Hepatocellular Carcinoma Datasets Research Articles

Prognostic Index for Liver Radiation (PILiR).

A Prognostic Index for Liver Radiation (PILiR) for improved patient selection for stereotactic liver-directed radiotherapy (SBRT) was developed. Using a large single-center database, 195 patients treated with SBRT for local control, including 66 with hepatocellular carcinoma (HCC) and 129 with metastatic liver disease, were analyzed. Only patients ineligible for alternative treatments were included. Overall survival was 11.9 months and 9.4 months in the HCC group and metastatic groups, respectively. In the combined dataset, Child-Pugh Score (CPS) (p = 0.002), serum albumin (p = 0.039), and presence of extrahepatic disease (p = 0.012) were significant predictors of early death on multivariable analysis and were included in the PILiR (total score 0 to 5). Median survival was 23.8, 9.1, 4.5, and 2.6 months for patients with 0, 1-2, 3, and 4-5 points, respectively. In the HCC dataset, CPS (p < 0.001) and gross tumor volume (p = 0.013) were predictive of early death. In the metastatic dataset, serum albumin (p < 0.001) and primary disease site (p = 0.003) were predictive of early death. The AUC for the combined, HCC, and metastatic datasets are 0.78, 0.84, and 0.80, respectively. Poor liver function (defined by CPS and serum albumin) and extrahepatic disease were most predictive of early death, providing clinically important expected survival information for patients and caregivers.

Exploring the mechanism of genistein in treating hepatocellular carcinoma through network pharmacology and molecular docking

Abstract Objectives Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with treatment options limited and outcomes often poor, especially in advanced stages. This study explores the therapeutic potential of genistein, a soybean-derived isoflavone, on HCC using network pharmacology to uncover its multi-targeted anti-cancer mechanisms. Methods Potential targets of genistein were predicted using databases such as Super-PRED, PharmMapper, and SwissTargetPrediction. Abnormally expressed genes in HCC tissues were analyzed from TCGA and GEO datasets, with genes linked to the prognosis of HCC patients selected as potential therapeutic targets. GO and KEGG pathway enrichment analyses were conducted for both genistein’s targets and the HCC-related gene set. Key targets were identified through network analysis using Cytoscape software. Molecular docking was performed with Autodock to assess the binding affinity between genistein and these key targets. The therapeutic effects of genistein on HCC were validated through animal experiments and cell line studies. Results This study identified 343 potential targets for genistein in treating hepatocellular carcinoma (HCC). Analyses revealed enrichment in cell cycle regulation pathways through GO and KEGG assessments. Transcriptomic data from HCC datasets unveiled 184 potential therapeutic targets, emphasizing cell cycle regulation. Notably, 12 proteins were identified as targets of both genistein and HCC treatment. Molecular docking studies demonstrated genistein’s strong binding affinity with CDC25C and MELK. In vitro and in vivo validations affirmed genistein’s role in inhibiting HCC proliferation by inducing G2/M phase arrest. This study elucidates genistein’s multi-target mechanism in suppressing HCC cell proliferation, supporting its potential clinical application in HCC treatment. Conclusions This study demonstrates that genistein effectively suppresses the malignant growth of HCC by interfering with the transition from the G2 to M phase, revealing a multifaceted mechanism of action.

Study on the prediction model of liver cancer based on chronic liver disease and the related molecular mechanism

Introduction and objectiveDue to the high heterogeneity of HCC, which leads to poor prognostic outcomes for patients, there is a need to develop a novel predictive model for accurate classification of HCC in order to improve patient survival rates. Materials and methodsThe data of the HCV, cirrhosis, and HCC were obtained from TCGA and GEO databases. Multivariable Cox regression analysis and survival analysis was conducted to assess the prognostic relevance of these differentially expressed genes. Single-cell sequencing was used to explore the intercellular interaction patterns and identify relevant signaling pathways. Drug sensitivity analysis was conducted to determine personalized treatment strategies for patients. ResultsIn this study, we conducted integrated analysis of hepatitis, cirrhosis, and hepatocellular carcinoma datasets and identified 10 liver disease progression genes associated with prognosis. These genes exhibited significant downregulation in expression as the disease advanced, suggesting their crucial involvement in HCC development. By performing multivariable Cox analysis, we established a prognostic model for liver disease progression to predict the prognosis of HCC patients. The model was validated using ROC analysis, demonstrating good accuracy and stability in prognostic evaluation. Single-cell sequencing analysis revealed that these genes primarily exert their effects through the MIF signaling pathway during HCC progression. Furthermore, we observed that patients in the low-risk group exhibited higher sensitivity to TACE treatment, while patients in the high-risk group showed better response to sorafenib treatment. ConclusionsIn summary, we have elucidated the key genes involved in the progression of liver diseases and established a precise prognostic model for assessing the prognosis of HCC patients. Our study provides novel insights and strategies for the treatment of HCC.

