Bioinformatics Identification of Lactate-Associated Genes in Hepatocellular Carcinoma: G6PD’s Role in Immune Modulation

Bioinformatics Identification of Lactate-Associated Genes in Hepatocellular Carcinoma: G6PD’s Role in Immune Modulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bioinformatics Identification of Lactate-Associated Genes in Hepatocellular Carcinoma: G6PD’s Role in Immune Modulation Hao-ran Qu, Chao-qun Wang, Su-juan Sun, Wen-wen Zhang, Xuan-shuang Du, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5748430/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Hepatocellular carcinoma (HCC) is a major global health issue, with poor prognosis often associated with dysregulated metabolic pathways, especially lactate metabolism. This study explored the prognostic significance of lactate-associated genes in HCC and their potential as therapeutic targets. Methods We analyzed RNA-seq and clinical data from 374 patients with HCC from The Cancer Genome Atlas (TCGA) database. Using Cox regression, LASSO analysis, and Kaplan-Meier survival curves, we identified key lactate-associated genes associated with patient outcomes. Functional validations, including Western blot, flow cytometry, and molecular docking studies, were performed to confirm the biological impact of these genes. Results G6PD, IK, and CALML5 were identified as significant prognostic markers for HCC. A prognostic model was developed that effectively stratified patients into risk groups, which correlated with survival. G6PD’s role in immune modulation and its potential as a drug target were validated through biochemical assays and computational analyses. Functional assays in HepG2 cells confirmed that alterations in G6PD expression affect T cell activity, with knockdown enhancing IFN-γ production and overexpression inhibiting it, demonstrating G6PD’s role in immune evasion. Conclusions This study establishes lactate metabolism genes, particularly G6PD, as key prognostic markers in HCC. The validation of G6PD’s immunomodulatory effects further supports its potential as a therapeutic target for strategies aimed at enhancing immune surveillance and treatment outcomes in HCC. Hepatocellular carcinoma lactate metabolism prognostic biomarkers G6PD PD-L1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally, characterized by its aggressive nature and poor prognosis [ 1 ]. The metabolic reprogramming of cancer cells, including altered lactate metabolism, plays a crucial role in HCC progression and therapeutic resistance. Lactate, traditionally viewed as a by-product of anaerobic glycolysis, has emerged as a key molecule in cancer, influencing both the tumor microenvironment and cancer cell signaling [ 2 – 4 ]. Recent studies have linked lactate metabolism to key pathways involved in cancer proliferation, metastasis, and immune evasion [ 5 ]. The Warburg effect [ 6 ], characterized by increased glucose uptake and lactate production even in the presence of oxygen, highlights the metabolic shift in cancer cells favoring lactate production. This metabolic shift is not merely a survival mechanism but also a strategic alteration that supports invasiveness and therapy resistance [ 7 , 8 ]. Lactate’s role extends beyond its metabolic functions; it serves as a signaling molecule that alters gene expression, modifies enzyme activities, and influences immune cell behavior in the tumor microenvironment [ 9 ]. Lactate inhibits immune surveillance by repressing the activity of immune cells, including T cells and natural killer cells, thereby contributing to immune evasion mechanisms in tumors [ 10 – 12 ]. Given the profound impact of lactate on HCC progression, identifying genetic markers linked to lactate metabolism may provide new prognostic tools and therapeutic targets. Studies have shown that genes involved in lactate production and transport are overexpressed in HCC and are correlated with poor patient outcomes [ 13 – 15 ]. Exploring the genetic underpinnings of lactate metabolism in HCC could provide valuable insights into its pathogenesis and uncover new avenues for targeted therapy. This study aimed to identify and evaluate the prognostic significance of lactate-associated genes in HCC by using bioinformatics approaches. We focused specifically on G6PD because of its strong association with tumor progression and immune cell infiltration, as revealed in large-scale transcriptomic datasets. We also investigated its role in regulating immune checkpoints, particularly PD-L1, through the mTOR pathway, and explored its potential as a therapeutic target to enhance anti-tumor immunity in HCC. By integrating single-cell RNA sequencing (scRNA-seq) data, the Cancer Genome Atlas (TCGA) datasets, and functional assays, this study aims to provide new insights into G6PD’s role in lactate metabolism, immune modulation, and HCC prognosis. Our findings highlight the complex interplay between metabolic reprogramming and immune regulation, providing new perspectives on how metabolic enzymes like G6PD contribute to tumor immune evasion and therapeutic resistance. Results Identification and Prognostic Relevance of Lactate-Associated Genes We used data from 371 hepatocellular carcinoma (HCC) patients with complete survival information from The Cancer Genome Atlas (TCGA) to evaluate the prognostic significance of lactate-associated genes. Univariate Cox regression analysis initially identified 128 lactate-associated genes significantly correlated with HCC prognosis from a list of 332 genes linked to lactate processes according to the literature [ 16 ]. The top ten genes significantly associated with HCC prognosis were G6PD, TCOF1, KIF2C, RAN, PPM1G, JPT1, CACYBP, CCT5, ENO1, and IK (Fig. 1 A). LASSO regression was then used to refine the selection of candidate prognostic genes, identifying 9 potential genes from the list (Figs. 1 B and 1 C). To confirm the independent prognostic value of the selected genes, we performed multivariate Cox regression analysis. This analysis identified three critical prognostic genes: CALML5, G6PD, and IK (Fig. 1 D). These genes were the strongest predictors of overall survival in HCC patients, adjusting for other clinical variables such as age, gender, and cancer stage. A prognostic model was constructed using these three genes (CALML5, G6PD, and IK), and risk scores were calculated based on their gene expression levels. Patients were stratified into high- and low-risk groups based on median risk scores (Fig. 1 E). The high-risk group showed significantly worse overall survival than the low-risk group, highlighting the prognostic relevance of the lactate-associated gene model. Finally, we performed univariate and multivariate Cox regression analyses to assess the impact of clinical variables, such as gene score and tumor stage, on prognosis. Both analyses showed that the gene score from the prognostic model and tumor stage were significantly correlated with survival outcomes in HCC patients (Fig. 1 F). Our analysis identified G6PD, CALML5, and IK as critical prognostic markers for HCC based on their association with lactate metabolism and survival. The prognostic model, incorporating these three genes, effectively stratifies patients into high- and low-risk groups, offering valuable insights for personalized treatment strategies in HCC. Construction and Validation of Lactate-Associated Gene Prognostic Model We constructed and validated a prognostic model based on lactate-associated genes (CALML5, G6PD, and IK), using the TCGA LIHC cohort, divided into training (Figs. 2 A-D) and internal validation (Figs. 2 E-H) sets, along with the external validation cohort (LIHC-JP) (Figs. 2 I-L). The model aimed to assess the predictive value of lactate metabolism-related genes in HCC and was evaluated across different datasets. In the training set, patients were stratified into high- and low-risk groups based on risk scores derived from the lactate-associated gene expression model. Kaplan-Meier survival analysis revealed a significant survival difference between the two groups. Patients in the high-risk group showed significantly poorer survival than those in the low-risk group, with increased mortality and decreased overall survival (OS) as risk scores increased (Figs. 2 A and 2 E). The distribution of risk scores and survival status were visualized to highlight survival differences (Figs. 2 B, C, F, and G). A heatmap of the three key genes in the prognostic model was generated to compare their expression patterns between high- and low-risk groups (Figs. 2 D and 2 H). This analysis showed that the expression of these genes was significantly higher in the high-risk group than in the low-risk group, reinforcing the association between elevated gene expression and poor prognosis. The heatmap also illustrated the distinct molecular characteristics of each risk group, confirming the prognostic relevance of lactate-associated genes in HCC. The external validation cohort (LIHC-JP) was used to validate the predictive value of the lactate-associated gene model. Similar to the training set, the high-risk group in the validation set showed significantly worse prognosis than the low-risk group (Fig. 2 I), confirming the robustness of the model across datasets. The external validation heatmap (Fig. 2 L) showed consistent expression patterns of the three key genes, confirming their relevance in predicting patient outcomes in an independent cohort. The distribution of risk scores and survival analysis in the external validation cohort mirrored the findings in the training set, further validating the prognostic utility of the model (Figs. 2 J and 2 K). These findings suggest that lactate metabolism-related genes may serve as valuable biomarkers for prognosis in HCC. Independent Prognostic Analysis and PCA We performed Decision Curve Analysis (DCA) to evaluate the clinical utility of our lactate-associated gene prognostic model. DCA plots (Figs. 3 A-C) showed that the model provides significant net clinical benefits across a wide range of threshold probabilities. This indicates that the model offers valuable predictive information for clinicians, assisting in decision-making for overall survival (OS) predictions. The net benefit curve suggests that our model outperforms traditional strategies, including clinical variables alone, by identifying patients who would benefit most from early intervention based on risk stratification. The model’s predictive accuracy was further assessed using Receiver Operating Characteristic (ROC) curves for 1-year, 2-year, and 3-year overall survival (OS) predictions. As shown in Figs. 3 D-F, the Area Under the Curve (AUC) values for both the TCGA cohort and external validation cohort were consistently above 0.65, with some exceeding 0.7 and even reaching 0.8 for certain time points. These findings highlight the strong performance of our model in accurately predicting OS, further supporting its potential utility in clinical practice for long-term survival predictions in HCC patients. To further validate the model’s ability to stratify patient risk, we performed Principal Component Analysis (PCA) on the full TCGA dataset, focusing on lactate-associated genes and those identified in the risk model. PCA results, shown in Figs. 3 G-I, revealed clear separation between high-risk and low-risk groups based on gene expression profiles. The PCA plots show that the risk genes effectively distinguish patients with better prognosis (low-risk) from those with worse prognosis (high-risk), supporting their utility in risk stratification. Our analysis confirms that the lactate-associated gene prognostic model is a robust and clinically relevant tool for predicting overall survival in HCC patients. Enrichment Analysis of High and Low-Risk Groups in HCC The circular plot illustrates Gene Ontology (GO) categories, highlighting key biological processes, such as cell cycle regulation and immune responses, which are more active in the high-risk group. This visualizes significant gene expression differences related to patient prognosis (Fig. 4 A). A dot plot details the top enriched Biological Processes, highlighting critical activities such as nuclear division and chromosome segregation, suggesting increased cellular proliferation in high-risk patients (Fig. 4 B). The bubble plot illustrates KEGG pathway analysis, identifying critical pathways, such as cell cycle and cytokine-cytokine receptor interaction, associated with high-risk groups (Fig. 4 C). GSEA plots compare high-risk and low-risk groups, with high-risk patients showing enrichment in pathways related to cell adhesion and metastasis, while low-risk patients exhibit activity in metabolic pathways, potentially correlating with better outcomes (Figs. 4 D-E). Immune Infiltration and Checkpoint Analysis The tumor microenvironment (TME) plays a crucial role in tumor progression and response to immunotherapy. To explore the relationship between immune cell infiltration and lactate-associated