The Systematic Analyses of RING Finger Gene Signature for Predicting the Prognosis of Patients with Hepatocellular Carcinoma

Author:

Zhang Chunfeng1,Yang Yang2,Wang Kun2,Chen Muhua2,Lu Min3,Hu Chenyu2,Du Xiaojuan4,Xing Baocai2,Liu Xiaofeng2ORCID

Affiliation:

1. Department of Medical Genetics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China

2. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital & Institute, Beijing 100142, China

3. Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China

4. Department of Cell Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China

Abstract

RING finger (RNF) proteins are frequently dysregulated in human malignancies and are tightly associated with tumorigenesis. However, the expression profiles of RNF genes in hepatocellular carcinoma (HCC) and their relations with prognosis remain undetermined. Here, we aimed at constructing a prognostic model according to RNF genes for forecasting the outcomes of HCC patients using the data from The Cancer Genome Atlas (TCGA) program. We collected HCC datasets to validate the values of our model in predicting prognosis of HCC patients from International Cancer Genome Consortium (ICGC) platform. Then, functional experiments were carried out to explore the roles of the representative RNF in HCC progression. A total of 107 differentially expressed RNFs were obtained between TCGA-HCC tumor and normal tissues. After comprehensive evaluation, a prognostic signature composed of 11 RNFs (RNF220, RNF25, TRIM25, BMI1, RNF216P1, RNF115, RNF2, TRAIP, RNF157, RNF145, and RNF19B) was constructed based on TCGA cohort. Then, the Kaplan-Meier (KM) curves and the receiver operating characteristic curve (ROC) were employed to evaluate predictive power of the prognostic model in testing cohort (TCGA) and validation cohort (ICGC). The KM and ROC curves illustrated the good predictive power in testing and validation cohort. The areas under the ROC curve are 0.77 and 0.76 in these two cohorts, respectively. Among the prognostic signature genes, BMI1 was selected as a representative for functional study. We found that BMI1 protein level was significantly upregulated in HCC tissues. Moreover, the inhibitor of BMI1, PTC-209, displayed an excellent anti-HCC effect in vitro. Enrichment analysis of BMI1 downstream targets showed that BMI1 might be involved in tumor immunotherapy. Together, our overall analyses revealed that the 11-RNFs prognostic signature might provide us latent chances for evaluating HCC prognosis and developing novel HCC therapy.

Funder

Science Foundation of Peking University Cancer Hospital

Publisher

Hindawi Limited

Subject

Oncology

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