Hepatocellular Carcinoma Samples Research Articles

UTP11 promotes the growth of hepatocellular carcinoma by enhancing the mRNA stability of Oct4

BackgroundSeveral publications suggest that UTP11 may be a promising gene engaged for involvement of hepatocellular carcinoma (HCC) pathology. However, there are extremely limited biological, mechanistic and clinical studies of UTP11 in HCC.MethodsTo anayze the UTP11 mRNA expression in HCC and normal clinical samples and further investigate the correlation between UTP11 expression and pathology and clinical prognosis via the Cancer Tissue Gene Atlas (TCGA) database. The protein levels of UTP11 were checked using the Human Protein Atlas (HPA) database. GO-KEGG enrichment was performed from Cancer Cell Line Encyclopedia (CCLE) database and TCGA dataset. The levels of UTP11 were tested with qRT-PCR and western blotting assays. Cell viability, immunofluorescence and flow cytometry assays and animal models were used to explore the potential involvement of UTP11 in regulating HCC growth in vitro and in vivo. The correlation of UTP11 and tumor stemness scores and stemness-associated proteins from TCGA database. The mRNA stability was treated with Actinomycin D, followed by testing the mRNA expression using qRT-PCR assay.ResultsUTP11 was highly expressed in HCC samples compared to normal tissues from TCGA database. Similarly, UTP11 protein expression levels were obviously elevated in HCC tissue samples from HPA database. Furthermore, UTP11 levels were correlated with poor prognosis in HCC patient samples in TCGA dataset. In addition, the UTP11 mRNA levels was notably enhanced in different HCC cell lines than in normal liver cells and knocking down UTP11 was obviously reduced the viability and cell death of HCC cells. UTP11 knockdown suppressed the tumor growth of HCC in vivo experiment and extended the mice survival time. GO-KEEG analysis from CCLE and TCGA database suggested that UTP11 might involve in RNA splicing and the stability of mRNA. Further, UTP11 was positively correlated with tumor stemness scores and stemness-associated proteins from TCGA database. Knockdown of UTP11 was reduced the expression of stem cell-related genes and regulated the mRNA stability of Oct4.ConclusionsUTP11 is potentially a diagnostic molecule and a therapeutic candidate for treatment of HCC.

Identification and validation of SLCO4C1 as a biological marker in hepatocellular carcinoma based on anoikis classification features.

Hepatocellular carcinoma (HCC) exhibits a high degree of invasiveness and is closely associated with rapid disease progression. Multiple lines of evidence indicate a strong correlation between anoikis resistance and tumor progression, invasion, and metastasis. Nevertheless, the classification of anoikis in HCC and the investigation of novel biological target mechanisms in this context continue to pose challenges, requiring further exploration. Combined with HCC samples from TCGA, GEO and ICGC databases, cluster analysis was conducted on anoikis genes, revealing novel patterns among different subtypes. Significant gene analysis of different gene subtypes was performed using WCGNA. The anoikis prognostic risk model was established by Lasso-Cox. Go, KEGG, and GSEA were applied to investigate pathway enrichment primarily observed in risk groups. We compared the disparities in immune infiltration, TMB, tumor microenvironment (TME), and drug sensitivity between the two risk groups. RT-qPCR and Western blotting were performed to validate the expression levels of SLCO4C1 in HCC. The biological functions of SLCO4C1 in HCC cells were assessed through various experiments, including CCK8 assay, colony formation assay, invasion migration assay, wound healing assay, and flow cytometry analysis. HCC was divided into 2 anoikis subtypes, and the subtypeB had a better prognosis. An anoikis prognostic model based on 12 (COPZ2, ACTG2, IFI27, SPP1, EPO, SLCO4C1, RAB26, STC2, RAC3, NQO1, MYCN, HSPA1B) risk genes is important for survival and prognosis. Significant differences were observed in immune cell infiltration, TME, and drug sensitivity analysis between the risk groups. SLCO4C1 was downregulated in HCC. SLCO4C1 downregulation promoted the proliferation, invasion, migration, and apoptosis of HCC cells. The tumor-suppressive role of SLCO4C1 in HCC has been confirmed. Our study presents a novel anoikis classification method for HCC that reveals the association between anoikis features and HCC. The anoikis feature is a critical biomarker bridging tumor cell death and tumor immunity. In this study, we provided the first evidence of SLCO4C1 functioning as a tumor suppressor in HCC.

