Independent Prognostic Factor For Hepatocellular Carcinoma Research Articles

Identification of a novel molecular classification for hepatocellular carcinoma based on disulfideptosis-related genes and its potential prognostic significance

BackgroundGlobally, hepatocellular carcinoma (HCC) is one of the most prevalent and deadly malignant tumors. A recent study proposed disulfidptosis, a novel form of regulated cell death (RCD), offering a new avenue for identifying tumor prognosis biomarkers and developing novel therapeutic targets.MethodsBased on the expression data of 14 disulfideptosis-related genes extracted from public databases, a new molecular classification of HCC called the “disulfidptosis score” was constructed and its relationship to tumor immunity and prognosis was evaluated.ResultsBased on the expression of disulfideptosis-related genes, we performed cluster analysis on HCC samples from the TCGA cohort, which classified these patients into three clusters: A, B, and C, and the differentially expressed genes of different clusters were analyzed. A disulfidptosis score model was constructed by differentially expressed genes associated with prognosis. Univariate and multivariate COX regression analysis showed that disulfidptosis score was an independent prognostic factor for HCC. In addition, in various disulfidptosis score groups, notable disparities were observed concerning the tumor immune microenvironment as well as the expression of immune checkpoint.ConclusionDisulfidptosis score have an important role in predicting HCC prognosis and help guide us in providing better immunotherapy options for patients.

Establishment of prognosis model of hepatocellular carcinoma based on prognosis related gene analysis and study on gene regulation mechanism of model

ObjectiveTo analyze the expression and mechanism of prognosis related differentially expressed genes (DEGs) in hepatocellular carcinoma (HCC), and establish a prognosis risk model of prognosis related DEGs. MethodsTranscriptome data and clinical information of 374 HCC samples were downloaded from TCGA data. Kaplan-Meier (KM) survival analysis screened prognostic genes in DEGs. Protein-protein interaction (PPI) network was constructed based on prognostic genes and key genes were screened. Cox regression was used to analyze the key genes and construct the prognostic risk model, calculate the risk score of each patient, divide the patients into low-risk group and high-risk group according to the median risk value, use KM analysis method for survival analysis and draw the survival curve, and use receiver operating characteristic (ROC) curve to evaluate the prognostic risk model, The relationship between risk score and clinicopathological features of HCC patients was analyzed. GEPIA and the human protein atlas (HPA) databases were used for expression verification of model genes. The mirDIP database is used to analyze the regulatory network of model genes. GSCAlite platform is used to analyze the mechanism and drug sensitivity of model genes. ResultsA total of 1987 DEGs were extracted from the transcriptome data of HCC and normal samples, of which 258 were related to the prognosis of HCC (P < 0.01). Six key genes (CDK1, CCNA2, BUB1, CDC20, CCNB1 and TOP2A) were screened from the PPI network based on prognostic related genes, and the prognostic risk model was established. Survival analysis showed that the overall survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.01). The AUC values of 1, 3 and 5 years in the prognostic risk model were 0.716, 0.678 and 0.633. Multivariate Cox regression analysis showed that patient age and patient risk score were independent risk factors for the prognosis of HCC. The model gene is highly expressed in HCC and can promote apoptosis, cell cycle and EMT pathway. In addition, the high expression of model gene produced drug resistance to trametinib, selumetinib and RDEA119 (refametinib). ConclusionThe prognostic risk model based on six prognostic related DEGs is an independent prognostic factor of HCC, which can effectively predict the survival and prognosis of HCC patients.

B-cell-specific signatures reveal novel immunophenotyping and therapeutic targets for hepatocellular carcinoma.

