Dataset Information

Comprehensive analysis of long non-coding RNA PVT1 gene interaction regulatory network in hepatocellular carcinoma using gene microarray and bioinformatics.

ABSTRACT: PVT1 has been reported to be involved in the tumorigenesis and development of different cancers. However, the role of PVT1 in hepatocellular carcinoma (HCC) remains unclear. In this study, we applied gene microarray analysis to detect differentially expressed genes (DEGs) between PVT1 RNAi groups and controls. We initially investigated and confirmed PVT1 expression in HCC using The Cancer Genome Atlas (TCGA). The potential genes and pathways associated with PVT1 were also analyzed. We also performed bioinformatics analyses (Gene Ontology (GO), pathway, Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses) to explore the underlying pathways and networks of these potential genes. We selected DLC1 for further analysis. Based on the TCGA database, PVT1 was markedly up-regulated in HCC, whereas DLC1 was down-regulated. Moreover, PVT1 expression negatively correlated with DLC1 in HCC, an observation that has been further validated in different cohorts with Oncomine. High expression of PVT1 was positively associated with gender, race, vascular invasion and pathological grade in HCC. Additionally, the ROC curve indicated that both PVT1 and DLC1 have high diagnostic value in HCC. We speculated that PVT1 might play a significant role in HCC development and progression via regulation of various pathways and genes, especially DLC1 and the Hippo signaling pathway. However, this mechanism should be confirmed by functional experiments.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC5622238 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Comprehensive analysis of long non-coding RNA PVT1 gene interaction regulatory network in hepatocellular carcinoma using gene microarray and bioinformatics.

Zhang Yu Y Dang Yi-Wu YW Wang Xiao X Yang Xia X Zhang Rui R Lv Zi-Li ZL Chen Gang G

American journal of translational research 20170915 9

PVT1 has been reported to be involved in the tumorigenesis and development of different cancers. However, the role of PVT1 in hepatocellular carcinoma (HCC) remains unclear. In this study, we applied gene microarray analysis to detect differentially expressed genes (DEGs) between PVT1 RNAi groups and controls. We initially investigated and confirmed PVT1 expression in HCC using The Cancer Genome Atlas (TCGA). The potential genes and pathways associated with PVT1 were also analyzed. We also perfo ...[more]

PMID: 28979669

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Project description:BackgroundLong noncoding RNAs (lncRNAs) are related to different biological processes in non-small cell lung cancer (NSCLC). However, the possible molecular mechanisms underlying the effects of the long noncoding RNA HOXA11-AS (HOXA11 antisense RNA) in NSCLC are unknown.MethodsHOXA11-AS was knocked down in the NSCLC A549 cell line and a high throughput microarray assay was applied to detect changes in the gene profiles of the A549 cells. Bioinformatics analyses (gene ontology (GO), pathway, Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses) were performed to investigate the potential pathways and networks of the differentially expressed genes. The molecular signatures database (MSigDB) was used to display the expression profiles of these differentially expressed genes. Furthermore, the relationships between the HOXA11-AS, de-regulated genes and clinical NSCLC parameters were verified by using NSCLC patient information from The Cancer Genome Atlas (TCGA) database. In addition, the relationship between HOXA11-AS expression and clinical diagnostic value was analyzed by receiver operating characteristic (ROC) curve.ResultsAmong the differentially expressed genes, 277 and 80 genes were upregulated and downregulated in NSCLC, respectively (fold change ≥2.0, P < 0.05 and false discovery rate (FDR) < 0.05). According to the degree of the fold change, six upregulated and three downregulated genes were selected for further investigation. Only four genes (RSPO3, ADAMTS8, DMBT1, and DOCK8) were reported to be related with the development or progression of NSCLC based on a PubMed search. Among all possible pathways, three pathways (the PI3K-Akt, TGF-beta and Hippo signaling pathways) were the most likely to be involved in NSCLC development and progression. Furthermore, we found that HOXA11-AS was highly expressed in both lung adenocarcinoma and squamous cell carcinoma based on TCGA database. The ROC curve showed that the area under curve (AUC) of HOXA11-AS was 0.727 (95% CI 0.663-0.790) for lung adenocarcinoma and 0.933 (95% CI 0.906-0.960) for squamous cell carcinoma patients. Additionally, the original data from TCGA verified that ADAMTS8, DMBT1 and DOCK8 were downregulated in both lung adenocarcinoma and squamous cell carcinoma, whereas RSPO3 expression was upregulated in lung adenocarcinoma and downregulated in lung squamous cell carcinoma. For the other five genes (STMN2, SPINK6, TUSC3, LOC100128054, and C8orf22), we found that STMN2, TUSC3 and C8orf22 were upregulated in squamous cell carcinoma and that STMN2 and USC3 were upregulated in lung adenocarcinoma. Furthermore, we compared the correlation between HOXA11-AS and de-regulated genes in NSCLC based on TCGA. The results showed that the HOXA11-AS expression was negatively correlated with DOCK8 in squamous cell carcinoma (r = -0.124, P = 0.048) and lung adenocarcinoma (r = -0.176, P = 0.005). In addition, RSPO3, ADAMTS8 and DOCK8 were related to overall survival and disease-free survival (all P < 0.05) of lung adenocarcinoma patients in TCGA.ConclusionsOur results showed that the gene profiles were significantly changed after HOXA11-AS knock-down in NSCLC cells. We speculated that HOXA11-AS may play an important role in NSCLC development and progression by regulating the expression of various pathways and genes, especially DOCK8 and TGF-beta pathway. However, the exact mechanism should be verified by functional experiments.

Project description:Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

Dataset Information

Comprehensive analysis of long non-coding RNA PVT1 gene interaction regulatory network in hepatocellular carcinoma using gene microarray and bioinformatics.

Publications

Comprehensive analysis of long non-coding RNA PVT1 gene interaction regulatory network in hepatocellular carcinoma using gene microarray and bioinformatics.

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