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Somatic mutations affect key pathways in lung adenocarcinoma.

ABSTRACT: Determining the genetic basis of cancer requires comprehensive analyses of large collections of histopathologically well-classified primary tumours. Here we report the results of a collaborative study to discover somatic mutations in 188 human lung adenocarcinomas. DNA sequencing of 623 genes with known or potential relationships to cancer revealed more than 1,000 somatic mutations across the samples. Our analysis identified 26 genes that are mutated at significantly high frequencies and thus are probably involved in carcinogenesis. The frequently mutated genes include tyrosine kinases, among them the EGFR homologue ERBB4; multiple ephrin receptor genes, notably EPHA3; vascular endothelial growth factor receptor KDR; and NTRK genes. These data provide evidence of somatic mutations in primary lung adenocarcinoma for several tumour suppressor genes involved in other cancers--including NF1, APC, RB1 and ATM--and for sequence changes in PTPRD as well as the frequently deleted gene LRP1B. The observed mutational profiles correlate with clinical features, smoking status and DNA repair defects. These results are reinforced by data integration including single nucleotide polymorphism array and gene expression array. Our findings shed further light on several important signalling pathways involved in lung adenocarcinoma, and suggest new molecular targets for treatment.

SUBMITTER: Ding L

PROVIDER: S-EPMC2694412 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Somatic mutations affect key pathways in lung adenocarcinoma.

Ding Li L Getz Gad G Wheeler David A DA Mardis Elaine R ER McLellan Michael D MD Cibulskis Kristian K Sougnez Carrie C Greulich Heidi H Muzny Donna M DM Morgan Margaret B MB Fulton Lucinda L Fulton Robert S RS Zhang Qunyuan Q Wendl Michael C MC Lawrence Michael S MS Larson David E DE Chen Ken K Dooling David J DJ Sabo Aniko A Hawes Alicia C AC Shen Hua H Jhangiani Shalini N SN Lewis Lora R LR Hall Otis O Zhu Yiming Y Mathew Tittu T Ren Yanru Y Yao Jiqiang J Scherer Steven E SE Clerc Kerstin K Metcalf Ginger A GA Ng Brian B Milosavljevic Aleksandar A Gonzalez-Garay Manuel L ML Osborne John R JR Meyer Rick R Shi Xiaoqi X Tang Yuzhu Y Koboldt Daniel C DC Lin Ling L Abbott Rachel R Miner Tracie L TL Pohl Craig C Fewell Ginger G Haipek Carrie C Schmidt Heather H Dunford-Shore Brian H BH Kraja Aldi A Crosby Seth D SD Sawyer Christopher S CS Vickery Tammi T Sander Sacha S Robinson Jody J Winckler Wendy W Baldwin Jennifer J Chirieac Lucian R LR Dutt Amit A Fennell Tim T Hanna Megan M Johnson Bruce E BE Onofrio Robert C RC Thomas Roman K RK Tonon Giovanni G Weir Barbara A BA Zhao Xiaojun X Ziaugra Liuda L Zody Michael C MC Giordano Thomas T Orringer Mark B MB Roth Jack A JA Spitz Margaret R MR Wistuba Ignacio I II Ozenberger Bradley B Good Peter J PJ Chang Andrew C AC Beer David G DG Watson Mark A MA Ladanyi Marc M Broderick Stephen S Yoshizawa Akihiko A Travis William D WD Pao William W Province Michael A MA Weinstock George M GM Varmus Harold E HE Gabriel Stacey B SB Lander Eric S ES Gibbs Richard A RA Meyerson Matthew M Wilson Richard K RK

Nature 20081001 7216

Determining the genetic basis of cancer requires comprehensive analyses of large collections of histopathologically well-classified primary tumours. Here we report the results of a collaborative study to discover somatic mutations in 188 human lung adenocarcinomas. DNA sequencing of 623 genes with known or potential relationships to cancer revealed more than 1,000 somatic mutations across the samples. Our analysis identified 26 genes that are mutated at significantly high frequencies and thus ar ...[more]

PMID: 18948947

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Project description:BackgroundThe objectives of this study were to identify hub genes and biological pathways involved in lung adenocarcinoma (LUAD) via bioinformatics analysis, and investigate potential therapeutic targets.AimTo determine reliable prognostic biomarkers for early diagnosis and treatment of LUAD.MethodsTo identify potential therapeutic targets for LUAD, two microarray datasets derived from the Gene Expression Omnibus (GEO) database were analyzed, GSE3116959 and GSE118370. Differentially expressed genes (DEGs) in LUAD and normal tissues were identified using the GEO2R tool. The Hiplot database was then used to generate a volcanic map of the DEGs. Weighted gene co-expression network analysis was conducted to cluster the genes in GSE116959 and GSE118370 into different modules, and identify immune genes shared between them. A protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes database, then the CytoNCA and CytoHubba components of Cytoscape software were used to visualize the genes. Hub genes with high scores and co-expression were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform enrichment analysis of these genes. The diagnostic and prognostic values of the hub genes were calculated using receiver operating characteristic curves and Kaplan-Meier survival analysis, and gene-set enrichment analysis was conducted. The University of Alabama at Birmingham Cancer data analysis portal was used to analyze relationships between the hub genes and normal specimens, as well as their expression during tumor progression. Lastly, validation of protein expression was conducted on the identified hub genes via the Human Protein Atlas database.ResultsThree hub genes with high connectivity were identified; cellular retinoic acid binding protein 2 (CRABP2), matrix metallopeptidase 12 (MMP12), and DNA topoisomerase II alpha (TOP2A). High expression of these genes was associated with a poor LUAD prognosis, and the genes exhibited high diagnostic value.ConclusionExpression levels of CRABP2, MMP12, and TOP2A in LUAD were higher than those in normal lung tissue. This observation has diagnostic value, and is linked to poor LUAD prognosis. These genes may be biomarkers and therapeutic targets in LUAD, but further research is warranted to investigate their usefulness in these respects.

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.

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Somatic mutations affect key pathways in lung adenocarcinoma.

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Somatic mutations affect key pathways in lung adenocarcinoma.

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