Immunotherapy presents both promises and challenges in treating hepatocellular carcinoma (HCC) due to its complex immunological microenvironment. The role of B cells, a key part of the immune system, remains uncertain in HCC. To identify B-cell-specific signatures and reveal novel immunophenotyping and therapeutic targets for HCC. Using the Tumor Immune Single-cell Hub 2 database, we identified B-cell-related genes (BRGs) in HCC. Gene enrichment analysis was performed to explore the possible collaboration between B cells and T cells in HCC. We conducted univariate Cox regression analysis using The Cancer Genome Atlas liver HCC collection dataset to find BRGs linked to HCC prognosis. Subsequently, least absolute shrinkage and selection operator regression was utilized to develop a prognostic model with 11 BRGs. The model was validated using the International Cancer Genome Consortium dataset and GSE76427. The risk score derived from the prognostic model emerged as an independent prognostic factor for HCC. Analysis of the immune microenvironment and cell infiltration revealed the immune status of various risk groups, supporting the cooperation of B and T cells in suppressing HCC. The BRGs model identified new molecular subtypes of HCC, each with distinct immune characteristics. Drug sensitivity analysis identified targeted drugs effective for each HCC subtype, enabling precision therapy and guiding clinical decisions. We clarified the role of B cells in HCC and propose that the BRGs model offers promising targets for personalized immunotherapy.

UBAP2 contributes to radioresistance by enhancing homologous recombination through SLC27A5 ubiquitination in hepatocellular carcinoma

Radiotherapy stands as an effective method in the clinical treatment of hepatocellular carcinoma (HCC) patients. However, both primary and acquired radioresistance limit its clinical application in HCC. Therefore, investigating the mechanism of radioresistance may provide other options for treating HCC. Based on single-cell RNA sequencing (scRNA-seq) and HCC transcriptome datasets, 227 feature genes with prognostic value were selected to establish the tSNE score. The tSNE score emerged as an independent prognostic factor for HCC and correlated with cell proliferation and radioresistance-related biological functions. UBAP2 was identified as the most relevant gene with the tSNE score, consistently elevated in human HCC samples, and positively associated with patient prognosis. Functionally, UBAP2 knockdown impeded HCC development and reduced radiation resistance in vitro and in vivo. The ectopic expression of SLC27A5 reversed the effects of UBAP2. Mechanically, we uncovered that UBAP2, through the ubiquitin-proteasome system, decreased the homologous recombination-related gene RAD51, not the non-homologous end-joining (NHEJ)-related gene CTIP, by degrading the antioncogene SLC27A5, thereby generating radioresistance in HCC. The findings recapitulated that UBAP2 promoted HCC progression and radioresistance via SLC27A5 stability mediated by the ubiquitin-proteasome pathway. It was also suggested that targeting the UBAP2/SLC27A5 axis could be a valuable radiosensitization strategy in HCC.

Construction of a circadian rhythm-relevant gene signature for hepatocellular carcinoma prognosis, immunotherapy and chemosensitivity prediction

AimsThis study explored the molecular and biologic mechanisms underlying the association between circadian rhythm disorders (CRD) and increased risk for hepatocellular carcinoma (HCC). BackgroundCRD are linked to increased risk for HCC, but the molecular and biologic mechanisms underlying this association are limited.ObjectiveThe study constructed and validated a CRD related gene model as an independent prognostic factor for HCC, providing insight into the molecular mechanisms linking CRD to increased HCC risk and identifying potential indicators for the efficacy of immunotherapy and anticancer drugs. This helps provide important clues for personalized treatment strategies for HCC patients. MethodsGene sets correlated with circadian rhythm were obtained from the Molecular Signatures Database (MSigDB) to intersect with differentially expressed genes (DEGs) between tumor samples and control samples in The Cancer Genome Atlas (TCGA) and HCCDB18 from Hepatocellular Carcinoma Cell DataBase (HCCDB). The CRD related gene model was developed by univariate Cox and stepwise multivariate analysis. Immune checkpoint blockade (ICB) therapy and anticancer drugs were analyzed using the tumor immune dysfunction and exclusion (TIDE) and pRRophetic, respectively. Seurat determined the cell type of HCC by analyzing single-cell data, and malignant cells were identified using Copykat. To detect the mRNA levels of genes in the CRD related gene model, quantitative real-time polymerase chain reaction (qRT-PCR) was carried out. ResultsThe activity of circadian rhythm in HCC tissue was significantly lower than that in control tissue. Subsequently, EZH2, IMPDH2, TYMS and SERPINE1 were selected to construct the CRD related gene model, which was an independent factor for HCC prognosis. Notably, low-risk patients had lower levels of immune cell infiltration and lower TIDE scores compared to high-risk patients with HCC, indicating that patients with a low risk may derive more benefit from immunotherapy. IMPDH2, TYMS and SERPINE1 expressed significantly higher in malignant cells than in benign epithelial cells. ConclusionsThis study presents a CRD related gene model to reveal the molecular perspective of the dependent mechanism of the association between CRD and cancer, which provides a potential indicator for understanding the preclinical efficacy of ICB and anticancer drugs.

