Pattern analysis of serum proteome distinguishes renal cell carcinoma from other urologic diseases and healthy persons.
Despite having a relatively low incidence, renal cell carcinoma (RCC) is one of the most lethal urologic cancers. For successful treatment including surgery, early detection is essential. Currently there is no screening method such as biomarker assays for early diagnosis of RCC. Surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF) is a recent technical advance that can be used to identify biomarkers for cancers. In this study, we investigated whether SELDI protein profiling and artificial intelligence analysis of serum could distinguish RCC from healthy persons and other urologic diseases (nonRCC). The SELDI-TOF data was acquired from a total of 36 serum samples with weak cation exchange-2 protein chip arrays and filtered using ProteinChip software. We used a decision tree algorithm c4.5 to classify the three groups of sera. Five proteins were identified with masses of 3900, 4107, 4153, 5352, and 5987 Da. These biomarkers can correctly separate RCC from healthy and nonRCC samples.
Won
Yonggwan
Y
Department of Computer Engineering, Chonnam National University Medical School, 5 Hakdong, Donggu, Gwangju 501-746, Korea.
Song
Ho-Jun
HJ
Kang
Taek Won
TW
Kim
Jung-Ja
JJ
Han
Byoung-Don
BD
Lee
Seung-Won
SW
D014408:Biomarkers, Tumor; D001798:Blood Proteins / Q000032:analysis*; D002292:Carcinoma, Renal Cell / Q000175:diagnosis*; D003663:Decision Trees; D006801:Humans; D007680:Kidney Neoplasms / Q000175:diagnosis*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D014570:Urologic Diseases / Q000175:diagnosis*
D014408:Biomarkers, Tumor; D001798:Blood Proteins