Subtype Of Hepatocellular Carcinoma Research Articles

Unraveling the relationship between anoikis-related genes and cancer-associated fibroblasts in liver hepatocellular carcinoma

This study intended to determine the molecular subtypes of liver hepatocellular carcinoma (LIHC) on the strength of anoikis-related genes (ARGs) and to assess their prognostic value and prospective relationship with immune cell infiltration and cancer-associated fibroblasts (CAFs). Univariate Cox regression analysis yielded 66 prognosis-related ARGs and classified LIHC into two distinct subtypes, with subtype A demonstrating overexpression of most prognosis-related ARGs and a significant survival disadvantage. Furthermore, a reliable prediction model was developed using ARGs to evaluate the risk of LIHC patients. This model served as an independent prognostic indicator and a quantitative tool for clinical prognostic prediction. Additionally, subtype-specific differences in immune cell infiltration were observed, and the risk score was potentially linked to immune-related characteristics. Moreover, the study identified a significant association between CAF score and LIHC prognosis, with a low CAF score indicating a favorable patient prognosis. In conclusion, this study reveals the molecular mechanisms underlying the development and progression of LIHC and identifies potential therapeutic targets for the disease.

Identification and characterization of RAC1-related immune and prognostic subtypes of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignant tumor with significant variability in prognosis among patients. Ras-related C3 botulinum toxin substrate 1 (RAC1) is a key focus in the area of cancer research. However, the molecular mechanisms of RAC1 in HCC remain incompletely elucidated. In this study, bioinformatics analysis was used, and public databases were used to obtain information about HCC cases. The samples were categorized into two groups of high and low expression based on the expression level of RAC1 gene. The limma package was used to calculate the differentially expressed genes between the two groups, and univariate Cox regression analysis was used to screen the prognostic related factors. Consensus clustering analysis was performed using the ConsensusClusterPlus package to identify molecular subtypes of HCC patients. Immune cell infiltration and ESTIMATE scores were assessed using the single sample gene set enrichment analysis and ESTIMATE algorithms. The sensitivity of different isoforms to chemotherapeutic agents was predicted by the oncoPredict package. Finally, we also performed cell function experiments to validate the biological role of RAC1 in vitro. Initially, we classified patients into high and low expression groups based on RAC1 gene expression levels and identified 195 up-regulated genes and 107 down-regulated genes. Through univariate Cox regression analysis, we screened out 169 prognosis-related factors. Furthermore, HCC patients were categorized into two subtypes. Subsequently, Kaplan-Meier survival curves showed that there was a significant difference in prognosis between the two molecular subtypes. Further analysis indicated substantial differences in gene expression levels and TIDE scores between two molecular subtypes. Moreover, these two subtypes exhibited varying sensitivity to chemotherapy drugs, as evidenced by differences in IC50 values. In addition, we found that the silence of RAC1 could effectively inhibit the migration and invasion of HCC cells in vitro. This study sheds light on the molecular intricacies of RAC1 in HCC and identifies patient populations that may benefit from immunotherapeutic interventions, with potential implications for tailored treatment strategies.

Application of Graph Models to the Identification of Transcriptomic Oncometabolic Pathways in Human Hepatocellular Carcinoma.

Whole-tissue transcriptomic analyses have been helpful to characterize molecular subtypes of hepatocellular carcinoma (HCC). Metabolic subtypes of human HCC have been defined, yet whether these different metabolic classes are clinically relevant or derive in actionable cancer vulnerabilities is still an unanswered question. Publicly available gene sets or gene signatures have been used to infer functional changes through gene set enrichment methods. However, metabolism-related gene signatures are poorly co-expressed when applied to a biological context. Here, we apply a simple method to infer highly consistent signatures using graph-based statistics. Using the Cancer Genome Atlas Liver Hepatocellular cohort (LIHC), we describe the main metabolic clusters and their relationship with commonly used molecular classes, and with the presence of TP53 or CTNNB1 driver mutations. We find similar results in our validation cohort, the LIRI-JP cohort. We describe how previously described metabolic subtypes could not have therapeutic relevance due to their overall downregulation when compared to non-tumoral liver, and identify N-glycan, mevalonate and sphingolipid biosynthetic pathways as the hallmark of the oncogenic shift of the use of acetyl-coenzyme A in HCC metabolism. Finally, using DepMap data, we demonstrate metabolic vulnerabilities in HCC cell lines.

