Molecular subtyping of hepatocellular carcinoma: A step toward precision medicine

Pan-Immune-Inflammation Value as a Prognostic Biomarker for Hepatocellular Carcinoma Patients Undergoing Hepatectomy

Purpose

Hepatocellular carcinoma (HCC) poses a substantial threat to global health, characterized by its high incidence and mortality rates. This research aims to assess the prognostic value of a systematic serum inflammation index, the pan-immune-inflammation value (PIV), in patients with HCC who have undergone hepatectomy.

Patients and Methods

A total of 1764 HCC patients who underwent surgery were included in the study. These patients were divided into two groups based on the median PIV value. The Cox regression model was utilized to ascertain the independent risk factors that influence the prognosis of patients. A PIV-based nomogram was constructed and its performance was evaluated by the C-index, calibration curve, ROC curve, and DCA curve. Finally, a comparison was made between the nomogram and existing staging models.

Results

Patients with elevated PIV exhibited diminished OS and RFS compared to those with lower PIV. Univariate and multivariate Cox analyses revealed that PIV is an independent predictor of prognosis. The PIV-based nomogram demonstrated excellent discrimination, calibration, and clinical net benefit. The proposed nomogram outperformed the other existing staging systems, as evidenced by a higher AUC value.

Conclusion

PIV exhibits potential as a prognostic factor for both OS and RFS in patients with HCC who have undergone hepatectomy. The PIV-based nomogram can serve as an additional tool in conjunction with the existing liver cancer staging system, thereby facilitating more personalized treatment decisions for clinicians.

Quantitative evaluation of tumor signal heterogeneity on gadoxetic acid-enhanced magnetic resonance imaging as a predictor of postoperative survival and antitumor immunity in hepatocellular carcinoma

AIM

We investigated the efficacy of quantitative evaluation of tumor signal heterogeneity on gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) to predict prognosis and antitumor immunity in patients with hepatocellular carcinoma (HCC) undergoing liver resection.

METHODS

A total of 297 patients who underwent curative resection for primary HCC were included. Tumor signal heterogeneity in the hepatobiliary phase (HBP) of EOB-MRI was quantified as the coefficient of variation (CV), calculated as the standard deviation divided by the mean signal intensity. Patients were classified into homogeneous (low CV) and heterogeneous (high CV) groups based on a cutoff value of 0.16 from receiver operating characteristic curve analysis. Tumor-infiltrating CD4+ and CD8+ T cells and PD-L1 expression were assayed by immunohistochemistry, and their associations with tumor signal heterogeneity were evaluated.

RESULTS

Among the 297 patients, 116 (39.1%) were classified into the heterogeneous group. The overall survival (OS) and recurrence-free survival (RFS) rates were significantly lower in the heterogeneous group (p < 0.001 for both). Multivariate analysis identified heterogeneous group as an independent prognostic factor for OS and RFS (p < 0.001 and p = 0.012, respectively). Extrahepatic recurrence was significantly more frequent in the heterogeneous group (18.1% vs. 7.7%, p = 0.024). CD4+ and CD8+ T cells were significantly decreased, and the PD-L1 positivity rate was significantly lower in the heterogeneous group (p < 0.001 for all).

CONCLUSIONS

The quantitative evaluation of tumor signal heterogeneity in the HBP of EOB-MRI using CV is useful for predicting postoperative prognosis in patients with HCC. Tumor signal heterogeneity may also reflect impaired local immunity and an immunologically "cold" tumor.

Multi-omic analysis reveals a CAF-stemness-governed classification in HCC liver transplant recipients beyond the Milan criteria

In patients with hepatocellular carcinoma (HCC) meeting the Milan criteria, liver transplantation (LT) is an effective therapy. This study aims to define the survival-related molecular biological features helping precisely identifying the patients with HCC beyond the Milan criteria who have acceptable outcomes. In the derivation cohort, integrated analyses of tumor tissues are conducted using RNA sequencing (RNA-seq), proteomic landscape, and transposase-accessible chromatin sequencing (ATAC-seq). Based on transcriptomics, three subgroups that significantly differ in overall survival were identified in the derivation cohort, and these findings are validated in an independent cohort. In-depth bioinformatics analysis using RNA-seq and proteomics reveals that the promotion of cancer stemness by cancer-associated fibroblasts (CAFs) can be responsible for the negative biological characteristics observed in high-risk HCC patients. The ATAC-seq identifies key factors regulating transcription, which may bridge CAF infiltration and stemness. Finally, we demonstrate that the CAF-derived CXCL12 sustains the stemness of HCC cells by promoting XRCC5 through CXCR4.

Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study

BACKGROUND

To explore the value of intratumoral and peritumoral radiomics in preoperative prediction of anaplastic lymphoma kinase (ALK) mutation status and survival in patients with lung adenocarcinoma.

METHODS

We retrospectively collected data from 505 eligible patients with lung adenocarcinoma from four hospitals (training and external validation sets 1-3). The CT-based radiomics features were extracted separately from the gross tumor volume (GTV) and GTV incorporating peritumoral 3-, 6-, 9-, 12-, and 15-mm regions (GPTV3, GPTV6, GPTV9, GPTV12, and GPTV15), and screened the most relevant features to construct radiomics models to predict ALK (+). The combined model incorporated radiomics scores (Rad-scores) of the best radiomics model and clinical predictors was constructed. Performance was evaluated using receiver operating characteristic (ROC) analysis. Progression-free survival (PFS) outcomes were examined using the Cox proportional hazards model.

RESULTS

In the four sets, 21.19% (107/505) patients were ALK (+). The GPTV3 radiomics model using a support vector machine algorithm achieved the best predictive performance, with the highest average AUC of 0.811 in the validation sets. Clinical TNM stage and pleural indentation were independent predictors. The combined model incorporating the GPTV3-Rad-score and clinical predictors achieved higher performance than the clinical model alone in predicting ALK (+) in three validation sets [AUC: 0.855 (95% CI: 0.766-0.919) vs. 0.648 (95% CI: 0.543-0.745), P = 0.001; 0.882 (95% CI: 0.801-0.962) vs. 0.634 (95% CI: 0.548-0.714), P < 0.001; 0.810 (95% CI: 0.727-0.877) vs. 0.663 (95% CI: 0.570-0.748), P = 0.006]. The prediction score of the combined model could stratify PFS outcomes in patients receiving ALK-TKI therapy (HR: 0.37; 95% CI: 0.15-0.89; P = 0.026) and immunotherapy (HR: 2.49; 95% CI: 1.22-5.08; P = 0.012).

CONCLUSION

The presented combined model based on GPTV3 effectively mined tumor features to predict ALK mutation status and stratify PFS outcomes in patients with lung adenocarcinoma.

Comprehensive multi-omics analyses expose a precision therapy strategy that targets replication stress in hepatocellular carcinoma using WEE1 inhibition

INTRODUCTION

Hepatocellular carcinoma (HCC) is an extremely heterogeneous malignancy with a poor prognosis, highlighting the need to target specific vulnerabilities within the tumor during treatment.

OBJECTIVES

This study employs multi-omics analysis techniques to provide novel insights into personalized therapeutic strategies for HCC patients.

METHODS

We performed proteomic and transcriptomic sequencing on 178 and 94 clinical samples of primary HCC without prior treatment, respectively. We employed an unbiased Kinome CRISPR-Cas9 library screening approach to systematically evaluate and identify novel therapeutic strategies that specifically target replication stress (RS). The synergy between oxaliplatin and adavosertib was verified using in vitro and in vivo models, including hydrodynamic injection, patient-derived organoids, and patient-derived xenografts.