gene expression, we analyzed the distribution of immune cells and immune checkpoint expression in high-risk and low-risk groups, based on the lactate-associated gene model. Our analysis revealed that the low-risk group exhibited a significantly higher proportion of stromal cells, suggesting a more active stromal component in these patients (Fig. 5 A). This finding suggests that stromal cells in the tumor microenvironment may contribute to more favorable immune conditions in low-risk patients. We also observed a strong positive correlation between G6PD expression and immune scores (Fig. 5 B). This suggests that higher G6PD expression may shape the immune landscape, potentially promoting immune cell infiltration and influencing the immune response in HCC. This positive correlation with immune scores highlights the potential role of G6PD in modulating the TME, especially in immune activation. To further assess immune cell infiltration, we used single-sample Gene Set Enrichment Analysis (ssGSEA) to evaluate the levels of 22 immune cell types in the high- and low-risk groups. Our results indicated increased macrophage (M0) infiltration in high-risk groups, whereas CD4 memory resting cells were more prevalent in low-risk groups (Fig. 5 C-E). This suggests that high-risk tumors may exhibit a more immunosuppressive TME, with macrophages, often involved in promoting immune evasion, being more abundant in these patients. Conversely, the increased presence of CD4 memory resting cells in the low-risk group may reflect a more balanced immune environment, potentially enhancing immune surveillance and tumor control. We further analyzed the expression of key immune checkpoints, including CTLA-4, CD274 (PD-L1), PDCD1 (PD-1), LAG3, and HAVCR2. These checkpoints play a crucial role in immune evasion and tumor progression. Our findings revealed differential expression of these checkpoints between the high- and low-risk groups (Fig. 5 F-G). Notably, G6PD and IK expression levels were strongly correlated with immune checkpoint expression, especially in the high-risk group. This suggests that G6PD and IK may regulate immune evasion by modulating immune checkpoint pathways, further supporting their potential role in promoting immune suppression and escape in HCC. Mutation Analysis and Drug Sensitivity in HCC Risk Groups Somatic mutation analysis within the TCGA dataset revealed distinct mutational profiles between high-risk and low-risk groups (Figs. 6 A-B). High-risk patients exhibited a higher frequency of mutations in key oncogenes and tumor suppressor genes, such as TP53 and CTNNB1. Notably, high-risk patients showed a higher tumor mutational burden (TMB), suggesting a potential correlation with poor prognosis. Tumor Mutational Burden analysis (Fig. 6 C) quantified TMB, revealing that high-risk patients consistently exhibited higher TMB values compared to low-risk groups. This difference was statistically significant and supports the association between higher TMB and increased risk scores. Survival analysis showed that patients with higher TMB, particularly those in the high-risk group, had poorer overall survival rates (Figs. 6 D-E). These findings suggest that TMB could serve as an independent prognostic factor in risk stratification. Drug sensitivity testing using data from the GDSC database revealed variable responses to chemotherapy agents between the groups. High-risk patients generally exhibited reduced sensitivity to drugs like sorafenib and doxorubicin, as reflected by higher IC50 values. Conversely, some agents, such as cisplatin and paclitaxel, exhibited relatively lower IC50 values in high-risk groups, suggesting a nuanced response pattern that may inform therapeutic decisions (Figs. 6 F-O). Gene Co-expression Network Analysis Using Weighted Gene Co-expression Network Analysis (WGCNA), we identified gene modules linked to tumor characteristics in HCC. The optimal soft-thresholding power for constructing a scale-free network was β = 13, with a scale-free fit index of 0.90, indicating a robust network topology (Fig. 7 A). Hierarchical clustering of genes revealed six distinct gene modules, each represented by a unique color (Fig. 7 B). The “Red” and “Green” modules showed significant correlations with clinical traits. The “Red” module had a strong positive correlation with tumor grade, indicating its association with more aggressive tumor features. In contrast, the “Green” module was most strongly correlated with tumor stage, suggesting its role in HCC progression (Fig. 7 C). Further analysis with Venn diagrams identified two genes, G6PD and IK, overlapping between our prognostic model's lactate-associated genes and the 'Red' and 'Green' modules, highlighting their relevance in HCC pathology (Fig. 7 D). Immunohistochemical staining confirmed that G6PD and IK were upregulated in HCC tissues compared to normal liver tissues, reinforcing their potential as biomarkers for tumor progression and therapeutic targets (Figs. 7 E-F). Single-Cell Transcriptomic Analysis We analyzed single-cell RNA sequencing (scRNA-seq) data to explore the cellular heterogeneity and expression profiles of lactate-associated genes in HCC. Using t-SNE dimensionality reduction, cells were grouped into distinct clusters representing major cell types, including T cells, hepatic cells, endothelial cells, myeloid cells, and mesenchymal cells (Figs. 8 A and 8 B). Analysis of Lactate-Related Gene (LRG) scores, based on key lactate-associated gene expression, showed significantly higher scores in tumor cells compared to normal cells (Fig. 8 C). Stratification by cell type revealed that mesenchymal and myeloid cells had the highest LRG scores, indicating their critical role in lactate metabolism within the tumor microenvironment (Fig. 8 D). Further investigation of specific lactate-associated genes revealed distinct expression patterns across cell types. G6PD, a key gene in lactate metabolism, was predominantly expressed in hepatic and mesenchymal cells (Fig. 8 E). Similarly, IK expression was enriched in myeloid and mesenchymal cells, highlighting its potential role in metabolic reprogramming of these cell populations (Fig. 8 F). These findings highlight the significance of lactate-associated genes in driving metabolic alterations and tumor progression at the single-cell level. Given G6PD’s prominent role in lactate metabolism and its expression in key cell types linked to tumor progression, we selected G6PD for further functional validation. Validation of G6PD expression in HCC To explore the functional impact of G6PD on immune responses in HCC, we performed functional assays. The TCGA LIHC dataset revealed that G6PD is significantly overexpressed in tumor tissues compared to normal tissues (Figure S1 A). Kaplan-Meier survival analysis indicated that high G6PD expression is associated with poor prognosis in HCC patients (HR = 2.15, Log-rank p = 8.6e-05; Figure S1 B). Stratification by cancer stage revealed a progressive increase in G6PD expression with advancing stages (Figure S1 C), and nodal metastasis (N1) correlated with higher G6PD levels compared to non-metastatic cases (Figure S1 D). Age-specific analysis further revealed that older patients had significantly higher G6PD expression than younger patients (Figure S1 E). These findings underscore the association of G6PD with tumor progression and adverse clinical outcomes in HCC. Further analysis in liver cancer cell lines (HepG2, Huh7, MHCC97H) revealed significantly higher G6PD mRNA and protein expression in cancer cells compared to the normal liver cell line L02 by western blot assays (Figures S1 F-G), suggesting that G6PD overexpression contributes to the metabolic alteration’s characteristic of HCC cells. G6PD and Immune Modulation in HCC Our analysis of G6PD in HCC indicates its significant role in regulating immune responses, particularly in CD8 + T cell activation. Immune infiltration analysis (Supplementary Fig. 2) showed that G6PD expression positively correlated with CD8 + T cell infiltration in the tumor microenvironment (Figures S2A-D). G6PD expression was also associated with immune cell types involved in immune suppression, such as macrophages. This suggests that G6PD may modulate both immune activation and suppression within the tumor. Functional assays showed that G6PD knockdown in HepG2 cells enhanced CD8 + T cell proliferation and IFN-γ secretion, indicating increased T cell activation (Figure S2E). In contrast, G6PD overexpression suppressed T cell activation, supporting the hypothesis that G6PD may contribute to immune evasion. These results suggest that G6PD modulates the immune response by regulating the tumor’s ability to influence CD8 + T cell activity. The effects of G6PD on immune cell activation and suppression markers, such as CD274 (PD-L1) in Fig. 5 , highlight its potential role in immune checkpoint regulation. G6PD Regulates PD-L1 Expression via the mTOR Pathway We further investigated the role of G6PD in regulating PD-L1 expression in HCC cells. Numerous studies have demonstrated that mTOR is a key upstream regulator of PD-L1 expression [ 17 – 19 ], playing a pivotal role in immune evasion by suppressing T cell activity [ 20 ]. Based on evidence that G6PD regulates immune activation and suppression, we hypothesized that it modulates PD-L1 expression via the mTOR signaling pathway. To test this hypothesis, we performed Western blot analyses to assess how G6PD knockdown and overexpression influence PD-L1 and mTOR signaling in HepG2 cells. Results depicted in Fig. 9 A show that G6PD knockdown significantly reduced phosphorylated mTOR (p-mTOR) and PD-L1 levels, suggesting that G6PD modulates PD-L1 expression via mTOR signaling. Conversely, G6PD overexpression in HepG2 cells increased p-mTOR and PD-L1 levels, further supporting that G6PD promotes immune evasion by upregulating PD-L1 through the mTOR pathway (Fig. 9 B). Rescue experiments, involving G6PD reintroduction in knockdown cells, restored p-mTOR and PD-L1 levels, confirming that G6PD specifically mediates these effects (Fig. 9 C). Furthermore, treatment with an mTOR inhibitor (Fig. 9 D) in G6PD-overexpressing cells significantly reduced p-mTOR and PD-L1 levels, reinforcing that G6PD exerts immune-modulatory effects via mTOR signaling. These findings indicate that G6PD impacts CD8 + T cell activation, immune cell infiltration, and PD-L1 expression via the mTOR pathway, thereby facilitating immune evasion in HCC. By regulating the immune checkpoint PD-L1, G6PD likely suppresses T cell activity, thus facilitating tumor growth and progression. Molecular Docking Analysis of G6PD with Candidate Drugs To investigate potential interactions between G6PD and candidate therapeutic compounds, molecular docking was performed using four drugs with the highest predicted sensitivity scores: Staurosporine_1034, Vinorelbine_2048, Vinblastine_1004, and Docetaxel_1007. The docking results revealed strong binding affinities between G6PD and the selected drugs, with binding energies below − 7 kcal/mol, suggesting stable interactions. Specifically, Staurosporine_1034 exhibited the highest binding affinity (-9.9 kcal/mol), forming one hydrogen bond within the active site. Vinorelbine_2048 showed a binding affinity of -7.6 kcal/mol, forming two hydrogen bonds that stabilized its interaction with G6PD. Vinblastine_1004 demonstrated a binding affinity of -8.8 kcal/mol, forming a single hydrogen bond. Docetaxel_1007 showed a binding affinity of -8.9 kcal/mol, forming three hydrogen bonds, indicating specific and robust binding. Visualization of the docking results revealed the structural alignment of each drug within the G6PD binding pocket, with key residues interacting through hydrogen bonds and hydrophobic interactions (Figs. 10 A-D) [ 21 ]. These findings suggest that the selected drugs may modulate G6PD activity by directly targeting its active site, potentially affecting lactate metabolism in HCC. Discussion Our study highlights the critical role of lactate metabolism in HCC progression and prognosis, identifying several lactate-associated genes, particularly G6PD, IK, and CALML5, as significant prognostic markers. These findings support the growing body of evidence indicating that cancer metabolism, particularly lactate production and utilization, significantly impacts tumor behavior and patient outcomes [ 13 – 15 ]. The association between high lactate production and aggressive tumor features, along with poor prognosis, is well established [ 22 ]. Lactate not only supports cancer cell growth and survival but also alters the tumor microenvironment, promoting cancer progression through immunosuppression and angiogenesis [ 2 , 10 – 12 ]. Our results specifically highlight the enzyme G6PD, a key player in the pentose phosphate pathway, involved in reductive biosynthesis and the generation of reducing equivalents. G6PD’s role in modulating PD-L1 expression via the mTOR signaling pathway, as demonstrated in our