PANoptosis-associated genes exhibit significant potential in the diagnosis of hepatocellular carcinoma

Introduction

Liver cancer, particularly hepatocellular carcinoma (HCC), poses a significant global health challenge due to its high mortality rate and limited therapeutic options. The epidemiology of liver cancer is intricate, and a multitude of risk factors contribute to its incidence. Chronic infections with hepatitis B virus and hepatitis C virus, coupled with lifestyle factors such as alcohol consumption and metabolic disorders like non-alcoholic fatty liver disease, are well-established risk factors for HCC (1). Despite advances in understanding these risk factors, the molecular pathogenesis of liver cancer remains poorly elucidated, hampering the development of effective targeted therapies (2).

The scarcity of actionable molecular targets in liver cancer presents a formidable barrier to the development of new treatments. Although progress has been made in identifying potential therapeutic targets, particularly within the PI3K/AKT/mTOR and Wnt/β-catenin pathways, many genetic alterations associated with liver cancer remain undruggable (3,4). Thus, ongoing research into the molecular mechanisms underpinning liver cancer urgently needs to be conducted to identify novel therapeutic targets that could enhance patient outcomes.

PANoptosis, a novel form of programmed cell death that includes mechanisms of apoptosis, pyroptosis, and necroptosis, has arisen as a significant area of interest in cancer research. This complex cell death pathway is orchestrated by the PANoptosome complex and plays a vital role in regulating immune responses and tumor progression. Recent studies have shown the role of PANoptosis across various cancers, including lung, colorectal, and breast cancers (5-9). In lung cancer, the genes associated with PANoptosis have been identified as potential prognostic biomarkers, offering insights into patient survival and treatment responses (5,6). The intricate interplay between PANoptosis and the tumor microenvironment in lung cancer suggests that targeting PANoptosis pathways could enhance anti-tumor immunity and improve therapeutic outcomes (7). In colorectal cancer, PANoptosis has been shown to affect the immune microenvironment, affecting the infiltration and functionality of immune cells. This has significant implications for the development of immunotherapy strategies, as understanding the role of PANoptosis could lead to more effective treatments (8,9). Additionally, the identification of PANoptosis-related gene signatures in colorectal cancer has laid the groundwork for predicting patient prognosis and chemosensitivity, further emphasizing the potential of PANoptosis as a promising therapeutic target (10).

In this study, we conducted a bioinformatics analysis to develop a prognostic model using PANoptosis-related genes associated with HCC. We also investigated the potential molecular mechanisms underlying PANoptosis-related subtypes in HCC. Our hypothesis is that PANoptosis plays a role in the development of HCC, and that genes related to PANoptosis may aid in the early diagnosis and treatment of this disease. We present this article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-356/rc).

Methods

The Cancer Genome Atlas (TCGA) clinical and transcriptomic data

The transcriptomic and clinical data of 370 patients with HCC were obtained from TCGA database (https://portal.gdc.cancer.gov). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Identification of key prognostic PANoptosis-related genes in HCC

A univariate Cox analysis (P<0.05) was conducted to identify the prognostic genes in HCC, and their expression patterns were displayed using the R package pheatmap (version 1.0.12). A functional enrichment analysis of these genes was conducted to identify the relevant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using clusterProfiler package.

HCC subtypes classified based on PANoptosis-related gene expression profiles

The link between the PANoptosis-related genes and HCC subtypes was examined by consensus clustering (k=2 to 6) of TCGA-ovarian cancer (OC) data using the ConsensusClusterPlus package (version 1.54.0). The Pheatmap package (version 1.0.12) was used to visualize the gene expression patterns, and a Kaplan-Meier analysis was conducted to examine the survival differences among the subgroups.

The underlying mechanisms between the high- and low-risk clusters

The DEseq2 package was used to identify the differentially expressed genes (DEGs) between the HCC clusters, applying a P<0.05 and |log₂fold change| >0.75 threshold, and the results were visualized using ggplot2. A heatmap of the DEGs was generated using pheatmap (version 1.0.12), and a functional enrichment analysis of the Gene Ontology (GO) and KEGG pathways was conducted using clusterProfiler.

Development of a prognostic model using the PANoptosis-related genes

The glmnet package was employed for the Cox regression analysis, which revealed the PANoptosis-related genes with prognostic value in TCGA-HCC cohort. The λ value that met the minimum criteria was chosen, and risk scores were computed by multiplying each gene’s expression level by its coefficient and summing the results. Based on the median risk scores, the patients were divided into low- and high-risk subgroups. The survival package was employed to evaluate variations in overall survival (OS) across these subgroups, with hazard ratios (HRs) and 95% confidence intervals (CIs) derived from the Cox proportional hazards analysis.

