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. 2024 Nov 18;150(12):506. doi: 10.1007/s00432-024-06031-7

Identification of a novel molecular classification for hepatocellular carcinoma based on disulfideptosis-related genes and its potential prognostic significance

Tao Wang 1, Yong Liu 1, Junjie Kong 1, Jun Liu 1,✉
PMCID: PMC11570565  PMID: 39551857

Abstract

Background

Globally, hepatocellular carcinoma (HCC) is one of the most prevalent and deadly malignant tumors. A recent study proposed disulfidptosis, a novel form of regulated cell death (RCD), offering a new avenue for identifying tumor prognosis biomarkers and developing novel therapeutic targets.

Methods

Based on the expression data of 14 disulfideptosis-related genes extracted from public databases, a new molecular classification of HCC called the “disulfidptosis score” was constructed and its relationship to tumor immunity and prognosis was evaluated.

Results

Based on the expression of disulfideptosis-related genes, we performed cluster analysis on HCC samples from the TCGA cohort, which classified these patients into three clusters: A, B, and C, and the differentially expressed genes of different clusters were analyzed. A disulfidptosis score model was constructed by differentially expressed genes associated with prognosis. Univariate and multivariate COX regression analysis showed that disulfidptosis score was an independent prognostic factor for HCC. In addition, in various disulfidptosis score groups, notable disparities were observed concerning the tumor immune microenvironment as well as the expression of immune checkpoint.

Conclusion

Disulfidptosis score have an important role in predicting HCC prognosis and help guide us in providing better immunotherapy options for patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-024-06031-7.

Keywords: Disulfidptosis, Hepatocellular carcinoma, Prognosis, Tumor immune microenvironment

Introduction

Globally, hepatocellular carcinoma (HCC) is one of the most prevalent and deadly malignant tumors. Unfortunately, the prognosis for HCC is extremely poor, with only 18% of patients surviving beyond 5 years. Research suggests that by the year 2030, HCC will claim the lives of over 1 million individuals (Jemal et al. 2017; Villanueva 2019). This disease primarily affects individuals with pre-existing liver conditions, including hepatitis B or C virus infections, alcoholism, or non-alcoholic fatty liver disease (NAFLD) (Llovet et al. 2016). Despite the development and application of multiple drugs for HCC, there has been no significant improvement in the survival rate over the past five years. Prognostic assessment based on biomarkers plays a critical role in the treatment of HCC patients. Serum alpha-fetoprotein (AFP) is the most widely used serological marker for HCC diagnosis and is included in international guidelines for HCC surveillance (EASL-EORTC clinical 2012; Bruix and Sherman 2005, 2011). Studies have indicated that elevated levels of AFP are associated with poor prognosis in HCC (Bai et al. 2017). However, it is disappointing that the reported sensitivity of AFP in predicting prognosis is approximately 60%, with a specificity of 80% (Lok et al. 2010; Marrero et al. 2009). Additional tumor markers, such as angiopoietin-2 or vascular endothelial growth factor, have shown potential in improving prognostic predictions through statistical modeling. However, they have yet to be integrated into personalized assessments for individual patients (Forner et al. 2018).

In recent years, with the rapid advancement of molecular biology technologies, including high-throughput sequencing, microarray, and various omics technologies, a deeper and more extensive comprehension regarding the molecular mechanism governing the advancement and progression of HCC has emerged (Lee et al. 2023). Therefore, it is crucial to identify effective biomarkers for predicting the prognosis of HCC and to discover novel targets for HCC therapy (Lee et al. 2023; Xing et al. 2021).

A recent study proposed disulfidptosis, a novel form of regulated cell death (RCD), resulting from the buildup of disulfide molecules within cells (Liu et al. 2023). The intracellular accumulation of disulfides, like cystine, can cause disulfide stress, which is highly toxic to cells (Joly et al. 2020; Liu et al. 2020). The reduced form of nicotinamide adenine dinucleotide phosphate (NADPH) can counteract disulfide stress by converting cystine to cysteine and ensuring cell survival. However, in the absence of glucose, NADPH levels decrease, leading to the accumulation of intracellular disulfide molecules and subsequent cell death. The study of RCD not only enhances our understanding of cellular homeostasis but also aids in the identification of new therapeutic targets for diseases, including cancer (Tang et al. 2019). The discovery of disulfidptosis offers a new avenue for identifying tumor prognosis biomarkers and developing novel therapeutic targets.

