Asunto(s)
Neoplasias/terapia , Proteína Quinasa C/genética , ARN Interferente Pequeño/farmacología , Progresión de la Enfermedad , Femenino , Humanos , Masculino , Dosis Máxima Tolerada , Neoplasias/genética , Neoplasias/patología , ARN Interferente Pequeño/administración & dosificación , Resultado del TratamientoRESUMEN
Using surface-enhanced laser desorption ionization mass spectrometry (SELDI/TOF-MS) and ProteinChip technology, coupled with a pattern-matching algorithm and serum samples, we screened for protein patterns to differentiate gastric cancer patients from noncancer patients. A classifier ensemble, consisting of 50 decision trees, correctly classified all gastric cancers and all controls of a training set (100% sensitivity and 100% specificity). Eight of 9 stage I gastric cancers (88.9% sensitivity for stage I) were correctly classified. In addition, 28 sera from gastric cancer patients taken in different hospitals were correctly classified (100% sensitivity). Furthermore, all 11 control sera obtained from patients without gastric cancer (100% specificity) were classified correctly and 29 of 30 healthy blood-donors were classified as noncancerous. ProteinChip technology in conjunction with bioinformatics allows the highly sensitive and specific recognition of gastric cancer patients.