Landscape Of Hepatocellular Carcinoma Research Articles

Integrated Single Cell Analysis Reveals An Atlas of Tumor Associated Macrophages in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC), one of the most prevalent cancers globally, is closely associated with tumor-associated macrophages (TAMs), including monocyte-derived macrophages and liver-resident Kupffer cells. Understanding TAM heterogeneity at the cellular level is crucial for developing effective HCC prevention and treatment strategies. In this study, we conducted an integrated single-cell analysis of four cohorts (GSE140228, GSE125449, GSE149614 and GSE156625) to elucidate the TAM landscape in HCC. We identified 284 gene markers, termed Panmyeloid markers, that characterize myeloid cells within this context. Our analysis distinguished six clusters of monocyte-derived macrophages (Macro1-Macro6) and four clusters of Kupffer cells (Kupffer1-Kupffer4). Notably, CXCL10 + macrophages and MT1G + Kupffer cells, predominantly located within tumor tissues, exhibited distinct functional characteristics relevant to HCC. We also explored cellular communication between TAMs and T cells, uncovering potential signaling pathways such as the CXCL10/CXCL11-CXCR3 and CXCL12-CXCR4 networks. These findings enhance our understanding of TAMs in HCC and open new avenues for targeted therapeutic interventions.

Exploring the Role of DNA Methylation Located in Cuproptosis-Related Genes: Implications for Prognosis and Immune Landscape in Hepatocellular Carcinoma.

Copper dysregulation has been linked to liver disease, cardiac dysfunction, neuropathy, and anemia. Previous investigations have been undertaken to demonstrate the impact of cuproptosis-related genes (CRGs) on the poor prognosis of hepatocellular carcinoma (HCC), while the prognostic significance and beneath molecular basis of DNA-methylation sites located in CRGs remain unknown. This study aims to identify CRG-located DNA-methylation sites linked to patient prognosis and establish a novel prognostic biomarkers combination for CRG-located DNA-methylation signature. The prognostic biomarkers combination was established through multivariate-Cox-regression after CRG-located DNA-methylation sites tied to the outcome of patients emerged by univariate-Cox-regression. The correlation between signature and immune cell infiltration levels, immune-checkpoint-associated genes was analyzed using spearman correlation and the difference was contrasted between different groups utilizing the Mann-Whitney-U test. Real-time quantitative methylation-specific polymerase chain reaction (RT-qMSP) was used to identify gene methylation. A novel prognostic biomarkers combination for CRG-located DNA-methylation signature was established. Subsequently, the independence of this methylation signature from clinical features and its correlation with immune infiltrative and immune checkpoints in HCC were also investigated. DNA methylation alterations can influence the onset, development, and treatment of various tumors by regulating the transcription of corresponding genes. Our analysis found that cg05706061 contained in prognosis signature was located in the promoter region of the cuproptosis-related gene SLC31A2. The DNA-methylation level of cg05706061 demonstrated significantly different between tumor and normal tissue, and significantly correlated with the expression of SLC31A2. We further investigated the promoter methylation status of SLC31A2 by qMSP, the result showed that the DNA-methylation level of SLC31A2 in HCC cell lines were significantly decreased compared with normal liver cells. Our findings reveal possible mechanisms of CRG-located DNA-methylation on the advancement of HCC and offers new perspectives for prognostic assessment and treatment options.

Immune landscape of hepatocellular carcinoma tumor microenvironment identifies a prognostic relevant model

BackgroundVarious studies highlighted that immune cell-mediated inflammatory processes play crucial roles in the progression and treatment of hepatocellular carcinoma (HCC). However, the immune microenvironment of HCC is still poorly characterized. Exploring the role of immune-related genes (IRGs) and describing the immune landscape in HCC would provide insights into tumor-immune co-evolution along HCC progression. MethodsWe integrated the datasets with complete prognostic information from the Cancer Genome Atlas (TCGA) database and GEO DataSets (GSE14520, GSE76427, and GSE54236) to construct a novel immune landscape based on the Cibersort algorithm and reveal the prognostic signature in HCC patients. ResultsTo describe the tumor microenvironment (TME) in HCC, immune infiltration patterns were defined using the CIBERSORT method, and a prognostic signature contains 5 types of immune cells, including 3 high-risk immune cells (T.cells. CD4. memory. resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8), were finally constructed. A novel prognostic index, based on prognostic immune risk score (pIRG), was developed using the univariate Cox regression analyses and LASSO Cox regression algorithm. Furthermore, the ROC curve and KM curve showed that the TME signatures had a stable value in predicting the prognosis of HCC patients in the internal training cohort, internal validation, and external validation cohort. Differential genes analysis and qPCR experiment showed that the expression levels of AKR1B10, LAPTM4B, MMP9, and SPP1 were significantly increased in high-risk patients, while the expression of CD5L was lower. Further analysis found that AKR1B10 and MMP9 were associated with higher M0 macrophage infiltration, while CD5L was associated with higher plasma cell infiltration. ConclusionsTaken together, we performed a comprehensive evaluation of the immune landscape of HCC and constructed a novel and robust prognostic prediction model. AKR1B10, LAPTM4B, MMP9, SPP1, and CD5L were involved in important processes in the HCC tumor microenvironment and were expected to become HCC prediction markers and potential targets of treatment.

