title: A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.
abstract: Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.
authors: School of Computer Science &Technology, Dalian University of Technology, 116024 Dalian, China.
authors: Xin
authors: X
authors: Huang
authors: Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
authors: Jun
authors: J
authors: Zeng
authors: Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
authors: Lina
authors: L
authors: Zhou
authors: Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
authors: Chunxiu
authors: C
authors: Hu
authors: Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
authors: Peiyuan
authors: P
authors: Yin
authors: School of Computer Science &Technology, Dalian University of Technology, 116024 Dalian, China.
authors: Xiaohui
authors: X
authors: Lin
mesh_terms: D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D001499:Bayes Theorem; D019985:Benchmarking; D014408:Biomarkers, Tumor / Q000097:blood*; D006528:Carcinoma, Hepatocellular / Q000097:blood / Q000175:diagnosis* / Q000209:etiology / Q000473:pathology; D004195:Disease Models, Animal; D018450:Disease Progression; D042241:Early Diagnosis; D005230:Fatty Acids, Nonesterified / Q000097:blood*; D006505:Hepatitis / Q000097:blood / Q000150:complications / Q000175:diagnosis* / Q000473:pathology; D006801:Humans; D008103:Liver Cirrhosis / Q000097:blood / Q000150:complications / Q000175:diagnosis* / Q000473:pathology; D008113:Liver Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000209:etiology / Q000473:pathology; D008244:Lysophosphatidylcholines / Q000097:blood*; D008297:Male; D055432:Metabolomics / Q000379:methods; D051381:Rats; D017207:Rats, Sprague-Dawley; D060388:Support Vector Machine; D013997:Time Factors
chemical_list: D014408:Biomarkers, Tumor; D005230:Fatty Acids, Nonesterified; D008244:Lysophosphatidylcholines