Hepatocellular carcinoma (HCC): Epidemiology, etiology and molecular classification

Abstract

Hepatocellular carcinoma (HCC), the primary malignancy of hepatocytes, is a diagnosis with bleak outcome. According to National Cancer Institute's SEER database, the average five-year survival rate of HCC patients in the US is 19.6% but can be as low as 2.5% for advanced, metastatic disease. When diagnosed at early stages, it is treatable with locoregional treatments including surgical resection, Radio-Frequency Ablation, Trans-Arterial Chemoembolization or liver transplantation. However, HCC is usually diagnosed at advanced stages when the tumor is unresectable, making these treatments ineffective. In such instances, systemic therapy with tyrosine kinase inhibitors (TKIs) becomes the only viable option, even though it benefits only 30% of patients, provides only a modest (~3months) increase in overall survival and causes drug resistance within 6months. HCC, like many other cancers, is highly heterogeneous making a one-size fits all option problematic. The selection of liver transplantation, locoregional treatment, TKIs or immune checkpoint inhibitors as a treatment strategy depends on the disease stage and underlying condition(s). Additionally, patients with similar disease phenotype can have different molecular etiology making treatment responses different. Stratification of patients at the molecular level would facilitate development of the most effective treatment option. With the increase in efficiency and affordability of "omics"-level analysis, considerable effort has been expended in classifying HCC at the molecular, metabolic and immunologic levels. This review examines the results of these efforts and the ways they can be leveraged to develop targeted treatment options for HCC.

Keywords: Epidemiology; Hepatocellular carcinoma; Immunological classification; Metabolic classification; Molecular classification; Risks and causative factors.

Fig. 1

An overview of different HCC etiologies and the potential mechanism causing HCC. Created with Biorender.com.

Fig. 2

An overview of multiple signaling pathways deregulated by HBV and their potential mechanisms. Dotted lines indicate an unknown mechanism. Binding of Wnt3 to Frizzled-7 receptor activates Wnt/β-catenin signaling through β-catenin accumulation and enhanced TCF-mediated transcription (Kim et al., 2008). HBV infection increases phosphorylation of c-Src kinase, causing an increase in HBV transcription and replication and disassembly of adherens junctions. The disassembly of adherens junctions, made of E-Cadherin bound to β-catenin, results in increased levels of intracellular E-Cadherin and β-catenin leading to EMT and increased TCF/LEF dependent β-catenin signaling (von Olshausen et al., 2018). Association of β-catenin with the scaffolding protein APC is required for its degradation by the APC/Axin/GSK3β complex. HBx protein competitively binds to APC, preventing its association with β-catenin, leading to increased β-catenin stabilization (Hsieh, Kim, Lim, Yu, & Jung, 2011). Increased stabilization of β-catenin leads to an increase in levels of its downstream effectors like c-MYC and a decrease in E-cadherin expression (Not shown in illustration). HBx also transcriptionally regulates MYH9 through β-catenin signaling and interacts with Non-muscle myosin heavy chain IIA (MYH9) to facilitate ubiquitin mediated GSK3β degradation, thus stabilizing β-catenin (Lin et al., 2020). HBx also enhances MAPK/ERK pathway by activating Ras-GTP complex formation (Benn & Schneider, 1994). The transcription factor AP1 is activated through this pathway (Cross, Wen, & Rutter, 1993). HBx induces phosphorylation and degradation of the NF-κB inhibitor, IκBα and a decrease in levels of the p52 precursor, p105 (Su & Schneider, 1996). The precore protein p22 also activates Wnt signaling potentially both in the nucleus and the cytoplasm (Tran et al., 2020). Created with Biorender.com.

Fig. 3

An overview of various molecular classification systems for HCC. For each classification system, the subgroup with better prognosis and worse prognosis are shaded yellow and gray, respectively. Correlating subgroups between multiple classification systems are connected using dotted lines. Created with Created with https://biorender.com.