Bioinformatics analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes

Lung adenocarcinoma (LAC) is the most frequent histologic type of lung cancer and rates of adenocarcinoma are increasing in most countries. Recently, several molecular markers have been identified to predict LAC. However, more prognostic makers and the underlying role of those makers are still imperative.

In this study, our objective was to identify a set of discriminating genes that can be used for characterization and prediction of response to LAC.

Using the bioinformatics analysis method, we merged two LAC datasets-GSE2514 and GSE7670 to find novel target genes and pathways to explain the pathogenicity.

The results showed that EDNRB (endothelin receptor type B), ADRB2 (beta-adrenergic receptor), S1PR1 (sphingosine-1-phosphate receptor 1), P2RY14 (PsY purinoceptor 14), LEPR (leptin-receptor), GHR (growth hormone receptor), PPM1D (protein phosphatase-1D), and GADD45B (growth arrest and DNA-damage-inducible, beta) have high degrees in response to LAC. Additionally, EDNRB, ADRB2, S1PR1, P2RY14, LEPR, and GHR may be involved in LAC through Neuroactive ligand-receptor interaction, but PPM1D and GADD45B may be through p53 signaling pathway. Some of our prediction had been demonstrated by previous reports, such as ADRB2, S1PR1, GHR, PPM1D, and GADD45B. Therefore, we hope our study could lay a basis for further study of other target genes, such as EDNRB, P2RY14, and LEPR.

It is effective to identify potential molecular marker for LAC and predict their underlying functions by bioinformatics analysis and graph clustering method. However, further experiments are still indispensable to confirm our conclusion.