Statistical analysis

Statistical significance was determined for differences with a P value of less than 0.05 and an absolute log₂fold change greater than 1. Additionally, a univariate Cox regression analysis was conducted to identify gene expressions significantly associated with survival, employing a P value threshold of less than 0.05.

Results

Identification of key prognostic PANoptosis-related genes in HCC

To elucidate the prognostic significance of the genes in HCC, a univariate Cox analysis of TCGA-HCC data was conducted, and 4,354 genes associated with patient outcomes were identified. The top 20 prognostic genes (UTP11, YBX1, CBX2, G6PD, KPNA2, SMS, NEIL3, GNL2, YARS1, MED8, DYNC1L, PSRC1, TRIP13, CENPA, CDCA8, PPM1G, EIF5B, CBARP, KIF2C, and GRPEL2) are shown in Figure 1A. An intersection analysis via a Venn diagram revealed 95 PANoptosis-related prognostic genes (Figure 1B). The KEGG pathway enrichment analysis of these genes implicated key pathways, such as proteasome function, endoplasmic reticulum protein processing, ErbB signaling, tumor necrosis factor (TNF) signaling, vascular endothelial growth factor (VEGF) signaling, and Ras signaling, involved in the pathogenesis of HCC (Figure 1C).

Figure 1 Identification of prognostic PANoptosis-related genes in HCC. (A) Top 20 prognostic genes in TCGA-HCC patients based on P values. (B) Venn diagram showing the prognostic PANoptosis-related genes. (C) The KEGG enrichment pathways associated with these prognostic PANoptosis-related genes in HCC. CI, confidence interval; HCC, hepatocellular carcinoma; HR, hazard ratio; KEGG, Kyoto Encyclopedia of Genes and Genomes; TCGA, The Cancer Genome Atlas.

HCC subtypes classified based on PANoptosis-related gene expression profiles

Despite growing interest in PANoptosis-related genes in oncology, their functional roles in HCC remain poorly understood. We employed consensus clustering to stratify TCGA-HCC patients into two distinct subtypes based on 95 PANoptosis-related genes (Figure 2A-2C). Both the clustering stability analysis (k=2) and principal component analysis (PCA) confirmed robust separation between the subtypes (Figure 2A,2B), and these findings were further validated by the gene expression patterns in a heatmap (Figure 2C). Notably, the low-risk subtype had significantly better OS than the high-risk subtype (HR: 1.595; 95% CI: 1.121–2.268; P=0.009; Figure 2D).

Figure 2 HCC subtypes classified based on PANoptosis-related genes in HCC. (A) PCA of two HCC subtypes. (B) Consensus clustering matrix (k=2) for subtype classification. (C) Heatmap depicting differential gene expression between subtypes. (D) Kaplan-Meier analysis of OS, stratified by subtype. C1, cluster 1; C2, cluster 2; CI, confidence interval; HCC, hepatocellular carcinoma; HR, hazard ratio; OS, overall survival; PC, principal component; PCA, principal component analysis; var., variation.

Variations in clinical characteristics between the two HCC subtypes

We examined the expression patterns of the key PANoptosis-related genes in relation to various clinical characteristics. The analysis of different subtypes revealed a significant association between the distribution of cluster 1 (C1) and cluster 2 (C2), and factors such as age, tumor-node-metastasis (TNM) classification, and grade staging (Figure 3). These results suggest that C2 may serve as a prognostic indicator of risk, potentially reflecting the progression of clinical symptoms.

Figure 3 Clinical characteristics of HCC subtypes. (A) Age distribution significantly differed between C1 and C2. (B) TNM stage distribution showed significant variation between the two subtypes. (C) Tumor grade distribution varied significantly between the two subtypes. (D) No significant differences between the two subtypes were observed in terms of radiation therapy distribution. C1, cluster 1; C2, cluster 2; CI, confidence interval; HCC, hepatocellular carcinoma; TNM, tumor-node-metastasis.

The underlying mechanisms between the high- and low-risk clusters

The volcano plot analysis revealed 967 DEGs between the C1 and C2 groups, with 886 genes upregulated and 81 downregulated in the C1 group relative to the C2 group. KEGG and GO analyses were conducted to examine the functional enrichment of these DEGs.

The GO analysis of the upregulated genes in the high-risk C1 group revealed their involvement in processes such as mitotic nuclear division, chromosome segregation, nuclear division, organelle fission, and cell cycle checkpoints (Figure 4A). Conversely, the downregulated genes were associated with pathways related to alcohol metabolism, terpenoid metabolism, isoprenoid metabolism, steroid metabolism, and carboxylic acid biosynthesis (Figure 4B).

