Global burden of leukemia attributable to occupational exposure to formaldehyde from 1990 to 2019.

The objective of this study was to evaluate the worldwide burden of leukemia owing to occupational exposure to formaldehyde (OEF) from 1990 to 2019. Data on leukemia due to OEF were obtained from the Global Burden of Disease Study (GBD) 2019. By region, age, sex, and disease subtype, the numbers and age-standardized rates (ASRs) associated with deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs) were analyzed. Annual average percentage change (AAPC) was used to estimate disease burden trends from 1990 to 2019. To measure the risk of leukemia due to OEF, the population attributable fraction (PAF) was introduced. From 1990 to 2019, the number of deaths, DALYs, YLLs, and YLDs for leukemia caused by OEF increased by 44%, 34%, 33%, and 124%, respectively. Regarding the change in ASRs, the age-standardized YLDs (ASYLDs) rate of leukemia due to OEF, which was 38.03% (AAPC = 1.17 [95% confidence interval [CI] 1.11, 1.23]), indicated an increased trend. But the age-standardized mortality rate (ASMR), age-standardized DALY (ASDALY) rate, and age-standardized YLL (ASYLL) rate showed decline trends, with - 11.90% (AAPC = - 0.41 [95% CI - 0.45, - 0.37]), - 14.19% (AAPC = - 0.5 [95% CI - 0.55, - 0.45]), and - 14.97% (AAPC = - 0.53 [95% CI - 0.58, - 0.48]), respectively. In terms of PAFs, there were increasing trends in PAFs of age-standardized deaths, ASDALYs, ASYLLs, and ASYLDs for leukemia caused by OEF, with 20.15% (95% uncertainty interval [UI] 11.76%, 30.25%), 36.28% (95% UI 21.46%, 53.42%), 51.91% (95% UI 35.05%, 72.07%), and 36.34% (95% UI 21.58%, 53.63%), respectively. Across the socio-demographic index (SDI) regions, the leukemia burden caused by OEF was concentrated in middle and high-middle SDI regions. Besides, OEF poses a more serious risk for acute leukemia among the leukemia subtype. Globally, leukemia caused by OEF remains a public health burden. Policies must be developed to avoid the burden of leukemia caused by OEF.

A large-scale transcriptional analysis reveals herb-derived ginsenoside F2 suppressing hepatocellular carcinoma via inhibiting STAT3.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common type of cancer that shows great morbidity and mortality rates. However, there are limited available drugs to treat HCC. AIM: The present work focused on discovering the potential anti-HCC compounds from traditional Chinese medicine (TCM) by employing high-throughput sequencing-based high-throughput screening (HTS2) together with the liver cancer pathway-associated gene signature. METHODS: HTS2 assay was adopted for identifying herbs. Protein-protein interaction (PPI) network analysis and computer-aided drug design (CADD) were used to identify key targets and screen the candidate natural products of herbs. Molecular docking, network pharmacology analysis, western blotting, immunofluorescent staining, subcellular fractionation experiment, dual-luciferase reporter gene assay, surface plasmon resonance (SPR) as well as nuclear magnetic resonance (NMR) were performed to validate the ability of compound binding with key target and inhibiting its function. Moreover, cell viability, colony-forming, cell cycle assay and animal experiments were performed to examine the inhibitory effect of compound on HCC. RESULTS: We examined the perturbation of 578 herb extracts on the expression of 84 genes from the liver cancer pathway, and identified the top 20 herbs significantly reverting the gene expression of this pathway. Signal transducer and activator of transcription 3  (STAT3)  was identified as one of the key targets of the liver cancer pathway by PPI network analysis. Then, by analyzing compounds from top 20 herbs utilizing CADD, we found ginsenoside F2 (GF2) binds to STAT3 with high affinity, which was further validated by the results from molecular docking, SPR and NMR. Additionally, our results showed that GF2 suppresses the phosphorylation of Y705 of STAT3, inhibits its nuclear translocation, decreases its transcriptional activity and inhibits the growth of HCC in vitro and in vivo. CONCLUSION: Based on this large-scale transcriptional study, a number of anti-HCC herbs were identified. GF2, a compound derived from TCM, was found to be a chemical basis of these herbs in treating HCC. The present work also discovered that GF2 is a new STAT3 inhibitor, which is able to suppress HCC. As such, GF2 represents a new potential anti-HCC therapeutic strategy.

