Using proteomic approaches to identify new biomarkers for detection and monitoring of ovarian cancer.
OBJECTIVE
Early detection and monitoring the treatment remain the most important factors in improving long-term survival of ovarian cancer patients. New biomarkers that individually or in combination improve the diagnostic performance of existing tumor markers are critically needed. This study uses proteomic approaches to identify new biomarkers for detection and monitoring of ovarian cancer.


METHODS
We analyzed protein profiles of three sets of sera using surface enhanced laser desorption and ionization time-of-light mass spectroscopy (SELDI-TOF-MS) on IMAC ProteinChip arrays and ProPeak software for bioinformatics data analysis. The first set of patients included 21 ovarian cancers, 18 benign diseases, and 20 normal patients. The second set included 32 ovarian cancers, 30 benign ovarian diseases, and 30 age-matched healthy controls. The third set included samples collected before and after chemotherapy from 18 ovarian cancer patients. All samples were collected at the Gynecology and Obstetrics Hospital of Fudan University in Shanghai, China. The datasets from low-intensity and high-intensity spectra were analyzed separately.


RESULTS
Seven peaks were selected for their contribution to the separation of ovarian cancers from controls using the first and second set of samples. The same dysregulation trends were confirmed for six of the seven peaks in independent validation using the third set of samples.


CONCLUSIONS
Using SELDI-TOF analysis of 195 unique specimens, we discovered with preliminary validation six distinct peaks that may potentially be useful in the detection and monitoring of ovarian cancer. Additional studies are required to determine the protein identities of these peaks and to further validate their performance as biomarkers.
Kong
Fanbing
F
Gynecology and Obstetrics Hospital of Fudan University, Shanghai, China.
Nicole White
C
C
Xiao
Xueyuan
X
Feng
Youji
Y
Xu
Congjian
C
He
Dacheng
D
Zhang
Zhen
Z
Yu
Yinhua
Y
D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000097:blood*; D001798:Blood Proteins / Q000032:analysis*; D019295:Computational Biology; D005260:Female; D006801:Humans; D008875:Middle Aged; D009363:Neoplasm Proteins / Q000097:blood*; D009367:Neoplasm Staging; D010051:Ovarian Neoplasms / Q000097:blood* / Q000175:diagnosis / Q000473:pathology; D040901:Proteomics / Q000379:methods; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods
D014408:Biomarkers, Tumor; D001798:Blood Proteins; D009363:Neoplasm Proteins