Construction of an Immunogenic Cell Death-Related Gene Signature and Genetic Subtypes for Predicting Prognosis, Immune Microenvironments, and Drug Sensitivity in Hepatocellular Carcinoma.

Immunogenic cell death (ICD) is a type of regulated cell death that modifies the immune response by releasing DAMPs or danger signals. Herein, we aimed to develop an ICD-related predictive model for patients with hepatocellular carcinoma (HCC) and investigate its applicability for predicting prognostic outcomes and immunotherapeutic responses. Differentially expressed genes of ICD were identified in the HCC and normal liver samples. A prognostic risk model and a nomogram containing clinicopathological features were created. To validate the effectiveness of the model, an external dataset was used. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, biological function and chemotherapeutic drug sensitivity were evaluated for different genetic subtypes and risk groups. A total of 35 ICD-related genes (ICDRGs) were identified between HCC and normal samples, 11 of which were significantly associated with overall survival (OS) in HCC patients. Four different genetic subtypes were formed and eight ICDRGs were selected to develop a risk prognostic model. The risk scores were shown to be an independent prognostic factor for HCC and positively correlated with pathological severity. Patients in the high-risk group had a higher frequency of TP53 mutations, increased expression of immune checkpoints and human leukocyte antigen genes. The inhibitory concentrations of chemotherapeutic drugs differed in different populations. In this study, we developed an ICDRG risk model and demonstrated its applicability in predicting survival outcomes, immune and chemotherapeutic responses in HCC patients. ICDRGs are expected to be used as novel biomarkers in the medical decision-making of HCC.

How CLSPN could demystify its prognostic value and potential molecular mechanism for hepatocellular carcinoma: A crosstalk study

Background & aimsCLSPN, a critical component of the S-phase checkpoint in response to DNA replication stress, has been implicated in the pathogenesis of multiple tumor types. The rising incidence of hepatocellular carcinoma (HCC) poses a significant challenge to global public health. Despite this, the specific functions of CLSPN in the development of HCC remain poorly understood. MethodsWe systematically evaluated the expression of CLSPN, prognosis and immune infiltration in patients with HCC and identified a competing endogenous RNA (ceRNA) network by using public database. The RT-qPCR, western blot, CCK8, transwell, flow cytometry, animal experiments, proteasome inhibition experiment, Co-IP assay and mass spectrometry were applied to explore its biological functions, post-transcriptional modifications and potential molecular mechanisms of CLSPN in HCC. ResultsWe verified the expression of CLSPN, and its high expression is an independent prognostic factor in HCC. The expression of CLSPN is also associated with the immune microenvironment of HCC. CLSPN silencing inhibited the proliferation, migration, invasion and cell cycle progression of HCC cells. We established a PSMA3-AS1/hsa-miR-101-3p/CLSPN regulator axis in HCC. CLSPN was influenced by ubiquitination and was involved in the Wnt/β-catenin pathway to regulate HCC progression. ConclusionsIt was the first time to comprehensively discover and identify the expression, prognosis, immunotherapy, RNAs regulator, posttranscriptional modification, and molecular mechanisms of CLSPN in HCC. These novel insights have the potential to expedite the development of personalized treatment strategies and translational medicine approaches for HCC patients.

Dysfunction of CCT3-associated network signals for the critical state during progression of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and is a serious threat to human health; thus, early diagnosis and adequate treatment are essential. However, there are still great challenges in identifying the tipping point and detecting early warning signals of early HCC. In this study, we aimed to identify the tipping point (critical state) of and key molecules involved in hepatocarcinogenesis based on time series transcriptome expression data of HCC patients. The phase from veHCC (very early HCC) to eHCC (early HCC) was identified as the critical state in HCC progression, with 143 genes identified as key candidate molecules by combining the DDRTree (dimensionality reduction via graph structure learning) and DNB (dynamic network biomarker) methods. Then, we ranked the candidate genes to verify their mRNA levels using the diethylnitrosamine (DEN)-induced HCC mouse model and identified five early warning signals, namely, CCT3, DSTYK, EIF3E, IARS2 and TXNRD1; these signals can be regarded as the potential early warning signals for the critical state of HCC. We identified CCT3 as an independent prognostic factor for HCC, and functions of CCT3 involving in the “MYCtargets_V1” and “E2F-Targets” are closely related to the progression of HCC. The predictive method combining the DDRTree and DNB methods can not only identify the key critical state before cancer but also determine candidate molecules of critical state, thus providing new insight into the early diagnosis and preemptive treatment of HCC.

