Characteristic of molecular subtypes based on PANoptosis-related genes and experimental verification of hepatocellular carcinoma

Results

To determine the character of PAN-RGs in the tumor progression of HCC, we acquired 14 PAN-RGs expression profile from the TCGA database. After the calculation of differential analysis, we observed that RIPK1, CASP6, CASP8, PYCARD, FADD, MAP3K7, TNFAIP3, RNF31, RBCK1, and PSTPIP2 were overexpressed in the HCC samples, nevertheless, the expression of NLRP3 was down expressed in the HCC samples (Figure 1A). On the basis of CNV file from the TCGA database, the CNV landscape of 14 PAN-RGs were explored and the result suggested higher amplification of RIPK1, NLRP3, RNF31, RIPK3, FADD, ZBP1, CASP8, and PYCARD, whereas CASP6, CASP1, MAP3K7, TNFAIP3, and PSTPIP2 showed higher deletion (Figure 1B). The PPI network illustrated a clear interaction of the 14 PAN-RGs (Figure 1C). In addition, the circle diagram displayed the location of the PAN-RGs on chromosome (Figure 1D). Mutation feature of PAN-RGs exhibited that the mutation frequency of NLRP3, CASP8, MAP3K7, RNF31 and RIPK3 was 2%, 1%, 1%, 1%, and 1%, respectively (Figure 1E).

Figure 1. Genetic expression and variation landscape of PAN-RGs in HCC. (A) Genetic expression of 14 PAN-RGs in normal and HCC samples. (B) CNV exploration of PAN-RGs. (C) Interaction network of 14 PAN-RGs. (D) The location of PAN-RGs on chromosome. (E) Mutation evaluation of PAN-RGs in HCC samples.

Molecular subtypes characteristic of HCC based on the PAN-RGs

In order to explore the molecular subtypes character of HCC, we enrolled 485 HCC samples to explain the relationship of PAN-RGs and tumorigenesis from the TCGA-LIHC dataset and GSE76427. The network plot illustrated the association of PAN-RGs and prognostic value. As shown in Figure 2A, a positive correlation was observed between the 14 PAN-RGs, and 5 prognostic risk factors were obtained (MAP3K7, CASP8, RBCK1, FADD, CASP6). After the estimated of an unsupervised consensus clustering analysis, the HCC samples displayed an optimal categorization of k = 3, with 216 HCC samples in PAN-RG cluster A, 116 HCC samples in PAN-RG cluster B, and 153 HCC samples in PAN-RG cluster C (Figure 2B). The PCA diagram displayed that the HCC samples in the different PAN-RG clusters could be clearly distinguished, indicating the accuracy of consensus clustering analysis (Figure 2C). Between the molecular subtypes of HCC, we observed a clear difference of prognosis in the different cluster subgroups, which the clinical prognosis of HCC in the cluster A was better than in cluster B and C (Figure 2D). The association of PAN-RGs and clinical features was displayed in a heatmap diagram, and the plot illustrated that the expression of PAN-RGs was greatly higher in PAN-RG cluster B (Figure 2E). These results demonstrate that the HCC samples could be accurately classify into different molecular subtypes based on the PAN-RGs expression, and associated with clinical prognosis for HCC.

Figure 2. Identification of molecular subtypes of PAN-RGs for HCC. (A) Prognostic factor exploration of PAN-RGs. (B) Unsupervised consensus clustering analysis. (C) PCA diagram of HCC samples in cluster A, B, and C. (D) Clinical prognostic outcome of HCC in different PAN-RG cluster subgroups. (E) Expression of PAN-RGs in different PAN-RG cluster subgroups and clinical features.

Exploration of immune infiltration in PAN-RG molecular subtypes

The TME feature of HCC samples in the different PAN-RG molecular subtypes was explored in the next study. On the basis of ESTIMATE algorithm, the proportion of stromal and immune cells in different PAN-RG cluster subgroups was evaluated. The ESTIMATE result revealed that the HCC samples in PAN-RG cluster B had higher stromal, immune, ESTIMATE scores, and lower tumor purity (Figure 3A). A remarkable distinction was explored in the immune infiltration of 23 kind immune cells in PAN-RG cluster A, B, and C via ssGSEA assessment (Figure 3B). Furthermore, giving the significant difference in immune infiltration, we further estimated the immunotherapy response of HCC in PAN-RG cluster A, B, and C. The IPS assessment analysis revealed that the HCC samples in the PAN-RG cluster C displayed a worse immunotherapy response to PD-1 and CTLA-4 than PAN-RG cluster A and B (Figure 3C–3F). According to the GSVA algorithm, the regulatory role of crucial KEGG in the development of HCC was explored. Between PAN-RG cluster A and B, several metabolism-associated pathways were greatly down-regulated of HCC in the PAN-RG cluster B, involving in linoleic acid metabolism, histidine metabolism, tyrosine metabolism, retinol metabolism, and fatty acid metabolism. Of note, some tumor-associated signaling pathways were up-regulated for those poor prognosis HCC samples in PAN-RG cluster B, such as ubiquitin mediated proteolysis, Fc gamma R-mediated phagocytosis and pancreatic cancer (Figure 3G). In PAN-RG cluster C, a series of down-regulation immune-associated pathways were explored, such as T cell receptor signaling pathway, B cell receptor signaling pathway, Toll like receptor signaling pathway, NOD like receptor signaling pathway (Figure 3H). These discoveries exhibit that the PAN-RG-based molecular subtypes of PAN-RG were closely associated with immune infiltration, and could reveal the immunotherapy response of HCC in different PAN-RG molecular subgroups.

