Increased CDC20 expression is associated with development and progression of hepatocellular carcinoma

Integrated GMPS and RAMP3 as a signature to predict prognosis and immune heterogeneity in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly fatal malignant worldwide. As different expression levels of specific genes can lead to different HCC outcomes, we aimed to develop a gene signature capable of predicting HCC prognosis.

METHODS

In this study, transcriptomic sequencing and relevant clinical data were extracted from public platforms. The guanine monophosphate synthase (GMPS)|receptor activity-modifying protein 3 (RAMP3) gene pair was developed based on the relative values of gene expression levels. Nomograms were developed using R software. Immune status was assessed through single-sample gene set enrichment analysis. GMPS knockdown was achieved through siRNA transfection. Quantitative reverse transcription PCR, apoptosis assays, and cell proliferation were performed to verify the function of GMPS|RAMP3 in HCC cells.

RESULTS

Here, a gene pair containing GMPS and RAMP3 was successfully constructed. We demonstrated that the GMPS|RAMP3 gene pair was an independent predictor with strong prognostic prediction power, based on which a nomogram was established. Functional analysis revealed that the enrichment of cell cycle-related pathways and immune status differed considerably between the two groups, with cell cycle-related genes highly expressed in the high GMPS|RAMP3 value group. Finally, cell experiments indicated that GMPS knockdown significantly repressed proliferation, promoted apoptosis, and enhanced the sensitivity of HCC cells to gemcitabine.

CONCLUSIONS

The gene pair GMPS|RAMP3 is a novel prognostic predictor of HCC, providing a promising approach to the treatment and assessment of immune heterogeneity in HCC.

Prognostic and Therapeutic Implications of Cell Division Cycle 20 Homolog in Breast Cancer

Cell division cycle 20 homolog (CDC20) is a well-known regulator of cell cycle progression. Abnormal expression of CDC20 leads to mitotic defects, which play a significant role in cancer development. In breast cancer (BC), CDC20 has been identified as a biomarker that has been linked to poor patient outcomes. In this study, we investigated the association of CDC20 with BC prognosis and immune cell infiltration by using multiple online databases, including UALCAN, KM plotter, TIMER2.0, HPA, TNM-plot, bc-GenExMiner, LinkedOmics, STRING, and GEPIA. The results demonstrate that BC patients have an elevated CDC20 expression in tumor tissues compared with the adjacent normal tissue. In addition, BC patients with overexpressed CDC20 had a median survival of 63.6 months compared to 169.2 months in patients with low CDC20 expression. Prognostic analysis of the examined data indicated that elevated expression of CDC20 was associated with poor prognosis and a reduction of overall survival in BC patients. These findings were even more prevalent in chemoresistance triple-negative breast cancer (TNBC) patients. Furthermore, the Gene Set Enrichment Analysis tool indicated that CDC20 regulates BC cells' cell cycle and apoptosis. CDC20 also significantly correlates with increased infiltrating B cells, CD4+ T cells, neutrophils, and dendritic cells in BC. In conclusion, the findings of this study suggest that CDC20 may be involved in immunomodulating the tumor microenvironment and provide evidence that CDC20 inhibition may serve as a potential therapeutic approach for the treatment of BC patients. In addition, the data indicates that CDC20 can be a reliable prognostic biomarker for BC.

Antitumor Effect of Apcin on Endometrial Carcinoma via p21-Mediated Cell Cycle Arrest and Apoptosis

OBJECTIVE

Endometrial carcinoma (EC) is a prevalent gynecological malignancy characterized by increasing incidence and mortality rates. This underscores the critical need for novel therapeutic targets. One such potential target is cell division cycle 20 (CDC20), which has been implicated in oncogenesis. This study investigated the effect of the CDC20 inhibitor Apcin on EC and elucidated the underlying mechanism involved.

