Dataset Information

Functional enrichment analysis based on long noncoding RNA associations.

ABSTRACT: BACKGROUND:Differential gene expression analysis using RNA-seq data is a popular approach for discovering specific regulation mechanisms under certain environmental settings. Both gene ontology (GO) and KEGG pathway enrichment analysis are major processes for investigating gene groups that participate in common biological responses or possess related functions. However, traditional approaches based on differentially expressed genes only detect a few significant GO terms and pathways, which are frequently insufficient to explain all-inclusive gene regulation mechanisms. METHODS:Transcriptomes of survivin (birc5) gene knock-down experimental and wild-type control zebrafish embryos were sequenced and assembled, and a differential expression (DE) gene list was obtained for traditional functional enrichment analysis. In addition to including DE genes with significant fold-change levels, we considered additional associated genes near or overlapped with differentially expressed long noncoding RNAs (DE lncRNAs), which may directly or indirectly activate or inhibit target genes and play important roles in regulation networks. Both the original DE gene list and the additional DE lncRNA-associated genes were combined to perform a comprehensive overrepresentation analysis. RESULTS:In this study, a total of 638 DE genes and 616 DE lncRNA-associated genes (lncGenes) were leveraged simultaneously in searching for significant GO terms and KEGG pathways. Compared to the traditional approach of only using a differential expression gene list, the proposed method of employing DE lncRNA-associated genes identified several additional important GO terms and KEGG pathways. In GO enrichment analysis, 60% more GO terms were obtained, and several neuron development functional terms were retrieved as complete annotations. We also observed that additional important pathways such as the FoxO and MAPK signaling pathways were retrieved, which were shown in previous reports to play important roles in apoptosis and neuron development functions regulated by the survivin gene. CONCLUSIONS:We demonstrated that incorporating genes near or overlapped with DE lncRNAs into the DE gene list outperformed the traditional enrichment analysis method for effective biological functional interpretations. These hidden interactions between lncRNAs and target genes could facilitate more comprehensive analyses.

SUBMITTER: Hung KS

PROVIDER: S-EPMC5998891 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Functional enrichment analysis based on long noncoding RNA associations.

Hung Kuo-Sheng KS Hsiao Chung-Chi CC Pai Tun-Wen TW Hu Chin-Hwa CH Tzou Wen-Shyong WS Wang Wen-Der WD Chen Yet-Ran YR

BMC systems biology 20180424 Suppl 4

<h4>Background</h4>Differential gene expression analysis using RNA-seq data is a popular approach for discovering specific regulation mechanisms under certain environmental settings. Both gene ontology (GO) and KEGG pathway enrichment analysis are major processes for investigating gene groups that participate in common biological responses or possess related functions. However, traditional approaches based on differentially expressed genes only detect a few significant GO terms and pathways, whi ...[more]

PMID: 29745842

Similar Datasets

Project description:Long noncoding RNAs (lncRNAs) serve important roles in the biology of autoimmune diseases and immune‑associated disorders. To identify lncRNAs specifically associated with psoriasis, the expression of lncRNAs from biopsies obtained from patients with psoriasis were compared with samples obtained from healthy volunteers using a microarray. Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) was performed to detect the expression of 10 identified dysregulated lncRNAs. Cis‑ and trans‑regulated target genes of lncRNAs were predicted. The results of microarray analysis indicated that 2,194 lncRNAs and 1,725 mRNAs were significantly dysregulated. Gene Ontology and pathway analyses among the dysregulated genes were performed. Co‑expression network analysis was also performed to study molecular interactions. Several identified pathways were associated with psoriasis. Among the 2,194 dysregulated lncRNAs, 1,549 of these had cis‑ or trans‑regulated predicted target genes. Among the 1,725 dysregulated mRNAs, 289 of the cis‑regulated target genes and 262 of the trans‑regulated target genes may be regulated by the differentially expressed lncRNAs; 10 differentially expressed lncRNAs were randomly selected and then validated. Of these lncRNAs, 7 exhibited the same expression profile as determined via microarray analysis, of which 3 lncRNAs were upregulated and 4 lncRNAs were downregulated. To the best of our knowledge, the present study is the first in which a microarray has been used to investigate the expression profile of lncRNAs associated with psoriasis. Additionally, the expression levels of the 10 aforementioned lncRNAs associated with psoriasis were validated in the present study for the first time using RT‑qPCR. The findings demonstrated that lncRNAs may contribute to the pathogenesis of psoriasis and suggested their potential diagnostic and therapeutic value. Furthermore, the findings of the present study suggest that the combined actions of several lncRNAs may contribute to the pathogenesis of psoriasis.

Project description:Intronic and intergenic long noncoding RNAs (lncRNAs) are emerging gene expression regulators. The molecular pathogenesis of renal cell carcinoma (RCC) is still poorly understood, and in particular, limited studies are available for intronic lncRNAs expressed in RCC. Microarray experiments were performed with two different custom-designed arrays enriched with probes for lncRNAs mapping to intronic genomic regions. Samples from 18 primary clear cell RCC tumors and 11 nontumor adjacent matched tissues were analyzed with 4k-probes microarrays. Oligoarrays with 44k-probes were used to interrogate 17 RCC samples (14 clear cell, 2 papillary, 1 chromophobe subtypes) split into four pools. Meta-analyses were performed by taking the genomic coordinates of the RCC-expressed lncRNAs, and cross-referencing them with microarray expression data from three additional human tissues (normal liver, prostate tumor and kidney nontumor samples), and with large-scale public data for epigenetic regulatory marks and for evolutionarily conserved sequences. A signature of 29 intronic lncRNAs differentially expressed between RCC and nontumor samples was obtained (false discovery rate (FDR) <5%). An additional signature of 26 intronic lncRNAs significantly correlated with the RCC five-year patient survival outcome was identified (FDR <5%, p-value M-bM-^IM-$0.01). We identified 4303 intronic antisense lncRNAs expressed in RCC, of which 25% were cis correlated (r >|0.6|) with the expression of the mRNA in the same locus across three human tissues. Gene Ontology (GO) analysis of those loci pointed to M-bM-^@M-^Xregulation of biological processesM-bM-^@M-^Y as the main enriched category. A module map analysis of all expressed protein-coding genes in RCC that had a significant (r M-bM-^IM-%|0.8|) trans correlation with the 20% most abundant lncRNAs identified 35 relevant (p <0.05) GO sets. In addition, we determined that 60% of these lncRNAs are evolutionarily conserved. At the genomic loci containing the intronic RCC-expressed lncRNAs, a strong association (p <0.001) was found between their transcription start sites and genomic marks such as CpG islands and histones methylation and acetylation. Intronic antisense lncRNAs are widely expressed in RCC tumors. Some of them are significantly altered in RCC in comparison with nontumor samples. The majority of these lncRNAs is evolutionarily conserved and possibly modulated by epigenetic modifications. Our data suggest that these RCC lncRNAs may contribute to the complex network of regulatory RNAs playing a role in renal cell malignant transformation. This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

Dataset Information

Functional enrichment analysis based on long noncoding RNA associations.

Publications

Functional enrichment analysis based on long noncoding RNA associations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets