Construction and validation of novel lactate metabolism molecular subtypes in hepatocellular carcinoma based on multi-omics analysis

doi:10.21203/rs.3.rs-3404975/v1

Download PDF

Research Article

Construction and validation of novel lactate metabolism molecular subtypes in hepatocellular carcinoma based on multi-omics analysis

https://doi.org/10.21203/rs.3.rs-3404975/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Purpose

Lactate, as a metabolic byproduct, has been found to promote carcinogenesis and induce immunosuppression in certain types of cancer. The objective of this study is to investigate the role of lactate metabolism-related regulatory genes in hepatocellular carcinoma (HCC).

Methods

In this study, we compiled transcriptomics data, genomic structural variation data (SNV, CNV) from The Cancer Genome Atlas (TCGA), as well as our proteomics data. We constructed the lactate metabolism score (LMScore) by integrating consensus clustering, differential analysis, and principal component analysis based on TCGA transcriptome data. Subsequently, we decoded the differences in clinical outcomes, mutation landscape, biological behaviors, immune characteristics and potential therapeutic drugs between distinct subtypes. Western blot (WB) was used to evaluate the protein levels of different tissues.

Results

The findings demonstrated a significant negative correlation between LMScore and the progression of HCC. Patients in distinct subtypes showed significant differences at multiple omics levels, such as transcriptome profiling, mutational landscape, and protein levels. Specifically, Patients in high LMScore subtype had a better prognosis and were more likely to benefit from immune therapy and clinical first- and second-line therapy including cisplatin and lenvatinib. Moreover, Patients in the low-score subtype exhibited high levels of immune cell infiltration, upregulation of immune checkpoints, and poor response of immunotherapy, suggesting an immune-suppressive microenvironment. Drug sensitivity analysis demonstrated these patients, who were not suitable for immunotherapy, could benefit more from drugs like paclitaxel and sorafenib. In addition, we found that TTC36 is a prognostic gene that not only affects the progression of HCC but also alters lactate metabolism levels. Upregulation of TTC36 can inhibit proliferation, invasion, migration, and lactate metabolism of HCC cell.

Conclusion

According to the lactate metabolism regulated genes, we constructed the LMScore, which can serve as powerful tool in stratifying HCC patients and decoding HCC heterogeneity. Moreover, this model can guide the treatment of immune therapy and clinical first- and second-line drugs, offering an optimized decision-making platform for HCC patients.

Hepatocellular carcinoma

Subtype

Lactate metabolism

Multi-omics

Immunity

Drugs

Hepatocellular carcinoma (HCC) originates from liver cells accounting for approximately 90% of primary malignant liver tumors and ranks sixth in global cancer incidence.[1]. Traditional treatments include surgical resection, percutaneous ablation, radiation therapy, and liver transplantation[2]. However, due to delayed diagnosis resulting from the asymptomatic early stage, the median survival period for advanced HCC is still less than 2 years. Owing to the significant heterogeneity of HCC, the current staging system is somewhat inadequate in accurately stratifying and selecting the target population for immunotherapy or targeted therapy. In addition, tumor cell metabolism changes impede immune response and pose obstacles to the treatment of liver cancer patients by affecting the tumor microenvironment[3]. Therefore, it is particularly important to study metabolism in liver cancer and explore a new method of stratifying patients, aiming to reduce intra-group differences and improve individual treatment benefits, which is crucial for enhancing clinical treatment and management.

Metabolic reprogramming is a significant hallmark of cancer, characterized by alterations in glucose metabolism, increased consumption of glutamine, and enhanced fatty acid synthesis[4–8]. The Warburg effect indicates a change in the metabolic process of tumor cells, where HCC can utilize glucose as an energy source with enough oxygen available, which ultimately results in the generation of substantial amounts of lactate.[9]. Lactate, when released from tumor cells via monocarboxylate transporters (MCTs), can activate G-protein coupled receptor 81 (GPR81), creating an acidic microenvironment[10].This acidic microenvironment intensifies tumor cell proliferation, metastasis, angiogenesis, and immune evasion, eventually resulting in poorer outcomes[11, 12]. Lactate is involved in regulating immune cells and tumor microenvironment, supporting the survival of malignant transformed cells[13, 14]. Lactate produced by tumor cells induces the polarization of macrophages from an M1 phenotype to an M2 phenotype, upregulating the expression of Arg 1 and VEGF, thereby promoting malignant tumor progression[15, 16]. At the same time, lactate increases the production of PD-L1 in tumor cells, which interacts with PD-1 receptors expressed on activated T cells, reducing their proliferation and effector functions[17]. Furthermore, high levels of lactate in the tumor microenvironment result in decreased cytotoxic activity of T cells, inhibition of NK cell cytolytic function, reduced production of IFN-γ, and limited activation of immune cells[18, 19]. Considering the important role of lactate metabolism in promoting tumor progression, regulating the tumor microenvironment, and immunotherapy, it holds great potential in the stratification and targeted therapy for HCC patients.

This study obtained transcriptome data from TCGA. we focused on lactate metabolism-related genes and constructed the LMScore to stratify HCC patients, predict patients’ prognosis, and provide precise guidance for individualized treatment using the PCA algorithm. Patients in the high LMScore subgroup were more likely to benefit from immunotherapy. Moreover, we assessed the immune-suppressive microenvironment of patients in low LMScore subgroups and predicted sensitive drugs to improve their survival and prognosis.

Data retrieval and processing

We downloaded RNA sequencing data for gene expression of the TCGA-LIHC cohort (in Fragments Per Kilobase Million, FPKM values) and the corresponding clinical information for 371 HCC samples from the XENA. We also downloaded the expression matrix file and clinical information for 115 HCC samples from the GSE76427 cohort (platform GPL10558) from the GEO database. After taking the intersection of genes, we merged the samples from TCGA-LIHC and GSE76427 to construct a meta-cohort. The clinical information of patients in TCGA-LIHC and GES76427 cohort were summarized in Table S1.The batch effects were eliminated by utilizing the "ComBat" function from the "SVA" package.[20]. PCA was used to validate the distribution of the expression profiles of TCGA-LIHC and GSE76427 before and after batch effects(Figure S1B). Samples that lacked complete survival data were excluded, resulting in a meta-cohort comprising 478 HCC samples and 17117 genes.

Acquisition and exploration of the lactate metabolism gene set

We downloaded the lactate metabolism gene set from the GSEA portal in January 2022[21]. We utilized the Gene Set Cancer Analysis (GSCA) database to analyze the mRNA expression profile of 14 Lactate metabolism genes (LMGs) thereby decoding the difference of survival prognosis, clinical staging, and pathway enrichment among them. The "survival" and "survminer" packages were utilized to identify the optimal cutoff point for LMGs in the meta-cohort through the "surv_cutpoint" function. Survival analysis was conducted on LMGs in the meta-cohort. Using the "igraph" R package, a network composed of 13 prognosis-related LMGs was visualized, and the attributes of nodes in the network were determined based on the source of genes and univariate Cox regression analysis. The correlation between genes defined the attributes of the edges.

Cluster analysis

We performed unsupervised clustering using the "ConsensusClusterPlus" R package based on 14 LMGs[22]. The clustering was conducted using the pam algorithm with the Euclidean distance measure, and we resampled 80% of the samples and repeated the process 50 times. The within-group correlation was found to be strong, while the between-group correlation was moderate for k = 2. Subsequently, we performed unsupervised clustering again, based on 163 LMRPGs. The cophenetic coefficient was utilized to determine the optimal rank. Typically, when the value of the cophenetic correlation coefficient begins to decrease, it is considered the optimal factorization rank. Here, the results indicated that the sample partitioning is more stable and reasonable while K = 3. We used the "pheatmap" R package to create a clustering heatmap, illustrating the expression of LMGs, clinical-pathological features, and their relationships with the clustering. Additionally, we conducted survival analysis to compare the overall survival (OS) differences among patients of different subtypes. To get information on immune cell genes, we obtained the "immune.xlsx" file from the literature[23]. Using the "GSVA" R package, we performed ssGSEA enrichment analysis on the integrated cohort samples and generated boxplots depicting the infiltration of 28 different immune cell types among the various patient subgroups

Gene set variation analysis and pathway enrichment analysis

We obtained the h.all.v7.4.symbols.gmt files as reference gene sets from the Molecular Signatures Database (MsigDB) (http://software.broadinstitute.org/gsea/msigdb/). We performed Gene Set Variation Analysis (GSVA) on different subtypes using the "GSVA" R package and generated a heatmap to visualize the analysis results[24]. Additionally, we conducted enrichment analysis on differentially expressed genes (DEGs) and visualized the results using the "ClusterProfiler" package.

Construction of the lactate metabolism score model

We developed a lactate metabolism scoring scheme to quantify the lactate metabolism level in individual patients. Specifically, we conducted principal component analysis (PCA) on LMRPGs expression profiles and then extracted principal components 1 and 2 as the signature scores. Finally, we defined the lactate metabolism score using a formula similar to previous studies: lactate metabolism score = ∑(PC1i + PC2i)[25, 26].

The "ggalluvial" package was utilized for generating Sankey diagrams to visualize the flow comparisons of HCC patients across different clusters, gene clusters, scores, and outcome events. Stacked bar charts, depicting the clinical pathological characteristics of different lactate metabolism score groups, were created to illustrate the percentages of the various components. Copy number variation and single nucleotide variation data of HCC patients were obtained from cBioPortal(cBioPortal for Cancer Genomics) to examine the differences in mutation landscapes between distinct subtypes. The gene mutation waterfall plot was generated using the "maftools" R package. The "spearman" approach was utilized to assess the correlation between immune cells, immune checkpoints, and lactate metabolism scores, visualized respectively through scatter plots and lollipop charts.

Immunotherapy and potential drug therapy

“ESTIMATE” was utilized to assess the immune scores and tumor purity in individuals with HCC. The ICI treatment score data for LIHC was obtained from The Cancer Imaging Archive (TCIA) database. Based on the LMScore, the benefits of PD-1 and CTLA-4 targeted therapies in patients of distinct subtypes were predicted. The TIDE scores for immune dysfunction and exclusion in HCC patients were downloaded from the TIDE database, and the immune evasion and immune exclusion of the two groups were analyzed[27]. MSI can lead to ineffective mismatch repair of DNA, resulting in a series of genetic mutations. The accumulation of these genetic mutations can cause tumor cells to produce a greater number of mutated antigens, triggering a stronger immune response from the immune system. Additionally, the "pRRophetic" package was employed to assess the therapeutic sensitivity and provide potential therapeutic drugs. (p < 0.001)[28].

Collection of liver cancer patient specimens

Tissue samples were collected from liver cancer patients who underwent surgical resection at the First Affiliated Hospital of Zhengzhou University. All specimens were diagnosed as hepatocellular carcinoma. All patients signed informed consent forms.

Cell culture and transfection

The liver cancer cell lines including HUH7, HepG2, HCCLM3, MHCC-97H, and Hep3B were all obtained from the Henan Organ Transplantation Research Center[29] and were cultured using Dulbecco's modified Eagle's medium (DMEM; HyClone/Thermo Fisher Scientific Inc,Switzerland)with 10% fetal bovine serum(FBS; LONSA SCIENCE S.R.L,Switzerland),1% streptomycin and penicillin. HCC cells were transfected with lipo8000 transfection reagent(Beyotime, China). Short hairpin RNA s(shRNA) targeting TTC36 were purchased from PPL company(China), with the following target sequences: (The sequence of TTC36-OE and TTC36-vector was provided in the supplemental document.)

