Correction for: Combined identification of ARID1A, CSMD1, and SENP3 as effective prognostic biomarkers for hepatocellular carcinoma.

The purpose of this study was to identify and validate novel prognostic biomarkers in human hepatocellular carcinoma (HCC). We analyzed gene expression profiles not only between 33 HCCs and their corresponding noncancerous liver tissues, but also between 25 HCCs and pooled normal liver tissues using cDNA microarrays containing 12800 genes. Functional analysis of differentially expressed genes involved in HCC carcinogenesis and tumor progression revealed that up-regulated and down-regulated genes are mainly associated with cell cycle and immune response, respectively. We detected two regions of cytogenetic changes only in poorly-differentiated HCCs using the expression data. We identified a 9-gene expression signature, which was able to predict differentiation degree and survival of HCC samples. Among the 9 most discriminatory genes, minichromosome maintenance protein 2 (MCM2), a significantly up-regulated gene involved in cell cycle pathway, was selected for further analysis. Overexpression of MCM2 protein related to poor-differentiation in HCC was validated using tissue microarray-based immunohistochemistry containing 96 HCCs. Our studies show that the 9-gene expression signature may serve as promising prognostic biomarkers involved in hepatocarcinogenesis and tumor progression.

Recently, increasing data have suggested that the lncRNA small nucleolar RNA host genes (SNHGs) were aberrantly expressed in hepatocellular carcinoma (HCC), but the association between the prognosis of HCC and their expression remained unclear. The purpose of this meta-analysis was to determine the prognostic significance of lncRNA SNHGs in HCC. We systematically searched Embase, Web of Science, PubMed, and Cochrane Library for eligible articles published up to February 2024. The prognostic significance of SNHGs in HCC was evaluated by hazard ratios (HRs) and 95% confidence intervals (CIs). Odds ratios (ORs) were used to assess the clinicopathological features of SNHGs. This analysis comprised a total of 25 studies covering 2314 patients with HCC. The findings demonstrated that over-expressed SNHGs were associated with larger tumor size, multiple tumor numbers, poor histologic grade, earlier lymphatic metastasis, vein invasion, advanced tumor stage, portal vein tumor thrombosis (PVTT), and higher alpha-fetoprotein (AFP) level, but not with hepatitis B virus (HBV) infection, and cirrhosis. In terms of prognosis, patients with higher SNHG expression were more likely to have shorter overall survival (OS), relapse-free survival (RFS), and disease-free survival (DFS). In conclusion, upregulation of SNHGs expression correlates with shorter OS, RFS, DFS, tumor size and numbers, histologic grade, lymphatic metastasis, vein invasion, tumor stage, PVTT, and AFP level, suggesting that SNHGs may serve as prognostic biomarkers in HCC.

Hepatocellular carcinoma (HCC) remains the main type of liver cancer. Understanding the molecular and immune mechanisms of HCC tumorigenesis are required to develop effective biomarkers. This study is designed to measure the circulating MMP9 and CEBPα to provide a diagnostic and prognostic biomarker for HCV-genotype (4) induced liver cirrhosis and carcinogenesis. This study included one hundred Egyptian patients, divided into two groups 50 patients each. The first group: classified into Chronic Liver Disease (CLD) without cirrhosis (n=25) and CLD with cirrhosis (n=25). The second group: classified into CLD patients with HCC, (n=25), and healthy control (25 volunteers). The expression of MMP9 and CEBPα genes were analysed using Real-Time PCR. Our results showed significant downregulation in MMP9 and CEBPα genes in cirrhotic and HCC patients (p< 0.001 and p<0.001) respectively. There was a significant (p< 0.001) diagnostic capacity between HCC patients against CLD with or without cirrhosis patients. Bioinformatics analysis revealed a relationship between MMP9 and CEBPα genes. In conclusion, the gradual decrease in the expression of MMP9 and CEBPα gene during the progression of the disease recommended use of MMP9 and CEBPα genes as a diagnostic and prognostic biomarker for both cirrhosis and HCC in HCV-genotype (4) patients.