In this study, the expression levels of disulfidptosis-related genes in patients with HCC were systematically evaluated and categorized into three distinct clusters. These clusters exhibited significant variations in prognosis, tumor immune microenvironment, and response to immunotherapy. Utilizing these disulfidptosis-related genes, a new model was created to predict HCC patients’ prognosis and response to immunotherapy. Thus, this research provides fresh insights into the molecular mechanisms of HCC development and opens up possibilities for developing effective treatments for this disease.

Materials and methods

Data extraction and Processing

The transcriptome data from 374 cases of HCC tissues and 50 cases of normal tissues, along with their corresponding clinical characteristics, were acquired from the Cancer Genome Atlas (TCGA) database. The gene expression data was then transformed from fragments per kilobase of exon per million mapped fragments (FPKM) to transcripts per kilobase million (TPM). Additionally, the RNA-sequencing transcriptome data of 243 and 81 samples of HCC tissues and their corresponding clinical characteristics were downloaded from the International Cancer Genome Consortium (ICGC) database (LIRI-JP dataset) and the Gene Expression Omnibus (GEO) database (GSE54236 dataset). These datasets served as the training and validation sets for analysis, respectively. Furthermore, tumor and adjacent normal tissue samples were collected from 12 patients who underwent surgery at the Shandong Provincial Hospital Affiliated to Shandong First Medical University. Complying with the Declaration of Helsinki, this study was approved by the hospital ethics committee. Written informed consent was obtained from all patients.

Acquirement of disulfidptosis-related genes

We retrieved the expression profiles of 14 genes associated with disulfidptosis from prior studies and extracted their expression data from HCC and normal tissue samples. To analyze the difference in expression between tumor and normal tissue samples, we used the Wilcoxon test.

RNA extraction and quantitative real-time PCR

The FastPure® Cell/Tissue Total RNA Isolation Kit V2 (Vazyme, RC112-01) was utilized to extract total RNA from 12 pairs of HCC tissues and their adjacent normal tissues. A Nanodrop 2000 spectrophotometer (Thermo Fisher, USA) was used to ascertain the purity and concentration of total RNA. Samples were then subjected to reverse transcription into cDNA using HiScript®III RT SuperMix for qPCR (Vazyme, RC323-01). qRT-PCR analysis was conducted on a QuantStudio 3 (Applied Biosystems, USA) using ChamQ Universal SYBR qPCR Master Mix (Vazyme, Q711-02/03) to determine the expression levels of disulfidptosis-related genes. Gene expression levels were normalized to GADPH and analyzed using the 2 − ΔΔCt method. The primers employed in this study can be found in Supplementary Table 1.

Cluster analysis for disulfidptosis-related genes

Utilizing the expression matrix of 14 genes associated with disulfidptosis in HCC samples procured from the TCGA database, we performed cluster analysis on the data using the “ConsensusClusterPlus” R package (Wilkerson and Hayes 2010). The survival rates of the diverse clusters were evaluated using the KM method, while the biological variances among them were assessed via gene set variation analysis (GSVA) (Hänzelmann et al. 2013).

Comparison of the tumor immune microenvironment between distinct clusters of disulfidptosis

We performed an investigation on the relative infiltrating levels of immune cells in HCC samples obtained from the TCGA database through the utilization of single-sample gene set enrichment analysis (ssGSEA) (Charoentong et al. 2017). Moreover, the assessment of immune stromal components’ ratios in the tumor microenvironment within Malignant Tumor tissues was accomplished using the Estimation of Stromal and Immune cells in Expression data (ESTIMATE) analysis (Yoshihara et al. 2013). To examine the disparities in TME among various clusters, we applied the Wilcoxon test. Additionally, we evaluated the differences in the expression of targeted immune checkpoint molecules across different clusters utilizing the “limma” R package.