TTC36-sh1, CGAGAAGAAGATGAAGTTT;

TTC36-sh2,GGCCATCTTCAACCCTGACAC;

TTC36-sh3,TCAAGCACAGCTGGAACAGTC;

TTC36-NC, GTTCTCCGAACGTGTCACGTT.

RT-qPCR

Cellular RNA was isolated using TRIzol(Invitrogen, USA), and cDNA was synthesized from the extracted RNA using a cDNA synthesis kit(Vazyme, China). Target gene primers(TsingkeBiotechnologyCo, Ltd., China), SYBR(Vazyme, China), and cDNA were mixed to prepare a 10µl reaction system. The 2-ΔΔCT method was used to calculate the expression level of RNA, with 18s serving as the reference gene. Primer sequences are here:

TTC36-F2: 5′-GGACTCCAAATGATCAGGCAGTG-3′,

TTC36-R2: 5′-GAGGTCCAATCCAACAATGTCTCC-3′;

18S-F: 5′-CGCTTCCTTACCTGGTTGAT-3′,

18S-R: 5′-GAGCGACCAAAGGAACGATA-3′.

Western blotting

Cellular total protein was extracted using RIPA buffer(Solarbio, China), and quantified with the BCA protein assay(seven, China). Electrophoresis was conducted on a 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS‐PAGE). Proteins were transferred onto a PVDF(millipore, USA) membrane at room temperature for a duration of 1 hour, using a constant current of 300 mA. Since the TTC36 protein has an approximate molecular weight of 25 kDa, the PVDF membrane was carefully cut around the corresponding 25 kDa marker. Following one-hour blocking step with skim milk, the PVDF membrane was incubated with the primary antibody at 4℃overnight. The membrane was subsequently washed with TBST and then incubated with a diluted secondary antibody at room temperature for one hour. Protein bands were visualized using an ECL((NCM Biotech, China)) detection reagent and detected with AmershamTM ImageQuantTM 800 (Amersham, UK) and Odyssey® Dlx Imaging System (LI‐COR Biosciences, USA). ImageJ software (NIH) was used to analyze the densitometric data. Antibodies against the following proteins were used as primary antibodies: GAPDH (Proteintech; 10494-1-AP), TTC36 (Abcam; ab122507).The following secondary antibodies were used: HRP‐conjugated anti‐rabbit IgG and anti‐mouse IgG (Cell Signaling Technology, USA).

EDU assay

Cells were seeded in a 96-well plate and incubated overnight in a cell culture incubator after EdU (5-ethynyl-2'-deoxyuridine) labeling. According to the instructions, the EdU detection was performed using the Edu kit (#C0071S, Shanghai Biotech, China). A 1:1000 dilution of the EdU solution was prepared using DMEM culture medium, and then added to the corresponding wells of the pre-planned 96-well plate, 100µl per well. The 96-well plate was incubated in a 37°C cell culture incubator for 4 hours, and then fixed with 4% paraformaldehyde at room temperature for 15 minutes. After completing the corresponding preparation work according to the instructions, cells were added with 50 µl of reaction solution for 30 minutes, followed by nuclear staining with Hoechst for 15 minutes. During the waiting period, we place the aluminum foil-wrapped 96-well plate in a dark area. Cell proliferation was imaged using a 20x fluorescence microscope.

Cell invasion and migration assays

The cells were seeded into six-well plates and cultured overnight. When cells reached 90% confluence, a single line of cells was scraped off in each well using a 100µl plastic pipette tips. Photographs were taken at 0 and 24 hours to record the degree of wound healing. HCC cell line was inoculated in upper Transwell chambers(Corning Costar, USA) with 200µl of DMEM culture medium without serum. The lower chambers were then filled with 500 µl of complete culture medium supplemented with 10% fetal bovine serum. Matrigel(Corning, USA) was applied to the bottom of the upper Transwell chambers to evaluate cell invasion ability. In order to assess cell migration ability, matrigel was not required on the bottom surface of the chambers. After a 24-hour incubation period, the chambers were extracted, fixed with 4% paraformaldehyde for 15 minutes, rinsed, and stained with crystal violet for 30 minutes. Five random fields were chosen for image capture, and cell counting was performed under a microscope.

Lactate measurement.

The transfected cells grown in a 6-well plate were digested using pancreatin(Solarbio, China). After centrifugation at 1000 RPM for 5 minutes, the supernatant was carefully discarded, leaving behind the pellet. The cell pellet was then dissolved in PBS(Solarbio, China) and kept for further use. Subsequently, the cells were disrupted using an ultrasonic homogenizer to obtain the cell lysate. The intracellular lactate levels in the transfected cells were determined using a lactate assay kit (Bestbio, China) and a BCA protein assay kit.

Statistical analysis

Data cleaning, statistical analysis, and visualization work were all conducted using R 4.2.3 and GraphPad Prism 9 software. Spearman correlation analysis was utilized to analyze the relationship between two continuous variables. The Overall Survival (OS) discrepancies between distinct subtypes were evaluated with Kaplan-Meier approach and Log-rank statistical analysis. When comparing two continuous variables, t-test or Wilcoxon rank-sum test was employed to evaluate differences. Pearson chi-square test or Fisher's exact test was used to compare categorical variables. The Cox proportional hazards model along with a 95% confidence interval (95% CI) was employed to assess the hazard ratio (HR). All statistical tests were two-sided, and a p-value below 0.05 was considered statistically significant.