BackgroundHepatocellular carcinoma (HCC) is a prevalent malignant tumor. Long non-coding RNAs (lncRNAs) have been demonstrated to be abnormally expressed in many tumors and act as crucial regulators in various biological processes. However, the expression and function of the recently identified macrophage-associated lncRNA ELMO1 antisense RNA 1 (ELMO1-AS1) in HCC are unclear.MethodsThe expression of ELMO1-AS1 was determined in HCC tissues and adjacent nontumorous tissues by quantitative real-time polymerase chain reaction (qRT-PCR). The Kaplan-Meier survival analysis and Cox regression analysis were performed to establish the correlation between the expression level and survival of HCC patients in a training set and a validation set, respectively. The overexpression experiments were also conducted to investigate the biological role of ELMO1-AS1 in HCC cells.ResultsWe uncovered that ELMO1-AS1 was significantly downregulated in HCC tissues, and high expression of ELMO1-AS1 is correlated with optimistic treatment outcome suggesting its potential as an independent prognostic biomarker for HCC. It was also found that overexpression of ELMO1-AS1 in HCC cells suppressed cell proliferation, migration and invasion and engulfment and cell motility 1 (ELMO1) may be a target of ELMO1-AS1.ConclusionOur results suggested that macrophage-associated lncRNA ELMO1-AS1 could be a crucial regulator involved in HCC progression and considered as a potential prognostic biomarker and therapeutic target for HCC.

Cell division cycle associated 5 (CDCA5) has been associated with the progression of several types of cancers. However, its possible role and mechanism in hepatocellular carcinoma (HCC) remain unknown. In the present study, immunohistochemical staining and real-time PCR were used to assess CDCA5 protein and mRNA levels in clinical samples. Statistical analysis was performed to explore the clinical correlation between CDCA5 protein expression and clinicopathological features and overall survival in HCC patients. Cell counting and colony formation assays were employed to analyse the effect of CDCA5 on cell proliferation, and flow cytometry was used to study the role of CDCA5 in cell cycle progression and apoptosis. Moreover, subcutaneous xenograft tumour models were implemented to predict the efficacy of targeting CDCA5 in HCC in vivo. We found that CDCA5 expression was significantly higher in HCC tumour tissues, was associated with clinicopathological characteristics, and predicted poor overall survival in HCC patients. Silencing of CDCA5 with small interfering RNA (siRNA) inhibited cell proliferation and induced G2/M cell cycle arrest in vitro. The xenograft growth assay revealed that CDCA5 downregulation impeded HCC growth in vivo. Further study indicated that CDCA5 depletion decreased the levels of ERK1/2 and AKT phosphorylation in vitro and in vivo. Taken together, these results indicate that CDCA5 may act as a novel prognostic biomarker and therapeutic target for HCC.

Eye absent homolog 4 (EYA4) has been demonstrated to be down‐regulated in hepatocellular carcinoma (HCC), but its biological function and the mechanism in HCC angiogenesis and metastasis remain largely unknown. Herein, we showed that EYA4 expression was frequently low in HCC tissue samples compared with matched adjacent non‐tumourous tissues. In the analysis of 302 HCC specimens, we revealed that decreased expression of EYA4 correlated with tumour differentiation status. Univariate and multivariate analyses identified EYA4 as an independent risk factor for recurrence‐free survival (RFS) and overall survival (OS) among the 302 patients. Functional assays showed that forced expression of EYA4 suppressed HCC cell migration, invasion and capillary tube formation of endothelial cells in vitro, as well as in vivo tumour angiogenesis and metastasis in a mouse model. Furthermore, mechanism study exhibited that EYA4 could inhibit HCC angiogenesis and metastasis by inhibiting c‐JUN/VEGFA pathway. Together, we provide proof that EYA4 is a novel tumour suppressor in HCC and a new prognostic biomarker and therapeutic target in HCC.

DNA methylation status is one of the most prevalent molecular alterations in human cancers. Identification of powerful diagnostic and prognostic biomarkers for hepatocellular carcinoma (HCC) without a biopsy is urgently required. The purpose of this study was to determine the methylation status of RASSF1A and SOCS-1genes as a non-invasive biomarker for HCC identification and prognosis. Methylation specific-PCR technique was performed to recognize the methylation status of RASSF1A and SOCS-1 genes in 100 patients with HCC, 100 patients with liver cirrhosis (LC) but without HCC were considered as cirrhotic liver control group and 100 healthy control. Methylation of RASSF1A and SOCS-1 genes were detected in 40% and 38% of HCC patients respectively, 14% and 20% of LC patients respectively. Methylation of SOCS-1 gene in peripheral blood of healthy control was 23%. Methylation of RASSF1A gene was associated with age, tumor size, vascular invasion and α fetoprotein (AFP), while SOCS-1 gene methylation was significantly associated with tumor size and AFP. Furthermore, using RASSF1A/ SOCS-1/ AFP panel improve diagnostic sensitivity for HCC 86% and specificity of 75%. RASSF1A and SOCS1 genes methylation status may play an important role in the process of hepatocarcinogenesis and may be used as diagnostic and prognostic noninvasive biomarkers for HCC when combined with serum AFP.

