Combination of Protein Coding and Noncoding Gene Expression As a Robust Prognostic Classifier in Stage I Lung Adenocarcinoma

Prognostic tests for patients with early-stage lung cancer may provide needed guidance on postoperative surveillance and therapeutic decisions. We used a novel strategy to develop and validate a prognostic classifier for early-stage lung cancer. Specifically, we focused on 42 genes with roles in lung cancer or cancer prognosis. Expression of these biologically relevant genes and their association with relapse-free survival (RFS) were evaluated using microarray data from 148 patients with stage I lung adenocarcinoma. Seven genes associated with RFS were further examined by quantitative reverse transcription PCR in 291 lung adenocarcinoma tissues from Japan, the United States, and Norway. Only BRCA1, HIF1A, DLC1, and XPO1 were each significantly associated with prognosis in the Japan and US/Norway cohorts. A Cox regression-based classifier was developed using these four genes on the Japan cohort and validated in stage I lung adenocarcinoma from the US/Norway cohort and three publicly available lung adenocarcinoma expression profiling datasets. The results suggest that the classifier is robust across ethnically and geographically diverse populations regardless of the technology used to measure gene expression. We evaluated the combination of the four-gene classifier with miRNA miR-21 (MIR21) expression and found that the combination improved associations with prognosis, which were significant in stratified analyses on stage IA and stage IB patients. Thus, the four coding gene classifier, alone or with miR-21 expression, may provide a clinically useful tool to identify high-risk patients and guide recommendations regarding adjuvant therapy and postoperative surveillance of patients with stage I lung adenocarcinoma.