ArticleJournal of Zhejiang University. Science. B2012
Combining proteomics, serum biomarkers and bioinformatics to discriminate between esophageal squamous cell carcinoma and pre-cancerous lesion.
Article in Journal of Zhejiang University. Science. B, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Comprehensive metabolic profiling uncovers early variation and biomarkers of esophageal squamous cell carcinoma.Science China. Life sciences · 2026Article
- Artificial intelligence technique in detection of early esophageal cancer.World journal of gastroenterology · 2020Review
- Anaphylatoxin C3a: A potential biomarker for esophageal cancer diagnosis.Molecular and clinical oncology · 2018Article
- Identification of Kininogen 1 as a Serum Protein Marker of Colorectal Adenoma in Patients with a Family History of Colorectal Cancer.Journal of Cancer · 2018Article
- Expression analysis of cyclooxygenase-2 in patients suffering from esophageal squamous cell carcinoma.PloS one · 2018Article
- Identification of MST1 as a potential early detection biomarker for colorectal cancer through a proteomic approach.Scientific reports · 2017Article
- A survey of computational tools for downstream analysis of proteomic and other omic datasets.Human genomics · 2015Review
- Identification of potential plasma biomarkers for esophageal squamous cell carcinoma by a proteomic method.International journal of clinical and experimental pathology · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveBiomarker assay is a noninvasive method for the early detection of esophageal squamous cell carcinoma (ESCC). Searching for new biomarkers with high specificity and sensitivity is very important for the early detection of ESCC. Serum surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS) is a high throughput technology for identifying cancer biomarkers using drops of sera.
methodsIn this study, 185 serum samples were taken from ESCC patients in a high incidence area and screened by SELDI. A support vector machine (SVM) algorithm was adopted to analyze the samples.
resultsThe SVM patterns successfully distinguished ESCC from pre-cancerous lesions (PCLs). Also, types of PCL, including dysplasia (DYS) and basal cell hyperplasia (BCH), and healthy controls (HC) were distinguished with an accuracy of 95.2% (DYS), 96.6% (BCH), and 93.8% (HC), respectively. A marker of 25.1 kDa was identified in the ESCC patterns whose peak intensity was observed to increase significantly during the development of esophageal carcinogenesis, and to decrease obviously after surgery.
conclusionsWe selected five ESCC biomarkers to form a diagnostic pattern which can discriminate among the different stages of esophageal carcinogenesis. This pattern can significantly improve the detection of ESCC.
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