Identification of a prognostic disulfidptosis-related gene signature in hepatocellular cancer.

Disulfidptosis regulate various biological processes in cancer. However, there is limited research on the genes related to disulfidptosis in predicting the prognosis of hepatocellular carcinoma (HCC). We aimed to develop a reliable disulfidptosis-related gene signature, which will characterize different HCC subtypes and predict their prognosis. The Cancer Genome Atlas (TCGA)-HCC dataset, comprising RNA sequencing data and clinical information, was obtained from the TCGA database. The crucial disulfidptosis-related genes were selected for bioinformatic analysis in HCC. HCC tumor classification was established through a consistent cluster analysis. The prognosis and immune-cell infiltration were investigated in association with a disulfidptosis-related HCC model. In TCGA-HCC patients, a total of 3,621 prognostic genes and 30 key prognostic disulfidptosis-related genes were identified. Using key prognostic disulfidptosis-related genes, TCGA-HCC patients were categorized into low- and high-risk clusters. The upregulated differentially expressed genes (DEGs) in high-risk cluster 1 (C1) could significantly impact cell cycle, DNA replication, and the p53 signaling pathway, whereas the pathways associated with the downregulated DEGs in high-risk C1 could significantly impact metabolism of xenobiotics by cytochrome P450, the PPAR signaling pathway, and tyrosine metabolism. Furthermore, the immune activity of the high-risk C1 group was different to that of the low-risk cluster 2 (C2) group. The 13 disulfidptosis-related genes were finally screened using least absolute shrinkage and selection operator (LASSO) regression analysis, including ANP32E, BOP1, RPN1, SLC7A11, PPIH, PCBP2, ME1, PRDX1, FLNC, INF2, MYH11, LRPPRC, and HNRNPM. The genes related to disulfidptosis are closely associated with tumor classification and immunity in patients with HCC. This is the first gene signature related to disulfidptosis demonstrated a strong predictive performance for the prognosis of HCC, which provide new perspectives for the diagnosis and treatment of HCC.

ADH4—a potential prognostic marker for hepatocellular carcinoma with possible immune-related implications

ObjectiveThis study aims to explore ADH4 expression in hepatocellular carcinoma (HCC), its prognostic impact, and its immune correlation to provide novel insights into HCC prognostication and treatment.MethodsHCC prognostic marker genes were rigorously selected using GEO database, Lasso regression, GEPIA, Kaplan-Meier and pROC analyses. The expression of interested markers (ADH4, DNASE1L3, RDH16, LCAT, HGFAC) in HCC and adjacent tissues was assessed by Immunohistochemistry (IHC). We observed that ADH4 exhibited low expression levels in liver cancer tissues and high expression levels in normal liver tissues. However, the remaining four genes did not manifest any statistically significant differences between hepatocellular carcinoma (HCC) tissue and adjacent non-cancerous tissue. Consequently, ADH4 became the primary focus of our research. ADH4 expression was validated by signed-rank tests and unpaired Wilcoxon rank sum tests across pan-cancer and HCC datasets. Clinical significance and associations with clinicopathological variables were determined using Kaplan-Meier, logistic regression and Cox analyses on TCGA data. The ADH4-related immune responses were explored by Spearman correlation analysis using TIMER2 data. CD68, CD4, and CD19 protein levels were confirmed by IHC in HCC and non-cancerous tissues.ResultsADH4 showed significant downregulation in various cancers, particularly in HCC. Moreover, low ADH4 expression was associated with clinicopathological variables and served as an independent prognostic marker for HCC patients. Additionally, ADH4 affects a variety of biochemical functions and may influence cancer development, prognosis, and treatment by binding to immune cells. Furthermore, at the immune level, the low expression pattern of ADH4 is TME-specific, indicating that ADH4 has the potential to be used as a target for cancer immunotherapy.ConclusionThis study highlights the diagnostic, prognostic and immunomodulatory roles of ADH4 in HCC. ADH4 could serve as a valuable biomarker for HCC diagnosis and prognosis, as well as a potential target for immunotherapeutic interventions.

