Integrative analysis unveils ECM signatures and pathways driving hepatocellular carcinoma progression: A multi-omics approach and prognostic model development.

Liver hepatocellular carcinoma (LIHC) is a highly lethal form of cancer that is among the deadliest cancer types globally. In terms of cancer-related mortality rates, liver cancer ranks among the top three, underscoring the severity of this disease. Insufficient analysis has been conducted to fully understand the potential value of the extracellular matrix (ECM) in immune infiltration and the prognostic stratification of LIHC, despite its recognised importance in the development of this disease. The scRNA-seq data of GSE149614 was used to conduct single-cell analysis on 10 LIHC samples. CellChat scores were calculated for seven cell populations in the descending cohort to investigate cellular communication, while PROGENy scores were calculated to determine tumour-associated pathway scores in different cell populations. The pathway analysis using GO and KEGG revealed the enrichment of ECM-associated genes in the pathway, highlighting the potential role of the ECM in LIHC development. By utilizing the TCGA-LIHC cohort, an ECM-based prognostic model for LIHC was developed using Lasso regression. Immune infiltration scores were calculated using two methods, and the performance of the ECM-related risk score was evaluated using an independent cohort from the CheckMate study. To determine the precise expression of ECM-associated risk genes in LIHC, we evaluated hepatocellular carcinoma cell lines using a range of assays, including Western blotting, invasion assays and Transwell assays. Using single-cell transcriptome analysis, we annotated the spatially-specific distribution of major immune cell types in single-cell samples of LIHC. The main cell types identified and annotated included hepatocytes, T cells, myeloid cells, epithelial cells, fibroblasts, endothelial cells and B cells. The utilisation of cellchat and PROGENy analyses enabled the investigation and unveiling of signalling interactions, protein functionalities and the prominent influential pathways facilitated by the primary immune cell types within the LIHC. Numerous tumour pathways, including PI2K, EGFR and TGFb, demonstrated a close correlation with the involvement of ECM in LIHC. Moreover, an evaluation was conducted to assess the primary ECM-related functional changes and biological pathway enrichment in LIHC. Differential genes associated with ECM were identified and utilised to create prognostic models. The prognostic stratification value of these models for LIHC patients was confirmed through validation in multiple databases. Furthermore, through immune infiltration analysis, it was discovered that ECM might be linked to the irregular expression and regulation of numerous immune cells. Additionally, histone acetylation was mapped against gene mutation frequencies and differential expression profiles. The prognostic stratification efficacy of the ECM prediction model constructed in the context of PD-1 inhibitor therapy was also examined, and it exhibited strong stratification performance. Cellular experiments, including Western blotting, invasion and Transwell assays, revealed that ECM-associated risk genes have a promoting effect on the development of LIHC. The creation of biomarkers for LIHC using ECM-related genes unveiled substantial correlations with immune microenvironmental infiltration and functional mutations in various tumour pathways. This enlightens us to the possibility that the influence of ECM on tumours may extend beyond simply promoting the fibrotic process and the stromal composition of tumours.

Identification of PPAR-related differentially expressed genes liver hepatocellular carcinoma and construction of a prognostic model based on data analysis and molecular docking.

Liver hepatocellular carcinoma (LIHC) is a significant global health issue with limited treatment options. In this study, single-cell RNA sequencing (scRNA-seq) data were used to explore the molecular mechanisms of LIHC development and identify potential targets for therapy. The expression of peroxisome proliferator-activated receptors (PPAR)-related genes was analysed in LIHC samples, and primary cell populations, including natural killer cells, T cells, B cells, myeloid cells, endothelial cells, fibroblasts and hepatocytes, were identified. Analysis of the differentially expressed genes (DEGs) between normal and tumour tissues revealed significant changes in gene expression in various cell populations. PPAR activity was evaluated using the 'AUCell' R software, which indicated higher scores in the normal versus the malignant hepatocytes. Furthermore, the DEGs showed significant enrichment of pathways related to lipid and glucose metabolism, cell development, differentiation and inflammation. A prognostic model was then constructed using 8 PPARs-related genes, including FABP5, LPL, ACAA1, PPARD, FABP4, PLIN1, HMGCS2 and CYP7A1, identified using least absolute shrinkage and selection operator-Cox regression analysis, and validated in the TCGA-LIHC, ICGI-LIRI and GSE14520 datasets. Patients with low-risk scores had better prognosis in all cohorts. Based on the expression of the eight model genes, two clusters of patients were identified by ConsensusCluster analysis. We also predicted small-molecule drugs targeting the model genes, and identified perfluorohexanesulfonic acid, triflumizole and perfluorononanoic acid as potential candidates. Finally, wound healing assay confirmed that PPARD can promote the migration of liver cancer cells. Overall, our study offers novel perspectives on the molecular mechanisms of LIHC and potential areas for therapeutic intervention, which may facilitate the development of more effective treatment regimens.

