4.4 Article

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

Journal

INTERNATIONAL JOURNAL OF GENERAL MEDICINE
Volume 16, Issue -, Pages 2519-2530

Publisher

DOVE MEDICAL PRESS LTD
DOI: 10.2147/IJGM.S420458

Keywords

hepatocellular carcinoma; stage; nomogram; TCGA

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This study aimed to establish a new model to predict the prognosis of hepatocellular carcinoma (HCC). Differential expression profiles of mRNA, miRNA, and lncRNA between early and late stage HCC were analyzed, and key molecules associated with prognosis and important immune cell types in HCC were identified. A nomogram incorporating age, gender, stage, and all important factors was constructed to predict the survival of HCC.
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 survivalassociated 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.

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