The Endless Sources of Hepatocellular Carcinoma Heterogeneity

Precision models in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) represents a global health challenge, and ranks among one of the most prevalent and deadliest cancers worldwide. Therapeutic advances have expanded the treatment armamentarium for patients with advanced HCC, but obstacles remain. Precision oncology, which aims to match specific therapies to patients who have tumours with particular features, holds great promise. However, its implementation has been hindered by the existence of numerous 'HCC influencers' that contribute to the high inter-patient heterogeneity. HCC influencers include tumour-related characteristics, such as genetic alterations, immune infiltration, stromal composition and aetiology, and patient-specific factors, such as sex, age, germline variants and the microbiome. This Review delves into the intricate world of HCC, describing the most innovative model systems that can be harnessed to identify precision and/or personalized therapies. We provide examples of how different models have been used to nominate candidate biomarkers, their limitations and strategies to optimize such models. We also highlight the importance of reproducing distinct HCC influencers in a flexible and modular way, with the aim of dissecting their relative contribution to therapy response. Next-generation HCC models will pave the way for faster discovery of precision therapies for patients with advanced HCC.

Evolutionary View of Liver Pathology

Evolutionary medicine emerged in the late twentieth century, integrating principles of natural selection and adaptation with the health sciences. Today, with a rapidly widening gap between the biology of Homo sapiens and its environment, maladaptation or maladaptive disorders can be detected in almost all diseases, including liver dysfunction. However, in hepatology, as in most medical specialties, evolutionary considerations are neglected because the majority of the medical community is not familiar with evolutionary principles. The aim of this brief review is to highlight an evolutionary approach that may facilitate understanding various liver diseases.

Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and is associated with high mortality rates due to late detection and aggressive progression. Peritumoral radiomics, an emerging technique that quantitatively analyzes the tissue surrounding the tumor, has shown significant potential in enhancing the management of HCC. This paper examines the role of peritumoral radiomics in improving diagnostic accuracy, guiding personalized treatment strategies, and refining prognostic assessments. By offering unique insights into the tumor microenvironment, peritumoral radiomics enables more precise patient stratification and informs clinical decision-making. However, the integration of peritumoral radiomics into routine clinical practice faces several challenges. Addressing these challenges through continued research and innovation is crucial for the successful implementation of peritumoral radiomics in HCC management, ultimately leading to improved patient outcomes.

Comprehensive RNA-Seq Gene Co-Expression Analysis Reveals Consistent Molecular Pathways in Hepatocellular Carcinoma across Diverse Risk Factors

Hepatocellular carcinoma (HCC) has the highest mortality rate and is the most frequent of liver cancers. The heterogeneity of HCC in its etiology and molecular expression increases the difficulty in identifying possible treatments. To elucidate the molecular mechanisms of HCC across grades, data from The Cancer Genome Atlas (TCGA) were used for gene co-expression analysis, categorizing each sample into its pre-existing risk factors. The R library BioNERO was used for preprocessing and gene co-expression network construction. For those modules most correlated with a grade, functional enrichments from different databases were then tested, which appeared to have relatively consistent patterns when grouped by G1/G2 and G3/G4. G1/G2 exhibited the involvement of pathways related to metabolism and the PI3K/Akt pathway, which regulates cell proliferation and related pathways, whereas G3/G4 showed the activation of cell adhesion genes and the p53 signaling pathway, which regulates apoptosis, cell cycle arrest, and similar processes. Module preservation analysis was then used with the no history dataset as the reference network, which found cell adhesion molecules and cell cycle genes to be preserved across all risk factors, suggesting they are imperative in the development of HCC regardless of potential etiology. Through hierarchical clustering, modules related to the cell cycle, cell adhesion, the immune system, and the ribosome were found to be consistently present across all risk factors, with distinct clusters linked to oxidative phosphorylation in viral HCC and pentose and glucuronate interconversions in non-viral HCC, underscoring their potential roles in cancer progression.

