A Novel Prognostic Signature for Combined T-cell Exhaustion and Mitochondrial Dysfunction in Hepatocellular Carcinoma

doi:10.21203/rs.3.rs-2691058/v1

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A Novel Prognostic Signature for Combined T-cell Exhaustion and Mitochondrial Dysfunction in Hepatocellular Carcinoma

https://doi.org/10.21203/rs.3.rs-2691058/v1

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Hepatocellular carcinoma (HCC) is a common lethal gastrointestinal cancer worldwide. Despite the involvement of T-cell exhaustion and mitochondrial dysfunction in HCC progression, there are no reliable biomarkers for these processes. In this study, we aimed to develop a gene signature that combines T-cell exhaustion- and mitochondrial dysfunction (TEMD)-related genes to predict HCC prognosis and direct immunotherapy and chemotherapy. Data of 240 and 370 patients were obtained from the International Cancer Genome Consortium database as well as the Cancer Genome Atlas, respectively. Using the R package Consensus ClusterPlus, TEMD-high and -low subtypes were generated based on the differential expression of TEMD-related genes and univariate Cox analysis. A two-gene prognostic signature comprising EZH2 and G6PD was constructed and regarded as an independent predictor of outcome. The TEMD high-risk group had a worse overall survival, higher immune infiltration levels, and higher expression of human leukocyte antigen and immune checkpoints. Tumor stemness indices, immunophenoscores, tumor immune dysfunction and exclusion scores, and half-maximal inhibitory concentrations were also compared for TEMD-based molecular subtypes and risk signature. Furthermore, stratification, nomogram construction, gene set enrichment analysis, and small-molecule drug response prediction were performed based on the TEMD-risk signature. Collectively, the proposed two-gene prognostic signature may help determine HCC prognosis and design individualized treatments.

Despite major advances in treatment, hepatocellular carcinoma (HCC) has a poor prognosis, with a 5-year survival rate of less than 30% (1-3). Complex etiology and considerable heterogeneity complicate the prognosis of HCC (4). To achieve accurate prognosis in patients with HCC and make informed treatment decisions, a novel prognostic indicator is urgently needed. T lymphocytes are the most important tumor killer effector cells in the body. The dysfunction of T-cell immunity can weaken the antitumor ability of the body and affect the prognosis of tumor.

T-cell exhaustion indicates the loss of T-cell function in patients with cancers and common chronic infections such as HBV. T-cell exhaustion is caused by persistent antigen exposure, wherein T cells produce few cytokines and cytotoxic effector molecules, downregulate activating receptors, and upregulate inhibitory receptors (5-7). T-cell exhaustion represents a major obstacle to successful cancer immunotherapy (8). Numerous efforts have been devoted to harness T-cell immunity by preventing or reversing exhaustion. Recently, the EPHA2 protein expressed on tumor cells has been shown to inhibit T cell-mediated antitumor responses; inhibiting EPHA2-reversed T-cell exhaustion, increasing the susceptibility of tumors to immunotherapy (9). Activating the TIM-3 pathway may also restore antitumor immunity and reverse T-cell exhaustion (10). Furthermore, the PD1 and CTLA4 antibody blockade has been effective in restoring T-cell exhaustion in patients (11). The combined blockade of PD1 and other receptors, such as TIM-3, provides even greater therapeutic benefits (10, 12). Various genes have been reported to induce or reverse T-cell exhaustion; however, whether these genes influence tumor immunity, the tumor microenvironment (TME), and prognosis in patients with HCC remains unclear.

Mitochondrial dysfunction has been implicated in cancer development and progression (13-14). The induction of mitochondrial dysfunction in tumor cells has emerged as a novel therapeutic approach. For example, gamitrinib, a small-molecule inhibitor targeting mitochondria, improves the efficacy of MAPK inhibitors in cancer cells, thereby causing mitochondrial dysfunction and blocking tumor bioenergetics (15). During cisplatin therapy, patients with cancer may experience mitochondrial dysfunction and decreased antioxidant levels, resulting in excessive reactive oxygen species (ROS) production and subsequent oxidative damage (16, 17). However, the mechanisms about mitochondrial dysregulation in cancer therapy remain unclear.

