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. 2025 Dec 23;17:154. doi: 10.1007/s12672-025-04328-8

Characterization of PANoptosis-related expression pattern, prognosis and tumor microenvironment in head and neck squamous cell carcinoma

Mengchi Zhang 1,#, Long Zhang 2,#, Tingting Miao 3, Xuegang Hu 2,✉
PMCID: PMC12835458  PMID: 41432823

Abstract

Objective

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies worldwide with a poor prognosis. PANoptosis is a novel programmed cell death pathway. Presently, the expression pattern and prognosis of PANoptosis in HNSCC remain not fully elucidated.

Methods

The expression profile and corresponding clinical information were downloaded from The Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO). Univariate cox regression and consensus clustering analysis were applied to identify PANoptosis-related molecular subtypes. The subtype specific pathways were identify through gene set variation analysis (GSVA). The differentially expressed genes (DEGs) between subtypes were identified using limma algorithm. Then, a PANoptosis-related signature was constructed using univariate cox regression analysis and lasso analysis based on the DEGs. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were exploited to evaluate the prognostic value of signature. In addition, the relationship between signature, immune microenvironment, and drug sensitivity was examined.

Results

A total of 707 tumor samples and 44 normal samples with corresponding clinical information were included in the study. Two molecular subtypes with distinct overall survival rates, immune cell infiltration and pathways were identified using clustering analysis. Compared to cluster N, cluster A was characterized by a favorable survival outcome and increasing immune cell infiltration. The TIDE analysis result indicated that patients in cluster A may have a better response to immunotherapy. In addition, a novel PANoptosis-related signature was constructed based on the DEGs from two clusters. The patients with high PANoptosis score were corresponding to a poor survival outcome. Cox regression analysis result revealed that the signature score could serve as an independent prognostic factors. Further drug analysis result suggest that patients in high PANoptosis group have more sensitivity to docetaxel drugs.

Conclusions

The two molecular subtypes and PANoptosis-related signature have the potential to underlying the molecular mechanism and provide reliable marker for the prognosis of HNSCC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-04328-8.

Introduction

Head and neck malignancies rank as the sixth most common type of cancer worldwide, with about 90% of cases were head and neck squamous cell carcinomas (HNSCC)[1, 2]. HNSCC is characterized by high rate of metastasis to cervical lymph nodes, enhanced invasiveness, and propensity for recurrence, ultimately leading to an unfavorable prognosis [3, 4] HNSCC is typically treated with surgery, chemotherapy, radiation therapy, immunotherapy, or a combination of these methods. Nonetheless, the efficacy is limited due to the tumor heterogeneity [5]. Presently, due to the progress in chemotherapy and targeted drugs, there has been a significant improved in the survival rate of patients with HNSCC. Accumulative evidence demonstrated that PD1/PD-L1 immune checkpoint therapy have achieved much progress in the advance stage HNSCC patients [6, 7]. However, patients who received from immunotherapy still experience resistance to PD 1/PD-L1 inhibitors [8]. Therefore, it is urgently to identify novel therapeutic targets to the treatment of HNSCC.

Programmed cell death (PCD) plays a crucial role in controlling cell numbers and maintaining cellular homeostasis [9, 10]. PANoptosis was identified as a novel form of PCD in 2019 [11]. It is emphasized the interconnection and synchronization among pyroptosis, apoptosis, and necroptosis [12], and cannot be defined by a single PCD mode alone. The regulation of PANoptosis is controlled by the PANoptosome multi-protein complex, which is made up of both upstream signaling molecules and receptors, acting as the pivotal trigger for inducing PANoptosis. PANoptosis is regulated by the PANoptosome complex, comprised of signaling molecules and receptors that act as the key trigger for initiating PANoptosis [13–15]. Despite numerous studies have revealed the prognostic value of the pyroptosis, apoptosis, and necroptosis in HNSCC [16–18], the relationship between PANoptosis and HNSCC, as we ll as the prognostic value and impact on immunity, are not fully elucidated. Consequently, comprehending the characteristics of PANoptosis may provide valuable insights into the molecular mechanisms of HNSCC, and may facilitate the clinical decision making in the treatment of HNSCC.

In the present study, we retrieved the expression profile of 501 HNSCC tumor samples to explore the PANoptosis-related molecular patterns and underlying the molecular mechanism of HNSCC, and a high confidence risk signature was constructed to predict the prognosis of HNSCC.

Materials and methods

Dataset acquisition

The raw RNAseq data of HNSC were retrieved from TCGA database (https://portal.gdc.cancer.gov/) and further converted into TPM (transcripts per kilo base million) value. The clinical information including age, gender, radiation, pharmaceutical, grader and stage, as well as mutation data were also obtained. After omitting incomplete clinical information, we were left with 545 patients, comprising of 44 healthy and 501 tumor samples, and the TCGA cohort was served as the training cohort. Two external validation datasets, GSE41613 (N = 97) [19] and GSE27020 (N = 109) [20], were utilized for validation, in addition to 66 PANoptosis-related genes from a previous studies (Supplementary Table 1) [21].

Characterization of differentially PANoptosis-related genes

The Limma (version: 3.64.3) R package was used to analyze the differential gene expression between normal and tumor tissue [22]. The Benjamini–Hochberg method was employed to calculate the adjusted P values for multiple tests. Genes with an adjusted P value < 0.05 were considered to be significantly differentially expressed.

Consensus clustering based on PANoptosis-prognostic genes

To begin with, we assessed differentially expressed PANoptosis-related genes to ascertain the predictive significance of HNSC patients from the TCGA database. To achieve this, we used the univariate Cox regression analysis method. In addition, to explore potential PANoptosis-related molecular subtypes, we utilizing the R software's ConcensusClusterPlus tool to execute consensus clustering based on the gene expression profiles [23]. To ensure the accuracy of the results, the optimal number of clusters was determined and this process was repeated 1000 times. The differentially expressed genes between subtypes were also identified using the “limma” R package with the threshold absolute fold change > 1.5 and adjusted P value < 0.05 [22].

Pathway and function enrichment analysis

To explore the mechanisms of the differentially expressed PANoptosis-related genes, the “clusterProfiler” package (version: 4.16.0) was used to perform functional enrichment analysis of Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) [24]. GSEA was employed to find out if there were significant distinctions in gene set expression between the two subtypes, with the adjusted p-value < 0.05 serving as the threshold. Also, the "GSVA" R package (version: 2.2.0) was utilized to gauge the level of changes in signaling pathways between the two subgroups with the aid of the "h.all.v2023.2.Hs.symbols.gmt" database [25].

Construction of PANoptosis-related risk model

We used the Least Absolute Shrinkage and Selection Operator (LASSO) analysis, which was done with the "glmnet" R package (version: 4.1), to reduce the number of prognostic genes. We conducted a tenfold cross-validation to determine the best LASSO parameters, then optimized our prognostic model through multivariate Cox regression analysis. The risk score was calculated for both the training and testing sets using the gene expression values and corresponding regression coefficients, which was risk score = Expression (genes) * Coefficients (genes). We then categorize patients into high- and low-risk group on the basis of median risk score cutoff. To assess the performance of the risk model, Kaplan–Meier analysis and time-dependent ROC curves were used.

Evaluation of prognostic value of PANoptosis-related risk model

Univariate and multivariate cox regression analysis was performed to determine the independence of the risk score and clinical info. By utilizing the "rms" R package, a nomogram was developed that incorporates the riskscore and prognostic clinicopathological variables (age, radiation and stage) to predict the overall survival of patients with HNSC. The C-index was computed using the bootstrap method with 1000 resamples, and the accuracy of the model was evaluated by examining the calibration curves. In addition, decision curve analysis was conducted to evaluate the clinical benefit of nomogram in 1-, 3-, and 5-years.

Immune cell infiltration

ssGSEA was used to measure the relative abundance of 28 immune cells in the TME by retrieving relevant marker genes from previously studies [26, 27].

Drug sensitivity

To determine the IC50 of specific chemotherapeutics and targeted therapy agents for HCC, we employed the "pRRophetic" R package. This package utilizes ridge regression and utilizes extensive data from the Genomics of Drug Sensitivity in Cancer (GDSC; www.cancerrxgene.org/) and TCGA database [28].

Statistical analysis

All analyses were performed using R programming language (version 4.1.2) and relevant packages. Wilcox's test was used to compare variables between the two groups. Chi-square tests were used to examine the relationship between risk groups and clinicopathological features. Spearman's correlation test was used to assess group correlations. A two-sided P-value of less than 0.05 was considered statistically significant. The p-value summaries were as follows: ***P < 0.001; **P < 0.01; *P< 0.05.

