Significance In Hepatocellular Carcinoma Research Articles

CSNK2A2 promotes hepatocellular carcinoma progression through activation of NF-κB pathway

Introduction and ObjectivesBreast and non-small cell lung cancers harbor an upregulated CSNK2A2 oncogene that encodes the protein kinase CK2 alpha', a catalytic subunit of the highly conserved serine/threonine kinase CK2. However, its role and biological significance in hepatocellular carcinoma (HCC) remains unclear. Materials and MethodsWestern-blotting and immunohistochemistry were used to measure the expression of CSNK2A2 in HCC tumor tissues and cell lines. CCK8, Hoechst staining, transwell, tube formation assay in vitro and nude mice experiments in vivo were used to measure the effects of CSNK2A2 on HCC proliferation, apoptosis, metastasis, angiogenesis and tumor formation. ResultsIn the study, we showed that CSNK2A2 was highly expressed in HCC comparison with matched control tissues, and was linked with lower survival of patients. Additional experiments indicated that silencing of CSNK2A2 promoted HCC cell apoptosis, while inhibited HCC cells migrating, proliferating, angiogenesis both in vitro and in vivo. These effects were also accompanied by reduced expression of NF-κB target genes, including CCND1, MMP9 and VEGF. Moreover, treatment with PDTC counteracted the promotional effects of CSNK2A2 on HCC cells. ConclusionsOverall, our results suggested that CSNK2A2 could promote HCC progression by activating the NF-κB pathway, and this could serve as a biomarker for future prognostic and therapeutic applications.

Glutamine synthetase-negative hepatocellular carcinoma has better prognosis and response to sorafenib treatment after hepatectomy.

Glutamine synthetase (GS) and arginase 1 (Arg1) are widely used pathological markers that discriminate hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma; however, their clinical significance in HCC remains unclear. We retrospectively analyzed 431 HCC patients: 251 received hepatectomy alone, and the other 180 received sorafenib as adjuvant treatment after hepatectomy. Expression of GS and Arg1 in tumor specimens was evaluated using immunostaining. mRNA sequencing and immunostaining to detect progenitor markers (cytokeratin 19 [CK19] and epithelial cell adhesion molecule [EpCAM]) and mutant TP53 were also conducted. Up to 72.4% (312/431) of HCC tumors were GS positive (GS+). Of the patients receiving hepatectomy alone, GS negative (GS-) patients had significantly better overall survival (OS) and recurrence-free survival (RFS) than GS+ patients; negative expression of Arg1, which is exclusively expressed in GS- hepatocytes in the healthy liver, had a negative effect on prognosis. Of the patients with a high risk of recurrence who received additional sorafenib treatment, GS- patients tended to have better RFS than GS+ patients, regardless of the expression status of Arg1. GS+ HCC tumors exhibit many features of the established proliferation molecular stratification subtype, including poor differentiation, high alpha-fetoprotein levels, increased progenitor tumor cells, TP53 mutation, and upregulation of multiple tumor-related signaling pathways. GS- HCC patients have a better prognosis and are more likely to benefit from sorafenib treatment after hepatectomy. Immunostaining of GS may provide a simple and applicable approach for HCC molecular stratification to predict prognosis and guide targeted therapy.

Identification of ferroptosis and drug resistance related hub genes to predict the prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Currently, overcoming the drug resistance in HCC is a critical challenge and ferroptosis has emerged as a promising therapeutic option for cancer. We aim to construct a new gene signature related to ferroptosis and drug resistance to predict the prognosis in HCC. The RNA-seq data of HCC patients was obtained from the Cancer Genome Atlas database. Using least absolute shrinkage and selection operator cox regression, Kaplan–Meier analysis, and differential analysis, we constructed a prognostic model consisting of six hub genes (TOP2A, BIRC5, VEGFA, HIF1A, FTH1, ACSL3) related to ferroptosis and drug resistance in HCC. Functional enrichment, pathway enrichment and GSEA analysis were performed to investigate the potential molecular mechanism, and construction of PPI, mRNA-miRNA, mRNA-RBP, mRNA-TF and mRNA-drugs interaction networks to predict its interaction with different molecules. Clinical prognostic characteristics were revealed by univariate, multivariate cox regression analysis and nomogram. We also analyzed the relationship between the signature, immune checkpoints, and drug sensitivity. The expression of the gene signature was detected in HCC cell lines and HPA database. Our prognostic model classified patients into high and low-risk groups based on the risk scores and found the expression level of the genes was higher in the high-risk group than the low-risk group, demonstrating that high expression of the hub genes was associated with poor prognosis in HCC. ROC analysis revealed its high diagnostic efficacy in both HCC and normal tissues. The proportional hazards model and calibration analysis confirmed that the model’s prediction was most accurate for 1- and 3-years survival. QRT-PCR showed the high expression level of the gene signature in HCC. Our study built a novel gene signature with good potential to predict the prognosis of HCC, which may provide new therapeutic targets and molecular mechanism for HCC diagnosis and treatment.