Etiology-independent activation of the LTβ-LTβR-RELB axis drives aggressiveness and predicts poor prognosis in HCC

BACKGROUND AND AIMS

HCC is the most common primary liver tumor, with an increasing incidence worldwide. HCC is a heterogeneous malignancy and usually develops in a chronically injured liver. The NF-κB signaling network consists of a canonical and a noncanonical branch. Activation of canonical NF-κB in HCC is documented. However, a functional and clinically relevant role of noncanonical NF-κB and its downstream effectors is not established.

APPROACH AND RESULTS

Four human HCC cohorts (total n = 1462) and 4 mouse HCC models were assessed for expression and localization of NF-κB signaling components and activating ligands. In vitro , NF-κB signaling, proliferation, and cell death were measured, proving a pro-proliferative role of v-rel avian reticuloendotheliosis viral oncogene homolog B (RELB) activated by means of NF-κB-inducing kinase. In vivo , lymphotoxin beta was identified as the predominant inducer of RELB activation. Importantly, hepatocyte-specific RELB knockout in a murine HCC model led to a lower incidence compared to controls and lower maximal tumor diameters. In silico , RELB activity and RELB-directed transcriptomics were validated on the The Cancer Genome Atlas HCC cohort using inferred protein activity and Gene Set Enrichment Analysis. In RELB-active HCC, pathways mediating proliferation were significantly activated. In contrast to v-rel avian reticuloendotheliosis viral oncogene homolog A, nuclear enrichment of noncanonical RELB expression identified patients with a poor prognosis in an etiology-independent manner. Moreover, RELB activation was associated with malignant features metastasis and recurrence.

CONCLUSIONS

This study demonstrates a prognostically relevant, etiology-independent, and cross-species consistent activation of a lymphotoxin beta/LTβR/RELB axis in hepatocarcinogenesis. These observations may harbor broad implications for HCC, including possible clinical exploitation.

Applications of single-cell multi-omics in liver cancer

Primary liver cancer, more specifically hepatocellular carcinoma (HCC), remains a significant global health problem associated with increasing incidence and mortality. Clinical, biological, and molecular heterogeneity are well-known hallmarks of cancer and HCC is considered one of the most heterogeneous tumour types, displaying substantial inter-patient, intertumoural and intratumoural variability. This heterogeneity plays a pivotal role in hepatocarcinogenesis, metastasis, relapse and drug response or resistance. Unimodal single-cell sequencing techniques have already revolutionised our understanding of the different layers of molecular hierarchy in the tumour microenvironment of HCC. By highlighting the cellular heterogeneity and the intricate interactions among cancer, immune and stromal cells before and during treatment, these techniques have contributed to a deeper comprehension of tumour clonality, hematogenous spreading and the mechanisms of action of immune checkpoint inhibitors. However, major questions remain to be elucidated, with the identification of biomarkers predicting response or resistance to immunotherapy-based regimens representing an important unmet clinical need. Although the application of single-cell multi-omics in liver cancer research has been limited thus far, a revolution of individualised care for patients with HCC will only be possible by integrating various unimodal methods into multi-omics methodologies at the single-cell resolution. In this review, we will highlight the different established single-cell sequencing techniques and explore their biological and clinical impact on liver cancer research, while casting a glance at the future role of multi-omics in this dynamic and rapidly evolving field.

Integrative analysis of senescence-related genes identifies robust prognostic clusters with distinct features in hepatocellular carcinoma

INTRODUCTION

Senescence refers to a state of permanent cell growth arrest and is regarded as a tumor suppressive mechanism, whereas accumulative evidence demonstrate that senescent cells play an adverse role during cancer progression. The scarcity of specific and reliable markers reflecting senescence level in cancer impede our understanding of this biological basis.

OBJECTIVES

Senescence-related genes (SRGs) were collected for integrative analysis to reveal the role of senescence in hepatocellular carcinoma (HCC).