RESULTS

In both proteomic- and transcriptomic-based subtyping analyses, subtypes characterized by hyperproliferative features demonstrated the poorest prognosis and the highest levels of RS. Among all first-line chemotherapeutic agents in these analyses, oxaliplatin accumulated the highest RS levels in HCC, while resistance remained a major challenge. With unbiased Kinome CRISPR loss-of-function gene screening, WEE1 was identified as a synthetic lethal target of oxaliplatin. The synergy between the WEE1 inhibitor adavosertib and oxaliplatin has been demonstrated in multiple in vitro and in vivo models. Mechanistically, adavosertib inhibits oxaliplatin-induced homologous recombination repair and G2/M checkpoint activation, leading to the accumulation of lethal DNA damage. Furthermore, patients with HCC showing high RS levels had poor prognoses and responded well to adavosertib and oxaliplatin combination treatments. This was validated by preclinical models and unsupervised clustering analysis.

CONCLUSIONS

Our findings provide promising insights into the precise therapeutic targeting of RS in HCC at both the proteomic and transcriptomic levels. Furthermore, our study highlights the potential of combining oxaliplatin with adavosertib as a treatment approach for HCC. In this study, we analyzed 178 and 94 pairs of clinical HCC samples using proteomic and transcriptomic sequencing, respectively. We discovered that the subtype characterized by high proliferation had the worst prognosis and highest RS level. Drug screening revealed that oxaliplatin promotes RS accumulation in HCC, but its resistance remains a challenge. Through unbiased CRISPR deletion-gene screening, WEE1 was identified as a lethal target of oxaliplatin. The WEE1 inhibitor adavosertib inhibits oxaliplatin-induced DNA repair, leading to lethal DNA damage accumulation. Furthermore, our clustering analysis based on RS levels demonstrated that HCC patients with high RS levels have poorer prognoses and be more beneficial from adavosertib and oxaliplatin combination therapy. These findings support an individualized treatment approach for HCC targeting RS based on WEE1 Inhibition.

IRGQ-mediated autophagy in MHC class I quality control promotes tumor immune evasion

The autophagy-lysosome system directs the degradation of a wide variety of cargo and is also involved in tumor progression. Here, we show that the immunity-related GTPase family Q protein (IRGQ), an uncharacterized protein to date, acts in the quality control of major histocompatibility complex class I (MHC class I) molecules. IRGQ directs misfolded MHC class I toward lysosomal degradation through its binding mode to GABARAPL2 and LC3B. In the absence of IRGQ, free MHC class I heavy chains do not only accumulate in the cell but are also transported to the cell surface, thereby promoting an immune response. Mice and human patients suffering from hepatocellular carcinoma show improved survival rates with reduced IRGQ levels due to increased reactivity of CD8+ T cells toward IRGQ knockout tumor cells. Thus, we reveal IRGQ as a regulator of MHC class I quality control, mediating tumor immune evasion.

Response to Bridging Therapy as a Prognostic Indicator of Post-Transplantation Hepatocellular Carcinoma Recurrence and Survival: A Systematic Review

Liver transplantation (LT) is one of the most effective treatments for hepatocellular carcinoma (HCC) in cirrhotic livers. Neoadjuvant bridging treatment in patients qualifying and listed for LT is advised but is still debatable owing to the low level of evidence. The aim of this study was to perform a systematic review to assess the prognostic value of bridging therapy, in terms of radiological and histopathological examination outcomes, for survival after LT. The systematic review was performed according to the PRISMA 2020 guidelines. The MEDLINE and Web of Science databases were searched. In total, five studies were included. An evaluation with the ROBINS-I resulted in studies classified as the following: moderate risk of bias (n = 1) and serious risk of bias (n = 4). The results of the analysis indicated that favorable LT outcomes were most common with complete response or partial radiological response. Poor radiological response or progressive disease during bridging treatment was generally associated with worse overall LT survival. There were not enough data to support the use of this approach to achieve a complete pathologic response. Radiological, pathological, histological, cellular, and molecular tumor features should be included in future LT qualification models.

IGSF1: a biomarker for predicting prognosis, immunotherapy response, and drug candidates in COVID-19 combined hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor prognosis and a common cause of cancer-related death worldwide, and despite ongoing therapeutic breakthroughs, patient survival benefits are limited. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of coronavirus disease 2019 (COVID-19) and poses a major threat to humanity worldwide. As the epidemic continues to develop, more and more people are infected with SARS-CoV-2, including patients with HCC. However, the relationship between COVID-19 and HCC has not yet been fully elucidated. Our study aimed to identify the shared genetic characteristics and molecular mechanisms between COVID-19 and HCC. The data involved in this study come from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression(GTEx), and Cancer Cell Line Encyclopedia(CCLE) databases. We used differentially expressed genes to perform enrichment analysis to reveal the biological landscape of COVID-19 combined with HCC. In addition, weighted gene co-expression network analysis (WGCNA) was used to study the co-expression network related to COVID-19 and HCC. We then combined the validation datasets to screen out immunoglobulin superfamily member 1 (IGSF1) as the most important core gene. Finally, we extensively studied the functional expression of IGSF1 in tumor samples, normal tissues, and cancer cell lines. The molecular mechanisms related to COVID-19 and HCC are rarely studied. Our study identifies IGSF1 as a potential therapeutic target and immune-related biomarker for patients with COVID-19 and HCC.

Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.

Single-cell characterization of infiltrating T cells identifies novel targets for gallbladder cancer immunotherapy

Gallbladder cancer (GBC) is among the most common malignancies of biliary tract system due to its limited treatments. The immunotherapeutic targets for T cells are appealing, however, heterogeneity of T cells hinds its further development. We systematically construct T cell atlas by single-cell RNA sequencing; and utilized the identified gene signatures of high_CNV_T cells to predict molecular subtyping towards personalized therapeutic treatments for GBC. We identified 12 T cell subtypes, where exhausted CD8+ T cells, activated/exhausted CD8+ T cells, and regulatory T cells were predominant in tumors. There appeared to be an inverse relationship between Th17 and Treg populations with Th17 levels significantly reduced, whereas Tregs were concomitantly increased. Furthermore, we first established subtyping criterion to identify three subtypes of GBC based on their pro-tumorigenic microenvironments, e.g., the type 1 group shows more M2 macrophages infiltration, while the type 2 group is infiltrated by highly exhausted CD8+ T cells, B cells and Tregs with suppressive activities. Our study provides valuable insights into T cell heterogeneity and suggests that molecular subtyping based on T cells might provide a potential immunotherapeutic strategy to improve GBC treatment.

Involvement of CircRNAs in regulating The "New Generation of Cancer Hallmarks": A Special Depiction on Hepatocellular Carcinoma

The concept of 'Hallmarks of Cancer' is an approach of reducing the enormous complexity of cancer to a set of guiding principles. As the underlying mechanism of cancer are portrayed, we find that we gain insight and additional aspects of the disease arise. The understanding of the tumor microenvironment (TME) brought a new dimension and led to the discovery of novel hallmarks such as senescent cells, non-mutational epigenetic reprogramming, polymorphic microbiomes and unlocked phenotypic plasticity. Circular RNAs (circRNAs) are single-stranded, covalently closed RNA molecules that are ubiquitous across all species. Recent studies on the circRNAs have highlighted their crucial function in regulating the formation of human malignancies through a range of biological processes. The primary goal of this review is to clarify the role of circRNAs in the most common form of liver cancer, hepatocellular carcinoma (HCC). This review also addressed the topic of how circRNAs affect HCC hallmarks, including the new generation hallmarks. Finally, the enormous applications that these rapidly expanding ncRNA molecules serve in the functional and molecular development of effective HCC diagnostic biomarkers and therapeutic targets.

Recent advances of engineered oncolytic viruses-based combination therapy for liver cancer

Liver cancer is a major malignant tumor, which seriously threatens human health and increases the economic burden on patients. At present, gene therapy has been comprehensively studied as an excellent therapeutic measure in liver cancer treatment. Oncolytic virus (OV) is a kind of virus that can specifically infect and kill tumor cells. After being modified by genetic engineering, the specificity of OV infection to tumor cells is increased, and its influence on normal cells is reduced. To date, OV has shown its effectiveness and safety in experimental and clinical studies on a variety of tumors. Thus, this review primarily introduces the current status of different genetically engineered OVs used in gene therapy for liver cancer, focuses on the application of OVs and different target genes for current liver cancer therapy, and identifies the problems encountered in OVs-based combination therapy and the corresponding solutions, which will provide new insights into the treatment of liver cancer.

Three molecular subtypes and a five-gene signature for hepatocellular carcinoma based on m7G-related classification

BACKGROUND

The current research investigated the heterogeneity of hepatocellular carcinoma (HCC) based on the expression of N7-methylguanosine (m7G)-related genes as a classification model and developed a risk model predictive of HCC prognosis, key pathological behaviors and molecular events of HCC.

METHODS

The RNA sequencing data of HCC were extracted from The Cancer Genome Atlas (TCGA)-live cancer (LIHC) database, hepatocellular carcinoman database (HCCDB) and Gene Expression Omnibus database, respectively. According to the expression level of 29 m7G-related genes, a consensus clustering analysis was conducted. The least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression algorithm were applied to create a risk prediction model based on normalized expression of five characteristic genes weighted by coefficients. Tumor microenvironment (TME) analysis was performed using the MCP-Counter, TIMER, CIBERSORT and ESTIMATE algorithms. The Tumor Immune Dysfunction and Exclusion algorithm was applied to assess the responses to immunotherapy in different clusters and risk groups. In addition, patient sensitivity to common chemotherapeutic drugs was determined by the biochemical half-maximal inhibitory concentration using the R package pRRophetic.

RESULTS

Three molecular subtypes of HCC were defined based on the expression level of m7G-associated genes, each of which had its specific survival rate, genomic variation status, TME status and immunotherapy response. In addition, drug sensitivity analysis showed that the C1 subtype was more sensitive to a number of conventional oncolytic drugs (including paclitaxel, imatinib, CGP-082996, pyrimethamine, salubrinal and vinorelbine). The current five-gene risk prediction model accurately predicted HCC prognosis and revealed the degree of somatic mutations, immune microenvironment status and specific biological events.

CONCLUSION

In this study, three heterogeneous molecular subtypes of HCC were defined based on m7G-related genes as a classification model, and a five-gene risk prediction model was created for predicting HCC prognosis, providing a potential assessment tool for understanding the genomic variation, immune microenvironment status and key pathological mechanisms during HCC development.

Gene expression signatures of stepwise progression of Hepatocellular Carcinoma

The molecular pathogenesis of Hepatocellular Carcinoma (HCC) is a complex process progressing from premalignant stages to cancer in a stepwise manner. Mostly, HCC is detected at advanced stages, leading to high mortality rates. Hence, characterising the molecular underpinnings of HCC from normal to cancer state through precancerous state may help in early detection and improve its prognosis and treatment. In this work, we analysed the transcriptomic profile of tumour and premalignant samples from HCC or chronic liver disease patients, who had undergone either total or partial hepatectomy. The normal samples from patients with metastatic cancer/polycystic liver disease/ cholangiocarcinoma were also included. A gene co-expression network approach was applied to identify hierarchical changes: modules, pathways, and genes related to different trajectories of HCC and patient survival. Our analysis shows that the progression from premalignant conditions to tumour is accompanied by differences in the downregulation of genes associated with HNF4A activity and the immune system and upregulation of cell cycle genes, bringing about variability in patient outcomes. However, an increase in immune and cell cycle activity is observed in premalignant samples. Interestingly, co-expression modules and genes from premalignant stages are associated with survival. THBD, a classical marker for dendritic cells, is a predictor of survival at the premalignant stage. Further, genes linked to microtubules, kinetochores, and centromere are altered in both premalignant and tumour conditions and are associated with survival. Our analysis revealed a three-way molecular axis of liver function, immune pathways, and cell cycle driving HCC pathogenesis.

Seven chromatin regulators as immune cell infiltration characteristics, potential diagnostic biomarkers and drugs prediction in hepatocellular carcinoma

Treatment is challenging due to the heterogeneity of hepatocellular carcinoma (HCC). Chromatin regulators (CRs) are important in epigenetics and are closely associated with HCC. We obtained HCC-related expression data and relevant clinical data from The Cancer Genome Atlas (TCGA) databases. Then, we crossed the differentially expressed genes (DEGs), immune-related genes and CRs to obtain immune-related chromatin regulators differentially expressed genes (IRCR DEGs). Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to select the prognostic gene and construct a risk model for predicting prognosis in HCC, followed by a correlation analysis of risk scores with clinical characteristics. Finally, we also carried out immune microenvironment analysis and drug sensitivity analysis, the correlation between risk score and clinical characteristics was analyzed. In addition, we carried out immune microenvironment analysis and drug sensitivity analysis. Functional analysis suggested that IRCR DEGs was mainly enriched in chromatin-related biological processes. We identified and validated PPARGC1A, DUSP1, APOBEC3A, AIRE, HDAC11, HMGB2 and APOBEC3B as prognostic biomarkers for the risk model construction. The model was also related to immune cell infiltration, and the expression of CD48, CTLA4, HHLA2, TNFSF9 and TNFSF15 was higher in high-risk group. HCC patients in the high-risk group were more sensitive to Axitinib, Docetaxel, Erlotinib, and Metformin. In this study, we construct a prognostic model of immune-associated chromatin regulators, which provides new ideas and research directions for the accurate treatment of HCC.

Identifying cancer driver genes in individual tumours

Cancer is a heterogeneous disease with a strong genetic component making it suitable for precision medicine approaches aimed at identifying the underlying molecular drivers within a tumour. Large scale population-level cancer sequencing consortia have identified many actionable mutations common across both cancer types and sub-types, resulting in an increasing number of successful precision medicine programs. Nonetheless, such approaches fail to consider the effects of mutations unique to an individual patient and may miss rare driver mutations, necessitating personalised approaches to driver-gene prioritisation. One approach is to quantify the functional importance of individual mutations in a single tumour based on how they affect the expression of genes in a gene interaction network (GIN). These GIN-based approaches can be broadly divided into those that utilise an existing reference GIN and those that construct de novo patient-specific GINs. These single-tumour approaches have several limitations that likely influence their results, such as use of reference cohort data, network choice, and approaches to mathematical approximation, and more research is required to evaluate the in vitro and in vivo applicability of their predictions. This review examines the current state of the art methods that identify driver genes in single tumours with a focus on GIN-based driver prioritisation.

Differentially expressed non-coding RNAs and their regulatory networks in liver cancer

The vast majority of human transcriptome is represented by various types of small RNAs with little or no protein-coding capability referred to as non-coding RNAs (ncRNAs). Functional ncRNAs include microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), which are expressed at very low, but stable and reproducible levels in a variety of cell types. ncRNAs regulate gene expression due to miRNA capability of complementary base pairing with mRNAs, whereas lncRNAs and circRNAs can sponge miRNAs off their target mRNAs to act as competitive endogenous RNAs (ceRNAs). Each miRNA can target multiple mRNAs and a single mRNA can interact with several miRNAs, thereby creating miRNA-mRNA, lncRNA-miRNA-mRNA, and circRNA-miRNA-mRNA regulatory networks. Over the past few years, a variety of differentially expressed miRNAs, lncRNAs, and circRNAs (DEMs, DELs, and DECs, respectively) have been linked to cancer pathogenesis. They can exert both oncogenic and tumor suppressor roles. In this review, we discuss the recent advancements in uncovering the roles of DEMs, DELs, and DECs and their networks in aberrant cell signaling, cell cycle, transcription, angiogenesis, and apoptosis, as well as tumor microenvironment remodeling and metabolic reprogramming during hepatocarcinogenesis. We highlight the potential and challenges in the use of differentially expressed ncRNAs as biomarkers for liver cancer diagnosis and prognosis.