Results

Landscape of PANoptosis-related genes

We first explored the expression level of 66 PANoptosis genes between normal and tumor tissue in TCGA-HNSC dataset. Subsequently, a total of 56 genes with 9 genes down-regulated and 47 genes up-regulated (Fig. 1A, B). Gene ontology enrichment analysis of these DEGs indicated that programmed necrotic cell death, extrinsic apoptotic signaling pathway and necrotic cell death were significantly enriched (Fig. 1C). KEGG enrichment result revealed genes mainly involved in apoptosis, necroptosis, TNF signaling pathway, and NOD − like receptor signaling pathway (Fig. 1D). By performing univariate cox regression analysis, 12 genes were comprised to 7 protective genes (CHMP7, RIPK1, FASLG, TP73, CASP2, BMF, GZMB) and 5 risky genes (IL1A, GSDME, FADD, BAK1, YWHAG) (Fig. 2A, B). CNV analysis result indicated that exhibited a prevalent frequency of CNV gain, while CHMP7 showed a significant frequency of CNV loss across diverse samples (Fig. 2C). The chromosomal locations of the CNV alterations for the 12 genes are depicted in Fig. 2D.

Fig. 1.

Fig. 1

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Characterization of PANoptosis genes. A Volcano plot of differentially expressed genes. B heatmap plot of differentially expressed genes. C GO enrichment analysis of differentially expressed genes. D KEGG enrichment analysis of differentially expressed genes

Fig. 2.

Fig. 2

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Landscape of PANoptosis genes. A forestplot of univariate cox regression analysis results of PANoptosis genes. B Network analysis showed the relationship of the prognostic genes. C CNV analysis of the prognostic genes. D Circos analysis of prognostic genes

Characterization of PANoptosis-related molecular subtypes

We then performed consensus clustering analysis on the prognostic PANoptosis genes. The optimal cluster number was selected K = 2 according to the CDF curve and the changed area under the curve (Fig. 3A, B). All patients were categorized into Cluster A (N = 267) and Cluster B (N = 234) (Fig. 3C). Compared to Cluster B, Cluster A have a better survival overall survival outcome (Fig. 3D). By using PCA analysis, it was possible to clearly distinguish between patients with PC in these two molecular subtypes, thus confirming the robustness of the clustering (Fig. 3E). Then, we examined the expression level of the DEGs between cluster A and B. As showed in Fig. 3F, GSDME, BAK1, FADD, YWHAG, IL1A were presented a significant high expression in cluster B, while BMF, CASP2, GZMB, TP73, FASLG, RIPK1, and CHMP7 were elevated in cluster A. Moreover, patients with advanced stage, grade and T, M, N cases were most enriched in cluster B, indicated the poor survival outcome of cluster B (Fig. 3G).

Fig. 3.

Fig. 3

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Construction of molecular subtypes based on PANoptosis-related genes. A CDF curve were depicted when clustering number k = 2 to k = 9. B Relative change of the area under the CDF curve. C Consensus heatmap when k = 2. D K-M curve analysis in cluster A and B. E PCA analysis could significantly distinguish between cluster A and B. F Comparisons of gene expression between cluster A and B. G The association between clinical factors and clusters

Immune infiltration and pathway enrichment of subtypes

Immune cell play an important role in inhibit tumor development and progression. Next, we examined the tumor immune cell infiltration level between cluster A and B. The results demonstrated a significant increase in the infiltration of activated B cell, activated CD4 T cell, activated CD8 T cell, effector memory CD8 T cell, immature B cell, memory B cell, regulatory T cell, T follicular helper cell, Type 1 T helper cell, CD56dim natural killer cell, eosinophil, macrophage, mast cell, MDSC, monocyte, natural killer cell and natural killer T cell in the TME of patients in cluster A, while CD56bright natural killer cell and neutrophil were showed a high infiltration level in cluster B (Fig. 4A). Moreover, the immune and stromal score were elevated in cluster A compared to cluster B, indicated that high immune infiltration of cluster A (Fig. 4B, C). TIDE analysis result revealed that patients in low risk group may have more likelihood to respond to immunotherapy (Fig. 4D–G). GSVA pathway enrichment analysis revealed that apoptosis, IL2 stat5 signaling pathway, hypoxia, notch signaling pathway, kras signaling pathway were significantly enriched in cluster A, while glycolysis pathway, MYC target pathway, TNFA signaling via NFKB pathway were enriched in cluster B (Fig. 5A). We also performed GSEA enrichment analysis between cluster A and B, and discovered that genes were significantly enriched in fanconi anemia pathway, cell adhesion molecules, antigen processing and presentation, Th1 And Th2 cell differentiation, thermogenesis in cluster A (Fig. 5B). Finally, we also performed differential expression analysis between cluster A and B, and identified 898 subtype associated DEGs (Supplementary table S2).