Identification of cuproptosis and ferroptosis-related subgroups and development of a signature for predicting prognosis and tumor microenvironment landscape in hepatocellular carcinoma.

Ferroptosis and cuproptosis play a crucial role in the progression and dissemination of hepatocellular carcinoma (HCC). The primary objective of this study was to develop a unique scoring system for predicting the prognosis and immunological landscape of HCC based on ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs). As the training cohort, we assembled a novel HCC cohort by merging gene expression data and clinical data from The Cancer Genome Atlas (TCGA) database, and Gene Expression Omnibus (GEO) database. The validation cohort consisted of 230 HCC cases taken from the International Cancer Genome Consortium (ICGC) database. Multiple genomic characteristics, such as tumor mutation burden (TMB), and copy number variations were analyzed concurrently. On the basis of the expression of CRGs and FRGs, patients were classified into cuproptosis and ferroptosis subtypes. Then, we constructed a risk model using least absolute shrinkage and selection operator (LASSO) analysis and Cox regression analysis based on ferroptosis and cuproptosis-related differentially expressed genes (DEGs). Patients were separated into two groups according to median risk score. We compared the immunophenotype, tumor microenvironment (TME), cancer stem cell index, and treatment sensitivity of two groups. Three subtypes of ferroptosis and two subtypes of cuproptosis were identified among the patients. A greater likelihood of survival (P<0.05) was expected for patients in FRGcluster B and CRGcluster B. After that, a confirmed risk signature for ferroptosis and cuproptosis was developed and tested. Patients in the low-risk group had significantly higher survival rates than those in the high-risk group, according to our study (P<0.001). There was also a strong correlation between the signature and other variables including immunophenoscore, TMB, cancer stem cell index, immunological checkpoint genes, and sensitivity to chemotherapeutics. Through this comprehensive research, we identified a unique risk signature associated with HCC patients' treatment status and prognosis. Our findings highlight FRGs' and CRGs' significance in clinical practice and imply ferroptosis and cuproptosis may be therapeutic targets for HCC patients.

Genomic and immune landscape in hepatocellular carcinoma: Implications for personalized therapeutics.

Hepatocellular carcinoma (HCC) is a globally prevalent malignancy, marked by genetic heterogeneity and intricate tumor microenvironment interactions. In this study, we undertook a detailed single-cell analysis of six active HCC patients, highlighting strong correlations between gene expression levels and cellular characteristics. UMAP clustering revealed seven distinct cell categories with associated gene expressions. A divergence was observed in tumor cells into high and low cuproptosis groups, each associated with distinct pathways: oxidative stress for the high cuproptosis group and inflammatory and angiogenesis pathways for the low group. CellChat analysis on the TCGA-LIHC cohort displayed unique intercellular interactions among hepatocytes, T cells, and other cells, with pathways like COLLAGEN and VEGF being pivotal. Functional enrichment analyses exposed pathways enriched between cuproptosis groups, with KEGG emphasizing diseases like Parkinson's. COX survival analysis identified key prognostic genes, revealing distinct survival rates between risk groups in TCGA and GSE14520 cohorts. Mutation data highlighted missense mutations, with TTN, TP53, and CTNNB1 being the most mutated in HCC. Immune infiltration analysis via CIBERSORTx indicated differences between risk groups in NK cells, neutrophils, and other cells. Our drug sensitivity investigation showed significant correlations between model genes and drug responsiveness, emphasizing the importance of patient risk stratification for therapeutic approaches. Further, ATP6V1G1 was recognized in its role in apoptosis and migration in HCC cells. In conclusion, our findings illuminate the complexities of HCC progression, potential predictive genetic markers for drug response, and the pivotal role of ATP6V1G1, suggesting avenues for targeted therapeutic strategies in HCC.

Establishment and validation of a novel lysosome-related gene signature for predicting prognosis and immune landscape in hepatocellular carcinoma.

Recent studies have shown that lysosomes not only provide energy for tumor cell growth, but also participate in the occurrence and development of malignant tumors by regulating various ways of tumor cell death. However, the role of lysosome associated genes (LSAGs) in hepatocellular carcinoma (HCC) remains unclear. Transcriptome data and clinical data of HCC were downloaded from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. We identified differential expression of LSAGs by comparing tumor tissue with normal liver tissue. Subsequently, we used univariate COX analysis and least absolute shrinkage and selection operator (LASSO) COX regression to construct the prognostic feature of LSAGs. Kaplan-Meier survival curve and receiver operating characteristic curve were used to evaluate the predictive ability of LSAGs feature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for functional enrichment analysis of risk differential genes. The relationship between LSAGs score and tumor microenvironment and chemotherapy drug sensitivity was analyzed. Finally, the cellular communication of tumor cells with high and low expression of model LSAGs was explored. We identified sixteen prognostic associated LSAGs, four of which were selected to construct prognostic feature of LSAGs. Patients in the low LSAGs group had a better prognosis than those in the high LSAGs group. GO and KEGG analyses showed that risk differential genes were enriched in leukocyte migration, cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway. The group with low LSAGs score had lower immune score. Patients in the high LSAGs group were more sensitive to drugs for chemotherapy. In addition, tumor cells with high expression of model LSAGs showed stronger association with immune cells through the interleukin-2 (IL2), fibroblast growth factor (FGF), adiponectin, and bone morphogenetic proteins (BMP) signaling pathways. We established a LSAGs signature that had the ability to predict clinical prognosis and immune landscape, proposing potential therapeutic targets for HCC.

Identification and validation of a cancer-associated fibroblasts-related scoring system to predict prognosis and immune landscape in hepatocellular carcinoma through integrated analysis of single-cell and bulk RNA-sequencing.

Cancer-associated fibroblasts (CAFs) regulate the malignant biological behaviour of hepatocellular carcinoma (HCC) as a significant component of the tumour immune microenvironment (TIME). This study aimed to develop a CAFs-based scoring system to predict the prognosis and TIME of patients with HCC. Data for the TCGA-LIHC and GSE14520 cohorts were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Single-cell RNA-sequencing data for HCC samples were retrieved from the GSE166635 cohort. The Least Absolute Shrinkage and Selection Operator algorithm was employed to develop a CAFs-related scoring system (CAFRss). The predictive value of the CAFRss was determined using Kaplan-Meier, Cox regression and Receiver Operating Characteristic curves. Additionally, the TIMER platform, single sample Gene Set Enrichment Analysis and the Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data algorithms were performed to determine the TIME landscape. Finally, the pRRophic algorithm was utilised for drug sensitivity analysis. The evaluation of the CAFRss system demonstrated its superior ability to predict the clinical outcome of patients with HCC. Additionally, CAFRss effectively distinguished HCC populations with distinct TIME landscapes. Furthermore, CAFRss-based risk stratification identified individuals with immune 'hot tumours' and predicted the survival of patients treated with ICBs. The developed CAFRss can serve as a predictive tool for determining the clinical outcome of HCC and differentiating populations with diverse TIME characteristics.

Identification of SGMS2 as a molecule involved in natural killer cell recruitment and its in-deep analysis in the liver cancer microenvironment: Evidence from large populations cohort.

Liver cancer, a common malignancy within the digestive system, presents with a particularly grim prognosis. Within the immune microenvironment, the role of natural killer (NK) cells in liver cancer remains unclear. We sourced data on clinical parameters and gene expressions for liver cancer patients from The Cancer Genome Atlas Program database and carried out all analyses using R software and its relevant codes. In our research, we delved into the genes intertwined with NK cells in hepatocellular carcinoma (HCC). Leveraging the QUANTISEQ and MCPCOUNTER algorithms to quantify NK cells, we spotlighted genes vital to the recruitment of NK cells. Among these genes, GDE1, WDFY3, DNAJB14, PKD2, DGAT2, SGMS2 and MKNK2 showed a strong correlation with patient outcomes. We also mapped out the single-cell expression trajectories of these genes within the HCC milieu. From our findings, SGMS2 emerged as a key gene warranting further scrutiny. Our in-depth analysis of SGMS2 shed light on its influence over specific biological pathways, its contribution to the immune landscape and its role in genomic instability within HCC. Drawing from this, we formulated a predictive model rooted in SGMS2-associated genes. This model showcased remarkable precision across both training and validation cohorts. Overall, our investigation underscored the profound implications of SGMS2, a gene pivotal to NK cell infiltration, in the landscape of HCC, thereby positioning it as a potential linchpin in oncological strategies.

Identification and validation of ferroptosis-related prognostic risk model and immune landscape in hepatocellular carcinoma

BackgroundFerroptosis has been paid much more attention on account of the correlation withtumorigenesis and development. However, the molecular characteristics and immune landscape of ferroptosis regulators in hepatocellular carcinoma (HCC) have not been fully elucidated. MethodsRNA-sequencing data and matched clinical data were collected from The Cancer Genome Atlas (TCGA) database, then underwent gene expression, genetic variations, prognostic risk model, and immune characterization analyses. An independent cohort from Gene Expression Omnibus (GEO) database was utilized to validate ferroptosis-related prognostic risk model. ResultsWe first identified the differentially expressed ferroptosis regulators between the tumor tissues and normal controls in HCC. Furthermore, the prognostic risk model based on ferroptosis regulators was constructed, of which the high risk group presented poor clinical outcomes compared to the low risk group. Importantly, the ferroptosis-related prognostic risk model consistently presented excellent prediction ability in recognizing the high and low risk patients according to the validation from an independent cohort. Subsequently, immune landscape analysis uncovered that most of ferroptosis regulatory genes were significantly associated with the infiltration of multiple immune cells and the expression of immune checkpoints in HCC. Moreover, the correlations of risk score with immune cells infiltration and immune checkpoints were determined in HCC. ConclusionOur study developed a prognostic risk model based on ferroptosis regulatory genes, which could accurately predict the patients’ prognosis. Immune characteristics analysisrevealed that ferroptosis regulatory genes were associated with immune cells infiltration and immune checkpoints in HCC.

Impact of apolipoprotein A1 on tumor immune microenvironment, clinical prognosis and genomic landscape in hepatocellular carcinoma.

Current knowledge on apolipoprotein A1 (APOA1) in hepatocellular carcinoma (HCC) is fragmented and even contradictory. Multi-dimensional analyses are required to comprehensively elucidate its value and underlying mechanism. We collected 49 RNA-seq datasets, 40 cell line types data and 70 scRNA pan-cancer datasets public available, including 17 HCC datasets (1754 tumor samples), and enrolled 73 pairs of HCC tissue and 516 blood samples independently from our clinics. APOA1 impacting on the HCC tumor microenvironment (TME) was analyzed using intensive data mining. Methylation sequencing, flow cytometry, quantitative PCR, western blot, immunohistochemistry and clinical chemistry assays were conducted for wet experimental investigation. The APOA1 ontology fingerprint indicated that it played various crucial biological roles in HCC, primarily involved in cholesterol efflux. Consistent findings at histology, serology, and clinical follow-up revealed that high APOA1 was a good prognosis indicator of HCC. Hypermethylation in the APOA1 promoter region was found in clinical samples which is in accordance with the reduction of APOA1 in HCC. The cell cycle, DNA replication, mismatch repair pathways, and tumor cell proliferation were less observed in the HCC APOA1high subgroup. The favorable immunoregulatory abilities of APOA1 showed interesting findings: a positive correlation between APOA1 and anti-tumor immune cells (NK, CD8+T cells) and a negative association with immune cells exerting immunosuppressive effects, including M2 macrophages. This is an integrative multidimensional exploration of APOA1 using bioinformatics and experiments. Both the prognostic value and anti-tumor effects based on APOA1 panoramic exploration in the HCC TME demonstrate a new potential clinical target for HCC assessment and intervention in the future.

A novel signature incorporating lipid metabolism- and immune-related genes to predict the prognosis and immune landscape in hepatocellular carcinoma.

Liver hepatocellular carcinoma (LIHC) is a highly malignant tumor with high metastasis and recurrence rates. Due to the relation between lipid metabolism and the tumor immune microenvironment is constantly being elucidated, this work is carried out to produce a new prognostic gene signature that incorporates immune profiles and lipid metabolism of LIHC patients. We used the "DEseq2" R package and the "Venn" R package to identify differentially expressed genes related to lipid metabolism (LRDGs) in LIHC. Additionally, we performed unsupervised clustering of LIHC patients based on LRDGs to identify their subgroups and immuno-infiltration and Gene Ontology (GO) enrichment analysis on the subgroups. Next, we employed multivariate, LASSO and univariate Cox regression analyses to determine variables and to create a prognostic profile on the basis of immune- and lipid metabolism-related differential genes (IRDGs and LRDGs). We separated patients into low- and high-risk groups in accordance with the best cut-off value of risk score. We conducted Decision Curve Analysis (DCA), Receiver Operating Characteristic curve analysis as a function of time as well as Survival Analysis to evaluate this signature's prognostic value. We incorporated the clinical characteristics of patients into the risk model to obtain a nomogram prognostic model. GEO14520 and ICGC-LIRI JP datasets were employed to externally confirm the accuracy and robustness of signature. The gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied for investigating the underlying mechanisms. Immune infiltration analysis was implemented to examine the differences in immune between both risk groups. Single-cell RNA sequencing (scRNA-SEQ) was utilized to characterize the genes that were involved in the distribution of signature and expression characteristics of different LIHC cell types. The patients' sensitivity in both risk groups to commonly used chemotherapeutic agents and semi-inhibitory concentrations (IC50) of the drugs was assessed using the GDSC database. On the basis of the differentially expressed genes (DEGs) in the two groups, the CMAP database was adopted for the prediction of potential small-molecule compounds. Small-molecule compounds were molecularly docked with prognostic markers. Lastly, we investigated the prognostic gene expression levels in normal and LIHC tissues with immunohistochemistry (IHC) and quantitative reverse transcription polymerase chain reaction(qRT-PCR). We built and verified a prognostic signature with seven genes that incorporated immune profiles and lipid metabolism. Patients were classified as low- and high-risk groups depending on their prognostic profiles. The overall survival (OS) was markedly lower in the high-risk group as compared to low-risk group. Time-dependent ROC curves more precisely predicted patients' survival at 1, 3 and 5 years; the area under the ROC curve was 0.81 (1 year), 0.75 (3 years) and 0.77 (5 years). The DCA curves showed the value of the prognostic genes in this signature for clinical applications. We included the patients' clinical characteristics in the risk model for both multivariate and univariate Cox regression analyses, and the findings revealed that the risk model represents an independent factor that influences OS in LIHC patients. With immune analysis, GSVA and GSEA, we identified that there are remarkable differences between the two risk groups in immune pathways, lipid metabolism, tumor development, immune cell infiltration and immune microenvironment, response to immunotherapy, and sensitivity to chemotherapy. Moreover, those with higher risk scores presented greater sensitivity to the chemotherapeutic agents. Experiments in vitro further elucidated the roles of SPP1 and FLT3 in the LIHC immune microenvironment. Furthermore, four small-molecule drugs that could target LIHC were screened. In vitro qRT-PCR , IHC revealed that the SPP1,KIF18A expressions were raised in LIHC in tumor samples, whereas FLT3,SOCS2 showed the opposite trend. We developed and verified a new signature comprising immune- and lipid metabolism-associated markers and to assess the prognosis and the immune status of LIHC patients. This signature can be applied to survival prediction, individualized chemotherapy, and immunotherapeutic guidance for patients with liver cancer. This study also provides potential targeted therapeutics and novel ideas for the immune evasion and progression of LIHC.