Long noncoding RNA SNHG3 interacts with microRNA-502-3p to mediate ITGA6 expression in liver hepatocellular carcinoma.

SNHG3, a long noncoding RNA (lncRNA), has been linked to poor outcomes in patients with liver hepatocellular carcinoma (LIHC). In this study, we found that SNHG3 was overexpressed in LIHC and associated with poor outcomes in patients with LIHC. Functional assays, including colony formation, spheroid formation, and in vivo assays showed that SNHG3 promoted stemness of cancer stem cells (CSC) and tumor growth in vivo by interacting with microRNA-502-3p (miR-502-3p). miR-502-3p inhibitor repressed the tumor-suppressing effects of SNHG3 depletion. Finally, by RNA pull-down, dual-luciferase reporter assay, m6A methylation level detection, and m6A-IP-qPCR assays, we found that miR-502-3p targeted YTHDF3 to regulate the translation of integrin alpha-6 (ITGA6) and targeted HBXIP to inhibit the m6A modification of ITGA6 through methyltransferase-like 3 (METTL3). Our study revealed that SNHG3 controls the YTHDF3/ITGA6 and HBXIP/METTL3/ITGA6 pathways by repressing miR-502-3p expression to sustain the self-renewal properties of CSC in LIHC.

A signature based on neutrophil extracellular trap-related genes for the assessment of prognosis, immunoinfiltration, mutation and therapeutic response in hepatocellular carcinoma.

BACKGROUND: Liver cancer is a highly lethal and aggressive form of cancer that poses a significant threat to patient survival. Within this category, liver hepatocellular carcinoma (LIHC) represents the most common subtype of liver cancer. Despite decades of research and treatment, the overall survival rate for LIHC has not significantly improved. Improved models are necessary to differentiate high-risk cases and predict possible treatment options for LIHC patients. Recent studies have identified a set of genes associated with neutrophil extracellular traps (NETs) that may contribute to tumor growth and metastasis; however, their prognostic value in LIHC has yet to be established. This study aims to construct a prognostic signature based on a set of NET-related genes (NRGs) for patients diagnosed with LIHC. METHODS: The transcriptomic data and clinical information concerning LIHC patients were procured from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium LIHC (ICLIHC) databases, respectively. To determine the NRG subtypes, the k-means algorithm was employed, along with consensus clustering. The aforementioned analysis aided the construction of a prognostic signature utilizing the last absolute shrinkage and selection operator Cox analysis. To validate the prognostic model, an external dataset, receiver operating characteristic curve, and principal component analysis were utilized. Moreover, the immune microenvironment and the proportion of immune cells between high- and low-risk cases were scrutinized by ESTIMATE and CIBERSORT algorithms. Finally, gene set enrichment analysis was executed to investigate the potential mechanism of NRGs in the pathogenesis and prognosis of LIHC. RESULTS: Two molecular subtypes of LIHC were identified based on the expression patterns of differentially expressed NRGs (DE-NRGs). The two subtypes demonstrated significant differences in survival rates and immune cell expression levels. The study results demonstrated the role of NRGs in antigen presentation, which led to the promotion of tumor immune escape. A risk model was developed and validated with strong overall survival prediction ability. The model, comprising 34 NRGs, showed a strong ability to predict prognosis. CONCLUSION: We built a dependable prognostic signature based on NRGs for LIHC. We identified that NRGs could have a significant interaction in LIHC's immune microenvironment and therapeutic response. This finding offers insight into the molecular mechanisms and targeted therapy for LIHC.

Exploration of YBX1 role in the prognostic value and immune characteristics by single-cell and bulk sequencing analysis for liver hepatocellular carcinoma.

