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. 2026 May 9;17:965. doi: 10.1007/s12672-026-05117-7

Systematic analysis of the mitophagy scoring system in the prognosis and immunotherapy of HCC patients

Yangjun Yin 1,#, Maixuan Qiu 1,#, Jun Shen 1,✉, Jianjun Yan 1,✉
PMCID: PMC13323770  PMID: 42105039

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-026-05117-7.

Keywords: Hepatocellular carcinoma, Mitophagy, Prognosis, Immunotherapy, Tumor mutation burden

Introduction

Hepatocellular carcinoma (HCC) represents a common malignant tumor with escalating global morbidity and mortality rates. Approximately 70% ~ 80% of patients remain asymptomatic during early disease stages, and the lack of widespread early screening programs leads to frequent diagnosis at advanced stages, thereby missing optimal surgical intervention opportunities [1, 2]. Emerging evidence indicates that genetic testing-guided targeted therapies may address current treatment limitations in HCC. However, the complexity of clinical classification and substantial tumor heterogeneity substantial HCC patients result in unsatisfactory responses to most targeted therapies based on gene expression variants, significantly hindering clinical translation [3]. As a current hot spot, gene set detection, such as m6A [4], cuproptosis [5] and other specific functional gene sets, has been used in a variety of cancer prognosis prediction models. For clinicians, there is a pressing need for clearer and more efficient method to classify patients and guide treatment decisions [6, 7]. Consequently,, developing novel multi- parameter prognostic biomarkers with well-defined gene functions and establishing refined HCC subtyping systems are crucial for achieving more precise diagnosis, staging, prognosis, and personalized treatment.

Mitochondria play essential roles in tumorigenesis, significantly influencing cancer cell proliferation and immune mechanisms [8]. Targeting mitochondrial regulation of tumor mitochondria is a new idea to fight cancer, and mitophagy, as an important mitochondrial quality control mechanism to eliminate damaged mitochondria, has been increasingly favored by researchers [9]. Previous studies demonstrate that mitophagy deficiency can compromise the stemness characteristics of HCC stem cells and reduce their tumorigenic potential [10]. Recently identified mitophagy related genes (MRGs), including ATG12 [11], MTERF3 [12], and CSNK2B [13]. have been implicated in HCC pathogenesis and represent potential molecular targets for therapy. The diversity in gene expression profiles enables molecular classification of cancers based on distinct molecular characteristics [14, 15]. Different mitophagy molecular typing was found to have important applications in the treatment and prognosis of in HCC. For instance, existing studies have constructed MRG signature with two subtypes that demonstrate important value for immunotherapy response and prognosis prediction in HCC patients [16]. Similarly, Wang et al. established the mitophagy associated risk scoring system by analyzing the DGE of mitophagy associated signaling pathways, which has the potential to be used as a prognostic tool for HCC [17]. Therefore, exploring the novel mitophagy-related signature can provide HCC patients with more treatment options and effectively guide clinical decision-making.

Previous mitophagy-related signatures for HCC were mostly constructed based on single transcriptomic data and only divided patients into two molecular subtypes, with limited consideration of genomic instability such as copy number variation (CNV) and lack of combined analysis with tumor mutation burden (TMB) for refined prognostic stratification. Additionally, existing studies have not fully characterized the correlation between mitophagy subtypes and the comprehensive immune cell infiltration landscape, nor have they conducted rigorous multi-tiered validation (internal cross-validation + independent cohort validation) for the constructed scoring system. To address these research gaps, this study integrates multi-omics data (CNV, transcriptome, somatic mutation) and clinical follow-up information to construct a novel mitophagy scoring system, and further explores its clinical value in combination with TMB, aiming to provide a more comprehensive and robust molecular stratification tool for HCC prognosis and immunotherapy prediction.

Methods

Data acquisition and processing

CNV profiles and corresponding clinical data of HCC patients were retrieved from The Cancer Genome Atlas (TCGA, https://tcga-data.nci.nih.gov/tcga/) and International Cancer Genome Consortium (ICGC, https://icgc.org/) databases. To identify mitophagy-related copy number variations in HCC, we conducted a systematic literature review using PubMed with the following search terms: (“copy number variation” AND HCC), (“copy number deletion” AND HCC), and (“chromosomal variation” AND HCC). Identified genomic loci were cross-referenced with TCGA and ICGC datasets to compile a comprehensive profile of mitophagy-associated CNVs in HCC.

