Identification of subclusters and prognostic genes based on glycolysis/gluconeogenesis in hepatocellular carcinoma

A Novel Signature Composed of Hypoxia, Glycolysis, Lactylation Related Genes to Predict Prognosis and Immunotherapy in Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. The hypoxic microenvironment in HCC enhances glycolysis and co-directed lactate accumulation, which leads to increased lactylation. However, the exact biological pattern remains to be elucidated. Therefore, we sought to identify hypoxia-glycolysis-lactylation (HGL) prognosis-related signatures and validate this in vitro.

METHODS

Transcriptomic data of patients with HCC were collected from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Differentially expressed HGL genes between HCC and normal tissues were obtained by DEseq2. The consensus clustering algorithm was employed to stratify patients into two distinct clusters. Subsequently, the single sample Gene Set Enrichment Analysis (ssGSEA), Tumor Immune Estimation Resource (TIMER) and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to assess immune infiltration and immune evasion. Least Absolute Shrinkage and Selection Operator (LASSO) and COX regression analysis were used to identify an HGL prognosis-related signature. Based on spatial transcriptome and histological data, we analyzed the expression of these genes in HCC and explored the function of Homer Scaffold Protein 1 (HOMER1) in HCC cells.

RESULTS

We identified 72 differentially expressed HGL genes and two HGL clusters. Cluster2, with better survival (p < 0.001), was significantly enriched in metabolic-related pathways. The HGL prognosis-related signature exhibited great predictive efficacy for patients in TCGA, ICGC, and GSE148355 databases (3-year area under the curve (AUC) = 0.822, 0.738, and 0.707, respectively). The elevated expression of HOMER1 in HCC was revealed by the combination of spatial transcriptome and histological data. Knocking down HOMER1 significantly inhibited the malignant progression of HCC cells.

CONCLUSIONS

We identified a signature with great predictive efficacy and discovered a gene, HOMER1, that influences the malignant progression of HCC with the potential to become a novel therapeutic target.

A Novel Prognostic Signature Integrating Immune and Glycolytic Pathways for Enhanced Prognosis and Immunotherapy Prediction in Hepatocellular Carcinoma

Background

This study aimed to establish an immune-glycolysis-related prognostic signature (IGRPS) to predict hepatocellular carcinoma (HCC) outcomes. Additionally, it explored the role of this signature in the tumor immune microenvironment (TIME), glycolytic pathways, and immunotherapy.

Methods

We analyzed RNA-seq, single-cell sequencing, and immune- and glycolysis-related gene datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Using weighted gene co-expression network analysis (WGCNA), F-test, and Cox regression, we identified key survival-related immune and glycolytic genes (SRIGRGs) and developed an IGRPS through multivariate Cox regression. The IGRPS's predictive performance was validated in training and validation cohorts using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and a prognostic nomogram. Its correlation with TIME and its ability to predict immunotherapy outcomes were also assessed. In vitro experiments were conducted to analyze the expression and function of IGRPS genes in HCC.

Results

Thirteen SRIGRGs were identified for constructing the IGRPS. Patients with low-risk scores had significantly longer survival times. The area under the curve (AUC) for ROC curves was over 0.73 for training and 0.7 for validation cohorts, with C-indices of 0.721 and 0.79, respectively. IGRPS was confirmed as an independent prognostic indicator. Patients in the low-risk group showed better responses to combined anti-CTLA4 and anti-PD-1 therapies. In vitro experiments indicated that PRKAG1 and B3GAT3 were upregulated, enhancing glycolysis and promoting HCC cell proliferation and migration.

Conclusion

The IGRPS, based on immune- and glycolysis-related genes, effectively predicted prognosis and immunotherapy responses in HCC patients.

The impact of metabolic reprogramming on tertiary lymphoid structure formation: enhancing cancer immunotherapy

BACKGROUND

Cancer immunotherapy has achieved unprecedented success in the field of cancer therapy. However, its potential is constrained by a low therapeutic response rate.