METHODS

The effects of Apcin on EC cell proliferation, apoptosis, and the cell cycle were evaluated using CCK8 assays and flow cytometry. RNA sequencing (RNA-seq) was subsequently conducted to explore the underlying molecular mechanism, and Western blotting and coimmunoprecipitation were subsequently performed to validate the results. Animal studies were performed to evaluate the antitumor effects in vivo. Bioinformatics analysis was also conducted to identify CDC20 as a potential therapeutic target in EC.

RESULTS

Treatment with Apcin inhibited proliferation and induced apoptosis in EC cells, resulting in cell cycle arrest. Pathways associated with apoptosis and the cell cycle were activated following treatment with Apcin. Notably, Apcin treatment led to the upregulation of the cell cycle regulator p21, which was verified to interact with CDC20 and consequently decrease the expression of downstream cyclins in EC cells. In vivo experiments confirmed that Apcin treatment significantly impeded tumor growth. Higher CDC20 expression was observed in EC tissue than in nonmalignant tissue, and increased CDC20 expression in EC patients was associated with shorter overall survival and progress free interval.

CONCLUSION

CDC20 is a novel molecular target in EC, and Apcin could be developed as a candidate antitumor drug for EC treatment.

Comprehensive analysis of the potential pathogenesis of COVID-19 infection and liver cancer

BACKGROUND

A growing number of clinical examples suggest that coronavirus disease 2019 (COVID-19) appears to have an impact on the treatment of patients with liver cancer compared to the normal population, and the prevalence of COVID-19 is significantly higher in patients with liver cancer. However, this mechanism of action has not been clarified.

AIM

To investigate the disease relevance of COVID-19 in liver cancer.

METHODS

Gene sets for COVID-19 (GSE180226) and liver cancer (GSE87630) were obtained from the Gene Expression Omnibus database. After identifying the common differentially expressed genes (DEGs) of COVID-19 and liver cancer, functional enrichment analysis, protein-protein interaction network construction and screening and analysis of hub genes were performed. Subsequently, the validation of the differential expression of hub genes in the disease was performed and the regulatory network of transcription factors and hub genes was constructed.

RESULTS

Of 518 common DEGs were obtained by screening for functional analysis. Fifteen hub genes including aurora kinase B, cyclin B2, cell division cycle 20, cell division cycle associated 8, nucleolar and spindle associated protein 1, etc., were further identified from DEGs using the "cytoHubba" plugin. Functional enrichment analysis of hub genes showed that these hub genes are associated with P53 signalling pathway regulation, cell cycle and other functions, and they may serve as potential molecular markers for COVID-19 and liver cancer. Finally, we selected 10 of the hub genes for in vitro expression validation in liver cancer cells.

CONCLUSION

Our study reveals a common pathogenesis of liver cancer and COVID-19. These common pathways and key genes may provide new ideas for further mechanistic studies.

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.

Anti-tumor target screening of sea cucumber saponin Frondoside A: a bioinformatics and molecular docking analysis

Cancer remains the leading cause of death worldwide. In spite of significant advances in targeted and immunotherapeutic approaches, clinical outcomes for cancer remain poor. The aim of the present study was to investigate the potential mechanisms and therapeutic targets of Frondoside A for the treatment of liver, pancreatic, and bladder cancers. The data presented in our study demonstrated that Frondoside A reduced the viability and migration of HepG2, Panc02, and UM-UC-3 cancer cell in vitro. Moreover, we utilized the GEO database to screen and identify for differentially expressed genes (DEGs) in liver, pancreatic, and bladder cancers, which resulted in the identification of 714, 357, and 101 DEGs, respectively. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation were performed using the Metascape database for DEGs that were significantly associated with cancer development. The protein-protein interaction (PPI) networks of the identified DEGs in liver, pancreatic, and bladder cancers were analyzed using Cytoscape 3.9.0 software, and subsequently identified potential key genes that were associated with these networks. Subsequently, their prognostic values were assessed by gene expression level analysis and Kaplan-Meier survival analysis (GEPIA). Furthermore, we utilized TIMER 2.0 to investigate the correlation between the expression of the identified key gene and cancer immune infiltration. Finally, molecular docking simulations were performed to assess the affinity of Frondoside A and key genes. Our results showed a significant correlation between these DEGs and cancer progression. Combined, these analyses revealed that Frondoside A involves in the regulation of multiple pathways, such as drug metabolism, cell cycle in liver cancer by inhibiting the expression of CDK1, TOP2A, CDC20, and KIF20A, and regulates protein digestion and absorption, receptor interaction in pancreatic cancer by down-regulation of ASPM, TOP2A, DLGAP5, TPX2, KIF23, MELK, LAMA3, and ANLN. While in bladder cancer, Frondoside A regulates muscle contraction, complement and coagulation cascade by increase FLNC expression. In conclusion, the present study offers valuable insights into the molecular mechanism underlying the anticancer effects of Frondoside A, and suggests that Frondoside A can be used as a functional food supplement or further developed as a natural anti-cancer drug.