Identification of differentially expressed genes (DEGs)

Mitophagy-related regulators were subjected to unsupervised hierarchical clustering using the R package ConsensusClusterPlus (v1.54.0). Kaplan-Meier survival analysis was performed with survival (v3.2–13) and survminer (v0.4.9) packages, with statistical significance determined by log-rank test. Differential expression analysis of mitophagy regulators across subgroups was visualized using pheatmap (v1.0.12). GEO datasets were analyzed through limma (v3.50.0) with significance thresholds set at |log2FC| >1 and adjusted p-value < 0.05. Gene Ontology (GO) enrichment analysis was conducted using clusterProfiler (v4.2.2).

Tumor immune microenvironment characterization

Immune cell infiltration patterns were analyzed through three complementary approaches: (1) Single-sample gene set enrichment analysis (ssGSEA) using GSVA (v1.42.0); (2) CIBERSORT deconvolution algorithm for immune cell subset quantification; (3) Principal component analysis (PCA)-based mitophagy scoring system. Immune checkpoint gene expression was compared using ggplot2 (v3.3.6) and reshape2 (v1.4.4).

Somatic mutation profiling

Somatic mutation data from TCGA MAF files were processed using maftools (v2.10.05). Mutation annotation and inter-group comparisons were performed with default parameters, retaining variants classified as “missense”, “nonsense”, or “frameshift”.

Validation strategy

To ensure the robustness of our mitophagy scoring system, we implemented a comprehensive validation strategy. Given the multi-omics nature of our model requiring both CNV and transcriptomic data alongside complete clinical follow-up, we first conducted rigorous internal validation. We performed 10-fold cross-validation repeated 100 times on the TCGA-LIHC cohort, which demonstrated consistent model performance with an average concordance index (C-index) of 0.72 (± 0.03), indicating good predictive reliability. We extensively searched for suitable independent validation cohorts containing all necessary data modalities. However, publicly available HCC datasets combining CNV profiles, transcriptomic data, and comprehensive clinical outcomes remain limited. This represents a common challenge in multi-omics research. Our cross-validation approach therefore provides the most robust available assessment of model performance while mitigating overfitting concerns.

Statistical analysis

All analyses were conducted in R (v4.2.1). Continuous variables were compared using Student’s t-test (parametric) or Wilcoxon rank-sum test (non-parametric). Categorical variables were analyzed by χ² test. Survival differences were assessed through Cox proportional hazards models. Correlation analyses utilized Spearman’s rank method. Multiple testing correction employed Benjamini-Hochberg procedure where applicable, with significance threshold set at FDR-adjusted p < 0.05.

Code availability

The custom code used for data processing, statistical analysis, and figure generation in this study is available upon reasonable request from the corresponding author. No standalone software or algorithm was developed as part of this work. All analyses were performed using publicly available R packages as described in the Methods section.

Results

Mitophagy subtype characterization in HCC

CNV frequencies of 17 mitophagy regulators in HCC are presented in Fig. 1a and b. Mutation profiling (Fig. 1c) identified HELZ as the most frequently mutated gene (2%), potentially contributing to HCC pathogenesis. A mitophagy regulatory network (Fig. 1d) highlighted RPS6KB1 and APPBP2 as prognostic risk factors and GCDH as a protective factor. Survival analysis (Figure S1) showed improved overall survival (OS) with low RPS6KB1/HELZ expression and high RNH1/IRF7/GCDH expression.

Fig. 1.

Fig. 1

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CNV analysis of Mitophagy related regulatory factors (a, b). Analysis of Mitophagy related gene mutations (c). The interaction between Mitophagy related genes was analyzed by one-way COX (d). Survival analysis between three mitophagy clusters (e). Analysis of immune infiltration between three mitophagy clusters (f)

Using consensus CDF curves and k-means clustering, HCC patients were stratified into three mitophagy clusters (A, B, C) based on regulator expression (Figure S2). Clinical feature distribution (Figure S3) and survival analysis (Fig. 1e) revealed cluster A had significantly worse OS than clusters B and C (P < 0.001). Immune infiltration analysis (Fig. 1f) showed clusters correlated with 19 immune cell types, including B cells, CD4+/CD8 + T cells, and dendritic cells (P < 0.05).

Mitophagy-related gene signatures

Difference analysis has been carried out between the three subtypes and 3655 overlapping DEGs have been obtained as shown in Fig. 2a Venn diagram. Using the gene transcriptome data for PCA grouping three gene clusters (A, B, and C) were obtained (Figure S4). The heatmap in figure S5 shows the distribution of clinical features and DEGs among the 3 gene clusters. Comparison of survival analyses revealed that in terms of survival probability, cluster B > A> C (p < 0.001, Fig. 2b). The immune infiltration difference of the three groups of gene clusters in 23 immune cells was shown in Fig. 2c, which was significantly correlated with 15 immune cells, but had no statistical difference with 8 immune cells. GO enrichment analysis revealed that DEGs were mainly enriched in triosephosphate metabolic process (biological process, BP), mitochondria (cellular component, CC), GTPase activity, and oxidoreductase (molecular function, MF) (Fig. 2d).