BACKGROUND: Y-box binding protein 1 (YBX1) plays a variety of roles in progression of multiple tumors. However, the role of YBX1 in prognostic value and immune regulation for liver hepatocellular carcinoma (LIHC) remains unclear. The present study aimed to examine the effect of YBX1 on the regulation of tumor immunity and survival prediction in LIHC patients. METHODS: YBX1-related expression profiles and single-cell and bulk sequencing analysis were performed using online databases. YBX1 expression was validated by a quantitative real-time PCR (qRT-PCR), western blotting and immunohistochemistry. Univariate/multivariate Cox regression analysis was performed to determine independent predictors of overall survival (OS). The ESTIMATE (i.e., Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) analysis were used to assess the relationships between YBX1 and LIHC immunity. RESULTS: YBX1 was over-expressed in LIHC tissues and cell lines. High YBX1 expression was significantly associated with poor OS. Univariate/multivariate Cox regression analysis revealed that YBX1 was an independent prognostic factor for LIHC. Gene set enrichment analysis revealed that YBX1 was associated with multiple signaling pathways correlated to LIHC. Additionally, YBX1 was expressed in multiple immune cells and was significantly correlated with immune cells, immune checkpoint markers and tumor immune microenvironment. The TIDE analysis demonstrated that LIHC patients with high YBX1 expression showed a higher T-cell dysfunction score and a higher exclusion score, as well as poorer immunotherapy response. CONCLUSIONS: YBX1 plays crucial oncogenic roles in LIHC and is closely associated with the immune defense system. YBX1 inhibition may serve as a potential treatment for LIHC.

G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma.

BACKGROUND: Liver Hepatocellular carcinoma (LIHC) exhibits a high incidence of liver cancer with escalating mortality rates over time. Despite this, the underlying pathogenic mechanism of LIHC remains poorly understood. MATERIALS & METHODS: To address this gap, we conducted a comprehensive investigation into the role of G6PD in LIHC using a combination of bioinformatics analysis with database data and rigorous cell experiments. LIHC samples were obtained from TCGA, ICGC and GEO databases, and the differences in G6PD expression in different tissues were investigated by differential expression analysis, followed by the establishment of Nomogram to determine the percentage of G6PD in causing LIHC by examining the relationship between G6PD and clinical features, and the subsequent validation of the effect of G6PD on the activity, migration, and invasive ability of hepatocellular carcinoma cells by using the low expression of LI-7 and SNU-449. Additionally, we employed machine learning to validate and compare the predictive capacity of four algorithms for LIHC patient prognosis. RESULTS: Our findings revealed significantly elevated G6PD expression levels in liver cancer tissues as compared to normal tissues. Meanwhile, Nomogram and Adaboost, Catboost, and Gbdt Regression analyses showed that G6PD accounted for 46%, 31%, and 49% of the multiple factors leading to LIHC. Furthermore, we observed that G6PD knockdown in hepatocellular carcinoma cells led to reduced proliferation, migration, and invasion abilities. Remarkably, the Decision Tree C5.0 decision tree algorithm demonstrated superior discriminatory performance among the machine learning methods assessed. CONCLUSION: The potential diagnostic utility of G6PD and Decision Tree C5.0 for LIHC opens up a novel avenue for early detection and improved treatment strategies for hepatocellular carcinoma.

AMPKα2 promotes tumor immune escape by inducing CD8+ T-cell exhaustion and CD4+ Treg cell formation in liver hepatocellular carcinoma.

BACKGROUND: Adenosine monophosphate-activated protein kinase (AMPK) is associated with the development of liver hepatocellular carcinoma (LIHC). AMPKα2, an α2 subunit of AMPK, is encoded by PRKAA2, and functions as the catalytic core of AMPK. However, the role of AMPKα2 in the LIHC tumor immune environment is unclear. METHODS: RNA-seq data were obtained from the Cancer Genome Atlas and Genotype-Tissue Expression databases. Using the single-cell RNA-sequencing dataset for LIHC obtained from the China National Genebank Database, the communication between malignant cells and T cells in response to different PRKAA2 expression patterns was evaluated. In addition, the association between PRKAA2 expression and T-cell evolution during tumor progression was explored using Pseudotime analysis, and the role of PRKAA2 in metabolic reprogramming was explored using the R "scMetabolis" package. Functional experiments were performed in LIHC HepG2 cells. RESULTS: AMPK subunits were expressed in tissue-specific and substrate-specific patterns. PRKAA2 was highly expressed in LIHC tissues and was associated with poor patient prognosis. Tumors with high PRKAA2 expression displayed an immune cold phenotype. High PRKAA2 expression significantly promoted LIHC immune escape. This result is supported by the following evidence: 1) the inhibition of major histocompatibility complex class I (MHC-I) expression through the regulation of interferon-gamma activity in malignant cells; 2) the promotion of CD8+ T-cell exhaustion and the formation of CD4+ Treg cells in T cells; 3) altered interactions between malignant cells and T cells in the tumor immune environment; and 4) induction of metabolic reprogramming in malignant cells. CONCLUSIONS: Our study indicate that PRKAA2 may contribute to LIHC progression by promoting metabolic reprogramming and tumor immune escape through theoretical analysis, which offers a theoretical foundation for developing PRKAA2-based strategies for personalized LIHC treatment.