Several research have indicated mitochondrial dysfunction as a driver of T-cell exhaustion (18-20). Vardhana et al. found that inhibiting mitochondrial oxidative phosphorylation in activated T cells suppresses cell proliferation and upregulates exhaustion-related genes (21). Furthermore, 4-1BB co-stimulation increases mitochondrial biogenesis and oxidative metabolism, reducing the tonic signaling-induced exhaustion of T cells (22). Based on these results, T-cell exhaustion and mitochondrial dysfunction might be linked. However, further research is required to determine the significance of their relationship in HCC prognosis.

Here, we developed a gene signature comprising T-cell exhaustion- and mitochondrial dysfunction (TEMD)-related genes (TEMDRGs) to predict HCC prognosis. Our findings may aid in designing personalized treatments and improving treatment outcomes.

2.1 Datasets and Identification of Differentially Expressed TEMDRGs

Gene expression and clinical information of HCC datasets were obtained from the The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/projects/TCGA-LIHC) and International Cancer Genome Consortium (ICGC; https://dcc.icgc.org/projects/LIHC-US) database. Gene expression was compared using transcripts per million (TPM) values, which were normalized using the log2 (TPM + 1) conversion format. TCGA-HCC participants were randomly assigned, at a 7:3 ratio, to two cohorts: the training cohort (N = 259) and testing (N = 111) cohort (Supplementary Table S1). The survival results of the ICGC cohort and the clinical characteristics of patients from the two databases are described in Supplementary Tables S2 and S3. The testing cohort and the entire HCC cohort of our study were used for the internal validation, whereas the ICGC-HCC cohort was used for external validation. Moreover, T-cell exhaustion-related genes (TERGs) and mitochondrial dysfunction-related genes (MDRGs) were retrieved from GeneCards (https://www.genecards.org/) (23). Our analysis focused on identifying differentially expressed TEMDRGs (DE-TEMDRGs) using the limma R package (v3.40.2) based on the intersection of TERGs and MDRGs.

2.2 Functional Enrichment Analysis

GO annotation and KEGG pathway enrichment analyses were conducted to study the function of DE-TEMDRGs (24). STRING was used to establish a PPI network, and then CytoScape (v3.2.1) was used to visualize the results.

2.3 Mutation Analysis of TEMDRGs

The GSCA tool was used to investigate genetic variations in DE-TEMDRGs in TCGA-HCC dataset, including single nucleotide variations (SNVs), missense mutations, nonsense mutations, and copy number variations (CNVs) (25).

2.4 Cluster Analysis

Among the DE-TEMDRGs, prognosis-related DE-TEMDRGs (PR-DE-TEMDRGs) were screened using univariate Cox regression analysis. In order to understanding the subtypes of TEMD-related molecules in HCC, the consensus clustering package ConsensusClusterPlus (v1.54.0) was used. To compare the prognosis between the subtypes, we performed a Kaplan–Meier (KM) survival analysis. The somatic mutation landscapes of the two clusters were analyzed using the R package maftools.

2.5 Construction and Validation of the Prognostic Model

The training and testing sets were randomly divided. The PR-DE-TEMDRGs were analyzed using multivariate Cox regression analyses and LASSO (26). According to the KM curve with a log-rank test, survival differences were compared between the low-risk group and high-risk group with the median risk score (1.808). Time-dependent receiver operating characteristic curves (ROCs) were constructed to evaluate the predictive power of the PR-DE-TEMDRGs. The same analysis was applied in the TCGA testing, entire TCGA-HCC, and ICGC-HCC cohorts to verify prognostic efficacy.