Fig. 4.

Fig. 4

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Immune landscape in molecular subtypes. A 28 immune cell infiltration level between cluster A and B. Evaluation of immune score (B) and stromal score in cluster A and B. The level of TIDE score (C), immunetherapy response rates (D), exclusion score (E) and dysfunction score (F) in cluster A and B

Fig. 5.

Fig. 5

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GSVA (A) and GSEA (B) pathway enrichment analysis between cluster A and B

Development of PANoptosis-related risk model

Among the 898 genes, we finally identified 324 prognostic genes, we then retained 168 genes that both existed in TCGA and GEO dataset. To construct a PANoptosis-related signature for HNSC, we then performed LASSO and multivariate cox regression analysis on these genes (Fig. 6A). The risk score for each patient in TCGA cohort and external validation cohort was calculated based on the previously risk formula. According to the median risk score, patients were divided into high- and low-risk group, respectively. KM survival analysis results revealed a significant difference in overall survival (OS) between high-risk and low-risk patients in the training cohort (P < 0.0001), GSE27020 cohort (P = 2e-04), and GSE41613 cohort (P < 0.0001) (Fig. 6B). ROC curve analysis result indicated that AUC value of 1-, 3- and 5- year prognosis of risk model in TCGA HNSC dataset were 0.719, 0.755, 0.738, respectively, which similarly with GSE27020 cohort (Fig. 6C) and GSE41613 cohort (Fig. 6D). Moreover, we also observed that patients with high risk score were corresponding to more dead cases (Figure S1A), which was consistent with GSE27020 cohort (Figure S1B) and GSE41613 cohort (Figure S1C).

Fig. 6.

Fig. 6

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Construction of PANoptosis-related model. A The coefficient landscape of prognostic genes and tenfold cross-validation curve of LASSO regression analysis. K-M survival curve and ROC curve analysis in TCGA (B), GSE27020 (C) and GSE41613 (D) cohorts, respectively

Prognostic value and immune infiltration of PANoptosis-related risk model

To further determine the independence of the risk model, we further conducted univariate and multivariate cox regression analysis on the risk score and clinical traits. The result revealed that the risk model can be served as an independent prognostic factors (Fig. 7A–B). We then constructed a nomogram based on the risk score, age, stage and radiation clinical traits (Fig. 7C). The calibration curve reveals that the overall survival from the actual and predicted values are in close agreement (Fig. 7D). The decision curve analysis result in 1-, 3- and 5-year overall survival revealed that patients could gain more clinical benefit from nomogram (Fig. 7E–G). In addition, we further evaluated immune infiltration of risk model. As showed in Fig. 8A, B, patients in low risk group corresponding to a higher infiltration level of activated CD8 T cell, activated CD4 T cell, activated B cell, macrophage, mast cell and monocyte, suggesting that a favourable survival outcome in low risk. We further evaluated chemo drug sensitivity of docetaxel between high- and low-risk group, and found that patients in high-risk group might benefit from the drug (Figure S2).

Fig. 7.

Fig. 7

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Prognostic value of risk model. Univariate (A) and multivariate (B) cox regression analysis of the risk score and clinical factors. C Construction of nomogram based on independent factors. D Calibration curve of the prediction efficiency of Nomogram in TCGA cohort. Decision curve analysis of nomogram in 1- (E) and 3- (F) and 5- (G) years overall survival

Fig. 8.

Fig. 8

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Immune cell infiltration in risk model. A Comparisons of 28 immune cells infiltration level in high- and low-risk group. B Correlation analysis of risk score and immune cells

Discussion

As a newly discovered PCD mode, PANoptosis have the ability to enhance anti-tumor immunity by modulating the accumulation of immune cells to induce cancer cell death [29]. The abnormal PANoptosis plays a key role in the prognosis and immune microenvironment of distinct malignancies. Wang et al. integrated 101 algorithms in 55 combinations to construct a reliable PANoptosis-related signature, and revealed the importance role of PANoptosis in shaping patient outcomes and promoting personalized treatment in breast cancer [30]. Zhao et al. developed a PANoptosis-related lncRNA signature and revealed the relationship between PANoptosis and immunotherapy [31]. Yang et al. discovered that PANoptosis-related signature exhibited a high performance in the prognosis for lymph node metastasis and advanced stage, and identified PANoptosis could serve as effective therapeutic target for HNSCC [32]. Wang et al. identified the efficacy of PANoptosis related to molecular subtypes and risk signature in predicting the overall survival rates and tumor microenvironment in colon cancer [33]. Qiang et al. construct a PANoptosis-related molecular subtype that can be utilized to personalized treatment and clinical decision -making in cervical cancer [34]. Overall, targeting PANoptosis may enhance the development of more effective strategies for tumor immunotherapy and chemotherapy. A better understanding of the role of PANoptosis in tumors could provide potentially therapeutic targets for tumor treatment.