Development and validation of a ubiquitin-proteasome system gene signature for prognostic prediction and immune microenvironment evaluation in hepatocellular carcinoma

BACKGROUND

The ubiquitin proteasome has a major role in the development of many tumors. However, the prognostic importance of ubiquitin proteasome-system genes (UPSGs) in hepatocellular carcinoma (HCC) is not fully defined.

METHODS

The TCGA and ICGC datasets were utilized to obtain transcriptional profiling data as well as clinicopathological information about HCC. The 3-UPSGs signature for the TCGA cohort was developed via univariate and LASSO Cox regression analyses. Differential expression of genes was demonstrated by qRT-PCR and immunohistochemistry (IHC). Biological pathways were studied using GSVA and GSEA. Six algorithms were used to compare immune infiltration between the two risk groups. Furthermore, drug sensitivity was measured using the "pRRophetic" R package. The predictive capacity of the 3-UPSGs signature for sensitivity to immunotherapy was also explored. Moreover, we performed a pan-cancer analysis of the 3-UPSGs signature.

RESULTS

A risk model containing 3 UPSGs (DCAF13, CDC20 and PSMB5) was developed. IHC and qRT-PCR results showed that signature genes were significantly overexpressed in HCC tissues. The high-risk group had a worse prognosis, with a higher clinicopathological grade, higher levels of tumor mutation burden (TMB), elevated levels of immune checkpoint (IC) expression, as well as increased sensitivity to immunotherapy. The two risk groups also differ in their sensitivity to chemotherapeutic drugs. Furthermore, the three UPSGs may play crucial roles in the progression of multiple types of cancers.

CONCLUSION

We created a 3-UPSGs signature to estimate the prognosis of HCC and to assist in individualized treatment.

The potential role of CDC20 in tumorigenesis, cancer progression and therapy: A narrative review

The cell division cycle 20 homologue (CDC20) is known to regulate the cell cycle. Many studies have suggested that dysregulation of CDC20 is associated with various pathological processes in malignant solid tumors, including tumorigenesis, progression, chemoradiotherapy resistance, and poor prognosis, providing a biomarker for cancer diagnosis and prognosis. Some researchers have demonstrated that CDC20 also regulates apoptosis, immune microenvironment, and tumor angiogenesis. In this review, we have systematically summarized the biological functions of CDC20 in solid cancers. Furthermore, we briefly synthesized multiple medicines that inhibited CDC20. We anticipate that CDC20 will be a promising and effective biomarker and therapeutic target for the treatment of human cancer.