Integrated Single-cell and Transcriptome Sequencing Analyses Identified PREX1 as an Immune-related Prognostic Biomarker for Liver Hepatocellular Carcinoma.

Background: PtdIns (3,4,5) P3-dependent Rac exchanger 1 (PREX1), also known as PREX1, a member of the Rac guanine nucleotide exchange factors (Rac-GEF) family. Studies have suggested that PREX1 plays a role in mediating oncogenic pathway activation and controlling various biological mechanisms in different types of cancer, including liver hepatocellular carcinoma (LIHC). However, the function of PREX1 in the pathogenesis of LIHC and its potential role on immunological regulation is not clearly elucidated. Methods: The expression level and the clinical role of PREX1 in LIHC was analyzed based on database from the Cancer Genome Atlas (TCGA), TNM plotter and University of Alabama Cancer Database (UALCAN). We investigated the relationship between PREX1 and immunity in LIHC by TISIDB, CIBERSORT and single cell analysis. Immunotherapy responses were assessed by the immunophenoscores (IPS). Moreover, biological functional assays were performed to further investigate the roles of PREX1 in liver cancer cell lines. Results: Higher expression of PREX1 in LIHC tissues than in normal liver tissues was found based on public datasets. Further analysis revealed that PREX1 was associated with worse clinical characteristics and dismal prognosis. Pathway enrichment analysis indicated that PREX1 participated in immune-related pathways. Through CIBERSORT and single cell analysis, we found a remarkable correlation between the expression of PREX1 and various immune cells, especially macrophages. In addition, high PREX1 expression was found to be associated with a stronger response to immunotherapy. Furthermore, in vitro assays indicated that depletion of PREX1 can suppress invasion and proliferation of LIHC cells. Conclusion: Elevated expression of PREX1 indicates poor prognosis, influences immune modulation and predicts sensitivity of immunosuppression therapy in LIHC. Our results suggested that PREX1 may be a prognostic biomarker and therapeutic target, offering new treatment options for LIHC.

Identification of Hub Genes in Liver Hepatocellular Carcinoma Based on Weighted Gene Co-expression Network Analysis.

Liver hepatocellular carcinoma (LIHC) is a malignant cancer with high incidence and poor prognosis. To investigate the correlation between hub genes and progression of LIHC and to provided potential prognostic markers and therapy targets for LIHC. Our study mainly used The Cancer Genome Atlas (TCGA) LIHC database and the gene expression profiles of GSE54236 from the Gene Expression Omnibus (GEO) to explore the differential co-expression genes between LIHC and normal tissues. The differential co-expression genes were extracted by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. The Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to annotate the function of differential genes. Then the hub genes were validated using protein-protein interaction (PPI) network. And the expression level and prognostic analysis were performed. The probable associations between the expression of hub genes and both tumor purity and infiltration of immune cells were explored by TIMER. A total of 68 differential co-expression genes were extracted. These genes were mainly enriched in complement activation (biological process), collagen trimer (cellular component), carbohydrate binding and receptor ligand activity (molecular function) and cytokine - cytokine receptor interaction. Then we demonstrated that the 10 hub genes (CFP, CLEC1B, CLEC4G, CLEC4M, FCN2, FCN3, PAMR1 and TIMD4) were weakly expressed in LIHC tissues, the qRT-PCR results of clinical samples showed that six genes were significantly downregulated in LIHC patients compared with adjacent tissues. Worse overall survival (OS) and disease-free survival (DFS) in LIHC patients were associated with the lower expression of CFP, CLEC1B, FCN3 and TIMD4. Ten hub genes had positive association with tumor purity. CFP, CLEC1B, FCN3 and TIMD4 could serve as novel potential molecular targets for prognosis prediction in LIHC.

Comprehensive intratumoral heterogeneity landscaping of liver hepatocellular carcinoma and discerning of APLP2 in cancer progression.