Background: Metallothioneins (MTs) play crucial roles in the modulation of zinc/copper homeostasis, regulation of neoplastic growth and proliferation, and protection against apoptosis. The present study attempted to visualize the prognostic landscape of MT functional isoforms and identify potential prognostic biomarkers in hepatocellular carcinoma (HCC). Methods: The transcriptional expression, comprehensive prognostic performances, and gene–gene interaction network of MT isoforms in HCC were evaluated via Oncomine, GEPIA, Kaplan–Meier plotter, and GeneMANIA databases. Characterized by good prognostic value in three external cohorts, MT1H was specifically selected as a potential prognostic biomarker in HCC with various clinicopathological features. Functional and pathway enrichment analyses of MT1H status were performed using cBioPortal, the Database for Annotation, Visualization, and Integrated Discovery (DAVID), and ssGSVA method. Results: MT1E/1F/1G/1H/1M/1X/2A was greatly downregulated in HCC. Prognostic analyses elucidated the essential correlations between MT1A/1B/1H/1X/2A/4 attenuation and poor overall survival, between MT1B/1H/4 downregulation and worse relapse-free survival, and between MT1A/1B/1E/1H/1M/2A/4 downregulation and diminished progression-free survival in HCC. Taken together, these results indicated the powerful prognostic value of MT1H among MTs in HCC. In-depth analyses suggested that MT1H may be more applicable to alcohol-derived HCC and involved in the downregulation of the inflammatory pathway, Jak–STAT pathway, TNF pathway, and Wnt signaling pathway. Conclusion: MT-specific isoforms displayed aberrant expression and varying prognostic value in HCC. MT1H repression in HCC was multi-dimensionally detrimental to patient outcomes. Therefore, MT1H was possibly associated with carcinogenesis and exploited as a novel prognostic biomarker and candidate therapeutic target for HCC.

ObjectiveEarly diagnosis and prognostic evaluation of hepatocellular carcinoma (HCC) remain significant challenges in clinical management. This study aims to identify prognostic biomarkers in HCC and to explore their implications in immune microenvironment infiltration.MethodsIn this study, we constructed a single-cell transcriptomic atlas of HCC, focusing on the expression profiles of T cell-related genes. Analytical approaches included cell-cell communication analysis and pseudotime trajectory analysis. To further predict and validate T cell-associated prognostic genes, we integrated transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Patients were stratified into high- and low-risk groups based on a prognostic model derived from these biomarkers. Immune infiltration levels in the tumor microenvironment were then evaluated across risk groups.ResultsA total of eight primary tumor samples and seven normal tissue samples were included as raw data in this study. Following stringent quality control and filtering, 53,477 cells were retained for downstream analysis. From these, we isolated 12,333 T cells, which were subjected to further clustering and annotation. The T cell subpopulations identified included 6314 natural killer T cells (NK T cells), 5199 effector memory CD4+ T cells, and 820 central memory CD8+ T cells. By integrating transcriptomic data from TCGA-LIHC and GEO datasets, we identified six prognostic biomarkers: LYZ, SPP1, EGR1, MARCO, FCN3, and PTTG1. A prognostic model was developed based on these biomarkers, enabling risk stratification into high- and low-risk groups. The model demonstrated robust predictive performance in estimating patient survival rates and immune cell infiltration levels within the tumor microenvironment.ConclusionThis study identified and validated prognostic biomarkers in HCC that effectively predict patient survival rates and immune infiltration characteristics. These findings provide a potential foundation for precision medicine strategies in HCC, offering novel insights into the tumor-immune microenvironment and its clinical implications.