Development of a Cancer-associated Fibroblast Signature for Evaluating Immunotherapy Response and Prognosis of Hepatocellular Carcinoma.

Alcohol abuse, non-alcoholic fatty liver disease (NAFLD), and hepatitis B and C are the main pathogenic factors of hepatocellular carcinoma (HCC). Though the current understanding of risk factors for HCC has been improved, patients with this type of cancer are normally diagnosed at advanced stages, posing significant challenges to effective treatment. This study analyzed the HCC datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database (GSE14520 and GSE116174). Stromal and immune cell infiltration in the tumor microenvironment (TME) was quantified by the ESTIMATE algorithm. To identify gene modules associated with cancer-associated fibroblasts (CAFs), weighted gene co-expression network analysis (WGCNA) was performed to develop gene co-expression networks. A CAF prognosis score (CAFPS) model was established based on the prognostic CAF genes screened by univariate and multivariate Cox regression analyses. To determine the role of the genes in the vital module in HCC, we conducted Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Finally, the relationship between CAFPS and drug sensitivity was analyzed using Genomic Data for Cancer Drug Sensitivity (GDSC). In this study, we found significant differences in immune scores, stromal scores, CAFs scores, and CD4/8 T-cell scores between normal samples and samples with different TNM staging. In particular, the proportion of CAFs was higher than all other cells in normal samples. Gene modules related to CAFs were identified by developing a gene co-expression network using WGCNA analysis. The lightyellow and greenyellow modules showed the highest correlation with CAF scores. Univariate COX analysis identified 12 genes related to HCC prognosis from a total of 191 genes in the two modules. The Kaplan-Meier (KM) survival analysis revealed that a high expression of these genes was associated with a lower survival chance. Based on the 12 genes obtained by univariate COX analysis, multivariate COX analysis was performed to construct a risk score model for the characteristics of CAFs (CAFPS). The KM survival curves of patients in the high CAFPS and low CAFPS groups showed that patients in the low CAFPS group had better survival. CAFs played a crucial role in the pathogenesis and treatment response of HCC. Targeting the CAFs milieu may provide therapeutic benefits, highlighting the importance of CAFS in developing a personalized treatment for HCC patients. Further studies are required to verify the current findings and explore their implications in clinical settings.

Mitochondrial GCN5L1 acts as a novel regulator for iron homeostasis to promote sorafenib sensitivity in hepatocellular carcinoma

BackgroundSorafenib resistance is becoming increasingly common and disadvantageous for hepatocellular carcinoma (HCC) treatment. Ferroptosis is an iron dependent programmed cell death underlying the mechanism of sorafenib. Iron is crucial for synthesis of cofactors essential to mitochondrial enzymes and necessary for HCC proliferation, while mitochondrial iron overload and oxidative stress are associated with sorafenib induced ferroptosis. However, the crosstalk among iron homeostasis and sorafenib resistance is unclear.MethodsWe conducted bioinformatics analysis of sorafenib treated HCC datasets to analyze GCN5L1 and iron related gene expression with sorafenib resistance. GCN5L1 deleted HCC cell lines were generated by CRISPR technology. Sorafenib resistant HCC cell line was established to validate dataset analysis and evaluate the effect of potential target.ResultsWe identified GCN5L1, a regulator of mitochondrial acetylation, as a modulator in sorafenib-induced ferroptosis via affecting mitochondrial iron homeostasis. GCN5L1 deficiency significantly increased sorafenib sensitivity in HCC cells by down-regulating mitochondrial iron transporters CISD1 expression to induce iron accumulation. Mitochondrial iron accumulation leads to an acceleration in cellular and lipid ROS. Sorafenib resistance is related to CISD1 overexpression to release mitochondrial iron and maintaining mitochondrial homeostasis. We combined CISD1 inhibitor NL-1 with sorafenib, which significantly enhanced sorafenib-induced ferroptosis by promoting mitochondrial iron accumulation and lipid peroxidation. The combination of NL-1 with sorafenib enhanced sorafenib efficacy in vitro and in vivo.ConclusionsOur findings demonstrate that GCN5L1/CISD1 axis is crucial for sorafenib resistance and would be a potential therapeutic strategy for sorafenib resistant HCC.