Prognostic analysis and risk assessment based on RNA editing in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and prognosis assessment is crucial for guiding treatment decisions. In this study, we aimed to develop a personalized prognostic model for HCC based on RNA editing. RNA editing is a post-transcriptional process that can affect gene expression and, in some cases, play a role in cancer development. By analyzing RNA editing sites in HCC, we sought to identify a set of sites associated with patient prognosis and use them to create a prognostic model. We gathered RNA editing data from the Synapse database, comprising 9990 RNA editing sites and 250 HCC samples. Additionally, we collected clinical data for 377 HCC patients from the Cancer Genome Atlas (TCGA) database. We employed a multi-step approach to identify prognosis-related RNA editing sites (PR-RNA-ESs). We assessed how patients in the high-risk and low-risk groups, as defined by the model, fared in terms of survival. A nomogram was developed to predict the precise survival prognosis of HCC patients and assessed the prognostic model's utility through a receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Our analysis identified 33 prognosis-related RNA editing sites (PR-RNA-ESs) associated with HCC patient prognosis. Using a combination of LASSO regression and cross-validation, we constructed a prognostic model based on 13 PR-RNA-ESs. Survival analysis demonstrated significant differences in the survival outcomes of patients in the high-risk and low-risk groups defined by this model. Additionally, the differential expression of the 13 PR-RNA-ESs played a role in shaping patient survival. Risk-prognostic investigations further distinguished patients based on their risk levels. The nomogram enabled precise survival prognosis prediction. Our study has successfully developed a highly personalized and accurate prognostic model for individuals with HCC, leveraging RNA editing data. This model has the potential to revolutionize clinical evaluation and medical management by providing individualized prognostic information. The identification of specific RNA editing sites associated with HCC prognosis and their incorporation into a predictive model holds promise for improving the precision of treatment strategies and ultimately enhancing patient outcomes in HCC.

Establishment and Validation of Novel Prognostic Subtypes in Hepatocellular Carcinoma Based on Bile Acid Metabolism Gene Signatures Using Bulk and Single-Cell RNA-Seq Data.

Hepatocellular carcinoma (HCC) is a highly detrimental cancer type and has limited therapeutic options, posing significant threats to human health. The development of HCC has been associated with a disorder in bile acid (BA) metabolism. In this study, we employed an integrative approach, combining various datasets and omics analyses, to comprehensively characterize the tumor microenvironment in HCC based on genes related to BA metabolism. Our analysis resulted in the classification of HCC samples into four subtypes (C1, C2a, C2b, and C3). Notably, subtype C2a, characterized by the highest bile acid metabolism score (BAMS), exhibited the highest survival probability. This subtype also demonstrated increased immune cell infiltration, lower cell cycle scores, reduced AFP levels, and a lower risk of metastasis compared to subtypes C1 and C3. Subtype C1 displayed poorer survival probability and elevated cell cycle scores. Importantly, the identified subtypes based on BAMS showed potential relevance to the gene expression of drug targets in currently approved drugs and those under clinical research. Genes encoding VEGFR (FLT4 and KDR) and MET were elevated in C2, while genes such as TGFBR1, TGFB1, ADORA3, SRC, BRAF, RET, FLT3, KIT, PDGFRA, and PDGFRB were elevated in C1. Additionally, FGFR2 and FGFR3, along with immune target genes including PDCD1 and CTLA4, were higher in C3. This suggests that subtypes C1, C2, and C3 might represent distinct potential candidates for TGFB1 inhibitors, VEGFR inhibitors, and immune checkpoint blockade treatments, respectively. Significantly, both bulk and single-cell transcriptome analyses unveiled a negative correlation between BA metabolism and cell cycle-related pathways. In vitro experiments further confirmed that the treatment of HCC cell lines with BA receptor agonist ursodeoxycholic acid led to the downregulation of the expression of cell cycle-related genes. Our findings suggest a plausible involvement of BA metabolism in liver carcinogenesis, potentially mediated through the regulation of tumor cell cycles and the immune microenvironment. This preliminary understanding lays the groundwork for future investigations to validate and elucidate the specific mechanisms underlying this potential association. Furthermore, this study provides a novel foundation for future precise molecular typing and the design of systemic clinical trials for HCC therapy.

