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TENET 2.0: Identification of key transcriptional regulators and enhancers in lung adenocarcinoma.

ABSTRACT: Lung cancer is the leading cause of cancer-related death and lung adenocarcinoma is its most common subtype. Although genetic alterations have been identified as drivers in subsets of lung adenocarcinoma, they do not fully explain tumor development. Epigenetic alterations have been implicated in the pathogenesis of tumors. To identify epigenetic alterations driving lung adenocarcinoma, we used an improved version of the Tracing Enhancer Networks using Epigenetic Traits method (TENET 2.0) in primary normal lung and lung adenocarcinoma cells. We found over 32,000 enhancers that appear differentially activated between normal lung and lung adenocarcinoma. Among the identified transcriptional regulators inactivated in lung adenocarcinoma vs. normal lung, NKX2-1 was linked to a large number of silenced enhancers. Among the activated transcriptional regulators identified, CENPA, FOXM1, and MYBL2 were linked to numerous cancer-specific enhancers. High expression of CENPA, FOXM1, and MYBL2 is particularly observed in a subgroup of lung adenocarcinomas and is associated with poor patient survival. Notably, CENPA, FOXM1, and MYBL2 are also key regulators of cancer-specific enhancers in breast adenocarcinoma of the basal subtype, but they are associated with distinct sets of activated enhancers. We identified individual lung adenocarcinoma enhancers linked to CENPA, FOXM1, or MYBL2 that were associated with poor patient survival. Knockdown experiments of FOXM1 and MYBL2 suggest that these factors regulate genes involved in controlling cell cycle progression and cell division. For example, we found that expression of TK1, a potential target gene of a MYBL2-linked enhancer, is associated with poor patient survival. Identification and characterization of key transcriptional regulators and associated enhancers in lung adenocarcinoma provides important insights into the deregulation of lung adenocarcinoma epigenomes, highlighting novel potential targets for clinical intervention.

SUBMITTER: Mullen DJ

PROVIDER: S-EPMC7515200 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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TENET 2.0: Identification of key transcriptional regulators and enhancers in lung adenocarcinoma.

Mullen Daniel J DJ Yan Chunli C Kang Diane S DS Zhou Beiyun B Borok Zea Z Marconett Crystal N CN Farnham Peggy J PJ Offringa Ite A IA Rhie Suhn Kyong SK

PLoS genetics 20200914 9

Lung cancer is the leading cause of cancer-related death and lung adenocarcinoma is its most common subtype. Although genetic alterations have been identified as drivers in subsets of lung adenocarcinoma, they do not fully explain tumor development. Epigenetic alterations have been implicated in the pathogenesis of tumors. To identify epigenetic alterations driving lung adenocarcinoma, we used an improved version of the Tracing Enhancer Networks using Epigenetic Traits method (TENET 2.0) in prim ...[more]

PMID: 32925947

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Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common cancers worldwide. The etiology and pathophysiology of LUAD remain unclear. The aim of the present study was to identify the key genes, miRNAs and transcription factors (TFs) associated with the pathogenesis and prognosis of LUAD.MethodsThree gene expression profiles (GSE43458, GSE32863, GSE74706) of LUAD were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by GEO2R.The Gene Ontology (GO) terms, pathways, and protein-protein interactions (PPIs) of these DEGs were analyzed. Bases on DEGs, the miRNAs and TFs were predicted. Furthermore, TF-gene-miRNA co-expression network was constructed to identify key genes, miRNAs and TFs by bioinformatic methods. The expressions and prognostic values of key genes, miRNAs and TFs were carried out through The Cancer Genome Atlas (TCGA) database and Kaplan Meier-plotter (KM) online dataset.ResultsA total of 337 overlapped DEGs (75 upregulated and 262 downregulated) of LUAD were identified from the three GSE datasets. Moreover, 851 miRNAs and 29 TFs were identified to be associated with these DEGs. In total, 10 hub genes, 10 key miRNAs and 10 key TFs were located in the central hub of the TF-gene-miRNA co-expression network, and validated using The Cancer Genome Atlas (TCGA) database. Specifically, seven genes (PHACTR2, MSRB3, GHR, PLSCR4, EPB41L2, NPNT, FBXO32), two miRNAs (hsa-let-7e-5p, hsa-miR-17-5p) and four TFs (STAT6, E2F1, ETS1, JUN) were identified to be associated with prognosis of LUAD, which have significantly different expressions between LUAD and normal lung tissue. Additionally, the miRNA/gene co-expression analysis also revealed that hsa-miR-17-5p and PLSCR4 have a significant negative co-expression relationship (r=?0.33, P=1.67e-14) in LUAD.ConclusionsOur study constructed a regulatory network of TF-gene-miRNA in LUAD, which may provide new insights about the interaction between genes, miRNAs and TFs in the pathogenesis of LUAD, and identify potential biomarkers or therapeutic targets for LUAD.

Project description:BackgroundThe development of human tumors is associated with the abnormal expression of various functional genes, and a massive tumor-based database needs to be deeply mined. Based on a multigene prediction model, access to urgent prognosis of patients has become possible.Materials and methodsWe selected three RNA expression profiles (GSE32863, GSE10072, and GSE43458) from the lung adenocarcinoma (LUAD) database of the Gene Expression Omnibus (GEO) and analyzed the differentially expressed genes (DEGs) between tumor and normal tissue using GEO2R program. After that, we analyzed the transcriptome data of 479 LUAD samples (54 normal tissue samples and 425 cancer tissue samples) and their clinical follow-up data from the (TCGA) database. Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) were used to assess the prediction model. Multivariate Cox analysis was used to identify independent predictors. TCGA pancreatic adenocarcinoma datasets were used to establish a nomogram model.ResultsWe found 98 significantly prognosis-related genes using KM and COX analysis, among which six genes were found to be the DEGs in GEO. Using multivariate analysis, it was found that a single gene could not be used as an independent predictor of prognosis. However, the risk score calculated by weighting these six genes could serve as an independent prognosis predictor. COX analysis performed with multiple covariates such as age, gender, tumor stage, and TNM typing showed that risk score could still be utilized as an independent risk factor for patient survival rate (p = 0.013) and had an applicable reliability (area under the curve, AUC = 0.665). By combining risk score and various clinical features, the nomogram model was constructed, which had been proven to have high consistency for the prediction of 3- and 5-year survival rate (concordance = 0.751) and high accuracy as tested by ROC (AUC = 0.71;AUC = 0.708).ConclusionWe proposed a method to predict the prognosis of LUAD by weighting multiple genes and constructed a nomogram model suitable for the prognostic evaluation of LUAD, which could provide a new tool for the identification of therapeutic targets and the efficacy evaluation of LUAD.

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TENET 2.0: Identification of key transcriptional regulators and enhancers in lung adenocarcinoma.

Publications

TENET 2.0: Identification of key transcriptional regulators and enhancers in lung adenocarcinoma.

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