functional assays, highlights its involvement in immune evasion by HCC cells. This finding establishes a potential link between metabolic reprogramming and immune checkpoint regulation, an important area for therapeutic intervention. The dual role of G6PD in metabolic modulation and immune regulation makes it a promising target for HCC therapy. Drugs that inhibit G6PD activity could not only disrupt cancer cell metabolic homeostasis but also enhance the immune response against tumors. Our molecular docking studies indicate that inhibitors such as Staurosporine could disrupt G6PD activity, suggesting a novel approach to curb HCC progression. This therapeutic strategy may be especially effective in patients with high G6PD expression and poor prognosis. The prognostic model based on lactate-associated genes effectively stratifies HCC patients into high- and low-risk categories, providing valuable insights for personalized treatment strategies. For example, patients in the high-risk category may benefit from more aggressive treatment regimens and close monitoring. Furthermore, the model’s strong performance across independent datasets highlights its potential utility in clinical settings, assisting in decision-making and improving patient management. Despite these promising findings, several challenges persist. The heterogeneity of HCC and the complex interplay of genetic, environmental, and metabolic factors present significant challenges in the uniform application of prognostic models. Future studies should aim to validate these findings in larger, multi-center cohorts and explore the interrelationships among various metabolic pathways in HCC. Additionally, integrating metabolic profiling with immunological and molecular characteristics could generate comprehensive models that capture the multifaceted nature of cancer [ 23 ]. Our analysis of immune infiltration emphasizes the intricate relationship between metabolic reprogramming and the immune landscape in HCC. Understanding how metabolic changes affect immune cell function and the efficacy of immunotherapy could lead to the development of combined metabolic and immune-modulating therapies. For instance, targeting metabolic enzymes alongside immune checkpoint inhibitors may improve therapeutic efficacy against HCC. In conclusion, this study not only confirms the importance of lactate metabolism in shaping clinical outcomes in HCC patients but also provides a foundation for future research and therapeutic development. G6PD is a key regulator of metabolic reprogramming and immune modulation in HCC. Our bioinformatics and functional validation studies provide strong evidence that G6PD contributes to immune evasion by modulating immune cell activity and immune checkpoint expression, particularly PD-L1, via the mTOR signaling pathway. These findings suggest that G6PD is not only a potential prognostic biomarker but also a promising target for immune-based therapies designed to enhance anti-tumor immunity and overcome immune evasion in HCC. Materials and Methods Data Collection and Preparation Hepatocellular carcinoma (HCC) data, including RNA-sequencing and clinical information, were downloaded from The Cancer Genome Atlas (TCGA) portal ( https://portal.gdc.cancer.gov/ ). A total of 374 HCC samples along with clinical data for 371 patients were included. Additionally, 332 lactate-associated genes were compiled from relevant literature sources, specifically from PMID: 38419085 [ 16 ]. An external validation dataset, LIHC-JP, was also used. Prognostic Gene Selection and Model Construction Initially, univariate Cox regression analysis was performed to assess the prognostic value of each lactate-associated gene. Genes with a p-value < 0.05 were considered significant and were further analyzed using Lasso regression to prevent overfitting and to refine the selection of variables. Multivariate Cox regression analysis was subsequently conducted to finalize the selection of variables, resulting in three lactate-associated prognostic genes. These genes were used to construct a prognostic model where each patient’s risk score was calculated using the formula: Risk score=∑(Expression level of Gene 𝑖 ×coefficient 𝑖 ) The TCGA dataset was divided into training and validation sets, with LIHC-JP serving as an external validation set. Kaplan-Meier curves were used to analyze survival differences between high and low-risk groups in these datasets. Enrichment Analysis Differentially expressed genes (DEGs) between risk groups were identified based on |log2FC| > 1.0 and a p-value < 0.05. Gene Ontology (GO) analysis for biological processes, cellular components, and molecular functions, along with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were conducted to explore the biological significance of these genes. Gene Set Enrichment Analysis (GSEA) was also performed to further understand pathway differences between high and low-risk groups. Immune Analysis The immune and stromal scores were estimated using the ESTIMATE algorithm to assess the impact of the tumor microenvironment on prognosis. Differences in immune cell infiltration between risk groups were analyzed using CIBERSORT. Correlations between gene expression and immune cell levels were assessed using Spearman’s rank correlation. Mutation Analysis Somatic mutation data from the TCGA dataset were analyzed using the "maftools" R package. Tumor Mutational Burden (TMB) was calculated, and HCC patients were classified into high and low TMB groups based on the median TMB value. Survival differences between these groups were examined. Drug Sensitivity Analysis Drug sensitivity was assessed using the "pRRophetic" R package, which predicts IC50 values for chemotherapeutic agents based on gene expression profiles. Drugs showing significant differences in IC50 values between high and low-risk groups were identified, and their potential efficacy was evaluated. Single-cell RNA Sequencing Analysis Single-cell RNA-seq data from GEO datasets GSE245906 and GSE149614 were processed using the "Seurat" R package. Data quality control, normalization, and batch effect removal were performed, followed by clustering and differential gene expression analysis. The "SingleR" package was used for cell type annotation, and "AUCell" was utilized to quantify lactate-related gene expression across cell types. Validation of Gene Expression The expression levels of key genes, G6PD and IK, were validated using immunohistochemistry data from the Human Protein Atlas ( https://www.proteinatlas.org/ ). This helped confirm their overexpression in HCC tissues compared to normal liver tissue. Cell culture and Lentivirus-infection Human normal hepatocytes L02, human hepatocellular carcinoma cells Huh7, HepG2 and MHCC97H were cultured in complete Dulbecco’s modified Eagle’s medium (DMEM) with 10% fetal bovine serum (FBS) (HyClone Laboratories, Logan, UT, USA) at 37°C in a 5% CO2 atmosphere. To generate stable knockdown or overexpressing cell lines for G6PD, G6PD shRNA plasmids (LV-Si-G6PD), G6PD overexpressing plasmids (LV-G6PD), and empty controls (LV-Sc-G6PD/LV-empty) were purchased from Santa Cruz Biotechnology. After coinfection of shRNA or overexpressing plasmids with the packaging plasmids psPAX2 (Addgene, Cat# 12260) and pMD2.G (Addgene, Cat# 12259) into HEK-293T cells, the lentivirus was harvested, titrated and stored for infecting HepG2 cells. These two cell lines ( G6PD -KD-HepG2 and G6PD -OE-HepG2) infected with lentivirus were screened with puromycin, and the stable cell lines were further confirmed with detection of WB. Cells were grown to 50% confluence in 6-well cell culture plates (Nest, China) and transfection was performed using Lipofectamine 2000 (Invitrogen, Cat# 11668030), according to the manufacturer’s instructions. In all the co-transfection experiments, the corresponding empty vectors were used as negative controls to ensure similar DNA concentrations. Immunohistochemistry (IHC) The collection and use of all human HCC samples were approved by the Ethical Committee of Wuhan University School of Medicine in Wuhan, China (2022007). Informed consent was obtained from each patient for the collection of HCC samples. IHC staining was performed on human liver tissue sections. The sections were processed by heating at 70°C in an oven for 30 min and dewaxed with xylene and alcohol for 10 min. Endogenous peroxidase was blocked in 0.3% H 2 O 2 for 20 min. The samples were further treated with citrate buffer at 95°C for 10 min and incubated with G6PD antibody (Cat#sc-373886, Santa Cruz Biotechnology, 1:100 dilution), DAKO EnVision containing horseradish peroxidase (HRP)-conjugated with anti-mouse antibody. Western Blot Analysis Western blotting was performed to validate the protein expression levels of G6PD and related signaling molecules such as mTOR and PD-L1. Cells were lysed using RIPA buffer (Cat#P0013D, Beyotime) containing protease and phosphatase inhibitors (Cat#P1045, Beyotime). Protein concentrations were measured using the BCA Protein Assay Kit (Cat#P0009, Beyotime). Equal amounts of protein were separated on SDS-PAGE gels and transferred to PVDF membranes. The membranes were blocked with 5% non-fat milk and incubated with primary antibodies against G6PD (Cat#sc-373886, Santa Cruz Biotechnology, 1:2000 dilution), mTOR (Cat#28273-1-AP, Proteintech, 1:1000 dilution), phospho-mTOR (Cat#67778-1-Ig, Proteintech, 1:1000 dilution), PD-L1 (Cat#83600-2-PBS, Proteintech, 1:1000 dilution), and GAPDH(Cat#60004-1-Ig, Proteintech, 1:10000 dilution, used as a loading control). Appropriate HRP-conjugated secondary antibodies (Cat#7074/7076, Cell Signaling Technology, 1:10000 dilution) were used, and protein bands were visualized using enhanced chemiluminescence (ECL) (Cat#A38556, ThermoFisher). Quantification of band intensity was performed using ImageJ software, and relative expression levels were calculated by normalizing to GAPDH. Flow Cytometry Analysis Flow cytometry was employed to assess the functional effects of G6PD modulation on T cell proliferation and IFN-γ secretion. Human CD8 + T cells were labeled with CFSE (Carboxyfluorescein succinimidyl ester, Cat#C34554, Invitrogen) and stimulated with anti-CD3 (Cat#341090, Biosciences) and anti-CD28 (Cat#348047, Biosciences) antibodies. These T cells were co-cultured with HepG2 cells, either with knocked-down (KD) or overexpressed (OE) G6PD, for three days. Post-incubation, cells were harvested and stained for CD8 (Cat#335805, Biosciences) and IFN-γ (Cat#562213, Biosciences). Flow cytometric analysis was conducted to determine T cell proliferation (by CFSE dilution) and IFN-γ production. The proliferation index and the percentage of IFN-γ + cells were calculated using FlowJo software. Structural analysis of G6PD and therapeutic agents From the "oncoppredict" results, we selected the four drugs with the highest predicted sensitivity for molecular docking analysis. The molecular structures were obtained from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ), and protein structures were retrieved from the Protein Data Bank (PDB; http://www.rcsb.org/ ). Protein and ligand files were converted to PDBQT (Protein Data Bank, Partial Charge (Q), & Atom Type (T)) format, water molecules were removed, and polar hydrogens were added to improve docking accuracy. Molecular docking simulations were conducted using AutoDock Vina 1.2.2 ( http://autodock.scripps.edu/ ) to evaluate drug-protein binding interactions. The therapeutic potential of each drug was assessed based on its docking score, which reflects the strength and affinity of the binding[ 24 ]. The G6PD and Staurosporine complex structure were visualized by PyMol. Statistical Analysis Statistical analyses were performed using R software (version 4.1.2). The Wilcoxon test was used for differential analysis, Spearman’s rank correlation for assessing correlations, and Kaplan-Meier method for survival analysis. The Benjamini-Hochberg method was applied for multiple testing correction. Significant differences were considered at p < 0.05. Declarations Ethics approval and consent to participate : Samples were collected with informed consent and in accordance with established biobanking protocols, ethical and legal standards. Tissue samples were approved by the Ethics Review Committee of Zhongnan Hospital of Wuhan University (Approval Number [2022]007). Clinical trial registration number : Not applicable. A vailability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This work was supported by National Natural Science Foundation of China grants 82272978 (ML); Natural Science Foundation Project of Hubei Province grant 2021CFB484 (ML); Open Foundation of Hubei Province Key Laboratory of Tumor Microenvironment and Immunotherapy (2024KZL07) Authors ’ contributions: Conceptualization: ML; Data curation: ML, CQW; Formal analysis: CQW, SJS, ML; Funding acquisition: ML, HYW; Investigation: CQW, SJS, WWZ, XSD, YASZ, XCW; Methodology: ML; Project administration: ML; Resources: ML, HYW; Software: HRQ, CQW, ML; Supervision: ML; Validation: HRQ, CQW, ML; Visualization: HRQ, CQW, ML; Writing – original draft: ML, HRQ, SJS; Writing – review and editing: ML, XLZ, HYW, QP, FLL, ML, HRQ. Acknowledgements : We would like to acknowledge the essential contributions of all staffs and students who participated in this work. References Llovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, Lencioni R, Koike K, Zucman-Rossi J, Finn RS. 