A Novel Four-Gene Signature Based on Nonsense-Mediated RNA Decay for Predicting Prognosis in Hepatocellular Carcinoma: Bioinformatics Analysis and Functional Validation

Abstract

Purpose

Nonsense-mediated RNA decay (NMD), a surveillance pathway for selective degradation of aberrant mRNAs, is associated with cancer progression. Its potential as a predictor for aggressive hepatocellular carcinoma (HCC) is unclear. Here, we present an innovative NMD risk model for predicting HCC prognosis.

Methods

The Cancer Genome Atlas (TCGA) data of 374 liver HCC (LIHC) and 50 normal liver samples were extracted. A risk model based on NMD-related genes was developed through least absolute shrinkage and selection operator Cox (LASSO-Cox) regression of the LIHC-TCGA data. Prognostic validation was done using GSE54236, GSE116174, and GSE76427 data. Univariate and multivariate Cox regression analyses were conducted to assess the prognostic value of the model. We also constructed nomograms for survival prediction. Tumor immune infiltration was evaluated using the CIBERSORT algorithm, and the tumor cell phenotype was assessed. Finally, mouse experiments verified UPF3B knockdown effects on HCC tumor characteristics.

Results

We developed a risk model based on four NMD-related genes (PABPC1, RPL8, SMG5, and UPF3B) and validated it using GSE54236, GSE116174, and GSE76427 data. The model effectively distinguished high- and low-risk groups corresponding to unfavorable and favorable HCC outcomes. Its prognostic prediction accuracy was confirmed through time-dependent ROC analysis, and clinical-use nomograms with calibration curves were developed. Single-cell RNA sequencing results indicated significantly higher expression of SMG5 and UPF3B in tumor cells. Knockdown of SMG5 and UPF3B inhibited HCC cell proliferation, invasion, and migration, while affecting cell-cycle progression and apoptosis. In vivo, UPF3B knockdown delayed tumor growth and increased immune cell infiltration.

Conclusion

Our NMD-related gene-based risk model can help identify therapeutic targets and biomarkers for HCC. Additionally, it assists clinicians in predicting the prognosis of HCC patients.

Keywords:

• nonsense-mediated RNA decay
• hepatocellular carcinoma
• biomarker
• LASSO
• survival
• tumor immunity

Data Sharing Statement

The datasets used in this study can be found in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The data for this study are available in the Cancer Genome Atlas (https://portal.gdc.cancer.gov/repository?facetTab=files); GSE54236, GSE116174 and GSE76427 datasets are available from the following website (https://www.ncbi.nlm.nih.gov/).

Ethics Approval and Consent to Participate

This study involved in the sample information and clinical data are from the TCGA database (https://portal.gdc.cancer.gov/) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/); the liver cancer cells were purchased from Wuhan Pricella Biotechnology Co., Ltd. The above study meets the conditions for exemption from ethical review in the Measures for Ethical Review of Life Sciences and Medical Research Involving Human Beings in China, so we obtained an ethical review exemption from the Human Research Ethics Committee of the Fourth Affiliated Hospital of Zhejiang University School of Medicine. The animal study has been evaluated by the Laboratory Animal Welfare Ethics Review Committee of Zhejiang University.

Acknowledgments

We acknowledge TCGA, GEO and VGDS database for providing their meaningful data.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare no competing interests in this work.

Additional information

Funding

This study was supported by National key research and development program (2022YFA1104600), Key Research and Development Program of Zhejiang Province (No.2020C03057), Key Science and Technology Program of Zhejiang Province (No. WKJ-ZJ-1818).