Abstract
Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multigene signatures in clinical practice is unclear, and the biologic importance of individual genes is difficult to assess, as the published signatures virtually do not overlap. Here, we describe a novel single institute cohort, including 196 non-small lung cancers (NSCLC) with clinical information and long-term follow-up. Gene expression array data were used as a training set to screen for single genes with prognostic impact. The top 450 probe sets identified using a univariate Cox regression model (significance level P < 0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n = 860). The meta-analysis revealed 14 genes that were significantly associated with survival (P < 0.001) with a false discovery rate <1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on tissue microarrays from 2 independent NSCLC cohorts, altogether including 617 NSCLC samples. Low CADM1 protein expression was significantly associated with shorter survival, with particular influence in the adenocarcinoma patient subgroup. Using a novel NSCLC cohort together with a meta-analysis validation approach, we have identified a set of single genes with independent prognostic impact. One of these genes, CADM1, was further established as an immunohistochemical marker with a potential application in clinical diagnostics.
Highlights
Lung cancer is the leading cause of cancer-related death worldwide [1]
The meta-analysis revealed 14 genes that were significantly associated with survival (P < 0.001) with a false discovery rate
Low cell adhesion molecule 1 (CADM1) protein expression was significantly associated with shorter survival, with particular influence in the adenocarcinoma patient subgroup
Summary
Even in early-stage patients treated by surgery, the risk of recurrence is high [2], and major efforts have been made to identify molecular markers that predict prognosis and response to additional therapy [3]. Microarray-based gene expression profiling has successfully been used in clinical cancer research to subclassify cancer entities, to predict prognosis or response to therapy, and to identify underlying mechanisms of tumor development [4]. In breast and colorectal cancer, prognostic gene expression signatures have been validated in independent patient cohorts and are tested in prospective randomized clinical trials [5, 6]. Several prognostic gene expression signatures have been published in non–small cell lung cancer Some prognostic multigene signatures have been confirmed in independent datasets [14,15,16], but the impact of individual genes has rarely
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