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Cancers (Basel) 2020, 12(10). Koppenol WH, Bounds PL, Dang CV. Otto Warburg's contributions to current concepts of cancer metabolism. Nat Rev Cancer. 2011;11(5):325–37. Li X, Yang Y, Zhang B, Lin X, Fu X, An Y, Zou Y, Wang J-X, Wang Z, Yu T. Lactate metabolism in human health and disease. Signal Transduct Target Therapy. 2022;7(1):305. Ngwa VM, Edwards DN, Philip M, Chen J. Microenvironmental Metabolism Regulates Antitumor Immunity. Cancer Res. 2019;79(16):4003–8. Brooks GA. The Science and Translation of Lactate Shuttle Theory. Cell Metab. 2018;27(4):757–85. Wang ZH, Peng WB, Zhang P, Yang XP, Zhou Q. Lactate in the tumour microenvironment: From immune modulation to therapy. EBioMedicine. 2021;73:103627. Yang Z, Yan C, Ma J, Peng P, Ren X, Cai S, Shen X, Wu Y, Zhang S, Wang X, et al. Lactylome analysis suggests lactylation-dependent mechanisms of metabolic adaptation in hepatocellular carcinoma. Nat Metab. 2023;5(1):61–79. Du D, Liu C, Qin M, Zhang X, Xi T, Yuan S, Hao H, Xiong J. Metabolic dysregulation and emerging therapeutical targets for hepatocellular carcinoma. Acta Pharm Sin B. 2022;12(2):558–80. Yang F, Hilakivi-Clarke L, Shaha A, Wang Y, Wang X, Deng Y, Lai J, Kang N. Metabolic reprogramming and its clinical implication for liver cancer. Hepatology. 2023;78(5):1602–24. Huang H, Chen K, Zhu Y, Hu Z, Wang Y, Chen J, Li Y, Li D, Wei P. A multi-dimensional approach to unravel the intricacies of lactylation related signature for prognostic and therapeutic insight in colorectal cancer. J Transl Med. 2024;22(1):211. Yu J, Ling S, Hong J, Zhang L, Zhou W, Yin L, Xu S, Que Q, Wu Y, Zhan Q et al. TP53/mTORC1-mediated bidirectional regulation of PD-L1 modulates immune evasion in hepatocellular carcinoma. J Immunother Cancer 2023, 11(11). Jeong SU, Hwang HS, Park JM, Yoon SY, Shin SJ, Go H, Lee JL, Jeong G, Cho YM. PD-L1 Upregulation by the mTOR Pathway in VEGFR-TKI-Resistant Metastatic Clear Cell Renal Cell Carcinoma. Cancer Res Treat. 2023;55(1):231–44. Zhao Y, Qi Y, Xia J, Duan M, Hao C, Yao W. The role of the PI3K/AKT/mTOR pathway in mediating PD-L1 upregulation during fibroblast transdifferentiation. Int Immunopharmacol. 2024;142(Pt B):113186. Mafi S, Mansoori B, Taeb S, Sadeghi H, Abbasi R, Cho WC, Rostamzadeh D. mTOR-Mediated Regulation of Immune Responses in Cancer and Tumor Microenvironment. Front Immunol. 2021;12:774103. Yu Z, Qiu B, Zhou H, Li L, Niu T. Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma. Cancer Cell Int. 2023;23(1):169. Chen S, Xu Y, Zhuo W, Zhang L. The emerging role of lactate in tumor microenvironment and its clinical relevance. Cancer Lett. 2024;590:216837. Wang K, Zerdes I, Johansson HJ, Sarhan D, Sun Y, Kanellis DC, Sifakis EG, Mezheyeuski A, Liu X, Loman N, et al. Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer. Nat Commun. 2024;15(1):3837. Gorgulla C, Boeszoermenyi A, Wang ZF, Fischer PD, Coote PW, Padmanabha Das KM, Malets YS, Radchenko DS, Moroz YS, Scott DA, et al. An open-source drug discovery platform enables ultra-large virtual screens. Nature. 2020;580(7805):663–8. Additional Declarations No competing interests reported. Supplementary Files supplementaryinformation1.2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5748430","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396841782,"identity":"6ee2a0cd-1f82-4c15-af20-8fe61adf17c0","order_by":0,"name":"Hao-ran Qu","email":"","orcid":"","institution":"Wuhan University Taikang Medical School, Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Hao-ran","middleName":"","lastName":"Qu","suffix":""},{"id":396841783,"identity":"8b04c7fb-6215-4a15-91b6-7c08e2cbf5bf","order_by":1,"name":"Chao-qun Wang","email":"","orcid":"","institution":"Renmin Hospital of Wuhan 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University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-lian","middleName":"","lastName":"Zhang","suffix":""},{"id":396841797,"identity":"5858d2b4-841b-4ff8-95fd-c0d9111cb695","order_by":11,"name":"Min Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAn0lEQVRIiWNgGAWjYDACdsYGhg8MB0BMAyK1MDM2MM4gUQsQ8ZCkRbeZuU3atu1OYgN78zYJhpo7hLWYHWZsk85te5bYwHOsTILh2DNitWw7nNggkWMmwdhwmEgtliAt8m9I0cIItoWHeC3Nlr3/Dhu38aQVWyQcI0bL8faHN36cOSzbz354440PNURogQM2EJFAgoZRMApGwSgYBXgAABgGOWf11QENAAAAAElFTkSuQmCC","orcid":"","institution":"Wuhan University Taikang Medical School, Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-01-02 03:23:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5748430/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5748430/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73079244,"identity":"045d396b-0d2a-4fa6-a0fa-7653e824f2dd","added_by":"auto","created_at":"2025-01-06 13:56:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":212536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation and Validation of Lactate Metabolism-Related Genes as Prognostic Markers in HCC.\u003c/strong\u003e (A) Univariate Cox regression analysis showing hazard ratios for the top ten lactate metabolism-related genes significantly associated with HCC prognosis, emphasizing their potential as biomarkers. (B) Visualization of the optimal lambda (λ) selection in Lasso regression, which minimizes model deviance and is used to identify the most significant genes for further analysis. (C) Coefficient profiles of selected genes across different lambda values in Lasso regression, illustrating the regularization path and the stability of each gene’s coefficient. (D) Multivariate Cox regression analysis demonstrating the prognostic impact of the final selected genes—CALML5, G6PD, and IK—each contributing uniquely to the risk model. (E and F) Prognostic models incorporating clinical variables and gene-based risk scores, illustrating the significant variables in both univariate and multivariate analyses that influence patient survival and categorizing patients into high- and low-risk groups based on their median risk scores.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/41120ed591eb18eca0453a5a.png"},{"id":73079241,"identity":"58d269ad-c074-457e-b33f-b68011c0172c","added_by":"auto","created_at":"2025-01-06 13:56:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":348945,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and Validation of Lactate-Associated Gene Prognostic Model in HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A), (E), and (I) Kaplan-Meier survival curves for the training set, internal validation set, and external validation set, respectively, demonstrating the overall survival differences between high- and low-risk groups as defined by the prognostic model, with p-values indicating significant separation. (B), (F), and (J) Risk score distributions for the training set, internal validation set, and external validation set, respectively, highlighting the progression from low to high risk across the patient spectrum. (C), (G), and (K) Survival status dot plots of patients in the training set, internal validation set, and external validation set, respectively, plotting survival time against the risk score, with markers indicating deceased (red) and alive (blue) status. (D), (H), and (L) Heatmaps of gene expression profiling in the training set, internal validation set, and external validation set, illustrating the expression levels of key prognostic genes across risk groups, with red indicating high expression and blue indicating low expression.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/33dbb3b0ae96085de0122f32.png"},{"id":73079253,"identity":"8919d2b7-b671-4499-82da-cc0741bd8008","added_by":"auto","created_at":"2025-01-06 13:56:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":314505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIndependent Prognostic Analysis and PCA of Lactate-Associated Genes. \u003c/strong\u003e(A-C) Concordance index plots illustrating the independent prognostic value of risk score, age, gender, grade, and stage over 1 to 10 years, demonstrating the model's ability to predict overall survival across time. (D-F) Receiver operating characteristic (ROC) curves for 1-year, 2-year, and 3-year overall survival predictions across the entire cohort, highlighting the Area Under the Curve (AUC) values to evaluate the prognostic model's accuracy. (G-I) Principal component analysis (PCA) plots showing the distribution of patient samples in the TCGA dataset based on their risk scores, distinguishing between high-risk (red) and low-risk (blue) groups, visually validating the model's effectiveness in stratifying patients by genetic profiles.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/df345d0fa565b9eab62484b8.png"},{"id":73079653,"identity":"69b5a37c-2f63-4448-8a36-88d38527f882","added_by":"auto","created_at":"2025-01-06 14:04:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":275689,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment Analysis of Lactate-Associated Genes. \u003c/strong\u003e(A) Circular representation of Gene Ontology (GO) enrichment across three domains—Biological Process, Cellular Component, and Molecular Function—with inner rings depicting specific GO terms and outer rings showing significant genes in high and low-risk groups. (B) Dot plot illustrating the top enriched Biological Process terms, ranked by q-value, emphasizing processes like the cell cycle, cytokine-cytokine receptor interaction, and glycolysis, which are prominent in high-risk groups. (C) Bubble plot detailing the KEGG pathway analysis identifying critical pathways that differentiate between high and low-risk groups, such as p53 signaling and cell adhesion molecules, with circle sizes representing the gene ratio and color intensity reflecting the q-value. (D) GSEA enrichment curves for high-risk groups showing the most significantly enriched pathways, such as KEGG_CELL_ADHESION_MOLECULES_CAMs, with colors representing different pathways. (E) GSEA enrichment curves for low-risk groups, highlighting pathways like KEGG_ALANINE_METABOLISM and KEGG_FATTY_ACID_METABOLISM, suggesting a distinct metabolic profile associated with lower cancer risk.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/a3f6c0022c3903b78e5372c1.png"},{"id":73079252,"identity":"939a6c6b-42ce-4acc-bae2-b9edde0e7421","added_by":"auto","created_at":"2025-01-06 13:56:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":394130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComprehensive Immune Profile and Checkpoint Expression in High and Low-Risk Hepatocellular Carcinoma Groups. \u003c/strong\u003e(A) Violin plots depicting the distribution of ESTIMATE scores, highlighting differences in immune and stromal scores between high and low-risk groups. (B) Heatmap showing the correlation between key lactate-associated genes (G6PD, IK, CALML5) and immune scores, highlighting the impact of these genes on the tumor microenvironment. (C) Bar plots illustrating the relative proportions of 22 immune cell types, assessed by single-sample gene set enrichment analysis (ssGSEA), across high and low-risk groups. (D) Box plots highlighting the variability and differential abundance of specific immune cells between the risk groups. (E) Heatmap summarizing the correlation coefficients between the expression of G6PD, IK, CALML5 genes and the infiltration levels of various immune cells. (F) Bar graph depicting gene expression levels of major immune checkpoints in high and low-risk groups. (G) Correlation heatmap displaying the relationships between immune checkpoint expression and lactate-associated genes.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/09d5422b934e9fa8f9d1d203.png"},{"id":73079654,"identity":"968cabf2-919b-4095-89fe-361e3b372947","added_by":"auto","created_at":"2025-01-06 14:04:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":214475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutation Analysis and Drug Sensitivity Correlation with Risk Groups in HCC.\u003c/strong\u003e (A-B) Mutation landscapes depicting the frequency and types of somatic mutations in high-risk and low-risk groups, highlighting key genes with significant differences. (C) Violin plots illustrating the distribution of tumor mutational burden (TMB) in high and low-risk groups, with a higher TMB observed in the high-risk group. (D-E) Survival curves comparing overall survival based on TMB and risk categories, emphasizing the prognostic significance of TMB alongside risk scores. (F-O) Box plots presenting the differential sensitivity of high and low-risk groups to various chemotherapy agents, as indicated by half-maximal inhibitory concentration (IC50) values, demonstrating distinct drug responses between the groups. Each plot corresponds to a specific therapeutic agent, suggesting potential for tailored treatment based on risk stratification.