Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process

Pei-Sung Hsu,Shang-Jyh Kao,Yu-Shan Wang,Kwan-Hwa Chi,Jiunn-Song Jiang,Su-Chen Huang,Chih-Chia Chang,Yi-Hsien Lin,Yuk-Wah Tsang,Wu-Ching Uen

doi:10.1186/1477-5956-9-20

Pei-Sung Hsu, Shang-Jyh Kao + Show 8 more

Open Access

https://doi.org/10.1186/1477-5956-9-20

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Abstract

BackgroundSurface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process.MethodsSera from 118 lung cancer patients, 72 healthy individuals, and 31 patients with inflammatory disease were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish lung cancer patients from sera was used. The two-stage analysis of distinguishing the healthy people and non-healthy patients (1st-stage) and then differentiating cancer patients from inflammatory disease patients (2nd-stage) to minimize the influence of inflammation was validated in the test group.ResultsIn the test group, the one-stage method had 87.2% sensitivity, 37.5% specificity, and 64.4% accuracy. The two-stage method had lower sensitivity (> 70.1%) but statistically higher specificity (80%) and accuracy (74.7%). The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis. This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area. This hypothesis was further tested using an SAA ELISA assay.ConclusionsInflammatory disease can severely interfere with the detection accuracy of SELDI profiles for lung cancer. Using a two-stage training process will improve the specificity and accuracy of detecting lung cancer.

Highlights

Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research
Ten protein peaks randomly selected over the course of the study were used to calculate the coefficient of variance (CV) as described previously [33]
Detection of lung cancers by one stage SELDI profile Serum samples were first analyzed from the training group using 134 random samples with or without lung cancer, using the SELDI Protein-Chip system

Summary

Introduction

Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. Lung cancer is the most common malignancy in the world, with poor overall 5-year survival rate [1]. SELDI-TOF-MS is currently one of the techniques used for cancer biomarker screening, including ovarian, breast, prostate, and liver cancers [9,10,11,12,13]. Serum amyloid A (SAA) is a sample of acute phase protein that has been detected by SELDI study. It belonged to a family of apolipoproteins and is reported as a serum biomarker of lung cancer and other malignancies, as well as many inflammatory diseases [14,21,22,23,24,25,26,27]

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