CDC23 knockdown suppresses the proliferation, migration and invasion of liver cancer via the EMT process

Liver cancer (LC) is a malignant tumour that is associated with high mortality rates worldwide. Cell division cycle 23 (CDC23) acts as an oncogene in papillary thyroid cancer. In addition, epithelial-mesenchymal transition (EMT) is frequently involved in the malignant metastasis of various cancer types. Therefore, we hypothesized that CDC23 may regulate the malignant biological behaviours of LC cells through EMT. Proliferation, colony formation and Transwell assays, western blotting and xenograft experiments were performed. The results of the present study showed that CDC23 was highly expressed in LC cell lines. In addition, it was found via multiple in vitro assays that CDC23 knockdown reduced the proliferation, migration and invasion of LC cell lines. Finally, an in vivo study confirmed that CDC23 knockdown inhibited the growth of xenograft LC in nude mice. More importantly, the changes in the levels of EMT-related marker proteins were analysed in the sh-CDC23 group compared with the sh-NC group of cells and xenografts. E-cadherin was upregulated, and N-cadherin and vimentin were significantly downregulated after CDC23 silencing. Taken together, these results revealed that the knockdown of CDC23 inhibits the progression of LC by regulating EMT and that CDC23 may be a novel therapeutic target for LC.

Exploring the pathogenesis of colorectal carcinoma complicated with hepatocellular carcinoma via microarray data analysis

Background: Despite the increasing number of research endeavors dedicated to investigating the relationship between colorectal carcinoma (CRC) and hepatocellular carcinoma (HCC), the underlying pathogenic mechanism remains largely elusive. The aim of this study is to shed light on the molecular mechanism involved in the development of this comorbidity. Methods: The gene expression profiles of CRC (GSE90627) and HCC (GSE45267) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) of psoriasis and atherosclerosis, three kinds of analyses were performed, namely, functional annotation, protein-protein interaction (PPI) network and module construction, and hub gene identification, survival analysis and co-expression analysis. Results: A total of 150 common downregulated differentially expressed genes and 148 upregulated differentially expressed genes were selected for subsequent analyses. The significance of chemokines and cytokines in the pathogenesis of these two ailments is underscored by functional analysis. Seven gene modules that were closely connected were identified. Moreover, the lipopolysaccharide-mediated signaling pathway is intricately linked to the development of both diseases. Finally, 10 important hub genes were identified using cytoHubba, including CDK1, KIF11, CDC20, CCNA2, TOP2A, CCNB1, NUSAP1, BUB1B, ASPM, and MAD2L1. Conclusion: Our study reveals the common pathogenesis of colorectal carcinoma and hepatocellular carcinoma. These common pathways and hub genes may provide new ideas for further mechanism research.

Genetic deviation associated with photodynamic therapy in HeLa cell

Photodynamic therapy (PDT) is a method that is used in cancer treatment. The main therapeutic effect is the production of singlet oxygen (1O2). Phthalocyanines for PDT produce high singlet oxygen with absorbers of about 600-700 nm.

AIM

It is aimed to analyze cancer cell pathways by flow cytometry analysis and cancer-related genes with q-PCR device by applying phthalocyanine L1ZnPC, which we use as photosensitizer in photodynamic therapy, in HELA cell line. In this study, we investigate the molecular basis of L1ZnPC's anti-cancer activity.

MATERIAL METHOD

The cytotoxic effects of L1ZnPC, a phthalocyanine obtained from our previous study, in HELA cells were evaluated and it was determined that it led to a high rate of death as a result. The result of photodynamic therapy was analyzed using q-PCR. From the data received at the conclusion of this investigation, gene expression values were calculated, and expression levels were assessed using the 2-∆∆Ct method to examine the relative changes in these values. Cell death pathways were interpreted with the FLOW cytometer device. One-Way Analysis of Variance (ANOVA) and the Tukey-Kramer Multiple Comparison Test with Post-hoc Test were used for the statistical analysis.

CONCLUSION

In our study, it was observed that HELA cancer cells underwent apoptosis at a rate of 80% with drug application plus photodynamic therapy by flow cytometry method. According to q-PCR results, CT values ​​of eight out of eighty-four genes were found to be significant and their association with cancer was evaluated. L1ZnPC is a new phthalocyanine used in this study and our findings should be supported by further studies. For this reason, different analyses are needed to be performed with this drug in different cancer cell lines. In conclusion, according to our results, this drug looks promising but still needs to be analyzed through new studies. It is necessary to examine in detail which signaling pathways they use and their mechanism of action. For this, additional experiments are required.