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/735ea6376ebea070dd2ed89d.png"},{"id":73079655,"identity":"bfb3b6e7-7465-4d79-8c9d-6edb1866fb6e","added_by":"auto","created_at":"2025-01-06 14:04:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":394697,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Co-expression Network Analysis and Correlation with Hepatocellular Carcinoma Progression. \u003c/strong\u003e(A) Scale independence plot for different soft-thresholding powers, indicating the optimal power where the scale-free topology fit index reaches 0.9. (B) Gene dendrogram obtained through average linkage hierarchical clustering, with module colors assigned at the dynamic tree cut to identify gene co-expression modules in the TCGA hepatocellular carcinoma dataset. (C) Heatmap showing the correlation between gene modules and clinical traits of HCC, including tumor grade and stage. Color intensity represents correlation coefficients, with red indicating positive and blue indicating negative correlations. (D) Venn diagram illustrating the overlap between genes identified in the predictive model and those in the significant 'Red' and 'Green' WGCNA modules. (E-F) Immunohistochemical staining of normal and tumor liver tissues for G6PD and IK, showing differential expression levels between normal and tumor tissues, highlighting their potential as biomarkers for HCC progression.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/2d4ad053834066806e26943b.png"},{"id":73079659,"identity":"7dc4d0fa-4550-4954-8aa0-c5ffb5d8de34","added_by":"auto","created_at":"2025-01-06 14:04:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":451028,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-Cell Transcriptomic Analysis of Lactate-Associated Genes in HCC. \u003c/strong\u003e(A) t-SNE plot illustrating the clustering of single cells from HCC and normal liver tissues, with clusters annotated according to their corresponding cell types. (B) t-SNE plot displaying the distribution of tumor and normal cells across the clusters. (C) Boxplot comparing Lactate-Related Gene (LRG) scores between normal and tumor groups, showing significantly higher scores in tumor cells. (D) Boxplot displaying the distribution of LRG scores across different cell types, indicating higher scores in mesenchymal and myeloid cells. (E) Dot plot showing the average expression of G6PD across various cell types, with higher expression levels observed in hepatic and mesenchymal cells. (F) Dot plot showing the average expression of IK across cell types, with notable enrichment in myeloid and mesenchymal cells.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/306a46c3ad993bab1fa2482a.png"},{"id":73079261,"identity":"035f2c3d-5111-4d67-9af9-6529e92242c1","added_by":"auto","created_at":"2025-01-06 13:56:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":352528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional Validation of G6PD in HCC. \u003c/strong\u003e(A-D) Western blot analysis of G6PD, phosphorylated mTOR (p-mTOR), mTOR, and PD-L1 expression in HepG2 cells under different experimental conditions.\u003cstrong\u003e \u003c/strong\u003e(A) Knockdown of G6PD with LV-Si-G6PD reduces p-mTOR and PD-L1 expression compared to the control (LV-Sc-G6PD). (B) Overexpression of G6PD (LV-G6PD) increases p-mTOR and PD-L1 expression compared to the control (LV-empty). (C) Restoration of G6PD expression in G6PD knockdown HepG2 cells (G6PD-KD-HepG2) reverses the reduction in p-mTOR and PD-L1 expression. (D) Treatment with an mTOR inhibitor decreases p-mTOR and PD-L1 expression in G6PD-overexpressing HepG2 cells (G6PD-OE-HepG2), demonstrating the dependency of PD-L1 regulation on mTOR signaling. (E) Flow cytometry analysis of T cell proliferation shows that G6PD knockdown enhances T cell proliferation (with higher IFN-γ production), while G6PD overexpression suppresses T cell activation, as indicated by CFSE dilution and IFN-γ levels.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/cfa67e8925abf1ee3a701712.png"},{"id":73079656,"identity":"5e758c18-7350-43f6-a21d-f751c95fb171","added_by":"auto","created_at":"2025-01-06 14:04:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":642957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular Docking Results of G6PD with Candidate Drugs. (A-D) Molecular docking of G6PD protein with four selected drugs: \u003c/strong\u003e(A) Staurosporine_1034, (B) Vinorelbine_2048, (C) Vinblastine_1004, and (D) Docetaxel_1007. The binding interactions are shown in detail, including hydrogen bonds and their respective distances. The overall protein structure is shown in magenta, while the drugs are depicted in green. Close-up views highlight the binding pockets and specific interactions between G6PD and each drug.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/1a342145328a0214a374ae76.png"},{"id":80836386,"identity":"d0f61ccb-6e18-4518-87e4-c6f9b7dc1a65","added_by":"auto","created_at":"2025-04-17 14:54:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4950935,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/287d9080-2be2-432f-acac-7ee76d8cd344.pdf"},{"id":73079245,"identity":"a041b897-90be-4399-a45b-479596d90ed3","added_by":"auto","created_at":"2025-01-06 13:56:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3906194,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryinformation1.2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5748430/v1/8f3f8aaa820abca0c5fb686c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatics Identification of Lactate-Associated Genes in Hepatocellular Carcinoma: G6PD’s Role in Immune Modulation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally, characterized by its aggressive nature and poor prognosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The metabolic reprogramming of cancer cells, including altered lactate metabolism, plays a crucial role in HCC progression and therapeutic resistance. Lactate, traditionally viewed as a by-product of anaerobic glycolysis, has emerged as a key molecule in cancer, influencing both the tumor microenvironment and cancer cell signaling [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have linked lactate metabolism to key pathways involved in cancer proliferation, metastasis, and immune evasion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The Warburg effect [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], characterized by increased glucose uptake and lactate production even in the presence of oxygen, highlights the metabolic shift in cancer cells favoring lactate production. This metabolic shift is not merely a survival mechanism but also a strategic alteration that supports invasiveness and therapy resistance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLactate\u0026rsquo;s role extends beyond its metabolic functions; it serves as a signaling molecule that alters gene expression, modifies enzyme activities, and influences immune cell behavior in the tumor microenvironment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Lactate inhibits immune surveillance by repressing the activity of immune cells, including T cells and natural killer cells, thereby contributing to immune evasion mechanisms in tumors [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the profound impact of lactate on HCC progression, identifying genetic markers linked to lactate metabolism may provide new prognostic tools and therapeutic targets. Studies have shown that genes involved in lactate production and transport are overexpressed in HCC and are correlated with poor patient outcomes [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Exploring the genetic underpinnings of lactate metabolism in HCC could provide valuable insights into its pathogenesis and uncover new avenues for targeted therapy.\u003c/p\u003e \u003cp\u003eThis study aimed to identify and evaluate the prognostic significance of lactate-associated genes in HCC by using bioinformatics approaches. We focused specifically on G6PD because of its strong association with tumor progression and immune cell infiltration, as revealed in large-scale transcriptomic datasets. We also investigated its role in regulating immune checkpoints, particularly PD-L1, through the mTOR pathway, and explored its potential as a therapeutic target to enhance anti-tumor immunity in HCC. By integrating single-cell RNA sequencing (scRNA-seq) data, the Cancer Genome Atlas (TCGA) datasets, and functional assays, this study aims to provide new insights into G6PD\u0026rsquo;s role in lactate metabolism, immune modulation, and HCC prognosis. Our findings highlight the complex interplay between metabolic reprogramming and immune regulation, providing new perspectives on how metabolic enzymes like G6PD contribute to tumor immune evasion and therapeutic resistance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and Prognostic Relevance of Lactate-Associated Genes\u003c/h2\u003e \u003cp\u003eWe used data from 371 hepatocellular carcinoma (HCC) patients with complete survival information from The Cancer Genome Atlas (TCGA) to evaluate the prognostic significance of lactate-associated genes. Univariate Cox regression analysis initially identified 128 lactate-associated genes significantly correlated with HCC prognosis from a list of 332 genes linked to lactate processes according to the literature [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The top ten genes significantly associated with HCC prognosis were G6PD, TCOF1, KIF2C, RAN, PPM1G, JPT1, CACYBP, CCT5, ENO1, and IK (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). LASSO regression was then used to refine the selection of candidate prognostic genes, identifying 9 potential genes from the list (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). To confirm the independent prognostic value of the selected genes, we performed multivariate Cox regression analysis. This analysis identified three critical prognostic genes: CALML5, G6PD, and IK (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). These genes were the strongest predictors of overall survival in HCC patients, adjusting for other clinical variables such as age, gender, and cancer stage. A prognostic model was constructed using these three genes (CALML5, G6PD, and IK), and risk scores were calculated based on their gene expression levels. Patients were stratified into high- and low-risk groups based on median risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). The high-risk group showed significantly worse overall survival than the low-risk group, highlighting the prognostic relevance of the lactate-associated gene model. Finally, we performed univariate and multivariate Cox regression analyses to assess the impact of clinical variables, such as gene score and tumor stage, on prognosis. Both analyses showed that the gene score from the prognostic model and tumor stage were significantly correlated with survival outcomes in HCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Our analysis identified G6PD, CALML5, and IK as critical prognostic markers for HCC based on their association with lactate metabolism and survival. The prognostic model, incorporating these three genes, effectively stratifies patients into high- and low-risk groups, offering valuable insights for personalized treatment strategies in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruction and Validation of Lactate-Associated Gene Prognostic Model\u003c/h3\u003e\n\u003cp\u003eWe constructed and validated a prognostic model based on lactate-associated genes (CALML5, G6PD, and IK), using the TCGA LIHC cohort, divided into training (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D) and internal validation (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-H) sets, along with the external validation cohort (LIHC-JP) (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI-L). The model aimed to assess the predictive value of lactate metabolism-related genes in HCC and was evaluated across different datasets. In the training set, patients were stratified into high- and low-risk groups based on risk scores derived from the lactate-associated gene expression model. Kaplan-Meier survival analysis revealed a significant survival difference between the two groups. Patients in the high-risk group showed significantly poorer survival than those in the low-risk group, with increased mortality and decreased overall survival (OS) as risk scores increased (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The distribution of risk scores and survival status were visualized to highlight survival differences (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C, F, and