Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

Measurement of serum alanine aminotransferase (ALT) is a common, readily available, and inexpensive laboratory assay in clinical practice. ALT activity is not only measured to detect liver disease, but also to monitor overall health. ALT activity is influenced by various factors, including viral hepatitis, alcohol consumption, and medication. Recently, the impact of metabolic abnormalities on ALT variation has raised concern due to the worldwide obesity epidemic. The normal ranges for ALT have been updated and validated considering the metabolic covariates in the various ethnic districts. The interaction between metabolic and demographic factors on ALT variation has also been discussed in previous studies. In addition, an extremely low ALT value might reflect the process of aging, and frailty in older adults has been raised as another clinically significant feature of this enzyme, to be followed with additional epidemiologic investigation. Timely updated, comprehensive, and systematic introduction of ALT activity is necessary to aid clinicians make better use of this enzyme.

We performed integrative network analyses to identify targets that can be used for effectively treating liver diseases with minimal side effects. We first generated co‐expression networks (CNs) for 46 human tissues and liver cancer to explore the functional relationships between genes and examined the overlap between functional and physical interactions. Since increased de novo lipogenesis is a characteristic of nonalcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC), we investigated the liver‐specific genes co‐expressed with fatty acid synthase (FASN). CN analyses predicted that inhibition of these liver‐specific genes decreases FASN expression. Experiments in human cancer cell lines, mouse liver samples, and primary human hepatocytes validated our predictions by demonstrating functional relationships between these liver genes, and showing that their inhibition decreases cell growth and liver fat content. In conclusion, we identified liver‐specific genes linked to NAFLD pathogenesis, such as pyruvate kinase liver and red blood cell (PKLR), or to HCC pathogenesis, such as PKLR, patatin‐like phospholipase domain containing 3 (PNPLA3), and proprotein convertase subtilisin/kexin type 9 (PCSK9), all of which are potential targets for drug development.