METHODS

Consensus clustering was used to subtype HCC based on SRGs. Several computational methods, including single sample gene set enrichment analysis (ssGSEA), fuzzy c-means algorithm, were performed. Data of drug sensitivities were utilized to screen potential therapeutic agents for different senescence patients. Additionally, we developed a method called signature-related gene analysis (SRGA) for identification of markers relevant to phenotype of interest. Experimental strategies consisting quantitative real-time PCR (qRT-PCR), β-galactosidase assay, western blot, and tumor-T cell co-culture system were used to validate the findings in vitro.

RESULTS

We identified three robust prognostic clusters of HCC patients with distinct survival outcome, mutational landscape, and immune features. We further extracted signature genes of senescence clusters to construct the senescence scoring system and profile senescence level in HCC at bulk and single-cell resolution. Senescence-induced stemness reprogramming was confirmed both in silico and in vitro. HCC patients with high senescence were immune suppressed and sensitive to Tozasertib and other drugs. We suggested that MAFG, PLIN3, and 4 other genes were pertinent to HCC senescence, and MAFG potentially mediated immune suppression, senescence, and stemness.

CONCLUSION

Our findings provide insights into the role of SRGs in patients stratification and precision medicine.

Alteration of serum bile acid profiles of HBV-related hepatocellular carcinoma identified by LC-MS/MS

BACKGROUND

Hepatocellular carcinoma closely related to metabolic disorders is a common and aggressive liver malignancy. The dysregulation of bile acid homeostasis has emerged as a key factor for the development and progression of HCC. We aimed to investigate the relationship between bile acids and HCC diagnosis and progression.

METHODS

A total of 744 HBV-related patients (including 396 HCC patients and 348 patients with chronic liver diseases) were enrolled in the current study. The baseline characteristics of patients were collected from electronic medical records, and the levels of bile acid profiles were determined by LC-MS/MS. Propensity score matching analysis was conducted to reduce the effect of selection bias, and receiver operating characteristic analysis was performed to evaluate the clinical application values of bile acid.

RESULTS

Significant differences were observed for most characteristics between the HCC group and the CLD group before PSM analysis. Patients with HCC were older and fatter (p < 0.05). After adjusting with a 1:1 ratio for age, gender and BMI, 42 HCC patients and 42 non-HCC patients were matched in 2 groups, respectively. The total bile acid level in HCC patients was lower than that in patients with chronic liver diseases before and after PSM analysis (p < 0.05). However, patients with HCC had significantly higher levels of DCA, LCA, and GLCA and lower levels of TCDCA, GUDCA, and TUDCA (p < 0.05, respectively). Besides, the TCDCA, TUDCA, GLCA, and GUDCA were significantly correlated with tumor procession. Moreover, the BAs profiles had a superior predictive ability for predicting the development of HCC even in patients with low serum AFP levels.

CONCLUSION

Patients with HCC had significantly lower levels of total bile acid, but higher levels of secondary bile acids (DCA, LCA, and GLCA). The levels of primary bile acid (TCDCA) were closely related to tumor size and stage, which indicated that the bile acids were involved in the HCC procession and had important clinical application values.

Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score

BACKGROUND

Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies.

AIM

To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS).

METHODS

A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05.

RESULTS

One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072).

CONCLUSION

Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.

Characterizing ligand-receptor interactions and unveiling the pro-tumorigenic role of CCL16-CCR1 axis in the microenvironment of hepatocellular carcinoma

Background

The heterogeneity of the tumor microenvironment significantly influences the prognosis of hepatocellular carcinoma (HCC) patients, with cell communication through ligand-receptor complexes playing a central role.

Methods

We conducted single-cell transcriptomic analysis on ten HCC tissues to identify ligand-receptor genes involved in malignant HCC cell communication using CellChat. Leveraging RNA-Seq data from the TCGA Liver Cancer (TCGA-LIHC) and Liver Cancer - RIKEN, JP (LIRI-JP) cohorts, we employed Cox regression analysis to screen for prognosis-related genes. Prognostic risk models were constructed through unsupervised clustering and differential gene expression analysis. Subsequently, a co-culture system involving tumor cells and macrophages was established. A series of experiments, including Transwell assays, immunofluorescence staining, immunoprecipitation, flow cytometry, and immunohistochemistry, were conducted to elucidate the mechanism through which HCC cells recruit macrophages via the CCL16-CCR1 axis.

