Current life science research studies often require data analysis in dry lab before validation their results in wet lab, especially, the study of microarray datasets. Study of gene expression data for tens of thousands of genes simultaneously under favourable conditions using microarray technology, the process of selecting responsive genes is important. In this study, inflammatory responsive genes were investigated by analyzing public microarray gene expression data of LPS-stimulated RAW 264.7 macrophage cells. Three microarray datasets with four time points (3, 6, 8, 18 hr) were retrieved from public database. Microarray data analysis with dry lab process includes a feature selection, co-expression network analysis, gene expression profile cluster analysis and protein-protein interaction network analysis were used in this study. The selected inflammatory responsive gene, SOCS3, was validated in wet lab with real-time RT-PCR. The SOCS3 gene shows a very volatile change in expression, this is different from the microarray data that has an increase as time changed. This may be due to induction of the SOCS3 gene may require co-stimulation of the stimulus to enhance expression stability. The SOCS3 protein is an important protein that regulates the negative response of cytokine signalling that helps prevent immune system disorders and inflammatory disease. Because of SOCS3 protein is active at the protein level, protein expression determination may be more reliable and unravel uncertainties suggested in the analyses of wet lab with only real-time RT-PCR.