STEAP2 promotes hepatocellular carcinoma progression via increased copper levels and stress-activated MAP kinase activity

Six Transmembrane Epithelial Antigen of Prostate 2 (STEAP2) belongs to a family of metalloreductases, which indirectly aid in uptake of iron and copper ions. Its role in hepatocellular carcinoma (HCC) remains to be characterized. Here, we report that STEAP2 expression was upregulated in HCC tumors compared with paired adjacent non-tumor tissues by RNA sequencing, RT-qPCR, Western blotting, and immunostaining. Public HCC datasets demonstrated upregulated STEAP2 expression in HCC and positive association with tumor grade. Transient and stable knockdown (KD) of STEAP2 in HCC cell lines abrogated their malignant phenotypes in vitro and in vivo, while STEAP2 overexpression showed opposite effects. STEAP2 KD in HCC cells led to significant alteration of genes associated with extracellular matrix organization, cell adhesion/chemotaxis, negative enrichment of an invasiveness signature gene set, and inhibition of cell migration/invasion. STEAP2 KD reduced intracellular copper levels and activation of stress-activated MAP kinases including p38 and JNK. Treatment with copper rescued the reduced HCC cell migration due to STEAP2 KD and activated p38 and JNK. Furthermore, treatment with p38 or JNK inhibitors significantly inhibited copper-mediated cell migration. Thus, STEAP2 plays a malignant-promoting role in HCC cells by driving migration/invasion via increased copper levels and MAP kinase activities. Our study uncovered a novel molecular mechanism contributing to HCC malignancy and a potential therapeutic target for HCC treatment.

Prognostic insights, immune infiltration, and therapeutic response: Cytoplasmic poly(A) tail regulators in hepatocellular carcinoma

The presence of a poly(A) tail is indispensable for the post-transcriptional regulation of gene expression in cancer. This dynamic and modifiable feature of transcripts is under the control of various nuclear and cytoplasmic proteins. This study aimed to develop a novel cytoplasmic poly(A)-related signature for predicting prognosis, clinical attributes, tumor immune microenvironment (TIME), and treatment response in hepatocellular carcinoma (HCC). Utilizing RNA-seq data from The Cancer Genome Atlas (TCGA), non-negative matrix factorization (NMF) and principal component analysis (PCA) were employed to categorize HCC patients into three clusters, thus demonstrating the pivotal prognostic role of cytoplasmic poly(A) tail regulators. Furthermore, machine learning algorithms such as least absolute shrinkage and selection operator (LASSO), survival analysis, and Cox proportional hazards modeling were able to distinguish distinct cytoplasmic poly(A) subtypes. As a result, a 5-gene signature derived from TCGA was developed and validated using International Cancer Genome Consortium (ICGC) HCC datasets. This novel classification based on cytoplasmic poly(A) regulators has the potential to improve prognostic predictions and provide guidance for chemotherapy, immunotherapy, and transarterial chemoembolization (TACE) in HCC.

Role of CENPF and NDC80 in the rehabilitation nursing of hepatocellular carcinoma and cirrhosis: An observational study.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors globally and often develops on the foundation of chronic liver disease or cirrhosis. Cirrhosis is a clinically prevalent chronic progressive liver disease characterized by diffuse liver damage resulting from long-term or repeated actions of 1 or more etiological factors. However, the impact of CENPF and nuclear division cycle 80 (NDC80) genes on rehabilitation nursing of HCC and cirrhosis remains unclear. HCC and cirrhosis datasets GSE63898 and GSE89377 profile files were downloaded from the gene expression omnibus database generated on platforms GPL13667 and GPL6947, respectively. Differentially expressed genes (DEGs) screening, weighted gene co-expression network analysis (WGCNA), construction and analysis of protein-protein interaction (PPI) networks, functional enrichment analysis, gene set enrichment analysis (GSEA), survival analysis, immune infiltration analysis, and comparative toxicogenomics database (CTD) analysis were conducted. Gene expression heatmaps were plotted. miRNAs regulating central DEGs were selected through TargetScan. A total of 626 DEGs were identified. According to gene ontology (GO) analysis, they were primarily enriched in small molecule metabolic processes, drug metabolic processes, binding of identical proteins, and lipid metabolic processes. Kyoto Encyclopedia of Gene and Genome (KEGG) analysis results indicated that the target genes were mainly enriched in metabolic pathways, phagosomes, glycine, serine, and threonine metabolism. The construction and analysis of the PPI network revealed 3 core genes (NDC80, CENPF, RRM2). Gene expression heatmaps showed that core genes (CENPF, NDC80) were highly expressed in HCC and cirrhosis samples. CTD analysis found that 2 genes (CENPF and NDC80) were associated with liver, jaundice, ascites, fever, dyspepsia, and hepatic encephalopathy. CENPF and NDC80 are highly expressed in HCC and cirrhosis, and CENPF and NDC80 might be the biomarkers of rehabilitation nursing of HCC and cirrhosis.