Integrative analysis of circadian clock with prognostic and immunological biomarker identification in ovarian cancer.

Objective: To identify circadian clock (CC)-related key genes with clinical significance, providing potential biomarkers and novel insights into the CC of ovarian cancer (OC). Methods: Based on the RNA-seq profiles of OC patients in The Cancer Genome Atlas (TCGA), we explored the dysregulation and prognostic power of 12 reported CC-related genes (CCGs), which were used to generate a circadian clock index (CCI). Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were used to identify potential hub genes. Downstream analyses including differential and survival validations were comprehensively investigated. Results: Most CCGs are abnormally expressed and significantly associated with the overall survival (OS) of OC. OC patients with a high CCI had lower OS rates. While CCI was positively related to core CCGs such as ARNTL, it also showed significant associations with immune biomarkers including CD8+ T cell infiltration, the expression of PDL1 and CTLA4, and the expression of interleukins (IL-16, NLRP3, IL-1ß, and IL-33) and steroid hormones-related genes. WGCNA screened the green gene module to be mostly correlated with CCI and CCI group, which was utilized to construct a PPI network to pick out 15 hub genes (RNF169, EDC4, CHCHD1, MRPL51, UQCC2, USP34, POM121, RPL37, SNRPC, LAMTOR5, MRPL52, LAMTOR4, NDUFB1, NDUFC1, POLR3K) related to CC. Most of them can exert prognostic values for OS of OC, and all of them were significantly associated with immune cell infiltration. Additionally, upstream regulators including transcription factors and miRNAs of key genes were predicted. Conclusion: Collectively, 15 crucial CC genes showing indicative values for prognosis and immune microenvironment of OC were comprehensively identified. These findings provided insight into the further exploration of the molecular mechanisms of OC.

Hallmark guided identification and characterization of a novel immune-relevant signature for prognostication of recurrence in stage I-III lung adenocarcinoma.

The high risk of postoperative mortality in lung adenocarcinoma (LUAD) patients is principally driven by cancer recurrence and low response rates to adjuvant treatment. Here, A combined cohort containing 1,026 stage I-III patients was divided into the learning (n = 678) and validation datasets (n = 348). The former was used to establish a 16-mRNA risk signature for recurrence prediction with multiple statistical algorithms, which was verified in the validation set. Univariate and multivariate analyses confirmed it as an independent indicator for both recurrence-free survival (RFS) and overall survival (OS). Distinct molecular characteristics between the two groups including genomic alterations, and hallmark pathways were comprehensively analyzed. Remarkably, the classifier was tightly linked to immune infiltrations, highlighting the critical role of immune surveillance in prolonging survival for LUAD. Moreover, the classifier was a valuable predictor for therapeutic responses in patients, and the low-risk group was more likely to yield clinical benefits from immunotherapy. A transcription factor regulatory protein-protein interaction network (TF-PPI-network) was constructed via weighted gene co-expression network analysis (WGCNA) concerning the hub genes of the signature. The constructed multidimensional nomogram dramatically increased the predictive accuracy. Therefore, our signature provides a forceful basis for individualized LUAD management with promising potential implications.

Deciphering the immune heterogeneity dominated by natural killer cells with prognostic and therapeutic implications in hepatocellular carcinoma.

Belonging to type 1 innate lymphoid cells (ILC1), natural killer (NK) cells play an important role not only in fighting microbial infections but also in anti-tumor response. Hepatocellular carcinoma (HCC) represents an inflammation-related malignancy and NK cells are enriched in the liver, making them an essential component of the HCC immune microenvironment. In this study, we performed single-cell RNA-sequencing (scRNA-seq) analysis to identify the NK cell marker genes (NKGs) and uncovered 80 prognosis-related ones by the TCGA-LIHC dataset. Based on prognostic NKGs, HCC patients were categorized into two subtypes with distinct clinical outcomes. Subsequently, we conducted LASSO-COX and stepwise regression analysis on prognostic NKGs to establish a five-gene (UBB, CIRBP, GZMH, NUDC, and NCL) prognostic signature-NKscore. Different mutation statuses of the two risk groups stratified by NKscore were comprehensively characterized. Besides, the established NKscore-integrated nomogram presented enhanced predictive performance. Single sample gene set enrichment analysis (ssGSEA) analysis was used to uncover the landscape of the tumor immune microenvironment (TIME) and the high-NKscore risk group was characterized with an immune-exhausted phenotype while the low-NKscore risk group held relatively strong anti-cancer immunity. T cell receptor (TCR) repertoire, tumor inflammation signature (TIS), and Immunophenoscore (IPS) analyses revealed differences in immunotherapy sensitivity between the two NKscore risk groups. Taken together, we developed a novel NK cell-related signature to predict the prognosis and immunotherapy efficacy for HCC patients.