Expression of lactate metabolism genes in HCC

Our study's workflow was depicted in Fig. 1. Based on the lactate metabolism-associated genes, Kaplan-Meier survival analysis were conducted (Fig. 2A and Figure S1C). The upregulation of prognostic protective factors such as LDHD, HAGH, and TP53 presented favorable outcomes in HCC patients. We further investigated the lactate metabolism gene set using the GSCA website. Figure 2B illustrated that the LDHA gene is a high-risk factor for multiple prognosis-related indicators (DFI, DSS, OS, PFS) in HCC patients, while LDHD and HAGH serve as significant protective factors. All of these were consistent with the results of survival curve analysis. Combining clinical staging, Fig. 2C indicated that the expression of prognostic risk factors such as LDHA and HIF1A increase with tumor stage progression, while the prognostic protective factors TP53, LDHD, and HAGH yield contrasting conclusion. Additionally, Fig. 2D suggested that LDHD and HAGH act as prognostic protective factors and may restrain the progression of HCC by suppressing cell cycle and EMT pathways potentially. We finally constructed a prognostic network diagram through co-expression analysis. As shown in Figure S1A, the lactate metabolism gene set includes prognostic risk factors and protective factors that are mutually regulated within a network, constituting an organic whole that collectively regulate the progression of HCC.

Cluster analysis

We conducted consistent clustering analysis based on lactate metabolism genes and subdivided the cohort into Cluster A (n = 181) and B (n = 297)(Fig. 3A). Survival analysis uncovered significant disparities between the two groups (Fig. 3B). Specifically, patients in Cluster A had worse survival rates compared to those in Cluster B. By visualizing the clinical pathological features and lactate metabolism genes of LIHC samples in a heatmap (Fig. 3C), we observed significant downregulation of the prognostic protective factors LDHD and HAGH in ClusterA. This finding aligns with our previous research results. Using the GSVA algorithm, we conducted enrichment analysis of HALLMARK pathways for subtypes A and B (Fig. 3D). The results revealed that ClusterA, characterized by a poorer prognosis, exhibited significant enrichment in cancer-related pathways such as PI3K_AKT_MTOR_SIGNALING and WNT_BETA_CATENIN_SIGNALING. GO and KEGG analyses revealed that differential genes between them were significant enrichment in pathways such as small molecule catabolic processes and metabolic pathways (Fig. 3E). Then, LMRPGs were used to perform consistent clustering on the meta cohort (Fig. 3F). The survival prognosis significantly differed among different geneclusters (Fig. 3G). Specifically, patients in the B genecluster displayed the best overall survival, whereas patients in the C genecluster had the poorest(P < 0.01).

Construction of a score model for lactate metabolism

Considering the heterogeneity and complexity of HCC, we attempted to construct a scoring system, LMScore, to quantify lactate metabolism and predict potential impact on individual tumor patients. The survival advantage of the B cluster and B genecluster were significantly higher than that of other subgroups, as demonstrated in Figs. 4A and Figs. 4B. Figures 4C illustrated patients with a higher LMScore had a more favorable prognosis. Sankey diagrams were constructed to visualize the relationships between cluster, genecluster, LMScore, and patients’ outcome events. Figure 4F revealed a significant correlation between the B cluster, B genecluster, and high LMScore, with the majority of patients in the high LMScore subtype being in a survival state. Considering the clinical information, Fig. 4D and Fig. 4E showed that patients with high LMScore have lower tumor staging and better outcome events compared to patients with low LMScore.

Additionally, we generated a waterfall plot to compare the differences in prevalence of single-gene mutations between the two subtypes. Figures 4G and 4H demonstrated there were high degree of overlap significantly mutated genes (SMGs) suggesting these gene mutations may be commonly presented in HCC. It was noteworthy that TP53, a tumor suppressor gene, CTNNB1, an oncogene, and TTN, the largest known protein-coding gene, were the top three SMGs in both subtypes. Here, TTN and CTNNB1 had a higher mutation frequency in the high LMScore subtype, while the high mutation rate of TP53 was characteristic of the low LMScore subtype. To further elucidate the differences in copy number alterations (CNAs) among different subtypes, we compiled the top 20 genes with copy number amplifications and deletions for each subtype patients (Fig. 4I-J). The results showed that there were no prominent differences in gene copy number variations (p-values > 0.05), indicating the presence of similar CNA events in both subtypes

Immunological microenvironment and immune cell infiltration.

This study aims to investigate the relationship among LMScore, immune cells and immune checkpoints in order to understand the impact of altered lactate metabolism on the tumor immune microenvironment. Figure 5A indicated that immune checkpoints in all families were significantly upregulated in the low LMScore subtype. Furthermore, correlation analysis also confirmed this point: except for eosinophils, LMScore exhibited significant negative correlations with various immune cell types, including CD4 + T cells, dendritic cells, type 2 T helper cells, MDSCs, and natural killer T cells (Fig. 5B). The ssGSEA algorithm was used to assess the infiltration abundance of 28 immune cells. The findings revealed that eosinophil infiltration was a distinctive immunological feature of the high-score subtype, while significantly elevated levels of active CD4 + T cells and myeloid-derived suppressor cells (MDSC) infiltration were characteristics of the low-score subtype (Fig. 5C). Furthermore, Fig. 5D confirmed a wide negative correlation between co-inhibitory immune checkpoints and LMScore. We further quantified the overall infiltration of immune cells of the distinct subtypes using the ESTIMATE algorithm. The findings demonstrated that the low LMScore subtype exhibited elevated immune scores (Fig. 5E) and ESTIMATE scores (Figure S1A), with statistically differences.