Integrative multiomics analysis identifies molecular subtypes and potential targets of hepatocellular carcinoma.

The liver is anatomically divided into eight segments based on the distribution of Glisson's triad. However, the molecular mechanisms underlying each segment and its association with hepatocellular carcinoma (HCC) heterogeneity are not well understood. In this study, our objective is to conduct a comprehensive multiomics profiling of the segmentation atlas in order to investigate potential subtypes and therapeutic approaches for HCC. A high throughput liquid chromatography-tandem mass spectrometer strategy was employed to comprehensively analyse proteome, lipidome and metabolome data, with a focus on segment-resolved multiomics profiling. To classify HCC subtypes, the obtained data with normal reference profiling were integrated. Additionally, potential therapeutic targets for HCC were identified using immunohistochemistry assays. The effectiveness of these targets were further validated through patient-derived organoid (PDO) assays. A multiomics profiling of 8536 high-confidence proteins, 1029 polar metabolites and 3381 nonredundant lipids was performed to analyse the segmentation atlas of HCC. The analysis of the data revealed that in normal adjacent tissues, the left lobe was primarily involved in energy metabolism, while the right lobe was associated with small molecule metabolism. Based on the normal reference atlas, HCC patients with segment-resolved classification were divided into three subtypes. The C1 subtype showed enrichment in ribosome biogenesis, the C2 subtype exhibited an intermediate phenotype, while the C3 subtype was closely associated with neutrophil degranulation. Furthermore, using the PDO assay, exportin 1 (XPO1) and 5-lipoxygenase (ALOX5) were identified as potential targets for the C1 and C3 subtypes, respectively. Our extensive analysis of the segmentation atlas in multiomics profiling defines molecular subtypes of HCC and uncovers potential therapeutic strategies that have the potential to enhance the prognosis of HCC.

Proteo-genomic characterization of cirrhosis-associated liver cancers reveals potential subtypes and therapeutic targets.

This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0. Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis. The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis. These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.

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.

Intertumoral Heterogeneity Based on MRI Radiomic Features Estimates Recurrence in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) heterogeneity impacts prognosis, and imaging is a potential indicator. To characterize HCC image subtypes in MRI and correlate subtypes with recurrence. Retrospective. A total of 440 patients (training cohort = 213, internal test cohort = 140, external test cohort = 87) from three centers. 1.5-T/3.0-T, fast/turbo spin-echo T2-weighted, spin-echo echo-planar diffusion-weighted, contrast-enhanced three-dimensional gradient-recalled-echo T1-weighted with extracellular agents (Gd-DTPA, Gd-DTPA-BMA, and Gd-BOPTA). Three-dimensional volume-of-interest of HCC was contoured on portal venous phase, then coregistered with precontrast and late arterial phases. Subtypes were identified using non-negative matrix factorization by analyzing radiomics features from volume-of-interests, and correlated with recurrence. Clinical (demographic and laboratory data), pathological, and radiologic features were compared across subtypes. Among clinical, radiologic features and subtypes, variables with variance inflation factor above 10 were excluded. Variables (P < 0.10) in univariate Cox regression were included in stepwise multivariate analysis. Three recurrence estimation models were built: clinical-radiologic model, subtype model, hybrid model integrating clinical-radiologic characteristics, and subtypes. Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, Fisher's exact test, Kaplan-Meier curves, log-rank test, concordance index (C-index). Significance level: P < 0.05. Two subtypes were identified across three cohorts (subtype 1:subtype 2 of 86:127, 60:80, and 36:51, respectively). Subtype 1 showed higher microvascular invasion (MVI)-positive rates (53%-57% vs. 26%-31%), and worse recurrence-free survival. Hazard ratio (HR) for the subtype is 6.10 in subtype model. Clinical-radiologic model included alpha-fetoprotein (HR: 3.01), macrovascular invasion (HR: 2.32), nonsmooth tumor margin (HR: 1.81), rim enhancement (HR: 3.13), and intratumoral artery (HR: 2.21). Hybrid model included alpha-fetoprotein (HR: 2.70), nonsmooth tumor margin (HR: 1.51), rim enhancement (HR: 3.25), and subtypes (HR: 5.34). Subtype model was comparable to clinical-radiologic model (C-index: 0.71-0.73 vs. 0.71-0.73), but hybrid model outperformed both (C-index: 0.77-0.79). MRI radiomics-based clustering identified two HCC subtypes with distinct MVI status and recurrence-free survival. Hybrid model showed superior capability to estimate recurrence. 3 TECHNICAL EFFICACY STAGE: 2.