The efficacy and safety of bevacizumab as a salvage therapy for patients with advanced hepatocellular carcinoma targeting immune tolerance

As is well understood that malignant tumour progression requires additional blood vessels to provide the nutrients necessary for growth. Many patients with advanced hepatocellular carcinoma (aHCC) experience disease progression after treatment with lenvatinib (Lenva) and immune checkpoint inhibitors (ICIs). Therefore, we designed a double-arm retrospective study to evaluate the antitumour activity of additional bevacizumab (Beva, an anti-vascular endothelial growth factor-targeting drug) as a means to reduce the blood vessels needed for tumour growth. Compared with the control group, the group that received Beva had prolonged progression-free survival (PFS) and a trend toward a benefit for overall survival duration. This study aimed to evaluate the anticancer effect of Beva in patients with aHCC who experienced tumour progression after treatment with Lenva+ICIs. From April 2021 to March 2023, we retrospectively included 20 patients as the experimental group and 21 patients as the control group. The patients in the experimental group experienced disease progression after receiving targeted therapy and ICIs, after which we added Beva to the treatment. The patients in the control group only received targeted therapy and ICIs. The efficacy endpoints were overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR), which were evaluated according to RECIST v1.1. Adverse events were assessed using NCI-CTCAE v5.0. Ultimately, 20 patients with aHCC in the experimental group of received Beva after disease progression, compared with 21 patients in the control group. The median OS was 12.6 mo (95% CI: 6.8-18.7) vs. 9.3 mo (95% CI: 4.3-14.4), and the median PFS was 6.9 mo (95% CI: 6.4-7.4) vs. 4.1 mo (95% CI: 2.4-5.8). The ORR for all patients was 5%, and the DCR for all patients was 70.0%. The median follow-up time for all patients was 7.5 mo (95% CI: 5.0-10.0). All patients had adverse events, but no fatal adverse events were observed. In conclusion, Bevacizumab is a drug resistant treatment option for patients with advanced hepatocellular carcinoma after Lenva+PD-1/PD-L1 treatment.

Characterizing the key genes of COVID-19 that regulate tumor immune microenvironment and prognosis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC), a highly heterogeneous malignant tumor associated with a poor prognosis, is a common cause of cancer-related deaths worldwide, with a limited survival benefit for patients despite ongoing therapeutic breakthroughs. Coronavirus disease 2019 (COVID-19), a severe infectious disease caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), is a global pandemic and a serious threat to human health. The increased susceptibility to SARS-CoV-2 infection and a poor prognosis in patients with cancer necessitate the exploration of the potential link between the two. No studies have investigated the relationship of COVID-19 genes with the prognosis and tumor development in patients with HCC. We screened prognosis-related COVID-19 genes in HCC, performed molecular typing, developed a stable and reliable COVID-19 genes signature for predicting survival, characterized the immune microenvironment in HCC patients, and explored new molecular therapeutic targets. Datasets of HCC patients, including RNA sequencing data and clinical information, were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Prognosis-related COVID-19 genes were identified by univariate Cox analysis. Molecular typing of HCC was performed using the consensus non-negative matrix factorization method (cNMF), followed by the analysis of survival, tumor microenvironment, and pathway enrichment for each subtype. Prognostic signatures were constructed using LASSO-Cox regression models, and receiver operating characteristic (ROC) curves were used to validate the predictive performance of the signature. The same approach was used for the test and external validation sets. Seven software packages were applied to determine the abundance of immune infiltration in HCC patients and investigate its relationship with the risk scores. Gene set enrichment analysis (GSEA) was used to explore the potential mechanisms by which the COVID-19 genes affect hepatocarcinogenesis and prognosis. Three types of machine learning methods were combined to identify the most critical genes in the signature and localize their expression at the single cell level. We identified 53 prognosis-related COVID-19 genes and classified HCC into two molecular subtypes (C1, C2) by using the NMF method. The prognosis of C2 was significantly better than that of C1, and the two subtypes differed remarkably in terms of the tumor immune microenvironment and biological functions. The 17 COVID-19 genes were screened using the LASSO regression method to develop a 17 COVID-19 genes signature, which demonstrated a good predictive performance for 1-, 2- and 3-year OS of patients with HCC. The risk score as an independent prognostic factor for HCC has better predictive accuracy than traditional clinical variables. Patients in the TCGA cohort were categorized by risk score into the high- and low-risk groups, with the high-risk group mainly enriched in the immune modulation-related pathways and the low-risk group mainly enriched in the metabolism-related pathways, suggesting that the COVID-19 genes may affect disease progression and prognosis by regulating the tumor immune microenvironment and metabolism in HCC. NOL10 was identified as the most critical gene in the signature and hypothesized to be a potential therapeutic target for HCC. Objectively, the COVID-19 genes signature developed in this study, as an independent prognostic factor in HCC patients, is closely associated with the prognosis and tumor immune microenvironment of HCC patients and indicates that they may regulate the development of HCC in multiple ways, providing us with new perspectives for understanding the molecular mechanisms of HCC and finding effective therapeutic targets.

Recent Advances in Image-Guided Locoregional Therapies for Primary Liver Tumors

Primary liver cancer is the leading cause of cancer-related deaths worldwide. with incidences predicted to rise over the next several decades. Locoregional therapies, such as radiofrequency or microwave ablation, are described as image-guided percutaneous procedures, which offer either a curative intent for early-stage hepatocellular carcinoma or bridging/downstaging for surgical resection or transplantation. Catheter-driven locoregional therapies, such as transarterial chemoembolization and radioembolization, induce tumor hypoxia, can be palliative, and improve survival for early-to-intermediate hepatocellular carcinoma and unresectable intrahepatic cholangiocarcinoma. Herein, we provide a comprehensive overview of the antineoplastic mechanisms underpinning locoregional therapies, different treatment approaches, and the current state of the literature for the efficacy of locoregional therapies for primary liver cancer. We also discuss emerging advancements, such as the adjuvant use of immunotherapies and molecular targeting agents with locoregional therapy, for the treatment of primary liver cancer.

Signature construction and molecular subtype identification based on immune-related genes for better prediction of prognosis in hepatocellular carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) immunotherapy is a focus of current research. We established a model that can effectively predict the prognosis and efficacy of HCC immunotherapy by analyzing the immune genes of HCC.

METHODS

Through the data mining of hepatocellular carcinoma in The Cancer Genome Atlas (TCGA), the immune genes with differences in tumor and normal tissues are screened, and then the univariate regression analysis is carried out to screen the immune genes with differences related to prognosis. The prognosis model of immune related genes is constructed by using the minimum absolute contraction and selection operator (lasso) Cox regression model in the TCGA training set data, The risk score of each sample was calculated, and the survival was compared with the Kaplan Meier curve and the receiver operating characteristic (ROC) curve to evaluate the predictive ability. Data sets from ICGC and TCGA were used to verify the reliability of signatures. The correlation between clinicopathological features, immune infiltration, immune escape and risk score was analyzed.

RESULTS

Seven immune genes were finally determined as the prognostic model of liver cancer. According to these 7 genes, the samples were divided into the high and low risk groups, and the results suggested that the high-risk group had a poorer prognosis, lower risk of immune escape, and better immunotherapy effect. In addition, the expression of TP53 and MSI was positively correlated in the high-risk group. Consensus clustering was performed to identify two main molecular subtypes (named clusters 1 and 2) based on the signature. It was found that compared with cluster 1, better survival outcome was observed in cluster 2.

CONCLUSION

Signature construction and molecular subtype identification of immune-related genes could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC immunotherapy.