Abstract 6175: Deep learning-based survival prediction for hepatocellular carcinoma using whole slide images

Abstract There is an increasing need for classifiers to effectively stratify prognosis in patients with hepatocellular carcinoma (HCC). Deriving interpretable prognostic features using deep-learning (DL)-based prognostic models from pathology images remains a challenge. In this study, we developed a computational approach based on DL to predict the overall survival (OS) of HCC patients from formalin-fixed paraffin-embedded histopathology whole slide images. A DL model for classifying good and poor prognosis was developed on a total of 460 patients with 2 independent cohorts from Seoul St. Mary's Hospital. The primary outcome was OS. The training set (SSM1, n = 292) included HCC patients that had undergone surgical resection. The classifier was then independently tested on the second HCC dataset (SSM2, n = 168). The area under the curve (AUC) for the receiver operating characteristic (ROC) curves was 0.850 and 0.781 for SSM1 and SSM2 datasets, respectively. In addition, we have tried to establish DL model for prediction of OS based on clinicopathological features, including Edmondson-Steiner grade, pT stage, vascular invasion, Ki-67 index, and cytokeratin 19 immunohistochemical expression. Random forest model was applied to classify the good and poor prognosis groups and The AUROCs for the random forest was 0.705 for the SSM1 dataset, which are inferior to that of DL prediction. These findings suggest that the DL techniques can be applied to refine the prognostic prediction of HCC patients. Citation Format: Sung Hak Lee, Hyun-Jong Jang. Deep learning-based survival prediction for hepatocellular carcinoma using whole slide images [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 6175.

Abstract 7035: Characterization of tissue-resident memory T cells in hepatocellular carcinoma through single-cell multi-omics

Abstract As the fifth leading cause of cancer-related mortality, liver cancer imposes a global burden. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with a five-year overall survival rate of less than 20% for advanced-stage cases. Even the most advanced immunotherapy for HCC has disparate response rates in each patient with HCC, which has led to efforts to unravel the underlying factors in the progression of HCC using bioinformatic approaches. In recent decades, numerous studies have explored T-cell subtypes with tissue-specific residency, suggesting their early protective functions and therapeutic potential within tumor microenvironment (TME). This study aimed to understand inter-individual and intercellular TME through single-cell RNA sequencing (scRNA-seq) and single-cell TCR sequencing (scTCR-seq) in five recurrent/non-recurrent HCC patients. We identified tumor-associated tissue-resident memory T (TA-TRM) cells and investigated the heterogeneous characteristics of two TRM clusters (CD69+ and CD103+). Whereas CD69+ TA-TRM showed high expression of genes associated with immunological activation, CD103+ TA-TRM exhibited upregulation of genes related to proliferation capacity. To validate the unique molecular patterns, we analyzed an independent HCC dataset (GSE140228) and observed high concordance of significant core marker genes for TA-TRM clusters between the two datasets. Moreover, we assessed the association of gene expression in TA-TRM cells with overall and recurrence-free survival by conducting survival analysis in two independent large-scale cohorts. The cross-validation approach was applied to estimate risk scores for patients with primary HCC tumors in The Cancer Genome Atlas (TCGA), and the trained model was evaluated using the International Cancer Genome Consortium (ICGC) data. The result of calculating the risk scores clearly indicates that TA-TRM cells are associated with survival in HCC. Therefore, our study characterized two distinct subtypes of TA-TRM cells in HCC, delineated their unique molecular signatures, and established their relevance to survival outcomes. The findings can potentially enhance the understanding of the roles of TA-TRM cells in the context of HCC. Citation Format: Mi-So Park, Hyunbin Cho, Hyeree Kim, Woong-Yang Park, Yong-Han Paik, Yeup Yoon, Wonseok Kang, Hong-Hee Won. Characterization of tissue-resident memory T cells in hepatocellular carcinoma through single-cell multi-omics [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 7035.

DNMT3L inhibits hepatocellular carcinoma progression through DNA methylation of CDO1: insights from big data to basic research.

