Diagnosis of gastric cancer using decision tree classification of mass spectral data

Abstract

Although gastric cancer is the second leading cause of cancer death worldwide, specific and sensitive biomarkers that can be used for its diagnosis are still unavailable. Attempting to improve on current approaches to the serological diagnosis of gastric cancer, we subjected serum samples from 245 individuals (including 127 gastric cancer patients, 100 age-and sex-matched healthy individuals, nine benign gastric lesion patients and nine colorectal cancer patients) for analysis by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. Peaks were detected with Ciphergen SELDI software version 3.1.1 and analyzed with Biomarker Patterns' software 5.0. We developed a classifier for separating the gastric cancer groups from the healthy groups. Three protein masses with 1468, 3935 and 7560 m/z were selected as a potential 'fingerprint' for the detection of gastric cancer. It was able to distinguish the gastric cancer patients from the health volunteers with a sensitivity of 95.6% and a specificity of 92.0% in the training set. In the blinding set, it was capable of differentiating the gastric cancer samples from the others with a specificity of 88.0%, a sensitivity of 85.3%, and an accuracy of 86.4%. These values were all higher than those achieved in a parallel analysis by measuring serum carcinoembryonic antigen (CEA) and carbohydrate antigen (CA)19-9 together. Therefore, the decision tree analysis of serum proteomic patterns has the potential to be used in gastric cancer diagnosis. (Cancer Sci 2007; 98: 37-43)

Identification of a high molecular weight kininogen fragment as a marker for early gastric cancer by serum proteome analysis

“…Using the MALDI-TOF ClinProt TM system, they also revealed that 1466 Da fibrinopeptide A levels were high in the sera of gastric cancer patients [19]. Su et al [20] also identified fibrinopeptide A as a candidate biomarker for gastric cancer. HMW kininogen fragments, however, have not been described as candidate biomarkers for gastric cancer.…”

“…Four previous reports used MALDI-TOF peptidomic analyses to identify biomarkers for gastric cancer [17][18][19][20]. By using SELDI-TOF MS, Ebert et al [17] reported a promising decision tree composed of three peptide peaks.…”

Discovery of biomarkers for gastric cancer: A proteomics approach

“…A proteomics approach that combines multiple technologies may contribute to the search of potential biomarkers with high sensitivity and high specificity. For instance, in recent studies, serum protein profiling was performed using a combination of SELDI-TOF-MS and ProteinChip array technology for the identification of gastric cancer in patients [25,26]. Also, plasma-free amino acid (PFAA) profiling has been used in conjunction with HPLC-ESI-MS for the identification of the different types of cancer and for its early detection [27].…”

“…Using a combination of proteomic techniques, apoC-I and apoC-III [29], thrombin light chain A [31], and three protein masses with 1468, 3935, and 7560 m/z [26] were identified as potential blood biomarkers for gastric cancer, with a high sensitivity (>89.9%) and high specificity (>71%). Moreover, MIF [32] and a 2209-Da peptide [33] were identified as potential blood biomarkers for gastric cancer (> 28% sensitivity and > 83.3% specificity) through large-scale research studies.…”

Search of potential gastric cancer biomarkers using low molecular weight blood plasma proteome profiling by mass spectrometry

“…Su Y. и др. [23] изучили образцы сыворотки крови 127 больных РЖ и 100 индивидов КГ. Авторы идентифицировали пики с m/z 1468, 3935 и 7560 Да, которые использовали для диагностики рака желудка.…”