Identification of prognosis-related differentially expressed genes between the distinct clusters of disulfidptosis

Principal component analysis (PCA) was utilized to determine whether samples could be stratified into distinct clusters based on disulfidptosis-related genes. Differentially expressed genes (DEGs) across disulfidptosis clusters were identified using the empirical Bayesian method approach with the “limma” R package. DEGs with P-values < 0.001 were deemed significantly different. By employing both the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) analysis of biological processes, we analyzed the pathways and biological processes primarily associated with these DEGs. By combining the survival information of the samples with univariate COX regression analysis on DEGs, we identified the prognostic-related genes within the DEGs. DEGs with a P-value < 0.001 were considered significantly associated with prognosis.

Construction of the disulfidptosis score

The expression matrix of the prognostic related DEGs was used to score samples in both the TCGA and ICGC databases via PCA. The scoring method employed was similar to the Genomic Grade Index (GGI):

graphic file with name M1.gif

We conducted survival analysis using signature scores PC1 and PC2 as well as the expression of prognosis-related genes, represented by i. The scores obtained from disulfidptosis were categorized into high and low groups to further analyze its impact on prognosis, clinical characteristic analysis, immune-related analysis, targeted immune checkpoint analysis, and immunotherapy analysis on the two groups of samples.

Statistical analysis

R (version 4.2.2) and GraphPad Prism (version 8.3.0) were utilized for all statistical analyses in this investigation. The comparison of disulfideptosis-related gene expression levels in HCC samples was conducted through the application of paired t-tests. To establish the optimal cutoff values for patient survival, the “survminer” package in R was employed by taking continuous variables into consideration. Prognostic analysis involved the creation of survival curves using the Kaplan-Meier method, and significance of differences was evaluated using the log-rank test. We assessed the predictive value of disulfideptosis scoring for prognosis by utilizing receiver operating characteristic (ROC) curves (implemented in the R package “timeROC”) and determining the area under the curve (AUC) values. To determine if the model acted as an independent prognostic factor for HCC, we conducted univariate and multivariate Cox regression analyses. For detecting differences, a p-value below 0.05 was considered statistically significant.

Results

The expression variation of disulfidptosis-Related genes in hepatocellular carcinoma

At the outset of our study, we analyzed a total of 374 cases of HCC and 50 normal samples from the TCGA cohort. Our findings revealed that, with the exception of IQGAP1, the expression levels of the other 13 genes differed significantly between the two groups of samples. Significantly, HCC tissues exhibited remarkable downregulation of MYH10, while the expression of the remaining genes experienced substantial upregulation in HCC tissues. (Fig. 1A).

Fig. 1.

Fig. 1

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Disulfidptosis-related genes in HCC. (A) The expression of disulfidptosis-related genes in tumor and normal tissues from the TCGA cohort; (B) The expression of disulfidptosis-related genes in tumor and normal tissues from clinical samples

Furthermore, we extracted RNA from 12 pairs of clinical tissue samples for qPCR analysis. Encouragingly, the results obtained from our qPCR experiments were consistent with those of the TCGA cohort, as mentioned earlier (Fig. 1B).We further conducted single-factor COX regression analysis and KM survival analysis on these 14 genes. The results indicate that high expression levels of 12 significantly upregulated genes are associated with poor prognosis (Supplementary Fig. 1A-L).

Different clusters based on Disulfidptosis-related genes exhibit different prognoses and biological processes

Based on the expression of genes related to disulfidptosis, we performed cluster analysis on HCC samples from the TCGA cohort using the “ConsensusClusterPlus” R package, which classified these patients into three clusters: A, B, and C (Fig. 2A). Using clinical survival data for survival analysis, the results indicated that cluster A had a significantly better prognosis than cluster B (Fig. 2B). Based on the clustering results, we generated a gene expression heatmap, which revealed that the expression of disulfidptosis-related genes was generally low in cluster A and high in cluster B (Fig. 2C), in agreement with the results from the single-gene KM survival analysis. Simultaneously, we use GSVA to explore the distinctions among various disulfidptosis clusters within biological processes. The findings indicate that these disulfidptosis clusters manifest significant variances in their biological behaviors (Supplementary Fig. 2).