Exploring the mechanism of Cassiae semen in regulating lipid metabolism through network pharmacology and experimental validation.

BACKGROUND: Multiple studies have assessed the role of Cassiae semen (CS) in regulating lipid metabolism. However, the mechanism of action of CS on non-alcoholic fatty liver disease (NAFLD) has seen rare scrutiny. OBJECTIVE: The objective of this study was to explore the regulatory mechanism of CS on lipid metabolism in NAFLD. METHODS: Components of CS ethanol extract (CSEE) were analyzed and identified using UPLC-Q-Orbirap HRMS. The candidate compounds of CS and its relative targets were extracted from the Traditional Chinese Medicine Systems Pharmacology, Swiss-Target-Prediction, and TargetNet web server. The Therapeutic Target Database, Genecards, Online Mendelian Inheritance in Man, and DisGeNET were searched for NAFLD targets. Binding affinity between potential core components and key targets was established employing molecular docking simulations. After that, free fatty acid (FFA)-induced HepG2 cells were used to further validate part of the network pharmacology results. RESULTS: Six genes, including Caspase 3 (CASP3), phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit α (PIK3CA), epidermal growth factor receptor (EGFR), and amyloid ß (A4) precursor protein (APP) were identified as key targets. The mitogen-activated protein kinase (MAPK) signaling pathway was found to associate closely with CS's effect on NAFLD. Per molecular docking findings, toralactone and quinizarin formed the most stable combinations with hub genes. About 0.1 (vs. FFA, P<0.01) and 0.2 (vs. FFA, P<0.05) mg/ml CSEE decreased lipid accumulation in vitro by reversing the up-regulation of CASP3, EGFR, and APP and the down-regulation of PIK3CA. CONCLUSION: CSEE can significantly reduce intracellular lipid accumulation by modulating the MAPK signaling pathway to decrease CASP3 and EGFR expression.

Hallmark-guided subtypes of hepatocellular carcinoma for the identification of immune-related gene classifiers in the prediction of prognosis, treatment efficacy, and drug candidates.

Hepatocellular carcinoma (HCC), accounting for ~90% of all primary liver cancer, is a prevalent malignancy worldwide. The intratumor heterogeneity of its causative etiology, histology, molecular landscape, and immune phenotype makes it difficult to precisely recognize individuals with high mortality risk or tumor-intrinsic treatment resistance, especially immunotherapy. Herein, we comprehensively evaluated the activities of cancer hallmark gene sets and their correlations with the prognosis of HCC patients using gene set variation analysis (GSVA) and identified two HCC subtypes with distinct prognostic outcomes. Based on these subtypes, seven immune-related genes (TMPRSS6, SPP1, S100A9, EPO, BIRC5, PLXNA1, and CDK4) were used to construct a novel prognostic gene signature [hallmark-guided subtypes-based immunologic signature (HGSIS)] via multiple statistical approaches. The HGSIS-integrated nomogram suggested an enhanced predictive performance. Interestingly, oncogenic hallmark pathways were significantly enriched in the high-risk group and positively associated with the risk score. Distinct mutational landscapes and immune profiles were observed between different risk groups. Moreover, immunophenoscore (IPS) and tumor immune dysfunction and exclusion (TIDE) analysis showed different sensitivities of HGSIS risk groups for immune therapy efficacy, and the pRRophetic algorithm indicated distinguishable responses for targeted/chemotherapies in different groups. KIF2C was picked out as the key target concerning HGSIS, and the top 10 small molecules were predicted to bind to the active site of KIF2C via molecular docking, which might be further used for candidate drug discovery of HCC. Taken together, our study offers novel insights for clinically significant subtype recognition, and the proposed signature may be a helpful guide for clinicians to improve the treatment regimens.

