Clinical Significance of Upregulation of EZH1 Expression in Hepatocellular Carcinoma Tissues.

BACKGROUND AND AIMS: The incidence and mortality of hepatocellular carcinoma (HCC) are increasing. It is urgent to develop more effective HCC biomarkers for diagnosis and treatment. This project intends to verify the expression of enhancer of zeste 1 polycomb repressive complex 2 subunit (EZH1) and its mechanism in HCC. METHODS: This study integrates global microarray and high-throughput sequencing datasets, combined with internal immunohistochemistry, to analyze the expression and prognostic value of EZH1 in HCC. Functional enrichment analysis was conducted to investigate transcriptional targets, which were achieved by intersecting HCC over-expressed genes, EZH1 co-expressed genes and putative transcriptional targets. The relationship between EZH1 and anticancer drugs was detected by drug sensitivity analysis. RESULTS: In this study, 84 datasets from 40 platforms (3,926 HCC samples and 3,428 non-cancerous liver tissues) were included to show the high expression of EZH1 in HCC. Immunohistochemistry with 159 HCC samples and 62 non-HCC samples confirmed the high expression level. HCC patients with high EZH1 expression had worse survival prognoses. Gene ontology and Reactome analysis revealed that metabolism-related pathways, including autophagy, are critical for HCC. Interestingly, as one of the EZH1 potential transcriptional targets, autophagy-related 7 (ATG7) appeared in the above pathways. ATG7 was positively correlated with EZH1, upregulated in HCC, and mediated poor prognosis. Upregulation of EZH1 was found to be in contact with HCC anti-tumor drug resistance. CONCLUSIONS: The upregulation of EZH1 expression can promote the occurrence of HCC and lead to poor clinical progression and drug resistance; these effects may be mediated by regulating ATG7.

Expression of IER3 in hepatocellular carcinoma: clinicopathology, prognosis, and potential regulatory pathways.

Background: Immediate early response 3 (IER3) is correlated to the prognosis of several cancers, but the precise mechanisms underlying the regulation by IER3 of the occurrence and development of hepatocellular carcinoma (HCC) remain unknown. Methods: The expression level of IER3 was examined by using in-house immunohistochemistry (IHC), public gene chip, and public RNA-sequencing (RNA-seq). The standardized mean difference (SMD) was calculated to compare the expression levels of IER3 between HCC patients and controls. The summary receiver operating characteristics (sROC) was plotted to comprehensively understand the discriminatory capability of IER3 between HCC and non-HCC group. The Kaplan-Meier curves and the combined hazard ratios (HRs) were used to determine the prognostic value of IER3 in HCC. Moreover, differentially expressed genes (DEGs) and co-expression genes (CEGs) were used to explored the molecular mechanisms of IER3 underlying HCC. hTFtarget was used to predict the transcription factors (TFs) of IER3. The binding site of TFs and the IER3 promoter region was forecasted using the JASPAR website. The relevant ChIP-seq data were used to determine whether TF peaks were present in the IER3 transcription initiation. Results: A significantly increased expression of IER3 protein was found in HCC tissue relative to non-HCC tissue as detected by IHC (p < 0.001). Compared to 1,263 cases of non-HCC tissues, IER3 in 1483 cases of HCC tissues was upregulated (SMD = 0.42, 95% confidence interval [CI] [0.09-0.76]). The sROC showed that IER3 had a certain ability at differentiating HCC tissues (area under the curve (AUC) = 0.65, 95% CI [0.61-0.69]). Comprehensive analysis of the effect of IER3 on the prognosis of patients with HCC demonstrated that higher IER3 expression was associated with poor prognosis in HCC (HRs = 1.30, 95% CI [1.03-1.64]). Pathway enrichment analysis revealed that IER3-related genes were mostly enriched in the PI3K-Akt signaling pathway, cancer-related signaling pathways, the p53 signaling pathway, and other signaling pathways. Regulatory factor X5 (RFX5) was identified as a possible regulator of IER3-related TF. Conclusion: IER3 may be a potential prognostic marker for HCC. The molecular mechanisms of IER3 in HCC warrant further study.

Clinical significance and molecular mechanism of angiotensin-converting enzyme 2 in hepatocellular carcinoma tissues.

During the pandemic of the coronavirus disease 2019, there exist quite a few studies on angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 infection, while little is known about ACE2 in hepatocellular carcinoma (HCC). The detailed mechanism among ACE2 and HCC still remains unclear, which needs to be further investigated. In the current study with a total of 6,926 samples, ACE2 expression was downregulated in HCC compared with non-HCC samples (standardized mean difference = -0.41). With the area under the curve of summary receiver operating characteristic = 0.82, ACE2 expression showed a better ability to differentiate HCC from non-HCC. The mRNA expression of ACE2 was related to the age, alpha-fetoprotein levels and cirrhosis of HCC patients, and it was identified as a protected factor for HCC patients via Kaplan-Meier survival, Cox regression analyses. The potential molecular mechanism of ACE2 may be relevant to catabolic and cell division. In all, decreasing ACE2 expression can be seen in HCC, and its protective role for HCC patients and underlying mechanisms were explored in the study.

Identifying the Prognostic Risk Factors of Synaptojanin 2 and Its Underlying Perturbations Pathways in Hepatocellular Carcinoma.