Prognostic stratification based on HIF-1α signaling for evaluating hypoxia status and immune landscape in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) has a desmoplastic and hypoxic tumor microenvironment (TME), resulting in poor prognosis and treatment resistance. This study aimed to construct a novel prognostic classifier to investigate the degree of hypoxia and immune profiles in HCC. Patients with HCC from public databases were classified into three HIF-1α clusters according to 16 reported HIF-1α-related genes. Then, an HIF-1α score system was constructed based on nine overlapping differentially expressed genes (ODEGs) among various HIF-1α clusters. Then, an HIF-1α score system was constructed based on nine overlapping differentially expressed genes (ODEGs) among various HIF-1α cluster. Besides, oncologic pathways and immune infiltration profiles were also investigated among HCCs with different HIF-1α scores. The reliable predictive abilities of the HIF-1α score system for patients’ survival were impressively suggested by the significant C-indexes and ROC analysis. All enrolled tumors were divided into high-, medium-, and low-HIF-1α score groups. Compared with the other two groups, the high HIF-1α score group exhibited highest enrichment of multiple oncogenic pathways, such as TNF-α signaling via NF-кB, IL6-JAK-STAT3 signaling, mTORC1 signaling, MYC signaling, Hedgehog signaling. Notably, higher HIF-1α scores correlated with advanced immunosuppressive TME. Besides, tumors with high HIF-1α scores represented high non-response rate to transcatheter arterial chemoembolization (TACE) and immune checkpoint blockade (ICB). In conclusion, we developed a novel HIF-1α score system to distinguish HCC with different degree of hypoxia and immune infiltration profiles.

Abstract 3959: Novel spliced isoform of the proteasome subunit ADRM1/Rpn13 promotes hepatocellular carcinoma (HCC) development through selective degradation of tumor suppressor p53