In the present study, 66 PANoptosis-related genes (PRGs) were exploited into the study. The differentially expression analysis result indicated that most of PRGs were highly expressed in tumor tissue, except TICAM1, ELANE, IGF1, RIPK3, APPL1, CHMP2B. Further cox regression analysis result indicated that GSDME, BAK1, FADD, YWHAG, IL1A were risky genes. Previously studies demonstrated that overexpression of FADD was correlated with several cancer-related pathways, including MAPK and MTOR signaling pathways in HNSCC [32]. Regarding GSDME, high expression level of it was observed in the tumor samples, compared to normal in HNSCC, which was consistently with our results [35]. YWHAG and BAK1 have been reported as a prognostic markers in HNSCC [36–39]. Ji et al. reported that Interleukin 1A (IL1A) mRNA and IL-1α protein play a role in promoting the synthesis of glutathione (GSH) to combat oxidative stress and enhance resistance to nutrition-starvation therapy in oral squamous cell carcinoma (OSCC) [40]. Collectively, these results indicated that PANoptosis play a crucial role in the progression of HNSCC. According to the PANoptosis-related genes, the molecular expression pattern was identified. All HNSCC patients were categorized into two distinct clusters. Interestingly, all risk genes including GSDME, BAK1, FADD, YWHAG, IL1A were highly expressed in cluster B, while protective genes including CHMP7, BMF, CASP2, TP73, RIPK1, GZMB and FASLG were clustered in cluster A. Thus, it is reasonable to interpreted that cluster B associated with poor survival outcome. Several studies have indicated that PANoptosis, by coordinating the crosstalk between pyroptosis, apoptosis, and necroptosis, not only directly induces tumor cell death but also modulates immune cell infiltration and activation in the tumor microenvironment, thereby impacting the response to immunotherapy [41]. Ma et al. and Wu et al. elucidated that activation of PANoptosis enhances CD8 + T cell infiltration and promotes interferon-γ secretion, thereby improving the efficacy of anti-PD-1 therapy in lung and melanoma cancers [42, 43]. In our study, we found that patients in Cluster A showed a higher levels of immune cell infiltration and favorable survival outcomes, suggesting that PANoptosis may enhance anti-tumor immunity in HNSCC through similar mechanisms. Moreover, studies also highlighted the potential of PANoptosis in regulating immune checkpoint expression. The activation of PANoptosis-related genes could decrease the expression of PD-L1, thereby reversing the immune-suppressive microenvironment [44]. Our analysis indicates that patients in Cluster A have lower TIDE scores, suggesting that the subtype may be more sensitive to immunotherapy, further supporting the positive role of PANoptosis in immune regulation. These findings offer valuable insights into the molecular mechanism of these subtypes of tumors and provide potential pathways for HNSCC subgroup screening.

By utilizing univariate Cox regression and LASSO analyses, a PANoptosis-related signature was develope to improve the prediction and characterization of HNSCC prognosis for each patient. The risk score for each patient was calculated based on 33 genes (AREG, HBEGF, LIMD2, PYGL, NFIC, PTPRS, AQP1, CD5, SCNN1D, SYCP2, CCR7, WNT7A, PGLYRP4, FCGBP, COL9A2, F2RL1, SEMA3C, EGFR, KRT8, CDKN2A, ANO1, EPHX3, CKM) expression and their corresponding coefficient. Most of these 33 genes have been identified that correlated with HNSCC. For example, high expression level of AREG was correlated with a better overall survival and progression free survival outcome as well as a heightened response to cetuximab in conjunction with chemotherapy in HNSCC [45]. Moreover, SCNN1D, CD5, AQP1, LIMD2 and PYGL were identified as potential biomarkers for the prognosis of HNSCC [46–49]. Li et al. found that low expression level of NFIC was associated with poor survival outcome of HNSCC [50]. Overall, these results indicated that these can be used as prediction marker for the prognosis of HNSCC. The patients were further categorized into high- and low- risk group, and KM curve analysis result indicated that a favorable survival outcome was observed in low-risk group. Through the multivariate cox regression analysis, the PANoptosis-related signature was identified as an independent prognostic factors. Previously studies have been demonstrated that the nomogram have great potential to be used as prediction tools for survival outcome [51, 52]. Therefore, a nomogram based on independent prognostic factors was constructed to predict the overall survival of HNSCC patients, and the calibration curve plots demonstrated that the actual survival rates were similar to the survival rates predicted by nomogram. These results indicated that our nomogram has a high accuracy in predicting HNSCC patients’ overall survival.

