Systematic analysis of the mitophagy scoring system in the prognosis and immunotherapy of HCC patients
Year: 2026
Type: article
Abstract: BACKGROUND: Mitophagy plays a vital role in hepatocellular carcinoma (HCC) progression. This study aims to construct a mitophagy-related scoring system and evaluate its prognostic value, association with tumor immune activity, and gene mutation profiles in HCC patients, thereby advancing personalized diagnosis and treatment. METHODS: Copy number variation (CNV) data of mitophagy regulators from TCGA and ICGC databases were integrated for consensus clustering. Principal component analysis (PCA) was employed to establish a mitophagy scoring system based on mitophagy clusters, gene clusters, and clinical outcomes. Kaplan-Meier survival analysis, Spearman correlation, and immune infiltration algorithms (CIBERSORT, ssGSEA) were used to assess clinical relevance, prognosis, and immunotherapy sensitivity. Tumor mutation burden (TMB) was combined with mitophagy scores for refined prognosis prediction. RESULTS: Three distinct mitophagy clusters (A, B, C) and gene clusters (A, B, C) were identified, showing significant differences in prognosis, immune cell infiltration (19 immune cell types, p < 0.05), and mutation profiles. The mitophagy scoring system stratified patients into high- and low-score groups. High-score patients exhibited better overall survival (p < 0.001), higher immune checkpoint expression (PD-1, CTLA4; p = 0.015 and p = 0.0007), and greater sensitivity to immunotherapy. Low-score patients had higher TP53 mutation rates (55%) and poorer outcomes. Combining mitophagy scores with TMB further improved prognostic accuracy (p < 0.001). CONCLUSION: The mitophagy scoring system serves as a robust prognostic biomarker and predictor of immunotherapy response in HCC, offering insights into tumor heterogeneity and clinical decision-making.
Source: Discover Oncology
Authors: Yangjun Yin, Maixuan Qiu, Jun Shen, Jianjun Yan
Institution: Second Military Medical University
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,690
OpenAlex ID: W7160708353