Dataset Information

Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma.

ABSTRACT: Recent technological advancements have permitted high-throughput measurement of the human genome, epigenome, metabolome, transcriptome, and proteome at the population level. We hypothesized that subsets of genes identified from omic studies might have closely related biological functions and thus might interact directly at the network level. Therefore, we conducted an integrative analysis of multi-omic datasets of non-small cell lung cancer (NSCLC) to search for association patterns beyond the genome and transcriptome. A large, complex, and robust gene network containing well-known lung cancer-related genes, including EGFR and TERT, was identified from combined gene lists for lung adenocarcinoma. Members of the hypoxia-inducible factor (HIF) gene family were at the center of this network. Subsequent sequencing of network hub genes within a subset of samples from the Transdisciplinary Research in Cancer of the Lung-International Lung Cancer Consortium (TRICL-ILCCO) consortium revealed a SNP (rs12614710) in EPAS1 associated with NSCLC that reached genome-wide significance (OR?=?1.50; 95% CI: 1.31-1.72; p?=?7.75?×?10-9). Using imputed data, we found that this SNP remained significant in the entire TRICL-ILCCO consortium (p?=?.03). Additional functional studies are warranted to better understand interrelationships among genetic polymorphisms, DNA methylation status, and EPAS1 expression.

SUBMITTER: Wang Z

PROVIDER: S-EPMC6021270 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma.

Wang Zhaoxi Z Wei Yongyue Y Zhang Ruyang R Su Li L Gogarten Stephanie M SM Liu Geoffrey G Brennan Paul P Field John K JK McKay James D JD Lissowska Jolanta J Swiatkowska Beata B Janout Vladimir V Bolca Ciprian C Kontic Milica M Scelo Ghislaine G Zaridze David D Laurie Cathy C CC Doheny Kimberly F KF Pugh Elizabeth K EK Marosy Beth A BA Hetrick Kurt N KN Xiao Xiangjun X Pikielny Claudio C Hung Rayjean J RJ Amos Christopher I CI Lin Xihong X Christiani David C DC

EBioMedicine 20180531

Recent technological advancements have permitted high-throughput measurement of the human genome, epigenome, metabolome, transcriptome, and proteome at the population level. We hypothesized that subsets of genes identified from omic studies might have closely related biological functions and thus might interact directly at the network level. Therefore, we conducted an integrative analysis of multi-omic datasets of non-small cell lung cancer (NSCLC) to search for association patterns beyond the g ...[more]

PMID: 29859855

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Project description:ObjectivesLung adenocarcinoma (LUAD) is recognized as one of the most prevalent and deadliest malignancies around the globe. The molecular mechanisms behind LUAD have not been fully elucidated. This study was launched to explore LUAD-associated hub genes and their enriched pathways using bioinformatics methods.MethodsInformation on GSE10072 was retrieved from the Gene Expression Omnibus (GEO) database and analyzed via the Limma package-based GEO2R tool to obtain the top 100 differentially expressed genes (DEGs) in LUAD. The protein-protein interaction (PPI) network of the DEGs was drawn using the STRING website and was shifted into Cytoscape to screen the top 6 hub genes via the CytoHubba application. Furthermore, the expression analysis and validation of hub genes in LUAD samples and cell lines were done using UALCAN, OncoDB, and GENT2 databases. Moreover, OncoDB was also used for analyzing hub gene DNA methylation levels. In addition, cBioPortal, GSEA tool, Kaplan-Meier (KM) plotter, Enrichr, CancerSEA, and DGIdb were performed to explore some other important aspects of hub genes in LUAD.ResultsWe identified Interleukin 6 (IL6), Collagen, type I, alpha 1 (COL1A1), TIMP metallopeptidase inhibitor 1 (TIMP1), CD34 molecule (CD34), Decorin (DCN), and Secreted Phosphoprotein 1 (SPP1) genes as the hub genes in LUAD, out of which IL6, CD34, and DCN were significantly down-regulated while COL1A1, TIMP1, and SPP1 were significantly up-regulated in LUAD cell lines and samples of diverse clinical variables. In this study, we also documented some important correlations between hub genes and other parameters such as DNA methylation, genetic alterations, Overall Survival (OS), and 14 important states at the single cell level. Lastly, we also identified hub genes associated with the ceRNA network and 11 important chemotherapeutic drugs.ConclusionWe identified 6 hub genes involved in the development and progression of LUAD. These hub genes can also be helpful in the accurate detection of LUAD and provide novel ideas for treatment.

Project description:Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.

Project description:Purpose: Lung adenocarcinoma is one of the most common malignancies. Though some historic breakthroughs have been made in lung adenocarcinoma, its molecular mechanisms of development remain elusive. The aim of this study was to identify the potential genes associated with the lung adenocarcinoma progression and to provide new ideas for the prognosis evaluation of lung adenocarcinoma. Methods: The transcriptional profiles of ten pairs of snap-frozen tumor and adjacent normal lung tissues were obtained by performing RNA-seq. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks in order to explore the associations of gene sets with the clinical features and to investigate the functional enrichment analysis of co-expression genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Set Enrichment Analysis (GSEA) analyses were performed using clusterProfiler. The protein-protein network (PPI) was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and hub genes were identified using Cytohubba in Cytoscape. Transcription factor enrichment analysis was performed by the RcisTarget program in R language. Results: Based on RNA-seq data, 1,545 differentially expressed genes (DEGs) were found. Eight co-expression modules were identified among these DEGs. The blue module exhibited a strong correlation with LUAD, in which ADCY4, RXFP1, AVPR2, CALCRL, ADRB1, RAMP3, RAMP2 and VIPR1 were hub genes. A low expression level of RXFP1, AVPR2, ADRB1 and VIPR1 was detrimental to the survival of LUAD patients. Genes in the blue module enriched in 86 Gene Ontology terms and five KEGG pathways. We also found that transcription factors EGR3 and EXOSC3 were related to the biological function of the blue module. Overall, this study brings a new perspective to the understanding of LUAD and provides possible molecular biomarkers for prognosis evaluation of LUAD.

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Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma.

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Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma.

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