High Expression of Ten Eleven Translocation 1 Is Associated with Poor Prognosis in Hepatocellular Carcinoma

Background

DNA methylation patterns have been found to be distinct between tumor and normal patients. However, the effect of DNA demethylation enzymes, ten eleven translocation (TET) proteins, has not been comprehensively characterized in liver cancer. In this research, we sought to unravel the linkage of TET proteins with prognosis, immune characteristics and biological pathways in hepatocellular carcinoma (HCC).

Materials and Methods

Four independent datasets with gene expression data and clinical data of HCC samples were downloaded from public databases. CIBERSORT, single sample Gene Set Enrichment Analysis (ssGSEA), MCP-counter, and TIMER were implemented to evaluate immune cell infiltration. limma was employed to screen differentially expressed genes (DEGs) between two groups. The demethylation-related risk model was established by using univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO), and stepwise Akaike information criterion (stepAIC).

Results

TET1 was significantly higher expressed in tumor samples than that in normal samples. HCC patients with advanced stages (III+IV) and grades (G3+G4) had higher TET1 expression compared to early stages (I+II) and grades (G1+G2). HCC samples with high TET1 expression had worse prognosis than that with low expression. High and low TET1 expression groups had distinct immune cell infiltration and response to immunotherapy and chemotherapy. We identified 90 DEGs related to DNA demethylation in high vs. low TET1 expression groups. Furthermore, we established a risk model based on 90 DEGs containing seven key prognostic genes (SERPINH1, CDC20, HACD2, SPHK1, UGT2B15, SLC1A5, and CYP2C9) with effectiveness and robustness in predicting HCC prognosis.

Conclusions

Our study suggested TET1 as a potential indicator in HCC progression. TET1 was closely involved in immune infiltration and activation of oncogenic pathways. The DNA demethylation-related risk model was potential to be applied for predicting HCC prognosis in clinics.

The structural flexibility of MAD1 facilitates the assembly of the Mitotic Checkpoint Complex

The spindle assembly checkpoint (SAC) safeguards the genome during cell division by generating an effector molecule known as the Mitotic Checkpoint Complex (MCC). The MCC comprises two subcomplexes: BUBR1:BUB3 and CDC20:MAD2, and the formation of CDC20:MAD2 is the rate-limiting step during MCC assembly. Recent studies show that the rate of CDC20:MAD2 formation is significantly accelerated by the cooperative binding of CDC20 to the SAC proteins MAD1 and BUB1. However, the molecular basis for this acceleration is not fully understood. Here, we demonstrate that the structural flexibility of MAD1 at a conserved hinge near the C-terminus is essential for catalytic MCC assembly. This MAD1 hinge enables the MAD1:MAD2 complex to assume a folded conformation in vivo. Importantly, truncating the hinge reduces the rate of MCC assembly in vitro and SAC signaling in vivo. Conversely, mutations that preserve hinge flexibility retain SAC signaling, indicating that the structural flexibility of the hinge, rather than a specific amino acid sequence, is important for SAC signaling. We summarize these observations as the 'knitting model' that explains how the folded conformation of MAD1:MAD2 promotes CDC20:MAD2 assembly.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

p53 directly downregulates the expression of CDC20 to exert anti-tumor activity in mantle cell lymphoma

BACKGROUND

Cell cycle dysregulation characterized by cyclin D1 overexpression is common in mantle cell lymphoma (MCL), while mitotic disorder was less studied. Cell division cycle 20 homologue (CDC20), an essential mitotic regulator, was highly expressed in various tumors. Another common abnormality in MCL is p53 inactivation. Little was known about the role of CDC20 in MCL tumorigenesis and the regulatory relationship between p53 and CDC20 in MCL.

