Prognostic model of hepatocellular carcinoma based on cancer grade

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. With highly invasive biological characteristics and a lack of obvious clinical manifestations, HCC usually has a poor prognosis and ranks fourth in cancer mortality. The aetiology and exact molecular mechanism of primary HCC are still unclear. AIM: To select the characteristic genes that are significantly associated with the prognosis of HCC patients and construct a prognosis model of this malignancy. METHODS: By comparing the gene expression levels of patients with different cancer grades of HCC, we screened out differentially expressed genes associated with tumour grade. By protein-protein interaction (PPI) network analysis, we obtained the top 2 PPI networks and hub genes from these differentially expressed genes. By using least absolute shrinkage and selection operator Cox regression, 13 prognostic genes were selected for feature extraction, and a prognostic risk model of HCC was established. RESULTS: The model had significant prognostic ability in HCC. We also analysed the biological functions of these prognostic genes. CONCLUSION: By comparing the gene profiles of patients with different stages of HCC, We have constructed a prognosis model consisting of 13 genes that have important prognostic value. This model has good application value and can be explained clinically.