Two-gene signature improves the discriminatory power of IASLC/ATS/ERS classification to predict the survival of patients with early-stage lung adenocarcinoma

In this study, we investigated the contribution of a gene expression-based signature (composed of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR) to survival prediction for early-stage lung adenocarcinoma categorized by the new International Association for the Study of Lung Cancer (IASLC)/the American Thoracic Society (ATS)/the European Respiratory Society (ERS) classification. We also aimed to verify whether gene signature improves the risk discrimination of IASLC/ATS/ERS classification in early-stage lung adenocarcinoma. Patients and

The two-gene signature composed of BRCA1 and ERBB3 was an independent risk factor of the IASLC/ATS/ERS classification, which can be used to improve the discriminatory power of prognosis prediction of the IASLC/ATS/ERS classification in early-stage lung adenocarcinoma. The two-gene signature combination with the IASLC/ATS/ERS classification might contribute to better patient stratification for adjuvant chemoradiotherapy or targeted therapy after the surgery.

Total RNA was extracted from 93 patients with pathologically confirmed TNM stage Ia and Ib lung adenocarcinoma. The mRNA expression levels of ten genes in the signature (BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, and SH3BGR) were detected using real-time polymerase chain reaction. Each patient was categorized according to the new IASLC/ATS/ERS classification by accessing hematoxylin-eosin-stained slides. The corresponding Kaplan-Meier survival analysis by the log-rank statistic, multivariate Cox proportional hazards modeling, and c-index calculation were conducted using the programming language R (Version 2.15.1) with the "risksetROC" package.

The multivariate analysis demonstrated that the risk factor of the ten-gene expression signature can significantly improve the discriminatory value of TNM staging in survival prediction, but not the value of the IASLC/ATS/ERS classification. Further analysis suggested that only BRCA1 and ERBB3 in the signature were independent risk factors after adjusting for the IASLC/ATS/ERS classification by Cox regression. A new algorithm of the two-gene expression signature containing BRCA1 and ERBB3 was generated. Adding the two-gene signature into the IASLC/ATS/ERS classification model further improved the discriminatory c-statistic from 0.728 to 0.756.