Dataset Information

Identification of potential prognostic long non?coding RNA signatures based on a competing endogenous RNA network in lung adenocarcinoma.

ABSTRACT: A number of experimental and computational studies have demonstrated the key roles of long non?coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in the tumorigenesis of lung adenocarcinoma (LUAC). However, there remains a requirement for prognostic candidate biomarkers acting as ceRNAs for the prediction of overall survival in patients with LUAC. The main goal of the present study was to identify novel lncRNAs associated with LUAC overall survival and assess their prognostic values. The study analyzed coding RNA and ncRNA expression profiles of patients with LUAC by retrieving existing RNA?sequencing datasets from The Cancer Genome Atlas database, and 2,507 differentially expressed mRNAs, 1,633 lncRNAs and 113 miRNAs were screened from patients with LUAC compared with those of adjacent normal samples (P<0.01 and |logFC|>2). Of these LUAC?specific RNAs, 134 lncRNAs, 21 miRNAs and 34 mRNAs were used to build an lncRNA?mRNA?miRNA ceRNA network, among which 8 lncRNAs and 9 mRNAs were associated with overall survival in patients with LUAC by acting as ceRNAs. Next, an lncRNA?based prognostic signature was constructed by risk scoring approach based on the expression levels of 9 prognosis?associated lncRNAs using Cox's regression analysis. Moreover, the prognostic capacity of the 9?lncRNA signature was independent of known clinical prognostic factors. These results provide novel insight into the potential of lncRNA ceRNAs to be candidate biomarkers associated with LUAC overall survival.

SUBMITTER: Wang X

PROVIDER: S-EPMC6196588 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Identification of potential prognostic long non‑coding RNA signatures based on a competing endogenous RNA network in lung adenocarcinoma.

Wang Xiaojuan X Ding Yawen Y Da Bangming B Fei Yan Y Feng Gang G

Oncology reports 20180921 6

A number of experimental and computational studies have demonstrated the key roles of long non‑coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in the tumorigenesis of lung adenocarcinoma (LUAC). However, there remains a requirement for prognostic candidate biomarkers acting as ceRNAs for the prediction of overall survival in patients with LUAC. The main goal of the present study was to identify novel lncRNAs associated with LUAC overall survival and assess their prognostic val ...[more]

PMID: 30272355

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Project description:Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, the function and application of circRNAs in lung adenocarcinoma (LUAD) are still unknown. In this study, we constructed a circRNA-associated competitive endogenous RNA (ceRNA) network to investigate the regulatory mechanism of LUAD procession and further constructed a prognostic signature to predict overall survival for LUAD patients. Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network. Based on the TargetScan prediction tool and Pearson correlation coefficient, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs, and 49 DEmRNAs. GO and KEGG enrichment indicated that the ceRNA network might be involved in the regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate Cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using the LASSO method in the TCGA-LUAD training cohort. By applying the signature, patients could be categorized into the high-risk or low-risk subgroups with significant survival differences (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009). The prognostic performance was confirmed in an independent GEO cohort (GSE42127, HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004). Multivariate Cox regression analysis proved that the three-gene signature was an independent prognostic factor. Combining the three-gene signature with clinical characters, a nomogram was constructed. The primary and external verification C-indexes were 0.717 and 0.716, respectively. The calibration curves for the probability of 3- and 5-year OS showed significant agreement between nomogram predictions and actual observations. Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and further constructed a useful prognostic signature to guide personalized treatment of LUAD patients.

