S-Space College of Medicine/School of Medicine (의과대학/대학원) Dept. of Biomedical Sciences (대학원 의과학과) Theses (Ph.D. / Sc.D._의과학과)
Development of Biomarkers for Hepatocellular Carcinoma Using Multiple Reaction Monitoring-Mass Spectrometry (MRM-MS) and Bioinformatics
- 의과대학 의과학과
- Issue Date
- 서울대학교 대학원
- Multiple Reaction Monitoring; Biomarker; Hepatocellular Carcinoma; Alpha-Fetoprotein; Glycosylated AFP; Global Data-Mining; Multi-Marker Panel
- 학위논문 (박사)-- 서울대학교 대학원 : 의과학과, 2015. 2. 김영수.
- Introduction: Hepatocellular carcinoma (HCC) is one of the most common cancers and is associated with a poor survival rate. Serum alpha-fetoprotein (AFP) has long been used as a diagnostic marker for HCC, albeit controversially. Although it remains widely used in clinics, the value of AFP in HCC diagnosis has recently been challenged due to its significant rates of false positive and false negative findings.
Methods: In chapter I, to improve the efficacy of AFP as HCC diagnostic marker, we developed a method of measuring total and glycosylated AFP by multiple reaction monitoring (MRM)-MS. In chapter II, the discovery of useful biomarkers for HCC, focused solely on the proteome, has been difficult
thus, wide-ranging global data mining of genomic and proteomic databases from previous reports would be valuable in screening biomarker candidates.
Results: In chapter I, we verified the total amount of AFP (nonglycopeptide levels) and the degree of glycosylated AFP (deglycopeptide levels) in 60 normal, 35 LC, and 60 HCC subjects. By MRM-MS analysis, the nonglycopeptide had 56.7% sensitivity, 68.3% specificity, and an AUC of 0.687, comparing the normal and HCC group, whereas the deglycopeptide had 93.3% sensitivity, 68.3% specificity, and an AUC of 0.859. In comparing the stage I HCC subgroup with the LC group, the nonglycopeptide had a sensitivity of 66.7%, specificity of 80.0%, and an AUC of 0.712, whereas the deglycopeptide had a sensitivity of 96.7%, specificity of 80.0%, and an AUC of 0.918.
In chapter II, global data mining was performed using 5 types of HCC data to screen for candidate biomarker proteins (cDNA microarray, copy number variation, somatic mutation, epigenetic, and quantitative proteomics data). Next, we applied MRM to verify HCC candidate biomarkers in individual serum samples from 3 groups: a healthy control group, before HCC treatment group, and after HCC treatment group. After determining the relative quantities of the candidate proteins by MRM, we compared their expression levels between the 3 groups, identifying 4 potential biomarkers (ANLN, FLNB, C4A, and AFP)
Conclusions: In chapter I, these data demonstrate that the discriminatory power of the deglycopeptide AFP is greater than that of the nonglycopeptide AFP comparing normal group with HCC group. We conclude that deglycopeptide can distinguish cancer status between normal subjects and HCC patients better than nonglycopeptide. In chapter II, the combination of 2 markers (ANLN, FLNB) improved the discrimination of the before HCC treatment group from the healthy control group compared with AFP. We conclude that the combination of global data mining and MRM verification enhances the screening of potential HCC biomarkers. This efficacious integrative strategy is applicable to the development of markers for cancer and other diseases.