2.6 Building and Validation of a Predictive Nomogram

Nomograms are extensively used for predicting cancer prognosis (27). Calibration curves were plotted using the R package to evaluate the nomogram predictive performance. Subsequently, we conducted a decision curve analysis (DCA) of the nomograms including all or only one independent prognostic factor.

2.7 Gene Set Enrichment Analysis

Using GSEA, we explored the potential molecular mechanisms associated with our established prognostic gene signature, obtaining KEGG pathway terms in C2, a gene set containing genes annotated by the same GO term in C5, and gene sets with oncogenic signatures in cancer in C6. A gene set with p value less than 0.05 and with the false discovery rate less than 0.25 was considered significantly enriched.

2.8 Analysis of Immune Status, Immunotherapy Response, and Chemotherapy Response

Twenty-two immune cell types in each tumor sample was analyzed using the CIBERSORT algorithm (28). Differences in immune infiltration between the groups were evaluated by Mann–Whitney U test. The expression of MHC molecules based on cluster analysis and the prognostic signature were compared. As a part of the immune checkpoint analysis, the expression of eight common immunoinhibitory factors (CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT, and SLGLEC15) was compared based on cluster and risk analyses.

Immune checkpoint inhibitor response is generally correlated with tumor stemness indices; thus, it could be a useful indicator of immunotherapy success. Therefore, stemness was compared among the groups. The mRNA stemness index of each sample was computed using one-class logistic regression machine learning with normalized gene expression profiles (29). Tumor immune dysfunction and exclusion (TIDE) scores are associated with a poor response to immune checkpoint blockade response. Therefore, we calculated the TIDE scores for TCGA molecular subtypes and risk groups using the TIDE database. The immunophenoscore (IPS), a scoring system for quantifying tumor immunogenicity, is positively correlated with immunotherapy response rates (30). IPS data were acquired from the Cancer Immunome Database (http://tcia.at/home/).

Doxorubicin, gemcitabine, sorafenib, and 5-fluorouracil are common systemic treatments for HCC. To compare the difference in sensitivity to these drugs between the groups, we used IC₅₀ values obtained from the Genomics of Drug Sensitivity in Cancer (GDSC).

2.9 Identification of Potentially Therapeutic Compounds

The L1000 fireworks display (L1000FWD) was used to interactively evaluate drug and small molecule-induced gene expression signatures. By comparing the regulated genes in different risk groups, we predicted drugs that might reverse or induce tumor growth. Considering p < 0.05 and enrichment scores ranging from –1 to 0, potential drug candidates for HCC were proposed. We obtained the 2D and 3D structures of these candidate drugs through the PubChem database.

2.10 Statistical Analysis

Statistical analyses were conducted using R software (v4.0.3). Correlation analysis was conducted using the R package corrplot and the Pearson method. Mann–Whitney U test was used to compare differences between the groups. Univariate and multivariate Cox regression analyses were conducted using the R package survival. Results with p < 0.05 were considered statistically significant.

3.1 DE-TEMDRGs in HCC

We obtained data of 370 tumor and 50 normal tissue samples from patients with HCC from TCGA. Using GeneCards, 413 TERGs and 2,415 MDRGs were obtained. Following the intersection of TERGs and MRGs, 135 TEMDRGs were obtained (Figure 1A, Supplementary Table S4). Of the 135 TEMDRGs, 32 TEMDRGs (29 upregulated and 3 downregulated) were differentially expressed in the TCGA-HCC cohort (Figure 1B, Supplementary Table S5). The GO term enrichment analysis revealed these TEMDRGs to mainly participate in the response to oxidative stress, negative regulation of phosphorylation, and regulation of mitochondrion organization. The KEGG pathway enrichment analysis suggested that the TEMDRGs were mainly involved in hepatitis B, the p53 signaling pathway, carbon metabolism, and the TNF signaling pathway (Figure 1C–1E). Next, we constructed a PPI network of the 32 TEMDRGs (Figure 1F).