Nevertheless, several limitations should be elucidated in this study. Firstly, all results were retrieved from data analysis and lack of experiments to validate it. Secondly, the samples size that enrolled in this study is still inadequate. Thus, it is necessary to include more clinical samples to ensure the robustness of the result. Thirdly, the signature contains too many genes and requires further reduction in the number of genes.

In conclusion, we identified two PANoptosis-related molecular subtypes and developed a PANoptosis-related signature that play a crucial role in predicting survival outcome and guiding immune therapy. These results could promote our understanding of PANoptosis of HNSCC and aid in developing more efficient treatment strategies.

Supplementary Information

12672_2025_4328_MOESM1_ESM.pdf (9.5MB, pdf)

Supplementary Material 1. Figure S1. Distribution of risk score, survival status and prognostic gene expression of patients in the TCGA (A), GSE27020 (B) and GSE41613 cohort (C), respectively

12672_2025_4328_MOESM2_ESM.pdf (9MB, pdf)

Supplementary Material 2. Figure S2. Evaluation of the immune cells type infiltration through distinct algorithms

12672_2025_4328_MOESM3_ESM.pdf (1.4MB, pdf)

Supplementary Material 3. Figure S3. Evaluation of chemo drug sensitivity between high- and low- risk group

12672_2025_4328_MOESM4_ESM.xlsx (14.4KB, xlsx)

Supplementary Material 4. Supplementary Table 1. The detail survival information of GSE27020 and GSE41613 cohort

12672_2025_4328_MOESM5_ESM.xlsx (9.7KB, xlsx)

Supplementary Material 5. Supplementary Table 2. The list of panoptosis-related genes.

12672_2025_4328_MOESM6_ESM.xlsx (110.2KB, xlsx)

Supplementary Material 6. Supplementary Table 3. Differentially expressed genes between A and B subtype

Acknowledgements

All authors would like to appreciate the TCGA and GEO databases for enabling the availability of high-quality data.

Author contributions

M.Z. and L.Z. were responsible for data collection, analysis and interpretations, and contributed to the draft of manuscript, T.M. and M.Z. contributed to data analysis and visualization. X.H. conceived and designed the study, and revised the manuscript critically for intellectual content. All authors reviewed and approved the final version of the manuscript.

Funding

This work was supported by Natural Science Research Project of Anhui Educational Committee (2023AH052590).

Data availability

The datasets presented in this study can be found in online repos itories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Materials.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

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

Mengchi Zhang and Long Zhang have contributed equally to this work.

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Associated Data

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

Supplementary Materials

12672_2025_4328_MOESM1_ESM.pdf (9.5MB, pdf)

Supplementary Material 1. Figure S1. Distribution of risk score, survival status and prognostic gene expression of patients in the TCGA (A), GSE27020 (B) and GSE41613 cohort (C), respectively

12672_2025_4328_MOESM2_ESM.pdf (9MB, pdf)

Supplementary Material 2. Figure S2. Evaluation of the immune cells type infiltration through distinct algorithms

12672_2025_4328_MOESM3_ESM.pdf (1.4MB, pdf)

Supplementary Material 3. Figure S3. Evaluation of chemo drug sensitivity between high- and low- risk group

12672_2025_4328_MOESM4_ESM.xlsx (14.4KB, xlsx)

Supplementary Material 4. Supplementary Table 1. The detail survival information of GSE27020 and GSE41613 cohort

12672_2025_4328_MOESM5_ESM.xlsx (9.7KB, xlsx)

Supplementary Material 5. Supplementary Table 2. The list of panoptosis-related genes.

12672_2025_4328_MOESM6_ESM.xlsx (110.2KB, xlsx)

Supplementary Material 6. Supplementary Table 3. Differentially expressed genes between A and B subtype

Data Availability Statement

The datasets presented in this study can be found in online repos itories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Materials.

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