Epigenetic mechanisms, such as histone deacetylases (HDACs), play an important role in the commencement and development of hepatocellular carcinoma (HCC). Previously, we have identified proteins with binds with HDAC1 and HDAC3 in liver cancer cells and also have shown that depletion of either HDAC1 or HDAC3 suppressed the expression of HDAC1/3 interacting proteins, including the prefoldin protein 2/6 (PFDN2/6), CR4-NOT transcription complex subunit 1 (CNOT1), and high mobility group 20B (HMG20b). In this study, online databases were utilized to analyze the expression of HDAC1/3, PFDN2/6, CNOT1, and HMG20b in a large panel of liver cancer cell lines, cancer tissues, and human normal and tumor liver tissues. These databases are “RNA Expression Atlas (https://www.ebi.ac.uk/gxa/home), Cancer Genome Atlas (TCGA), gene expression profiling interactive analysis (GEPIA), integrative molecular databases of hepatocellular carcinoma (HCCDB), human protein atlas, and Kaplan-Meier Plotter for RNA sequences in liver cancer (http://kmplot.com/analysis/index.php?p=service&cancer=liver_rnaseq#). The expression of these genes was verified experimentally in human HepG2 cells via semi-quantitative RT PCR. The results showed that all these genes are expressed in twenty-three human liver cancer cell lines, higher expression in human liver cancer than normal tissues. However, HDAC1 and PFDN2 are expressed at higher levels than other genes analyzed in this study. The analysis of these six genes in 364 human liver cancer patients by Kaplan Meier plotter predicted HDAC1 and PFDN2 as poor, while HMG20b as a favorable prognostic biomarker in hepatocellular carcinoma. PFDN2 and HMG20b are novel prognostic markers of hepatocellular carcinoma, identified first in this study. Further clinical studies are needed to verify the expression and patient survival concerning the expresion of PFDN2 and HMG20b in human hepatocellular carcinoma patients.

It is urgent to explore effective diagnostic and prognostic biomarkers for hepatocellular carcinoma (HCC). Now, both lncRNAs and lipid metabolism are involved in tumor pathogenesis. Long noncoding RNA, RP11‐466I1.1, could likely be linked to lipid metabolism according to our bioinformatics analysis, yet studies about RP11‐466I1.1 expression in tumors and its potential functions are still lacking. We aimed to explore the expression and correlations with clinical features of a long noncoding RNA, RP11‐466I1.1, and further analyze its diagnostic and prognostic values in hepatocellular carcinoma. Expression levels of RP11‐466I1.1 were detected by quantitative real‐time PCR (qRT‐PCR) in tissue and serum level, and expression differences were analyzed by independent 2‐tailed t tests. Clinical features were obtained, and their correlations with RP11‐466I1.1 were analyzed by chi‐squared test. Receiver operating characteristic (ROC) curve was performed to assess the diagnostic value. Kaplan‐Meier method and log‐rank test were used to evaluate the prognostic value of RP11‐466I1.1. Results showed that RP11‐466I1.1 was upregulated in HCC tissues (P < .01) and serum (P < .05). Significant upregulation of RP11‐466I1.1 in HCC tissues with poor histological grade (P < .01) and incomplete tumor capsule (P < .01) was found compared to that with better histological grade and complete tumor capsule, respectively. The diagnostic value of RP11‐466I1.1 was not supported by ROC curve analysis (AUROC=0.665, P = .079). Yet, the significant correlation of RP11‐466I1.1 with poor prognosis indicated its potential prognostic value in HCC. This study suggested that RP11‐466I1.1 is distinctively expressed in HCC and may serve as a promising novel prognostic biomarker. The concrete mechanisms of RP11‐466I1.1 playing roles in HCC pathogenesis need further study.