Comprehensive Bioinformatic Analysis Reveals an Autophagy-related Gene Signature for Predicting Outcome, Immune Status, and Drug Sensitivity in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world, but molecular complexity and tumor heterogeneity make predictive models for HCC prognosis ineffective. Many recent studies have suggested that autophagy is important in tumor progression. Using autophagy-related genes (ARGs), we attempted to create a novel signature for individual prognosis prediction in patients with HCC. Differentially expressed ARGs (DE-ARGs) in HCC and normal samples were screened using TCGA datasets. Univariate Cox and multivariate Cox regression analyses were performed to determine ARGs related to survival in HCC. An autophagy-based signature was constructed using LASSO, and its correlation with the prognosis and the immune infiltration of HCC patients was explored. In this study, we screened 32 survival-related DE-ARGs by analyzing TCGA datasets. Functional enrichment indicated that the 32 DE-ARGs may play important functional and regulatory roles in cellular autophagy, the regulation of multiple signaling pathways, as well as in the context of cancer and neurological diseases. Based on PPI Network, we identified several hub genes. LASSO Cox regression analysis selected five genes (CASP8, FOXO1, PRKCD, SPHK1, and SQSTM1) for a novel prognostic model. AUCs of 0.752, 0.686, and 0.665 in the TCGA whole set indicated that the model accurately predicted 1-, 3-, and 5-year overall survival, respectively. Cox regression analysis showed that the five-gene signature is an independent and robust predictor in patients with HCC. The high-risk group demonstrated higher levels of immune cell infiltration and exhibited a strong correlation with the immune microenvironment and tumor stem cells. In addition, we further identified PRKCD and SQSTM1 as critical regulators involved in HCC progression. The expression levels of PRKCD and SQSTM1 genes play a crucial role in chemotherapy drug sensitivity and resistance. We introduce here a novel ARG-based predictive feature for HCC patients. Effective use of this signature will aid in determining a patient's prognosis and may lead to novel approaches to clinical decision-making and therapy.

Prognostic value and potential mechanism of cellular senescence and tumor microenvironment in hepatocellular carcinoma: Insights from bulk transcriptomics and single-cell sequencing analysis.

The high mortality rate and postoperative recurrence of hepatocellular carcinoma (HCC) contribute to the burden on society and healthcare. The prognostic value and underlying mechanisms of cellular senescence and tumor microenvironment (TME) in HCC remain unclear. Bulk transcriptomic data were obtained from 368 HCC samples in The Cancer Genome Atlas-liver hepatocellular carcinoma cohort and 64 samples from the GSE116174 dataset. Single-cell RNA sequencing (scRNA-seq) data of HCC were obtained from the GSE149614 dataset, including 18 tumor samples from 10 patients. Prognosis-related cellular senescence genes and immune cells were identified through univariate analysis. Least absolute shrinkage and selection operator regression analysis was performed to construct the CellAge score and TME score, both of which were identified as independent prognostic factors for HCC based on multivariate Cox analysis. The combined CellAge and TME scores showed improved prognostic stratification for HCC patients, as confirmed by multivariate Cox analysis (p < .001). The gene set enrichment analysis (GSEA) revealed enrichment of the extracellular matrix receptor interaction signaling pathway in the group with high CellAge scores and low TME scores, which exhibited a worse prognosis. Single-cell sequencing results revealed higher expression activity of the cAMP response element modulator (CREM) extended transcription factor in HCC cells and most immune cells, indicating its involvement in TME remodeling. Finally, the tumor immune dysfunction and exclusion (TIDE) analysis demonstrated that the combined scores could predict the outcomes of immune therapy in patients with HCC. In conclusion, cellular senescence contributes to TME remodeling in HCC, and the developed CellAge and TME scores serve as independent prognostic factors and predictors of immune therapy in HCC.

A novel disulfidptosis-related lncRNAs signature for predicting survival and immune response in hepatocellular carcinoma.