Impact of anesthesia-related genes on prognosis and tumor microenvironment in hepatocellular carcinoma: A comprehensive analysis.

Hepatocellular carcinoma (HCC), renowned for its bleak prognosis and high recurrence rates, necessitates innovative strategies for prognosis assessment and therapeutic intervention. In this pursuit, we systematically investigated the influence of anesthesia-related genes (ANARGs) on HCC outcomes. Leveraging data from The Cancer Genome Atlas (TCGA), our study scrutinized RNA sequencing data and clinical profiles from 374 HCC patients alongside 50 non-tumor liver samples to unravel ANARG expression patterns and their clinical relevance. Employing consensus clustering, we segregated HCC samples into two distinct subtypes based on ANARG profiles, unveiling significant survival disparities between them. Further differential expression analysis pinpointed pivotal genes and pathways distinguishing these subtypes, notably implicating lipid metabolism and the MTOR signaling pathway in HCC pathogenesis. A prognostic model, comprising five key ANARGs (DAGLA, CYP26B, HAVCR, G6PD and AKR1B), exhibited robust predictive capability for patient outcomes, validated across independent patient cohorts. Furthermore, immune infiltration analysis uncovered a nuanced interplay between ANARG expression and the tumor immune microenvironment, spotlighting variations in immune cell infiltration and function across the identified HCC subtypes. This comprehensive analysis underscores not only the prognostic significance of ANARGs in HCC but also their potential to modulate the tumor microenvironment, providing novel insights for tailoring anesthetic management and therapeutic strategies in HCC care. Our findings advocate for a more integrative approach to HCC management, amalgamating molecular profiling with traditional clinical parameters to refine patient stratification and personalize treatment strategies.

Apolipoprotein H-based prognostic risk correlates with liver lipid metabolism disorder in patients with HBV-related hepatocellular carcinoma

Background/Aim: Chronic hepatitis B patients often develop concomitant fatty liver disease, which is associated with increased risk of liver cirrhosis and hepatocarcinoma. Our previous studies have shown that apolipoprotein H (APOH) levels are gradually decreased in patients with chronic HBV infection at different stages of disease progression, and APOH deficiency disrupted hepatic lipid metabolism and caused fatty liver. We focus on the relationship between APOH and hepatocellular carcinoma (HCC) in the context of chronic HBV infection. Methods and resultsAPOH was downregulated at the transcriptional level in HBV-related HCC patients from open-source human liver transcriptome databases, and relatively high expression of APOH might be a favourable prognostic marker in HCC. APOH downregulation was positively associated with tumour grade and HCC subtypes. The analysis result of CHCC-HBV database showed that APOH-associated differential expression genes (DEGs) enriched in lipid metabolic pathways and downregulated APOH correlated with macrophage, neutrophil and CD8 T cell infiltration levels. Next, in vitro experiments were performed and APOH gene was silenced in HepG2.2.15 cells, an HBV producing human HCC cells. Further transcriptomic assay and analysis revealed the DEGs were enriched in cholesterol metabolism. The subsequent RT-qPCR experiments identified that CYP7A1 expression was higher upregulated in APOH silencing HepG2.2.15 cells than vehicle control cells (p < 0.05). Finally, demographic data of patients with HBV-related HCC were enrolled, and serum APOH levels were analysed using ELISA. Serum APOH levels were significantly lower in patients with HCC than in healthy controls (p < 0.05), and positively correlated with triglyceride level in healthy controls (p < 0.05). In HBV-HCC patients, serum APOH levels were positively correlated with albumin levels and negatively correlated with alkaline phosphatase (ALP), total bilirubin, and INR levels (p < 0.05). ConclusionAPOH downregulation disrupted liver lipid metabolism to potentially affect the overall survival in patients with HBV-related HCC.

Anthocyanin-Mediated Autophagy in Hepatocellular Carcinoma: Gene Associations and Prognostic Implications.