G). A heatmap of the three key genes in the prognostic model was generated to compare their expression patterns between high- and low-risk groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). This analysis showed that the expression of these genes was significantly higher in the high-risk group than in the low-risk group, reinforcing the association between elevated gene expression and poor prognosis. The heatmap also illustrated the distinct molecular characteristics of each risk group, confirming the prognostic relevance of lactate-associated genes in HCC. The external validation cohort (LIHC-JP) was used to validate the predictive value of the lactate-associated gene model. Similar to the training set, the high-risk group in the validation set showed significantly worse prognosis than the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI), confirming the robustness of the model across datasets. The external validation heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL) showed consistent expression patterns of the three key genes, confirming their relevance in predicting patient outcomes in an independent cohort. The distribution of risk scores and survival analysis in the external validation cohort mirrored the findings in the training set, further validating the prognostic utility of the model (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK). These findings suggest that lactate metabolism-related genes may serve as valuable biomarkers for prognosis in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eIndependent Prognostic Analysis and PCA\u003c/h3\u003e\n\u003cp\u003eWe performed Decision Curve Analysis (DCA) to evaluate the clinical utility of our lactate-associated gene prognostic model. DCA plots (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C) showed that the model provides significant net clinical benefits across a wide range of threshold probabilities. This indicates that the model offers valuable predictive information for clinicians, assisting in decision-making for overall survival (OS) predictions. The net benefit curve suggests that our model outperforms traditional strategies, including clinical variables alone, by identifying patients who would benefit most from early intervention based on risk stratification. The model\u0026rsquo;s predictive accuracy was further assessed using Receiver Operating Characteristic (ROC) curves for 1-year, 2-year, and 3-year overall survival (OS) predictions. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F, the Area Under the Curve (AUC) values for both the TCGA cohort and external validation cohort were consistently above 0.65, with some exceeding 0.7 and even reaching 0.8 for certain time points. These findings highlight the strong performance of our model in accurately predicting OS, further supporting its potential utility in clinical practice for long-term survival predictions in HCC patients. To further validate the model\u0026rsquo;s ability to stratify patient risk, we performed Principal Component Analysis (PCA) on the full TCGA dataset, focusing on lactate-associated genes and those identified in the risk model. PCA results, shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-I, revealed clear separation between high-risk and low-risk groups based on gene expression profiles. The PCA plots show that the risk genes effectively distinguish patients with better prognosis (low-risk) from those with worse prognosis (high-risk), supporting their utility in risk stratification. Our analysis confirms that the lactate-associated gene prognostic model is a robust and clinically relevant tool for predicting overall survival in HCC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEnrichment Analysis of High and Low-Risk Groups in HCC\u003c/h3\u003e\n\u003cp\u003eThe circular plot illustrates Gene Ontology (GO) categories, highlighting key biological processes, such as cell cycle regulation and immune responses, which are more active in the high-risk group. This visualizes significant gene expression differences related to patient prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). A dot plot details the top enriched Biological Processes, highlighting critical activities such as nuclear division and chromosome segregation, suggesting increased cellular proliferation in high-risk patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The bubble plot illustrates KEGG pathway analysis, identifying critical pathways, such as cell cycle and cytokine-cytokine receptor interaction, associated with high-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). GSEA plots compare high-risk and low-risk groups, with high-risk patients showing enrichment in pathways related to cell adhesion and metastasis, while low-risk patients exhibit activity in metabolic pathways, potentially correlating with better outcomes (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eImmune Infiltration and Checkpoint Analysis\u003c/h3\u003e\n\u003cp\u003eThe tumor microenvironment (TME) plays a crucial role in tumor progression and response to immunotherapy. To explore the relationship between immune cell infiltration and lactate-associated gene expression, we analyzed the distribution of immune cells and immune checkpoint expression in high-risk and low-risk groups, based on the lactate-associated gene model. Our analysis revealed that the low-risk group exhibited a significantly higher proportion of stromal cells, suggesting a more active stromal component in these patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). This finding suggests that stromal cells in the tumor microenvironment may contribute to more favorable immune conditions in low-risk patients. We also observed a strong positive correlation between G6PD expression and immune scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This suggests that higher G6PD expression may shape the immune landscape, potentially promoting immune cell infiltration and influencing the immune response in HCC. This positive correlation with immune scores highlights the potential role of G6PD in modulating the TME, especially in immune activation. To further assess immune cell infiltration, we used single-sample Gene Set Enrichment Analysis (ssGSEA) to evaluate the levels of 22 immune cell types in the high- and low-risk groups. Our results indicated increased macrophage (M0) infiltration in high-risk groups, whereas CD4 memory resting cells were more prevalent in low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-E). This suggests that high-risk tumors may exhibit a more immunosuppressive TME, with macrophages, often involved in promoting immune evasion, being more abundant in these patients. Conversely, the increased presence of CD4 memory resting cells in the low-risk group may reflect a more balanced immune environment, potentially enhancing immune surveillance and tumor control. We further analyzed the expression of key immune checkpoints, including CTLA-4, CD274 (PD-L1), PDCD1 (PD-1), LAG3, and HAVCR2. These checkpoints play a crucial role in immune evasion and tumor progression. Our findings revealed differential expression of these checkpoints between the high- and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-G). Notably, G6PD and IK expression levels were strongly correlated with immune checkpoint expression, especially in the high-risk group. This suggests that G6PD and IK may regulate immune evasion by modulating immune checkpoint pathways, further supporting their potential role in promoting immune suppression and escape in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMutation Analysis and Drug Sensitivity in HCC Risk Groups\u003c/h2\u003e \u003cp\u003eSomatic mutation analysis within the TCGA dataset revealed distinct mutational profiles between high-risk and low-risk groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). High-risk patients exhibited a higher frequency of mutations in key oncogenes and tumor suppressor genes, such as TP53 and CTNNB1. Notably, high-risk patients showed a higher tumor mutational burden (TMB), suggesting a potential correlation with poor prognosis. Tumor Mutational Burden analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) quantified TMB, revealing that high-risk patients consistently exhibited higher TMB values compared to low-risk groups. This difference was statistically significant and supports the association between higher TMB and increased risk scores. Survival analysis showed that patients with higher TMB, particularly those in the high-risk group, had poorer overall survival rates (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E). These findings suggest that TMB could serve as an independent prognostic factor in risk stratification. Drug sensitivity testing using data from the GDSC database revealed variable responses to chemotherapy agents between the groups. High-risk patients generally exhibited reduced sensitivity to drugs like sorafenib and doxorubicin, as reflected by higher IC50 values. Conversely, some agents, such as cisplatin and paclitaxel, exhibited relatively lower IC50 values in high-risk groups, suggesting a nuanced response pattern that may inform therapeutic decisions (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-O).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGene Co-expression Network Analysis\u003c/h3\u003e\n\u003cp\u003eUsing Weighted Gene Co-expression Network Analysis (WGCNA), we identified gene modules linked to tumor characteristics in HCC. The optimal soft-thresholding power for constructing a scale-free network was β\u0026thinsp;=\u0026thinsp;13, with a scale-free fit index of 0.90, indicating a robust network topology (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Hierarchical clustering of genes revealed six distinct gene modules, each represented by a unique color (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The \u0026ldquo;Red\u0026rdquo; and \u0026ldquo;Green\u0026rdquo; modules showed significant correlations with clinical traits. The \u0026ldquo;Red\u0026rdquo; module had a strong positive correlation with tumor grade, indicating its association with more aggressive tumor features. In contrast, the \u0026ldquo;Green\u0026rdquo; module was most strongly correlated with tumor stage, suggesting its role in HCC progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Further analysis with Venn diagrams identified two genes, G6PD and IK, overlapping between our prognostic model's lactate-associated genes and the 'Red' and 'Green' modules, highlighting their relevance in HCC pathology (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Immunohistochemical staining confirmed that G6PD and IK were upregulated in HCC tissues compared to normal liver tissues, reinforcing their potential as biomarkers for tumor progression and therapeutic targets (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSingle-Cell Transcriptomic Analysis\u003c/h3\u003e\n\u003cp\u003eWe analyzed single-cell RNA sequencing (scRNA-seq) data to explore the cellular heterogeneity and expression profiles of lactate-associated genes in HCC. Using t-SNE dimensionality reduction, cells were grouped into distinct clusters representing major cell types, including T cells, hepatic cells, endothelial cells, myeloid cells, and mesenchymal cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Analysis of Lactate-Related Gene (LRG) scores, based on key lactate-associated gene expression, showed significantly higher scores in tumor cells compared to normal cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Stratification by cell type revealed that mesenchymal and myeloid cells had the highest LRG scores, indicating their critical role in lactate metabolism within the tumor microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Further investigation of specific lactate-associated genes revealed distinct expression patterns across cell types. G6PD, a key gene in lactate metabolism, was predominantly expressed in hepatic and mesenchymal cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). Similarly, IK expression was enriched in myeloid and mesenchymal cells, highlighting its potential role in metabolic reprogramming of these cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). These findings highlight the significance of lactate-associated genes in driving metabolic alterations and tumor progression at the single-cell level. Given G6PD\u0026rsquo;s prominent role in lactate metabolism and its expression in key cell types linked to tumor progression, we selected G6PD for further functional validation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eValidation of G6PD expression in HCC\u003c/h2\u003e \u003cp\u003eTo explore the functional impact of G6PD on immune responses in HCC, we performed functional assays. The TCGA LIHC dataset revealed that G6PD is significantly overexpressed in tumor tissues compared to normal tissues (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Kaplan-Meier survival analysis indicated that high G6PD expression is associated with poor prognosis in HCC patients (HR\u0026thinsp;=\u0026thinsp;2.15, Log-rank p\u0026thinsp;=\u0026thinsp;8.6e-05; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Stratification by cancer stage revealed a progressive increase in G6PD expression with advancing stages (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC), and nodal metastasis (N1) correlated with higher G6PD levels compared to non-metastatic cases (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD). Age-specific analysis further revealed that older patients had significantly higher G6PD expression than younger patients (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE). These findings underscore the association of G6PD with tumor progression and adverse clinical outcomes in HCC. Further analysis in liver cancer cell lines (HepG2, Huh7, MHCC97H) revealed significantly higher G6PD mRNA and protein expression in cancer cells compared to the normal liver cell line L02 by western blot assays (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eF-G), suggesting that G6PD overexpression contributes to the metabolic alteration\u0026rsquo;s characteristic of HCC cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eG6PD and Immune Modulation in HCC\u003c/h2\u003e \u003cp\u003eOur analysis of G6PD in HCC indicates its significant role in regulating immune responses, particularly in CD8\u003csup\u003e+\u003c/sup\u003e T cell activation. Immune infiltration analysis (Supplementary Fig.