The accumulation of intracellular disulfides induces a novel and unique form of metabolic-related cell death known as disulfidptosis. A previous study revealed the prognostic value of a risk model of disulfidptosis-related genes in hepatocellular carcinoma (HCC). However, to date, no studies have investigated the relationship between disulfidptosis-related long non-coding RNAs (DRLs) and HCC. In this study, we collected and analyzed RNA sequencing data from 370 HCC samples to explore the DRLs in the tumorigenesis and development of HCC. By employing Lasso Cox regression and multivariate Cox regression analyses, we identified five prognostic DRLs, which were used to construct a prognostic signature. The signature was subsequently validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, Cox regression analyses, nomograms, and calibration curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed, revealing that the DRLs signature was associated with HCC and several cancer-related pathways. Furthermore, the DRLs signature showed correlations with the infiltration of M0 and M1 macrophages, immune-related functions, and multiple immune checkpoints, including PDCD1, LAG3, CTLA4, TIGIT, CD47, and others. Analysis using the tumor immune dysfunction and exclusion (TIDE) approach demonstrated that the DRLs signature could predict the response to immunotherapy. Finally, we screened potential chemotherapy drugs that could sensitize HCC. In conclusion, our novel DRLs signature provides valuable insights into predicting patient survival and immunotherapy responses.

Liver cancer-specific mutations in functional domains of ADAR2 lead to the elevation of coding and non-coding RNA editing in multiple tumor-related genes.

Mutation is the major cause of phenotypic innovations. Apart from DNA mutations, the alteration on RNA such as the ADAR-mediated A-to-I RNA editing could also shape the phenotype. These two layers of variations have not been systematically combined to study their collective roles in cancers. We collected the high-quality transcriptomes of ten hepatocellular carcinoma (HCC) and the matched control samples. We systematically identified HCC-specific mutations in the exonic regions and profiled the A-to-I RNA editome in each sample. All ten HCC samples had mutations in the CDS of ADAR2 gene (dsRNA-binding domain or catalytic domain). The consequence of these mutations converged to the elevation of ADAR2 efficiency as reflected by the global increase of RNA editing levels in HCC. The up-regulated editing sites (UES) were enriched in the CDS and UTR of oncogenes and tumor suppressor genes (TSG), indicating the possible roles of these target genes in HCC oncogenesis. We present the mutation-ADAR2-UES-oncogene/TSG-HCC axis that explains how mutations at different layers would finally lead to abnormal phenotype. In the light of central dogma, our work provides novel insights into how to fully take advantage of the transcriptome data to decipher the consequence of mutations.

Comprehensive Analysis of the Immunosuppressive Function of Regulatory T Cells in Human Hepatocellular Carcinoma Tissues.

Immune-based therapies are commonly employed to combat hepatocellular carcinoma (HCC). However, the presence of immune-regulating elements, especially regulatory T cells (Tregs), can dramatically impact the treatment efficacy. A deeper examination of the immune-regulation mechanisms linked to these inhibitory factors and their impact on HCC patient outcomes is warranted. We employed multicolor fluorescence immunohistochemistry (mIHC) to stain Foxp3, cytokeratin, and nuclei on an HCC tissue microarray (TMA). Leveraging liver cancer transcriptome data from TCGA, we built a prognostic model focused on Treg-associated gene sets and represented it with a nomogram. We then sourced liver cancer single-cell RNA sequencing data (GSE140228) from the GEO database, selectively focusing on Treg subsets, and conducted further analyses, including cell-to-cell communication and pseudo-time trajectory examination. Our mIHC results revealed a more substantial presence of Foxp3+Tregs in HCC samples than in adjacent normal tissue samples (P < .001). An increased presence of Foxp3+Tregs in HCC samples correlated with unfavorable patient outcomes (HR = 1.722, 95% CI:1.023-2.899, P = .041). The multi-factorial prognosis model we built from TCGA liver cancer data highlighted Tregs as a standalone risk determinant for predicting outcomes (HR = 3.84, 95% CI:2.52-5.83, P < .001). Re-analyzing the scRNA-seq dataset (GSE140228) showcased distinctive gene expression patterns in Tregs from varying tissues. Interactions between Tregs and other CD4+T cell types were predominantly governed by the CXCL13/CXCR3 signaling pathway. Communication pathways between Tregs and macrophages primarily involved MIF-CD74/CXCR4, LGALS9/CD45, and PTPRC/MRC1. Additionally, macrophages could influence Tregs via HLA-class II and CD4 interactions. An elevated presence of Tregs in HCC samples correlated with negative patient outcomes. Elucidating the interplay between Tregs and other immune cells in HCC could provide insights into the modulatory role of Tregs within HCC tissues.