DNMT3L is a crucial DNA methylation regulatory factor, yet its function and mechanism in hepatocellular carcinoma (HCC) remain poorly understood. Bioinformatics-based big data analysis has increasingly gained significance in cancer research. Therefore, this study aims to elucidate the role of DNMT3L in HCC by integrating big data analysis with experimental validation. Dozens of HCC datasets were collected to analyze the expression of DNMT3L and its relationship with prognostic indicators, and were used for molecular regulatory relationship evaluation. The effects of DNMT3L on the malignant phenotypes of hepatoma cells were confirmed in vitro and in vivo. The regulatory mechanisms of DNMT3L were explored through MSP, western blot, and dual-luciferase assays. DNMT3L was found to be downregulated in HCC tissues and associated with better prognosis. Overexpression of DNMT3L inhibits cell proliferation and metastasis. Additionally, CDO1 was identified as a target gene of DNMT3L and also exhibits anti-cancer effects. DNMT3L upregulates CDO1 expression by competitively inhibiting DNMT3A-mediated methylation of CDO1 promoter. Our study revealed the role and epi-transcriptomic regulatory mechanism of DNMT3L in HCC, and underscored the essential role and applicability of big data analysis in elucidating complex biological processes.

Prognostic and metabolic characteristics of a novel cuproptosis-related signature in patients with hepatocellular carcinoma

Cuproptosis is a novel discovered mode of programmed cell death. To identify the molecular regulatory patterns related to cuproptosis, this study was designed for exploring the correlation between cuproptosis-related genes (CRGs) and the prognosis, metabolism, and treatment of hepatocellular carcinoma (HCC). Cancer Genome Atlas (TCGA) database was used to screen 363 HCC samples, which were categorized into 2 clusters based on the expression of CRGs. Survival analysis demonstrated that overall survival (OS) was better in Cluster 1 than Cluster 2 which might to be relevant to differences in metabolic based on functional analysis. With LASSO regression analysis and univariate COX regression, 8 prognosis-related genes were screened, a differently expressed genes (DEGs) were then constructed (HCC patients' DEGs)-based signature. The signature's stability was also validated in the 2 independent cohorts and test cohorts (GSE14520, HCC dataset in PCAWG). The 1-year, 3-year, and 5-year area under the curve (AUC) were 0.756, 0.706, and 0.722, respectively. The signature could also well predict the response to chemotherapy, targeted and transcatheter arterial chemoembolization (TACE) by providing a risk score. Moreover, the correlation was uncovered by the research between the metabolism and risk score. In conclusion, a unique cuproptosis-related signature that be capable of predicting patients' prognosis with HCC, and offered valuable insights into chemotherapy, TACE and targeted therapies for these patients has been developed.

Deciphering the immune modulation through deep transcriptomic profiling and therapeutic implications of DNA damage repair pattern in hepatocellular carcinoma

AimsDNA damage repair (DDR) plays a pivotal role in hepatocellular carcinoma (HCC), driving oncogenesis, progression, and therapeutic response. However, the mechanisms of DDR mediated immune cells and immuno-modulatory pathways in HCC are yet ill-defined. MethodsOur study introduces an innovative deep machine learning framework for precise DDR assessment, utilizing single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. Single-cell RNA sequencing data were obtained and in total 85,628 cells of primary or post-immunotherapy cases were analyzed. Large-scale HCC datasets, including 1027 patients in house together with public datasets, were used for 101 machine-learning models and a novel DDR feature was derived at single-cell resolution (DDRscore). Druggable targets were predicted using the reverse phase protein array (RPPA) proteomic profiling of 169 HCC patients and RNA-seq data from 22 liver cancer cell lines. ResultsOur investigation reveals a dynamic interplay of DDR with natural killer cells and B cells in the primary HCC microenvironment, shaping a tumor-promoting immune milieu through metabolic programming. Analysis of HCC post-immunotherapy demonstrates elevated DDR levels that induces epithelial-mesenchymal transition and fibroblast-like transformation, reshaping the fibrotic tumor microenvironment. Conversely, attenuated DDR promotes antigen cross-presentation by dendritic cells and CD8+ T cells, modulating the inflammatory tumor microenvironment. Regulatory network analysis identifies the CXCL10-CXCR3 axis as a key determinant of immunotherapeutic response in low DDR HCC, potentially regulated by transcription factors GATA3, REL, and TBX21. Using machine learning techniques by combining bulk RNA-seq data in house together with public datasets, we introduce DDRscore, a robust consensus DDR scoring system to predict overall survival and resistance to PD-1 therapy in HCC patients. Finally, we identify BRAF as a potential therapeutic target for high DDRscore patients. ConclusionOur comprehensive findings advance our understanding of DDR and the tumor microenvironment in HCC, providing insights into immune regulatory mechanisms mediated via DDR pathways.