3.2 Genetic variation of DE-TEMDRGs

We analyzed CNVs and SNVs of the 32 DE-TEMDRGs in the TCGA-HCC cohort. Among the 75 HCC samples, 56 (74.67%) exhibited genetic mutations (Figure 2A–2B). The most common variant classification, variant type, and SNV class were missense mutation, SNP, and C>T, respectively (Figure 2A). Of the 32 DE-TEMDRGs, PRKDC (29%) was the most frequently mutated gene, followed by MIK617 (15%) and BAX (7%) (Figure 2B). Figure 2C shows that the SNV mutation patterns tended to be dominated by C>T and T>C transitions. Notably, most DE-TEMDRGs exhibited CNVs in HCC (Figure 2D). Among the distribution of CNV classes, the highest heterozygous amplification rates were noted in PARP1, PRKDC, and BAK1; the highest heterozygous deletion rates were noted in TAT, CCNA2, and CXCL10; and the highest homozygous amplification rate was noted in PARP1, SOCS3, and PRKDC (Figure 2E–2F). Figure 2G shows the heatmap of CNV and mRNA expression in the TCGA cohort. BAG6 and PARP1 were the two most common genes whose mRNA expression was significantly correlated with CNV.

3.3 Identification of HCC Molecular Subtypes Based on PR-DE-MDRTEGs

The univariate analysis showed that 27 genes were associated with HCC prognosis, among which TAT and AR were protective factors, whereas the other genes were risk factors (Supplementary Table S6). The patients with HCC were divided into two subclusters using ConsensusClusterPlus (v1.54.0) (Figure 3A). A prominent clustering effect, as well as consistency and stability within subgroups, was observed (Supplementary Table S7, Supplementary Figure S1A–S1I). Cluster C1 showed high PR-DE-MDRTEG expression, indicative of a TEMD-high subtype. Conversely, cluster C2 showed low expression, indicative of a TEMD-low subtype (Figure 3B). Survival analyses demonstrated that patients with the TEMD-low subtype generally had a favorable prognosis, whereas those with the TEMD-high subtype had poorer prognoses (Figure 3C). Clinical parameters, such as stage and grade, were significantly different between the clusters (Supplementary Figure S2A–S2E).

GSEA was conducted to compare the two subgroups to further identify the activated signaling pathways. The gene sets significantly enriched in the TEMD-high groups included WP_T cell_antigen_receptor_TCR_signaling, WP_T cell_receptor_and_costimulatory_signaling, WP_oxidative_stress, WP_tgf-β_signaling_pathway, and KEGG_pathways_in_cancer (Supplementary Table S8, Figure 3D). In addition, distinct somatic mutation profiles were observed between these subtypes. Mutations in TP53 (40%), TTN (26%), and CTNNB (22%) were highly prevalent in the TEMD-high subtype, whereas CTNNB (26%), TTN (24%), and MUC16 (16%) mutations were most prominent in the TEMD-low subtype (Figure 3E).

3.4 Tumor Microenvironment Immune Landscape, and Chemotherapy Response in the TEMD subtypes

Evidence suggest that T-cell exhaustion and mitochondrial dysfunction affect antitumor immune responses. Thus, we determined the immune landscape within tumor microenvironments of the two TEMD-based subtypes. Patients with the TEMD-high subtype exhibited increased proportions of activated T regulatory cells (Tregs) and M0 macrophages, whereas patients with the TEMD-low subtype had an increased percentage of monocytes (Figure 4A). In addition, the TEMD-high subtype showed high levels of human leukocyte antigen (HLA), as well as upregulation of immune checkpoint expression, compared to the TEMD-low subtype (Figure 4B–C). The stemness score for the TEMD-high subtype was higher compared to the TEMD-low subtype (Figure 4D).

