Identification of a Subtype of Hepatocellular Carcinoma with Poor Prognosis Based on Expression of Genes within the Glucose Metabolic Pathway

Fasting in combination with the cocktail Sorafenib:Metformin blunts cellular plasticity and promotes liver cancer cell death via poly-metabolic exhaustion

PURPOSE

Dual-Interventions targeting glucose and oxidative metabolism are receiving increasing attention in cancer therapy. Sorafenib (S) and Metformin (M), two gold-standards in liver cancer, are known for their mitochondrial inhibitory capacity. Fasting, a glucose-limiting strategy, is also emerging as chemotherapy adjuvant. Herein, we explore the anti-carcinogenic response of nutrient restriction in combination with sorafenib:metformin (NR-S:M).

RESULTS

Our data demonstrates that, independently of liver cancer aggressiveness, fasting synergistically boosts the anti-proliferative effects of S:M co-treatment. Metabolic and Cellular plasticity was determined by the examination of mitochondrial and glycolytic activity, cell cycle modulation, activation of cellular apoptosis, and regulation of key signaling and metabolic enzymes. Under NR-S:M conditions, early apoptotic events and the pro-apoptotic Bcl-xS/Bcl-xL ratio were found increased. NR-S:M induced the highest retention in cellular SubG1 phase, consistent with the presence of DNA fragments from cellular apoptosis. Mitochondrial functionality, Mitochondrial ATP-linked respiration, Maximal respiration and Spare respiratory capacity, were all found blunted under NR-S:M conditions. Basal Glycolysis, Glycolytic reserve, and glycolytic capacity, together with the expression of glycogenic (PKM), gluconeogenic (PCK1 and G6PC3), and glycogenolytic enzymes (PYGL, PGM1, and G6PC3), were also negatively impacted by NR-S:M. Lastly, a TMT-proteomic approach corroborated the synchronization of liver cancer metabolic reprogramming with the activation of molecular pathways to drive a quiescent-like status of energetic-collapse and cellular death.

CONCLUSION

Altogether, we show that the energy-based polytherapy NR-S:M blunts cellular, metabolic and molecular plasticity of liver cancer. Notwithstanding the in vitro design of this study, it holds a promising therapeutic tool worthy of exploration for this tumor pathology.

Association of mitochondrial phosphoenolpyruvate carboxykinase with prognosis and immune regulation in hepatocellular carcinoma

Mitochondrial phosphoenolpyruvate carboxykinase (PCK2), a mitochondrial isoenzyme, supports the growth of cancer cells under glucose deficiency conditions in vitro. This study investigated the role and potential mechanism of PCK2 in the occurrence and development of Hepatocellular carcinoma (HCC). The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and other databases distinguish the expression of PCK2 and verified by qRT-PCR and Western blotting. Kaplan-Meier was conducted to assess PCK2 survival in HCC. The potential biological function of PCK2 was verified by enrichment analysis and gene set enrichment analysis (GSEA). The correlation between PCK2 expression and immune invasion and checkpoint was found by utilizing Tumor Immune Estimation Resource (TIMER). Lastly, the effects of PCK2 on the proliferation and metastasis of hepatocellular carcinoma cells were evaluated by cell tests, and the expressions of Epithelial mesenchymal transformation (EMT) and apoptosis related proteins were detected. PCK2 is down-regulated in HCC, indicating a poor prognosis. PCK2 gene mutation accounted for 1.3% of HCC. Functional enrichment analysis indicated the potential of PCK2 as a metabolism-related therapeutic target. Subsequently, we identified several signaling pathways related to the biological function of PCK2. The involvement of PCK2 in immune regulation was verified and key immune checkpoints were predicted. Ultimately, after PCK2 knockdown, cell proliferation and migration were significantly increased, and N-cadherin and vimentin expression were increased. PCK2 has been implicated in immune regulation, proliferation, and metastasis of hepatocellular carcinoma, and is emerging as a novel predictive biomarker and metabolic-related clinical target.