Hepatocellular carcinoma (HCC) is a globally prevalent malignancy accompanied by high incidence, poor outcomes, and high mortality. Anthocyanins can inhibit tumor proliferation, migration, invasion, and promote apoptosis. Moreover, autophagy-related genes (ARGs) may play vital roles in HCC progression. This study aimed to decipher the mechanisms through which anthocyanins influence HCC via ARGs and to establish a novel prognostic model. Based on data from public databases, differential analysis and the Venn algorithm were employed to detect intersecting genes among differentially expressed genes (DEGs), anthocyanin- related targets, and ARGs. Consensus clustering was implemented to delineate molecular subtypes of HCC. The prognostic model was developed by Cox regression analyses. CIBIRSORT was engaged to assess the immune cell infiltration. Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve were utilized to evaluate the predictive efficiency of the prognostic signature. A total of 36 intersecting genes were identified from overlapping 1524 ARGs, 537 anthocyanin- related targets, and 5247 DEGs. Consensus clustering determined three molecular subtypes (cluster 1, cluster 2, and cluster 3). Cluster 1 showed worse outcomes and remarkably higher abundances of plasma cells and T follicular helper cells. Furthermore, four prognostic signatures [KDR (Kinase insert domain receptor), BAK1 (BCL2 antagonist/killer 1), HDAC1 (Histone deacetylase 1), and CDK2 (Cyclin-dependent kinase 2)] were identified and showing substantial predictive efficacy. This investigation identified three molecular subtypes of HCC patients and proposed a promising prognostic signature comprising KDR, BAK1, HDAC1, and CDK2, which could supply further robust evidence for additional clinical and functional studies.

Proteomic analysis of DEN and CCl4-induced hepatocellular carcinoma mouse model.

Hepatocellular carcinoma (HCC) seriously threatens human health, mostly developed from liver fibrosis or cirrhosis. Since diethylnitrosamine (DEN) and carbon tetrachloride (CCl4)-induced HCC mouse model almost recapitulates the characteristic of HCC with fibrosis and inflammation, it is taken as an essential tool to investigate the pathogenesis of HCC. However, a comprehensive understanding of the protein expression profile of this model is little. In this study, we performed proteomic analysis of this model to elucidate its proteomic characteristics. Compared with normal liver tissues, 432 differentially expressed proteins (DEPs) were identified in tumor tissues, among which 365 were up-regulated and 67 were down-regulated. Through Gene Ontology (GO) analysis, Ingenuity Pathway Analysis (IPA), protein-protein interaction networks (PPI) analysis and Gene-set enrichment analysis (GSEA) analysis of DEPs, we identified two distinguishing features of DEN and CCl4-induced HCC mouse model in protein expression, the upregulation of actin cytoskeleton and branched-chain amino acids metabolic reprogramming. In addition, matching DEPs from the mouse model to homologous proteins in the human HCC cohort revealed that the DEN and CCl4-induced HCC mouse model was relatively similar to the subtype of HCC with poor prognosis. Finally, combining clinical information from the HCC cohort, we screened seven proteins with prognostic significance, SMAD2, PTPN1, PCNA, MTHFD1L, MBOAT7, FABP5, and AGRN. Overall, we provided proteomic data of the DEN and CCl4-induced HCC mouse model and highlighted the important proteins and pathways in it, contributing to the rational application of this model in HCC research.

A prognostic signature established based on genes related to tumor microenvironment for patients with hepatocellular carcinoma.

Complex cellular signaling network in the tumor microenvironment (TME) could serve as an indicator for the prognostic classification of hepatocellular carcinoma (HCC) patients. Univariate Cox regression analysis was performed to screen prognosis-related TME-related genes (TRGs), based on which HCC samples were clustered by running non-negative matrix factorization (NMF) algorithm. Furthermore, the correlation between different molecular HCC subtypes and immune cell infiltration level was analyzed. Finally, a risk score (RS) model was established by LASSO and Cox regression analyses (CRA) using these TRGs. Functional enrichment analysis was performed using gene set enrichment analysis (GSEA). HCC patients were divided into three molecular subtypes (C1, C2, and C3) based on 704 prognosis-related TRGs. HCC subtype C1 had significantly better OS than C2 and C3. We selected 13 TRGs to construct the RS model. Univariate and multivariate CRA showed that the RS could independently predict patients' prognosis. A nomogram integrating the RS and clinicopathologic features of the patients was further created. We also validated the reliability of the model according to the area under the receiver operating characteristic (ROC) curve value, concordance index (C-index), and decision curve analysis. The current findings demonstrated that the RS was significantly correlated with CD8+ T cells, monocytic lineage, and myeloid dendritic cells. This study provided TRGs to help classify patients with HCC and predict their prognoses, contributing to personalized treatments for patients with HCC.

