Supplemental Figure 2. The performance of the 4-coding gene classifier in 9 independent cohorts of stage I lung squamous cell carcinoma patients. The combined analysis uses the 8 cohorts for which overall survival data was available. For each cohort, cases were categorized as high, medium or low based on tertiles. P-values were obtained by the log-rank test for trend.
ARTICLE ABSTRACT
Background: We previously developed a prognostic classifier using the expression levels of BRCA1, HIF1A, DLC1, and XPO1 that identified stage I lung adenocarcinoma patients with a high risk of relapse. That study evaluated patients in five independent cohorts from various regions of the world. In an attempt to further validate the classifier, we have used a meta-analysis–based approach to study 12 cohorts consisting of 1,069 tumor–node–metastasis stage I lung adenocarcinoma patients from every suitable, publically available dataset.Methods: Cohorts were obtained through a systematic search of public gene expression datasets. These data were used to calculate the risk score using the previously published 4-gene risk model. A fixed effect meta-analysis model was used to generate a pooled estimate for all cohorts.Results: The classifier was associated with prognosis in 10 of the 12 cohorts (P < 0.05). This association was highly consistent regardless of the ethnic diversity or microarray platform. The pooled estimate demonstrated that patients classified as high risk had worse overall survival for all stage I [HR, 2.66; 95% confidence interval (CI), 1.93–3.67; P < 0.0001] patients and in stratified analyses of stage IA (HR, 2.69; 95% CI, 1.66–4.35; P < 0.0001) and stage IB (HR, 2.69; 95% CI, 1.74–4.16; P < 0.0001) patients.Conclusions: The 4-gene classifier provides independent prognostic stratification of stage IA and stage IB patients beyond conventional clinical factors.Impact: Our results suggest that the 4-gene classifier may assist clinicians in decisions about the postoperative management of early-stage lung adenocarcinoma patients. Cancer Epidemiol Biomarkers Prev; 23(12); 2884–94. ©2014 AACR.