In light of the significant differences in TMEs between the subtypes, we assessed their responses to immunotherapy and chemotherapy. Compared to the TEMD-low subtype, TEMD-high tumors had a higher TIDE score and lower IPS score (Figure 4E–F), indicative of a less prominent immunotherapy response (31-33). Meanwhile, TEMD-high subtypes had a lower IC₅₀ for chemotherapy agents (Figure 4G), indicating a better response to chemotherapy.

3.5 Construction of a TEMD-Related Prognosis Signature and Clinical Correlation Analysis

The TCGA-HCC cohort was randomly divided into training group and testing group at a ratio of 7:3. To avoid overfitting, LASSO analysis was used, with three PR-DE-TEMDRGs retained (Figure 5A–B). Subsequently, two risk genes, EZH2 and G6PD, were retained to construct the optimal LRPS (Figure 5C–D). Their expression was higher in TCGA-HCC (Figure 5E) and the ICGC-HCC (Supplementary Figure S3A) samples than in normal liver samples. Furthermore, the protein levels of these genes showed the same trend in the Human Protein Atlas database (Supplementary Figure S3B). The prognostic value of these two genes and their correlations with clinicopathological characteristics were further investigated. In both TCGA (Figure 5F–G) and ICGC (Supplementary Figure S3C–S3D) HCC cohorts, EZH2 and G6PD expression levels were positively correlated with stage and grade. According to the KM analyses, patients with high EZH2 and G6PD levels had a poor outcome in both TCGA (Figure 5H–5I) and ICGC (Supplementary Figure S3E–S3F).

3.6 Identification and Validation of the TEMD Prognostic Signature

A signature of the two TEMD genes was identified. Risk score was calculated according to the following formular: risk score = EZH2 × 0.33188 + G6PD × 0.21697. Patients of the TCGA training cohort were divided to low-risk group and high-risk group based on the median value of the risk score (1.808). A scatter plot was created to visualize the distribution of risk scores and their relationship with outcome in the training cohort (Figure 6A). The TEMD signature was further validated both internally (TCGA testing and entire TCGA-HCC cohort) and externally (ICGC-HCC cohort) (Figure 6A). The KM analysis revealed that compared to the low-risk group, the high-risk group exhibited a poor outcome in not only the TCGA training cohort (p < 0.001) but also the TCGA testing (p = 0.038), TCGA-HCC (p < 0.001), and ICGC-HCC (p < 0.001) cohorts (Figure 6B). The prognostic model showed an area under the ROC curve of 0.841, 0.738, and 0.747 at 1, 3, and 4 years, respectively, in the training cohort. Furthermore, it showed a robustness predictive value in the test cohort, entire TCGA cohort, and external ICGC cohort (Figure 6C), which indicated a good predictive value.

3.7 Correlation Between the TEMD Signature and Clinical characteristics

The correlation between the TEMD signature and clinical features was compared. In the TCGA-HCC cohort, high-risk scores were associated with advanced stage (Figure 7A), advanced grade (Figure 7B), and HBV infection (Figure 7C) but not HCV infection (Figure 7D). The stratified survival analysis revealed that advanced stages (Figure 7E) and a history of HBV (Figure 7G) or HCV (Figure 7H) were associated with poor overall survival (OS). However, the grade-specific survival analysis did not reveal any differences (Figure 7F). The high-risk score group was significantly associated with poorer outcomes across all subgroups (Supplementary Figure S4A–S4B, S4D), except in those without a history of HBV infection (Supplementary Figure S4C). In the ICGC-HCC cohort, the risk scores of HCC samples with early stage and grade were relatively less than those with advanced stage and grade (Figure 7I–7J) and also tended to have a better prognosis (Figure 7K–7L). However, no significant differences were observed among the different subtypes for prognosis between the high-risk group and low-risk group owing to the low number of cases (Supplementary Figure S4E–S4F).

