Label-free quantification of analytes using liquid chromatography-mass spectrometry (LC-MS) is gaining recognition as a very good strategy for biomarker discovery. However, such quantification is not addressed adequately in the instrument-specific software packages. In particular, alignment of LC-MS data presents a significant challenge in label-free quantification and comparison of biomolecules. This challenge coupled with biological variability and disease heterogeneity in human populations has restricted recent advances in LC- MS-based biomarker discovery studies. This project brings together experts in bioinformatics, biostatistics, biochemistry, clinical cancer research, chromatography, and mass spectrometry to develop novel analytical tools for LC-MS-based label-free quantification and comparison of glycans and peptides in serum and plasma. Specifically, a novel probabilistic-based mixture regression model and a new clustering-based method will be investigated for alignment of LC-MS data and for identification of patient subgroups. LC-MS data from spike-in studies will be utilized to develop and optimize the proposed alignment methods and to compare their performance with other existing solutions. The optimized algorithms and statistical methods will be applied to identify peptide and glycan candidate biomarkers for early detection of HCC. This will be accomplished by using two LC-MS technologies to evaluate the expression of peptides and glycans in serum and plasma samples collected from HCC patients and cirrhotic controls. The candidate biomarkers will be validated using isotope dilution mass spectrometric assays. Our proposal to perform both peptide and glycan profiling studies based on serum and plasma samples from the same participants is a unique opportunity to explore an integromic approach for marker discovery. Furthermore, this project will capitalize on markers identified in this study and other previous studies to investigate key metabolic and signaling pathways that may be related to the progression of HCC. This will enhance our understanding of the disease progression and the functional involvement of the markers in metabolic and signaling pathways, which could be used to design and test improved treatment strategies.
PROJECT NARRATIVE This project will lead to the development of novel open source analytical tools for label-free quantification of peptides and glycans in serum and plasma using liquid chromatography-mass spectrometry (LC-MS) technologies. The availability of such tools will assist the research community in advancing the promising LC- MS-based biomarker discovery research. The proposed tools will be utilized to find and validate early- diagnosis biomarkers of hepatocellular carcinoma (HCC). Defining clinically applicable biomarkers that detect early-stage HCC in a high-risk population of cirrhotic patients has potentially far-reaching consequences for disease management and patient health. This project is important because most HCC patients are diagnosed at a late stage, where the treatment options are limited. There is a pressing need to identify biomarkers that could be used for early detection of HCC. In addition to screening high-risk populations for early signs of disease, the resulting biomarkers could be used to design and test improved treatment strategies.
|Zuo, Yiming; Cui, Yi; Di Poto, Cristina et al. (2016) INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery. Methods 111:12-20|
|Tsai, Tsung-Heng; Wang, Minkun; Ressom, Habtom W (2016) Preprocessing and Analysis of LC-MS-Based Proteomic Data. Methods Mol Biol 1362:63-76|
|Wang, Minkun; Yu, Guoqiang; Ressom, Habtom (2016) Integrative Analysis of Proteomic, Glycomic, and Metabolomic Data for Biomarker Discovery. IEEE J Biomed Health Inform :|
|Wang, Minkun; Tsai, Tsung-Heng; Di Poto, Cristina et al. (2016) Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies. BMC Genomics 17 Suppl 4:545|
|Tsai, Tsung-Heng; Song, Ehwang; Zhu, Rui et al. (2015) LC-MS/MS-based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma. Proteomics 15:2369-81|
|Ranjbar, Mohammad R Nezami; Tadesse, Mahlet G; Wang, Yue et al. (2015) Bayesian Normalization Model for Label-Free Quantitative Analysis by LC-MS. IEEE/ACM Trans Comput Biol Bioinform 12:914-27|
|Wang, Minkun; Yu, Guoqiang; Ressom, Habtom W (2015) Integrative analysis of LC-MS based glycomic and proteomic data. Conf Proc IEEE Eng Med Biol Soc 2015:8185-8|
|Tsai, Tsung-Heng; Wang, Minkun; Di Poto, Cristina et al. (2014) LC-MS profiling of N-Glycans derived from human serum samples for biomarker discovery in hepatocellular carcinoma. J Proteome Res 13:4859-68|
|Wang, Jinlian; Zuo, Yiming; Liu, Lun et al. (2014) Identification of functional modules by integration of multiple data sources using a Bayesian network classifier. Circ Cardiovasc Genet 7:206-17|
|Zuo, Yiming; Yu, Guoqiang; Tadesse, Mahlet G et al. (2014) Biological network inference using low order partial correlation. Methods 69:266-73|
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