Immunotherapy and targeted therapy

To further elucidate the relationship between LMScore and immune checkpoint inhibitor(ICIs), Fig. 6A demonstrated that patients with a high LMScore exhibit a higher response rate to ICIs. Therefore, patients in low-scoring subtype were more susceptible to developing resistance to immune therapy with ctla4 and PD1. Immune evasion can lead to resistance against immunotherapy. TIDE scoring system can evaluate immune evasion and predict the impact of immune therapy on tumor patients. The results illustrated that the TIDE and Exclusion scores of the low LMScore subtype were significantly higher than those of the high-score subtype (Fig. 6B). MSI scoring is an important indicator for measuring the tumor's responsiveness to immunotherapy. In the context of immunotherapy, a higher MSI score is generally associated with better treatment response and prognosis. Therefore, patients in the high LMScore subtype exhibited considerably higher response rates to immunotherapy compared to those in the low LMScore subtype (Fig. 6C).

Furthermore, we compared the differences in drug sensitivity between distinct subtypes patients by exploring the area under the receiver operating characteristic curve (ROC) (Fig. 6D). Specifically, we conducted a comparison of commonly used first-line and second-line treatments for liver cancer in clinical settings. This comparison included chemotherapy drugs such as Paclitaxel, Cisplatin, Oxaliplatin, Capecitabine, and Mitoxantrone, as well as targeted therapy drugs like Sorafenib, Lenvatinib, Osimertinib, and Nintedanib. Results showed that the patients of high LMScore subtype were more sensitive to cisplatin, oxaliplatin, capecitabine, lenvatinib, and nintedanib. In contrast, patients in low LMScore subtype, benefit more from paclitaxel, mitoxantrone, sorafenib, and axitinib. Sensitivity predictions for unapplied drugs were also made for distinct LMScore subtypes. The results indicated that the low LMScore subtype may benefit from vincristine and volasertib treatment, while the high LMScore subtype may benefit from RITA(Figure S2A-C). Supplementary figures(Figure S2D-E) were used to validate the connection between high expression of the molecular SLC7A11 and HIF1A and resistance to sorafenib, confirming previous reports[30].

Expression of LMRPGs in HCC.

The successful construction of the LMScore accomplished the clear classification of HCC patients, showing significant differences in prognosis between distinct subtypes. Our goal was to identify crucial hub genes that impact the prognostic variations between the two subtypes. In this study, the LMRPGs were shown in Table S2. According to the flowchart depicted in Figure S3A, we identified three significant genes of interest: CFHR4, TTC36, and SLC10A1. After considering the diagnostic efficacy of these target genes in HCC subgroups including Tumor-Normal as well as high/low LMScore, we ultimately selected TTC36 as the hub gene (Figure S3B-D). TTC36, a gene associated with lactate metabolism, exhibits notable variations in expression levels between the distinct LMScore subtypes. Moreover, TTC36 expression was significantly correlated with LMScore (Figure S3E). QPCR were conducted using tumor and adjacent normal tissues from patients with HCC at our experimental center. The mRNA expression of TTC36 was downregulated in HCC tumor tissues (Fig. 7A). The Western Blot (WB) results were consistent with qPCR(Fig. 7B). Combined with the HPA database, TTC36 is an independent prognostic factor (Fig. 7C). Finally, we used a lactate detection assay kit to assess the impact of TTC36 expression on intracellular lactate levels in tumor cells. The graph illustrates that the intracellular lactate levels in the TTC36-OE group were significantly lower compared to the control group. Conversely, the lactate levels in the TTC36-KD group were higher than those in the TTC36-NC group.

Functional phenotypes of TTC36 in HCC cells.

We utilized a panel of liver cancer cell lines including HepG2, HUH7, MHCC-97H, HCCLM3, and Hep3B. WB and PCR results showed that TTC36 was significantly upregulated in HUH7 cells and downregulated in LM3 cells (Fig. 7D-E). Accordingly, we selected HUH7 and LM3 cell lines to respectively manipulate the expression of TTC36, aiming to achieve knockdown and overexpression. Results indicated in contrast to the Vector group, the OE group transfected with the overexpression plasmid showed a significant enhancement of TTC36 at both transcription and translation levels. (Fig. 7F-G). After knocking down TTC36, we examined the functional phenotypic changes of HCC cells. The graph demonstrated that parallel to the control group, the cell viability of the TTC36-KD group was substantially elevated, while the overexpressed group showed a noticeable decrease in cell viability (Fig. 8A). Scratch results revealed that compared to the TTC36-vector, the scratch width of the TTC36-OE group was wider, indicating limited cell migration ability. Conversely, knocking down TTC36 results in a narrower scratch compared to the negative control (NC) group, thus promoting the migration ability of tumor cells (Fig. 8B). The Transwell assay yielded consistent results, indicating that TTC36-OE inhibited the metastasis of LM3 cells, whereas TTC36-sh1 promoted its progression (Fig. 8C). The above results all have statistical differences.

Tumor cells can convert glucose into lactate through glycolysis even under aerobic conditions due to the energy demand for proliferation. This metabolic shift alters the tumor microenvironment, providing a growth advantage for tumor cells. lactate, is now acknowledged to exert a crucial part in tumor metabolism changes and regulation of the tumor microenvironment [31]. However, the lactate in HCC is not fully understood. Therefore, it is of utmost importance to conduct in-depth research on the impact of lactate on HCC patient prognosis, tumor microenvironment, and personalized therapy.

Inherent driving factors of tumor heterogeneity are reflected in the genomic alterations of tumor cells[32]. Approximately 18–40% of HCC patients have CTNNB1 mutations, which play a vital role in liver metabolism regulation and tumor patients’ prognosis[33]. TTN gene mutations are the most observed in gastric-esophageal cancer and are significantly associated with the prognosis of thyroid cancer patients[34, 35]. TP53 is a well-known tumor suppressor gene involving in cell growth, proliferation, and DNA damage repair mechanisms[36]. HCC patients with TP53 mutations have shorter OS and recurrence-free survival compared to those with wild-type TP53, which is in line with the high TP53 mutation rate in the low LMScore subtype associated with poor prognosis in this study. Chromosomal structural alterations are a major characteristic of the cancer genome. A common phenomenon in this regard is the amplification and deletion of longer segments of DNA, referred to as copy number alterations (CNA). In this work, gene mutations were found to be of considerable importance in the prognosis of patients in different LMScore subtypes than that of chromosomal variations.