Synaptojanin 2 (SYNJ2) regulates cell proliferation and apoptosis via dephosphorylating plasma membrane phosphoinositides. Aim of this study is to first seek the full-scale expression levels and potential emerging roles of SYNJ2 in hepatocellular carcinoma (HCC). We systematically analyzed SYNJ2 mRNA expression and protein levels in HCC tissues based on large-scale data and in-house immunohistochemistry (IHC). The clinical significance and risk factors for SYNJ2-related HCC cases were identified. A nomogram of prognosis was created and its performance was validated by concordance index (C-index) and shown in calibration plots. Based on the identified differentially coexpressed genes (DCGs) of SYNJ2, enriched annotations and potential pathways were predicted, and the protein interacting networks were mapped. Upregulated SYNJ2 in 3,728 HCC and 3,203 non-HCC tissues were verified and in-house IHC showed higher protein levels of SYNJ2 in HCC tissues. Pathologic T stage was identified as a risk factor. Upregulated mRNA levels and mutated SYNJ2 might cause a poorer outcome. The C-index of the nomogram model constructed by SYNJ2 level, age, gender, TNM classification, grade, and stage was evaluated as 0.643 (95%CI = 0.619-0.668) with well-calibrated plots. A total of 2,533 DCGs were extracted and mainly functioned together with SYNJ2 in metabolic pathways. Possible transcriptional axis of CTCF/POLR2A-SYNJ2/INPP5B (transcription factor-target) in metabolic pathways was discovered based on ChIP-seq datasets. In summary, transcriptional regulatory axis CTCF/POLR2A-SYNJ2 might influence SYNJ2 expression levels. Increased SYNJ2 expression level could be utilized for predicting HCC prognosis and potentially accelerates the occurrence and development of HCC via metabolic perturbations pathways.

Expression of Cell Division Cycle Protein 45 in Tissue Microarrays and the CDC45 Gene by Bioinformatics Analysis in Human Hepatocellular Carcinoma and Patient Outcomes.

BACKGROUND Hepatocellular carcinoma (HCC) causes a heavy disease burden worldwide. Cell division cycle 45 (Cdc45) and its encoding gene (CDC45) have been studied for a long time, but their expression patterns and roles in liver carcinogenesis and advanced HCC deterioration are still incompletely understood. This study integrated tissue microarray and bioinformatics analyses to explore the expression and clinical value of CDC45 and Cdc45 in HCC. MATERIAL AND METHODS In HCC, the expression and relationships with clinic-pathological parameters of CDC45 and Cdc45 were investigated by integrating the RNA-sequencing data, downloaded from The Cancer Genome Atlas and Oncomine databases, and tissue microarray with immunohistochemistry staining. Co-expressed genes and genetic alterations of CDC45 separately obtained from Oncomine and cBioPortal databases were identified to shed light on the potential mechanisms of CDC45 in HCC. RESULTS CDC45 and Cdc45 were both overexpressed in HCC tissues, and the CDC45 level progressively increased from stage I to III. The survival outcomes of the group with high CDC45 expression were significantly worse compared with the group with low expression. Amplification and deep deletion were 2 major significant alteration types in HCC patients, and the outcomes were worse in patients with altered versus unaltered CDC45. NUDT1, E2F1, CCNE2, MCM5, and CENPM were identified as the most significantly co-expressed genes. CONCLUSIONS CDC45 and Cdc45 were both upregulated in HCC, and increased expression levels and genetic alternations of CDC45 were correlated with worse prognosis in HCC patients. CDC45 may promote HCC by co-expressing with NUDT1, E2F1, CCNE2, MCM5, and CENPM.

A nine-miRNA signature as a potential diagnostic marker for breast carcinoma: An integrated study of 1,110 cases.

Growing evidence indicates that microRNAs (miRNAs) play critical roles in the initiation and progression of breast carcinoma (BC) and are promising diagnostic biomarkers. In the present study, we aimed to identify a multi-marker miRNA pool with high diagnostic performance for BC. We collected miRNA expression profiles of BC samples and normal breast tissues from The Cancer Genome Atlas (TCGA) and screened differentially expressed miRNAs by conducting two­sample t-tests and by calculating log2 fold-change (log2FC) ratios. Statistical significance was established at p<0.001 and |log2FC| >1. Then, we generated receiver operating characteristic (ROC) curves, calculated the area under the curve (AUC) with a 95% confidence interval (95% CI), and calculated the diagnostic sensitivity and specificity using MedCalc software. Additionally, we predicted the targets of candidate miRNAs using 10 online databases: TarBase, miRTarBase, TargetScan, TargetMiner, microRNA.org, RNA22, PicTar-vert, miRDB, PITA and PolymiRTS. Target genes that were predicted by at least four algorithms were chosen, and cooperative targets of multiple miRNAs were further selected for GO and KEGG pathway analyses through the DAVID online tool. Eventually, a total of 66 differentially expressed miRNAs were identified after miRNA expression profiles were analyzed in BC and normal breast samples. Of these, we selected nine dysregulated miRNAs as candidate diagnostic markers: seven upregulated miRNAs (hsa-miR-21, hsa-miR-96, hsa-miR-183, hsa-miR­182, hsa-miR-141, hsa-miR-200a and hsa-miR-429) and two downregulated miRNAs (hsa-miR-139 and hsa-miR­145). The ROC curve for the combination of these nine differently expressed miRNAs showed extremely high diagnostic accuracy, with an AUC of 0.995 (95% CI, 0.988­0.999) and diagnostic sensitivity and specificity of 98.7 and 98.9%, respectively. In conclusion, the combination of these nine miRNAs significantly improved the accuracy of breast cancer diagnosis.