Construction of a ceRNA network in hepatocellular carcinoma and comprehensive analysis of immune infiltration patterns.

Abstract

BACKGROUND:

Hepatocellular carcinoma (HCC) is a type of refractory malignant tumor with high fatality rate. Currently, immunotherapy and competitive endogenous RNA (ceRNA) are research hotspots in HCC, but the relationship between ceRNA and the immune microenvironment in HCC is unclear.

METHODS:

Firstly, a differentially expressed circRNA-miRNA-mRNA network was constructed from the GEO database, and functional enrichment analysis was performed. Next, combine the TCGA database to construct a ceRNA prognosis-related subnetwork. Establish a risk prediction model based on the mRNA in the sub-network, and evaluate the impact of the model on the prognosis. Use clinical samples to verify the expression of genes in the model. Finally, we analyzed the distribution of tumor infiltrating immune cells (TIC) in HCC, and explored the correlation between mRNAs in the ceRNA sub-network and immune infiltration.

RESULTS:

We used the HCC ceRNA network (including 12 circRNA, 5 miRNA, and 8 mRNA) as a starting point for the identification of target genes (PSMD10, ESR1 and PPARGC1A) in the ceRNA prognosis-related subnetwork to establish a risk prediction model and elucidated its important role in predicting the poor prognosis of HCC. The differences in mRNA expression verified by clinical samples are consistent with the database. In addition, we found that the mRNAs in the ceRNA prognosis subnetwork are closely related to different types of TICs and immune checkpoints.

CONCLUSIONS:

This study is expected to serve as a reference for the study of mechanisms underlying liver cancer, the screening of prognostic markers and the evaluation of the immune response.

• Authors: Zhifan Zuo, Tingsong Chen, Yue Zhang, Lei Han, Bo Liu, Bin Yang, Tao Han, Zhendong Zheng
• Journal: American journal of translational research (Am J Transl Res) Vol. 13 Issue 12 Pg. 13356-13379 ( 2021) ISSN: 1943-8141 [Print] United States
• PMID: 35035681 (Publication Type: Journal Article)
• Copyright: AJTR Copyright © 2021.