Figure 4 Identification of mechanisms for distinguishing between high- and low-risk clusters. (A) GO enrichment analysis of the upregulated DEGs. (B) GO enrichment analysis of the downregulated DEGs. (C) KEGG pathway analysis of the upregulated DEGs. (D) KEGG pathway analysis of the downregulated DEGs. DEG, differentially expressed gene; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

The KEGG analysis revealed the participation of the upregulated genes in pathways such as the cell cycle, oocyte meiosis, DNA replication, extracellular matrix (ECM)-receptor interaction, cellular senescence, central carbon metabolism in cancer, mismatch repair, progesterone-mediated oocyte maturation, and the p53 signaling pathway (Figure 4C). Conversely, the downregulated genes were primarily involved in the metabolism of xenobiotics by cytochrome, retinol metabolism, drug metabolism through cytochrome P450, chemical carcinogenesis via DNA adducts, complement and coagulation cascades, and peroxisome proliferator-activated receptor (PPAR) signaling pathways (Figure 4D).

These observations are consistent with previous studies that have identified the cell cycle, ECM-receptor interaction, and p53 signaling pathways as significant tumor markers (10-12). Overall, the results indicated that the C1 OC subtype displayed greater migratory and proliferative capabilities than the C2 subtype.

Development of a prognostic model using PANoptosis-related genes

Through the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses, the 95 PANoptosis-related genes were distilled into a 36-gene prognostic signature using the optimal λ value (Figure 5A). The risk score was calculated as follows: risk score = (0.6412) × LMNB2 + (−1.3474) × CHMP2B + (0.9781) × EIF2S1 + (−0.5935) × MLKL + (−0.7597) × NMT1 + (−0.5645) × DBNL + (0.9192) × PSMA1 + (0.5428) × PRKCD + (0.7693) × MAP2K2 + (0.6247) × PARP2 + (0.1241) × SFN + (0.4736) × CASP7 + (0.6114) × YWHAB + (−0.6684) × PIK3CA + (0.5576) × PSMB5 + (−0.6655) × ACTB + (0.3818) × LMNA + (−0.447) × PTK2 + (−0.9012) × PSMD3 + (−0.4507) × ENDOG + (0.3313) × CTSB + (−0.9311) × PSMC4 + (−0.4684) × CHMP3 + (0.4246) × MAPK1 + (0.7553) × PSMC2 + (−0.7573) × RPS27A + (−0.2555) × E2F1 + (0.2181) × CTSV + (0.5805) × NRAS + (−0.5085) × TUBA1B + (1.0891) × PSMB2 + (−0.6775) × ACIN1 + (1.2241) × HTRA2 + (−0.9946) × PSMC3 + (0.9141) × PSME3 + (−0.4346) × BAX.

Figure 5 Prognostic model based on PANoptosis-related genes. (A) LASSO regression stratified TCGA-HCC patients into low-risk (blue) and high-risk (red) groups based on PANoptosis-related gene expression. (B) Kaplan-Meier survival analysis comparing the high- and low-risk groups. (C) AUC curves of the two groups of TCGA-HCC patients. AUC, area under the curve; CI, confidence interval; HCC, hepatocellular carcinoma; HR, hazard ratio; LASSO, least absolute shrinkage and selection operator; TCGA, The Cancer Genome Atlas.

Patients from TCGA-HCC dataset were categorized into low- and high-risk groups based on this signature. Notably, the low-risk group had significantly better OS than the high-risk group (HR: 5.905; 95% CI: 3.861–9.031; P<0.001; Figure 5B). The prognostic accuracy of the model was further validated by a receiver operating characteristic (ROC) curve analysis, yielding area under the curve (AUC) values of 0.826, 0.865, and 0.854 for 1-, 3-, and 5-year survival, respectively (Figure 5C).

Discussion

This study conducted a univariate Cox analysis of TCGA-HCC data and identified 4,354 genes associated with patient prognosis. A Venn diagram intersection analysis revealed that 95 genes were associated with PANoptosis. By consensus clustering, we divided TCGA-HCC patients into two subtypes based on these 95 genes, and a clustering stability analysis and PCA confirmed significant differences between the subtypes. The OS of the low-risk subtype was significantly better than that of the high-risk subtype. The GO analysis of the high-risk C1 group revealed that the upregulated genes were involved in processes such as mitosis, chromosome segregation, and cell cycle checkpoints, while the downregulated genes were associated with pathways related to alcohol metabolism and steroid metabolism. The KEGG analysis showed that upregulated genes were involved in pathways related to the cell cycle and DNA replication, while the downregulated genes primarily participated in drug metabolism and chemical carcinogenesis pathways. Using LASSO and Cox regression, 36 prognostic markers were selected from the 95 PANoptosis-related genes. Based on these markers, the patients were classified into low- and high-risk groups; the OS of the low-risk group was significantly better than that of the high-risk group. The prognostic accuracy of the model was validated by a ROC curve analysis, with AUC values of 0.826, 0.865, and 0.854 for 1-, 3-, and 5-year survival, respectively.