Role of CCR1/5/7 in hepatocellular carcinoma: a study on prognostic evaluation, molecular subtyping, and association with immune infiltration.

This study aims to assess the prognostic value of the C-C motif chemokine receptor (CCR) gene family in hepatocellular carcinoma (HCC) and its relationship with immune infiltration and molecular subtypes of HCC. The evaluation of the GSE14520 dataset and TCGA database confirmed the prognostic significance of CCR. Building upon the correlation between CCR1, CCR5, and CCR7 and favorable prognosis, we further validated the prognostic importance of CCR1, CCR5, and CCR7 in ICGC database and an independent cohort from Guangxi autonomous region. Then, we constructed a risk prognosis model. Additionally, we observed significant positive correlations between CCR1, CCR5, and CCR7 and the infiltration of B cells, T cells, and macrophages in HCC. Subsequently, we conducted CCK assays, Transwell assays, and colony formation assays to evaluate the molecular biological functions of CCR1, CCR5, and CCR7. These experiments further confirmed that upregulation of CCR1, CCR5, and CCR7 can individually inhibit the proliferation, migration, and stemness of HCC cells. By analyzing the relationship between expression levels and tumor mutation frequency, we discovered that patients with high CCR1 expression were more likely to be classified as non-proliferative HCC. Similar conclusions were observed for CCR5 and CCR7. The association of CCR1, CCR5, and CCR7 with the molecular subtypes of HCC suggests that they may serve as intermediary molecules linking immune status and molecular subtypes in HCC. In summary, CCR1, CCR5, and CCR7 have the potential to serve as prognostic biomarkers for HCC and regulate HCC progression by influencing immune cell infiltration.

Abstract 2695: A higher percentage of M2 macrophages in non-tumor part predict the tumor recurrence in patients with hepatocellular carcinoma after surgical resection

Abstract The primary hurdle in improving outcomes for hepatocellular carcinoma (HCC) patients after curative treatment is the high early recurrence rate. The existing literature indicates the presence of type 2 macrophages (M2), renowned for their immunosuppressive capabilities, has been implicated in creating a microenvironment conducive to tumor growth and recurrence. However, the intricate interplay involving M2 macrophages and the recurrence of HCC is underexplored. Therefore, we conducted an analysis involving HCC patients who underwent hepatectomy at our hospital between 2013 and 2017. We collected samples from both the tumor and non-tumor regions and subjected them to bulk RNA sequencing for comprehensive molecular profiling. The proportion of immune cells was calculated by CIBERSORTx.In our study, we enrolled fifty patients, with sixty percent presenting with cirrhosis and seventy-three percent having viral hepatitis. Twenty-three patients experienced tumor recurrence, with a median time to recurrence of 12.6 months. First, we found no correlation between five TIMR subtypes defined by TIMELASER (Xue R, Nature, 2022) and recurrence. However, as for the proportions of immune cell, a significantly lower NK/Treg ratio (p= 0.0163) and high within the resected tumor section in recurrent group was found. On the other hand, the ratio of M2 macrophages to total macrophages (M2/M) was significantly higher within the non-tumor region in patients experiencing tumor recurrence after hepatectomy (p=0.0357). Furthermore, the expression of the ACGT2 gene in non-tumor part, which strongly correlates with Treg function, was significantly higher (p=0.0056) in recurrent group. In addition, the innate T cells score was increased in non-tumor pat in recurrent group but with borderline significance (p=0.0628). However, in multivariable analysis, only a higher M2/M ratio within the non-tumor region (HR 3.756, p=0.0072) were identified as independent risk factors for tumor recurrence. We also found that patients with a higher M2/M ratio had a lower percentage of cirrhotic patients (p=0.0049) and less association with TIME-IA (immune activation) subtype of HCC by TIMELASER classification (p=0.0030). In conclusion, a higher M2/M ratio within the non-tumor region predict the tumor recurrence in patients with hepatocellular carcinoma. Citation Format: Po-Ting Lin, Wei-Ting Ku, Tsung-Han Wu, Cheng-Heng Wu, Chan-Keng Yang, Wei Teng, Yung-Chang Lin, Chun-Yen Lin. A higher percentage of M2 macrophages in non-tumor part predict the tumor recurrence in patients with hepatocellular carcinoma after surgical resection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2695.

