Support vector machines coupled with proteomics approaches for detecting biomarkers predicting chemotherapy resistance in small cell lung cancer

Han,Mingyong; Dai,Jianjian; Zhang,Ying; Lin,Qi; Jiang,Man; Xu,Xiaoya; Liu,Qi; Jia,Jihui

doi:10.3892/or.2012.2037

Support vector machines coupled with proteomics approaches for detecting biomarkers predicting chemotherapy resistance in small cell lung cancer

Authors:
- Mingyong Han
- Jianjian Dai
- Ying Zhang
- Qi Lin
- Man Jiang
- Xiaoya Xu
- Qi Liu
- Jihui Jia
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Affiliations: Cancer Therapy and Research Center, Shandong Provincial Hospital, Shandong University, Jinan 250021, P.R. China, Department of Microbiology/Key Laboratory for Experimental Teratology of Chinese Ministry of Education, School of Medicine, Shandong University, Jinan 250012, P.R China
Published online on: September 17, 2012 https://doi.org/10.3892/or.2012.2037
Pages: 2233-2238

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Abstract

The aim of this study was to identify serum protein fingerprints of small cell lung cancer (SCLC) and potential biomarkers related to chemotherapy resistance of SCLC with surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS). A total of 60 SCLC patients and 48 age- and sex-matched healthy individuals were enrolled. The chemotherapy regimen was cisplatin plus etoposide. All patients received two cycles of chemotherapy. Serum protein profiles were detected using SELDI-TOF MS and the spectra were analyzed with support vector machines (SVMs). Western blotting was performed to verify the results of SELDI-TOF MS. Three top scored peaks, at m/z of 6269, 9043 and 13124 Da, were finally selected as potential biomarkers for detection of SCLC. The SVM classifier separated the SCLC from the healthy samples in the blind test, with a sensitivity of 92.4% and a specificity of 92.5%. For the 56 eligible chemotherapy patients, 4 had a complete response (7.14%), 39 patients had a partial response (69.6%), 9 patients had a stable disease (16.1%) and 4 patients had a progressive disease (7.14%). The model constructed using two protein peaks with m/z of 8830 and 10468 Da separated the chemotherapy-resistant group from the chemotherapy-sensitive group with a sensitivity of 80.0% and a specificity of 80.0%. Initial protein database searching identiﬁed 10468 Da as S100-A9 which was conﬁrmed by western blotting. The present results suggest that the combination of SELDI-TOF MS with SVM may provide a useful means in the search for serum biomarkers for predicting chemotherapy resistance in patients with SCLC.

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