Comprehensive analysis of ferroptosis-related genes for clinical and biological significance in hepatocellular carcinoma

ObjectiveThis study aims to build a prognostic model of hepatocellular carcinoma (HCC) with ferroptosis-associated genes and explore their molecular function.MethodsGene expression data and clinical information were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases and the International Cancer Genome Consortium (ICGC). A ferroptosis-associated gene set was obtained from the FerrDb database to identify differentially expressed genes. Then, we performed pathway enrichment analysis and immune infiltration analysis. A combined model based on ferroptosis-associated genes for predicting the overall survival of HCC was built by univariate and multivariate Cox regression analyses. Quantitative real-time polymerase chain reaction, Western blotting, colony formation, CCK-8, and EdU incorporation assays were performed to clarify the function of CAPG in the regulation of cell proliferation in human HCC. Ferroptosis was evaluated by glutathione (GSH), malondialdehyde (MDA), and total iron detection.ResultsForty-nine ferroptosis-related genes were significantly correlated with HCC, 19 of which had prognostic significance. CAPG, SLC7A11 and SQSTM1 were used to construct a novel risk model. The areas under the curves (AUCs) were 0.746 and 0.720 (1 year) in the training and validation groups, respectively. The survival analysis indicated that patients with high risk scores exhibited worse survival in the training and validation groups. The risk score was also identified as an independent prognostic factor of overall survival (OS), which established and validated the predictive abilities of the nomogram. The risk score was also significantly correlated with the expression of immune checkpoint genes. In vitro data showed that CAPG knockdown dramatically suppressed HCC cell proliferation, and the underlying molecular mechanisms might be that the silencing of CAPG reduced the expression of SLC7A11 and promoted ferroptosis.ConclusionThe established risk model can be used to predict the prognosis of HCC. At the mechanistic level, CAPG may drive HCC progression by regulating SLC7A11, and ferroptosis activation in HCC patients with high CAPG expression may serve as a potential therapeutic strategy.

A novel cuproptosis-related lncRNA signature predicts the prognosis and immunotherapy for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most serious malignant tumors with a poor prognosis worldwide. Cuproptosis is a novel copper-dependent cell death form, involving mitochondrial respiration and lipoylated components of the tricarboxylic acid (TCA) cycle. Long non-coding RNAs (lncRNAs) have been demonstrated to affect the tumorigenesis, growth, and metastasis of HCC. We explored the potential roles of cuproptosis-related lncRNAs in predicting the prognosis for HCC. The RNA-seq transcriptome data, mutation data, and clinical information data of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analyses were performed to identify a prognostic cuproptosis-related lncRNA signature. The receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of the lncRNA signature for HCC. The enrichment pathways, immune functions, immune cell infiltration, tumor mutation burden, and drug sensitivity were also analyzed. We constructed a prognostic model consisting of 8 cuproptosis-related lncRNAs for HCC. The patients were divided into high-risk group and low-risk group according to the riskscore calculated using the model. Kaplan-Meier analysis revealed that the high-risk lncRNA signature was correlated with poor overall survival [hazard ratio (HR) =1.009, 95% confidence interval (CI) = 1.002-1.015; p= 0.010)] of HCC. A prognostic nomogram incorporated the lncRNA signature and clinicopathological features were constructed and showed favorable performance for predicting prognosis of HCC patients. In addition, the most immune-related functions were significantly different between the high-risk and low-risk groups. Tumor mutation burden (TMB) and immune checkpoints were also expressed differently between the two risk groups. Finally, HCC patients with low-risk score were more sensitive to several chemotherapy drugs. The novel cuproptosis-related lncRNA signature could be used to predict prognosis and evaluate the effect of chemotherapy for HCC.

Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma

We used pyroptosis-related genes to establish a risk–score model for prognostic prediction of liver hepatocellular carcinoma (LIHC) patients. A total of 52 pyroptosis-associated genes were identified. Then, data for 374 LIHC patients and 50 normal individuals were acquired from the TCGA database. Through gene expression analyses, differentially expressed genes (DEGs) were determined. The 13 pyroptosis-related genes (PRGs) confirmed as potential prognostic factors through univariate Cox regression analysis were entered into Lasso and multivariate Cox regression to build a PRGs prognostic signature, containing four PRGs (BAK1, GSDME, NLRP6, and NOD2) determined as independent prognostic factors. mRNA levels were evaluated by qRT-PCR, while overall survival (OS) rates were assessed by the Kaplan–Meier method. Enrichment analyses were done to establish the mechanisms associated with differential survival status of LIHC patients from a tumor immunology perspective. Additionally, a risk score determined by the prognostic model could divide LIHC patients into low- and high-risk groups using median risk score as cut-off. A prognostic nomogram, derived from the prognostic model and integrating clinical characteristics of patients, was constructed. The prognostic function of the model was also validated using GEO, ICGC cohorts, and online databases Kaplan–Meier Plotter. Small interfering RNA-mediated knockdown of GSDME, as well as lentivirus-mediated GSDME knockdown, were performed to validate that knockdown of GSDME markedly suppressed growth of HCC cells both in vivo and in vitro. Collectively, our study demonstrated a PRGs prognostic signature that had great clinical value in prognosis assessment.

NK cell marker gene-based model shows good predictive ability in prognosis and response to immunotherapies in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the fourth leading cause of malignancy worldwide, and its progression is influenced by the immune microenvironment. Natural killer (NK) cells are essential in the anti-tumor response and have been linked to immunotherapies for cancers. Therefore, it is important to unify and validate the role of NK cell-related gene signatures in HCC. In this study, we used RNA-seq analysis on HCC samples from public databases. We applied the ConsensusClusterPlus tool to construct the consensus matrix and cluster the samples based on their NK cell-related expression profile data. We employed the least absolute shrinkage and selection operator regression analysis to identify the hub genes. Additionally, we utilized the CIBERSORT and ESTIMATE web-based methods to perform immune-related evaluations. Our results showed that the NK cell-related gene-based classification divided HCC patients into three clusters. The C3 cluster was activated in immune activation signaling pathways and showed better prognosis and good clinical features. In contrast, the C1 cluster was remarkably enriched in cell cycle pathways. The stromal score, immune score, and ESTIMATE score in C3 were much higher than those in C2 and C1. Furthermore, we identified six hub genes: CDC20, HMOX1, S100A9, CFHR3, PCN1, and GZMA. The NK cell-related genes-based risk score subgroups demonstrated that a higher risk score subgroup showed poorer prognosis. In summary, our findings suggest that NK cell-related genes play an essential role in HCC prognosis prediction and have therapeutic potential in promoting NK cell antitumor immunity. The six identified hub genes may serve as useful biomarkers for novel therapeutic targets.

Ubiquitin-proteasome system-based signature to predict the prognosis and drug sensitivity of hepatocellular carcinoma.

Background: Ubiquitin-proteasome system (UPS) is implicated in cancer occurrence and progression. Targeting UPS is emerging as a promising therapeutic target for cancer treatment. Nevertheless, the clinical significance of UPS in hepatocellular carcinoma (HCC) has not been entirely elucidated. Methods: Differentially expressed UPS genes (DEUPS) were screened from LIHC-TCGA datasets. The least absolute shrinkage and selection operator (LASSO) and stepwise multivariate regression analysis were conducted to establish a UPS-based prognostic risk model. The robustness of the risk model was further validated in HCCDB18, GSE14520, and GSE76427 cohorts. Subsequently, immune features, clinicopathologic characteristics, enrichment pathways, and anti-tumor drug sensitivity of the model were further evaluated. Moreover, a nomogram was established to improve the predictive ability of the risk model. Results: Seven UPS-based signatures (ATG10, FBXL7, IPP, MEX3A, SOCS2, TRIM54, and PSMD9) were developed for the prognostic risk model. Individuals with HCC with high-risk scores presented a more dismal prognosis than those with low-risk scores. Moreover, larger tumor size, advanced TNM stage, and tumor grade were observed in the high-risk group. Additionally, cell cycle, ubiquitin-mediated proteolysis, and DNA repair pathways were intimately linked to the risk score. In addition, obvious immune cell infiltration and sensitive drug response were identified in low-risk patients. Furthermore, both nomogram and risk score showed a significant prognosis-predictive ability. Conclusion: Overall, we established a novel UPS-based prognostic risk model in HCC. Our results will facilitate a deep understanding of the functional role of UPS-based signature in HCC and provide a reliable prediction of clinical outcomes and anti-tumor drug responses for patients with HCC.