Results

Single-cell analysis unveiled significant interactions between malignant HCC cells and macrophages, identifying 76 related ligand-receptor genes. Patients were classified into three subtypes based on the expression patterns of eight prognosis-related ligand-receptor genes. The subtype with the worst prognosis exhibited reduced infiltration of T cell-related immune cells, downregulation of immune checkpoint genes, and increased M2-like tumor-associated macrophage scores. In vitro experiments confirmed the pivotal role of the CCL16-CCR1 axis in the recruitment and M2 polarization of tumor-associated macrophages. Clinical samples demonstrated a significant association between CCL16 protein expression levels and advanced stage, lymph node metastasis, and distant metastasis. Immunohistochemistry and immunofluorescence staining further confirmed the correlation between CCL16 and CCR1, CD68, and CD206, as well as CD68+CCR1+ macrophage infiltration.

Conclusions

Our study identified molecular subtypes, a prognostic model, and immune microenvironment features based on ligand-receptor interactions in malignant HCC cell communication. Moreover, we revealed the pro-tumorigenic role of HCC cells in recruiting M2-like tumor-associated macrophages through the CCL16-CCR1 axis.

Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study

Introduction

Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients.

Methods

Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed.

Results

The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656-0.840) and 0.702 (95% CI: 0.547-0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815-0.957) and 0.820 (95% CI: 0.648-0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not.

Conclusion

Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.

Fluorescence microscopy imaging of mitochondrial metabolism in cancer cells

Mitochondrial metabolism is an important contributor to cancer cell survival and proliferation that coexists with enhanced glycolytic activity. Measuring mitochondrial activity is useful to characterize cancer metabolism patterns, to identify metabolic vulnerabilities and to identify new drug targets. Optical imaging, especially fluorescent microscopy, is one of the most valuable tools for studying mitochondrial bioenergetics because it provides semiquantitative and quantitative readouts as well as spatiotemporal resolution of mitochondrial metabolism. This review aims to acquaint the reader with microscopy imaging techniques currently used to determine mitochondrial membrane potential (ΔΨm), nicotinamide adenine dinucleotide (NADH), ATP and reactive oxygen species (ROS) that are major readouts of mitochondrial metabolism. We describe features, advantages, and limitations of the most used fluorescence imaging modalities: widefield, confocal and multiphoton microscopy, and fluorescent lifetime imaging (FLIM). We also discus relevant aspects of image processing. We briefly describe the role and production of NADH, NADHP, flavins and various ROS including superoxide and hydrogen peroxide and discuss how these parameters can be analyzed by fluorescent microscopy. We also explain the importance, value, and limitations of label-free autofluorescence imaging of NAD(P)H and FAD. Practical hints for the use of fluorescent probes and newly developed sensors for imaging ΔΨm, ATP and ROS are described. Overall, we provide updated information about the use of microscopy to study cancer metabolism that will be of interest to all investigators regardless of their level of expertise in the field.

Metabolomics-Guided Identification of a Distinctive Hepatocellular Carcinoma Signature

BACKGROUND

Hepatocellular carcinoma (HCC) is a major contributor to cancer-related morbidity and mortality burdens globally. Given the fundamental metabolic activity of hepatocytes within the liver, hepatocarcinogenesis is bound to be characterized by alterations in metabolite profiles as a manifestation of metabolic reprogramming.

METHODS

HCC and adjacent non-tumoral liver specimens were obtained from patients after HCC resection. Global patterns in tissue metabolites were identified using non-targeted ¹H Nuclear Magnetic Resonance (¹H-NMR) spectroscopy whereas specific metabolites were quantified using targeted liquid chromatography-mass spectrometry (LC/MS).