MAIN BODY

Tertiary lymphoid structure (TLS) plays a crucial role in antitumor immunity and is associated with a good prognosis. Metabolic reprogramming, as a hallmark of the tumor microenvironment, can influence tumor immunity and promote the formation of follicular helper T cells and germinal centers. However, many current studies focus on the correlation between metabolism and TLS formation factors, and there is insufficient direct evidence to suggest that metabolism drives TLS formation. This review provided a comprehensive summary of the relationship between metabolism and TLS formation, highlighting glucose metabolism, lipid metabolism, amino acid metabolism, and vitamin metabolism.

CONCLUSIONS

In the future, an in-depth exploration of how metabolism affects cell interactions and the role of microorganisms in TLS will significantly advance our understanding of metabolism-enhanced antitumor immunity.

Metabolic pathway activation and immune microenvironment features in non-small cell lung cancer: insights from single-cell transcriptomics

Introduction

In this study, we aim to provide a deep understanding of the tumor microenvironment (TME) and its metabolic characteristics in non-small cell lung cancer (NSCLC) through single-cell RNA sequencing (scRNAseq) data obtained from public databases. Given that lung cancer is a leading cause of cancer-related deaths globally and NSCLC accounts for the majority of lung cancer cases, understanding the relationship between TME and metabolic pathways in NSCLC is crucial for developing new treatment strategies.

Methods

Finally, machine learning algorithms were employed to construct a risk signature with strong predictive power across multiple independent cohorts. After quality control, 29,053 cells were retained, and PCA along with UMAP techniques were used to distinguish 13 primary cell subpopulations. Four highly activated metabolic pathways were identified within malignant cell subpopulations, which were further divided into seven distinct subgroups showing significant differences in differentiation potential and metabolic activity. WGCNA was utilized to identify gene modules and hub genes closely associated with these four metabolic pathways.

Results

Our analysis showed that DEGs between tumor and normal tissues were predominantly enriched in immune response and cell adhesion pathways. The comprehensive examination of our model revealed substantial variations in clinical and pathological characteristics, enriched pathways, cancer hallmarks, and immune infiltration scores between high-risk and low-risk groups. Wet lab experiments validated the role of KRT6B in NSCLC, demonstrating that KRT6B expression is elevated and it stimulates the proliferation of cancer cells.

Discussion

These observations not only enhance our understanding of metabolic reprogramming and its biological functions in NSCLC but also provide new perspectives for early detection, prognostic evaluation, and targeted therapy. However, future research should further explore the specific mechanisms of these metabolic pathways and their application potentials in clinical practice.

A Gluconeogenesis-Related Genes Model for Predicting Prognosis, Tumor Microenvironment Infiltration, and Drug Sensitivity in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a prevalent malignancy within the digestive system, known for its poor prognosis. Gluconeogenesis, a critical metabolic pathway, is responsible for the synthesis of glucose in the normal liver. This study aimed to examine the role of gluconeogenesis-related genes (GRGs) in HCC and evaluate their impact on the tumor microenvironment infiltration and drug sensitivity in HCC.

Methods

We retrieved gene expression and clinical pathological data of HCC from The Cancer Genome Atlas (TCGA) database. This dataset was utilized to develop a prognosis model. The data from The International Cancer Genome Consortium (ICGC) served as an independent validation cohort. A least absolute shrinkage and selection operator (LASSO) regression analysis was applied to a curated panel of GRGs to construct and validate the predictive model. Furthermore, unsupervised consensus clustering, based on the expression levels of GRGs, categorized HCC patients into distinct subgroups.

Results

A four-gene prognostic model, referred to as GRGs, has been successfully developed with high accuracy and stability for the prediction of HCC patient prognosis. This model enables the stratification of patients into high or low risk groups based on individual risk scores, revealing significant differences in immune infiltration patterns and anti-tumor drug responses. Unsupervised consensus clustering analysis delineated four distinct subgroups of patients, each characterized by a unique prognosis and tumor immune microenvironment (TIME).

Conclusion

This study is the first to develop a prognostic model incorporating 4-GRGs that effectively predicts the prognosis, tumor microenvironment infiltration, and drug sensitivity in HCC patients. The model based on 4 GRGs may contribute to predict the prognosis, immunotherapy and chemotherapy response of HCC patients.