RASSF4 Attenuates Metabolic Dysfunction-Associated Steatotic Liver Disease Progression via Hippo Signaling and Suppresses Hepatocarcinogenesis

Background & AimsMetabolic dysfunction-associated steatotic liver disease (MASLD) is a dynamic chronic liver disease closely related to metabolic abnormalities, such as diabetes and obesity. MASLD can further progress to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis, cirrhosis and even hepatocellular carcinoma (HCC). However, the mechanisms underlying the progression of MASLD and further progression to liver fibrosis and liver cancer are unknown. MethodsIn this study, we performed transcriptome analysis in livers from mice with MASLD and found suppression of a potential anti-oncogene, RAS association domain protein 4 (RASSF4). RASSF4 expression levels were measured in liver or tumor tissues of patients with MASH or HCC, respectively. We established RASSF4 overexpression and knockout mouse models. The effects of RASSF4 were evaluated by qPCR, western blotting, histopathological analysis, wound healing assays, Transwell assays, EdU incorporation assays, colony formation assays, sorafenib sensitivity assays and tumorigenesis assays. ResultsRASSF4 was significantly downregulated in MASH and HCC samples. Using liver-specific RASSF4 knockout mice, we demonstrated that loss of hepatic RASSF4 exacerbated hepatic steatosis and fibrosis. In contrast, RASSF4 overexpression prevented steatosis in MASLD mice. In addition, RASSF4 in hepatocytes suppressed the activation of hepatic stellate cells (HSCs) by reducing TGF-β secretion. Moreover, we found that RASSF4 is an independent prognostic factor for HCC. Mechanistically, we found that RASSF4 in the liver interacts with MST1 to inhibit YAP nuclear translocation through the Hippo pathway. ConclusionsThese findings establish RASSF4 as a therapeutic target for MASLD and HCC.

LncRNA CRNDE Drives the Progression of Hepatocellular Carcinoma by inducing the Immunosuppressive Niche.

As a crucial protumorigenic long noncoding RNA, colorectal tumor differential expression (CRNDE) has been confirmed to facilitate the progression of various cancers. However, its role in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) is still unclear. Here we determined that CRNDE was upregulated in HCC samples and that CRNDE-positive cells were predominantly enriched in malignant tumor cells. In vivo functional assays revealed that CRNDE-induced tumor cells supported HCC progression, recruited abundant granulocyte myeloid-derived suppressor cells (G-MDSCs) and restricted the infiltration of T cells. In terms of mechanisms, CRNDE bound with Toll-like receptor 3 (TLR3) and activated NF-κB signaling to increase the secretion of c-x-c motif chemokine ligand 3 (CXCL3). CRNDE knockdown could significantly suppress the accumulation of G-MDSCs and enhance the infiltration of T cells in the TME of HCC in vivo. Taken together, our study reveals the CRNDE-NF-κB-CXCL3 axis plays a crucial role in driving the immunosuppressive niche to facilitate HCC progression by recruiting G-MDSCs.

Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC), ranking as the second-leading cause of global mortality among malignancies, poses a substantial burden on public health worldwide. Anoikis, a type of programmed cell death, serves as a barrier against the dissemination of cancer cells to distant organs, thereby constraining the progression of cancer. Nevertheless, the mechanism of genes related to anoikis in HCC is yet to be elucidated. This paper's data (TCGA-HCC) were retrieved from the database of the Cancer Genome Atlas (TCGA). Differential gene expression with prognostic implications for anoikis was identified by performing both the univariate Cox and differential expression analyses. Through unsupervised cluster analysis, we clustered the samples according to these DEGs. By employing the least absolute shrinkage and selection operator Cox regression analysis (CRA), a clinical predictive gene signature was generated from the DEGs. The Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to determine the proportions of immune cell types. The external validation data (GSE76427) were procured from Gene Expression Omnibus (GEO) to verify the performance of the clinical prognosis gene signature. Western blotting and immunohistochemistry (IHC) analysis confirmed the expression of risk genes. In total, 23 prognostic DEGs were identified. Based on these 23 DEGs, the samples were categorized into four distinct subgroups (clusters 1, 2, 3, and 4). In addition, a clinical predictive gene signature was constructed utilizing ETV4, PBK, and SLC2A1. The gene signature efficiently distinguished individuals into two risk groups, specifically low and high, demonstrating markedly higher survival rates in the former group. Significant correlations were observed between the expression of these risk genes and a variety of immune cells. Moreover, the outcomes from the validation cohort analysis aligned consistently with those obtained from the training cohort analysis. The results of Western blotting and IHC showed that ETV4, PBK, and SLC2A1 were upregulated in HCC samples. The outcomes of this paper underscore the effectiveness of the clinical prognostic gene signature, established utilizing anoikis-related genes, in accurately stratifying patients. This signature holds promise in advancing the development of personalized therapy for HCC.

