We previously developed a network phenotyping strategy (NPS), a graph theory-based transformation of clinical practice data, for recognition of two primary subgroups of hepatocellular cancer (HCC), called S and L, which differed significantly in their tumor masses. In the current study, we have independently validated this result on 641 HCC patients from another continent. We identified the same HCC subgroups with mean tumor masses 9 cmxn (S) and 22 cmxn (L), p<10-14. The means of survival distribution (not available previously) for this new cohort were also significantly different (S was 12 months, L was 7 months, p<10-5). We characterized nine unique reference patterns of interactions between tumor and clinical environment factors, identifying four subtypes for S- and five subtypes for L-phenotypes, respectively. In L phenotype, all reference patterns were PVT (portal vein thrombosis) positive, all platelet/AFP levels were high, and all were chronic alcohol consumers. L had phenotype landmarks with worst survival. S phenotype interaction patterns were PVT negative, with low platelet/AFP levels. We demonstrated that tumor-clinical environment interaction patterns explained how a given parameter level can have a different significance within a different overall context. Thus, baseline bilirubin is low in S1 and S4, but high in S2 and S3, yet all are S subtype patterns, with better prognosis than in L. Gender and age, representing macro-environmental factors, and bilirubin, INR and AST levels representing micro-environmental factors, had a major impact on subtype characterization. Clinically important HCC phenotypes are therefore represented by complete parameter relationship patterns and cannot be replaced by individual parameter levels.