Advanced mass spectrometry-based multi-omics technologies for exploring the pathogenesis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the primary hepatic malignancies and is the third most common cause of cancer related death worldwide. Although a wealth of knowledge has been gained concerning the initiation and progression of HCC over the last half century, efforts to improve our understanding of its pathogenesis at a molecular level are still greatly needed, to enable clinicians to enhance the standards of the current diagnosis and treatment of HCC. In the post-genome era, advanced mass spectrometry driven multi-omics technologies (e.g., profiling of DNA damage adducts, RNA modification profiling, proteomics, and metabolomics) stand at the interface between chemistry and biology, and have yielded valuable outcomes from the study of a diversity of complicated diseases. Particularly, these technologies are being broadly used to dissect various biological aspects of HCC with the purpose of biomarker discovery, interrogating pathogenesis as well as for therapeutic discovery. This proof of knowledge-based critical review aims at exploring the selected applications of those defined omics technologies in the HCC niche with an emphasis on translational applications driven by advanced mass spectrometry, toward the specific clinical use for HCC patients. This approach will enable the biomedical community, through both basic research and the clinical sciences, to enhance the applicability of mass spectrometry-based omics technologies in dissecting the pathogenesis of HCC and could lead to novel therapeutic discoveries for HCC.

How advancement in biological network analysis methods empowers proteomics.

Proteomics provides important information--that may not be inferable from indirect sources such as RNA or DNA--on key players in biological systems or disease states. However, it suffers from coverage and consistency problems. The advent of network-based analysis methods can help in overcoming these problems but requires careful application and interpretation. This review considers briefly current trends in proteomics technologies and understanding the causes of critical issues that need to be addressed--i.e., incomplete data coverage and inter-sample inconsistency. On the coverage issue, we argue that holistic analysis based on biological networks provides a suitable background on which more robust models and interpretations can be built upon; and we introduce some recently developed approaches. On consistency, group-based approaches based on identified clusters, as well as on properly integrated pathway databases, are particularly useful. Despite that protein interactions and pathway networks are still largely incomplete, given proper quality checks, applications and reasonably sized data sets, they yield valuable insights that greatly complement data generated from quantitative proteomics.