Fig. 2.

Fig. 2

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Cluster analysis on HCC samples based on the expression of disulfidptosis-related genes. (A) Consensus clustering matrix for k = 3; (B) Survival analysis of patients in distinct disulfidptosis clusters; (C) Heatmap depicting the expression levels of disulfidptosis-related genes in distinct disulfidptosis clusters

Tumor Immune characteristics among different disulfidptosis clusters

We utilized ssGSEA analysis to examine the presence of 23 various immune cell types in distinct clusters. The findings illustrated that the levels of infiltrating activated CD4 + T cells, activated dendritic cells, natural killer cells, regulatory T cells, type 2 T helper cells, and other immune cells were notably greater in cluster B compared to cluster A. Conversely, the infiltration of neutrophils was significantly higher in cluster A than in cluster B (Fig. 3A). Furthermore, we employed the ESTIMATE algorithm to assign scores to different clusters, and the outcomes indicated that the immune score, stromal score, and ESTIMATE score were considerably higher in cluster A than in cluster B (p < 0.05). (Fig. 3B-D).

Fig. 3.

Fig. 3

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Tumor immune landscape in disulfidptosis clusters. (A) ssGSEA of patients in distinct disulfidptosis clusters, the asterisks represent the statistical p value between the three disulfidptosis clusters (*p < 0.05; **p < 0.01; ***p < 0.001); (B) ESTIMATE score of patients in distinct disulfidptosis clusters; (C) Immune score of patients in distinct disulfidptosis clusters; (D) Stromal score of patients in distinct disulfidptosis clusters

Constructing a disulfidptosis score model

Principal component analysis (PCA) was performed on the results of the clustering analysis, revealing that the expression levels of genes related to disulfidptosis can be used to separate different clusters (Fig. 4A). Using an empirical Bayesian method, we identified 6586 DEGs between disulfidptosis clusters (Fig. 4B). Enrichment analysis of these genes using KEGG and GO revealed that they are enriched in the PI3k-Akt pathway, MAPK pathway, as well as in biological processes such as cellular metabolism and cell cycle regulation. (Fig. 4C, D).

Fig. 4.

Fig. 4

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Molecular characteristics of differentially expressed genes (DEGs) among disulfidptosis clusters. (A) PCA among distinct disulfidptosis clusters; (B) DEGs extracted among three disulfidptosis clusters; (C) GO enrichment analysis for the DEGs. (D) KEGG pathway analysis for the DEGs

Using univariate Cox regression analysis for DEGs, we obtained 3594 DEGs significantly associated with prognosis (p < 0.05). Based on the expression of these 3594 genes, we scored the samples and calculated the optimal cutoff value to divide them into high- and low- disulfidptosis score groups. Differential analysis of the scores among different clusters revealed that Cluster B had significantly higher scores than the other two groups, while Cluster A had the lowest scores (Supplementary Fig. 3A, B).

Survival and immunological correlation analysis among different disulfidptosis score groups

Survival analysis of different disulfidptosis score groups showed that the low-scoring group had significantly better survival than the high-scoring group (Supplementary Fig. 3C), and the same results were observed in samples from different tumor stages (Supplementary Fig. 3D, E). In addition, univariate and multivariate analyses of age, sex, pathological grade, tumor stage, and disulfidptosis score revealed that the disulfidptosis score was an independent prognostic factor for HCC (p = 0.029, HR:1.005, 95% CI:1.001–1.010) (Fig. 5A, B). The AUC curve showed that the disulfidptosis score had AUC values of 0.7009, 0.5976, 0.6253, and 0.6237 for predicting 1-, 2-, 3-, and 4-year overall survival, respectively (Fig. 5C).