Cancer Stem Cells from Definition to Detection and Targeted Drugs

Cancers remain the second leading cause of mortality in the world. Preclinical and clinical studies point an important role of cancer/leukaemia stem cells (CSCs/LSCs) in the colonisation at secondary organ sites upon metastatic spreading, although the precise mechanisms for specific actions are still not fully understood. Reviewing the present knowledge on the crucial role of CSCs/LSCs, their plasticity, and population heterogeneity in treatment failures in cancer patients is timely. Standard chemotherapy, which acts mainly on rapidly dividing cells, is unable to adequately affect CSCs with a low proliferation rate. One of the proposed mechanisms of CSC resistance to anticancer agents is the fact that these cells can easily shift between different phases of the cell cycle in response to typical cell stimuli induced by anticancer drugs. In this work, we reviewed the recent studies on CSC/LSC alterations associated with disease recurrence, and we systematised the functional assays, markers, and novel methods for CSCs screening. This review emphasises CSCs' involvement in cancer progression and metastasis, as well as CSC/LSC targeting by synthetic and natural compounds aiming at their elimination or modulation of stemness properties.

Integration with Transcriptomic and Metabolomic Analyses Reveals the In Vitro Cytotoxic Mechanisms of Chinese Poplar Propolis by Triggering the Glucose Metabolism in Human Hepatocellular Carcinoma Cells

Natural products serve as a valuable reservoir of anticancer agents. Chinese poplar propolis (CP) has exhibited remarkable antitumor activities, yet its precise mechanisms of action remain elusive. This study aims to elucidate the in vitro cytotoxic mechanisms of CP in human hepatocellular carcinoma cells (HepG2) through comprehensive transcriptomic and metabolomic analyses. Our evidence suggested that CP possesses a great potential to inhibit the proliferation of HepG2 cells by targeting the glucose metabolism. Notably, CP exhibited a dose- and time-dependent reduction in the viability of HepG2 cells. Transcriptome sequencing unveiled significant alterations in the cellular metabolism, particularly within glucose metabolism pathways. CP effectively restrained glucose consumption and lactic acid production. Moreover, the CP treatment led to a substantial decrease in the mRNA expression levels of key glucose transporters (GLUT1 and GLUT3) and glycolytic enzymes (LDHA, HK2, PKM2, and PFK). Correspondingly, CP suppressed some key protein levels. Cellular metabolomic analysis demonstrated a marked reduction in intermediary products of glucose metabolism, specifically fructose 1,6-bisphosphate and acetyl-CoA, following CP administration. Finally, key compounds in CP were screened, and apigenin, pinobanksin, pinocembrin, and galangin were identified as potential active agents against glycolysis. It indicates that the effectiveness of propolis in inhibiting liver cancer is the result of the combined action of several components. These findings underscore the potential therapeutic value of propolis in the treatment of liver cancer by targeting glycolytic pathways.

A High-Throughput Sequencing Data-Based Classifier Reveals the Metabolic Heterogeneity of Hepatocellular Carcinoma