Biomarker Identification in Liver Cancers Using Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) Imaging: An Approach for Spatially Resolved Metabolomics.

Liver cancers are characterized by interindividual and intratumoral heterogeneity, which makes early diagnosis and the development of therapies challenging. Desorption electrospray ionization mass spectrometry (DESI-MS) imaging is a potent and sensitive MS ionization technique for direct, unaltered 2D and 3D imaging of metabolites in complex biological samples. Indeed, DESI gently desorbs and ionizes analyte molecules from the sample surface using an electrospray source of highly charged aqueous spray droplets in ambient conditions. DESI-MS imaging of biological samples allows untargeted analysis and characterization of metabolites in liver cancers to identify new biomarkers of malignancy. In this chapter, we described a detailed protocol using liver cancer samples collected and stored for histopathology examination, either as frozen or as formalin-fixed, paraffin-embedded specimens. Such hepatocellular carcinoma samples can be subjected to DESI-MS analyses, illustrating the capacity of spatially resolved metabolomics to distinguish malignant lesions from adjacent normal liver tissue.

Pan-cancer analysis suggests that LY6H is a potential biomarker of diagnosis, immunoinfiltration, and prognosis.

LY6H, a member of the lymphocyte antigen-6(LY6) gene family, is located on human chromosomes 6, 8, 11 and 19. This superfamily is characterized by the presence of LU domains. It has demonstrated its emerging significance in various cancers including adenocarcinoma, bladder cancer, ovarian cancer and skin cancer. However, comprehensive pan-cancer analyses have not been conducted to investigate its role in diagnosis, prognosis and immunological prediction. By conducting comprehensive analysis of patient data obtained from publicly available databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), University of Alabama at Birmingham (UALCAN), The Comparative Toxicological Genomics Database (CTD), cBioportal, cancerSEA, and UCSC, we systematically investigated the differential expression of LY6H in 33 different types of human tumors. Additionally, we thoroughly analyzed the diagnostic, prognostic, and immunoinfiltration value of LY6H. Simultaneously, we examined the correlation between LY6H and tumor stemness, methylation patterns, drug sensitivity, gene alterations as well as single cell functions. Furthermore, protein-protein interaction networks and gene-gene interaction networks for LY6H were constructed. Moreover, we also explored the network relationship between LY6H and chemical compounds or genes. The results revealed that LY6H exhibited high expression levels in most cancers which were further validated through Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Immunohistochemistry (IHC) analysis using Hepatocellular carcinoma (HCC) samples. Moreover, LY6H displayed early diagnostic potential in 12 tumors while also showing positive or negative correlations with prognosis across different tumor types. Additionally, it was found that LY6H played a pivotal role in regulating immune-infiltrating cells across multiple cancers whereas the correlation between LY6H expression and immune-related genes varied depending on their specific types. Furthermore, the expression of LY6H was significantly associated with DNA methylation patterns in 21 cancers. Therefore, LY6H could serve as an adjunctive biomarker for early tumor detection as well as a prognostic indicator for diverse malignancies.

Eosinophil-Associated Genes are Potential Biomarkers for Hepatocellular Carcinoma Prognosis.

Background: Eosinophils, a type of white blood cell originating from the bone marrow, are widely believed to play a crucial role in inflammatory processes, including allergic reactions and parasitic infections. However, the relationship between eosinophils and liver cancer is not well understood. Methods: Tumor immune infiltration scores were calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Key modules and hub genes associated with eosinophils were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Univariate and multivariate Cox analyses, along with LASSO regression, were used to identify prognostic genes and create a risk model. The Tumor Immune Dysfunction and Exclusion (TIDE) score was used to evaluate the immunotherapeutic significance of the eosinophil-associated gene risk score (ERS) model. Experiments such as flow cytometry, immunohistochemical analysis, real-time quantitative PCR (RT-qPCR), and Western blotting were used to determine gene expression levels and the status of eosinophil infiltration in tumors. Results: A risk trait model including 4 eosinophil-associated genes (RAMP3, G6PD, SSRP1, PLOD2) was developed by univariate Cox analysis and Lasso screening. Pathologic grading (p < 0.001) and model risk scores (p < 0.001) were found to be independent predictors of hepatocellular carcinoma (HCC) patient survival. Western blotting revealed higher levels of eosinophil peroxidase (EPX) in HCC tissues compared to adjacent normal tissues. Immunohistochemistry showed that eosinophils mainly infiltrated the connective tissue around HCC. The HCC samples showed low expression of RAMP3 and high expression of G6PD, SSRP1, and PLOD2, as detected by IHC and RT-qPCR analysis. The in vivo mouse experiments showed that IL-33 treatment induced the recruitment of eosinophils and reduced the number of intrahepatic tumor nodules. Conclusion: Overall, eosinophil infiltration in HCC is significantly correlated with patient survival. The risk assessment model based on eosinophil-related genes serves as a reliable clinical prognostic indicator and provides insights for precise treatment of HCC.