Fig. 5.

Fig. 5

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Prognostic value of the disulfidptosis score model. (A) Univariate independent prognostic analysis; (B) Multivariate independent prognostic analysis; (C) Receiver operating characteristic (ROC) curves of disulfidptosis score for predicting the 1/2/3/4-years survival

Immunological correlation analysis of the disulfidptosis score revealed that it was positively correlated with CD4 + T cells and Th2 cells, but negatively correlated with eosinophils and neutrophils (Fig. 6A). In addition, PD-1, PD-L1, and CTLA4 were expressed at significantly higher levels in the high- disulfidptosis score group than in the low- disulfidptosis score group (Fig. 6B-D), suggesting that anti-PD-1/PD-L1/CTLA4 immunotherapy may be more effective for the high- disulfidptosis score group.

Fig. 6.

Fig. 6

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Immunological correlation analysis among different disulfidptosis score groups. (A) Immunocorrelation analysis; (B) PD-L1 expression in distinct disulfidptosis score groups; (C) CTLA4 expression in distinct disulfidptosis score groups; (D) PD-1 expression in distinct disulfidptosis score groups

Validating results using the ICGC and GEO database cohort

We validated our results using the ICGC and GEO database cohort. In the ICGC database cohort, Kaplan-Meier method results showed that the low- disulfidptosis score group had significantly better survival than the high- disulfidptosis score group (Supplementary Fig. 4A); univariate and multivariate analysis results showed that the disulfidptosis score was an independent prognostic factor (p = 0.007, HR:1.014, 95% CI:1.004–1.024) (Supplementary Fig. 4B, C); the AUC curve showed that the disulfidptosis score had AUC values of 0.7593, 0.694, 0.7261, and 0.7712 for predicting 1-, 2-, 3-, and 4-year overall survival, respectively (Supplementary Fig. 4D).

Due to the incomplete clinical information of GSE54236 cohort, we only utilized Kaplan-Meier method and ROC curves to validate the prognostic predictive value of the disulfidoptosis scoring model in the GEO database cohort. Kaplan-Meier method results showed that the low- disulfidptosis score group had significantly better survival than the high- disulfidptosis score group also (Supplementary Fig. 5A); the AUC curve indicated that the disulfidptosis score yielded AUC values of 0.62525, 0.66178, 0.81635, and 0.85443 for predicting 1-, 2-, 3-, and 4-year overall survival, respectively (Supplementary Fig. 5B). These results suggest that the disulfidptosis score has predictive value for HCC patients.

Discussion

Globally, HCC ranks high among malignant tumors in terms of both morbidity and mortality (Foerster et al. 2022). However, our understanding of the etiology of HCC and the progression of tumors remains extremely limited. In previous studies, various forms of RCD have been identified, demonstrating significant involvement in diverse human ailments, including cancer, neurodegenerative disorders, and autoimmune diseases. Mounting evidence suggests a profound correlation between RCD and the occurrence as well as progression of tumors (Gong et al. 2023; Peng et al. 2022; Tong et al. 2022).

Disulfidptosis is a recently discovered novel form of cell death (Liu et al. 2023). The excessive accumulation of cystine due to high expression of SLC7A11 can disrupt normal disulfide bonds between cytoskeletal proteins, leading to cell death (Chen et al. 2022; Xu et al. 2022). Studies have indicated that cancer cells with elevated SLC7A11 expression can mitigate cytotoxicity by reducing cystine to more soluble cysteine; however, restricting the intake of NADPH and glucose can hinder this process and result in cell death (Liu et al. 2020). As a novel form of regulated cell death, the role of disulfidptosis in hepatocellular carcinoma remains unclear. Research on molecular typing based on disulfidptosis-related genes could deepen our understanding of its role in HCC and potentially provide new methods for predicting patient prognosis, while also serving as a novel therapeutic target.