Metabolic heterogeneity plays a key role in poor outcomes in malignant tumors, but its role in hepatocellular carcinoma (HCC) remains largely unknown. In the present study, we aim to disentangle the metabolic heterogeneity features of HCC by developing a classification system based on metabolism pathway activities in high-throughput sequencing datasets. As a result, HCC samples were classified into two distinct clusters: cluster 1 showed high levels of glycolysis and pentose phosphate pathway activity, while cluster 2 exhibited high fatty acid oxidation and glutaminolysis status. This metabolic reprogramming-based classifier was found to be highly correlated with several clinical variables, including overall survival, prognosis, TNM stage, and 𝛼-fetoprotein (AFP) expression. Of note, activated oncogenic pathways, a higher TP53 mutation rate, and increased stemness were also observed in cluster 1, indicating a causal relationship between metabolic reprogramming and carcinogenesis. Subsequently, distinct metabolism-targeted therapeutic strategies were proven in human HCC cell lines, which exhibit the same metabolic properties as corresponding patient samples based on this classification system. Furthermore, the metabolic patterns and effects of different types of cells in the tumor immune microenvironment were explored by referring to both bulk and single-cell data. It was found that malignant cells had the highest overall metabolic activities, which may impair the anti-tumor capacity of CD8+ T cells through metabolic competition, and this provided a potential explanation for why immunosuppressive cells had higher overall metabolic activities than those with anti-tumor functions. Collectively, this study established an HCC classification system based on the gene expression of energy metabolism pathways. Its prognostic and therapeutic value may provide novel insights into personalized clinical practice in patients with metabolic heterogeneity.

KCNN4 Promotes the Stemness Potentials of Liver Cancer Stem Cells by Enhancing Glucose Metabolism

The presence of liver cancer stem cells (LCSCs) is one of the reasons for the treatment failure of hepatocellular carcinoma (HCC). For LCSCs, one of their prominent features is metabolism plasticity, which depends on transporters and ion channels to exchange metabolites and ions. The K⁺ channel protein KCNN4 (Potassium Calcium-Activated Channel Subfamily N Member 4) has been reported to promote cell metabolism and malignant progression of HCCs, but its influence on LCSC stemness has remained unclear. Here, we demonstrated that KCNN4 was highly expressed in L-CSCs by RT-PCR and Western blot. Then, we illustrated that KCNN4 promoted the stemness of HC-C cells by CD133⁺CD44⁺ LCSC subpopulation ratio analysis, in vitro stemness transcription factor detection, and sphere formation assay, as well as in vivo orthotopic liver tumor formation and limiting dilution tumorigenesis assays. We also showed that KCNN4 enhanced the glucose metabolism in LCSCs by metabolic enzyme detections and seahorse analysis, and the KCNN4-promoted increase in LCSC ratios was abolished by glycolysis inhibitor 2-DG or OXPHOS inhibitor oligomycin. Collectively, our results suggested that KCNN4 promoted LCSC stemness via enhancing glucose metabolism, and that KCNN4 would be a potential molecular target for eliminating LCSCs in HCC.

Metabolism-Associated Gene Signatures for FDG Avidity on PET/CT and Prognostic Validation in Hepatocellular Carcinoma

Introduction

The prognostic value of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in hepatocellular carcinoma (HCC) was established in previous reports. However, there is no evidence suggesting the prognostic value of transcriptomes associated with tumor FDG uptake in HCC. It was aimed to elucidate metabolic genes and functions associated with FDG uptake, followed by assessment of those prognostic value.

Methods

Sixty HCC patients with Edmondson–Steiner grade II were included. FDG PET/CT scans were performed before any treatment. RNA sequencing data were obtained from tumor and normal liver tissue. Associations between each metabolism-associated gene and tumor FDG uptake were investigated by Pearson correlation analyses. A novel score between glucose and lipid metabolism-associated gene expression was calculated. In The Cancer Genome Atlas Liver Hepatocellular Carcinoma dataset, the prognostic power of selected metabolism-associated genes and a novel score was evaluated for external validation.

Results

Nine genes related to glycolysis and the HIF-1 signaling pathway showed positive correlations with tumor FDG uptake; 21 genes related to fatty acid metabolism and the PPAR signaling pathway demonstrated negative correlations. Seven potential biomarker genes, PFKFB4, ALDOA, EGLN3, EHHADH, GAPDH, HMGCS2, and ENO2 were identified. A metabolic gene expression balance score according to the dominance between glucose and lipid metabolism demonstrated good prognostic value in HCC.