Taohong Siwu Decoction exerts anticancer effects on breast cancer via regulating MYC, BIRC5, EGF and PIK3R1 revealed by HTS2 technology.

Taohong Siwu Decoction (TSD), a classical gynecological prescription that was firstly reported 600 years ago, has been widely used in the adjuvant treatment of breast cancer (BRCA) in China. However, the mechanism of action of TSD in treating BRCA has remained unclear. Here, high-throughput sequencing-based high-throughput screening (HTS2) technology was used to reveal the molecular mechanism of TSD, combination with bioinformatics and systems pharmacology in this study. Firstly, our results showed that TSD exerts an anticancer effect on BRCA cells by inhibiting cell proliferation, migration and inducing apoptosis as well as cell-cycle arrest. And our results from HTS2 suggested that herbs of TSD could significantly inhibit KRAS pathway and pathway in cancer, and activate apoptosis pathway, p53 pathway and hypoxia pathway, which may lead to the anticancer function of TSD. Further, we found that TSD clearly regulates MYC, BIRC5, EGF, and PIK3R1 genes, which play an important role in the development and progression of tumor and have significant correlation with overall survival in BRCA patients. By molecular docking, we discovered that Pentagalloylglucose, a compound derived from TSD, might directly bind to and inhibit the function of BRD4, which is a reported transcriptional activator of MYC gene, and thus repress the expression of MYC. Taken together, this study explores the mechanism of TSD in anti-BRCA by combining HTS2 technology, bioinformatics analysis and systems pharmacology.

Identification of a Tumor Immunological Phenotype-Related Gene Signature for Predicting Prognosis, Immunotherapy Efficacy, and Drug Candidates in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the predominant subtype of primary liver cancer and represents a highly heterogeneous disease, making it hard to predict the prognosis and therapy efficacy. Here, we established a novel tumor immunological phenotype-related gene index (TIPRGPI) consisting of 11 genes by Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to predict HCC prognosis and immunotherapy response. TIPRGPI was validated in multiple datasets and exhibited outstanding performance in predicting the overall survival of HCC. Multivariate analysis verified it as an independent predictor and a TIPRGPI-integrated nomogram was constructed to provide a quantitative tool for clinical practice. Distinct mutation profiles, hallmark pathways, and infiltration of immune cells in tumor microenvironment were shown between the TIPRGPI high and low-risk groups. Notably, significant differences in tumor immunogenicity and tumor immune dysfunction and exclusion (TIDE) were observed between the two risk groups, suggesting a better response to immune checkpoint blockade (ICB) therapy of the low-risk group. Besides, six potential drugs binding to the core target of the TIPRGPI signature were predicted via molecular docking. Taken together, our study shows that the proposed TIPRGPI was a reliable signature to predict the risk classification, immunotherapy response, and drugs candidate with potential application in the clinical decision and treatment of HCC. The novel "TIP genes"-guided strategy for predicting the survival and immunotherapy efficacy, we reported here, might be also applied to more cancers other than HCC.

Action Mechanism Underlying Improvement Effect of Fuzi Lizhong Decoction on Nonalcoholic Fatty Liver Disease: A Study Based on Network Pharmacology and Molecular Docking.