Integrated analysis of lactate-related genes identifies POLRMT as a novel marker promoting the proliferation, migration and energy metabolism of hepatocellular carcinoma via Wnt/β-Catenin signaling.

Hepatocellular carcinoma (HCC) is a prevalent and deadly form of cancer globally with typically unfavorable outcomes. Increasing research suggests that lactate serves as an important carbon contributor to cellular metabolism and holds a crucial part in the progression, sustenance, and treatment response of tumors. However, the contribution of lactate-related genes (LRGs) in HCC is still unclear. In this study, we analyzed TCGA datasets and screened 21 differentially expressed LRGs related to long-term survivals in HCC patients. Pan-cancer assays revealed that 21 LRGs expression exhibited a dysregulated level in man types of tumors and associated with clinical prognosis of tumor patients. The analysis of 21 LRGs successfully classified HCC samples into two molecular subtypes, and these two subtypes showed significant differences in clinical information, gene expression, and immune characteristics. Subsequently, based on the aforementioned 21 LRGs, a novel prognostic signature (DTYMK, IRAK1, POLRMT, MPV17, UQCRH, PDSS1, SLC16A3, SPP1 and LDHD) was generated by LASSO-Cox regression analysis. Survival assays demonstrated that the signature performed well in predicting the overall survival of patients with HCC. The results of Gene Set Variation Analysis indicated that the high GSVA scores were associated with poor prognosis. Moreover, we also investigated the correlation between GSVA scores and various signaling pathways in HCC. Among the nine prognostic genes, our attention focused on POLRMT which was highly expressed in HCC specimens based on TCGA datasets and several HCC cell lines. In addition, functional assays indicated that POLRMT distinctly promoted the proliferation, migration and energy metabolism of HCC cells via regulating Wnt/β-Catenin signaling. Overall, through the establishment of a novel prognostic signature, we have provided potential clinical value for assessing the prognosis of HCC patients. Furthermore, our study has identified the high expression of POLRMT in HCC and demonstrated its crucial role in HCC cell proliferation. These findings hold great importance in advancing our understanding of the pathophysiology of HCC, identifying new therapeutic targets, and improving patient survival rates.

Inactivation of ERK1/2 in cancer-associated hepatic stellate cells suppresses cancer-stromal interaction by regulating extracellular matrix in fibrosis.

The ERK1/2 pathway is involved in epithelial-mesenchymal transformation and cell cycle of tumor cells in hepatocellular carcinoma (HCC). In the present study, we investigated the involvement of ERK1/2 activation on hepatic stellate cells (HSCs). We identified ERK1/2 phosphorylation in activated HSCs of HCC samples. We found that tumor cells promoted the migration and invasion capacity of HSCs by activating ERK1/2 phosphorylation. Using high throughput transcriptome sequencing analysis, we found that ERK1/2 inhibition altered genes significantly correlated to signaling pathways involved in extracellular matrix remodeling. We screened genes and demonstrated that the ERK1/2 inhibition-related gene set significantly correlated to cancer-associated fibroblast infiltration in TCGA HCC tumor samples. Moreover, inhibition of ERK1/2 suppressed tumor cell-induced enhancement of HSC migration and invasion by regulating expression of fibrosis markers FAP, FN1 and COL1A1. In a tumor cell and HSC splenic co-transplanted xenograft mouse model, inhibition of ERK1/2 suppressed liver tumor formation by downregulating fibrosis, indicating ERK1/2 inhibition suppresses tumor-stromal interactions in vivo. Taken together, our data indicate that inhibition of ERK1/2 in tumor-associated HSCs suppresses tumor-stromal interactions and progression. Furthermore, inhibition of ERK1/2 may be a potential target for HCC treatment.

