Establishment and preliminary validation of a diagnostic prediction model for esophageal squamous cell carcinoma based on MALDI-TOF MS
10.13602/j.cnki.jcls.2019.02.01
- VernacularTitle:基于MALDI-TOF MS技术食管鳞癌诊断预测模型的建立及初步验证
- Author:
Ting ZHANG
1
,
2
;
Nana WANG
2
,
3
;
Lu YANG
1
,
2
;
Yingjian CHEN
4
;
Chengjin HU
4
Author Information
1. Department of Medical Laboratory, Weifang Medical University
2. Department of Laboratory Medicine, The 960th hospital of the PLA
3. Graduate School, Taishan Medical University
4. Department of Laboratory Medicine, The 960th hospital of the PLA
- Publication Type:Journal Article
- Keywords:
matrix-assisted laser desorption / ionization time-of-flight mass spectrometry;
proteomics;
esophageal squamous cell carcinoma;
diagnostic prediction model;
tumor marker
- From:
Chinese Journal of Clinical Laboratory Science
2019;37(2):81-86
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a diagnostic prediction model for esophageal squamous cell carcinoma (ESCC) and search the potential biomarkers of ESCC.
Methods:Serum samples from 59 patients with ESCC and 57 healthy controls were collected, and randomly divided into the training group (44 patients and 42 healthy controls) and validation group (15 patients and 15 healthy controls). Serum proteins/peptides were extracted and purified with weak cation-exchange chromatography Magnetic Beads (WCX-MB), and detected by the matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF MS). Then the differentially expressed proteins/peptides were screened out, and a diagnostic prediction model for ESCC was established and preliminarily validated.
Results:The ClinProTools software identified 31 differential peptide peaks (P＜0.05), among which 18 peaks had significant difference (P＜0.01). Compared with healthy controls, 8 peaks were up-regulated in ESCC patients, while 10 peaks were down-regulated. Among them, the areas under the receiver operating characteristics (ROC) curve (AUC ROC ) of m/z 2 660.84 and m/z 5 336.49 peaks were 0.95 and 0.91, respectively, and their expressions were up-regulated in ESCC patients. The validation results showed that the accuracy, sensitivity and specificity of the diagnostic prediction model established by the genetic algorithm (GA) were 93.10%, 92.90% and 93.30%, respectively.
Conclusion:The established diagnostic prediction model may be used for the auxiliary diagnosis of ESCC. Two peptide peaks of m/z 2 660.84 and m/z 5 336.49 may be the potential biomarkers of ESCC.
