Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.
Precise cancer classification is a central challenge in clinical cancer research such as diagnosis, prognosis and metastasis prediction. Most existing cancer classification methods based on gene or metabolite biomarkers were limited to single genomics or metabolomics, and lacked integration and utilization of multiple 'omics' data. The accuracy and robustness of these methods when applied to independent cohorts of patients must be improved. In this study, we propose a directed random walk-based method to evaluate the topological importance of each gene in a reconstructed gene-metabolite graph by integrating information from matched gene expression profiles and metabolomic profiles. The joint use of gene and metabolite information contributes to accurate evaluation of the topological importance of genes and reproducible pathway activities. We constructed classifiers using reproducible pathway activities for precise cancer classification and risk metabolic pathway identification. We applied the proposed method to the classification of prostate cancer. Within-dataset experiments and cross-dataset experiments on three independent datasets demonstrated that the proposed method achieved a more accurate and robust overall performance compared to several existing classification methods. The resulting risk pathways and topologically important differential genes and metabolites provide biologically informative models for prostate cancer prognosis and therapeutic strategies development.
Department of Mathematics, Heilongjiang Institute of Technology, Harbin, 150050, China.
Wei
W
Liu
Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Xuefeng
X
Bai
Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Yuejuan
Y
Liu
Department of Mathematics, Heilongjiang Institute of Technology, Harbin, 150050, China.
Wei
W
Wang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Junwei
J
Han
Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Qiuyu
Q
Wang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yanjun
Y
Xu
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Chunlong
C
Zhang
Department of Biostatistics, Anhui Agricultural University, Hefei, 230030, China.
Shihua
S
Zhang
Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Xuecang
X
Li
Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Zhonggui
Z
Ren
Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Jian
J
Zhang
Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Chunquan
C
Li
D014408:Biomarkers, Tumor / Q000378:metabolism; D053898:Biosynthetic Pathways / Q000235:genetics; D019992:Databases as Topic; D023281:Genomics; D006801:Humans; D008297:Male; D053858:Metabolic Networks and Pathways / Q000235:genetics; D055432:Metabolomics; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000145:classification / Q000235:genetics* / Q000378:metabolism*; D015203:Reproducibility of Results; D012984:Software
D014408:Biomarkers, Tumor