Abstract 7102: The central dogma of hepatocellular carcinoma: Genomic, transcriptomic, and proteomic changes

Abstract Cancer is a disease condition characterized by remarkable heterogeneity, wherein numerous molecular features display considerable disparities even with the same patient cohorts, leading to varied treatment outcomes. The primary factor contributing to the substantial differences observed across cancers stems from unique combinations of genetic mutations. Epigenetic alterations further contribute to differential gene expression and splicing, thereby enhancing transcriptomic diversity. The protein products originating from the cancer transcriptome constitute the specific intracellular and extracellular environment, consequently exerting supplementary effects on epigenetic, transcriptomic, and proteomic changes. The complex interplay and mutual influence between genetic aberrations, transcriptomes, and proteomes significantly contribute to shaping the extensive spectrum of molecular characteristics observed in cancer. With label-free mass spectrometry, RNA-sequencing, and low-pass whole genome sequencing, the quantification of the proteome, transcriptome, and genetic abbreviation of the cancer can be evaluated. With the mouse hydrodynamic tail vein injection (HDVI) models of hepatocellular carcinoma (HCC), different subtypes of HCC derived from different genetic abbreviations can be generated. With the proteome, transcriptome, and genetic abbreviation data of the better-defined HCC from HDVI models and HCC patients, the patient-derived HCC can be classified according to the origin of the genetic abbreviation used in the generation of the HDVI-derived HCC. Furthermore, the proteome of patient-derived plasma will be analyzed, and the signature change unique to HCC classified by different origins of genetic abbreviation will provide an abstract of the proteome, transcriptome, and genetic abbreviation of the HCC tissue. The transcriptomics and proteomics data of HDVI mice produced from five distinct sets of genetic abbreviations have been measured. The proteome and transcriptome of tissue, as well as the proteome of plasma, from a small cohort of HCC patients were studied and classified using this resource to illuminate the fundamental landscape of HCC. The final results will be presented and discussed at the meeting. Citation Format: Hoi Tang MA, Chun Yin YU, Lau Yan NG. The central dogma of hepatocellular carcinoma: Genomic, transcriptomic, and proteomic changes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7102.

Imaging and prognostic characterization of fat-containing hepatocellular carcinoma subtypes.

Steatohepatitic hepatocellular carcinoma (SH-HCC) is characterized by intratumoral fat with > 50% inflammatory changes. However, intratumoral fat (with or without inflammation) can also be found in not-otherwise specified HCC (NOS-HCC). We compared the imaging features and outcome of resected HCC containing fat on pathology including SH-HCC (> 50% steatohepatitic component), NOS-HCC with < 50% steatohepatitic component (SH-NOS-HCC), and fatty NOS-HCC (no steatohepatitic component). From September 2012 to June 2021, 94 patients underwent hepatic resection for fat-containing HCC on pathology. Imaging features and categories were assessed using LIRADS v2018. Fat quantification was performed on chemical-shift MRI. Recurrence-free and overall survival were estimated. Twenty-one patients (26%) had nonalcoholic steatohepatitis (NASH). The median intra-tumoral fat fraction was 8%, with differences between SH-HCC and SH-NOS-HCC (9.5% vs. 5% p = 0.03). There was no difference in major LI-RADS features between all groups; most tumors were classified as LR-4/5. A mosaic architecture on MRI was rare (7%) in SH-HCC, a fat in mass on CT was more frequently depicted (48%) in SH-HCC. A combination of NASH with no mosaic architecture on MRI or NASH with fat in mass on CT yielded excellent specificity for diagnosing SH-HCC (97.6% and 97.7%, respectively). The median recurrence-free and overall survival were 58 and 87months, with no difference between groups (p = 0.18 and p = 0.69). In patients with NASH, an SH-HCC may be suspected in L4/LR-5 observations with no mosaic architecture at MRI or with fat in mass on CT. Oncological outcomes appear similar between fat-containing HCC subtypes.