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.

SNRPB promotes the progression of hepatocellular carcinoma via regulating cell cycle, oxidative stress, and ferroptosis.

Small Nuclear Ribonucleoprotein Polypeptides B and B1 (SNRPB) have been linked to multiple human cancers. However, the mechanism of SNRPB in hepatocellular carcinoma (HCC) and whether SNRPB has a synergistic effect with sorafenib in the treatment of HCC remain unclear. In this study, bioinformatic analysis found that SNRPB was an independent prognostic factor for HCC that exerted a critical effect on the progression of HCC. SNRPB was linked with immune checkpoints, cell cycle, oxidative stress and ferroptosis in HCC. Single cell sequencing analysis found that HCC cell subset with high expression of SNRPB, accounted for a higher proportion in HCC cells with higher stages, had higher expression levels of the genes which promote cell cycle, inhibit oxidative stress and ferroptosis, and had higher cell cycle score, lower oxidative stress score and ferroptosis score. Single-sample gene set enrichment analysis (ssGSEA) analysis found that 17 oxidative stress pathways and 68 oxidative stress-ferroptosis related genes were significantly correlated with SNRPB risk scores. SNRPB knockdown induced cell cycle G2/M arrest and restrained cell proliferation, while downregulated the expression of CDK1, CDK4, and CyclinB1. The combined treatment (SNRPB knockdown+sorafenib) significantly inhibited tumor growth. In addition, the expression of SLC7A11, which is closely-related to ferroptosis, decreased significantly in vitro and in vivo. Therefore, SNRPB may promote HCC progression by regulating immune checkpoints, cell cycle, oxidative stress and ferroptosis, while its downregulation inhibits cell proliferation, which enhances the therapeutic effect of sorafenib, providing a novel basis for the development of HCC therapies.

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.

SRSF10 facilitates HCC growth and metastasis by suppressing CD8+T cell infiltration and targeting SRSF10 enhances anti-PD-L1 therapy

Background and AimsRNA splicing is an essential step in regulating the gene posttranscriptional expression. Serine/arginine-rich splicing factors (SRSFs) are splicing regulators with vital roles in various tumors. Nevertheless, the expression patterns and functions of SRSFs in hepatocellular carcinoma (HCC) are not fully understood. MethodsFlow cytometry and immunofluorescent staining were used to determine the CD8+T cell infiltration. Orthotopic HCC model, lung metastasis model, DEN/CCl4 model, Srsf10△hep model, and Srsf10HepOE model were established to evaluate the role of SRSF10 in HCC and the efficacy of combination treatment. ResultsSRSF10 was one of the most survival-relevant genes among SRSF members and was an independent prognostic factor for HCC. SRSF10 facilitated HCC growth and metastasis by suppressing CD8+T cell infiltration. Mechanistically, SRSF10 down-regulated the p53 protein by preventing the exon 6 skipping (exon 7 in mouse) mediated degradation of MDM4 transcript, thus inhibiting CD8+T cell infiltration. Elimination of CD8+T cells or overexpression of MDM4 removed the inhibitory role of SRSF10 knockdown in HCC growth and metastasis. SRSF10 also inhibited the IFNα/γ signaling pathway and promoted the HIF1α-mediated up-regulation of PD-L1 in HCC. Hepatocyte-specific SRSF10 deficiency alleviated the DEN/CCl4-induced HCC progression and metastasis, whereas hepatocyte-specific SRSF10 overexpression deteriorated these effects. Finally, SRSF10 knockdown enhanced the anti-PD-L1-mediated anti-tumor activity. ConclusionsSRSF10 promoted HCC growth and metastasis by repressing CD8+T cell infiltration mediated by the MDM4-p53 axis. Furthermore, SRSF10 suppressed the IFNα/γ signaling pathway and induced the HIF1α signal mediated PD-L1 up-regulation. Targeting SRSF10 combined with anti-PD-L1 therapy showed promising efficacy.