3.8 Development of a Clinical Nomogram

Univariate and multivariate Cox regression analyses revealed that risk score, stage, and HCV status were independent prognostic factors in the TCGA-HCC cohort (Figure 8A–8B). In addition, risk score and stage remained independent prognostic factors in the ICGC-HCC cohort (Supplementary Figure S5A–S5B), which was consistent with the findings of the TCGA cohort.

A nomogram for OS prediction was then developed for TCGA (Figure 8C) and ICGC (Supplementary Figure S5C) cohorts. The calibration curves showed good concordance between nomogram-predicted and actual survival in TCGA (Figure 8D) and ICGC cohorts (Supplementary Figure S5D). The DCA curves revealed that the nomogram offered a greater net benefit in both cohorts (Figure 8E–8G and Supplementary Figure S5E–S5G). According to these results, the predictive nomogram for OS was better than clinical factors alone and could assist clinical management.

3.9 Analysis of T-Cell Exhaustion and Mitochondrial Dysfunction

To examine whether TEMD signatures correlate with T-cell exhaustion status in HCC samples, we compared the expression of marker genes in the low-risk group and high-risk group. STAT5B, CCR8, PDCD1, TGFB1, LAG3, CTLA4, HAVCR2, TIGIT, CXCL13, LAYN, CD274, IDO1, IDO2, PDCD1LG2, and VSIR are established marker genes of T-cell exhaustion (34-36). Figure 9A shows a higher expression of marker genes in the high-risk group, indicating greater T-cell exhaustion relative to that in the low-risk group.

The GSEA data showed that high-risk tumors exhibited significantly enriched pathways related to T-cell exhaustion (including regulation of T-cell activation (37), TGF-β signaling (38, 39), NOTCH signaling (40), and IFNG signaling (41) (Figure 9B–9E).

Both HCC groups were subjected to GSEA to assess mitochondrial dysfunction status. The analysis revealed various mitochondrial dysfunction-related pathways (including ROS metabolic process, respiratory system, ATPase activity, glycolysis, and glycerophospholipid metabolic process) as significantly enriched (Supplementary Figure S6A–S6E).

3.10 Analysis of Immune Cell Infiltration, Immune Checkpoint Expression, and Immunotherapy Response

To investigate the relationship of our TEMD prognostic signature with immunotherapy efficacy, we compared the immune infiltration levels between the high-risk group and low-risk group. High-risk scores were associated with less infiltrating activated CD4+ T cells, Tfh cells, plasma B cells, Tregs, resting myeloid dendritic cells, neutrophils, etc. (Figure 10A). In addition, the high-risk group presented upregulation of immune checkpoints and high HLA gene expression (Figure 10B–10C). Furthermore, the stemness score was higher for the high-risk group (Figure 10D).

The high-risk group presented a lower IPS score and higher TIDE score than the low-risk group (Figure 10E–10F). The TEMD signature-based risk score positively correlated with TIDE score (Figure 10H). These findings suggested a better response to immunotherapy in low-risk patients.

3.11 Chemotherapy Response Analysis and TMED-Related Small-Molecule Drug Screening

Based on GDSC data, responses to common chemotherapeutic agents were predicted for the two groups. Interestingly, the high-risk group had higher IC₅₀ values for doxorubicin, gemcitabine, sorafenib, and 5-fluorouracil (Figure 10H), suggesting that these patients may not respond to chemotherapy.

The L1000fwd web-based tool was used to identify small-molecule targets for HCC treatment. According to the differentially expressed genes (DEGs) (Supplementary Table S9), we screened the three most common small-molecule drugs as drug candidates for patients with HCC (Supplementary Table S10). A 2D display of ephedrine (Figure 11A), digoxin (Figure 12B), and tacrolimus (Figure 11C) is available in the PubChem database, along with a 3D display of ephedrine (Figure 11D).

This study revealed a TEMD-related signature composed of EZH2 and G6PD to predict the clinical outcome and therapeutic response in patients with HCC. We believe that our findings may improve the accuracy of survival assessments in HCC.

