Test on existence of histology subtype-specific prognostic signatures among early stage lung adenocarcinoma and squamous cell carcinoma patients using a Cox-model based filter

Biology Direct

Suyan Tian¹  Chi Wang³  Ming-Wen An² 

[1] Division of Clinical Epidemiology, First Hospital of Jilin University, 71Xinmin Street, Changchun 130021, Jilin, China

[2] Department of Mathematics, Vassar College, Poughkeepsie 12604, NY, USA

[3] Department of Biostatistics and Markey Cancer Center, University of Kentucky, 800 Rose St., Lexington 40536, KY, USA

关键词: Feature selection algorithm;    Gene expression barcode;    Histology-subtype specific;    Prognosis;    Cox model;    Squamous cell carcinoma (SCC);    Adenocarcinoma (AC);    Non-small cell lung cancer (NSCLC);   

Others  :  1180726
DOI  :  10.1186/s13062-015-0051-z

 received in 2014-08-12, accepted in 2015-03-24,  发布年份 2015

【 摘 要 】

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.

Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two major histology subtypes of NSCLC. It has been demonstrated that fundamental differences exist in their underlying mechanisms, which motivated us to postulate the existence of specific genes related to the prognosis of each histology subtype.

Results

In this article, we propose a simple filter feature selection algorithm with a Cox regression model as the base. Applying this method to real-world microarray data identifies a histology-specific prognostic gene signature. Furthermore, the resulting 32-gene (32/12 for AC/SCC) prognostic signature for early-stage AC and SCC samples has superior predictive ability relative to two relevant prognostic signatures, and has comparable performance with signatures obtained by applying two state-of-the art algorithms separately to AC and SCC samples.

Conclusions

Our proposal is conceptually simple, and straightforward to implement. Furthermore, it can be easily adapted and applied to a range of other research settings.

Reviewers

This article was reviewed by Leonid Hanin (nominated by Dr. Lev Klebanov), Limsoon Wong and Jun Yu.

【 授权许可】

   
2015 Tian et al.; licensee BioMed Central.

【 图 表 】

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