Fig. 2.

Fig. 2

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The difference analysis between the three groups is carried out and the overlapped difference gene is obtained (a). Survival analysis of three gene clusters (b). Immune cell differences among three gene clusters (c). GO enrichment analysis of differential genes (d)

Establishment of the mitophagy scoring system

As shown in the Sankey diagram of Fig. 3a, HCC patients were classified into three mitophagy clusters, three gene clusters and two mitophagy score groups, and the risk scoring system was constructed based on their prognosis. The different mitophagy cluster components (Fig. 3b) and gene clusters (Fig. 3c) all showed significant differences in mitophagy scores. Pearson correlation analysis revealed significant associations between mitophagy score and 17 immune cells types, while no significant correlation was observed with 6 other immune populations (Fig. 3d). Survival analysis revealed that patients with high mitophagy scores had significantly better overall survival compared to those with low scores (Fig. 3e). When incorporating TMB, patients with high TMB (H-TMB) showed poorer survival outcomes than those with low TMB groups (L-TMB) (Fig. 3f). Then combined analysis of mitophagy scores and TMB stratified patients into four distinct prognostic groups: L-TMB + L-score, L-TMB + H-score, H-TMB + L-score, and H-TMB + H-score. As shown in Fig. 3g, patients in the H-TMB + L-score group had the poorest survival, while those in the L-TMB + H-score group exhibited the most favorable outcomes (p < 0.001).

Fig. 3.

Fig. 3

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Alluvial diagram of gene cluster distribution in groups with different mitophagy scores and survival outcomes (a). Compare mitophagy scores between gene cluster groups (b). Compare mitophagy scores between mitophagy cluster groups (c) Correlation between Mitophagy score and immune cells (d). Survival analysis between high and low mitophagy score groups, L and H TMB groups, H-TMB + H-score, H-TMB + L-score, L-TMB + L-score and L-TMB + H-score groups (e-g)

Correlations with gene mutations, ICI response, and clinical features

Analysis of gene mutation differences between mitophagy score groups revealed that the gene mutation rate in the high score group was 83.75%, among which CTNNB1 had the highest mutation rate, which was 29% (Fig. 4a), and that in the low score group, the gene mutation rate was 88.06%, and the mutation rate of TP53 was as high as 55% (Fig. 4b). Furthermore, analyzing the mitophagy score group and the sensitivity of immune checkpoint inhibitors (ICI) treatment found that the high mitophagy score correlated significantly with immune checkpoints PD1 positivity (p = 0.015) and CTLA4 positivity (p = 0.0007) (Fig. 4c-e). Finally, analysis of the relationship between mitophagy score and clinical characteristics of HCC revealed that patients who were older (p = 0.014, Fig. 5a), male (p = 0.01, Fig. 5b), and with low tumor stage (p = 0.00091, Fig. 5e) had a higher mitophagy score, whereas G stage (Fig. 5c) and lymph node metastasis (N) (Fig. 5d), and T stage (Fig. 5f) had no significant correlation with the mitophagy score.

Fig. 4.

Fig. 4

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Mutations of genes in the high mitophagy score group (a). Mutations of genes in the low mitophagy score group (b). Mitophagy score group and the sensitivity of immune checkpoint therapy (c-e)

Fig. 5.

Fig. 5

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Relationship between mitophagy score and clinical characteristics of HCC patients (a-f)

Prognostic significance across clinical subgroups

Through Kaplan-Meier analysis of the prognostic relationship between mitophagy score and HCC patients under different states, it was found that high-rating grouping could significantly improve the survival rate of patients with different tumor stages, T stages, age, and M0, G1, G3, and N0 patients (figure S6a-r). It showed that mitophagy score played a crucial role in the prognostic development of HCC.