MRI imaging features for predicting macrotrabecular-massive subtype hepatocellular carcinoma: a systematic review and meta-analysis.

To identify significant MRI features associated with macrotrabecular-massive hepatocellular carcinoma (MTM-HCC), and to assess the distribution of Liver Imaging Radiology and Data System (LI-RADS, LR) category assignments. PubMed and EMBASE were searched up to March 28, 2023. Random-effects model was constructed to calculate pooled diagnostic odds ratios (DORs) and 95% confidence intervals (CIs) for each MRI feature for differentiating MTM-HCC from NMTM-HCC. The pooled proportions of LI-RADS category assignments in MTM-HCC and NMTM-HCC were compared using z-test. Ten studies included 1978 patients with 2031 HCCs (426 (20.9%) MTM-HCC and 1605 (79.1%) NMTM-HCC). Six MRI features showed significant association with MTM-HCC: tumor in vein (TIV) (DOR = 2.4 [95% CI, 1.6-3.5]), rim arterial phase hyperenhancement (DOR =2.6 [95% CI, 1.4-5.0]), corona enhancement (DOR = 2.6 [95% CI, 1.4-4.5]), intratumoral arteries (DOR = 2.6 [95% CI, 1.1-6.3]), peritumoral hypointensity on hepatobiliary phase (DOR = 2.2 [95% CI, 1.5-3.3]), and necrosis (DOR = 4.2 [95% CI, 2.0-8.5]). The pooled proportions of LI-RADS categories in MTM-HCC were LR-3, 0% [95% CI, 0-2%]; LR-4, 11% [95% CI, 6-16%]; LR-5, 63% [95% CI, 55-71%]; LR-M, 12% [95% CI, 6-19%]; and LR-TIV, 13% [95% CI, 6-22%]. In NMTM-HCC, the pooled proportions of LI-RADS categories were LR-3, 1% [95% CI, 0-2%]; LR-4, 8% [95% CI, 3-15%]; LR-5, 77% [95% CI, 71-82%]; LR-M, 5% [95% CI, 3-7%]; and LR-TIV, 6% [95% CI, 2-11%]. MTM-HCC had significantly lower proportion of LR-5 and higher proportion of LR-M and LR-TIV categories. Six MRI features showed significant association with MTM-HCC. Additionally, compared to NMTM-HCC, MTM-HCC are more likely to be categorized LR-M and LR-TIV and less likely to be categorized LR-5. Several MR imaging features can suggest macrotrabecular-massive hepatocellular carcinoma subtype, which can assist in guiding treatment plans and identifying potential candidates for clinical trials of new treatment strategies. • Macrotrabecular-massive hepatocellular carcinoma is a subtype of HCC characterized by its aggressive nature and unfavorable prognosis. • Tumor in vein, rim arterial phase hyperenhancement, corona enhancement, intratumoral arteries, peritumoral hypointensity on hepatobiliary phase, and necrosis on MRI are indicative of macrotrabecular-massive hepatocellular carcinoma. • Various MRI characteristics can be utilized for the diagnosis of the macrotrabecular-massive hepatocellular carcinoma subtype. This can prove beneficial in guiding treatment decisions and identifying potential candidates for clinical trials involving novel treatment approaches.

RNA processing modification mediated subtypes illustrate the distinctive features of tumor microenvironment in hepatocellular carcinoma.

Multiple transcript isoforms of genes can be formed by processing and modifying the 5' and 3' ends of RNA. Herein, the aim of this study is to uncover the characteristics of RNA processing modification (RPM) in hepatocellular carcinoma (HCC), and to identify novel biomarkers and potential targets for treatment. Firstly, integrated bioinformatics analysis was carried out to identify risk prognostic RPM regulators (RPMRs). Then, we used these RPMRs to identify subtypes of HCC and explore differences in immune microenvironment and cellular function improvement pathways between the sub-types. Finally, we used the principal component analysis algorithms to estimate RPMscore, which were applied to 5 cohorts. Lower RPMscore among patients correlated with a declined survival rate, increased immune infiltration, and raised expression of immune checkpoints, aligning with the "immunity tidal model theory". The RPMscore exhibited robust, which was validated in multiple datasets. Mechanistically, low RPMscore can create an immunosuppressive microenvironment in HCC by manipulating tumor-associated macrophages. Preclinically, patients with high RPMscore might benefit from immunotherapy. The RPMscore is helpful in clustering HCC patients with distinct prognosis and immunotherapy. Our RPMscore model can help clinicians to select personalized therapy for HCC patients, and RPMscore may act a part in the development of HCC.