Abstract The Ubiquitin-Proteasome System (UPS) is the major pathway in eukaryotic cells that regulates intracellular protein degradation and turnover. Cumulative evidence has suggested that defects in the UPS machinery are involved in the pathogenesis of human diseases, including cancer. The proteasome-associated polyubiquitin receptor ADRM1 (also known as Rpn13) orchestrates the process of substrate recruitment with deubiquitination in the proteasome through its N-terminal ubiquitin-binding domain and C-terminal activation of deubiquitinating enzyme. Using long-read SMRT-seq to profile the RNA splicing landscape in hepatocellular carcinoma (HCC), we previously reported on an unannotated alternative spliced variant of ADRM1 in human hepatocellular carcinoma (HCC) (Hepatology 2019). This novel isoform exhibits an exon 9 skipping (ADRM1-ΔEx9) that resulted in an altered C-terminus of the protein. In this study, we undertook further investigations to characterize the detailed functional effects of ADRM1-ΔEx9 in HCC biology and the underlying mechanism. Initial study by junction-specific Taqman PCR assay showed that ADRM1-ΔEx9 is more frequently upregulated in HCC tumors than the canonical full-length counterpart (ADRM1-FL). There is an interchange between the two isoforms as tumor develops which underscores an isoform-switch in favor of HCC development. Indeed, our result showed that ADRM1-ΔEx9, but not ADRM1-FL, correlated significantly with inferior patient survival. Functionally, ADRM1-ΔEx9 knockdown profoundly suppressed proliferation by inducing spontaneous apoptosis in HCC cell lines and patient-derived HCC organoids. The corresponding effects were less apparent from ADRM1-FL knockdown. Consistently, overexpression of ADRM1-ΔEx9 in normal human liver organoids promoted pre-malignant features alongside increased in-vitro propagation time. Ubiquitin Proteome profiling revealed that ADRM1-ΔEx9 led to a reduced expression of the pivotal tumor suppressor p53. A parallel transcriptome also substantiated that ADRM1-ΔEx9 modulated genes involved in the p53 and apoptosis pathways. Collectively, our study demonstrated that the non-canonical spliced isoform ADRM1-ΔEx9 confers growth and pro-survival advantages in HCC through mediating selective degradation of tumor suppressor p53 protein. Citation Format: Yanmei Sun, Mingjing Xu, Ho Lee Wan, Alissa M. Wong, Kelvin K. Ng, Nathalie Wong. Novel spliced isoform of the proteasome subunit ADRM1/Rpn13 promotes hepatocellular carcinoma (HCC) development through selective degradation of tumor suppressor p53. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3959.

A novel risk score model based on pyroptosis-related genes for predicting survival and immunogenic landscape in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer worldwide, with high incidence and mortality. Pyroptosis, a form of inflammatory-regulated cell death, is closely associated with oncogenesis. Expression profiles of HCC were downloaded from the TCGA database and validated using the ICGC and GEO databases. Consensus clustering analysis was used to determine distinct clusters. The pyroptosis-related genes (PRGs) included in the pyroptosis-related signature were selected by univariate Cox regression and LASSO regression analysis. Kaplan-Meier and receiver operating characteristic (ROC) analyses were performed to estimate the prognostic potential of the model. The characteristics of infiltration of immune cells between different groups of HCC were explored. Two independent clusters were identified according to PRG expression. Cluster 2 showed upregulated expression, poor prognosis, increased immune cell infiltration and worse immunotherapy response than cluster 1. A prognostic risk signature consisting of five genes (GSDME, NOD1, PLCG1, NLRP6 and NLRC4) was identified. In the high-risk score group, HCC patients showed decreased survival rates. In particular, multiple clinicopathological characteristics and immune cell infiltration were significantly associated with the risk score. Notably, the 5 PRGs in the risk score have been implicated in carcinogenesis, immunological pathways and drug sensitivity. A prognostic signature comprising five PRGs can be used as a potential prognostic factor for HCC. The PRG-related signature provides an in-depth understanding of the association between pyroptosis and chemotherapy or immunotherapy for HCC patients.

Crosstalk between 5-methylcytosine and N6-methyladenosine machinery defines disease progression, therapeutic response and pharmacogenomic landscape in hepatocellular carcinoma

BackgroundAccumulated evidence highlights the significance of the crosstalk between epigenetic and epitranscriptomic mechanisms, notably 5-methylcytosine (5mC) and N6-methyladenosine (m6A). Herein, we conducted a widespread analysis regarding the crosstalk between 5mC and m6A regulators in hepatocellular carcinoma (HCC).MethodsPan-cancer genomic analysis of the crosstalk between 5mC and m6A regulators was presented at transcriptomic, genomic, epigenetic, and other multi-omics levels. Hub 5mC and m6A regulators were summarized to define an epigenetic and epitranscriptomic module eigengene (EME), which reflected both the pre- and post-transcriptional modifications.Results5mC and m6A regulators interacted with one another at the multi-omic levels across pan-cancer, including HCC. The EME scoring system enabled to greatly optimize risk stratification and accurately predict HCC patients’ clinical outcomes and progression. Additionally, the EME accurately predicted the responses to mainstream therapies (TACE and sorafenib) and immunotherapy as well as hyper-progression. In vitro, 5mC and m6A regulators cooperatively weakened apoptosis and facilitated proliferation, DNA damage repair, G2/M arrest, migration, invasion and epithelial-to-mesenchymal transition (EMT) in HCC cells. The EME scoring system was remarkably linked to potential extrinsic and intrinsic immune escape mechanisms, and the high EME might contribute to a reduced copy number gain/loss frequency. Finally, we determined potential therapeutic compounds and druggable targets (TUBB1 and P2RY4) for HCC patients with high EME.ConclusionsOur findings suggest that HCC may result from a unique synergistic combination of 5mC-epigenetic mechanism mixed with m6A-epitranscriptomic mechanism, and their crosstalk defines therapeutic response and pharmacogenomic landscape.