Single-molecule RNA in situ detection in clinical FFPE tissue sections by vsmCISH

Although RNA plays a vital role in gene expression, it is less used as an in situ biomarker for clinical diagnostics than DNA and protein. This is mainly due to technical challenges caused by the low expression level and easy degradation of RNA molecules. To tackle this issue, methods that are sensitive and specific are needed. Here, we present an RNA single-molecule chromogenic in situ hybridization assay based on DNA probe proximity ligation and rolling circle amplification. When the DNA probes hybridize into close proximity to the RNA molecules, they form a V-shape structure and mediate the circularization of circle probes. Thus, our method was termed vsmCISH. We successfully applied our method to assess HER2 mRNA expression status in invasive breast cancer tissue and investigated the utility of albumin mRNA ISH for differentiating primary from metastatic liver cancer. The promising results on clinical samples indicate that our method has great potential for application in diagnosing diseases using RNA biomarkers.

Aberrantly Expressed MicroRNAs in Cancer-Associated Fibroblasts and Their Target Oncogenic Signatures in Hepatocellular Carcinoma

Cancer-associated fibroblasts (CAFs) contribute to tumor progression, and microRNAs (miRs) play an important role in regulating the tumor-promoting properties of CAFs. The objectives of this study were to clarify the specific miR expression profile in CAFs of hepatocellular carcinoma (HCC) and identify its target gene signatures. Small-RNA-sequencing data were generated from nine pairs of CAFs and para-cancer fibroblasts isolated from human HCC and para-tumor tissues, respectively. Bioinformatic analyses were performed to identify the HCC-CAF-specific miR expression profile and the target gene signatures of the deregulated miRs in CAFs. Clinical and immunological implications of the target gene signatures were evaluated in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA_LIHC) database using Cox regression and TIMER analysis. The expressions of hsa-miR-101-3p and hsa-miR-490-3p were significantly downregulated in HCC-CAFs. Their expression in HCC tissue gradually decreased as HCC stage progressed in the clinical staging analysis. Bioinformatic network analysis using miRWalks, miRDB, and miRTarBase databases pointed to TGFBR1 as a common target gene of hsa-miR-101-3p and hsa-miR-490-3p. TGFBR1 expression was negatively correlated with miR-101-3p and miR-490-3p expression in HCC tissues and was also decreased by ectopic miR-101-3p and miR-490-3p expression. HCC patients with TGFBR1 overexpression and downregulated hsa-miR-101-3p and hsa-miR-490-3p demonstrated a significantly poorer prognosis in TCGA_LIHC. TGFBR1 expression was positively correlated with the infiltration of myeloid-derived suppressor cells, regulatory T cells, and M2 macrophages in a TIMER analysis. In conclusion, hsa-miR-101-3p and hsa-miR-490-3p were substantially downregulated miRs in CAFs of HCC, and their common target gene was TGFBR1. The downregulation of hsa-miR-101-3p and hsa-miR-490-3p, as well as high TGFBR1 expression, was associated with poor clinical outcome in HCC patients. In addition, TGFBR1 expression was correlated with the infiltration of immunosuppressive immune cells.

Classification of the mitochondrial ribosomal protein-associated molecular subtypes and identified a serological diagnostic biomarker in hepatocellular carcinoma

Purpose

The objective of this study was to sort out innovative molecular subtypes associated with mitochondrial ribosomal proteins (MRPs) to predict clinical therapy response and determine the presence of circulating markers in hepatocellular carcinoma (HCC) patients.

Methods

Using an unsupervised clustering method, we categorized the relative molecular subtypes of MRPs in HCC patients. The prognosis, biological properties, immune checkpoint inhibitor and chemotherapy response of the patients were clarified. A signature and nomogram were developed to evaluate the prognosis. Enzyme-linked immunosorbent assay (ELISA) measured serum mitochondrial ribosomal protein L9 (MRPL9) levels in liver disease patients and normal individuals. Receiver operating characteristic (ROC) curves were conducted to calculate the diagnostic effect. The Cell Counting Kit 8 was carried out to examine cell proliferation, and flow cytometry was used to investigate the cell cycle. Transwell assay was applied to investigate the potential of cell migration and invasion. Western blot detected corresponding changes of biological markers.

Results

Participants were classified into two subtypes according to MRPs expression levels, which were characterized by different prognoses, biological features, and marked differences in response to chemotherapy and immune checkpoint inhibitors. Serum MRPL9 was significantly higher in HCC patients than in normal individuals and the benign liver disease group. ROC curve analysis showed that MRPL9 was superior to AFP and Ferritin in differentiating HCC from healthy and benign patients, or alone. Overexpressed MRPL9 could enhance aggressiveness and facilitate the G1/S progression in HCC cells.

Conclusion

We constructed novel molecular subtypes based on MRPs expression in HCC patients, which provided valuable strategies for the prediction of prognosis and clinical personalized treatment. MRPL9 might act as a reliable circulating diagnostic biomarker and therapeutic target for HCC patients.

Clinical Significance of TUBGCP4 Expression in Hepatocellular Carcinoma

We aim to investigate the expression and clinical significance of the tubulin gamma complex-associated protein 4 (TUBGCP4) in hepatocellular carcinoma (HCC). The mRNA expression of TUBGCP4 in HCC tissues was analyzed using The Cancer Genome Atlas (TCGA) database. Paired HCC and adjacent nontumor tissues were obtained from HCC patients to measure the protein expression of TUBGCP4 by immunohistochemistry (IHC) and to analyze the relationship between TUBGCP4 protein expression and the clinicopathological characteristics and the prognosis of HCC patients. We found that TUBGCP4 mRNA expression was upregulated in HCC tissues from TCGA database. IHC analysis showed that TUBGCP4 was positively expressed in 61.25% (49/80) of HCC tissues and 77.5% (62/80) of adjacent nontumor tissues. The Chi-square analysis indicated that the positive rate of TUBGCP4 expression between HCC tissues and the adjacent nontumor tissues was statistically different (P < 0.05). Furthermore, we found that TUBGCP4 protein expression was correlated with carbohydrate antigen (CA-199) levels of HCC patients (P < 0.05). Further, survival analysis showed that the overall survival time and tumor-free survival time in the TUBGCP4 positive group were significantly higher than those of the negative group (P < 0.05), indicating that the positive expression of TUBGCP4 was related to a better prognosis of HCC patients. COX model showed that TUBGCP4 was an independent prognostic factor for HCC patients. Our study indicates that TUBGCP4 protein expression is downregulated in HCC tissues and has a relationship with the prognosis of HCC patients.

Molecular subtyping and IMScore based on immune-related pathways, oncogenic pathways, and DNA damage repair pathways for guiding immunotherapy in hepatocellular carcinoma patients

Background

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. Although immunotherapy provides hope for advanced HCC patients, the outcomes are not satisfactory and vary by individual case. In this study, we sought to establish novel molecular subtypes and a stable model based on tumor-related pathways for guiding the immunotherapy in HCC patients.

Methods

A total of 15 pathways including immune pathways, stromal pathways, oncogenic pathways, and DNA damage repair pathways were used to construct molecular subtypes through consensus clustering. Immune characteristics, gene mutations, and genomic alterations including copy number variations and homologous recombination deficiency (HRD) were analyzed in different clusters. The Tumor Immune Dysfunction and Exclusion (TIDE) framework was used to predict the response to immunotherapy. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were employed to screen prognostic genes for constructing a risk model.

Results

Three clusters/subtypes were constructed including Immune-E, Immune-D and Stromal-E. Immune-D had the worst prognosis and high enrichment of HRD pathways. Immune-E had higher immune infiltration, higher expression of major histocompatibility complex (MHC)-related genes, and higher expression of PD1, PDL1, CTLA4, and LAG3. TP53 alterations frequently occurred in Immune-D. Immune-E had a relatively high response to immunotherapy and was sensitive to chemotherapeutic drugs. Moreover, we constructed an IMScore model that was effective to classify HCC patients into different risk groups, and the IMScore had a better performance than the TIDE score.

Conclusions

This study revealed the complex interaction among the tumor microenvironment (TME), genomic alterations, and tumor-related pathways by exploring the molecular difference of 3 subtypes. The IMScore model has potential to provide guidance for immunotherapy in HCC patients.

Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.

Advances in multi-omics research on viral hepatitis

Viral hepatitis is a major global public health problem that affects hundreds of millions of people and is associated with significant morbidity and mortality. Five biologically unrelated hepatotropic viruses account for the majority of the global burden of viral hepatitis, including hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). Omics is defined as the comprehensive study of the functions, relationships and roles of various types of molecules in biological cells. The multi-omics analysis has been proposed and considered key to advancing clinical precision medicine, mainly including genomics, transcriptomics and proteomics, metabolomics. Overall, the applications of multi-omics can show the origin of hepatitis viruses, explore the diagnostic and prognostics biomarkers and screen out the therapeutic targets for viral hepatitis and related diseases. To better understand the pathogenesis of viral hepatitis and related diseases, comprehensive multi-omics analysis has been widely carried out. This review mainly summarizes the applications of multi-omics in different types of viral hepatitis and related diseases, aiming to provide new insight into these diseases.

Nascent Proteome and Glycoproteome Reveal the Inhibition Role of ALG1 in Hepatocellular Carcinoma Cell Migration

Asparagine-linked glycosylation protein 1 homolog (ALG1) participates in the initial stage of protein N-glycosylation and N-glycosylation has been implicated in the process of hepatocellular carcinoma (HCC) progression. However, whether ALG1 plays a role in human HCC remains unknown. In this study, the expression profile of ALG1 in tumorous and corresponding adjacent non-tumor tissues was analyzed. The relationship of ALG1 expression with clinical features and prognosis of HCC patients was also evaluated using immuno-histochemical method. Here we found ALG1 decreased in HCC tissues compared with adjacent normal liver tissues, which predicted an unfavorable prognosis. Combined with RNA interference, nascent proteome and glycoproteome were determined systematically in Huh7 cell line. Bioinformatics analysis indicated that the differentially expressed proteins participating in the response of ALG1 knockdown were most significantly associated with cell-cell adhesion. Functional studies confirmed that knockdown of ALG1 reduced cell adhesion capacity, and promoted cell migration. Furthermore, down-regulation of H8N2 (on N-glycosite N651) and H5N4S2F1 (on N-glycosite N692) from N-cadherin was identified as a feature of ALG1 knockdown. Our findings revealed that ALG1 controlled the expression of glycosylated N-cadherin and played a role in HCC migration, with implications for prognosis.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00050-5.

Morphomolecular Classification Update on Hepatocellular Adenoma, Hepatocellular Carcinoma, and Intrahepatic Cholangiocarcinoma

Hepatocellular adenomas (HCAs), hepatocellular carcinomas (HCCs), and intrahepatic cholangiocarcinomas (iCCAs) are a highly heterogeneous group of liver tumors with diverse pathomolecular features and prognoses. High-throughput gene sequencing techniques have allowed discovery of distinct genetic and molecular underpinnings of these tumors and identified distinct subtypes that demonstrate varied clinicobiologic behaviors, imaging findings, and complications. The combination of histopathologic findings and molecular profiling form the basis for the morphomolecular classification of liver tumors. Distinct HCA subtypes with characteristic imaging findings and complications include HNF1A-inactivated, inflammatory, β-catenin-activated, β-catenin-activated inflammatory, and sonic hedgehog HCAs. HCCs can be grouped into proliferative and nonproliferative subtypes. Proliferative HCCs include macrotrabecular-massive, TP53-mutated, scirrhous, clear cell, fibrolamellar, and sarcomatoid HCCs and combined HCC-cholangiocarcinoma. Steatohepatitic and β-catenin-mutated HCCs constitute the nonproliferative subtypes. iCCAs are classified as small-duct and large-duct types on the basis of the level of bile duct involvement, with significant differences in pathogenesis, molecular signatures, imaging findings, and biologic behaviors. Cross-sectional imaging modalities, including multiphase CT and multiparametric MRI, play an essential role in diagnosis, staging, treatment response assessment, and surveillance. Select imaging phenotypes can be correlated with genetic abnormalities, and identification of surrogate imaging markers may help avoid genetic testing. Improved understanding of morphomolecular features of liver tumors has opened new areas of research in the targeted therapeutics and management guidelines. The purpose of this article is to review imaging findings of select morphomolecular subtypes of HCAs, HCCs, and iCCAs and discuss therapeutic and prognostic implications. Online supplemental material is available for this article. ^©RSNA, 2022.

Epigenetic remodelling in human hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer, being the sixth most commonly diagnosed cancer and the fourth leading cause of cancer-related death. As other heterogeneous solid tumours, HCC results from a unique synergistic combination of genetic alterations mixed with epigenetic modifications.In HCC the patterns and frequencies of somatic variations change depending on the nearby chromatin. On the other hand, epigenetic alterations often induce genomic instability prone to mutations. Epigenetics refers to heritable states of gene expression without alteration to the DNA sequence itself and, unlike genetic changes, the epigenetic modifications are reversible and affect gene expression more extensively than genetic changes. Thus, studies of epigenetic regulation and the involved molecular machinery are greatly contributing to the understanding of the mechanisms that underline HCC onset and heterogeneity. Moreover, this knowledge may help to identify biomarkers for HCC diagnosis and prognosis, as well as future new targets for more efficacious therapeutic approaches.In this comprehensive review we will discuss the state-of-the-art knowledge about the epigenetic landscape in hepatocarcinogenesis, including evidence on the diagnostic and prognostic role of non-coding RNAs, modifications occurring at the chromatin level, and their role in the era of precision medicine.Apart from other better-known risk factors that predispose to the development of HCC, characterization of the epigenetic remodelling that occurs during hepatocarcinogenesis could open the way to the identification of personalized biomarkers. It may also enable a more accurate diagnosis and stratification of patients, and the discovery of new targets for more efficient therapeutic approaches.

The Detection and Verification of Two Heterogeneous Subgroups and a Risk Model Based on Ferroptosis-Related Genes in Hepatocellular Carcinoma

^#Background. Because of the heterogeneity of hepatocellular carcinoma (HCC) and the complex nature of the tumor microenvironment (TME), the long-term efficacy of therapy continues to be a clinical challenge. It is necessary to classify and refine the appropriate treatment intervention decision-making in this kind of tumor. Methods. We used “ConsensusClusterPlus” to establish a stable molecular classification based on the ferroptosis-related genes (FRGs) expression obtained from FerrDb. The clinical features, immune infiltration, DNA damage, and genomic changes of different subclasses were evaluated. The least absolute shrinkage and selection operator regression (LASSO) method and univariate Cox regression were utilized to construct the ferroptosis-related prognosis risk score (FPRS) model, and the association between the FPRS model and HCC molecular characteristics, immune features, and immunotherapy was studied. Results. We identified two ferroptosis subclasses, C1 with poor prognosis and a higher proportion of patients in the middle and late stages infected with HBV and HCV, having higher DNA damage including aneuploidy, HRD, fraction altered, and the number of segments, and higher probability of gene mutation and copy number mutation. FPRS model was constructed on the basis of differentially expressed genes (DEGs) between C1 and C2, which showed a higher area under the curve (AUC) in predicting overall survival rate in the training set and independent verification cohort and could reflect the clinical characteristics and response to immunotherapy of different patients, being an independent prognostic factor of HCC. Conclusion. Here, we revealed two novel molecular subgroups based on FRGs and develop an FPRS model consisting of six genes that can help predict prognosis and select patients suitable for immunotherapy.

Patients with polyclonal hepatocellular carcinoma are at a high risk of early recurrence and have a poor recurrence-free survival period

Background/purpose of the study

Tumor heterogeneity based on copy number variations is associated with the evolution of cancer and its clinical grade. Clonal composition (CC) represents the number of clones based on the distribution of B-allele frequency (BAF) obtained from a genome-wide single nucleotide polymorphism (SNP) array. A higher CC number represents a high degree of heterogeneity. We hypothesized and evaluated that the CC number in hepatocellular carcinoma (HCC) tissues might be associated with the clinical outcomes of patients.

