  Background
  Non-small cell lung cancer (NSCLC) is the predominant histological type of
lung cancer, accounting for up to 85% of cases. Disease stage is commonly used
to determine adjuvant treatment eligibility of NSCLC patients, however, it is
an imprecise predictor of the prognosis of an individual patient. Currently,
many researchers resort to microarray technology for identifying relevant
genetic prognostic markers, with particular attention on trimming or extending
a Cox regression model.
  Among NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two
major histology subtypes. It has been demonstrated that there exist fundamental
differences in the underlying mechanisms between them, which motivated us to
postulate there might exist specific genes relevant to prognosis of each
histology subtype.
  Results
  In this article, we propose a simple filterer feature selection algorithm
with a Cox regression model as the base. Applying this method to a real-world
microarray data, no evidence has been found to support the existence of
histology-specific prognostic gene signature. Nevertheless, a 31-gene
prognostic gene signature for the early-stage AC and SCC samples is obtained,
which provides comparable performance when compared with other relevant
signatures.
  Conclusions
  Our proposal is conceptually simple and straightforward to implement.
Therefore, it is expected that other researchers, especially those with less
statistical knowledge and experience, can adapt this method readily to test
their own research hypotheses.