Significance of RNA Processing Factors in Prognosis and Treatment of Hepatocellular Carcinoma: Implications for Precision Medicine

Hepatocellular carcinoma (HCC) is a malignant tumor with heterogenous nature and high mortality worldwide. RNA processing has been implicated in tumorigenesis, progression, and prognosis to develop early diagnosis and targeted therapy for cancer, including HCC. This study aimed to identify and validate a novel RNA processing gene signature for predicting HCC prognosis. Transcriptome and clinical data of HCC samples were obtained from Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Gene Expression Omnibus databases, and RNA processing factors from the AmiGO database. The least absolute shrinkage and selection operator and multivariate Cox regression analysis were performed to examine prognostic gene signatures. Correlation analysis of prognostic models with clinical features and HallMark pathway-based Genomic Variation Analysis enrichment analysis were performed. We identified seven distinguished RNA processing factors to build the prognostic model for HCC patients. ROC curve analysis of the TCGA-HCC, ICGC-HCC, and GSE76427 cohorts determined the ability to predict prognosis through a sevengene-based model (AUC &gt; 0.7). A nomogram to forecast the overall survival among HCC patients was established. In addition, 7 cell types (CD3+ cells, CD8+ T cells, macrophage/monocyte, monocyte, myeloid dendritic cells, neutrophils and cancer-associated fibroblast) significantly differed between the two risk groups. Furthermore, enrichment analysis showed significant enrichment in fatty acid metabolism, lipogenesis, pancreatic beta cells, and bile acid metabolism in the low-risk group, while in the high-risk group, DNA repair, protein secretion, and mitotic pathways were significantly enriched. Analyzing the Tumor Immune Dysfunction and Exclusion results demonstrated that immune checkpoint blockade therapy was poorly efficacious in the high-risk group. This study constructed and validated a novel prognostic signature related to RNA processing factors in HCC, improving therapeutic strategies for HCC. Building such prognostic signatures could pave the way for developing targeted therapy and precision medicine in HCC.

Anti-BIRC5 autoantibody serves as a valuable biomarker for diagnosing AFP-negative hepatocellular carcinoma.

Autoantibodies targeting tumor-associated antigens (TAAbs) have emerged as promising biomarkers for early cancer detection. This research aimed to assess the diagnostic capacity of anti-BIRC5 autoantibody in detecting AFP-negative hepatocellular carcinoma (ANHCC). This research was carried out in three stages (discovery phase, validation phase, and evaluation phase) and included a total of 744 participants. Firstly, the anti-BIRC5 autoantibody was discovered using protein microarray, exhibiting a higher positive rate in ANHCC samples (ANHCCs) compared to normal control samples (NCs). Secondly, the anti-BIRC5 autoantibody was validated through enzyme-linked immunosorbent assay (ELISA) in 85 ANHCCs and 85 NCs from two clinical centers (Zhengzhou and Nanchang). Lastly, the diagnostic usefulness of the anti-BIRC5 autoantibody for hepatocellular carcinoma (HCC) was evaluated by ELISA in a cohort consisting of an additional 149 AFP-positive hepatocellular carcinoma samples (APHCCs), 95 ANHCCs and 244 NCs. The association of elevated autoantibody to high expression of BIRC5 in HCC was further explored by the database from prognosis, immune infiltration, DNA methylation, and gene mutation level. In the validation phase, the area under the ROC curve (AUC) of anti-BIRC5 autoantibody to distinguish ANHCCs from NCs in Zhengzhou and Nanchang centers was 0.733 and 0.745, respectively. In the evaluation phase, the AUCs of anti-BIRC5 autoantibody for identifying ANHCCs and HCCs from NCs were 0.738 and 0.726, respectively. Furthermore, when combined with AFP, the AUC for identifying HCCs from NCs increased to 0.914 with a sensitivity of 77.5% and specificity of 91.8%. High expression of BIRC5 gene is not only correlated with poor prognosis of HCCs, but also significantly associated with infiltration of immune cells, DNA methylation, and gene mutation. The findings suggest that the anti-BIRC5 autoantibody could serve as a potential biomarker for ANHCC, in addition to its supplementary role alongside AFP in the diagnosis of HCC. Next, we can carry out specific verification and explore the function of anti-BIRC5 autoantibody in the occurrence and development of HCC.

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.