Hepatocellular carcinoma is one of the most common primary malignant tumors with the third highest mortality rate worldwide. This study aims to identify key genes associated with hepatocellular carcinoma prognosis using the Gene Expression Omnibus (GEO) database and provide a theoretical basis for discovering novel prognostic biomarkers for hepatocellular carcinoma. Hepatocellular carcinoma-related datasets were retrieved from the GEO database. Differentially expressed genes (DEGs) were identified using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and key genes were identified using Cytoscape software. The University of Alabama at Birmingham Cancer Data Analysis Resource (UALCAN) was used to analyze the expression levels of key genes in normal and hepatocellular carcinoma tissues, as well as their associations with pathological grade, clinical stage, and patient survival. The Human Protein Atlas (THPA) was used to further validate the impact of key genes on overall survival. Expression levels of key genes in the blood of hepatocellular carcinoma patients were evaluated using the expression atlas of blood-based biomarkers in the early diagnosis of cancers (BBCancer). A total of 78 DEGs were identified from the GEO database. GO and KEGG analyses indicated that these genes may contribute to hepatocellular carcinoma progression by promoting cell division and regulating protein kinase activity. Sixteen key genes were screened via Cytoscape and validated using UALCAN and THPA. These genes were overexpressed in hepatocellular carcinoma tissues and were associated with disease progression and poor prognosis. Finally, BBCancer analysis showed that ASPM and NCAPG were also elevated in the blood of hepatocellular carcinoma patients. This study identified 16 key genes as potential prognostic biomarkers for hepatocellular carcinoma, among which ASPM and NCAPG may serve as promising blood-based markers for hepatocellular carcinoma.

Purpose: Exploring the molecular mechanisms of prognosis in patients with hepatocellular carcinoma (HCC) could assist in identifying novel biomarkers for effective therapy against this deadly disease. Hence, this study was designed to determine the prognostic biomarkers of HCC and to investigate potential mechanisms. Experimental Design: The RNA-Seq data as well as clinical data were downloaded from The Cancer Genome Atlas (TCGA) database, aiming at identifying differentially expressed genes (DEGs) between nontumorous tissues and HCC. Then, weighted gene coexpression network analysis (WGCNA) was performed for determining significant modules associated with overall survival (OS). Results: The mRNA expression profiles from 374 HCC patients in TCGA were included to determine 3270 DEGs. Then, WGCNA was carried out on the 3270 DEGs, and 10 coexpressed gene modules were determined. Pearson’s correlation analysis revealed that the turquoise module significantly positively correlated with OS and the blue module significantly negatively correlated with OS, followed by seven hub genes with high connectivity were identified in these two modules. Based on the Kaplan-Meier (K-M) survival curves and receiver operating characteristic (ROC) curves, the seven hub genes could predict prognosis for patients with HCC well, and the results were validated by GSE54236 dataset contained 78 HCC patients. Additionally, the univariate and multivariate Cox regression analyses showed that the seven hub genes were independent prognostic factors for patients with HCC. Conclusions: In conclusion, the current results provide novel insights into the prognostic biomarkers of HCC, which may contribute to the development of molecular targeted therapy for HCC. Citation Format: Yi Bai, Junyu Long, Jianzhen Lin, Xu Yang, Dongxu Wang, Xiaobo Yang, Xinting Sang, Jiahui Liu, Haitao Zhao. Identifying prognostic biomarkers of hepatocellular carcinoma by weighted gene coexpression network analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3988.

NIFK, an independent prognostic biomarker of hepatocellular carcinoma, is correlated with immune infiltration

The objective of this study was to identify the prognostic biomarkers for hepatocellular carcinoma (HCC) by analyzing the N6-methyladenosine (m6A) RNA modification. HCC is a complex malignant tumor induced by various pathogenic factors. m6A RNA modification and its regulators influence the tumorigenesis and advancement of HCC. RNA sequencing and clinical data were extracted from the TCGA-LIHC and ICGC-LIRI-JP database. Single-cell RNA sequencing data were processed using Seurat and Harmony packages. ConsensusClusterPlus identified molecular subtypes, and ssGSEA quantified m6A regulator-related gene sets. Differentially expressed genes were analyzed, followed by the establishment of a risk model. qRT-PCR validated mRNA expression in Huh7, Hep 3B, and Hep G2 cells and normal hepatocytes. Four molecular subtypes based on m6A regulator transcriptional profiles of m6A regulators were identified, each exhibiting unique clinical, prognostic, and pathway characteristics. A robust risk model distinguished the high- and low-risk groups, revealing obvious differences in immune cells infiltration and chemotherapeutic drug sensitivity. qRT-PCR confirmed significant differential expression of key genes (TRNP1, KIF20A, and CFRHR3) in HCC cell lines and normal hepatocytes. In conclusion, the established risk model may serve as a perspective tool for prognostic prediction, and provide insights into the functions of m6A involved in HCC.