Mitochondrial Quality Regulation Genes as Prognostic Markers in Hepatocellular Carcinoma: Tumor Microenvironment, Therapeutic Response, and Drug Sensitivity Analysis.

Authors

Binbin Li, Lijun Zeng, Zhilong He, Lunqi Luo, Yongmei Luo, Xinyu Yi, Zhimin Li, Hongbo Zhu, Yuehua Li

Published in

Drug development research. Volume 87. Issue 6. Pages e70372.

Abstract

Aberrant expression of mitochondrial quality regulation genes (MQRGs) is intricately linked to mitochondrial dysfunction and the progression of hepatocellular carcinoma (HCC), highlighting the urgent need for reliable prognostic biomarkers. In this study, we aimed to identify differentially expressed MQRGs from 20 candidate genes by analyzing transcriptomic profiles and clinical records from the TCGA (n = 371) and GEO (n = 167) datasets. The identified prognostic MQRGs, along with associated subtype differentially expressed genes (DEGs, n = 156), underwent LASSO and multivariate Cox regression analyses to construct a risk model, which was subsequently validated through time-dependent ROC analysis, Kaplan-Meier curves, and in vitro RT-qPCR. Our findings established a robust 4-MQRG signature comprising ANXA10, BAMBI, AKR1B15, and SPINK1, which revealed that patients classified as high risk had significantly shorter overall survival compared to their low-risk counterparts (p < 0.001). The predictive accuracy of this signature was noteworthy, yielding 1-, 3-, and 5-year AUCs of 0.725, 0.696, and 0.747 in the training cohort (n = 243) and 0.676, 0.627, and 0.592 in the testing cohort (n = 242), respectively. Furthermore, high-risk scores were associated with distinct immunosuppressive tumor microenvironments and varying sensitivity to systemic therapies. The dysregulated expression of the four genes was corroborated by RT-qPCR and analysis of the HPA database. In conclusion, this validated MQRG-based prognostic signature serves as an accurate tool for survival prediction and risk stratification, thus offering valuable biomarker support for personalized therapeutic approaches in HCC.

PMID:
42687711
Bibliographic data and abstract were imported from PubMed on 03 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.