In this study, through clustering analysis, we have identified three distinct clustering patterns of disulfidptosis within HCC. To further evaluate the relationship between disulfidptosis and prognosis, pathway signatures and immune cell infiltration, we have constructed a disulfidptosis score model and validated the results using cohorts from the ICGC and GEO database. The results of the analysis indicate that the group with a low score for disulfidptosis shows significantly better survival compared to the group with a high score. It is important to note that the score for disulfidptosis emerges as an independent factor for predicting the prognosis of HCC, potentially offering a new prognostic model for this condition.

The results of KEGG and GO enrichment analyses indicate that the DEGs between different molecular subtypes of disulfidoptosis are enriched in the PI3k-Akt pathway, MAPK pathway, as well as in biological processes such as cellular metabolism and cell cycle regulation. This suggests that different molecular subtypes of disulfidoptosis may regulate tumor cell metabolism and cell cycle through the PI3k-Akt and MAPK pathways, thereby influencing proliferation and apoptosis (Ullah et al. 2022; Wagner and Nebreda 2009; Yu et al. 2022). This could be one of the reasons for the significant prognostic differences observed between the molecular subtypes.

The tumor immune microenvironment (TIME) encompasses various components, including tumor cells, immune cells, and cytokines. The intricate interactions between these elements can gradually transform TIME into a state that suppresses the host’s immune response. The delicate balance between tumor-promoting and anti-tumor inflammatory mediators ultimately dictates the tumor’s progression and the efficacy of anti-tumor immunity (Lv et al. 2022). The TIME encompasses various components, including tumor cells, immune cells, and cytokines. The intricate interactions between these elements can gradually transform the TIME into a state that suppresses the host’s immune response. The delicate balance between tumor-promoting and anti-tumor inflammatory mediators ultimately dictates the tumor’s progression and the efficacy of anti-tumor immunity (Drake et al. 2006; Khong and Restifo 2002; Thomas and Massagué 2005). As a result, the functionality of anti-tumor immune cells is impeded, posing significant challenges in sustaining an effective anti-tumor immune response. Despite variations observed in different types of cancer and various populations, the role of TIME in tumor development appears to be similar (Locy et al. 2018).

In this study, we conducted an analysis of the levels of TIME-related cells in different disulfidptosis score groups to investigate the relationship between disulfidptosis scores and TIME. The results revealed significant differences in TIME among various disulfidptosis score groups. The disulfidptosis score exhibited a positive correlation with CD4 + T cells and Th2 cells, while displaying a negative correlation with eosinophils, neutrophils, and other cell types. Previous research suggests that eosinophils can inhibit tumor growth through various mechanisms (Grisaru et al. 2021). For instance, activated eosinophils inhibit the growth of prostate cancer cells in vitro by secreting IL-10 and IL-12. Moreover, they can hinder tumor metastasis by enhancing the expression of the adhesion molecule E-cadherin(Furbert-Harris et al. 2003). Additionally, co-culture studies involving eosinophils and various tumor cell lines in mice or humans have directly observed eosinophil-mediated cytotoxicity (Lucarini et al. 2017; Kataoka et al. 2004; Munitz et al. 2005). Simultaneously, independent studies indicate that eosinophils exhibit anti-tumor activity independently of IL-5 and CD8 + T cells (Reichman et al. 2019). Whereas Th2 cells in tumors are often associated with poor prognosis. Research proposes the existence of a complex cross talk model among tumor cells, cancer-associated fibroblasts (CAFs), Th2 cells, and other immune cells that may favor tumor promotion. Recruited CD4 + GATA-3 + Th2 cells in this intricate network might exert pro-tumor effects (Monte et al. 2011). The notable differences in the TIME among various disulfidptosis score groups may be one of the contributing factors to the poorer prognosis observed in the high disulfidptosis score group.