Figure 3. The TME characteristic of HCC in the PAN-RG molecular subgroups. (A) Evaluation of ESTIMATE score in PAN-RG cluster subtypes. (B) The immune infiltration exploration of HCC in PAN-RG cluster A, B, and C. (C–F) Immunotherapy response investigation of PAN-RG molecular subgroups via TCIA database. (G, H) GSVA algorithm shows the different regulation KEGG pathways in PAN-RG cluster A, B, and C.

Generation of DEGs and gene-cluster subgroups identification for HCC

In order to better understand the molecular biological function of the PAN-RG cluster subgroups, differential expression analysis was carried out to identify the DEGs between the PAN-RG cluster subgroups with the screening threshold set at |fold change| ≥ 1 and p.adjust < 0.001. On the basis of difference analysis calculation, we obtained 1077 intersection DEGs between the PAN-RG cluster subgroups. GO enrichment analysis exhibited that the PAN-RG cluster-based DEGs were enriched in T cell activation, leukocyte cell-cell adhesion, external side of plasma membrane, and immune receptor activity (Figure 4A). KEGG analysis result displayed that Epstein-Barr virus infection, tuberculosis, and phagosome were enriched of the PAN-RG cluster-based DEGs (Figure 4B). Under the estimation of univariate Cox analysis, we acquired 376 DEGs which associated with clinical prognosis for HCC. On the basis of unsupervised consensus clustering analysis, the HCC samples were successfully divided into 3 cluster subgroups (K = 3), including 151 HCC samples in gene-cluster A, 219 HCC samples in gene-cluster B, and 115 HCC samples in gene-cluster C (Figure 4C). The clinical survival results of HCC samples displayed that the prognosis outcome of HCC in gene-cluster C was even worse than those HCC samples in gene-cluster A and gene-cluster B (p < 0.001, Figure 4D). The heatmap plot exhibited the expression profile of prognosis DEGs in the different clinical variates, PAN-RG- and gene-cluster subgroups (Figure 4E). Additionally, we found that the gene-cluster C with worse clinical prognosis displayed higher expression level of PAN-RGs (Figure 4F).

Figure 4. Identification of DEGs in the PAN-RG molecular subtypes. (A) GO and (B) KEGG enrichment assessment of the PAN-RG cluster-based DEGs. (C) Unsupervised consensus clustering of the DEGs in HCC. (D) Clinical prognosis analysis of HCC in gene-cluster subgroups. (E) Heatmap plot illustrates the relationship of clinical variates and prognostic DEGs in PAN-RG- and gene-cluster subgroups. (F) PAN-RGs expression landscape of HCC in gene-cluster A, B, and C.

Establishment of risk subgroups for HCC based on the prognostic DEGs

Based on the prognostic factors for HCC, the LASSO algorithm selected 10 feature variates from the 376 DEGs (Supplementary Figure 1). Then, 7 feature variates were obtained for the risk score calculation via multivariate Cox analysis. According to the 7 feature variates, the HCC samples from the TCGA-LIHC and GSE76427 were divided into training cohort and test cohort under the division set at 1:1 via package “caret”. In the PAN-RG cluster subgroups, we observed that the risk score of HCC samples in cluster B was greatly higher than those in the cluster A, and C (Figure 5A). As for gene-cluster subgroups, the HCC samples in gene-cluster C with worse prognosis had remarkable risk score than other gene-cluster subgroups (Figure 5B). The Sankey plot displayed the detail relationship of clinical survival outcome, risk score, PAN-RG cluster, and gene-cluster (Figure 5C). PCA diagram illustrated that the risk score could clearly distinguish the HCC samples with low- and high-risk score (Figure 5D). After the estimation of clinical prognostic outcome for HCC samples, an obvious difference between low- and high-risk groups for HCC was observed in the entire cohort (p < 0.001, Figure 5E). In the training cohort and test cohort, the same clinical prognosis outcome was obtained, the HCC samples with low-risk score had better survival rate (Supplementary Figure 2). The ROC of risk score revealed that the AUC was 0.755 (cutoff: 1.142), indicating a favorable diagnostic ability for HCC (Figure 5F). These results demonstrate that establishment of the risk score could divide the HCC samples into different risk subgroups and could accurately evaluate the clinical prognosis for HCC.