\u0026nbsp;2) showed that G6PD expression positively correlated with CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration in the tumor microenvironment (Figures S2A-D). G6PD expression was also associated with immune cell types involved in immune suppression, such as macrophages. This suggests that G6PD may modulate both immune activation and suppression within the tumor. Functional assays showed that G6PD knockdown in HepG2 cells enhanced CD8\u003csup\u003e+\u003c/sup\u003e T cell proliferation and IFN-γ secretion, indicating increased T cell activation (Figure S2E). In contrast, G6PD overexpression suppressed T cell activation, supporting the hypothesis that G6PD may contribute to immune evasion. These results suggest that G6PD modulates the immune response by regulating the tumor\u0026rsquo;s ability to influence CD8\u003csup\u003e+\u003c/sup\u003e T cell activity. The effects of G6PD on immune cell activation and suppression markers, such as CD274 (PD-L1) in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, highlight its potential role in immune checkpoint regulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eG6PD Regulates PD-L1 Expression via the mTOR Pathway\u003c/h2\u003e \u003cp\u003eWe further investigated the role of G6PD in regulating PD-L1 expression in HCC cells. Numerous studies have demonstrated that mTOR is a key upstream regulator of PD-L1 expression [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], playing a pivotal role in immune evasion by suppressing T cell activity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Based on evidence that G6PD regulates immune activation and suppression, we hypothesized that it modulates PD-L1 expression via the mTOR signaling pathway. To test this hypothesis, we performed Western blot analyses to assess how G6PD knockdown and overexpression influence PD-L1 and mTOR signaling in HepG2 cells. Results depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA show that G6PD knockdown significantly reduced phosphorylated mTOR (p-mTOR) and PD-L1 levels, suggesting that G6PD modulates PD-L1 expression via mTOR signaling. Conversely, G6PD overexpression in HepG2 cells increased p-mTOR and PD-L1 levels, further supporting that G6PD promotes immune evasion by upregulating PD-L1 through the mTOR pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Rescue experiments, involving G6PD reintroduction in knockdown cells, restored p-mTOR and PD-L1 levels, confirming that G6PD specifically mediates these effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Furthermore, treatment with an mTOR inhibitor (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD) in G6PD-overexpressing cells significantly reduced p-mTOR and PD-L1 levels, reinforcing that G6PD exerts immune-modulatory effects via mTOR signaling. These findings indicate that G6PD impacts CD8\u003csup\u003e+\u003c/sup\u003e T cell activation, immune cell infiltration, and PD-L1 expression via the mTOR pathway, thereby facilitating immune evasion in HCC. By regulating the immune checkpoint PD-L1, G6PD likely suppresses T cell activity, thus facilitating tumor growth and progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Docking Analysis of G6PD with Candidate Drugs\u003c/h2\u003e \u003cp\u003eTo investigate potential interactions between G6PD and candidate therapeutic compounds, molecular docking was performed using four drugs with the highest predicted sensitivity scores: Staurosporine_1034, Vinorelbine_2048, Vinblastine_1004, and Docetaxel_1007. The docking results revealed strong binding affinities between G6PD and the selected drugs, with binding energies below \u0026minus;\u0026thinsp;7 kcal/mol, suggesting stable interactions. Specifically, Staurosporine_1034 exhibited the highest binding affinity (-9.9 kcal/mol), forming one hydrogen bond within the active site. Vinorelbine_2048 showed a binding affinity of -7.6 kcal/mol, forming two hydrogen bonds that stabilized its interaction with G6PD. Vinblastine_1004 demonstrated a binding affinity of -8.8 kcal/mol, forming a single hydrogen bond. Docetaxel_1007 showed a binding affinity of -8.9 kcal/mol, forming three hydrogen bonds, indicating specific and robust binding. Visualization of the docking results revealed the structural alignment of each drug within the G6PD binding pocket, with key residues interacting through hydrogen bonds and hydrophobic interactions (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-D) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These findings suggest that the selected drugs may modulate G6PD activity by directly targeting its active site, potentially affecting lactate metabolism in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study highlights the critical role of lactate metabolism in HCC progression and prognosis, identifying several lactate-associated genes, particularly G6PD, IK, and CALML5, as significant prognostic markers. These findings support the growing body of evidence indicating that cancer metabolism, particularly lactate production and utilization, significantly impacts tumor behavior and patient outcomes [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe association between high lactate production and aggressive tumor features, along with poor prognosis, is well established [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Lactate not only supports cancer cell growth and survival but also alters the tumor microenvironment, promoting cancer progression through immunosuppression and angiogenesis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Our results specifically highlight the enzyme G6PD, a key player in the pentose phosphate pathway, involved in reductive biosynthesis and the generation of reducing equivalents. G6PD\u0026rsquo;s role in modulating PD-L1 expression via the mTOR signaling pathway, as demonstrated in our functional assays, highlights its involvement in immune evasion by HCC cells. This finding establishes a potential link between metabolic reprogramming and immune checkpoint regulation, an important area for therapeutic intervention.\u003c/p\u003e \u003cp\u003eThe dual role of G6PD in metabolic modulation and immune regulation makes it a promising target for HCC therapy. Drugs that inhibit G6PD activity could not only disrupt cancer cell metabolic homeostasis but also enhance the immune response against tumors. Our molecular docking studies indicate that inhibitors such as Staurosporine could disrupt G6PD activity, suggesting a novel approach to curb HCC progression. This therapeutic strategy may be especially effective in patients with high G6PD expression and poor prognosis.\u003c/p\u003e \u003cp\u003eThe prognostic model based on lactate-associated genes effectively stratifies HCC patients into high- and low-risk categories, providing valuable insights for personalized treatment strategies. For example, patients in the high-risk category may benefit from more aggressive treatment regimens and close monitoring. Furthermore, the model\u0026rsquo;s strong performance across independent datasets highlights its potential utility in clinical settings, assisting in decision-making and improving patient management.\u003c/p\u003e \u003cp\u003eDespite these promising findings, several challenges persist. The heterogeneity of HCC and the complex interplay of genetic, environmental, and metabolic factors present significant challenges in the uniform application of prognostic models. Future studies should aim to validate these findings in larger, multi-center cohorts and explore the interrelationships among various metabolic pathways in HCC. Additionally, integrating metabolic profiling with immunological and molecular characteristics could generate comprehensive models that capture the multifaceted nature of cancer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analysis of immune infiltration emphasizes the intricate relationship between metabolic reprogramming and the immune landscape in HCC. Understanding how metabolic changes affect immune cell function and the efficacy of immunotherapy could lead to the development of combined metabolic and immune-modulating therapies. For instance, targeting metabolic enzymes alongside immune checkpoint inhibitors may improve therapeutic efficacy against HCC.\u003c/p\u003e \u003cp\u003eIn conclusion, this study not only confirms the importance of lactate metabolism in shaping clinical outcomes in HCC patients but also provides a foundation for future research and therapeutic development. G6PD is a key regulator of metabolic reprogramming and immune modulation in HCC. Our bioinformatics and functional validation studies provide strong evidence that G6PD contributes to immune evasion by modulating immune cell activity and immune checkpoint expression, particularly PD-L1, via the mTOR signaling pathway. These findings suggest that G6PD is not only a potential prognostic biomarker but also a promising target for immune-based therapies designed to enhance anti-tumor immunity and overcome immune evasion in HCC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Preparation\u003c/h2\u003e \u003cp\u003eHepatocellular carcinoma (HCC) data, including RNA-sequencing and clinical information, were downloaded from The Cancer Genome Atlas (TCGA) portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A total of 374 HCC samples along with clinical data for 371 patients were included. Additionally, 332 lactate-associated genes were compiled from relevant literature sources, specifically from PMID: 38419085 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. An external validation dataset, LIHC-JP, was also used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Gene Selection and Model Construction\u003c/h2\u003e \u003cp\u003eInitially, univariate Cox regression analysis was performed to assess the prognostic value of each lactate-associated gene. Genes with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant and were further analyzed using Lasso regression to prevent overfitting and to refine the selection of variables. Multivariate Cox regression analysis was subsequently conducted to finalize the selection of variables, resulting in three lactate-associated prognostic genes. These genes were used to construct a prognostic model where each patient\u0026rsquo;s risk score was calculated using the formula:\u003c/p\u003e \u003cp\u003eRisk score=\u0026sum;(Expression level of Gene \u003csub\u003e\u0026#119894;\u003c/sub\u003e\u0026times;coefficient \u003csub\u003e\u0026#119894;\u003c/sub\u003e )\u003c/p\u003e \u003cp\u003eThe TCGA dataset was divided into training and validation sets, with LIHC-JP serving as an external validation set. Kaplan-Meier curves were used to analyze survival differences between high and low-risk groups in these datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment Analysis\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes (DEGs) between risk groups were identified based on |log2FC| \u0026gt; 1.0 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Gene Ontology (GO) analysis for biological processes, cellular components, and molecular functions, along with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were conducted to explore the biological significance of these genes. Gene Set Enrichment Analysis (GSEA) was also performed to further understand pathway differences between high and low-risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eImmune Analysis\u003c/h2\u003e \u003cp\u003eThe immune and stromal scores were estimated using the ESTIMATE algorithm to assess the impact of the tumor microenvironment on prognosis. Differences in immune cell infiltration between risk groups were analyzed using CIBERSORT. Correlations between gene expression and immune cell levels were assessed using Spearman\u0026rsquo;s rank correlation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMutation Analysis\u003c/h2\u003e \u003cp\u003eSomatic mutation data from the TCGA dataset were analyzed using the \"maftools\" R package. Tumor Mutational Burden (TMB) was calculated, and HCC patients were classified into high and low TMB groups based on the median TMB value. Survival differences between these groups were examined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDrug Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eDrug sensitivity was assessed using the \"pRRophetic\" R package, which predicts IC50 values for chemotherapeutic agents based on gene expression profiles. Drugs showing significant differences in IC50 values between high and low-risk groups were identified, and their potential efficacy was evaluated.