Identification of therapeutic targets and prognostic biomarkers among CXC chemokines in hepatocellular carcinoma microenvironment.

Hepatocellular carcinoma (HCC) is characterized by occult onset, rapid progression and poor prognosis. CXC chemokines play an important role in tumor microenvironment and development. The potential mechanistic values of CXC chemokines as clinical biomarkers and therapeutic targets in HCC have not been fully clarified. ONCOMINE, UALCAN, GEPIA, cBioPortal, SurvExpress, MethSurv, SurvivalMeth, String, GeneMANIA, DAVID, Metascape, TRRUST, LinkedOmics, and Timer were applied in this study. The transcriptional levels of CXCL9/16/17 in HCC tissues were significantly elevated while CXCL1/2/5/6/7/12/14 were significantly reduced. significant correlation was found between the expression of CXC3/5 and the pathological stage of HCC patients. High level of CXCL4 was associated with a longer disease-free survival. For overall survival, lower expressions of CXCL1/3/5/8 and higher expressions of CXCL2 were associated with a better outcome. In addition, the prognostic values of CXC chemokines signature in HCC were explored in four independent cohorts, the high-risk group displayed unfavorable survival outcome compared with the low-risk group. And for the prognostic value of the DNA methylation of CXC chemokines, we identified the CpGs which were significantly associated with prognosis in HCC patients. DNA methylation signature analysis also showed a statistically significant association between the high- and low-risk group. For potential mechanism, the neighbor gene networks, interaction analyses, functional enrichment analyses of CC chemokine receptors in HCC were performed, the transcription factor targets, kinase targets, and miRNA targets of CXC chemokines were also identified in HCC. We also found significant correlations among CXC chemokines expression and the infiltration of immune cells, the tumor infiltration levels among HCC with different somatic copy number alterations of these chemokine receptors were also assessed. Moreover, the Cox proportional hazard model showed that CCR2/6/8/12, B_cell, macrophage and dendritic _cell were significantly related to the clinical outcome of HCC patients. CXC chemokines might serve as therapeutic targets and prognostic biomarkers in HCC.

Analysis of risk factors of hepatocellular carcinoma and establishment of a clinical prognosis model.

Liver cancer is a common malignancy of the digestive system. Hepatocellular carcinoma (HCC) accounts for the most majority of these tumors and it has brought a heavy medical burden to underdeveloped countries and regions. Many factors affect the prognosis of HCC patients, however, there is no specific statistical model to predict the survival time of clinical patients. This study derived a risk factor signature of HCC and reliable clinical prediction model by statistically analyzing The Surveillance, Epidemiology, and End Results (SEER) database patient information using an open source package in the python environment.

Integrated Analysis of N1-Methyladenosine Methylation Regulators-Related lncRNAs in Hepatocellular Carcinoma

N1-methyladenosine (m1A) and long non-coding RNAs (lncRNAs) play significant roles in tumor progression in hepatocellular carcinoma (HCC). However, their association with HCC is still unclear. In this study, lncRNAs related to m1A were extracted from the mRNA expression matrix in The Cancer Genome Atlas (TCGA) database. Five m1A-related lncRNAs (AL031985.3, NRAV, WAC-AS1, AC026412.3, and AC099850.4) were identified based on lasso Cox regression and they generated a prognostic signature of HCC. The prognostic signature was identified as an independent prognosis factor in HCC patients. Moreover, the prognostic signature achieved better performance than TP53 mutation status or tumor mutational burden (TMB) scores in the stratification of patient survival. The immune landscape indicated that most immune checkpoint genes and immune cells were distributed differently between both risk groups. A higher IC50 of chemotherapeutics (sorafenib, nilotinib, sunitinib, and gefitinib) was observed in the high-risk group, and a lower IC50 of gemcitabine in the low-risk group, suggesting the potential of the prognostic signature in chemosensitivity. In addition, fifty-five potential small molecular drugs were found based on drug sensitivity and NRAV expression. Together, five m1A-related lncRNAs generated a prognostic signature that could be a promising prognostic prediction approach and therapeutic response assessment tool for HCC patients.