Development and validation of a novel cellular senescence-related prognostic signature for predicting the survival and immune landscape in hepatocellular carcinoma

Background: Cellular senescence is a typical irreversible form of life stagnation, and recent studies have suggested that long non-coding ribonucleic acids (lncRNA) regulate the occurrence and development of various tumors. In the present study, we attempted to construct a novel signature for predicting the survival of patients with hepatocellular carcinoma (HCC) and the associated immune landscape based on senescence-related (sr) lncRNAs. Method: Expression profiles of srlncRNAs in 424 patients with HCC were retrieved from The Cancer Genome Atlas database. Lasso and Cox regression analyses were performed to identify differentially expressed lncRNAs related to senescence. The prediction efficiency of the signature was checked using a receiver operating characteristic (ROC) curve, Kaplan–Meier analysis, Cox regression analyses, nomogram, and calibration. The risk groups of the gene set enrichment analysis, immune analysis, and prediction of the half-maximal inhibitory concentration (IC50) were also analyzed. Quantitative real-time polymerase chain reaction (qPCR) was used to confirm the levels of AC026412.3, AL451069.3, and AL031985.3 in normal hepatic and HCC cell lines. Results: We identified 3 srlncRNAs (AC026412.3, AL451069.3, and AL031985.3) and constructed a new risk model. The results of the ROC curve and Kaplan–Meier analysis suggested that it was concordant with the prediction. Furthermore, a nomogram model was constructed to accurately predict patient prognosis. The risk score also correlated with immune cell infiltration status, immune checkpoint expression, and chemosensitivity. The results of qPCR revealed that AC026412.3 and AL451069.3 were significantly upregulated in hepatoma cell lines. Conclusion: The novel srlncRNA (AC026412.3, AL451069.3, and AL031985.3) signatures may provide insights into new therapies and prognosis predictions for patients with HCC.

DNA methylation regulators-related molecular patterns and tumor immune landscape in hepatocellular carcinoma

Increasing evidence showed that the dysregulation of DNA methylation regulators is a decisive feature of almost all cancer types and affects tumor progressions. However, few studies focused on the underlying influences of DNA methylation regulators-related genes (DMRegs) in immune cell-infiltration characteristics, tumor microenvironment (TME) and immunotherapy in HCC patients. In our study, the alterations of DNA methylation regulators modification patterns (DMRPs) were clustered from hepatocellular carcinoma (HCC) samples based on the expression of DNA methylation regulators as well as genetic and transcriptional features. In addition, based on molecular identification of three distinct molecular subtypes, we found that different DMRPs alterations were related to different clinicopathological characteristics, prognosis, and immune cells infiltration features. Moreover, we constructed and validated a DNA methylation regulators-related genes score (DMRegs_score) to predict the survival of HCC patients. A high DMRegs _score, which was characterized by more TP53 wild mutation, high expression of PD-1, CTLA-4, and remarkable immunity activation, was indicative of poor prognosis. Furthermore, we validated the expression of eight genes which were used for the prognostic signature in this risk score by RT-qPCR using tissues from our center. More importantly, DMRegs_score was highly correlated with targeted drug sensitivity. Additionally, we developed a highly accurate scoring system that could be used to improve the clinical applicability of DMRegs _score. In conclusion, these findings may contribute to a better understanding of DNA methylation regulators and provide new strategies for evaluating prognosis and developing more effective combination therapy for HCC patients.

Immunosuppressive landscape in hepatocellular carcinoma revealed by single-cell sequencing.

Background/AimsHepatocellular carcinoma (HCC), accounting for 75-85% of primary liver cancer cases, is the third leading cause of cancer-related death worldwide. The purpose of this research was to examine the tumor immune microenvironment (TIME) in HCC.MethodsWe investigated the HCC TIME by integrated analysis of single-cell and bulk-tissue sequencing data to reveal the landscape of major immune cell types.ResultsRegulatory T(Treg) cells were found to be specifically distributed in the TIME of HCC. Several immune checkpoints, including TNFRSF4, TIGIT and CTLA4, were found to be uniquely overexpressed in Treg cells, and the glycolysis/gluconeogenesis pathway was enriched in Treg cells. We also discovered the presence of two NK-cell subsets with different cytotoxic capacities, one in an activated state with antitumor effects and another with an exhausted status. In addition, memory B cells in HCC were found to exist in a unique state, with high proliferation, low differentiation, and low activity, which was induced by overexpression of PRAP1 and activation of the MIF-CD74 axis.ConclusionsWe revealed the TIME landscape in HCC, highlighting the heterogeneity of major immune cell types and their potential mechanisms in the formation of an immunosuppressive environment. Hence, blocking the formation of the TIME could be a useful therapeutic strategy for HCC.

A novel pyroptosis risk model composed of NLRP6 effectively predicts the prognosis of hepatocellular carcinoma patients.

