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Background Hepatocellular carcinoma (HCC) is a heterogeneous disease with both tumor and liver factors being involved. Aims To investigate HCC clinical phenotypes and factors related to HCC size. Methods Prospectively-collected HCC patients' data from a large Italian database were arranged according to the maximum tumor diameter (MTD) and divided into tumor size terciles, which were then compared in terms of several common clinical parameters and patients' survival. Results An higer MTD tercile was significantly associated with increased blood alpha-fetoprotein (AFP), gamma-glutamyl transpeptidase (GGTP), and platelet levels. Patients with higher platelet levels had larger tumors and higher GGTP levels, with lower bilirubin levels. However, patients with the highest AFP levels had larger tumors and higher bilirubin levels, reflecting an aggressive biology. AFP correlation analysis revealed the existence of 2 different groups of patients: those with higher and with lower AFP levels, each with different patient and tumor characteristics. The Cox proportional-hazard model showed that a higher risk of death was correlated with GGTP and bilirubin levels, tumor size and number, and portal vein thrombosis (PVT), but not with AFP or platelet levels. Conclusions An increased tumor size was associated with increased blood platelet counts, AFP and GGTP levels. Platelet and AFP levels were important indicators of tumor size, but not of survival.

Previous work has shown that two general processes contribute to hepatocellular cancer (HCC) prognosis: liver damage, monitored by indices such as blood bilirubin, prothrombin time (PT), and aspartate aminostransferase (AST); and tumor biology, monitored by indices such as tumor size, tumor number, presence of portal vein thrombosis (PVT) and blood alpha-fetoprotein (AFP) levels. These processes may affect one another, with prognostically significant interactions between multiple tumor and host parameters. These interactions form a context that provide personalization of the prognostic meaning of these factors for every patient. Thus, a given level of bilirubin or tumor diameter might have a different significance in different personal contexts. We previously applied network phenotyping strategy (NPS) to characterize interactions between liver function indices of Asian HCC patients and recognized two clinical phenotypes, S and L, differing in tumor size and tumor nodule numbers. Our aim was to validate the applicability of the NPS-based HCC S/L classification on an independent European HCC cohort, for which survival information was additionally available. Four sets of peripheral blood parameters, including AFP-platelets, derived from routine blood parameter levels and tumor indices from the ITA.LI.CA database, were analyzed using NPS, a graph-theory-based approach that compares personal patterns of complete relationships between clinical data values to reference patterns with significant association to disease outcomes. Without reference to the actual tumor sizes, patients were classified by NPS into two subgroups with S and L phenotypes. These two phenotypes were recognized using solely the HCC screening test results, consisting of eight common blood parameters, paired by their significant correlations, including an AFP-platelets relationship. These trends were combined with patient age, gender, and self-reported alcoholism into NPS personal patient profiles. We subsequently validated (using actual scan data) that patients in L phenotype group had 1.5× larger mean tumor masses relative to S, P = 6 × 10(-16). Importantly, with the new data, liver test pattern-identified S-phenotype patients had typically 1.7× longer survival compared to L-phenotype patients. NPS integrated the liver, tumor, and basic demographic factors. Cirrhosis-associated thrombocytopenia was typical for smaller S tumors. In L tumor phenotype, typical platelet levels increased with the tumor mass. Hepatic inflammation and tumor factors contributed to more aggressive L tumors, with parenchymal destruction and shorter survival. NPS provides integrative interpretation for HCC behavior, identifying two tumor and survival phenotypes by clinical parameter patterns. The NPS classifier is provided as an Excel tool. The NPS system shows the importance of considering each tumor marker and parameter in the total context of all the other parameters of an individual patient.