Figure 5. Evaluation of risk score for HCC samples based on the prognostic DEGs. (A) Risk score of HCC samples in PAN-RGs cluster subgroups. (B) Risk score of HCC samples in gene cluster-subgroups. (C) Association of risk score and clinical survival status for HCC samples in the PAN-RG- and gene-cluster subgroups. (D) The PCA plot of HCC samples in the risk subgroups. (E) Prognosis analysis of HCC samples in the risk subgroups. (F) The diagnostic effectiveness of the risk score for HCC.

Independent prognostic evaluation of risk score for HCC

Considering the role of the risk score in estimating clinical prognosis outcome for HCC, we further explored the independent prognostic significance of risk score. The clinical variates of HCC were acquired from the TCGA-LIHC and GSE76427, including age, gender and stage. Under the calculation of univariate Cox analysis, the HCC-based stage (HR = 1.670 (1.383–2.018), P < 0.001) and risk score (HR = 1.343 (1.251–1.443), P < 0.001) were related to poor prognosis for HCC (Figure 6A). For multivariate Cox analysis, the risk score (HR = 1.313 (1.220–1.412), P < 0.001) was regarded as an independent prognosis factor for HCC (Figure 6B). The AUC of risk score, age, gender and HCC-based stage was 0.755, 0.544, 0.492 and 0.661, showing a favorable predictive ability of risk score for HCC (Figure 6C). As displayed in Figure 6D–6I, the result of univariate/multivariate Cox analysis suggested that the risk score was an independent prognosis indicator for HCC in the training cohort and test cohort. Moreover, the AUC of risk score in the training cohort and test cohort was 0.803 and 0.698. Taken together, our results illustrate that the risk score is an independent prognostic factor for HCC, showing a favorable diagnostic power than other clinicopathological characteristics.

Figure 6. Exploration of independent prognostic for risk score in HCC. (A, B) Univariate and multivariate Cox analysis in entire cohort. (C) ROC curve analysis of risk score and clinical variates of HCC. Univariate/multivariate Cox analysis and ROC curve in training cohort (D–F) and test cohort (G–I).

Development of nomogram based on risk score and clinicopathological features for HCC

We developed a nomogram model to explore the one-, three-, and five years clinical outcome for HCC based on the risk score and clinical variates. As illustrated in Figure 7A–7C, the nomogram results in entire, training and test cohorts revealed that the risk score could accurately estimate the survival probability for HCC. The DCA curve exhibited that the development of nomogram in estimating clinical prognosis for HCC was better than other variates in the entire, training and test cohorts (Figure 7D–7F). Furthermore, the calibration analysis showed that the clinical outcome of nomogram assessed in one-, three-, and five years was consisted with the real survival status for HCC, indicating the favorable accuracy of nomogram (Figure 7G–7I).

Figure 7. Development of the nomogram for evaluating the survival probability in HCC. (A–C) Nomogram development of risk score and clinical variates in entire, training and test cohorts. (D–F) DCA curve analysis. (G–I) Calibration curve estimation between the actual OS and predicted OS.

The immune infiltration evaluation of risk subgroups for HCC

The immune infiltration of HCC samples in the two risk subgroups were further explored in the subsequent investigation. The correlation analysis indicated that the risk score was positively correlated with type 17 T helper cell, but negatively associated with eosinophil, activated CD8⁺ T cell, activated B cell, type 1 T helper cell, mast cell, gamma delta T cell and immature B cell (Figure 8A). Moreover, we explored the potential relationship between 7 independent prognostic factors and immune infiltration, and the heatmap displayed a clearly positive association of 4 prognostic factors (S100A9, HMOX1, RGL4 and IL18RAP) and most of immune cells, however, another 3 prognostic factors (TRIM21, TRAF3 and TMC7) were negatively correlated with most of immune cells (Figure 8B). These results illustrated a potential relation between the risk score and immune infiltration.

Figure 8. Assessment of immune infiltration of HCC samples in the risk subgroups. (A) Correlation coefficient of risk score and immune infiltration. (B) Correlation analysis of 8 prognostic factors and immune infiltration. (C) ESTIMATE score. (D) Immune infiltration evaluation of HCC samples in the risk subgroups.