Conclusions

The transcriptomic evidence of this study strongly supports the prognostic power of FDG PET/CT and indicates the potential usefulness of FDG PET/CT imaging biomarkers to select appropriate patients for metabolism-targeted therapy in HCC.

A Starvation-Based 9-mRNA Signature Correlates With Prognosis in Patients With Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the world’s most prevalent and lethal cancers. Notably, the microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients.

Methods

The mRNA expression matrix and corresponding clinical information of HCC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) was used to distinguish different genes in the hunger metabolism gene in liver cancer and adjacent tissues. Gene Set Enrichment Analysis (GSEA) was used to identify biological differences between high- and low-risk samples. Univariate and multivariate analyses were used to construct prognostic models for hunger-related genes. Kaplan-Meier (KM) and receiver-operating characteristic (ROC) were used to assess the model accuracy. The model and relevant clinical information were used to construct a nomogram, protein expression was detected by western blot (WB), and transwell assay was used to evaluate the invasive and metastatic ability of cells.

Results

First, we used univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We then used multivariate analysis to build a model with nine genes. Finally, we divided the sample into low- and high-risk groups according to the median of the risk score. KM can be used to conclude that the prognosis of high- and low-risk samples is significantly different, and the prognosis of high-risk samples is worse. The prognostic accuracy of the 9-mRNA signature was also tested in the validation data set. GSEA was used to identify typical pathways and biological processes related to 9-mRNA, cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway, as well as biological processes related to the model. As evidenced by WB, EIF2S1 expression was increased after starvation. Overall, EIF2S1 plays an important role in the invasion and metastasis of liver cancer.

Conclusions

The 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients. However, its mechanism of action warrants further investigation.

Identification and Characterization of Robust Hepatocellular Carcinoma Prognostic Subtypes Based on an Integrative Metabolite-Protein Interaction Network

Metabolite-protein interactions (MPIs) play key roles in cancer metabolism. However, our current knowledge about MPIs in cancers remains limited due to the complexity of cancer cells. Herein, the authors construct an integrative MPI network and propose a MPI network based hepatocellular carcinoma (HCC) subtyping and mechanism exploration workflow. Based on the expressions of hub proteins on the MPI network, two prognosis-distinctive HCC subtypes are identified. Meanwhile, multiple interdependent features of the poor prognostic subtype are observed, including hypoxia, DNA hypermethylation of metabolic pathways, fatty acid accumulation, immune pathway up-regulation, and exhausted T-cell infiltration. Notably, the immune pathway up-regulation is probably induced by accumulated unsaturated fatty acids which are predicted to interact with multiple immune regulators like SRC and TGFB1. Moreover, based on tumor microenvironment compositions, the poor prognostic subtype is further divided into two sub-populations showing remarkable differences in metabolism. The subtyping shows a strong consistency across multiple HCC cohorts including early-stage HCC. Overall, the authors redefine robust HCC prognosis subtypes and identify potential MPIs linking metabolism to immune regulations, thus promoting understanding and clinical applications about HCC metabolism heterogeneity.

Development and validation of glycolysis-related prognostic score for prediction of prognosis and chemosensitivity of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with aggressive biological behaviour. Its rapid proliferation and tumour growth require reprogramming of glucose metabolism or the Warburg effect. However, the association between glycolysis-related genes with clinical features and prognosis of PDAC is still unknown. Here, we used the meta-analysis to correlate the hazard ratios (HR) of 106 glycolysis genes from MSigDB by the cox proportional hazards regression analysis in 6 clinical data sets of PDAC patients to form a training cohort, and a single group of PDAC patients from the TCGA, ICGC, Arrayexpress and GEO databases to form the validation cohort. Then, a glycolysis-related prognosis (GRP) score based on 29 glycolysis prognostic genes was established in 757 PDAC patients from the training composite cohort and validated in 267 ICGC-CA validation cohort (all P < .05). In addition, including PADC, the prognostic value was also confirmed in other 7 out of 30 pan-cancer cohorts. The GRP score was significantly related to specific metabolism pathways, immune genes and immune cells in the patients with PADC (all P < .05). Finally, by combining with immune cells, the GRP score also well-predicted the chemosensitivity of patients with PADC in the TCGA cohort (AUC = 0.709). In conclusion, this study developed a GRP score for patients with PDAC in predicting prognosis and chemosensitivity for PDAC.

