Prognostic Analysis and Biomarkers Identification of Immune Infiltration in Early and Late Stage Hepatocellular Carcinoma Based on TCGA Data

BACKGROUND: Hepatocellular carcinoma (HCC) is a major cause of cancer death in the world. The aim of this study was to establish a new model to predict the prognosis of HCC. MATERIALS AND METHODS: The mRNA, miRNA and lncRNA expression profiles of early (stage I–II) and late (stage III–IV) stage HCC patients were acquired from The Cancer Genome Atlas (TCGA) database. The differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs) and lncRNAs (DElncRNAs) were identified between early and late stage HCC. Key molecules associated with the prognosis, and important immune cell types in HCC were identified. The nomogram based on incorporating age, gender, stage, and all important factors was constructed to predict the survival of HCC. RESULTS: A total of 1516 DEmRNAs, 97 DEmiRNAs and 87 DElncRNAs were identified. A DElncRNA-DEmiRNA-DEmRNA regulatory network including 78 mRNAs, 50 miRNAs and 1 lncRNA was established. Among the regulatory network, 11 molecules were significantly correlated with the prognosis of HCC based on Lasso regression analysis. Then, Preadipocytes and 3 survival-associated DEmRNAs were identified as crucial biomarkers. Subsequently, a nomogram with a differentiation degree of 0.758, including 1 immune cell, 11 mRNAs and 3 miRNAs, was generated. CONCLUSION: Our study constructed a model by incorporating clinical information, significant biomarkers and immune cells to predict the survival of HCC, which achieved a good performance.