Recently, hepatic mitoribosomal defects have been reported to promote glucose partitioning into glycolytic flux and lactate synthesis, resulting in T-cell exhaustion and HCC progression (42). Oxidative stress and ROS overproduction are well-known consequences of mitochondrial dysfunction (43, 44). During chronic antigen exposure, oxidative stress promotes the expression of TOX, which protects T cells from bioenergy disasters, thus promoting survival at the expense of mitochondrial capacity (21). Additionally, TOX expression increases in functionally exhausted PD1^high CD8+ tumor-infiltrating T cells in HCC (45). NFAT activity is enhanced in response to calcium-driven mitochondrial ROS, which contributes to T-cell exhaustion (21). There is evidence that the NFAT pathway drives HCC cell proliferation, with tumors exhibiting NFAT2 upregulation (46). It has also been reported that NFTAc1, which belongs to the NFAT family, triggers tumor cell apoptosis in HCC (47). Cancer immunotherapy trials have shown that agents that reverse effector T-cell exhaustion promote antitumor immunity (48). These findings indicate that a comprehensive understanding of the interaction between T-cell exhaustion and mitochondrial dysfunction in HCC may help guide the development of new treatments. Owing to the availability of large-scale sequencing data, we can now construct prognostic features to guide personalized clinical treatment and predict undesired outcomes (49).

EZH2 is a histone methyltransferase and one of the key components of PRC2 (50). It plays both tumor-promoting and tumor-suppressing roles. It has been shown that somatic mutations in the EZH2 enzymatic domain lead to increased H3K27 trimethylation, which leads to the epigenetic reprogramming of B cells and developing B cell lymphoma (51-54). In addition, EZH2 overexpression leads to rapid tumor progression in breast, bladder, ovarian, prostate, and melanoma cancers (55-57). However, nonsense mutations and inactivation deletions of EZH2 have been found in different cancers, indicating that EZH2 may play a tumor-suppressor role in these conditions (58-61). Some studies have suggested that EZH2 overexpression contributes to HCC progression and its malignant features (62, 63). In a preclinical animal study, lentivirus carrying shRNAs or siRNAs against EZH2 was found to be effective against HCC (64). In addition, several studies have reported that EZH2 mRNA or protein expression is upregulated in HCC tumor tissues, with high levels of expression predicting poor outcomes (65, 66).

G6PD, which is located on the X chromosome, regulates oxidative damage in red blood cells (67). G6PD plays a critical role in the growth of rapidly dividing cells, as observed during tumorigenesis (68, 69) and embryonic development (70). As a result of its synergistic role with NADPH oxidase 4, G6PD regulates ROS production and maintains the cellular redox balance, which is often closely associated with tumorigenesis (71, 72). G6PD is also implicated in tumor cell proliferation, apoptosis, migration, and invasion (73-76). Studies have reported that G6PD is upregulated in bladder cancer (77), breast cancer (78, 79), and esophageal squamous cell carcinoma (80, 81). HCC patients with high G6PD expression have a poor prognosis, high level of metastasis, and poor tumor grade (82). G6PD inhibits ferroptosis in HCC by inhibiting cytochrome P450 oxidoreductases (83). Furthermore, high G6PD expression induces epithelial–mesenchymal transition, causing HCC cells to migrate and invade (74). The upregulation of G6PD in HCC inhibits cell proliferation by directly interfering with BAG chaperone 3 (84). Based on these findings, G6PD may be used as a prognostic indicator and a drug target.

Using our TEMD-based signature, we predicted 10 potential therapeutic drugs against HCC based on DEGs between the high-risk group and the low-risk group. A cohort study of the national population revealed that long-term users of digitoxin had a reduced risk of male-predominant gastrointestinal cancers, particularly HCC (85). The anticancer potential of digitoxin has been reported and confirmed in various tumors including lung cancer (86), pancreatic cancer (87), glioma (88), prostate cancer (89), melanoma (90), and HCC (91). In addition, researchers have found that sorafenib combined with digitoxin significantly impairs primary HepG2 cell growth, possibly by inhibiting ERK and hypoxia signaling (92). Tacrolimus exhibits antiproliferative properties in HCC cells by arresting the cells in the G0/G1 phase (93). Furthermore, tacrolimus suppresses T immune responses by inducing apoptosis (94). To date, no research has determined how ephedrine contributes to the development of HCC. The goal of our follow-up research is to study how these drugs interact with HCC.

