title: Progressive lung cancer determined by expression profiling and transcriptional             regulation.
abstract: Clinically, our ability to predict disease outcome for patients with early stage lung cancer is currently poor. To address this issue, tumour specimens were collected at surgery from non-small cell lung cancer (NSCLC) patients as part of the European Early Lung Cancer (EUELC) consortium. The patients were followed-up for three years post-surgery and patients who suffered progressive disease (PD, tumour recurrence, metastasis or a second primary) or remained disease-free (DF) during follow-up were identified. RNA from both tumour and adjacent-normal lung tissue was extracted from patients and subjected to microarray expression profiling. These samples included 36 adenocarcinomas and 23 squamous cell carcinomas from both PD and DF patients. The microarray data was subject to a series of systematic bioinformatics analyses at gene, network and transcription factor levels. The focus of these analyses was 2-fold: firstly to determine whether there were specific biomarkers capable of differentiating between PD and DF patients, and secondly, to identify molecular networks which may contribute to the progressive tumour phenotype. The experimental design and analyses performed permitted the clear differentiation between PD and DF patients using a set of biomarkers implicated in neuroendocrine signalling and allowed the inference of a set of transcription factors whose activity may differ according to disease outcome. Potential links between the biomarkers, the transcription factors and the genes p21/CDKN1A and Myc, which have previously been implicated in NSCLC development, were revealed by a combination of pathway analysis and microarray meta-analysis. These findings suggest that neuroendocrine-related genes, potentially driven through p21/CDKN1A and Myc, are closely linked to whether or not a NSCLC patient will have poor clinical outcome.
authors.affiliation: School of Computer Science, The University of Manchester, Manchester,                     UK.
authors.forename: Namshik
authors.initials: N
authors.lastname: Han
authors.forename: Zulkifli
authors.initials: Z
authors.lastname: Dol
authors.forename: Olga
authors.initials: O
authors.lastname: Vasieva
authors.forename: Russell
authors.initials: R
authors.lastname: Hyde
authors.forename: Triantafillos
authors.initials: T
authors.lastname: Liloglou
authors.forename: Olaide
authors.initials: O
authors.lastname: Raji
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Brambilla
authors.forename: Christian
authors.initials: C
authors.lastname: Brambilla
authors.forename: Yves
authors.initials: Y
authors.lastname: Martinet
authors.forename: Gabriella
authors.initials: G
authors.lastname: Sozzi
authors.forename: Angela
authors.initials: A
authors.lastname: Risch
authors.forename: Luis M
authors.initials: LM
authors.lastname: Montuenga
authors.forename: Andy
authors.initials: A
authors.lastname: Brass
authors.forename: John K
authors.initials: JK
authors.lastname: Field
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics / Q000378:metabolism* / Q000473:pathology; D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000235:genetics* / Q000378:metabolism; D002294:Carcinoma, Squamous Cell / Q000235:genetics / Q000378:metabolism* / Q000473:pathology; D057225:Data Mining; D018450:Disease Progression; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics / Q000378:metabolism* / Q000473:pathology; D008297:Male; D053858:Metabolic Networks and Pathways; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D025341:Principal Component Analysis; D049490:Systems Biology; D014158:Transcription, Genetic
chemical_list: D014408:Biomarkers, Tumor