Single-cell transcriptome analysis reveals a cancer-associated fibroblast marker gene signature in hepatocellular carcinoma that predicts prognosis

Single-cell transcriptome analysis reveals a cancer-associated fibroblast marker gene signature in hepatocellular carcinoma that predicts prognosis

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

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

Comprehensive analysis of cuproptosis-related prognostic gene signature and tumor immune microenvironment in HCC.

Background: Copper is an indispensable mineral element involved in many physiological metabolic processes. Cuproptosis is associated with a variety of cancer such as hepatocellular carcinoma (HCC). The objective of this study was to examine the relationships between the expression of cuproptosis-related genes (CRGs) and tumor characteristics, including prognosis and microenvironment of HCC. Methods: The differentially expressed genes (DEGs) between high and low CRGs expression groups in HCC samples were identified, and further were analyzed for functional enrichment analysis. Then, CRGs signature of HCC was constructed and analyzed utilizing LASSO and univariate and multivariate Cox regression analysis. Prognostic values of CRGs signature were evaluated by Kaplan-Meier analysis, independent prognostic analysis and nomograph. The expression of prognostic CRGs was verified by Real-time quantitative PCR (RT-qPCR) in HCC cell lines. In addition, the relationships between prognostic CRGs expression and the immune infiltration, tumor microenvironment, antitumor drugs response and m6A modifications were further explored using a series of algorithms in HCC. Finally, ceRNA regulatory network based on prognostic CRGs was constructed. Results: The DEGs between high and low CRG expression groups in HCC were mainly enriched in focal adhesion and extracellular matrix organization. Besides, we constructed a prognostic model that consists of CDKN2A, DLAT, DLST, GLS, and PDHA1 CRGs for predicting the survival likelihood of HCC patients. And the elevated expression of these five prognostic CRGs was substantially in HCC cell lines and associated with poor prognosis. Moreover, immune score and m6A gene expression were higher in the high CRG expression group of HCC patients. Furthermore, prognostic CRGs have higher mutation rates in HCC, and are significantly correlated with immune cell infiltration, tumor mutational burden, microsatellite instability, and anti-tumor drug sensitivity. Then, eight lncRNA-miRNA-mRNA regulatory axes that affected the progression of HCC were predicted. Conclusion: This study demonstrated that the CRGs signature could effectively evaluate prognosis, tumor immune microenvironment, immunotherapy response and predict lncRNA-miRNA-mRNA regulatory axes in HCC. These findings extend our knowledge of cuproptosis in HCC and may inform novel therapeutic strategies for HCC.

Prognostic 7-SLC-Gene Signature Identified via Weighted Gene Co-Expression Network Analysis for Patients with Hepatocellular Carcinoma.

Solute carrier (SLC) proteins play an important role in tumor metabolism. But SLC-associated genes' prognostic significance in hepatocellular carcinoma (HCC) remained elusive. We identified SLC-related factors and developed an SLC-related classifier to predict and improve HCC prognosis and treatment. From the TCGA database, corresponding clinical data and mRNA expression profiles of 371 HCC patients were acquired, and those of 231 tumor samples were derived from the ICGC database. Genes associated with clinical features were filtered using weighted gene correlation network analysis (WGCNA). Next, univariate LASSO Cox regression studies developed SLC risk profiles, with the ICGC cohort data being used in validation. Univariate Cox regression analysis revealed that 31 SLC genes (P < 0.05) were related to HCC prognosis. 7 (SLC22A25, SLC2A2, SLC41A3, SLC44A1, SLC48A1, SLC4A2, and SLC9A3R1) of these genes were applied in developing a SLC gene prognosis model. Samples were classified into the low-andhigh-risk groups by the prognostic signature, with those in the high-risk group showing a significantly worse prognosis (P < 0.001 in the TCGA cohort and P=0.0068 in the ICGC cohort). ROC analysis validated the signature's prediction power. In addition, functional analyses showed enrichment of immune-related pathways and different immune status between the two risk groups. The 7-SLC-gene prognostic signature established in this study helped predict the prognosis, and was also correlated with the tumor immune status and infiltration of different immune cells in the tumor microenvironment. The current findings may provide important clinical indications for proposing a novel combination therapy consists of targeted anti-SLC therapy and immunotherapy for HCC patients.