Project description:BackgroundThis study aimed to develop multi-RNA-based models using a competing endogenous RNA (ceRNA) regulatory network to provide survival risk prediction in head and neck squamous cell carcinoma (HNSCC).MethodsAll long non-coding RNA (lncRNA), microRNA (miRNA), and mRNA expression data and clinicopathological features related to HNSCC were derived from The Cancer Genome Atlas. Differentially expressed RNAs were calculated using R. Prognostic factors were identified using univariate Cox regression analysis. Functional analysis was performed using GO, KEGG pathways, and PPI network. Based on the results, we derived a risk signature and compared high- and low-risk subgroups using LASSO regression analysis. Survival analysis and the relationship between risk signature and clinicopathological features were performed using log-rank tests and Cox regression analysis. A ceRNA regulatory network was constructed, and prognostic lncRNAs and miRNA expression levels were validated in vitro and in vivo.ResultsA list of 207 lncRNAs, 18 miRNAs and 362 mRNAs related to overall survival was established. Five lncRNAs (HOTTIP, LINC00460, RMST, SFTA1P, and TM4SF19-AS1), one miRNA (hsa-miR-206), and one mRNA (STC2) were used to construct the ceRNA network. Three prognostic models contained 13 lncRNAs, eight miRNAs, and 17 mRNAs, which correlated with the patient status, disease-free survival (DFS), stage, grade, T stage, N stage, TP53 mutation status, angiolymphatic invasion, HPV status, and extracapsular spread. KEGG pathway analysis revealed significant enrichment of "Transcriptional misregulation in cancer" and "Neuroactive ligand-receptor interaction." In addition, HOTTIP, LINC00460, miR-206 and STC2 were validated in GTEx data, GEO microarrays and six HNSCC cell lines.ConclusionsOur findings clarify the interaction of ceRNA regulatory networks and crucial clinicopathological features. These results show that prognostic biomarkers can be identified by constructing multi-RNA-based prognostic models, which can be used for survival risk prediction in patients with HNSCC.

Project description:BackgroundNon-small cell lung cancer (NSCLC) is a major subtype of lung cancer with high malignancy and bad prognosis, consisted of lung adenocarcinomas (LUAD) and lung squamous cell carcinomas (LUSC) chiefly. Multiple studies have indicated that competing endogenous RNA (ceRNA) network centered long noncoding RNAs (lncRNAs) can regulate gene expression and the progression of various cancers. However, the research about lncRNAs-mediated ceRNA network in LUAD is still lacking.MethodsIn this study, we analyzed the RNA-seq database from The Cancer Genome Atlas (TCGA) and obtained dysregulated lncRNAs in NSCLC, then further identified survival associated lncRNAs through Kaplan-Meier analysis. Quantitative real time PCR (qRT-PCR) was performed to confirm their expression in LUAD tissues and cell lines. The ceRNA networks were constructed based on DIANA-TarBase and TargetScan databases and visualized with OmicShare tools. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to investigate the potential function of ceRNA networks.ResultsIn total, 1,437 and 1,699 lncRNAs were found to be up-regulated in LUAD and LUSC respectively with 895 lncRNAs overlapping (|log2FC| > 3, adjusted P value <0.01). Among which, 222 lncRNAs and 46 lncRNAs were associated with the overall survival (OS) of LUAD and LUSC, and 18 out of 222 up-regulated lncRNAs were found to have inverse correlation with LUAD patients' OS (|log2FC| > 3, adjusted P value < 0.02). We selected 3 lncRNAs (CASC8, LINC01842 and VPS9D1-AS1) out of these 18 lncRNAs and confirmed their overexpression in lung cancer tissues and cells. CeRNA networks were further constructed centered CASC8, LINC01842 and VPS9D1-AS1 with 3 miRNAs and 100 mRNAs included respectively.ConclusionThrough comprehensively analyses of TCGA, our study identified specific lncRNAs as candidate diagnostic and prognostic biomarkers for LUAD. The novel ceRNA network we created provided more insights into the regulatory mechanisms underlying LUAD.