Research Progress on Dendritic Cells in Hepatocellular Carcinoma Immune Microenvironments

Dendritic cells (DCs) are antigen-presenting cells that play a crucial role in initiating immune responses by cross-presenting relevant antigens to initial T cells. The activation of DCs is a crucial step in inducing anti-tumor immunity. Upon recognition and uptake of tumor antigens, activated DCs present these antigens to naive T cells, thereby stimulating T cell-mediated immune responses and enhancing their ability to attack tumors. It is particularly noted that DCs are able to cross-present foreign antigens to major histocompatibility complex class I (MHC-I) molecules, prompting CD8+ T cells to proliferate and differentiate into cytotoxic T cells. In the malignant progression of hepatocellular carcinoma (HCC), the inactivation of DCs plays an important role, and the activation of DCs is particularly important in anti-HCC immunotherapy. In this review, we summarize the mechanisms of DCs activation in HCC, the involved regulatory factors and strategies to activate DCs in HCC immunotherapy. It provides a basis for the study of HCC immunotherapy through DCs activation.

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data

Background

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction.

Methods

Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts.

Results

We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions.

Conclusions

This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.

A hypoxia-glycolysis-lactate-related gene signature for prognosis prediction in hepatocellular carcinoma

BACKGROUND

Liver cancer ranks sixth in incidence and third in mortality globally and hepatocellular carcinoma (HCC) accounts for 90% of it. Hypoxia, glycolysis, and lactate metabolism have been found to regulate the progression of HCC separately. However, there is a lack of studies linking the above three to predict the prognosis of HCC. The present study aimed to identify a hypoxia-glycolysis-lactate-related gene signature for assessing the prognosis of HCC.

METHODS

This study collected 510 hypoxia-glycolysis-lactate genes from Molecular Signatures Database (MSigDB) and then classified HCC patients from TCGA-LIHC by analyzing their hypoxia-glycolysis-lactate genes expression. Differentially expressed genes (DEGs) were screened out to construct a gene signature by LASSO-Cox analysis. Univariate and multivariate regression analyses were used to evaluate the independent prognostic value of the gene signature. Analyses of immune infiltration, somatic cell mutations, and correlation heatmap were conducted by "GSVA" R package. Single-cell analysis conducted by "SingleR", "celldex", "Seurat", and "CellCha" R packages revealed how signature genes participated in hypoxia/glycolysis/lactate metabolism and PPI network identified hub genes.

RESULTS

We classified HCC patients from TCGA-LIHC into two clusters and screened out DEGs. An 18-genes prognostic signature including CDCA8, CBX2, PDE6A, MED8, DYNC1LI1, PSMD1, EIF5B, GNL2, SEPHS1, CCNJL, SOCS2, LDHA, G6PD, YBX1, RTN3, ADAMTS5, CLEC3B, and UCK2 was built to stratify the risk of HCC. The risk score of the hypoxia-glycolysis-lactate gene signature was further identified as a valuable independent factor for estimating the prognosis of HCC. Then we found that the features of clinical characteristics, immune infiltration, somatic cell mutations, and correlation analysis differed between the high-risk and low-risk groups. Furthermore, single-cell analysis indicated that the signature genes could interact with the ligand-receptors of hepatocytes/fibroblasts/plasma cells to participate in hypoxia/glycolysis/lactate metabolism and PPI network identified potential hub genes in this process: CDCA8, LDHA, YBX1.

CONCLUSION

The hypoxia-glycolysis-lactate-related gene signature we built could provide prognostic value for HCC and suggest several hub genes for future HCC studies.

Immmunometabolism of systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a potentially fatal chronic autoimmune disease which is underlain by complex dysfunction of the innate and adaptive immune systems. Although a series of well-defined genetic and environmental factors have been implicated in disease etiology, neither the development nor the persistence of SLE is well understood. Given that several disease susceptibility genes and environmental factors interact and influence inflammatory lineage specification through metabolism, the field of immunometabolism has become a forefront of cutting edge research. Along these lines, metabolic checkpoints of pathogenesis have been identified as targets of effective therapeutic interventions in mouse models and validated in clinical trials. Ongoing studies focus on mitochondrial oxidative stress, activation of the mechanistic target of rapamycin, calcium signaling, glucose utilization, tryptophan degradation, and metabolic cross-talk between gut microbiota and the host immune system.