Pyroptosis is a unique inflammatory-related cell death process, and inflammation is considered to be a key factor in hepatocellular carcinoma (HCC). However, the pyroptosis landscape in HCC has not been thoroughly studied. Clinical data and RNA sequencing data of HCC patients were collected from The Cancer Genome Atlas database, and differentially expressed genes (DEGs) associated with pyroptosis were discovered. The relationship between DEGs and prognosis was studied. Using The Cancer Genome Atlas cohort, a least absolute shrinkage and selection operator regression model was built on the basis of pyroptosis-related DEGs, which was then verified by the Gene Expression Omnibus (GEO) cohort. Functional enrichment analysis and immunological states were also studied between distinct risk subgroups. Finally, the potential tumor suppressive function of NLRP6 in HCC was analyzed. In total, 26 pyroptosis-related DEGs were identified. Consensus clustering results showed patients with high levels of pyroptosis were associated with higher tumor stage, grade, and poor prognosis. The least absolute shrinkage and selection operator risk model was built using six genes linked with prognosis (GSDMC, GSDME, NOD2, NLRP6, CASP8, and SCAF11). Risk score was an independent risk factor that suggested shortened overall survival in both the training cohort (HR: 3.52, 95% CI: 1.351-9.193) and validation cohort (HR: 3.31, 95% CI: 1.435-7.617). Meanwhile, the low-risk population had higher immunological activity. We also found a novel potential tumor suppressor gene NLRP6, which may negatively regulate the E2F and MYC pathways and be associated with higher immune cell infiltration levels, which lead to better prognosis. This study revealed the pyroptosis landscape of HCC and provided a promising clinical prognosis evaluation model. Additionally, some new targets related to prognosis such as NLRP6 are proposed for further study.

Molecular profile of hepatocellular carcinoma (HCC) in older versus younger adults: Does age matter?

477 Background: HCC is increasingly prevalent in older adults with rising incidence and an aging population worldwide. Retrospective studies show older patients with HCC may have an increased survival compared to younger patients. However, data is lacking regarding the genomic and biologic differences, that if identified, would potentially change how we treat this disease in younger vs. older patients. Hence, there is a need to better characterize the molecular landscape of the disease in an age-specific manner. We analyzed the association of age with genomic alterations and therapeutic response to sorafenib in a cohort of advanced HCC that had undergone comprehensive molecular profiling. Methods: 487 HCC samples (excluding variants) were analyzed using Next Generation Sequencing (592 gene panel, NextSeq), Whole Exome and Whole Transcriptome Sequencing (NovaSeq), and IHC at Caris Life Sciences (Phoenix, AZ). PD-L1 positivity was determined by IHC (SP-142 clone, cutoff ≥1, 1%). Tumor mutational burden (TMB) was a measure of total somatic mutations per Mb. Immune cell populations were determined by Microenvironment Cell Population (MCP) counter analysis of RNA expression data. Overall survival (OS) calculated from tissue collection to last contact and time on treatment (TOT) with sorafenib were extracted from insurance claims and calculated using Kaplan-Meier curves. Statistical analysis was done using Chi-square, Fisher Exact and Wilcoxon rank sum tests, with p values adjusted for multiple comparisons and q&lt;0.05. Results: Differences in the molecular landscape of HCC stratified by patient age were assayed using a ternary classification based on 1 standard deviation from the mean age (mean age=65; &lt;53: A1 (n=51), 53-77: A2 (n=361), &gt;77: A3 (n=75)). With age, mutational frequencies in CTNNB1 (A1=13.04%, A2=33.43%, A3=38.24%) and TERT (A1=25%, A2=68.84%, A3=76.92%) increased, while ATM (A1=6.52%, A2=0.93%, A3=1.49%) decreased (p&lt;0.05, q&gt;0.05). There were fold increases in median TMB (A2/A1=1.33, A3/A1=1.33, p&lt;0.01), LAG3 (A2/A1=1.75, A3/A1=1.93 p&lt;0.01), CTLA4 (A2/A1=2.05, A3/A1=2.15, p&lt;0.05) expression; median cell fractions of CD8+ T cells (A2/A1=1.37, A3/A1=1.50, p&lt;0.05) &amp; B cells (A3/A1=3.01 p&lt;0.05) increased while cancer associated fibroblasts (A1/A2=0.62, A1/A3=0.69, p&lt;0.01) decreased with age. PD-L1 was not statistically significant. While there was no change in OS, reduced TOT with sorafenib was observed in patients aged&gt;65 (p=0.013). Conclusions: Increased alterations in oncogenic drivers and estimates of CD8+ T cells and B cells were observed in the elderly population with HCC. The enhanced presence of co-inhibitory molecules suggests potential immune evasion. While we observed reduced TOT with sorafenib, additional studies are needed to elucidate the impact of molecular alterations on outcomes with sorafenib and newer therapies (i.e. immunotherapy) in older adults.