INTRODUCTION: As the sixth most common type of cancer worldwide, liver hepatocellular carcinoma (LIHC) emerges as grave public health danger owing to its chemotherapy-resistant feature. Disulfidoptosis is a newly discovered programmed cell death process affecting the normal actin cytoskeleton structure. METHODS: Single-cell RNA (scRNA)-seq data were procured from GSE149614 and GSE202642 datasets. We utilized uniform manifold approximation and projection and clustering algorithm Louvian for dimensionality reduction and FindAllMarkers function for determining the differentially expressed genes (DEGs). Monocle2 and SCENIC were utilized to perform pseudo-time series and transcription factor analysis for selected subgroups. A series of in vitro experiments, including colony formation assay (CFA), flow cytometry targeting apoptosis and cell cycle, was applied to investigate how APLP2 regulated the LIHC progression. Two cell lines of LIHC cells, HepG2, and Huh7, were used for si-APLP2 transfection. RESULTS: Tumor heterogeneity landscape of LIHC was depicted by detailed subgroup analysis. We found T and B cells were enriched with POU2F1 and HES1 activity. Inflammatory cancer-associated fibroblasts interacted with the cancer cells, uniquely through COL1A1/SDC1, COL1A2/SDC1 and LUM/ITGB1 pathways. The transformation from normal hepatocytes to malignant cells was displayed by cell trajectory analysis. State4, which was determined as malignant cells, was enriched in PI3K, hypoxia, and Epidermal growth factor receptor pathway, and enriched with Nuclear Receptor Subfamily 2 Group F Member 1 transcription factor activity. We observed an intense communication from the cancer cells to endothelial cells, mainly through the Vitronectin (VTN) to Kinase Insert Domain Receptor (KDR) pathway. A prognostic model targeting LIHC was constructed based on the disulfidoptosis-based DEGs, namely APLP2, PDIA6, YBX1, SPP1, whose accuracy was validated in multiple cohorts. Knockdown of APLP2 significantly increased the apoptosis and delayed cell cycle progression of LIHC cell line. CONCLUSION: A prognostic model targeting LIHC was constructed based on the disulfidoptosis-related DEGs, which displayed high stability and accuracy in multiple cohorts. APLP2 played an active role in the carcinogenesis of LIHC by regulating the apoptosis and cell cycle.

Analysis of Somatic Mutations in the TCGA-LIHC Whole Exome Sequence to Identify the Neoantigen for Immunotherapy in Hepatocellular Carcinoma.

There are numerous clinically proven methods for treating cancer worldwide. Immunotherapy has been used to treat cancer with significant success in the current studies. The purpose of this work is to identify somatically altered target gene neoantigens and investigate liver cancer-related immune cell interaction and functional changes for potential immunotherapy in future clinical trials. Clinical patient data from the Cancer Genome Atlas (TCGA) database were used in this investigation. The R maf utility package was used to perform somatic analysis. The 17-mer peptide neoantigens were extracted using an in-house Python software called Peptide.py. Additionally, the epitope analysis was conducted using NetMHCpan4.1 program. Neopeptide immunogenicity was assessed using DeepCNN-Ineo, and tumor immune interaction, association with immune cells, correlation, and survival analysis were assessed using the TIMER web server. Based on somatic mutation analysis, we have identified the top 10 driver genes (TP53, TNN, CTNNB1, MUC16, ALB, PCLO, MUC4, ABCA13, APOB, and RYR2). From the superfamily of 20 HLA (Human leukocyte antigens) allele epitopes, we discovered 5653 neopeptides. Based on T cell receptor face hydrophobic analysis, these neopeptides were subjected to immunogenicity investigation. A mutation linked to tumor growth may have an impact on immune cells. According to this study's correlation and survival analysis, all driver genes may function as immune targets for liver cancer. These genes are recognized to be immune targets. In the future, immune checkpoint inhibitors may be developed to prolong patient survival times and prevent hepatocellular carcinoma (HCC) through immunotherapy.

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.

FANCI is Associated with Poor Prognosis and Immune Infiltration in Liver Hepatocellular Carcinoma.

Objective: This study aimed to validate FANCI as a potential marker for both prognosis and therapy in liver hepatocellular carcinoma. Method: FANCI expression data were acquired from GEPIA, HPA, TCGA, and GEO databases. The impact of clinicopathological features was analyzed by UALCAN. The prognosis of Liver Hepatocellular Carcinoma (LIHC) patients with highly expressed FANCI was constructed utilizing Kaplan-Meier Plotter. GEO2R was employed to identify differentially expressed genes (DEGs). Metascape was used to analyze functional pathways correlations. Protein-Protein interaction (PPI) networks were generated by Cytoscape. Furthermore, molecular complex detection (MCODE) was utilized to recognize Hub genes, which were selected to establish a prognostic model. Lastly, the relationship between FANCI and immune cell infiltration in LIHC was examined. Results: Compared to adjacent tissues, FANCI expression levels were significantly higher in LIHC tissues and were positively correlated to the cancer grade, stage, and prior hepatitis B virus (HBV) infection. High expression of FANCI was found to be associated with poor prognosis in LIHC (HR=1.89, p<0.001). DEGs that were positively correlated with FANCI were involved in various processes, including the cell cycle, VEGF pathway, immune system processes, and biogenesis of ribonucleoproteins. MCM10, TPX2, PRC1, and KIF11 were identified as key genes closely related to FANCI and poor prognosis. A reliable five-variable prognostic model was constructed with strong predictive capability. Lastly, a positive correlation was observed between FANCI expression and tumor-infiltration levels of CD8+ T cells, B cells, regulatory T (Tregs), CD4+ T helper 2 (Th2), and macrophage M2 cells. Conclusion: FANCI may hold promise as a potential biomarker for predicting prognostic outcomes, and a valuable therapeutic target for LIHC patients, with a focus on anti-proliferation, anti-chemoresistance, and combination with immunotherapy.

