lncRNAs) are recently emerging as a novel promising biomarker for cancer diagnosis and prognosis. Despite these previous investigations, the expression pattern and diagnostic role of lncRNAs in oral squamous cell carcinoma (OSCC) remain unclear. In this study, we identified six novel lncRNA biomarkers (LINC01697, LINC02487, LOC105376575, AC005083.1, SLC8A1-AS1, and U62317.1) from a list of 29 differentially expressed lncRNAs using the least absolute shrinkage and selection operator (LASSO) method in the discovery dataset of 167 OSCC samples and 45 normal oral tissues. Using the logistic regression method, we constructed a six lncRNAs-based diagnostic risk model (6lncRNAScore) which was able to differentiate between OSCC samples and control samples with high performance with AUC of 0.995 and high diagnostic specificity of 88.9% and sensitivity of 98.2% in the discovery dataset. The diagnostic performance of the 6lncRNAScore was further validated in another two independent OSCC dataset with AUC of 0.968 and 1.0. Functional enrichment analysis for lncRNA biomarkers-related mRNAs suggested that lncRNAs biomarkers tended to be involved in the lipid metabolic process. Together, our study highlighted the importance of lncRNAs in OSCC and demonstrated the utility of lncRNA expression as a promising biomarker for early diagnosis of OSCC.

| S-EPMC7174591 | biostudies-literature

Revealing potential long non-coding RNA biomarkers in lung adenocarcinoma using long non-coding RNA-mediated competitive endogenous RNA network.

Project description:In our study, we aimed to reveal potential long non-coding RNAs (lncRNA) biomarkers in lung adenocarcinoma (LAD) using lncRNA-mediated competing endogenous RNAs (ceRNAs) network (LMCN). Competing lncRNA-mRNA interactions were identified using the hypergeometric test. Co-expression analysis for the competing lncRNA-mRNA interactions was implemented, and relying on the weight value >0.8, a highly competitive LMCN was further constructed. Degree distribution, betweenness and closeness for LMCN were carried out to analyze the network structure. Functional analyses of mRNAs in LMCN were carried out to further explore the biological functions of lncRNAs. Biclique algorithm was utilized to extract competing modules from the LMCN. Finally, we verified our findings in an independent sample set using qRT-PCR. Based on degrees >60, we identified 4 hubs, including DLEU2, SNHG12, HCP5, and LINC00472. Furthermore, 2 competing modules were identified, and LINC00472 in module 1 functioned as a hub in both LMCN and module. Functional implications of lncRNAs demonstrated that lncRNAs were related to histone modification, negative regulation of cell cycle, neuroactive ligand-receptor interaction, and regulation of actin cytoskeleton. qRT-PCR results demonstrated that lncRNAs LINC00472, and HCP5 were down-regulated in LAD tissues, while the expression level of SNHG12 was up-regulated in LAD tissues. Our study sheds novel light on the roles of lncRNA-related ceRNA network in LAD and facilitates the detection of potential lncRNA biomarkers for LAD diagnosis and treatment. Remarkably, in our study, LINC00472, HCP5, and SNHG12 might be potential biomarkers for LAD management.

| S-EPMC5572850 | biostudies-literature

iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data.

Project description:BackgroundLong intergenic non-coding RNAs (lincRNAs) are emerging as a novel class of non-coding RNAs and potent gene regulators. High-throughput RNA-sequencing combined with de novo assembly promises quantity discovery of novel transcripts. However, the identification of lincRNAs from thousands of assembled transcripts is still challenging due to the difficulties of separating them from protein coding transcripts (PCTs).ResultsWe have implemented iSeeRNA, a support vector machine (SVM)-based classifier for the identification of lincRNAs. iSeeRNA shows better performance compared to other software. A public available webserver for iSeeRNA is also provided for small size dataset.ConclusionsiSeeRNA demonstrates high prediction accuracy and runs several magnitudes faster than other similar programs. It can be integrated into the transcriptome data analysis pipelines or run as a web server, thus offering a valuable tool for lincRNA study.

| S-EPMC3582448 | biostudies-literature

Identification of long non-coding RNA-related and -coexpressed mRNA biomarkers for hepatocellular carcinoma.

Project description:BACKGROUND:While changes in mRNA expression during tumorigenesis have been used widely as molecular biomarkers for the diagnosis of a number of cancers, the approach has limitations. For example, traditional methods do not consider the regulatory and positional relationship between mRNA and lncRNA. The latter has been largely shown to possess tumor suppressive or oncogenic properties. The combined analysis of mRNA and lncRNA is likely to facilitate the identification of biomarkers with higher confidence. RESULTS:Therefore, we have developed an lncRNA-related method to identify traditional mRNA biomarkers. First we identified mRNAs that are differentially expressed in Hepatocellular Carcinoma (HCC) by comparing cancer and matched adjacent non-tumorous liver tissues. Then, we performed mRNA-lncRNA relationship and coexpression analysis and obtained 41 lncRNA-related and -coexpressed mRNA biomarkers. Next, we performed network analysis, gene ontology analysis and pathway analysis to unravel the functional roles and molecular mechanisms of these lncRNA-related and -coexpressed mRNA biomarkers. Finally, we validated the prediction and performance of the 41 lncRNA-related and -coexpressed mRNA biomarkers using Support Vector Machine model with five-fold cross-validation in an independent HCC dataset from RNA-seq. CONCLUSIONS:Our results suggested that mRNAs expression profiles coexpressed with positionally related lncRNAs can provide important insights into early diagnosis and specific targeted gene therapy of HCC.

| S-EPMC6357343 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data