Project description:Schizophrenia (SCZ) is a serious psychiatric condition with a 1% lifetime risk. SCZ is one of the top ten global causes of disabilities. Despite numerous attempts to understand the function of genetic factors in SCZ development, genetic components in SCZ pathophysiology remain unknown. The competing endogenous RNA (ceRNA) network has been demonstrated to be involved in the development of many kinds of diseases. The ceRNA hypothesis states that cross-talks between coding and non-coding RNAs, including long non-coding RNAs (lncRNAs), via miRNA complementary sequences known as miRNA response elements, creates a large regulatory network across the transcriptome. In the present study, we developed a lncRNA-related ceRNA network to elucidate molecular regulatory mechanisms involved in SCZ. Microarray datasets associated with brain regions (GSE53987) and lymphoblasts (LBs) derived from peripheral blood (sample set B from GSE73129) of SCZ patients and control subjects containing information about both mRNAs and lncRNAs were downloaded from the Gene Expression Omnibus database. The GSE53987 comprised 48 brain samples taken from SCZ patients (15 HPC: hippocampus, 15 BA46: Brodmann area 46, 18 STR: striatum) and 55 brain samples taken from control subjects (18 HPC, 19 BA46, 18 STR). The sample set B of GSE73129 comprised 30 LB samples (15 patients with SCZ and 15 controls). Differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified using the limma package of the R software. Using DIANA-LncBase, Human MicroRNA Disease Database (HMDD), and miRTarBase, the lncRNA- associated ceRNA network was generated. Pathway enrichment of DEmRNAs was performed using the Enrichr tool. We developed a protein-protein interaction network of DEmRNAs and identified the top five hub genes by the use of STRING and Cytoscape, respectively. Eventually, the hub genes, DElncRNAs, and predictive miRNAs were chosen to reconstruct the subceRNA networks. Our bioinformatics analysis showed that twelve key DEmRNAs, including BDNF, VEGFA, FGF2, FOS, CD44, SOX2, NRAS, SPARC, ZFP36, FGG, ELAVL1, and STARD13, participate in the ceRNA network in SCZ. We also identified DLX6-AS1, NEAT1, MINCR, LINC01094, DLGAP1-AS1, BABAM2-AS1, PAX8-AS1, ZFHX4-AS1, XIST, and MALAT1 as key DElncRNAs regulating the genes mentioned above. Furthermore, expression of 15 DEmRNAs (e.g., ADM and HLA-DRB1) and one DElncRNA (XIST) were changed in both the brain and LB, suggesting that they could be regarded as candidates for future biomarker studies. The study indicated that ceRNAs could be research candidates for investigating SCZ molecular pathways.

Project description:Gastric cancer (GC) is a common type of cancer, and identification of novel diagnostic biomarkers associated with this disease is important. The present study aimed to identify novel diagnostic biomarkers associated with the prognosis of GC, using an integrated bioinformatics approach. Differentially expressed long non-coding RNAs (lncRNAs) associated with GC were identified using Gene Expression Omnibus datasets (GSE58828, GSE72305 and GSE99416) and The Cancer Genome Atlas database. A competing endogenous RNA network that incorporated five lncRNAs [long intergenic non-protein coding RNA 501 (LINC00501), LINC00365, SOX21 antisense divergent transcript 1 (SOX21-AS1), GK intronic transcript 1 (GK-IT1) and DLEU7 antisense RNA 1 (DLEU7-AS1)], 29 microRNAs and 114 mRNAs was constructed. Gene Ontology and protein-protein interaction network analyses revealed that these lncRNAs may be involved in 'biological regulation', 'metabolic process', 'cell communication', 'developmental process', 'cell proliferation', 'reproduction' and the 'cell cycle'. The results of receiver operating characteristic curve analysis demonstrated that LINC00501 (AUC=0.819), LINC00365 (AUC=0.580), SOX21-AS1 (AUC=0.736), GK-IT1 (AUC=0.823) and DLEU7-AS1 (AUC=0.932) had the potential to become valuable diagnostic biomarkers for GC. Associations with clinicopathological characteristics demonstrated that LINC00501 expression was significantly associated with sex (P=0.015) and tumor grade (P=0.022). Furthermore, LINC00365 expression was significantly associated with lymph node metastasis (P=0.025). Gene set enrichment analysis revealed that LINC00501, LINC00365 and SOX21-AS1 were enriched in signaling pathways associated with GC. Reverse transcription-quantitative PCR analysis demonstrated that LINC00501 expression (P=0.043) was significantly upregulated in GC tissues, whereas the expression levels of LINC00365 (P=0.033) and SOX21-AS1 (P=0.037) were significantly downregulated in GC tissues. Taken together, the results of the present study suggest that LINC00501, LINC00365, SOX21-AS1, GK-IT1 and DLEU7-AS1 may be used as novel diagnostic biomarkers for GC, and may be functionally associated with GC development and progression.

Dataset Information

Identification of potential prognostic long non?coding RNA signatures based on a competing endogenous RNA network in lung adenocarcinoma.

Publications

Identification of potential prognostic long non‑coding RNA signatures based on a competing endogenous RNA network in lung adenocarcinoma.

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