The complex tumor microenvironment (TME) has an impact on tumor progression and its immune response, with tumor-infiltrating immune cells being one of the most crucial components in the TME. CD8 + T cells represent a crucial subset of T cells in the adaptive immune response against tumors[37]. CD4 + T cells actively participate in the signal transduction for antigen recognition by T cell receptors (TCR)[38]. Tregs, also known as regulatory T cells, constitute a distinct subset of CD4 + T cells, playing a pivotal role in maintaining immune tolerance and suppressing immune responses[39]. MDSCs suppress immune responses primarily by inhibiting T cell activation and promoting immune tolerance. In terms of innate immunity, MDSCs downregulate the expression of NKG2D by binding to TGF-β on the cell membrane, thereby inhibiting the function of NK cells[40]. In terms of adaptive immunity, MDSCs inhibit T cell survival and their immune response by competitively depleting cysteine in the internal environment and generating reactive oxygen species. MDSCs exert a pivotal role in shaping the immunosuppressive microenvironment of tumors, enhancing immune evasion of tumor cells, and promoting tumor angiogenesis, malignant growth, and metastasis.

Lactate can regulate the immune response in the tumor microenvironment[41]. Previous studies have shown that in melanoma, the accumulation of lactate impairs the secretion of cytokines, particularly IFN-r, by tumor-infiltrating T cells and NK cells, thus inhibiting immune surveillance and promoting tumor growth[42].In HCC, high levels of lactate accumulation promote the survival of Treg cells and impair the anti-tumor response of effector T cells and natural killer cells, exerting an immunosuppressive effect[31]. Here, we observed that Tregs, MDSC, and other immune suppressive cells were more abundant in patients of low LMScore subtype, suggesting a potential inclination towards an immunosuppressive microenvironment. The TIDE indicated that patients within the low LMScore face challenges in benefiting from immunotherapy due to immune escape and the presence of an immunosuppressive microenvironment. Lastly, we employed the pRRophetic algorithm to assess the sensitivity of clinically used first-line and second-line medications for different patients subtypes with respect to liver cancer, providing guidance for the clinical management of immune-tolerant patients.

Here, we identified three genes with significant differences in expression levels between high and low LMScore subtypes: CFHR4, TTC36, and SLC10A1. The CFHR4 gene encodes complement factor H-related protein 4, which, along with other complement factor-related proteins, have a hand in regulating the activity and functionality of the complement system[43]. The SLC10A1 gene encodes a protein that is critically involved in bile acid reabsorption and recycling. Taking into account of the diagnostic efficacy of gene expression in distinguishing distinct LMScore subtypes of HCC patients, we ultimately selected TTC36 as the hub gene. TTC36, which encodes a protein with three adjacent Tetratricopeptide repeat (TPR) motifs, is significantly downregulated in liver cancer. TPR proteins act as scaffolds for protein-protein interactions and play a role in various cellular functions, including transcription, protein translocation, and degradation[44]. Despite the growing body of research, the function and interaction of the TTC36 gene remain poorly understood. Currently, this gene get involved in various diseases and biological processes, such as tumors, neurological disorders, and cardiovascular diseases[45, 46]. We further demonstrated that TTC36 is significantly under-expressed in HCC and inhibits HCC cell proliferation, invasion, migration, and lactate metabolism in in vitro experiments. However, the specific mechanism of action remains unclear, necessitating further research in the future.

Our study introduces a novel approach by incorporating a lactate metabolism gene set to establish LMScore and classify HCC patients into two distinct subtypes. This method holds great promise in effectively stratifying HCC tumor patients and providing guidance for late-stage treatments. However, it is important to acknowledge the current limitations of our research. Firstly, it should be noted that all patients included in this study were part of a retrospective analysis. Therefore, the LMScore established in the research requires validation through prospective studies. Secondly, we indirectly evaluated the immunotherapy of HCC patients using bioinformatics methods, as there is a lack of application of large-scale immunotherapy cohorts. Lastly, it is necessary to include more clinically eligible patients in our research and undertake clinical experimental studies to further validate our findings.

In conclusion, this study constructed an LMScore based on transcriptomics, and combined with data of genomic structural variation and proteomics to exam the differences lied in the two subtypes patients. Our work not only contributes to understand the potential role of lactate metabolism in influencing the heterogeneity of HCC, but also provides a reliable platform for disease monitoring and individualized treatment strategies.

HCC, Hepatocellular Carcinoma; LMRPGs, Lactate Metabolism-Related and Prognostic Genes; TCGA, The Cancer Genome Atlas; GEO, Gene Expression Omnibus; FPKM, Fragments Per Kilobase Million; GSCA, Gene Set Cancer Analysis; LMG, Lactate Metabolism Genes; MsigDB, Molecular Signatures Database; GSVA, Gene Set Variation Analysis; GO, Gene Ontology; WB, Western Blotting; RT-qPCR, Real Time -Quantitative Polymerase Chain Reaction; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, Differentially Expressed Genes; PCA, Principal Component Analysis; ssGSEA, single-sample Gene Set Enrichment Analysis; GSEA, Gene-set Enrichment Analysis; LMScore, Lactate Metabolism Score; TCIA, The Cancer Imaging Archive; TIDE, Tumor Immune Dysfunction And Exclusion; shRNA, Short Hairpin RNA; PVDF, Polyvinylidene Fluoride; TBST, Tris-Buffered Saline with Tween; EDU, 5-Ethynyl-2'-deoxyuridine; OS, Overal Survival; DFI, Disease-Free Interval; DSS, Disease-Specific Survival; PFS, Progression-Free Survival; PAS, Proportion of Ambiguous Clustering; CNAs, Copy number alterations; MDSC, Myeloid-Derived Suppressor Cells; ICI, Immune Checkpoint Inhibitor; MSI, Microsatellite instability; ROC, Receiver Operating Characteristic; AUC, Area Under the Curve