Chemical Complementarity of Tumor Resident, Adaptive Immune Receptor CDR3s and Previously Defined Hepatitis C Virus Epitopes Correlates with Improved Outcomes in Hepatocellular Carcinoma.

To better understand how adaptive immune receptors (IRs) in hepatocellular carcinoma (HCC) microenvironments are related to disease outcomes, we employed a chemical complementarity scoring algorithm to quantify electrostatic complementarity between HCC tumor TRB or IGH complementarity-determining region 3 (CDR3) amino acid (AA) sequences and previously characterized hepatitis C virus (HCV) epitopes. High electrostatic complementarity between HCC-resident CDR3s and 12 HCV epitopes was associated with greater survival probabilities, as indicated by two distinct HCC IR CDR3 datasets. Two of the HCV epitopes, HCV*71871 (TRB) and HCV*13458 (IGH), were also determined to represent significantly larger electrostatic CDR3-HCV epitope complementarity in HCV-positive HCC cases, compared with HCV-negative HCC cases, with the CDR3s representing yet a third, independent HCC dataset. Overall, the results indicated the utility of CDR3 AA sequences as biomarkers for HCC patient stratification and as potential guides for the development of therapeutic reagents.

CCDC167 promotes hepatocellular carcinoma cell proliferation and is associated with the immune microenvironment of hepatocellular carcinoma

Coiled-coil domain-containing (CCDC)family proteins play an important role in tumour progression, however, the role of CCDC167 in hepatocellular carcinoma remains unclear. To investigate the role of coiled-coil structural domain 167 (CCDC167) in hepatocellular carcinoma progression, we downloaded three hepatocellular carcinoma datasets (TCGA-LIHC, ICGC-LIRI and GSE76427) and analysed the mechanisms by which CCDC167 regulates hepatocellular carcinoma progression using MetaScape, CPTAC, HPA, TISCH and TISIDB.“ pRRophetic” package predicted the chemotherapeutic agents associated with CCDC167. The results showed that CCDC167 was highly expressed in hepatocellular carcinoma tissues and was associated with poor prognosis in patients with hepatocellular carcinoma. Knockdown of CCDC167 inhibits the proliferative capacity of hepatocellular carcinoma cells. CCDC167 was involved in the regulation of cell cycle, DNA replication and oxidative phosphorylation. Hepatocellular carcinoma samples with high expression of CCDC167 showed higher immune scores and C4 (lymphocyte depleted) subtypes. CCDC167 was associated with a variety of immune molecules (CD274, IL6R, and KDR), not only that, but CCDC167 was positively correlated with Treg infiltration and negatively correlated with T cells CD4 memory resting. High expression of CCDC167 increased immune escape in hepatocellular carcinoma patients. CCDC167 was associated with sensitivity to cisplatin in hepatocellular carcinoma patients. CCDC167 promoted the proliferative capacity of hepatocellular carcinoma cells. Our study suggests that CCDC167 is a novel and potentially valuable prognostic marker for hepatocellular carcinoma, and that CCDC167 iCCDC167 not only promotes hepatocellular carcinoma cell proliferation but is also associated with immune function in hepatocellular carcinoma patients.

Cellular senescence throws new insights into patient classification and pharmacological interventions for clinical management of hepatocellular carcinoma.

