The incidence of hepatocellular carcinoma (HCC) and HCC-related deaths has increased over the last few decades. There are several risk factors of HCC such as viral hepatitis (B, C), cirrhosis, tobacco and alcohol use, aflatoxin-contaminated food, pesticides, diabetes, obesity, nonalcoholic fatty liver disease (NAFLD), and metabolic and genetic diseases. Diagnosis of HCC is based on different methods such as imaging ultrasonography (US), multiphasic enhanced computed tomography (CT), magnetic resonance imaging (MRI), and several diagnostic biomarkers. In this review, we examine the epidemiology of HCC worldwide and in Egypt as well as risk factors associated with the development of HCC and, finally, provide the updated diagnostic biomarkers for the diagnosis of HCC, particularly in the early stages of HCC. Several biomarkers are considered to diagnose HCC, including downregulated or upregulated protein markers secreted during HCC development, circulating nucleic acids or cells, metabolites, and the promising, recently identified biomarkers based on quantitative proteomics through the isobaric tags for relative and absolute quantitation (iTRAQ). In addition, a diagnostic model used to improve the sensitivity of combined biomarkers for the diagnosis of early HCC is discussed.

Early detection of hepatocellular carcinoma (HCC) is crucial in providing more effective therapies. As routine laboratory variables are readily accessible, this study aimed to develop a simple non-invasive model for predicting hepatocellular cancer. Two groups of patients were recruited: an estimation group (n = 300) and a validation group (n = 625). Each comprised two categories: hepatocellular cancer and liver cirrhosis. Logistic regression analyses and receiver operating characteristic (ROC) curves were used to develop and validate the HCC-Mark model comprising AFP, high-sensitivity C-reactive protein, albumin and platelet count. This model was tested in cancer patients classified by the Barcelona Clinic Liver Cancer (BCLC), Cancer of Liver Italian Program (CLIP) and Okuda systems, and was compared with other non-invasive models for predicting hepatocellular cancer. HCC-Mark produced a ROC AUC of 0.89 (95% CI 0.85–0.90) for discriminating hepatocellular carcinoma from liver cirrhosis in the estimation group and 0.90 (0.86–0.90) in the validation group (both p < 0.0001). This AUC exceeded all other models, that had AUCs from 0.41 to 0.81. AUCs of HCC-Mark for discriminating patients with a single focal lesion, absent macrovascular invasion, tumour size <2 cm, BCLC (0-A), CLIP (0–1) and Okuda (stage Ι) from cirrhotic patients were 0.88 (0.85–0.90), 0.87 (0.85–0.89), 0.89 (0.85–0.93), 0.87 (0.84–0.89), 0.85 (0.82–0.87) and 0.86 (0.83–0.89), respectively (all p < 0.0001). HCC-Mark is an accurate and validated model for the detection of hepatocellular cancer and certain of its clinical features.