OBJECTIVE: This study aimed to decipher the bioactive compounds and potential mechanism of traditional Chinese medicine (TCM) formula Fuzi Lizhong Decoction (FLD) for nonalcoholic fatty liver disease (NAFLD) treatment via an integrative network pharmacology approach. METHODS: The candidate compounds of FLD and its relative targets were obtained from the TCMSP and PharmMapper web server, and the intersection genes for NAFLD were discerned using OMIM, GeneCards, and DisGeNET. Then, the PPI and component-target-pathway networks were constructed. Moreover, GO enrichment and KEGG pathway analysis were performed to investigate the potential signaling pathways associated with FLD's effect on NAFLD. Eventually, molecular docking simulation was carried out to validate the binding affinity between potential core components and key targets. RESULTS: A total of 143 candidate active compounds and 129 relative drug targets were obtained, in which 61 targets were overlapped with NAFLD. The PPI network analysis identified ALB, MAPK1, CASP3, MARK8, and AR as key targets, mainly focusing on cellular response to organic cyclic compound, steroid metabolic process, and response to steroid hormone in the biological processes. The KEGG pathway analysis demonstrated that 16 signaling pathways were closely correlated with FLD's effect on NALFD with cancer pathways, Th17 cell differentiation, and IL-17 signaling pathways as the most significant ones. In addition, the molecular docking analysis revealed that the core active compounds of FLD, such as 3'-methoxyglabridin, chrysanthemaxanthin, and Gancaonin H, had a high binding activity with such key targets as ALB, MAPK1, and CASP3. CONCLUSIONS: This study suggested that FLD exerted its effect on NAFLD via modulating multitargets with multicompounds through multipathways. It also demonstrated that the network pharmacology-based approach might provide insights for understanding the interrelationship between complex diseases and interventions of the TCM formula.

Characterization of cellular senescence patterns predicts the prognosis and therapeutic response of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a prevalent malignancy with a high mortality rate. Cellular senescence, an irreversible state of cell cycle arrest, plays a paradoxical role in cancer progression. Here, we aimed to identify Hepatocellular carcinoma subtypes by cellular senescence-related genes (CSGs) and to construct a cellular senescence-related gene subtype predictor as well as a novel prognostic scoring system, which was expected to predict clinical outcomes and therapeutic response of Hepatocellular carcinoma. Methods: RNA-seq data and clinical information of Hepatocellular carcinoma patients were derived from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). The "multi-split" selection was used to screen the robust prognostic cellular senescence-related genes. Unsupervised clustering was performed to identify CSGs-related subtypes and a discriminant model was obtained through multiple statistical approaches. A CSGs-based prognostic model-CSGscore, was constructed by LASSO-Cox regression and stepwise regression. Immunophenoscore (IPS) and Tumor Immune Dysfunction and Exclusion (TIDE) were utilized to evaluate the immunotherapy response. Tumor stemness indices mRNAsi and mDNAsi were used to analyze the relationship between CSGscore and stemness. Results: 238 robust prognostic differentially expressed cellular senescence-related genes (DECSGs) were used to categorize all 336 hepatocellular carcinoma patients of the TCGA-LIHC cohort into two groups with different survival. Two hub genes, TOP2A and KIF11 were confirmed as key indicators and were used to form a precise and concise cellular senescence-related gene subtype predictor. Five genes (PSRC1, SOCS2, TMEM45A, CCT5, and STC2) were selected from the TCGA training dataset to construct the prognostic CSGscore signature, which could precisely predict the prognosis of hepatocellular carcinoma patients both in the training and validation datasets. Multivariate analysis verified it as an independent prognostic factor. Besides, CSGscore was also a valuable predictor of therapeutic responses in hepatocellular carcinoma. More downstream analysis revealed the signature genes were significantly associated with stemness and tumor progression. Conclusion: Two subtypes with divergent outcomes were identified by prognostic cellular senescence-related genes and based on that, a subtype indicator was established. Moreover, a prognostic CSGscore system was constructed to predict the survival outcomes and sensitivity of therapeutic responses in hepatocellular carcinoma, providing novel insight into hepatocellular carcinoma biomarkers investigation and design of tailored treatments depending on the molecular characteristics of individual patients.

Integrative analysis identifies key mRNA biomarkers for diagnosis, prognosis, and therapeutic targets of HCV-associated hepatocellular carcinoma.