Comprehensive Analysis of NKX3.2 in Liver Hepatocellular Carcinoma by Bigdata.

Background and Objectives: The gene NKX3.2 plays a role in determining cell fate during development, and mutations of NKX3.2 have been studied in relation to human skeletal diseases. However, due to the lack of studies on the link between NKX3.2 and cancer, we aimed to provide insights into NKX3.2 as a new prognostic biomarker for liver hepatocellular carcinoma (LIHC). Materials and Methods: The clinical significance of LIHC was investigated using open gene expression databases. We comprehensively analyzed NKX3.2 expression in LIHC using Gene Expression Profiling Interactive Analysis 2, Tumor Immune Estimation Resource (TIMER), and Kaplan-Meier plotter databases. Then, we investigated the association between NKX3.2 expression and tumor-infiltrating immune cells (TIICs). Results: NKX3.2 expression was higher in the primary tumor group compared to the normal group, and expression was higher in fibrolamellar carcinoma (FLC) compared to other subtypes. When the prognostic value of NKX3.2 was evaluated, highly expressed NKX3.2 significantly improved the overall survival and had an unfavorable prognosis. In addition, NKX3.2 expression was associated with immune cell infiltration. Patients with low gene expression and high macrophage expression had a poorer survival rate than those with low NKX3.2 and low macrophage expression (p = 0.0309). Conclusions: High NKX3.2 expression may induce poorer prognosis in LIHC. In addition, these findings can be used as basic data due to the lack of available related research. However, further in vivo studies are essential to gain a deeper understanding of the biological role of NKX3.2 in LIHC and its potential implications for cancer development and progression.

The correlation of EZH2 expression with the progression and prognosis of hepatocellular carcinoma.

BACKGROUND: Enhancer of Zeste homologue 2 (EZH2) is a polycomb group gene and an epigenetic regulator that inhibits transcription, a modification associated with gene silencing. EZH2 plays an essential role in humoral and cell-mediated adaptive immunity. The purpose of the current study is to investigate the prognostic potential of EZH2 and to comprehensively analyse the correlation between EZH2 and immune infiltration in multiple cancer cases, especially liver hepatocellular carcinoma. METHODS: EZH2 expression across cancers was explored through Oncomine, HPA, and GEPIA2. Additionally, the prognostic value of EZH2 analysis across cancers was based on the GEPIA2, TCGA portal, Kaplan-Meier Plotter, and LOGpc databases. Based on GO and KEGG analyses, GSEA helped demonstrate the biological processes through which EZH2 might lead to HCC development. GEPIA and TIMER were adopted to detect the possible relationship of EZH2 expression with tumour-infiltrating immune cells (TIICs). RESULTS: EZH2 overexpression levels were associated with poor prognosis of cancer, especially hepatocellular carcinoma. A high EZH2 expression level is related to a poor prognosis of HCC, especially in disease histology and stage III. The EZH2 expression level was positively correlated with critical gene markers of TAMs, M2 macrophages, M1 macrophages, and monocytes. Further analysis revealed that EZH2 genes were mainly related to DNA recombination, mitotic cell cycle phase transition, and chromosome segregation. CONCLUSION: EZH2 plays an essential role in the immune microenvironment and is a potential prognostic marker and immunotherapy target for hepatocellular carcinoma.

System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma.