Cellular senescence, a state of stable growth arrest, is intertwined with human cancers. However, characterization of cellular senescence-associated phenotypes in hepatocellular carcinoma (HCC) remains unexplored. To address this issue, we delineated cellular senescence landscape across HCC. We enrolled two HCC datasets, TCGA-LIHC and International Cancer Genome Consortium (ICGC). Unsupervised clustering was executed to probe tumor heterogeneity based upon cellular senescence genes. Least absolute shrinkage and selection operator algorithm were utilized to define a cellular senescence-relevant scoring system. TRNP1 expression was measured in HCCs and normal tissues through immunohistochemistry, immunoblotting and quantitative real-time polymerase chain reaction. The influence of TMF-regulated nuclear protein (TRNP)1 on HCC senescence and growth was proven via a series of experiments. TCGA-LIHC patients were classified as three cellular senescence subtypes, named C1-3. The robustness and reproducibility of these subtypes were proven in the ICGC cohort. C2 had the worst overall survival, C1 the next, and C3 the best. C2 presented the highest levels of immune checkpoints, abundance of immune cells, and immunogenetic indicators. Thus, C2 might possibly respond to immunotherapy. C2 had the lowest somatic mutation rate, while C1 presented the highest copy number variations. A cellular senescence-relevant gene signature was generated, which can predict patient survival, and chemo- or immunotherapeutic response. Experimentally, it was proven that TRNP1 presented the remarkable upregulation in HCCs. TRNP1 knockdown induced apoptosis and senescence of HCC cells and attenuated tumor growth. These findings provide a systematic framework for assessing cellular senescence in HCC, which decode the tumor heterogeneity and tailor the pharmacological interventions to improve clinical management.

PGAM1 Inhibition Promotes HCC Ferroptosis and Synergizes with Anti-PD-1 Immunotherapy.

The combination of immunotherapy and molecular targeted therapy exhibits promising therapeutic efficacy in hepatocellular carcinoma (HCC), but the underlying mechanism is still unclear. Here, phosphoglycerate mutase 1 (PGAM1) is identified as a novel immunometabolic target by using a bioinformatic algorithm based on multiple HCC datasets. PGAM1 is highly expressed in HCC and associated with a poor prognosis and a poor response to immunotherapy. In vitro and in vivo experiments indicate that targeting PGAM1 inhibited HCC cell growth and promoted the infiltration of CD8+ T-cells due to decreased enzymatic activity. Mechanistically, inhibition of PGAM1 promotes HCC cell ferroptosis by downregulating Lipocalin (LCN2) by inducing energy stress and ROS-dependent AKT inhibition, which can also downregulate Programmed death 1-ligand1 (PD-L1). Moreover, an allosteric PGAM1 inhibitor (KH3) exhibits good antitumor effects in patient-derived xenograft (PDX) models and enhanced the efficacy of anti-PD-1 immunotherapy in subcutaneous and orthotopic HCC models. Taken together, the findings demonstrate that PGAM1 inhibition exerts an antitumor effect by promoting ferroptosis and CD8+ T-cell infiltration and can synergize with anti-PD-1 immunotherapy in HCC. Targeting PGAM1 can be a promising new strategy of "killing two birds with one stone" for HCC treatment.

Clinical Significance of Apurinic/Apyrimidinic Endodeoxyribonuclease 1 and MicroRNA Axis in Hepatocellular Carcinoma.

Identification of prognostic factors for hepatocellular carcinoma (HCC) opens new perspectives for therapy. Circulating and cellular onco-miRNAs are noncoding RNAs which can control the expression of genes involved in oncogenesis through post-transcriptional mechanisms. These microRNAs (miRNAs) are considered novel prognostic and predictive factors in HCC. The apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1) contributes to the quality control and processing of specific onco-miRNAs and is a negative prognostic factor in several tumors. The present work aims to: a) define APE1 prognostic value in HCC; b) identify miRNAs regulated by APE1 and their relative target genes and c) study their prognostic value. We used The Cancer Genome Atlas (commonly known as TCGA) data analysis to evaluate the expression of APE1 in HCC. To identify differentially-expressed miRNAs (DEmiRNAs) upon APE1 depletion through specific small interfering RNA, we used NGS and nanostring approaches in the JHH-6 HCC tumor cell line. Bioinformatics analyses were performed to identify signaling pathways involving APE1-regulated miRNAs. Microarray analysis was performed to identify miRNAs correlating with serum APE1 expression. APE1 is considerably overexpressed in HCC tissues compared to normal liver, according to the TCGA-liver HCC (known as LIHC) dataset. Enrichment analyses showed that APE1-regulated miRNAs are implicated in signaling and metabolic pathways linked to cell proliferation, transformation, and angiogenesis, identifying Cyclin Dependent Kinase 6 and Lysosomal Associated Membrane Protein 2 as targets. miR-33a-5p, miR-769, and miR-877 are related to lower overall survival in HCC patients. Through array profiling, we identified eight circulating DE-miRNAs associated with APE1 overexpression. A training phase identified positive association between sAPE1 and miR-3180-3p and miR-769. APE1 regulates specific miRNAs having prognostic value in HCC.