Hepatitis C virus-associated HCC (HCV-HCC) is a prevalent malignancy worldwide and the molecular mechanisms are still elusive. Here, we screened 240 differentially expressed genes (DEGs) of HCV-HCC from Gene expression omnibus (GEO) and the Cancer Genome Atlas (TCGA), followed by weighted gene coexpression network analysis (WGCNA) to identify the most significant module correlated with the overall survival. 10 hub genes (CCNB1, AURKA, TOP2A, NEK2, CENPF, NUF2, CDKN3, PRC1, ASPM, RACGAP1) were identified by four approaches (Protein-protein interaction networks of the DEGs and of the significant module by WGCNA, and diagnostic and prognostic values), and their abnormal expressions, diagnostic values, and prognostic values were successfully verified. A four hub gene-based prognostic signature was built using the least absolute shrinkage and selection operator (LASSO) algorithm and a multivariate Cox regression model with the ICGC-LIRI-JP cohort (N =112). Kaplan-Meier survival plots (P = 0.0003) and Receiver Operating Characteristic curves (ROC = 0.778) demonstrated the excellent predictive potential for the prognosis of HCV-HCC. Additionally, upstream regulators including transcription factors and miRNAs of hub genes were predicted, and candidate drugs or herbs were identified. These findings provide a firm basis for the exploration of the molecular mechanism and further clinical biomarkers development of HCV-HCC.

Mining TCGA Data for Key Biomarkers Related to Immune Microenvironment in Endometrial cancer by Immune Score and Weighted Correlation Network Analysis.

Background: Endometrial cancer (EC) is one of the most lethal gynecological cancers around the world. The aim of this study is to identify the potential immune microenvironment-related biomarkers associated with the prognosis for EC. Methods: RNA-seq data and clinical information of EC patients were derived from The Cancer Genome Atlas (TCGA). The immune score of each EC sample was obtained by ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) was used to identify the interesting module and potential key genes concerning the immune score. The expression patterns of the key genes were then verified via the GEPIA database. Finally, CIBERSORT was applied to evaluate the relative abundances of 22 immune cell types in EC. Results: Immune scores were significantly associated with tumor grade and histology of EC, and high immune scores may exert a protective influence on the survival outcome for EC. WGCNA indicated that the black module was significantly correlated with the immune score. Function analysis revealed it mainly involved in those terms related to immune regulation and inflammatory response. Moreover, 11 key genes (APOL3, C10orf54, CLEC2B, GIMAP1, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GYPC, IFFO1, TAGAP) were identified from the black module, validated by the GEPIA database, and revealed strong correlations with infiltration levels of multiple immune cell types, as was the prognosis of EC. Conclusion: In this study, 11 key genes showed abnormal expressions and strong correlations with immune infiltration in EC, most of which were significantly associated with the prognosis of EC. These findings made them promising therapeutic targets for the treatment of EC.

Identification of Potential Hub Genes Related to Diagnosis and Prognosis of Hepatitis B Virus-Related Hepatocellular Carcinoma via Integrated Bioinformatics Analysis.

Hepatocellular carcinoma (HCC) is a common malignant cancer with poor survival outcomes, and hepatitis B virus (HBV) infection is most likely to contribute to HCC. But the molecular mechanism remains obscure. Our study intended to identify the candidate potential hub genes associated with the carcinogenesis of HBV-related HCC (HBV-HCC), which may be helpful in developing novel tumor biomarkers for potential targeted therapies. Four transcriptome datasets (GSE84402, GSE25097, GSE94660, and GSE121248) were used to screen the 309 overlapping differentially expressed genes (DEGs), including 100 upregulated genes and 209 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were used to explore the biological function of DEGs. A PPI network based on the STRING database was constructed and visualized by the Cytoscape software, consisting of 209 nodes and 1676 edges. Then, we recognized 17 hub genes by CytoHubba plugin, which were further validated on additional three datasets (GSE14520, TCGA-LIHC, and ICGC-LIRI-JP). The diagnostic effectiveness of hub genes was assessed with receiver operating characteristic (ROC) analysis, and all hub genes displayed good performance in discriminating TNM stage I patient samples and normal tissue ones. For prognostic analysis, two prognostic key genes (TOP2A and KIF11) out of the 17 hub genes were screened and used to develop a prognostic signature, which showed good potential for overall survival (OS) stratification of HBV-HCC patients. Gene Set Enrichment Analysis (GSEA) was performed in order to better understand the function of this prognostic gene signature. Finally, the miRNA-mRNA regulatory relationships of all hub genes in human liver were predicted using miRNet. In conclusion, the current study gives further insight on the pathogenesis and carcinogenesis of HBV-HCC, and the identified DEGs provide a promising direction for improving the diagnostic, prognostic, and therapeutic outcomes of HBV-HCC.