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Recent technological advances have enabled the generation of various omic data (e.g., genomics, proteomics, metabolomics and glycomics) in a high-throughput manner. The integration of multi-omic data sets is desirable to unravel the complexity of a biological system. In this paper, we propose a new approach to investigate both inter and intra relationships for multi-omic data sets by using regularized canonical correlation analysis and graphical lasso. The application of this novel approach on real multi-omic data sets helps identify hub proteins and their neighbors that may be missed by typical statistical analysis to serve as biomarker candidates. Also, the integration of data from various cellular components (i.e., proteins, metabolites and glycans) offers the potential to discover more reliable biomarker candidates for complex disease.