Author contributions

HL and JG conceived and carried out the experiments. HL wrote the paper. HL, ML, Long L, and Lei L integrated and analyzed the data. BH and ZW helped to collect clinical samples. WG and SZ advised on the experimental design and substantially contributed to critically revise the article. HL, JG, ML edited and revised the manuscript. All authors approved this manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (82103282), Henan Province Medical Science and Tech-nology Research Plan (SBGJ20210306), Henan Charity General Federation of Hepatobiliary Care Fund (GDXZ2023002), "Science and Technology to create Central Plains" Young Talent Lifting Project (2023HYTP041).

Ethics approval:

All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, China(2019-KY-21).

Informed Consent Statement:

Informed consent was obtained from all individual participants included in the study.

Data Availability Statement:

The bioinformatics data used in support of the findings of this study are public. Further inquiries can be directed to the corresponding author.

Acknowledgments:

It is with great gratitude that we acknowledge the TCGA, GEO, and GSCA databases for providing data.

Conflict of interest:

The authors have no relevant financial or non-financial interests to disclose.

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71(3):209–249.
Wang H, Lu Z, Zhao X: Tumorigenesis, diagnosis, and therapeutic potential of exosomes in liver cancer. J Hematol Oncol 2019, 12(1):133.
Sun R, Zhang Z, Bao R, Guo X, Gu Y, Yang W, Wei J, Chen X, Tong L, Meng J et al: Loss of SIRT5 promotes bile acid-induced immunosuppressive microenvironment and hepatocarcinogenesis. J Hepatol 2022, 77(2):453–466.
Dai W, Xiang W, Han L, Yuan Z, Wang R, Ma Y, Yang Y, Cai S, Xu Y, Mo S et al: PTPRO represses colorectal cancer tumorigenesis and progression by reprogramming fatty acid metabolism. Cancer Commun (Lond) 2022, 42(9):848–867.
Dai W, Xu L, Yu X, Zhang G, Guo H, Liu H, Song G, Weng S, Dong L, Zhu J et al: OGDHL silencing promotes hepatocellular carcinoma by reprogramming glutamine metabolism. J Hepatol 2020, 72(5):909–923.
Hay N: Reprogramming glucose metabolism in cancer: can it be exploited for cancer therapy? Nat Rev Cancer 2016, 16(10):635–649.
Li Z, Zhang H: Reprogramming of glucose, fatty acid and amino acid metabolism for cancer progression. Cell Mol Life Sci 2016, 73(2):377–392.
Pavlova NN, Thompson CB: The Emerging Hallmarks of Cancer Metabolism. Cell Metab 2016, 23(1):27–47.
Xia P, Zhang H, Lu H, Xu K, Jiang X, Jiang Y, Gongye X, Chen Z, Liu J, Chen X et al: METTL5 stabilizes c-Myc by facilitating USP5 translation to reprogram glucose metabolism and promote hepatocellular carcinoma progression. Cancer Commun (Lond) 2023, 43(3):338–364.
Roland CL, Arumugam T, Deng D, Liu SH, Philip B, Gomez S, Burns WR, Ramachandran V, Wang H, Cruz-Monserrate Z et al: Cell surface lactate receptor GPR81 is crucial for cancer cell survival. Cancer Res 2014, 74(18):5301–5310.
Boedtkjer E, Pedersen SF: The Acidic Tumor Microenvironment as a Driver of Cancer. Annu Rev Physiol 2020, 82:103–126.
Wang JX, Choi SYC, Niu X, Kang N, Xue H, Killam J, Wang Y: Lactic Acid and an Acidic Tumor Microenvironment suppress Anticancer Immunity. Int J Mol Sci 2020, 21(21).
Gao Y, Zhou H, Liu G, Wu J, Yuan Y, Shang A: Tumor Microenvironment: Lactic Acid Promotes Tumor Development. J Immunol Res 2022, 2022:3119375.
Ye L, Jiang Y, Zhang M: Crosstalk between glucose metabolism, lactate production and immune response modulation. Cytokine Growth Factor Rev 2022, 68:81–92.
Hirschhaeuser F, Sattler UG, Mueller-Klieser W: Lactate: a metabolic key player in cancer. Cancer Res 2011, 71(22):6921–6925.
Zhang A, Xu Y, Xu H, Ren J, Meng T, Ni Y, Zhu Q, Zhang WB, Pan YB, Jin J et al: Lactate-induced M2 polarization of tumor-associated macrophages promotes the invasion of pituitary adenoma by secreting CCL17. Theranostics 2021, 11(8):3839–3852.
Feng J, Yang H, Zhang Y, Wei H, Zhu Z, Zhu B, Yang M, Cao W, Wang L, Wu Z: Tumor cell-derived lactate induces TAZ-dependent upregulation of PD-L1 through GPR81 in human lung cancer cells. Oncogene 2017, 36(42):5829–5839.
Elia I, Rowe JH, Johnson S, Joshi S, Notarangelo G, Kurmi K, Weiss S, Freeman GJ, Sharpe AH, Haigis MC: Tumor cells dictate anti-tumor immune responses by altering pyruvate utilization and succinate signaling in CD8(+) T cells. Cell Metab 2022, 34(8):1137–1150.e1136.
Jedlička M, Feglarová T, Janstová L, Hortová-Kohoutková M, Frič J: Lactate from the tumor microenvironment - A key obstacle in NK cell-based immunotherapies. Front Immunol 2022, 13:932055.
Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD: The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012, 28(6):882–883.
Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP: Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011, 27(12):1739–1740.
Wilkerson MD, Hayes DN: ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010, 26(12):1572–1573.
Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z: Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep 2017, 18(1):248–262.
Hänzelmann S, Castelo R, Guinney J: GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013, 14:7.
Yang H, Zou X, Yang S, Zhang A, Li N, Ma Z: Identification of lactylation related model to predict prognostic, tumor infiltrating immunocytes and response of immunotherapy in gastric cancer. Front Immunol 2023, 14:1149989.
Zhang W, Xiao P, Tang J, Wang R, Wang X, Wang F, Ruan J, Yu S, Tang J, Huang R et al: m6A Regulator-Mediated Tumour Infiltration and Methylation Modification in Cervical Cancer Microenvironment. Front Immunol 2022, 13:888650.
Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, Li Z, Traugh N, Bu X, Li B et al: Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 2018, 24(10):1550–1558.
Geeleher P, Cox N, Huang RS: pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One 2014, 9(9):e107468.
Cui H, Guo D, Zhang X, Zhu Y, Wang Z, Jin Y, Guo W, Zhang S: ENO3 Inhibits Growth and Metastasis of Hepatocellular Carcinoma via Wnt/β-Catenin Signaling Pathway. Front Cell Dev Biol 2021, 9:797102.
Li Y, Yang W, Zheng Y, Dai W, Ji J, Wu L, Cheng Z, Zhang J, Li J, Xu X et al: Targeting fatty acid synthase modulates sensitivity of hepatocellular carcinoma to sorafenib via ferroptosis. J Exp Clin Cancer Res 2023, 42(1):6.
Xu Y, Hao X, Ren Y, Xu Q, Liu X, Song S, Wang Y: Research progress of abnormal lactate metabolism and lactate modification in immunotherapy of hepatocellular carcinoma. Front Oncol 2022, 12:1063423.
Andre F, Filleron T, Kamal M, Mosele F, Arnedos M, Dalenc F, Sablin MP, Campone M, Bonnefoi H, Lefeuvre-Plesse C et al: Genomics to select treatment for patients with metastatic breast cancer. Nature 2022, 610(7931):343–348.
Xu C, Xu Z, Zhang Y, Evert M, Calvisi DF, Chen X: β-Catenin signaling in hepatocellular carcinoma. J Clin Invest 2022, 132(4).
Duan Q, Tang C, Ma Z, Chen C, Shang X, Yue J, Jiang H, Gao Y, Xu B: Genomic Heterogeneity and Clonal Evolution in Gastroesophageal Junction Cancer Revealed by Single Cell DNA Sequencing. Front Oncol 2021, 11:672020.
Han X, Chen J, Wang J, Xu J, Liu Y: TTN mutations predict a poor prognosis in patients with thyroid cancer. Biosci Rep 2022, 42(7).
Lindemann A, Takahashi H, Patel AA, Osman AA, Myers JN: Targeting the DNA Damage Response in OSCC with TP53 Mutations. J Dent Res 2018, 97(6):635–644.
Lees JR: CD8 + T cells: The past and future of immune regulation. Cell Immunol 2020, 357:104212.
Meitei HT, Lal G: T cell receptor signaling in the differentiation and plasticity of CD4(+) T cells. Cytokine Growth Factor Rev 2023, 69:14–27.
Zheng Y, Chen Z, Han Y, Han L, Zou X, Zhou B, Hu R, Hao J, Bai S, Xiao H et al: Immune suppressive landscape in the human esophageal squamous cell carcinoma microenvironment. Nat Commun 2020, 11(1):6268.
Li H, Han Y, Guo Q, Zhang M, Cao X: Cancer-expanded myeloid-derived suppressor cells induce anergy of NK cells through membrane-bound TGF-beta 1. J Immunol 2009, 182(1):240–249.
Wang ZH, Peng WB, Zhang P, Yang XP, Zhou Q: Lactate in the tumour microenvironment: From immune modulation to therapy. EBioMedicine 2021, 73:103627.
Brand A, Singer K, Koehl GE, Kolitzus M, Schoenhammer G, Thiel A, Matos C, Bruss C, Klobuch S, Peter K et al: LDHA-Associated Lactic Acid Production Blunts Tumor Immunosurveillance by T and NK Cells. Cell Metab 2016, 24(5):657–671.
Skerka C, Chen Q, Fremeaux-Bacchi V, Roumenina LT: Complement factor H related proteins (CFHRs). Mol Immunol 2013, 56(3):170–180.
Jiang L, Kwong DL, Li Y, Liu M, Yuan YF, Li Y, Fu L, Guan XY: HBP21, a chaperone of heat shock protein 70, functions as a tumor suppressor in hepatocellular carcinoma. Carcinogenesis 2015, 36(10):1111–1120.
Song L, Guo X, Zhao F, Wang W, Zhao Z, Jin L, Wu C, Yao J, Ma Z: TTC36 inactivation induce malignant properties via Wnt-β-catenin pathway in gastric carcinoma. J Cancer 2021, 12(9):2598–2609.
Xie Y, Lv X, Ni D, Liu J, Hu Y, Liu Y, Liu Y, Liu R, Zhao H, Lu Z et al: HPD degradation regulated by the TTC36-STK33-PELI1 signaling axis induces tyrosinemia and neurological damage. Nat Commun 2019, 10(1):4266.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Construction and validation of novel lactate metabolism molecular subtypes in hepatocellular carcinoma based on multi-omics analysis

Status:

Version 1

Abstract

Purpose

Methods

Results

Conclusion

Figures

Introduction

Methods

Statistical analysis

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1