Insights on epithelial cells at the single-cell level in hepatocellular carcinoma prognosis and response to chemotherapy.

Background: Hepatocellular carcinoma (HCC) originates from Epithelial cells, and epithelial lineage plasticity has become a promising research direction for advancing HCC treatment. This study aims to focus on Epithelial cells to provide target insights for detecting HCC prognosis and response to drug therapy. Methods: Single-cell RNA sequencing (scRNA-seq) data from GSE149614 were clustered using Seurat, and the differentiation and evolution of epithelial cells were analyzed by Monocle 2. Scissor+ and Scissor- Epithelial cells associated with the prognostic phenotypes of bulk RNA-seq of HCC were screened using the Scissor algorithm for differential analysis to screen candidate genes. Candidate genes were overlapped with prognostic related genes screened by univariate Cox regression, and the Least Absolute Shrinkage and Selection Operator (LASSO) sparse penalty was imposed on the intersection genes to construct a risk assessment system. Results: Eight major cell subpopulations of HCC were identified, among which the proportion of epithelial cells in non-tumor liver tissues and HCC tissues was significantly different, and its proportion increased with advanced clinical stage. During the progression of HCC, the whole direction of epithelial cells differentiation trajectory was towards enhanced cell proliferation. Differential analysis between Scissor+ and Scissor- epithelial cells screened 1,265 upregulated and 191 downregulated prognostic candidate genes. Wherein, the upregulated genes were enriched in Cell processes, Genetic information processing, Metabolism and Human disease with Infection. Nevertheless, immune system related pathways took the main proportions in downregulated genes enriched pathways. There were 17 common genes between upregulated candidate genes and prognostic risk genes, of which CDC20, G6PD and PLOD2 were selected as components for constructing the risk assessment system. Risk score showed a significant correlation with tumor stage, epithelial-mesenchymal transition (EMT) related pathways and 22 therapeutic drugs, and was an independent prognostic factor for HCC. Conclusion: This study revealed the cellular composition of HCC, the differentiation evolution and functional landscape of epithelial cells in the further deterioration of HCC, and established a 3-gene risk model, which was closely related to clinical features, EMT, and drug sensitivity prediction. These findings provided insights in patient prognosis and drug therapy detection for HCC.

CENPL accelerates cell proliferation, cell cycle, apoptosis, and glycolysis via the MEK1/2-ERK1/2 pathway in hepatocellular carcinoma

Centromere protein L (CENPL) is involved in the mitotic process of eukaryotic cells and the development of various types of cancer. However, its role in hepatocellular carcinoma (HCC) remains unclear. This study aimed to investigate the expression and clinical significance of CENPL in HCC, and explore its involvement in regulating HCC cell proliferation, apoptosis, cell cycle, and glycolysis both in vivo and in vitro. CENPL expression was analyzed in HCC and normal liver tissues using The Cancer Genome Atlas, Gene Expression Omnibus mining, real-time quantitative polymerase chain reaction, and immunohistochemistry. Functional assays were used to assess the role of CENPL in HCC cell proliferation, apoptosis, cell cycle, and glycolysis. The potential pathways underlying the regulatory effects of CENPL, as well as the expression of mitogen-activated protein kinase (MAPK) signaling pathway-related molecules and markers of proliferation and glycolysis were investigated. CENPL was significantly upregulated in HCC tissue and associated with multiple clinicopathological features and poor patient prognosis. Univariate and multivariate analyses demonstrated that CENPL may serve as an independent prognostic factor for HCC. Upregulation of CENPL in HCC regulated tumor proliferation and glycolytic processes. Mechanistic studies revealed that differentially expressed genes between the CENPL-overexpressing and control groups were mainly concentrated in the MAPK signaling pathway. Pathway inhibition analysis indicated that CENPL activated the MEK1/2-ERK1/2 signaling pathway to promote proliferation and glycolysis in HCC cells. This study elucidated the role of CENPL in regulating cell proliferation, apoptosis, cell cycle, and glycolysis in HCC. CENPL may represent a therapeutic target and prognostic biomarker for HCC.