The present study has some limitations. First, the validation of our signature relied solely on retrospectively obtained data from the ICGC database, indicating that more prospective research is required to confirm the clinical value of the signature. Second, further studies on animal models are needed to determine the roles of the two selected genes in HCC development. Finally, the broad implications of the signature for multidrug sensitivity warrant further drug screening.

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Availability of data and material

Data and materials described in this manuscript, including all relevant raw data, will be freely available upon request to any researcher wishing to use them for non-commercial purposes.

Competing interests

The authors declare no competing interests.

Funding

This research was supported by the Clinical Research Project of Shanghai Municipal Health Commission (202140100), Minsheng Research Project of Pudong New Area Science and Technology Development Fund (PKJ2022-Y39), and Medical Discipline Construction Project of Pudong Health Committee of Shanghai (PWYts2021-18).

Authors' contributions

JL and MGH carried out the main analysis and wrote the manuscript; YL and MHL initiated the project, provided advice on the analysis and edited the manuscript; HW and WCG assisted with checking methods and data; YG accessed the data; ZJL provided advice on the analysis and manuscript; HHS initiated the project and provided advice on the analysis and manuscript.

Acknowledgements

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Authors' information (optional)

Jie Liu¹, Mingang He², Ying Liu², Menghan Liu³,Hao Wang², Wenchao Gu⁴, Yang Gao², Zengjun Li², Haihan Song⁴

1Cancer Center, Shandong Public Health Clinical Center, Public Health Clinical Center Affiliated to Shandong University, Jinan, China.

2Department of Gastrointestinal Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.

3School of Clinical and Basic Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.

4Department of Pulmonary and Critical Care Medicine, Shanghai Pudong New Area People's Hospital, Shanghai, China.

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A Novel Prognostic Signature for Combined T-cell Exhaustion and Mitochondrial Dysfunction in Hepatocellular Carcinoma

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Abstract

Figures

1 Introduction

2 Materials And Methods

2.1 Datasets and Identification of Differentially Expressed TEMDRGs

2.2 Functional Enrichment Analysis

2.3 Mutation Analysis of TEMDRGs

2.4 Cluster Analysis

2.5 Construction and Validation of the Prognostic Model

2.6 Building and Validation of a Predictive Nomogram

2.7 Gene Set Enrichment Analysis

2.8 Analysis of Immune Status, Immunotherapy Response, and Chemotherapy Response

2.9 Identification of Potentially Therapeutic Compounds

2.10 Statistical Analysis

3 Results

3.1 DE-TEMDRGs in HCC

3.2 Genetic variation of DE-TEMDRGs

3.3 Identification of HCC Molecular Subtypes Based on PR-DE-MDRTEGs

3.4 Tumor Microenvironment Immune Landscape, and Chemotherapy Response in the TEMD subtypes

3.5 Construction of a TEMD-Related Prognosis Signature and Clinical Correlation Analysis

3.6 Identification and Validation of the TEMD Prognostic Signature

3.7 Correlation Between the TEMD Signature and Clinical characteristics

3.8 Development of a Clinical Nomogram

3.9 Analysis of T-Cell Exhaustion and Mitochondrial Dysfunction

3.10 Analysis of Immune Cell Infiltration, Immune Checkpoint Expression, and Immunotherapy Response

3.11 Chemotherapy Response Analysis and TMED-Related Small-Molecule Drug Screening

4 Discussion

Declarations

References

Additional Declarations

Supplementary Files

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