HCC is a complex and heterogeneous disease, with its development influenced by a multitude of factors, including genetic, environmental, and lifestyle elements. One emerging area of interest in understanding the pathogenesis of HCC is the role of PANoptosis, a form of programmed cell death that integrates pyroptosis, apoptosis, and necroptosis (5,6). This multifaceted cell death pathway has been shown to play a significant role in shaping the tumor microenvironment and influencing cancer progression (5,6). This pathway, orchestrated by the PANoptosome complex, plays a pivotal role in modulating immune responses and tumor progression. Empirical studies have underscored its relevance across various cancer types, including lung, colorectal, and breast cancers (5-9). In the context of lung cancer, genes associated with PANoptosis have the potential to function as prognostic biomarkers, providing valuable insights into patient survival and treatment response (5,6). Furthermore, the strategic targeting of PANoptosis may bolster anti-tumor immunity and enhance therapeutic outcomes. In colorectal cancer, PANoptosis exerts a significant influence on the immune microenvironment, thereby affecting immune cell dynamics and informing immunotherapeutic approaches (8,9). The identification of PANoptosis gene signatures holds promise for predicting patient prognosis and treatment sensitivity, and these genes present themselves as viable therapeutic targets.

In this study, through LASSO and Cox regression analyses, 95 PANoptosis-related genes were distilled into a 36-gene prognostic signature (comprising LMNB2, CHMP2B, EIF2S1, MLKL, NMT1, DBNL, PSMA, PRKCD, MAP2K2, PARP2, SFN, CASP7, YWHAB, PIK3CA, PSMB5, ACTB, LMNA, PTK2, PSMD3, ENDOG, CTSB, PSMC4, CHMP3, MAPK1, PSMC2, RPS27A, E2F1, CTSV, NRAS, TUBA1B, PSMB2, ACIN1, HTRA2, PSMC3, PSME3, and BAX).

LMNB2, LMNB1, and LMNA are associated with mitochondrial permeability transition-driven necrosis-related genes in HCC, and have been found to be significantly correlated with patient survival (1). These findings suggest their potential as prognostic biomarkers and therapeutic targets, and provide insights into the pathogenesis of HCC (11). CASP7, a member of the caspase family, plays a regulatory role in apoptosis, a process often dysregulated in cancer. Its involvement in HCC, particularly concerning mitochondrial permeability transition and necrosis-related pathways, highlights its potential as a therapeutic target for inducing apoptosis (11). Beta-actin (ACTB), a cytoskeletal protein, is implicated in HCC through mutations in its three-prime untranslated region, which affect miR-1 and miR-29a, thereby influencing cancer cell migration and invasion via downstream target genes (12). PTK2 (FAK) is involved in the regulation of proliferation, survival, and migration in HCC through the circ_0082319/miR-505-3p axis (13). PSMD3, a component of the 26S proteasome, is linked to cancer progression and serves as a target for modulating the ubiquitin-proteasome system in HCC (14). ENDOG, associated with genome stability, is regulated by autophagy in HCC, highlighting the role of autophagic pathways in genomic instability and cancer progression (15). Collectively, these studies emphasize the complex nature of HCC and the critical importance of integrating genomic, proteomic, and functional analyses to identify key molecular drivers and potential therapeutic targets.

Our study highlights the role of PANoptosis-associated genes in HCC, indicating that these genes may serve as crucial biomarkers for diagnosis. Given the recent findings that 5-fluorouracil (5-FU) can induce PANoptosis in cancer cells, especially those resistant to apoptosis, there is a compelling rationale for investigating PANoptosis as a therapeutic target in HCC (16,17). This approach could potentially provide a novel avenue for overcoming resistance to conventional chemotherapy, which remains a significant challenge in HCC treatment. Furthermore, the evidence you cited regarding capecitabine’s efficacy in patients who do not respond to sorafenib emphasizes the need for tailored treatment strategies (16,17). Capecitabine, as a prodrug of 5-FU, could indeed leverage the mechanisms of PANoptosis to enhance therapeutic outcomes (16,17). This highlights an exciting direction for future research: investigating the synergistic effects of PANoptosis-inducing agents and existing therapies.

This study had certain limitations. Our findings are derived from a retrospective analysis; thus, they need to be confirmed through prospective studies. Although our study offers promising insights into PANoptosis-related genes for diagnosing HCC, future research should involve independent cohorts to validate our predictive model. This step is essential to confirm the robustness of our results and their clinical applicability. Our study’s reliance on historical data may introduce biases affecting reproducibility. Moreover, future research needs to carry out functional experiments on these genes to better understand their role in HCC.

Conclusions

For HCC patients, PANoptosis-related genes have a strong association with tumor classification. PANoptosis-related gene signatures exhibited robust predictive performance for HCC prognosis, presenting new angles for HCC diagnosis and therapy.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-356/rc

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-356/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-356/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: L. Huleatt)