RESULTS

Principal component analysis (PCA) within our ¹H-NMR dataset identified a principal component (PC) one of 53.3%, along which the two sample groups were distinctively clustered. Univariate analysis of tissue specimens identified more than 150 metabolites significantly altered in HCC compared to non-tumoral liver. For LC/MS, PCA identified a PC1 of 45.2%, along which samples from HCC tissues and non-tumoral tissues were clearly separated. Supervised analysis (PLS-DA) identified decreases in tissue glutathione, succinate, glycerol-3-phosphate, alanine, malate, and AMP as the most important contributors to the metabolomic signature of HCC by LC/MS.

CONCLUSIONS

Together, ¹H-NMR and LC/MS metabolomics have the capacity to distinguish HCC from non-tumoral liver. The characterization of such distinct profiles of metabolite abundances underscores the major metabolic alterations that result from hepatocarcinogenesis.

Comparative RNA-Sequencing Analysis Reveals High Complexity and Heterogeneity of Transcriptomic and Immune Profiles in Hepatocellular Carcinoma Tumors of Viral (HBV, HCV) and Non-Viral Etiology

Background and Objectives: Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, is the leading cause of cancer-related mortality. It arises and progresses against fibrotic or cirrhotic backgrounds mainly due to infection with hepatitis viruses B (HBV) or C (HCV) or non-viral causes that lead to chronic inflammation and genomic changes. A better understanding of molecular and immune mechanisms in HCC subtypes is needed. Materials and Methods: To identify transcriptional changes in primary HCC tumors with or without hepatitis viral etiology, we analyzed the transcriptomes of 24 patients by next-generation sequencing. Results: We identified common and unique differentially expressed genes for each etiological tumor group and analyzed the expression of SLC, ATP binding cassette, cytochrome 450, cancer testis, and heat shock protein genes. Metascape functional enrichment analysis showed mainly upregulated cell-cycle pathways in HBV and HCV and upregulated cell response to stress in non-viral infection. GeneWalk analysis identified regulator, hub, and moonlighting genes and highlighted CCNB1, ACTN2, BRCA1, IGF1, CDK1, AURKA, AURKB, and TOP2A in the HCV group and HSF1, HSPA1A, HSP90AA1, HSPB1, HSPA5, PTK2, and AURKB in the group without viral infection as hub genes. Immune infiltrate analysis showed that T cell, cytotoxic, and natural killer cell markers were significantly more highly expressed in HCV than in non-viral tumors. Genes associated with monocyte activation had the highest expression levels in HBV, while high expression of genes involved in primary adaptive immune response and complement receptor activity characterized tumors without viral infection. Conclusions: Our comprehensive study underlines the high degree of complexity of immune profiles in the analyzed groups, which adds to the heterogeneous HCC genomic landscape. The biomarkers identified in each HCC group might serve as therapeutic targets.

Custom gene expression panel for evaluation of potential molecular markers in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. It is a highly heterogeneous disease with poor prognosis and limited treatment options, which highlights the need for reliable biomarkers. This study aims to explore molecular markers that allow stratification of HCC and may lead to better prognosis and treatment prediction.

MATERIALS AND METHODS

We studied 20 candidate genes (HCC hub genes, potential drug target genes, predominant somatic mutant genes) retrieved from literature and public databases with potential to be used as the molecular markers. We analysed expression of the genes by RT-qPCR in 30 HCC tumour and adjacent non-tumour paired samples from Vietnamese patients. Fold changes in expression were then determined using the 2-∆∆CT method, and unsupervised hierarchical clustering was generated using Cluster v3.0 software.

RESULTS

Clustering of expression data revealed two subtypes of tumours (proliferative and normal-like) and four clusters for genes. The expression profiles of the genes TOP2A, CDK1, BIRC5, GPC3, IGF2, and AFP were strongly correlated. Proliferative tumours were characterized by high expression of the c-MET, ARID1A, CTNNB1, RAF1, LGR5, and GLUL1 genes. TOP2A, CDK1, and BIRC5 HCC hub genes were highly expressed (> twofold) in 90% (27/30), 83% (25/30), and 83% (24/30) in the tissue samples, respectively. Among the drug target genes, high expression was observed in the GPC3, IGF2 and c-MET genes in 77% (23/30), 63% (19/30), and 37% (11/30), respectively. The somatic mutant Wnt/ß-catenin genes (CTNNB1, GLUL and LGR5) and TERT were highly expressed in 40% and 33% of HCCs, respectively. Among the HCC marker genes, a higher percentage of tumours showed GPC3 expression compared to AFP expression [73% (23/30) vs. 43% (13/30)].