Identification and validation of a prognostic-related mutant gene DNAH5 for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide and has a poor prognosis. Thus, there is a need for an effective biomarker to improve and predict the prognosis of HCC. RNA sequencing data, simple nucleotide variation data, and clinical data of HCC patients from The Cancer Genome Atlas (TCGA) to identify mutant genes, simple nucleotide variation data, and clinical data of HCC patients from the International Cancer Genome Consortium (ICGC) to validate the prognostic value of mutant genes were the data sources of the present study. To identify the overall survival (OS)-related mutant genes, a Kaplan-Meier (KM) analysis was conducted. We carried out univariate Cox and multivariate Cox regression analyses to identify the independent prognostic factors. We also conducted a correlation analysis of immune cells and mutant genes. To explore the molecular mechanisms of mutant genes, we conducted a gene set enrichment analysis (GSEA). A nomogram was constructed to help predict the prognosis of HCC. In addition, we explored the expression profile of mutant genes in HCC based on a TCGA dataset, an ICGC dataset, and our own HCC tissue samples. We identified and validated a mutant gene, dynein axonemal heavy chain 5 (DNAH5), which was negatively related to the OS of HCC patients. Univariate Cox and multivariate Cox regression analyses revealed that the mutant gene DNAH5 could act as an independent prognostic factor for HCC. Most pathways of the mutant gene DNAH5 were involved in cancer development and progression based on GSEA analysis. The mutant gene DNAH5 was negatively correlated with monocytes, naive CD4 T cells, activated dendritic cells, and activated mast cells. In addition, the mRNA and protein levels of DNAH5 had a significantly higher level of expression in the tissue samples of patients with HCC. A nomogram consisting of the pathological stage, DNAH5, and tumor mutation burden (TMB) performed well. The mutant gene DNAH5 had a significantly higher level of expression in the tissue samples of patients with HCC, could act as an independent prognostic factor for HCC, and is a potential new immunotherapy target for HCC.

Integrated analysis of SKA1-related ceRNA network and SKA1 immunoassays in HCC: A study based on bioinformatic.

Hepatocellular carcinoma (HCC) poses a global health challenge. Effective biomarkers are required for early diagnosis to improve survival rates of patients with HCC. Spindle and kinetochore-associated complex subunits 1 (SKA1) is essential for proper chromosome segregation in the mitotic cell cycle. Previous studies have shown that overexpression of SKA1 is associated with a poor prognosis in various cancers. The expression, prognostic value, and clinical functions of SKA1 in HCC were evaluated with several bioinformatics web portals. Additionally, we identified target long non-coding RNAs (lncRNAs) and microRNAs by analyzing messenger RNA (mRNA)-miRNA and miRNA-lncRNA interaction data and elucidated the potential competing endogenous RNA (ceRNA) mechanism associated with SKA1. High SKA1 expression was associated with poor prognosis in patients with HCC. Furthermore, multivariate Cox regression analysis revealed that SKA1 expression was an independent prognostic factor for HCC. GO and KEGG analyses showed that SKA1 is related to the cell cycle checkpoints, DNA replication and repair, Rho GTPases signaling, mitotic prometaphase, and kinesins. Gene set enrichment analysis revealed that high levels of SKA1 are associated with cancer-promoting pathways. DNA methylation of SKA1 in HCC tissues was lower than that in normal tissues. Ultimately, the following 9 potential ceRNA-based pathways targeting SKA1 were identified: lncRNA: AC026401.3, Small Nucleolar RNA Host Gene 3 (SNHG3), and AC124798.1-miR-139-5p-SKA1; lncRNA: AC26356.1, Small Nucleolar RNA Host Gene 16 (SNHG16), and FGD5 Antisense RNA 1-miR-22-3p-SKA1; lncRNA: Cytoskeleton Regulator RNA (CYTOR), MIR4435-2 Host Gene, and differentiation antagonizing non-protein coding RNA-miR-125b-5p-SKA1. SKA1 expression levels significantly correlated with immune cell infiltration and immune checkpoint genes in the HCC tissues. SKA1 is a potential prognostic biomarker for HCC. This study provides a meaningful direction for research on SKA1-related mechanisms, which will be beneficial for future research on HCC-related molecular biological therapies and targeted immunotherapy.