Over the past decade, unprecedented advancements have been made in cancer immunotherapy, with the most widely utilized immunotherapeutic agents targeting the cytotoxic T lymphocyte-associated protein 4 (CTLA-4), programmed cell death protein-1 (PD-1), and its ligand (PD-L1) immune checkpoint pathways (Bagchi et al. 2021; Liu and Zheng 2020; Rowshanravan et al. 2018; Yi et al. 2022). While chemotherapy and radiotherapy remain the primary treatment modalities for most cancer types, immune checkpoint inhibitors (ICI) have emerged as first-line therapies for many tumors. CTLA-4, primarily expressed by T cells, serves as an inhibitory receptor, suppressing T cell activity and upregulating during T cell activation. CTLA-4 plays a pivotal role in immune response inhibition through distinct mechanisms both intracellularly and extracellularly (Krummel and Allison 1995; Valk et al. 2008; Zhang et al. 2021) PD-1, identified in 1992 as a protein involved in regulating programmed T cell death, exhibits significantly elevated expression on exhausted T cells (Ishida et al. 1992; Ahmadzadeh et al. 2009; Salmaninejad et al. 2019). Similar to CTLA-4, the binding of PD-1 to its ligand (PD-L1) leads to the suppression of T cell immune response (Freeman et al. 2000). Extensive preclinical research has unveiled the enhancement of anti-tumor immune effects through targeting the CTLA-4/PD-1/PD-L1 pathway (Hirano et al. 2005; Iwai et al. 2002; Peggs and Quezada 2010). A multitude of antibodies targeting CTLA-4/PD-1/PD-L1 have gained approval for the treatment of cancers in recent years (Bagchi et al. 2021).

Nonetheless, a portion of patients with advanced HCC fails to exhibit the expected response when undergoing immune checkpoint blockade therapy. Specifically, anti-PD-1 medications like nivolumab and pembrolizumab displayed a mere 15% objective response rate (ORR) in individuals who had received sorafenib treatment beforehand (Zhu et al. 2018; El-Khoueiry et al. 2017), showing a similar scenario in anti-CTLA-4 treatment (Duffy et al. 2017). In this study, we conducted an analysis comparing the expression levels of CTLA-4, PD-1, and PD-L1 in different disulfidptosis score groups. The findings revealed a significantly higher expression of PD-1, PD-L1, and CTLA-4 in the high disulfidptosis score group compared to the low score group. This may indicate that patients in the high disulfidptosis score group exhibit a more favorable response to ICI therapy, providing valuable guidance for devising improved treatment strategies for these patients.

This study has the following limitations. Firstly, due to insufficient accessible information, we lacked our own clinical cohort to validate the prognostic predictive value of the disulfidptosis score in hepatocellular carcinoma, relying instead on transcriptome data from public databases for this validation. Secondly, the absence of comprehensive data on responses to immunotherapy prevented us from fully assessing the disulfidptosis score’s predictive value for such responses; we could only estimate its impact by comparing the expression of common immune checkpoint molecules between high and low scoring groups. In the future, we aim to establish clinical cohorts to further validate the stability and universality of the disulfidptosis score model and assess its predictive value regarding responses to immunotherapy. Lastly, since the disulfidptosis score model is derived from the expression levels of multiple prognostic-related genes rather than a few key genes, it presents challenges in designing foundational experiments to further explore and validate the underlying mechanisms predicting hepatocellular carcinoma prognosis, marking another limitation of this study.

To sum up, this investigation thoroughly and systematically examines the expression patterns of regulatory factors associated with disulfidptosis in HCC, as well as their relationship with prognosis and immune cell infiltration. These findings have the potential to aid clinicians in identifying novel prognostic biomarkers for HCC and in seeking more effective immune therapies.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Figure 1–5 (1.7MB, docx)
Supplementary Table 1 (14.7KB, docx)

Author contributions

All authors contributed to the study conception and design. TW extracted data. TW and JK analysed the data. TW and YL drafted the manuscript. TW, JK and JL contributed to a critical revision of the manuscript. All authors have read and approved the final version of the manuscript.

Funding

This work was supported by the Department of Science and Technology of Shandong Province, China (No. ZR2023MH172).

Data availability

No datasets were generated or analysed during the current study.

Declarations

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.

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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 Figure 1–5 (1.7MB, docx)
Supplementary Table 1 (14.7KB, docx)

Data Availability Statement

No datasets were generated or analysed during the current study.

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