BACKGROUND: Liver hepatocellular carcinoma (LIHC) ranks sixth among the most common types of cancer with a high mortality rate. Cuproptosis is a newly discovered type of cell death in tumor, which is characterized by accumulation of intracellular copper leading to the aggregation of mitochondrial lipoproteins and destabilization of proteins. Thus, understanding the exact effects of cuproptosis-related genes in LIHC and determining their prognosticvalue is critical. However, the prognostic model of LIHC based on cuproptosis-related genes has not been reported. METHODS: Firstly, we downloaded transcriptome data and clinical information of LIHC patients from TCGA and GEO (GSE76427), respectively. We then extracted the expression of cuproptosis-related genes and established a prognostic model by lasso cox regression analysis. Afterwards, the prediction performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic curve (ROC). Then, the prognostic model and the expression levels of the three genes were validated using the dataset from GEO. Subsequently, we divided LIHC patients into two subtypes by non-negative matrix factorization (NMF) classification and performed survival analysis. We constructed a Sankey plot linking different subtypes and prognostic models. Next, we calculate the drug sensitivity of each sample from patients in the high-risk group and low-risk group by the R package pRRophetic. Finally, we verified the function of LIPT1 in LIHC. RESULTS: Using lasso cox regression analysis, we developed a prognostic risk model based on three cuproptosis-related genes (GCSH, LIPT1 and CDKN2A). Both in the training and in the test sets, the overall survival (OS) of LIHC patients in the low-risk group was significantly longer than that in the high-risk group. By performing NMF cluster, we identified two molecular subtypes of LIHC (C1 and C2), with C1 subtype having significantly longer OS and PFS than C2 subtype. The ROC analysis indicated that our model had a precisely predictive capacity for patients with LIHC. The multivariate Cox regression analysis indicated that the risk score is an independent predictor. Subsequently, we identified 71 compounds with IC50 values that differed between the high-risk and low-risk groups. Finally, we determined that knockdown of LIPT1 gene expression inhibited proliferation and invasion of hepatoma cells. CONCLUSION: In this study, we developed a novel prognostic model for hepatocellular carcinoma based on cuproptosis-related genes that can effectively predict the prognosis of LIHC patients. The model may be helpful for clinicians to make clinical decisions for patients with LIHC and provide valuable insights for individualized treatment. Two distinct subtypes of LIHC were identified based on cuproptosis-related genes, with different prognosis and immune characteristics. In addition, we verified that LIPT1 may promote proliferation, invasion and migration of LIHC cells. LIPT1 might be a new potential target for therapy of LIHC.

A risk score model based on TGF-ß pathway-related genes predicts survival, tumor microenvironment and immunotherapy for liver hepatocellular carcinoma.

BACKGROUND: Transforming growth factor-beta (TGF-ß) signal is an important pathway involved in all stages of liver hepatocellular carcinoma (LIHC) initiation and progression. Therefore, targeting TGF- ß pathway may be a potential therapeutic strategy for LIHC. Prediction of patients' tumor cells response requires effective biomarkers. METHODS: From 54 TGF-ß-related genes, this research determined the genes showing the greatest relation to LIHC prognosis, and developed a risk score model with 8 TGF-ß-related genes. The model divided LIHC patients from different datasets and platforms into low- and high-risk groups. Multivariate Cox regression analysis confirmed that the model was an independent prognostic factor for LIHC. The differences in genetic mutation, immune cell infiltration, biological pathway, response to immunotherapy or chemotherapy, and tumor microenvironment in LIHC samples showing different risks were analyzed. RESULTS: Compared with low-risk group, in the training set and test set, high-risk group showed shorter survival, lower stromal score and higher M0 macrophages scores, regulatory T cells (Tregs), helper follicular T cells. Moreover, high-risk samples showed higher sensitivity to cisplatin, imatinib, sorafenib and salubrinal and pyrimethamine. High-risk group demonstrated a significantly higher Tumor Immune Dysfunction and Exclusion (TIDE) score, but would significantly benefit less from taking immunotherapy and was less likely to respond to immune checkpoint inhibitors. CONCLUSIONS: In general, this work provided a risk scoring model based on 8 TGF-ß pathway-related genes, which might be a new potential tool for predicting LIHC.

Identification and Verification of Ubiquitin D as a Gene Associated with Hepatitis C Virus-Induced Hepatocellular Carcinoma.

INTRODUCTION: Accumulated studies have suggested that hepatitis C virus (HCV) infection is one of the leading causes for hepatocellular carcinoma (HCC). However, the mechanisms underlying the effect of HCV on the occurrence of HCC are still poorly understood. METHODS: HCV infection datasets (GSE82177 and GSE17856) and HCC datasets (The Cancer Genome Atlas Liver Hepatocellular Carcinoma and GSE89377) were downloaded from Gene Expression Omnibus or TCGA for analysis. The common differentially expressed genes in the above four datasets were identified by R software. The expression of ubiquitin D (UBD) in HCV-infected HepG2 cells was detected by RT-qPCR and Western blot, respectively. The interaction between NS3 and p53 was detected by co-immunoprecipitation. The influence of UBD on the proliferation and migration ability of HepG2 cells was evaluated by CCK-8 and wound healing assay, respectively. RESULTS: UBD was upregulated in both HCV-infected samples and HCC samples. HCV NS3 interacted with p53 and inhibited its expression. HCV NS3-induced UBD promoted the proliferation and migration of HepG2 cells. CONCLUSION: Our results suggest that HCV NS3-induced UBD is positively correlated with the development of HCV-related HCC during HCV infection. Targeting UBD could be a potential strategy for preventing and treating HCV-induced HCC.

