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ORIGINAL RESEARCH ARTICLE

Construction and validation of a prognostic signature based on related genes in hepatocellular carcinoma

Hao Wang1† ,  Runze Jiang2† ,  Jianqi Li3† ,  Kai Cui4 ,  Mingang He5 ,  Yang Gao5 ,  Bingbing Ren5 ,  Changsheng Yan5 ,  Zengjun Li5*
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1 Department of General Surgery, Shandong Provincial Public Health Clinical Center, Jinan, Shandong, China
2 Department of Traditional Chinese Medicine Innovation, Traditional Chinese Medicine Innovation Research Institute, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
3 Department of Gastrointestinal Surgery, The Third Affiliated Hospital of Shandong First Medical University, Jinan, Shandong, China
4 Department of Hepatobiliary Surgery, Shandong Cancer Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
5 Department of Colorectal Surgery Second Ward, Shandong Cancer Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
†These authors contributed equally to this work.
EJMO, 025170149 https://doi.org/10.36922/EJMO025170149
Received: 27 April 2025 | Revised: 4 July 2025 | Accepted: 16 July 2025 | Published online: 2 October 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Introduction: Given the poor prognosis associated with hepatocellular carcinoma (HCC), identifying reliable biomarkers is critical to aid in prognostication and customized treatment planning.

Objective: This study aimed to develop a prognostic gene signature based on anoikis- and autophagy-related genes (AARGs) to predict HCC prognosis.

Methods: We analyzed differentially expressed AARGs associated with HCC prognosis using messenger RNA expression profiles and clinicopathological data from the Cancer Genome Atlas Liver Hepatocellular Carcinoma Database. Validation was performed using data from the International Cancer Genome Consortium. AARG signatures were constructed using univariate Cox regression and the least absolute shrinkage and selection operator method.

Results: We identified 13 AARGs, of which nine showed significant associations with overall survival (OS). Three AARGs—baculoviral IAP repeat containing 5, mitogen-activated protein kinase 3, and BRI1-associated kinase 1—were selected to establish the prognostic AARGs signature. Statistical analyses confirmed the prognostic capacity of the model. Gene set enrichment analysis was performed to explore potential molecular mechanisms. Further investigations included assessments of clinical parameters, immune landscape, immune checkpoint blockade response, tumor stemness, and chemotherapy sensitivity. Immunohistochemistry was conducted to compare protein expression between normal and tumor tissues. Patients in the high-risk group exhibited advanced tumor stage, shorter survival time, and poorer prognosis, along with anoikis resistance, elevated autophagy, and immunosuppression. The nomogram incorporating the AARG signature showed strong predictive performance for OS.

Conclusion: We developed and validated a new AARGs-based signature that effectively predicts prognosis for HCC patients.

Keywords
Anoikis
Autophagy
Prognosis
Hepatocellular carcinoma
Differentially expressed genes
Funding
This work was supported by the Natural Science Fund of Shandong Province (ZR2020LZL003).
Conflict of interest
The authors declare no competing interests.
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