Discussion

Mitophagy, a critical regulator of cellular homeostasis, plays multifaceted roles in tumor biology from embryonic development to cancer progression [18, 19]. In HCC, dysregulated mitophagy disrupts mitochondrial quality control while facilitating tumor immune evasion and metastatic dissemination. Recognizing its potential as a therapeutic target, several recent studies have explored mitophagy-related gene signatures for HCC prognosis prediction. Wang et al. [20] developed a risk scoring system based on metabolic patterns; while others have proposed signatures using LASSO regression or machine learning approaches [14, 21]. These studies collectively underscore the prognostic and immunomodulatory significance of mitophagy in HCC, yet most have relied primarily on transcriptomic data. These studies collectively underscore the prognostic and immunomodulatory significance of mitophagy in HCC, yet most have relied primarily on transcriptomic data. Building upon this foundation, our study offers a complementary perspective through integrated multi-omics analysis. Unlike previous transcriptome-based models, we incorporated CNV data with transcriptomic profiles to construct a mitophagy scoring system. This approach captures both genomic instability and functional expression, potentially providing a more comprehensive reflection of mitophagy dysregulation in HCC. As hallmarks of tumor heterogeneity, CNVs are frequently altered in tumor development [22, 23]. We identified 17 mitophagy regulators with recurrent CNV alterations in HCC. Among these, RPS6KB1 and APPBP2 emerged as prognostic risk factors, consistent with their oncogenic roles in other malignancies [24, 25], while GCDH correlated with favorable outcomes, aligning with its tumor-suppressive function in melanoma [26]. These findings underscore CNV analysis may offer additional insights into molecular drivers of HCC progression beyond transcriptomic profiling alone.

The mitophagy scoring system effectively stratified patients into high- and low-risk groups with distinct survival outcomes. Notably, we explored the combination of mitophagy scores with TMB. Our analysis revealed that the low-TMB/high-mitophagy score subgroup exhibited the most favorable prognosis, whereas the high-TMB/low-mitophagy score subgroup had the worst outcomes. While prior studies have noted associations between mitophagy subtypes and immune features [16], and TMB itself is a well-established prognostic factor in HCC, the integration of mitophagy scores with TMB for refined four-tier stratification represents an exploratory attempt to address the limitations of single biomarkers in heterogeneous HCC populations. This dual-parameter approach, if validated in future studies, may contribute to more nuanced risk assessment.

The tumor immune microenvironment is a critical determinant of treatment response [27], and our analysis linked mitophagy scores to immune checkpoint expression. High mitophagy scores correlated with increased PD1/CTLA4 positivity, consistent with findings from Liu et al. [16] who reported higher immune checkpoint gene expression in certain mitophagy subtypes. This aligns with a more immunogenic microenvironment in high-score patients—characterized by enriched T cell infiltration and checkpoint activation [28, 29]—suggests a consistent pattern across studies. In contrast, low-mitophagy score patients exhibit immunosuppressive features, including reduced immune cell infiltration and checkpoint expression, that may contribute to poor prognosis and ICI resistance. These observations, while correlative, offer a rationale for further investigating mitophagy status as a potential factor in personalized ICI selection for HCC.

A notable finding in our analysis was the inverse relationship between TP53 mutation status and PD-L1 activity in the low mitophagy score group. While TP53 mutations are generally associated with increased immune checkpoint expression in some cancers [30], we observed high TP53 mutation rates (55%) coupled with lower PD-L1 activity in low mitophagy score patients. This apparent paradox may reflect the complex, context-dependent regulation of PD-L1. The loss of wild-type p53 function can impair the interferon-gamma (IFN-γ) signaling pathway, a primary driver of PD-L1 transcription [31]. Additionally, the low mitophagy score might define an immunosuppressive microenvironment lacking the T-cell-derived signals necessary for PD-L1 induction. This observation, while preliminary, highlights a potential interplay between genomic instability, mitochondrial quality control, and immune regulation that warrants future investigation.

Another intriguing finding of our study is that high mitophagy scores were associated with better survival in male and older patients, which contrasts with the general epidemiology of HCC. It is necessary to clarify that this finding does not imply that older patients have stronger immune responses or that male patients have a more robust immune status—on the contrary, aging is associated with immune senescence and a relatively weakened immune system, and females generally exhibit a stronger adaptive and innate immune response than males, which is also one of the reasons for the higher prevalence of HBV infection (a major etiological factor of HCC) in males However, it should be acknowledged that incomplete etiological records are a common limitation of public databases, and not all patients in our study cohort have clear and complete records of HBV infection or other etiological factors. We hypothesize that this sex- and age-related survival phenomenon may be attributed to the heterogeneous regulatory effects of mitophagy on the tumor microenvironment in different HCC populations: for example, mitophagy may modulate the immune microenvironment of HBV-related HCC (predominant in male patients) through specific molecular pathways, and aging may trigger compensatory activation of mitophagy in HCC cells, which in turn shapes an immunogenic tumor microenvironment to exert a protective effect. Further investigations combining complete etiological data, larger sample sizes and in vitro experiments are needed to unravel the underlying sex- and age-related differences in mitophagy-mediated immune regulation in HCC.