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSingle-cell RNA Sequencing Analysis\u003c/h2\u003e \u003cp\u003eSingle-cell RNA-seq data from GEO datasets GSE245906 and GSE149614 were processed using the \"Seurat\" R package. Data quality control, normalization, and batch effect removal were performed, followed by clustering and differential gene expression analysis. The \"SingleR\" package was used for cell type annotation, and \"AUCell\" was utilized to quantify lactate-related gene expression across cell types.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eValidation of Gene Expression\u003c/h2\u003e \u003cp\u003eThe expression levels of key genes, G6PD and IK, were validated using immunohistochemistry data from the Human Protein Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This helped confirm their overexpression in HCC tissues compared to normal liver tissue.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCell culture and Lentivirus-infection\u003c/h2\u003e \u003cp\u003eHuman normal hepatocytes L02, human hepatocellular carcinoma cells Huh7, HepG2 and MHCC97H were cultured in complete Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium (DMEM) with 10% fetal bovine serum (FBS) (HyClone Laboratories, Logan, UT, USA) at 37\u0026deg;C in a 5% CO2 atmosphere.\u003c/p\u003e \u003cp\u003eTo generate stable knockdown or overexpressing cell lines for G6PD, G6PD shRNA plasmids (LV-Si-G6PD), G6PD overexpressing plasmids (LV-G6PD), and empty controls (LV-Sc-G6PD/LV-empty) were purchased from Santa Cruz Biotechnology. After coinfection of shRNA or overexpressing plasmids with the packaging plasmids psPAX2 (Addgene, Cat# 12260) and pMD2.G (Addgene, Cat# 12259) into HEK-293T cells, the lentivirus was harvested, titrated and stored for infecting HepG2 cells. These two cell lines (\u003cem\u003eG6PD\u003c/em\u003e-KD-HepG2 and \u003cem\u003eG6PD\u003c/em\u003e-OE-HepG2) infected with lentivirus were screened with puromycin, and the stable cell lines were further confirmed with detection of WB.\u003c/p\u003e \u003cp\u003eCells were grown to 50% confluence in 6-well cell culture plates (Nest, China) and transfection was performed using Lipofectamine 2000 (Invitrogen, Cat# 11668030), according to the manufacturer\u0026rsquo;s instructions. In all the co-transfection experiments, the corresponding empty vectors were used as negative controls to ensure similar DNA concentrations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eImmunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eThe collection and use of all human HCC samples were approved by the Ethical Committee of Wuhan University School of Medicine in Wuhan, China (2022007). Informed consent was obtained from each patient for the collection of HCC samples. IHC staining was performed on human liver tissue sections. The sections were processed by heating at 70\u0026deg;C in an oven for 30 min and dewaxed with xylene and alcohol for 10 min. Endogenous peroxidase was blocked in 0.3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e for 20 min. The samples were further treated with citrate buffer at 95\u0026deg;C for 10 min and incubated with G6PD antibody (Cat#sc-373886, Santa Cruz Biotechnology, 1:100 dilution), DAKO EnVision containing horseradish peroxidase (HRP)-conjugated with anti-mouse antibody.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eWestern Blot Analysis\u003c/h2\u003e \u003cp\u003eWestern blotting was performed to validate the protein expression levels of G6PD and related signaling molecules such as mTOR and PD-L1. Cells were lysed using RIPA buffer (Cat#P0013D, Beyotime) containing protease and phosphatase inhibitors (Cat#P1045, Beyotime). Protein concentrations were measured using the BCA Protein Assay Kit (Cat#P0009, Beyotime). Equal amounts of protein were separated on SDS-PAGE gels and transferred to PVDF membranes. The membranes were blocked with 5% non-fat milk and incubated with primary antibodies against G6PD (Cat#sc-373886, Santa Cruz Biotechnology, 1:2000 dilution), mTOR (Cat#28273-1-AP, Proteintech, 1:1000 dilution), phospho-mTOR (Cat#67778-1-Ig, Proteintech, 1:1000 dilution), PD-L1 (Cat#83600-2-PBS, Proteintech, 1:1000 dilution), and GAPDH(Cat#60004-1-Ig, Proteintech, 1:10000 dilution, used as a loading control). Appropriate HRP-conjugated secondary antibodies (Cat#7074/7076, Cell Signaling Technology, 1:10000 dilution) were used, and protein bands were visualized using enhanced chemiluminescence (ECL) (Cat#A38556, ThermoFisher). Quantification of band intensity was performed using ImageJ software, and relative expression levels were calculated by normalizing to GAPDH.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eFlow Cytometry Analysis\u003c/h2\u003e \u003cp\u003eFlow cytometry was employed to assess the functional effects of G6PD modulation on T cell proliferation and IFN-γ secretion. Human CD8\u003csup\u003e+\u003c/sup\u003e T cells were labeled with CFSE (Carboxyfluorescein succinimidyl ester, Cat#C34554, Invitrogen) and stimulated with anti-CD3 (Cat#341090, Biosciences) and anti-CD28 (Cat#348047, Biosciences) antibodies. These T cells were co-cultured with HepG2 cells, either with knocked-down (KD) or overexpressed (OE) G6PD, for three days. Post-incubation, cells were harvested and stained for CD8 (Cat#335805, Biosciences) and IFN-γ (Cat#562213, Biosciences). Flow cytometric analysis was conducted to determine T cell proliferation (by CFSE dilution) and IFN-γ production. The proliferation index and the percentage of IFN-γ\u0026thinsp;+\u0026thinsp;cells were calculated using FlowJo software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eStructural analysis of G6PD and therapeutic agents\u003c/h2\u003e \u003cp\u003eFrom the \"oncoppredict\" results, we selected the four drugs with the highest predicted sensitivity for molecular docking analysis. The molecular structures were obtained from PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and protein structures were retrieved from the Protein Data Bank (PDB; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Protein and ligand files were converted to PDBQT (Protein Data Bank, Partial Charge (Q), \u0026amp; Atom Type (T)) format, water molecules were removed, and polar hydrogens were added to improve docking accuracy. Molecular docking simulations were conducted using AutoDock Vina 1.2.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://autodock.scripps.edu/\u003c/span\u003e\u003cspan address=\"http://autodock.scripps.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to evaluate drug-protein binding interactions. The therapeutic potential of each drug was assessed based on its docking score, which reflects the strength and affinity of the binding[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The G6PD and Staurosporine complex structure were visualized by PyMol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R software (version 4.1.2). The Wilcoxon test was used for differential analysis, Spearman\u0026rsquo;s rank correlation for assessing correlations, and Kaplan-Meier method for survival analysis. The Benjamini-Hochberg method was applied for multiple testing correction. Significant differences were considered at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eSamples were collected with informed consent and in accordance with established biobanking protocols, ethical and legal standards. Tissue samples were approved by the Ethics Review Committee of Zhongnan Hospital of Wuhan University (Approval Number [2022]007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration number\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003evailability\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of data and materials:\u003c/strong\u003e The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by National Natural Science Foundation of China grants 82272978 (ML); Natural Science Foundation Project of Hubei Province grant 2021CFB484 (ML); Open Foundation of Hubei Province Key Laboratory of Tumor Microenvironment and Immunotherapy (2024KZL07)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions:\u0026nbsp;\u003c/strong\u003eConceptualization: ML; Data curation: ML, CQW; Formal analysis: CQW, SJS, ML; Funding acquisition: ML, HYW; Investigation: CQW, SJS, WWZ, XSD, YASZ, XCW; Methodology: ML; Project administration: ML;\u0026nbsp;Resources: ML, HYW; \u0026nbsp;Software: HRQ, CQW, ML; Supervision: ML; Validation: HRQ, CQW, ML; Visualization: HRQ, CQW, ML; Writing \u0026ndash; original draft: ML, HRQ, SJS; Writing \u0026ndash; review and editing: ML, XLZ, HYW, QP, FLL, ML, HRQ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eWe would like to acknowledge the essential contributions of all staffs and students who participated in this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLlovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, Lencioni R, Koike K, Zucman-Rossi J, Finn RS. 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Nature. 2020;580(7805):663\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, lactate metabolism, prognostic biomarkers, G6PD, PD-L1","lastPublishedDoi":"10.21203/rs.3.rs-5748430/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5748430/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHepatocellular carcinoma (HCC) is a major global health issue, with poor prognosis often associated with dysregulated metabolic pathways, especially lactate metabolism. This study explored the prognostic significance of lactate-associated genes in HCC and their potential as therapeutic targets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed RNA-seq and clinical data from 374 patients with HCC from The Cancer Genome Atlas (TCGA) database. Using Cox regression, LASSO analysis, and Kaplan-Meier survival curves, we identified key lactate-associated genes associated with patient outcomes. Functional validations, including Western blot, flow cytometry, and molecular docking studies, were performed to confirm the biological impact of these genes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eG6PD, IK, and CALML5 were identified as significant prognostic markers for HCC. A prognostic model was developed that effectively stratified patients into risk groups, which correlated with survival. G6PD\u0026rsquo;s role in immune modulation and its potential as a drug target were validated through biochemical assays and computational analyses. Functional assays in HepG2 cells confirmed that alterations in G6PD expression affect T cell activity, with knockdown enhancing IFN-γ production and overexpression inhibiting it, demonstrating G6PD\u0026rsquo;s role in immune evasion.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study establishes lactate metabolism genes, particularly G6PD, as key prognostic markers in HCC. The validation of G6PD\u0026rsquo;s immunomodulatory effects further supports its potential as a therapeutic target for strategies aimed at enhancing immune surveillance and treatment outcomes in HCC.\u003c/p\u003e","manuscriptTitle":"Bioinformatics Identification of Lactate-Associated Genes in Hepatocellular Carcinoma: G6PD’s Role in Immune Modulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-06 13:56:02","doi":"10.21203/rs.3.rs-5748430/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0cb71b54-b0d6-47df-9207-cb710c1d7d21","owner":[],"postedDate":"January 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-17T14:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-06 13:56:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5748430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5748430","identity":"rs-5748430","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}