Toxicological effects of polystyrene nanoplastics and perfluorooctanoic acid to Gambusia affinis.

Plastic pollution has attracted huge attention from public and scientific community in recent years. In the environment, nanoplastics (NPs, <100 nm) can interact with persistent organic pollutants (POPs) such as perfluorooctanoic acid (PFOA) and may exacerbate associated toxic impacts. The present study aims to explore the single and combined ecotoxicological effects of PFOA and polystyrene nanoplastics (PS-NPs, 80 nm) on the PI3K/AKT3 signaling pathway using a freshwater fish model Gambusia affinis. Fish were exposed individually to PS-NPs (200 µg/L) and PFOA (50, 500, 5000 µg/L) and their chemical mixtures for 96 h. Our results showed that the co-exposure significantly altered the mRNA relative expression of PI3K, AKT3, IKKß and IL-1ß, compared to corresponding single exposure and control groups, indicating that the PFOA-NP co-exposure can activate the PI3K/AKT3 signaling pathway. The bioinformatic analyses showed that AKT3 had more probes and exhibited a significantly sensitive correlation with DNA methylation, compared to other genes (PIK3CA, IKBKB, and IL1B). Further, the mRNA expressions of PIK3CA, AKT3, and IKBKB had a significant correlation with copy number variation (CNV) in human liver hepatocellular carcinoma (LIHC). And PIK3CA had the highest mutation rate among other genes of interest for LIHC. Moreover, AKT3 showed a relatively lower expression in TAM and CAF cells, compared to PIK3CA, IKBKB, and IL1B. Besides, hsa-mir-155-5p was closely correlated with AKT3, PIK3CA, IKBKB, and IL1B. In summary, these results provide evidence that NPs could enhance the carcinogenic effects of POPs on aquatic organisms and highlight possible targets of LIHC induced by PFOA-NP co-exposure.

Five metastasis-related mRNAs signature predicting the survival of patients with liver hepatocellular carcinoma.

BACKGROUNDS: Liver hepatocellular carcinoma (HCC) is one of the most malignant tumors, of which prognosis is unsatisfactory in most cases and metastatic of HCC often results in poor prognosis. In this study, we aimed to construct a metastasis- related mRNAs prognostic model to increase the accuracy of prediction of HCC prognosis. METHODS: Three hundred seventy-four HCC samples and 50 normal samples were downloaded from The Cancer Genome Atlas (TCGA) database, involving transcriptomic and clinical data. Metastatic-related genes were acquired from HCMBD website at the same time. Two hundred thirty-three samples were randomly divided into train dataset and test dataset with a proportion of 1:1 by using caret package in R. Kaplan-Meier method and univariate Cox regression analysis and lasso regression analysis were performed to obtain metastasis-related mRNAs which played significant roles in prognosis. Then, using multivariate Cox regression analysis, a prognostic prediction model was established. Transcriptome and clinical data were combined to construct a prognostic model and a nomogram for OS evaluation. Functional enrichment in high- and low-risk groups were also analyzed by GSEA. An entire set based on The International Cancer Genome Consortium(ICGC) database was also applied to verify the model. The expression levels of SLC2A1, CDCA8, ATG10 and HOXD9 are higher in tumor samples and lower in normal tissue samples. The expression of TPM1 in clinical sample tissues is just the opposite. RESULTS: One thousand eight hundred ninety-five metastasis-related mRNAs were screened and 6 mRNAs were associated with prognosis. The overall survival (OS)-related prognostic model based on 5 MRGs (TPM1,SLC2A1, CDCA8, ATG10 and HOXD9) was significantly stratified HCC patients into high- and low-risk groups. The AUC values of the 5-gene prognostic signature at 1 year, 2 years, and 3 years were 0.786,0.786 and 0.777. A risk score based on the signature was a significantly independent prognostic factor (HR = 1.434; 95%CI = 1.275-1.612; P < 0.001) for HCC patients. A nomogram which incorporated the 5-gene signature and clinical features was also built for prognostic prediction. GSEA results that low- and high-risk group had an obviously difference in part of pathways. The value of this model was validated in test dataset and ICGC database. CONCLUSION: Metastasis-related mRNAs prognostic model was verified that it had a predictable value on the prognosis of HCC, which could be helpful for gene targeted therapy.