CONCLUSION

The custom panel and molecular markers from this study may be useful for diagnosis, prognosis, biomarker-guided clinical trial design, and prediction of treatment outcomes.

A Novel Cuproptosis-Related Signature Identified DLAT as a Prognostic Biomarker for Hepatocellular Carcinoma Patients

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common type of liver cancers, with more than a million cases per year by 2025. Cuproptosis is a novel form of programmed cell death, and is caused by mitochondrial lipoylation and destabilization of iron-sulfur proteins triggered by copper, which was considered as a key player in various biological processes. However, the roles of cuproptosis-related genes (CRGs) in HCC remain largely unknown.

METHODS

In the present study, we constructed and validated a four CRGs signature for predicting the overall survival (OS) of HCC patients in both The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases.

RESULTS

Patients with high CRGs risk score showed shorter OS than those with low CRGs risk score. Functional analysis suggested that the CRGs-based prognostic signature was associated with metabolism remodeling which facilitated liver cancer progression. In addition, reduced infiltration of CD8⁺ T cells and increased macrophages were found in HCCs from patients with high CRGs risk score. As one of the four CRGs, higher expression of dihydrolipoamide S-acetyltransferase (DLAT) was accompanied by higher expression of program death ligand 1 (PD-L1) in HCC. Further, we confirmed that DLAT was up-regulated and correlated with poor prognosis in a clinical HCC cohort.

CONCLUSION

In conclusion, our study constructed a four CRGs signature prognostic model and identified DLAT as an independent prognostic factor for HCC, thus providing new clues for understanding the association between cuproptosis and HCC.

Biochemical Mechanisms of Sirtuin-Directed Protein Acylation in Hepatic Pathologies of Mitochondrial Dysfunction

Mitochondrial protein acetylation is associated with a host of diseases including cancer, Alzheimer’s, and metabolic syndrome. Deciphering the mechanisms regarding how protein acetylation contributes to disease pathologies remains difficult due to the complex diversity of pathways targeted by lysine acetylation. Specifically, protein acetylation is thought to direct feedback from metabolism, whereby nutritional status influences mitochondrial pathways including beta-oxidation, the citric acid cycle, and the electron transport chain. Acetylation provides a crucial connection between hepatic metabolism and mitochondrial function. Dysregulation of protein acetylation throughout the cell can alter mitochondrial function and is associated with numerous liver diseases, including non-alcoholic and alcoholic fatty liver disease, steatohepatitis, and hepatocellular carcinoma. This review introduces biochemical mechanisms of protein acetylation in the regulation of mitochondrial function and hepatic diseases and offers a viewpoint on the potential for targeted therapies.

Implications of genetic heterogeneity in hepatocellular cancer

Hepatocellular carcinoma (HCC) exhibits a remarkable degree of heterogeneity, not only at an inter-patient level but also between and within tumors in the same patient. The advent of next-generation sequencing (NGS)-based technologies has allowed the creation of high-resolution atlases of HCC. This review outlines recent findings from genomic, epigenomic, transcriptomic, and proteomic sequencing that have yielded valuable insights into the spatial and temporal heterogeneity of HCC. The high heterogeneity of HCC has both clinical and therapeutic implications. The challenges in prospectively validating molecular classifications for HCC either for prognostication or for prediction of therapeutic response are partly due to the immense heterogeneity in HCC. Moreover, the heterogeneity of HCC tumors combined with the lack of commonly mutated, druggable targets severely limits treatment options for HCC. Recently, immune checkpoint inhibitors and combination therapies have shown promise for advanced HCC, while T cell therapies and vaccines are currently being investigated. Yet, immunotherapies show benefit only in a limited subset of patients, making it imperative to decipher tumor heterogeneity in HCC in order to enable optimal patient selection. This review summarizes the cutting-edge research on heterogeneity in HCC and explores the implications of heterogeneity on stratifying patients and developing biomarkers and therapies for HCC.

