Primary hepatocellular cancer (HCC) classification systems are based on histopathology and radiology, yet clinical intuition and experience suggest that natural history and disease progression have distinctive clinical features, consistent with a cluster of homogeneous entities within a heterogeneous cohort. We built a rigorous, sequenced, graph-based strategy of network phenotyping analysis (NPA) to combine data smoothing to minimize stochasticity, multivariate analysis to identify ambiguity and prioritize key variables, and k-partite graphs to visualize coherence. In 890 unresectable HCC patients, we selected 13 baseline clinical variables. After rank ordering by survival, we found data structure that exhibited coherence between variables, implying heterogeneity in which survival varied depending on the associated variable profile. The NPA data compression identified five distinctive clinical phenotypes, based only on gender, age, and levels of serum bilirubin, serum alpha-fetoprotein (AFP), and serum gamma glutamyl transpeptidase (GGTP). The phenotypic profiles for gender and age were substantially different. Young, male patients had a low survival, while elderly women had a long survival. Novel clusters included young men, with high AFP levels for their level of bilirubin, as well as women of all ages, with high GGTP values for their level of AFP. The identification of phenotypic groups of HCC may be of value in studies designed to understand their underlying pathophysiology. We conclude that NPA offers a new useful tool in the reclassification of HCC.
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