SOCS5-RBMX stimulates SREBP1-mediated lipogenesis to promote metastasis in steatotic HCC with HBV-related cirrhosis

Abnormal lipid metabolism promotes hepatocellular carcinoma (HCC) progression, which engenders therapeutic difficulties owing to unclear mechanisms of the phenomenon. We precisely described a special steatotic HCC subtype with HBV-related cirrhosis and probed its drivers. Hematoxylin-eosin (HE) staining of 245 HCC samples revealed a special HCC subtype (41 cases) characterized by HBV-related cirrhosis and intratumoral steatosis without fatty liver background, defined as steatotic HCC with HBV-related cirrhosis (SBC-HCC). SBC-HCC exhibits a larger tumor volume and worse prognosis than non-SBC-HCC. Screening for driver genes promoting fatty acid (FA) biosynthesis in the Gao’s HBV-related cirrhosis HCC cases and GSE121248’ HBV-related HCC cases revealed that high expression of SOCS5 predicts increased FA synthesis and that SOCS5 is upregulated in SBC-HCC. Through proteomics, metabolomics, and both in vivo and in vitro experiments, we demonstrated that SOCS5 induces lipid accumulation to promote HCC metastasis. Mechanistically, through co-IP and GST-pulldown experiments, we found that the SOCS5-SH2 domain, especially the amino acids Y413 and D443, act as critical binding sites for the RBMX-RRM domain. SOCS5-RBMX costimulates the promoter of SREBP1, inducing de novo lipogenesis, while mutations in the SH2 domain, Y413, and D443 reverse this effect. These findings precisely identified SBC-HCC as a special steatotic HCC subtype and highlighted a new mechanism by which SOCS5 promotes SBC-HCC metastasis.

Cuproptosis-related molecular classification and gene signature of hepatocellular carcinoma and experimental verification.

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor overall prognosis. Cuproptosis, a recently proposed mode of copper-dependent cell death, plays a critical role in the malignant progression of various tumors; however, the expression and prognostic value of cuproptosis-related regulatory genes in HCC remain unclear. Genomic, genetic, and expression profiles of ten key cuproptosis-related regulatory genes were analyzed using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset and protein expression data from the Human Protein Atlas (HPA) database. Unsupervised clustering of HCC patients based on these ten key cuproptosis-related regulatory genes was used to identify different HCC subtypes and analyze the differences in clinical and immune characteristics among subtypes. Subsequently, univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox analyses were used to establish a cuproptosis-related prognostic signature, and the accuracy of prognostic signature prediction was internally validated by Kaplan-Meier survival analysis and time-dependent receiver operating characteristic curve in TCGA training and testing cohorts. The prognostic signature was externally validated using TCGA-LIHC entire cohort and International Cancer Genome Consortium Liver Cancer (ICGC-LIRI) cohorts. Finally, the expression landscape of cuproptosis-related regulatory genes in prognostic signature was explored by quantitative real-time polymerase chain reaction (qRT-PCR), western blotting and immunohistochemistry (IHC) experiments. Ten cuproptosis-related genes were differentially expressed in normal and HCC tissues. Unsupervised clustering identified two subtypes and HCC patients with these two subtypes had different clinical prognoses and immune characteristics, as well as different degrees of response to immunotherapy. Lipoyltransferase 1 (LIPT1), dihydrolipoamide s-acetyltransferase (DLAT), and cyclin dependent kinase inhibitor 2A (CDKN2A) were selected to construct a prognostic signature, which significantly distinguished HCC patients with different survival periods in the TCGA training and testing cohorts and was well validated in both the TCGA-LIHC entire cohort and ICGC-LIRI cohort. The risk score of the prognostic signature was confirmed to be an independent prognostic factor, and nomograms were generated to effectively predict the probability of HCC patient survival. The qRT-PCR, western blotting and IHC results also revealed a significant imbalance in the expression of these cuproptosis-related genes in HCC. The classification and prognostic signature based on cuproptosis-related regulatory genes helps to explain the heterogeneity of HCC, which may contribute to the individualized treatment of patients with the disease.