High-Mobility Group AT-Hook 1 Served as a Prognosis Biomarker and Associated with Immune Infiltrate in Hepatocellular Carcinoma

BACKGROUND

The protein high-mobility group AT-hook 1 (HMGA1) has been demonstrated that modulated cellular proliferation, invasion, and apoptosis with a poor prognosis in miscellaneous carcinomas. However, the mechanism of circumstantial carcinogenesis and association with the immune microenvironment of HMGA1 in hepatocellular carcinoma (HCC) had not been extensively explored.

METHODS

The gene expression, clinicopathological correlation, and prognosis analysis were performed in the data obtained from TCGA. The results were further validated by ICGC and GEO database and external validation cohort from Guangxi. The HMGA1 protein expression was further examined in the HPA database. Biological function analyses were conducted by GSEA, STRING database, and Coexpedia online tool. Using TIMER and CIBERSORT method, the relationship between immune infiltrate and HMGA1 was investigated.

RESULTS

In HCC, HMGA1 had much higher transcriptional and proteomic expression than in corresponding paraneoplastic tissue. Patients with high HMGA1 expression had a poor prognosis and unpromising clinicopathological features. High HMGA1 expression was closely related to the cell cycle, tumorigenesis, substance metabolism, and immune processes by regulating complex signaling pathways. Notably, HMGA1 may be associated with TP53 mutational carcinogenesis. Moreover, increased HMGA1 expression may lead to an increase in immune infiltration and a decrease in tumor purity in HCC. CIBERSORT analysis elucidated that the amount of B cell naive, B cell memory, T cells gamma delta, macrophages M2, and mast cell resting decreased when HMGA1 expression was high, whereas T cells follicular helper, macrophages M0, and Dendritic cells resting increased.

CONCLUSION

In conclusions, HMGA1 is a potent prognostic biomarker and a sign of immune infiltration in HCC, which may be a potential immunotherapy target for HCC.

VDAC Modulation of Cancer Metabolism: Advances and Therapeutic Challenges

Most anionic metabolites including respiratory substrates, glycolytic adenosine triphosphate (ATP), and small cations that enter mitochondria, and mitochondrial ATP moving to the cytosol, cross the outer mitochondrial membrane (OMM) through voltage dependent anion channels (VDAC). The closed states of VDAC block the passage of anionic metabolites, and increase the flux of small cations, including calcium. Consequently, physiological or pharmacological regulation of VDAC opening, by conditioning the magnitude of both anion and cation fluxes, is a major contributor to mitochondrial metabolism. Tumor cells display a pro-proliferative Warburg phenotype characterized by enhanced aerobic glycolysis in the presence of partial suppression of mitochondrial metabolism. The heterogeneous and flexible metabolic traits of most human tumors render cells able to adapt to the constantly changing energetic and biosynthetic demands by switching between predominantly glycolytic or oxidative phenotypes. Here, we describe the biological consequences of changes in the conformational state of VDAC for cancer metabolism, the mechanisms by which VDAC-openers promote cancer cell death, and the advantages of VDAC opening as a valuable pharmacological target. Particular emphasis is given to the endogenous regulation of VDAC by free tubulin and the effects of VDAC-tubulin antagonists in cancer cells. Because of its function and location, VDAC operates as a switch to turn-off mitochondrial metabolism (closed state) and increase aerobic glycolysis (pro-Warburg), or to turn-on mitochondrial metabolism (open state) and decrease glycolysis (anti-Warburg). A better understanding of the role of VDAC regulation in tumor progression is relevant both for cancer biology and for developing novel cancer chemotherapies.