A Novel Gene Pair CSTF2/DPE2A Impacts Prognosis and Cell Cycle of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC), one of the commonest cancers at present, possesses elevated mortality. This study explored the predictive value of CSTF2/PDE2A for HCC prognosis. In this study, clinical information and RNA sequencing expression profiles of HCC patients were acquired from common databases. Kaplan-Meier curve compound with time-dependent ROC curve, nomogram model, and univariate/multivariate Cox analysis were carried out to access the prediction capacity of CSTF2/PDE2A. The immune status, tumor microenvironment, drug sensitivity, biological function and pathway between HCC and adjacent non-tumorous tissue were analyzed and compared. Finally, RT-qPCR, Western blot, and apoptosis assays were performed to verify the effect on HCC cells of CSTF2/PDE2A. The optimal cut-off value of CSTF2, PDE2A and CSTF2/PDE2A was 6.95, 0.95 and 3.63, respectively. In TCGA and ICGC cohorts, the high group of CSTF2/PDE2A presented higher OS compared to low group. The area under the curve (AUC) for OS at 1-, 2-, and 3-years predicted by CSTF2/PDE2A were 0.731/0.695, 0.713/0.732 and 0.689/0.755, higher than the counterparts of the single gene CSTF2 and PDE2A. Multivariate Cox analysis revealed that CSTF2/PDE2A (HR = 1.860/3.236, 95% CI = 1.265-2.733/1.575-6.645) was an independent prognostic factor for HCC. The OS nomogram model created according to five independent factors including CSTF2/PDE2A showed excellent capacity for HCC prognosis. Furthermore, the immune status of the CSTF2/PDE2A high group was deleted, cell cycle-related genes and chemotherapy resistance were increased. Finally, cell experiments revealed distinct differences in the proliferation, apoptosis, protein and mRNA expression of HCC cells after si-CSTF2 transfection compared with the negative control. Taken together, the gene pair CSTF2/PDE2A is able to forecast the prognosis of HCC and regulates cell cycle, which is promising as a novel prognostic predictor of HCC.

A novel signature of the ligand and receptor genes associated with disulfidoptosis for prediction of prognosis, immunologic therapy responses in hepatocellular carcinoma

BackgroudWe aimed to explore the prognostic features of ligand and receptor genes associated with disulfidoptosis in hepatocellular carcinoma (HCC) and establish a risk signature utilizing these genes to predict the prognosis of HCC patients. MethodsWe used scRNA-seq data from GSE166635 to differentiate malignant cells from normal cells using “copykat”.The study thoroughly examined the disparities in disulfidoptosis scores and the associated gene expressions between malignant and normal cells.We identified key ligand and receptor genes that are specific to HCC cells.Subsequently, Correlation analysis was conducted to ascertain the ligand and receptor genes associated with disulfidoptosis.We performed univariate Cox regression analysis to identify prognostic ligand and receptor genes associated with disulfidoptosis.We employed LASSO to construct a risk signature using prognostic ligand and receptor genes associated with disulfidoptosis.Lastly, we developed a nomogram model that integrates the risk signature with clinicopathological characteristics. ResultsMalignant cells displayed a marked increase in disulfidoptosis scores and the expression of associated genes compared to normal cells.We identified 110 receptor and ligand genes significantly associated with disulfidoptosis, and narrowed them down to create a risk signature comprising eight genes.Multivariate analysis confirmed the risk signature as an independent prognostic factor for HCC and validated its predictive value for immunotherapy outcomes.A novel nomogram was developed, incorporating stage information and the risk signature derived from disulfidoptosis-related receptor and ligand genes, demonstrating excellent predictive accuracy and reliability in HCC prognosis prediction. ConclusionRisk signatures based on disulfidoptosis-associated ligand and receptor genes can effectively predict HCC prognosis and may inform immunotherapy strategies.

Lenvatinib or Sorafenib Treatment Causing a Decrease in Skeletal Muscle Mass, an Independent Prognostic Factor in Hepatocellular Carcinoma: A Survival Analysis Using Time-Varying Covariates.

