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A large variation in prognosis is observed despite the use of clinical prognostic factors in patients with advanced non-small cell lung cancer (NSCLC). It is likely that this variation is due to the different biological properties of the tumour cells. In this work we aimed to identify gene signature that could predict survival in advanced NSCLC. Total RNA was extracted from five 5 μm-thick sections of the FFPE using the High Pure RNA Paraffin Kit (Roche). RNA amplification was performed using WT-Ovation™ FFPE RNA Amplification System V2 (NuGen). The amplified cDNA was then labelled and hybridised onto Illumina HumanRef-8 v3.0 Expression BeadChips. Microarray data analysis was subsequently performed using Genespring GX version 9.0. Out of 75 FFPE samples, only 32 had sufficient RNA quality and quantity for microarray gene expression analysis. Patients were grouped into long and short survival groups based on the time to cancer-related death. After normalization and filtration, 19,002 genes were selected for differential gene expression analysis. A total of 440 genes differed significantly between the long and short survival groups (ANOVA, p <; 0.05, with Benjamini and Hochberg False Discovery Rate multiple testing correction). Unsupervised Hierarchial Clustering with Pearson correlation and average linkage identified two broad clusters of patients corresponding to the long and short survival. Thirteen genes were selected based on the TTest, 2-fold expression changes, principal components analysis and univariate Cox regression analysis and risk scores were calculated for each patient. These gene signatures were independent predictors of survival. The model was validated with a published microarray data from 130 patients with NSCLC. Using Gene Set Analysis (GSA), we found certain biological processes including metastasis and chemotherapy resistance were up-regulated in the short survival group while TID pathway and MAPKKK cascade were enriched in th- - e long survival group. As the conclusion, there is several distinct gene expression profiles associated with survival of patients with advanced stage NSCLC. Survival outcomes in advanced NSCLC could be predicted based on a 13-gene signature.