Methods

Somatic mutation, whole transcriptome, and CC number based on copy number variations of 36 frozen tissue samples of operably resected HCC tissues were analyzed by targeted deep sequencing, transcriptome analysis, and SNP array.

Results

The samples were classified into the heterogeneous tumors as poly-CC (n = 26) and the homogeneous tumors as mono-CC (n = 8). The patients with poly-CC had a higher rate of early recurrence and a significantly shorter recurrence-free survival period than the mono-CC patients (7.0 months vs. not reached, p = 0.0084). No differences in pathogenic non-synonymous mutations, such as TP53, were observed between the two groups when targeted deep sequencing was applied. A transcriptome analysis showed that cell cycle-related pathways were enriched in the poly-CC tumors, compared to the mono-CC tumors. Poly-CC HCC is highly proliferative and has a high risk of early recurrence.

Conclusion

CC is a possible candidate biomarker for predicting the risk of early postoperative recurrence and warrants further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12072-021-10278-4.

Nucleic acid biomarkers to assess graft injury after liver transplantation

Many risk factors and complications impact the success of liver transplantation, such as ischaemia-reperfusion injury, acute rejection, and primary graft dysfunction. Molecular biomarkers have the potential to accurately diagnose, predict, and monitor injury progression or organ failure. There is a critical opportunity for reliable and non-invasive biomarkers to reduce the organ shortage by enabling i) the assessment of donor organ quality, ii) the monitoring of short- and long-term graft function, and iii) the prediction of acute and chronic disease development. To date, no established molecular biomarkers have been used to guide clinical decision-making in transplantation. In this review, we outline the recent advances in cell-free nucleic acid biomarkers for monitoring graft injury in liver transplant recipients. Prior work in this area can be divided into two categories: biomarker discovery and validation studies. Circulating nucleic acids (CNAs) can be found in the extracellular environment pertaining to different biological fluids such as bile, blood, urine, and perfusate. CNAs that are packaged into extracellular vesicles may facilitate intercellular and interorgan communication. Thus, decoding their biological function, cellular origins and molecular composition is imperative for diagnosing causes of graft injury, guiding immunosuppression and improving overall patient survival. Herein, we discuss the most promising molecular biomarkers, their state of development, and the critical aspects of study design in biomarker research for early detection of post-transplant liver injury. Future advances in biomarker studies are expected to personalise post-transplant therapy, leading to improved patient care and outcomes.

Identification of molecular subtypes and prognostic signature for hepatocellular carcinoma based on genes associated with homologous recombination deficiency

Hepatocellular carcinoma (HCC) is a rapidly developing digestive tract carcinoma. The prognosis of patients and side effects caused by clinical treatment should be better improved. Nonnegative matrix factorization (NMF) clustering was performed using 109 homologous recombination deficiency (HRD)-related of HCC genes from The Cancer Genome Atlas (TCGA) database. Limma was applied to analyze subtype differences. Immune scores and clinical characteristics of different subtypes were compared. An HRD signature were built with least absolute shrinkage operator (LASSO) and multivariate Cox analysis. Performance of the signature system was then assessed by Kaplan–Meier curves and receiver operating characteristic (ROC) curves. We identified two molecular subtypes (C1 and C2), with C2 showing a significantly better prognosis than C1. C1 contained 3623 differentially expressed genes. A 4-gene prognostic signature for HCC was established, and showed a high predicting accuracy in validation sets, entire TCGA data set, HCCDB18 and GSE14520 queues. Moreover, the risk score was validated as an independent prognostic marker for HCC. Our research identified two molecular subtypes of HCC, and proposed a novel scoring system for evaluating the prognosis of HCC in clinical practice.

Bioinformatics Analysis Identifies Precision Treatment with Paclitaxel for Hepatocellular Carcinoma Patients Harboring Mutant TP53 or Wild-Type CTNNB1 Gene

Hepatocellular carcinoma (HCC) is an aggressive and chemoresistant cancer type. The development of novel therapeutic strategies is still urgently needed. Personalized or precision medicine is a new trend in cancer therapy, which treats cancer patients with specific genetic alterations. In this study, a gene signature was identified from the transcriptome of HCC patients, which was correlated with the patients’ poorer prognoses. This gene signature is functionally related to mitotic cell cycle regulation, and its higher or lower expression is linked to the mutation in tumor protein p53 (TP53) or catenin beta 1 (CTNNB1), respectively. Gene–drug association analysis indicated that the taxanes, such as the clinically approved anticancer drug paclitaxel, are potential drugs targeting this mitotic gene signature. Accordingly, HCC cell lines harboring mutant TP53 or wild-type CTNNB1 genes are more sensitive to paclitaxel treatment. Therefore, our results imply that HCC patients with mutant TP53 or wild-type CTNNB1 genes may benefit from the paclitaxel therapy.

Integrative proteomics reveals the role of E3 ubiquitin ligase SYVN1 in hepatocellular carcinoma metastasis

BACKGROUND

Tumor metastasis is a major factor for poor prognosis of hepatocellular carcinoma (HCC), but the relationship between ubiquitination and metastasis need to be studied more systematically. We analyzed the ubiquitinome of HCC in this study to have a more comprehensive insight into human HCC metastasis.

METHODS

The protein ubiquitination levels in 15 HCC specimens with vascular invasion and 15 without vascular invasion were detected by ubiquitinome. Proteins with significantly different ubiquitination levels between HCCs with and without vascular invasion were used to predict E3 ubiquitin ligases associated with tumor metastasis. The topological network of protein substrates and corresponding E3 ubiquitin ligases was constructed to identify the key E3 ubiquitin ligase. Besides, the growth, migration and invasion ability of LM3 and HUH7 hepatoma cell lines with and without SYVN1 expression interference were measured by cell proliferation assay, subcutaneous tumor assay, umphal vein endothelium tube formation assay, transwell migration and invasion assays. Finally, the interacting proteins of SYVN1 were screened and verified by protein interaction omics, immunofluorescence, and immunoprecipitation. Ubiquitin levels of related protein substrates in LM3 and HUH7 cells were compared in negative control, SYVN1 knockdown, and SYVN1 overexpression groups.

RESULTS

In this study, our whole-cell proteomic dataset and ubiquitinomic dataset contained approximately 5600 proteins and 12,000 ubiquitinated sites. We discovered increased ubiquitinated sites with shorter ubiquitin chains during the progression of HCC metastasis. In addition, proteomic and ubiquitinomic analyses revealed that high expression of E3 ubiquitin-protein ligase SYVN1 is related with tumor metastasis. Furthermore, we found that SYVN1 interacted with heat shock protein 90 (HSP90) and impacted the ubiquitination of eukaryotic elongation factor 2 kinase (EEF2K).

CONCLUSIONS

The ubiquitination profiles of HCC with and without vascular invasion were significantly different. SYVN1 was the most important E3 ubiquitin-protein ligase responsible for this phenomenon, and it was related with tumor metastasis and growth. Therefore, SYVN1 might be a potential therapeutic target for HCC.

A Five-Gene-Based Prognostic Signature for Hepatocellular Carcinoma

Objective: This study intends to identify potential prognostic marker genes associated with the prognosis of patients suffering from hepatocellular carcinoma (HCC) based on TCGA and GEO analysis.

Methods: TCGA-LIHC cohort was downloaded and the data related to HCC were extracted from The Cancer Genome Atlas (TCGA) database and subjected to differential analysis. HCC-related gene expression datasets were retrieved from the GEO database, followed by differential analysis. After intersection of the results of TCGA and GEO databases, gene interaction analysis was performed to obtain the core genes. To identify the genes related to the prognosis of HCC patients, we conducted univariate and multivariate Cox analyses.