This study aimed to assess the effects of lenvatinib (LEN) or sorafenib (SOR) treatment for hepatocellular carcinoma (HCC) on body composition and changes in body composition on survival. This study enrolled 77 HCC patients. Skeletal muscle index (SMI), subcutaneous and visceral adipose tissue indices (SATI and VATI), AFP, PIVKA-II, and ALBI scores were analyzed at the time of LEN/SOR introduction, three months after the introduction, at treatment discontinuation, and the last observational time. The differences between chronological changes in these values were analyzed using a paired t-test. The Cox proportional hazards model was used to analyze prognostic factors using time-varying covariates. The chronological changes in each factor were 45.5-43.6-40.6-39.8 (cm2/m2) for SMI, 41.7-41.6-36.3-33.7 (cm2/m2) for SATI, 41.9-41.1-37.1-34.8 (cm2/m2) for VATI, 2.379-26.42-33.61-36.32 (×103 ng/mL) for AFP, 9.404-13.39-61.34-25.70 (×103 mAU/mL) for PIVKA-II, and -2.56--2.38--1.99--1.90 for the ALBI score. The presence of pre-treatment (p = 0.042), AFP (p = 0.002), PIVKA-II (p < 0.001), ALBI score (p < 0.001), and SMI (p = 0.001) were independent prognostic factors. Skeletal muscle mass decreases significantly during LEN/SOR treatment and is an independent prognostic factor for HCC.

Characterizing the key genes of COVID-19 that regulate tumor immune microenvironment and prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), a highly heterogeneous malignant tumor associated with a poor prognosis, is a common cause of cancer-related deaths worldwide, with a limited survival benefit for patients despite ongoing therapeutic breakthroughs. Coronavirus disease 2019 (COVID-19), a severe infectious disease caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), is a global pandemic and a serious threat to human health. The increased susceptibility to SARS-CoV-2 infection and a poor prognosis in patients with cancer necessitate the exploration of the potential link between the two. No studies have investigated the relationship of COVID-19 genes with the prognosis and tumor development in patients with HCC. We screened prognosis-related COVID-19 genes in HCC, performed molecular typing, developed a stable and reliable COVID-19 genes signature for predicting survival, characterized the immune microenvironment in HCC patients, and explored new molecular therapeutic targets. Datasets of HCC patients, including RNA sequencing data and clinical information, were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Prognosis-related COVID-19 genes were identified by univariate Cox analysis. Molecular typing of HCC was performed using the consensus non-negative matrix factorization method (cNMF), followed by the analysis of survival, tumor microenvironment, and pathway enrichment for each subtype. Prognostic signatures were constructed using LASSO-Cox regression models, and receiver operating characteristic (ROC) curves were used to validate the predictive performance of the signature. The same approach was used for the test and external validation sets. Seven software packages were applied to determine the abundance of immune infiltration in HCC patients and investigate its relationship with the risk scores. Gene set enrichment analysis (GSEA) was used to explore the potential mechanisms by which the COVID-19 genes affect hepatocarcinogenesis and prognosis. Three types of machine learning methods were combined to identify the most critical genes in the signature and localize their expression at the single cell level. We identified 53 prognosis-related COVID-19 genes and classified HCC into two molecular subtypes (C1, C2) by using the NMF method. The prognosis of C2 was significantly better than that of C1, and the two subtypes differed remarkably in terms of the tumor immune microenvironment and biological functions. The 17 COVID-19 genes were screened using the LASSO regression method to develop a 17 COVID-19 genes signature, which demonstrated a good predictive performance for 1-, 2- and 3-year OS of patients with HCC. The risk score as an independent prognostic factor for HCC has better predictive accuracy than traditional clinical variables. Patients in the TCGA cohort were categorized by risk score into the high- and low-risk groups, with the high-risk group mainly enriched in the immune modulation-related pathways and the low-risk group mainly enriched in the metabolism-related pathways, suggesting that the COVID-19 genes may affect disease progression and prognosis by regulating the tumor immune microenvironment and metabolism in HCC. NOL10 was identified as the most critical gene in the signature and hypothesized to be a potential therapeutic target for HCC. Objectively, the COVID-19 genes signature developed in this study, as an independent prognostic factor in HCC patients, is closely associated with the prognosis and tumor immune microenvironment of HCC patients and indicates that they may regulate the development of HCC in multiple ways, providing us with new perspectives for understanding the molecular mechanisms of HCC and finding effective therapeutic targets.