Identification of a five-immune gene model as an independent prognostic factor in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a common malignant tumor with a poor prognosis. We aimed to identify a new prognostic model of HCC based on differentially expressed (DE) immune genes.

Methods

The DE immune genes were identified based on an analysis of 374 cases of HCC and 50 adjacent non-tumor specimens from the Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, Lasso regression, and multivariate Cox analysis were used to construct the model based on the training group. Survival analysis and the receiver operating characteristic (ROC) curves were used to evaluate model performance. The testing group and the entire group were subsequently used for validation of the model.

Results

A five-immune gene model consisted of HSPA4, ISG20L2, NDRG1, EGF, and IL17D was identified. Based on the model, the overall survival was significantly different between the high-risk and low-risk groups (P = 7.953e-06). The AUCs for the model at 1- and 3-year were 0.849 and 0.74, respectively. The reliability of the model was confirmed using the validation groups. The risk score was identified as an independent prognostic parameter and closely related to the content of immune cells from human HCC specimens.

Conclusion

We identified a five-immune gene model that can be used as an independent prognostic marker for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08012-2.

Synthesis, Kinetic and Conformational Studies of 2-Substituted-5-(β-d-glucopyranosyl)-pyrimidin-4-ones as Potential Inhibitors of Glycogen Phosphorylase

Dysregulation of glycogen phosphorylase, an enzyme involved in glucose homeostasis, may lead to a number of pathological states such as type 2 diabetes and cancer, making it an important molecular target for the development of new forms of pharmaceutical intervention. Based on our previous work on the design and synthesis of 4-arylamino-1-(β-d-glucopyranosyl)pyrimidin-2-ones, which inhibit the activity of glycogen phosphorylase by binding at its catalytic site, we report herein a general synthesis of 2-substituted-5-(β-d-glucopyranosyl)pyrimidin-4-ones, a related class of metabolically stable, C-glucosyl-based, analogues. The synthetic development consists of a metallated heterocycle, produced from 5-bromo-2-methylthiouracil, in addition to protected d-gluconolactone, followed by organosilane reduction. The methylthio handle allowed derivatization through hydrolysis, ammonolysis and arylamine substitution, and the new compounds were found to be potent (μM) inhibitors of rabbit muscle glycogen phosphorylase. The results were interpreted with the help of density functional theory calculations and conformational analysis and were compared with previous findings.

Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis

As liver hepatocellular carcinoma (LIHC) has high morbidity and mortality rates, improving the clinical diagnosis and treatment of LIHC is an important issue. The advent of the era of precision medicine provides us with new opportunities to cure cancers, including the accumulation of multi-omics data of cancers. Here, we proposed an integration method that involved the Fisher ratio, Spearman correlation coefficient, classified information index, and an ensemble of decision trees (DTs) for biomarker identification based on an unbalanced dataset of LIHC. Then, we obtained 34 differentially expressed genes (DEGs). The ability of the 34 DEGs to discriminate tumor samples from normal samples was evaluated by classification, and a high area under the curve (AUC) was achieved in our studied dataset and in two external validation datasets (AUC = 0.997, 0.973, and 0.949, respectively). Additionally, we also found three subtypes of LIHC, and revealed different biological mechanisms behind the three subtypes. Mutation enrichment analysis showed that subtype 3 had many enriched mutations, including tumor protein p53 (TP53) mutations. Overall, our study suggested that the 34 DEGs could serve as diagnostic biomarkers, and the three subtypes could help with precise treatment for LIHC.