Early detection is one of the greatest challenges in the study of oncology. The five-year survival rate is more than 90% for early esophageal cancer patients, but only 10%–15% for patients in late or advanced stages (Yang, 1980
; Lu et al., 1988
). Therefore, prevention and early detection are both very important for improving the prognosis of ESCC. Recent advances in protein profiling technologies for identifying candidate novel tumor biomarkers have raised great interest in searching for cancer biomarkers. New cancer biomarkers could be used as indicators of early-stage disease.
Tumor biomarkers such as CEA, CA 19-9, and SCCA have been widely investigated in the treatment of esophageal cancer patients (Tanaka et al., 2010
). However, the application of these markers to the clinical diagnosis of esophageal cancer is still limited by their low sensitivity and specificity. As a soluble CYFRA 21-1, the probability of clinical utilization of CYFRA 21-1 in esophageal cancer has also been tested. CYFRA 21-1 shows higher sensitivity than CEA, CA 19-9, and SCCA (Brockmann et al., 2000
; Kawaguchi et al., 2000
). However, the sensitivity of all these markers is still less than 10%, and therefore they do not meet the high requirements for early esophageal cancer detection (Nakamura et al., 1998
). In our results, we also found that the sensitivity and specificity of individual CEA, CA 19-9, and CYFRA 21-1 biomarkers were very low in screening for esophageal cancer.
ESCC has a multi-factorial nature. An effective detection method can be achieved if we choose a combined diagnosis model instead of using single biomarkers. A combination of SELDI-MS and ProteinChip technology could provide a high-throughput proteomic profiling tool (Adam et al., 2002
). The “fingerprints” of ESCC and a unique diagnostic model can also be established if a sophisticated bioinformatics tool is adopted for complex data analysis.
In our study, sera from many groups were collected to build the specific protein profiling model. Markers showing differential expression in the ESCC and HC groups were the first focus. We identified many markers that changed gradually in the ESCC, PCL, and HC groups. The markers 5.6, 7.9, 15.9, and 25.1 kDa showed significant differences among all three groups (p<0.05). The markers 8.0, 16.0, 16.2, and 25.1 kDa that showed increased expression in the ESCC group showed significantly decreased expression (p<0.05) in the postoperative patients. The marker 25.1 kDa was of particular interest because it increased significantly and progressively in the HC, PCL, and ESCC groups and decreased significantly after operation. This marker was selected to build the SVM pattern for the diagnosis of ESCC. The role of biomarker 25.1 kDa is very important in esophageal carcinogenesis.
To estimate the sensitivity and specificity of the combination model established here, 1 000 tests and training sets were compiled by random selection. The sensitivity and specificity were assessed according to the average of the 1 000 tests. Every combination of the markers screened out in the first step was considered. This method may be more time-consuming compared to a stepwise approach, but it can find the best marker combination.
Using ProteinChip technology, different groups of serum protein biomarkers for ESCC were identified with different expression characteristics in the HC, BCH, DYS, and ESCC groups. The aim of our study is to identify biomarkers for ESCC diagnosis as well as to screen the biomarkers associated with the carcinogenesis of ESCC, and to profile the differential protein expression patterns before and after operation. We created SVM patterns distinguishing ESCC from DYS, BCH, and HC groups with high sensitivity, specificity, and overall accuracy. The pattern discriminating BCH from HC yielded unsatisfactory results.
We carried out purification of these protein biomarkers. Other researchers have also announced successful purification of biomarkers using tryptic peptide mapping (Rai et al., 2002
) or amino acid sequencing technology (Klade et al., 2001
). The marker 28 061 Da, which showed a marked decrease in the expression in the DYS group, was identified as apolipoprotein A-1 by our primary bioinformatics analysis. The down-regulation of this protein in ovarian cancer was reported by Hu et al. (2005
). However, many biomarkers of low abundance need further purification.
In conclusion, we identified and selected five biomarker protein patterns to construct a five-peak SVM model for early detection of ESCC. This SVM diagnosis model was used to discriminate among the different stages of esophageal carcinogenesis. The results showed that the specificity and sensitivity of the five-peak SVM model for a blind test were 96.8% and 87.1%, respectively. The advantages of the five-peak SVM diagnosis model compared to a single biomarker indicate that there is a great potential to improve the detection of ESCC by using this kind of combined biomarker model.