Results: Based on differential analysis of TCGA database, 854 genes were differentially expressed in HCC, any of which might link to the occurrence and progression of HCC. Meanwhile, joint analysis of HCC-related gene expression datasets in the GEO database screened 214 genes. Five core genes CDC20, TOP2A, RRM2, UBE2C and AOX1 were significantly associated with the prognosis of HCC patients and the risk model based on these five genes effectively predicted the prognosis of HCC patients.

Conclusion: Collectively, our data suggest that CDC20, TOP2A, RRM2, UBE2C and AOX1 may be the key genes affecting the prognosis of patients with HCC. The five-gene signature could accurately predict the prognosis of HCC patients.

DNA Damage Repair Profiles Alteration Characterize a Hepatocellular Carcinoma Subtype With Unique Molecular and Clinicopathologic Features

Hepatocellular carcinoma (HCC) is one of the most common malignancies and displays high heterogeneity of molecular phenotypes. We investigated DNA damage repair (DDR) alterations in HCC by integrating multi-omics data. HCC patients were classified into two heterogeneous subtypes with distinct clinical and molecular features: the DDR-activated subtype and the DDR-suppressed subtype. The DDR-activated subgroup is characterized by inferior prognosis and clinicopathological features that result in aggressive clinical behavior. Tumors of the DDR-suppressed class, which have distinct clinical and molecular characteristics, tend to have superior survival. A DDR subtype signature was ultimately generated to enable HCC DDR classification, and the results were confirmed by using multi-layer date cohorts. Furthermore, immune profiles and immunotherapy responses are also different between the two DDR subtypes. Altogether, this study illustrates the DDR heterogeneity of HCCs and is helpful to the understanding of personalized clinicopathological and molecular mechanisms responsible for unique tumor DDR profiles.

Brilliant glycans and glycosylation: Seq and ye shall find

Proteoglycosylation is the addition of monosaccharides or glycans to the protein peptide chain. This is a common post-translational modification of proteins with a variety of biological functions. At present, more than half of all biopharmaceuticals in clinic are modified by glycosylation. Most glycoproteins are potential drug targets and biomarkers for disease diagnosis. Therefore, in-depth study of glycan structure of glycoproteins will ultimately improve the sensitivity and specificity of glycoproteins for clinical disease detection. With the deepening of research, the function and application value of glycans and glycosylation has gradually emerged. This review systematically introduces the latest research progress of glycans and glycosylation. It encompasses six cancers, four viruses, and their latest discoveries in Alzheimer's disease, allergic diseases, congenital diseases, gastrointestinal diseases, inflammation, and aging.

A scalable and reproducible preparation for the antitumor protein TLC, a human-derived telomerase inhibitor

Telomerase, which is overexpressed in approximately 90% of liver cancer cells, is an ideal target for anti-liver cancer therapy. LPTS, a putative liver tumor suppressor, is the only human-derived protein that can bind telomerase directly and inhibit the extension of telomere activity. Our previous studies demonstrated that TAT-LPTS-LC (TLC), a recombinant protein fused by the C-terminal 133-328 fragment of LPTS and TAT peptides, could be delivered into cells to inhibit telomerase-positive hepatoma cell growth in vitro and in vivo with very low toxicity. In the present study, E. coli strains which expressed TLC in abundance were screened and cultured in a laboratory bioreactor. A reproducible protein separation process was built, and this process was suitable for industrial amplification. The yields of TLC protein were up to 184 mg in one batch with a purity of approximately 95%. The purified TLC protein had a similar inhibitory effect on telomerase activity in vitro compared with those purified by Ni-affinity chromatography. Furthermore, TLC protein could be delivered into the cell nucleus to increase the doubling time of the cell and suppress cell growth in telomerase-positive liver cancer cell lines. Cell growth inhibition was negatively correlated with telomere length, suggesting that TLC is a highly targeted telomerase-telomere anticancer agent. These results will contribute to future preclinical studies of the TLC protein.

Genomic Landscape of Liquid Biopsy for Hepatocellular Carcinoma Personalized Medicine

Hepatocellular carcinoma (HCC) is the sixth most frequently diagnosed cancer and the third leading cause of cancer-related deaths worldwide. Advanced-stage HCC patients have poor survival rates and this requires the discovery of novel clear biomarkers for HCC early diagnosis and prognosis, identifying risk factors, distinguishing HCC from non-HCC liver diseases, and assessment of treatment response. Liquid biopsy has emerged as a novel minimally invasive approach to enable monitoring tumor progression, metastasis, and recurrence. Since the liquid biopsy analysis has relatively high specificity and low sensitivity in cancer early detection, there is a risk of bias. Next-generation sequencing (NGS) technologies provide accurate and comprehensive gene expression and mutational profiling of liquid biopsies including cell-free circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and genomic components of extracellular vesicles (EVs) including micro-RNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). Since HCC is a highly heterogeneous cancer, HCC patients can display various genomic, epigenomic, and transcriptomic patterns and exhibit varying sensitivity to treatment options. Identification of individual variabilities in genomic signatures in liquid biopsy has the potential to greatly enhance precision oncology capabilities. In this review, we highlight and critically discuss the latest progress in characterizing the genomic landscape of liquid biopsy, which can advance HCC personalized medicine.

Artificial neural networks for multi-omics classifications of hepato-pancreato-biliary cancers: towards the clinical application of genetic data

PURPOSE

Several multi-omics classifications have been proposed for hepato-pancreato-biliary (HPB) cancers, but these classifications have not proven their role in the clinical practice and been validated in external cohorts.

PATIENTS AND METHODS

Data from whole-exome sequencing (WES) of The Cancer Genome Atlas (TCGA) patients were used as an input for the artificial neural network (ANN) to predict the anatomical site, iClusters (cell-of-origin patterns) and molecular subtype classifications. The Ohio State University (OSU) and the International Cancer Genome Consortium (ICGC) patients with HPB cancer were included in external validation cohorts. TCGA, OSU and ICGC data were merged, and survival analyses were performed using both the 'classic' survival analysis and a machine learning algorithm (random survival forest).

RESULTS

Although the ANN predicting the anatomical site of the tumour (i.e. cholangiocarcinoma, hepatocellular carcinoma of the liver, pancreatic ductal adenocarcinoma) demonstrated a low accuracy in TCGA test cohort, the ANNs predicting the iClusters (cell-of-origin patterns) and molecular subtype classifications demonstrated a good accuracy of 75% and 82% in TCGA test cohort, respectively. The random survival forest analysis and Cox' multivariable survival models demonstrated that models for HPB cancers that integrated clinical data with molecular classifications (iClusters, molecular subtypes) had an increased prognostic accuracy compared with standard staging systems.

CONCLUSION

The analyses of genetic status (i.e. WES, gene panels) of patients with HPB cancers might predict the classifications proposed by TCGA project and help to select patients suitable to targeted therapies. The molecular classifications of HPB cancers when integrated with clinical information could improve the ability to predict the prognosis of patients with HPB cancer.

Molecular subtyping of hepatocellular carcinoma: A step toward precision medicine

Hepatocellular carcinoma (HCC) is one of the most prevalent and fatal digestive tumors. Treatment for this disease has been constraint by heterogeneity of this group of tumors, which has greatly limited the progress in personalized therapy. Although existing studies have revealed the genetic and epigenetic blueprints that drive HCCs, many of the molecular mechanisms that lead to HCCs remain elusive. Recent advances in techniques for studying functional genomics, such as genome sequencing and transcriptomic analyses, have led to the discovery of molecular mechanisms that participate in the initiation and evolution of HCC. Integrative multi-omics analyses have identified several molecular subtypes of HCC associated with specific molecular characteristics and clinical outcomes. Deciphering similar molecular features among highly heterogeneous HCC patients is a prerequisite to implementation of personalized therapeutics. This review summarizes the current research progresses in precision therapy on the backbone of molecular subtypes of HCC.