Comprehensive analysis of cell death genes in hepatocellular carcinoma based on multi-omics data.

Hepatocellular carcinoma (HCC) is a common tumor of the digestive system. Cell death is an essential process in normal tissue that consists of 3 classical pathways: apoptosis, necrosis and autophagy. To perform a comprehensive analysis of the impact of cell death on liver cancer. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Cancer Genome Atlas (TCGA) datasets were used to analyze the relationships between mutations in cell death-related genes and clinical variables of HCC. Then, we applied the DESeq2 package to identify aberrantly expressed genes in HCC and their related biological functions through a Pearson correlation analysis. Finally, a cell death-related signature of HCC was constructed using the single-factor Cox regression. We identified the genes involved in apoptosis, necrosis and autophagy, of which TP53 and SPTA1 had the highest frequency of mutations. The results revealed that cell death-related tumor mutational burden (TMB) was significant for the pathologic stage and had a strong relationship with the prognosis. Moreover, 53 cell death-related genes that are differentially expressed in HCC were screened, and 3 of them were correlated with HCC prognosis. Harvey rat sarcoma viral oncogene homolog (HRAS) affected the infiltration of immune cells and was closely correlated with ferroptosis. Peptidylprolyl isomerase A (PPIA) played a significant role in mitochondrial pathways. At last, we constructed a cell death-related signature of HCC using 10 prognosis-related genes and a nomogram based on 3 variables (expression, group and stage). This study provided a comprehensive analysis of cell death-related genes in HCC based on multi-omics data, identified the contribution of each variable to clinical outcome and predicted the survival probability of HCC patients more directly.

Identification of a prognostic cuproptosis-related signature in hepatocellular carcinoma

BackgroundCuproptosis is a new type of copper-induced cell death that is characterized by the aggregation of lipoylated tricarboxylic acid (TCA) cycle proteins. However, its role in hepatocellular carcinoma (HCC) remains unclear. The goal of this research is to develop a cuproptosis-related signature predicting the prognosis of HCC.MethodsThe cuproptosis-related genes were defined using Pearson correlation coefficients. LASSO-Cox regression analysis was used to evaluate the prognostic values of cuproptosis-related genes to construct a cuproptosis-related prognostic model. The immune microenvironment analysis was performed by “ssGSEA” to reveal the associated immune cell infiltration patterns with the cuproptosis-related genes signature. The expression levels of one of the prognostic genes PDXK were then verified in HCC samples by Western Blot and immunohistochemistry. The potential roles of target genes in cuproptosis were further explored during in-vitro experiments.ResultsA total of 136 cuproptosis-related genes were discovered using Pearson correlation analysis in HCC. A cuproptosis-related signature that included 5 cuproptosis-related genes (PDXK, HPN, SLC25A28, RNFT1, CLEC3B) was established in the TCGA-LIHC training cohort. TCGA validation cohort and another two external validation cohorts confirmed the robustness of the signature’s predictive value. Moreover, a nomogram using the risk score was created to best predict the survival of HCC patients. The immune microenvironment analysis revealed distinct immune infiltrations patterns between different risk groups based on the signature model. Furthermore, the upregulation of PDXK was confirmed in HCC tumor tissues in 30 clinical HCC specimens. The knockdown of PDXK reduced the proliferation, migration and invasion of HCC cells. Besides, the expression of PDXK was upregulated after the induction of cuproptosis by elesclomol–CuCL2, which could be suppressed when pretreated with a copper ion chelator. And PDXK deficiency increased the sensitivity of HCC cells to cuproptosis inducer.ConclusionOur study identified a new cuproptosis-related gene signature that could predict the prognosis of HCC patient. Besides, the upregulated PDXK could promote the proliferation and metastasis of HCC. And PDXK deficiency facilities cuproptosis in HCC. Therefore, these fundings highlighted that PDXK might serve as a potential diagnostic and therapeutic target for HCC.

Human Epidermal Growth Factor Receptor 2 (HER2) Expression by Immunohistochemistry and Its Clinical Significance in Hepatocellular Carcinoma: A Single-Center Analysis.