Based on the ESTIMATE assessment algorithm, we observed a remarkable difference of immune, ESTIMATE and tumor purity between the two risk subgroups (Figure 8C). Additionally, the ssGSEA algorithm suggested that the proportion of activated B cell, CD8⁺ T cell, eosinophil, mast cell and type 1 T helper cell was higher of HCC samples in the high-risk group, whereas the proportion of natural killer T cell was greatly higher for HCC in the low-risk group (Figure 8D). These results illustrate a clear distinction in immune infiltration of HCC samples in low- and high-risk groups, and associated with the risk score.

Genetic mutation feature and immunotherapy response of HCC in the risk subgroups

In first, we investigated the mutation characteristic of somatic in the risk subgroups for HCC. As illustrated in Figure 9A, 9B, about 149 samples had somatic mutation in 190 samples (78.42%), with lower mutation frequency of TP53 (17%), TTN (22%), ALB (7%) and RYR2 (6%). In high-risk group, we observed 144 samples had somatic mutation in171 samples (84.21%), with lower mutation frequency of CTNNB1 (24%) and MUC16 (13%). Based on the TCIA database, the immunotherapy response to PD-1 and CTLA-4 of HCC samples in the risk subgroups was further explored. The IPS evaluation analysis revealed that the low-risk group was more sensitive to PD-1 and CTLA-4 treatment (Figure 9C–9F). The analysis of immune checkpoints displayed that the expression profile of most immune checkpoints was higher in the high-risk group (Figure 9G). These results demonstrate that the risk score is associated with mutation feature and could indicate the immunotherapy response of HCC samples in the risk subgroups.

Figure 9. Genetic mutation characteristic and immunotherapy response of risk subgroups. Evaluation of somatic mutation landscape of HCC samples in (A) low- and (B) high-risk groups. (C–F) Immunotherapy response exploration of risk subgroups. (G) Difference analysis of immune checkpoints in the risk subgroups.

Prediction of potential chemotherapeutic compounds for HCC in risk subgroups

To better understand the application of the risk model in the clinical treatment for HCC, we explored several potential chemotherapeutic compounds which may benefit for the treatment of HCC samples in the risk subgroups based on the GDSC database. As implied in Figure 10, the IC50 of VX-680, sorafenib, pyrimethamine, KIN001-135, GW843682X, GNF-2, crizotinib and CGP-082996 in the high-risk group was greatly lower than low-risk group; notably, the IC50 of TGX221, roscovitine, parthenolide and erlotinib was higher in the high-risk group. Overall, these findings illustrate that the risk model could indicate the chemotherapeutic compounds response of HCC samples in the different risk subgroups.

Figure 10. Exploration of potential chemotherapeutic compounds for HCC in the different risk subgroups. The drug sensitivity evaluation of (A) VX-680; (B) TGX221; (C) Sorafenib; (D) Roscovitine; (E) Pyrimethamin; (F) Parthenolide; (G) KIN001-135; (H) GW843682X; (I) GNF-2; (J) Erlotinib; (K) Crizotinib and (L) CGP-082996.

Analysis of expression profile for prognostic signatures

We used HCC clinical samples to detect the expression levels of selected prognostic signatures. As shown in Figure 11, HCC tumor tissues contain high levels of HMOX1, S100A9, TMC7, TRAF3 and TRIM21, whereas the expression of IL8RAP and RGL4 were higher in the control group (Figure 11A–11G).

Figure 11. Validation of mRNA level for prognostic signatures in HCC. (A) HMOX1; (B) IL18RAP; (C) RGL4; (D) S100A9; (E) TMC7; (F) TRAF3; (G) TRIM21.

Author Contributions

DD and HR conceived and designed the study. SY and NK analyzed the data and drafted the manuscript. HR collected clinical samples and HR designed and completed the experimental portion. TQ and CX acquired the financial support. All authors agreed to submit to the current journal, gave final approval of the version to be published.

Conflicts of Interest

The authors declare no conflicts of interest related to this study.

Ethical Statement and Consent

The study was approved by the human ethics committee of the Affiliated Hospital of Qingdao University and the Ethics Office of Qingdao University. All patients and cadaver family members provided written informed consent and the project was in accordance with the Helsinki Declaration of 1975.

Funding

This study was supported by the National Natural Science Foundation of China (82172478); the Young Taishan Scholars Program (tsqn201909190); Shandong Higher Education Young Science and Technology Support Program (2021KJ048); Postdoctoral Science Foundation of China (2022T150340; 2021M701813); Qingdao Postdoctoral Applied Research Project (2020); Innovation Fund of National Orthopedics and Sports Rehabilitation Clinical Medicine Research Center (2021-NCRC-CXJJ-ZH-02).

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