title: Identification of suitable genes contributes to lung adenocarcinoma clustering by multiple meta-analysis methods.
abstract: BACKGROUND
  With the widespread application of high-throughput technology, numerous meta-analysis methods have been proposed for differential expression profiling across multiple studies.
  
  
  OBJECTIVE
  We identified the suitable differentially expressed (DE) genes that contributed to lung adenocarcinoma (ADC) clustering based on seven popular multiple meta-analysis methods.
  
  
  METHODS
  Seven microarray expression profiles of ADC and normal controls were extracted from the ArrayExpress database. The Bioconductor was used to perform the data preliminary preprocessing. Then, DE genes across multiple studies were identified. Hierarchical clustering was applied to compare the classification performance for microarray data samples. The classification efficiency was compared based on accuracy, sensitivity and specificity.
  
  
  RESULTS
  Across seven datasets, 573 ADC cases and 222 normal controls were collected. After filtering out unexpressed and noninformative genes, 3688 genes were remained for further analysis. The classification efficiency analysis showed that DE genes identified by sum of ranks method separated ADC from normal controls with the best accuracy, sensitivity and specificity of 0.953, 0.969 and 0.932, respectively. The gene set with the highest classification accuracy mainly participated in the regulation of response to external stimulus (P = 7.97E-04), cyclic nucleotide-mediated signaling (P = 0.01), regulation of cell morphogenesis (P = 0.01) and regulation of cell proliferation (P = 0.01).
  
  
  CONCLUSIONS
  Evaluation of DE genes identified by different meta-analysis methods in classification efficiency provided a new perspective to the choice of the suitable method in a given application. Varying meta-analysis methods always present varying abilities, so synthetic consideration should be taken when providing meta-analysis methods for particular research.
authors.affiliation: Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
authors.forename: Ze-Hui
authors.initials: ZH
authors.lastname: Yang
authors.affiliation: Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China. ruizhengmed@yeah.net.
authors.forename: Rui
authors.initials: R
authors.lastname: Zheng
authors.affiliation: Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Zhang
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics*; D000077192:Adenocarcinoma of Lung; D016022:Case-Control Studies; D016000:Cluster Analysis; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D015201:Meta-Analysis as Topic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*
keywords: differentially expressed genes; hierarchical clustering; lung adenocarcinoma; meta-analysis methods; sum of ranks