Human epidermal growth factor receptor 2 (HER2) is a member of the tyrosine kinase receptor family. It has been identified as an oncogene and is associated with poor outcomes in multiple tumortypes. In hepatocellular carcinoma (HCC), there are contradictory data regarding the HER2 expression and its role in tumor development and progression. Some studies have identified HER2 expression as an early event during tumorigenesis, which decreases with progression and metastasis. Additional data provided evidence that treatment with anti-HER2 therapy resulted in local response and reduction in the metastasis rate in HCC micemodels. Patients with histological diagnoses of HCC between 2010 and 2020 were included. HER2 staining was performed by immunohistochemistry (IHC), and scoring was done in accordance with the gastric cancer guidelines as 0, 1+, 2+, and 3+. Clinicopathological features were accessed by medical records. This study aims to evaluate HER2 expression by IHC in HCCand to correlate this expression with some clinicopathological features such as Barcelona Clinic Liver Cancer (BCLC) staging,number of hepatic lesions,alpha-fetoprotein level, underlying liver disease, presence of livercirrhosis, Child-Pugh score, and tumor recurrence. A total of 57 specimens from 54 patients were included. Of the patients, 85% were men, and the median age at diagnosis was 71 years (interquartile range: 59-75 years). Regarding stage, 61% were at stage 0-A of BCLC. Of the patients,57% had a solitary HCC nodule. Concerning treatment, surgery was performed in 50% of the patients.HER2 expression was identified in seven patients: five in the membrane and two in the cytoplasm. Concerning the membrane staining, HER2 expression was scored as 1+/2+ in 7.4% (n = 4 patients). Of the patients with HER2 expression, fourhad a BCLC stage of 0-A and a single HCC nodule; alpha-fetoprotein was <400 ng/mL in all cases. There was no correlation to clinicopathological features. In one patient with HER2 2+ expressionat diagnosis, this expression was not identified at tumor progression. Median disease-free survival in HER2 with IHC scores 1+/2+ and cytoplasmatic was 38 months versus 22 months in HER2 with a score of 0 (p = 0.604). HER2 expression is a rare event in HCC. It was not possible to identify any relation to clinicopathological features. However, when we relate our data to previous trials, HER2 appears to be an early event in the course of HCC.

TLR2 Gene Polymorphism and MicroRNA 301 gene Expression Level as a Signature for Hepatocellular Carcinoma in Egyptian Patients

TLR2 Gene Polymorphism and MicroRNA 301 gene Expression Level as a Signature for Hepatocellular Carcinoma in Egyptian Patients

Comprehensive analysis of cuproptosis-related lncRNAs for prognostic significance and immune microenvironment characterization in hepatocellular carcinoma.

Cuproptosis was characterized as a novel type of programmed cell death. Recently, however, the role of cuproptosis-related long noncoding RNAs (CRLs) in tumors has not yet been studied. Identifying a predictive CRL signature in hepatocellular carcinoma (HCC) and investigating its putative molecular function were the goals of this work. Initially, Pearson's test was used to assess the relationship between lncRNAs and cuproptosis-associated genes obtained from HCC data of The Cancer Genome Atlas (TCGA). By implementing differential expression and univariate Cox analysis, 61 prognostic CRLs were subsequent to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. A prognostic risk score model was then constructed to evaluate its ability to predict patients' survival when combined with clinicopathological parameters in HCC. The five-lncRNA prognostic signature categorized the HCC patients into high- and low-risk groups. The low-risk group exhibited more sensitivity to elesclomol than the high-risk one. Surprisingly, distinct mitochondrial metabolism pathways connected to cuproptosis and pivotal immune-related pathways were observed between the two groups via gene set enrichment analysis (GSEA). Meanwhile, there were substantial differences between the high-risk group and the low-risk group in terms of tumor-infiltrating immune cells (TIICs). Furthermore, a positive relationship was shown between the risk score and the expression of immune checkpoints. Additionally, differential expression of the five lncRNAs was confirmed in our own HCC samples and cell lines via RT-qPCR. Finally, in vitro assays confirmed that WARS2-AS1 and MKLN1-AS knockdown could sensitize HCC cells to elesclomol-induced cuproptosis. Overall, our predictive signature may predict the prognosis of HCC patients in an independent manner, give a better understanding of how CRLs work in HCC, and offer therapeutic reference for patients with HCC.