Our study has several limitations that necessitate cautious interpretation of the conclusions: this is a retrospective analysis based on public database data, with incomplete clinical and etiological information recording and potential sample selection bias, and the incomplete etiological data further hinders the in-depth exploration of the interplay between mitophagy, HCC etiological factors (e.g., HBV infection) and clinical prognosis; despite rigorous technical validation of the mitophagy scoring system via 10-fold cross-validation repeated 100 times with a stable concordance index, the study lacks direct biological validation through in vitro cell and in vivo animal experiments, with all conclusions derived merely from bioinformatic analysis and data mining without verifying the scoring system’s regulatory mechanisms in HCC progression, immune response and gene mutation; the small sample size of certain clinical subgroups (e.g., M0, G1) impairs the reliability of subgroup survival and correlation analyses, and also restricts in-depth biological validation and multi-dimensional stratification analysis of the scoring system in different subpopulations; the association between high mitophagy scores and potential better ICI response is only inferred from bioinformatic analyses of immune checkpoint expression and immune cell infiltration patterns, with no supporting data from actual clinical treatment outcomes of HCC patients receiving ICIs; additionally, the mitophagy scoring system remains a purely bioinformatic model to date, whose clinical applicability awaits verification in real-world clinical samples with standardized detection methods established. To address these limitations and further enhance the scientificity and clinical value of the mitophagy scoring system, we will conduct targeted follow-up research: launch large-sample prospective clinical cohort studies with complete collection of clinical and etiological information to reduce retrospective biases and validate the system’s prognostic and ICI response predictive value in actual clinical settings; design and perform in vitro and in vivo functional experiments to complete biological validation of the system and elucidate its underlying molecular regulatory mechanisms; expand the sample size of various HCC clinical subgroups for more detailed subgroup analysis to improve the system’s applicability in different populations; collect real-world data of HCC patients receiving ICI therapy to validate the system’s predictive value for immunotherapy efficacy; and optimize the scoring system by incorporating more clinical and molecular biomarkers while developing standardized detection methods for its key indicators, so as to explore its translational value in HCC precision oncology.

It should be emphasized that the findings of this study are preliminary and represent a bioinformatic exploration of mitophagy-related patterns in HCC. While our results are partially in confirmation with overlapping papers published elsewhere—which have also underscored the prognostic significance of mitophagy in various cancers—this study provides a distinct multi-omics perspective by integrating CNV and TMB data. Nevertheless, the clinical utility and biological mechanisms of the proposed scoring system require further prospective validation.

Conclusion

In conclusion, this study presents a mitophagy scoring system that integrates CNV and transcriptomic data, offering a multi-omics perspective on HCC prognosis and immune features. While building upon existing mitophagy-related research, our exploratory combination of mitophagy scores with TMB and the observation of demographic-specific patterns may offer additional avenues for risk stratification. Further validation in independent cohorts and experimental models will be essential to determine the clinical utility of this approach.

Supplementary Information

Supplementary Material 1 (4.4MB, pdf)

Acknowledgements

Not Applicable.

Author contributions

Y.J. Y and M.X. Q contributed to the study conception and design. Material preparation, data collection and analysis were performed by J. S and J.J. Y. The first draft of the manuscript was written by Y.J. Y, M.X. Q, J. S and J.J. Y and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

No funding was received to assist with the preparation of this work.

Data availability

The datasets analyzed during the current study are available in public repositories. The TCGA-LIHC dataset can be accessed at https://portal.gdc.cancer.gov/(project ID: TCGA-LIHC). The ICGC-LIRI-JP dataset can be accessed at https://dcc.icgc.org/(dataset ID: LIRI-JP). All data were downloaded in accordance with the data access policies of each repository. No new datasets were generated in this study.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Not applicable.

Consent for publish

Not applicable.

Consent for publication

Not applicable.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yangjun Yin and Maixuan Qiu contributed equally to the study.

Contributor Information

Jun Shen, Email: shenjundr@163.com.

Jianjun Yan, Email: yanjianjundr@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (4.4MB, pdf)

Data Availability Statement

The datasets analyzed during the current study are available in public repositories. The TCGA-LIHC dataset can be accessed at https://portal.gdc.cancer.gov/(project ID: TCGA-LIHC). The ICGC-LIRI-JP dataset can be accessed at https://dcc.icgc.org/(dataset ID: LIRI-JP). All data were downloaded in accordance with the data access policies of each repository. No new datasets were generated in this study.

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