METHODS

CDC20 expression was detected in MCL patients and MCL cell lines harboring mutant p53 (Jeko and Mino cells) and wild-type p53 (Z138 and JVM2 cells). Z138 and JVM2 cells were treated with CDC20 inhibitor apcin, p53 agonist nutlin-3a, or in combination, and then cell proliferation, cell apoptosis, cell cycle, cell migration and invasion were determined by CCK-8, flow cytometry and Transwell assays. The regulatory mechanism between p53 and CDC20 was revealed by dual-luciferase reporter gene assay and CUT&Tag technology. The anti-tumor effect, safety and tolerability of nutlin-3a and apcin were investigated in vivo in the Z138-driven xenograft tumor model.

RESULTS

CDC20 was overexpressed in MCL patients and cell lines compared with their respective controls. The typical immunohistochemical marker of MCL patients, cyclin D1, was positively correlated with CDC20 expression. CDC20 high expression indicated unfavorable clinicopathological features and poor prognosis in MCL patients. In Z138 and JVM2 cells, either apcin or nutlin-3a treatment could inhibit cell proliferation, migration and invasion, and induce cell apoptosis and cell cycle arrest. GEO analysis, RT-qPCR and WB results showed that p53 expression was negatively correlated with CDC20 expression in MCL patients, Z138 and JVM2 cells, while this relationship was not observed in p53-mutant cells. Dual-luciferase reporter gene assay and CUT&Tag assay revealed mechanistically that CDC20 was transcriptionally repressed by p53 through directly binding p53 to CDC20 promoter from - 492 to + 101 bp. Moreover, combined treatment of nutlin-3a and apcin showed better anti-tumor effect than single treatment in Z138 and JVM2 cells. Administration of nutlin-3a/apcin alone or in combination confirmed their efficacy and safety in tumor-bearing mice.

CONCLUSIONS

Our study validates the essential role of p53 and CDC20 in MCL tumorigenesis, and provides a new insight for MCL therapeutics through dual-targeting p53 and CDC20.

Differentially Expressed Cell Cycle Genes and STAT1/3-Driven Multiple Cancer Entanglement in Psoriasis, Coupled with Other Comorbidities

Psoriasis is a persistent T-cell-supported inflammatory cutaneous disorder, which is defined by a significant expansion of basal cells in the epidermis. Cell cycle and STAT genes that control cell cycle progression and viral infection have been revealed to be comorbid with the development of certain cancers and other disorders, due to their abnormal or scanty expression. The purpose of this study is to evaluate the expression of certain cell cycle and STAT1/3 genes in psoriasis patients and to determine the types of comorbidities associated with these genes. To do so, we opted to adopt the in silico methodology, since it is a quick and easy way to discover any potential comorbidity risks that may exist in psoriasis patients. With the genes collected from early research groups, protein networks were created in this work using the NetworkAnalyst program. The crucial hub genes were identified by setting the degree parameter, and they were then used in gene ontology and pathway assessments. The transcription factors that control the hub genes were detected by exploring TRRUST, and DGIdb was probed for remedies that target transcription factors and hubs. Using the degree filter, the first protein subnetwork produced seven hub genes, including STAT3, CCNB1, STAT1, CCND1, CDC20, HSPA4, and MAD2L1. The hub genes were shown to be implicated in cell cycle pathways by the gene ontology and Reactome annotations. The former four hubs were found in signaling pathways, including prolactin, FoxO, JAK/STAT, and p53, according to the KEGG annotation. Furthermore, they enhanced several malignancies, including pancreatic cancer, Kaposi's sarcoma, non-small cell lung cancer, and acute myeloid leukemia. Viral infections, including measles, hepatitis C, Epstein-Barr virus, and HTLV-1 and viral carcinogenesis were among the other susceptible diseases. Diabetes and inflammatory bowel disease were conjointly annotated. In total, 129 medicines were discovered in DGIdb to be effective against the transcription factors BRCA1, RELA, TP53, and MYC, as opposed to 10 medications against the hubs, STAT3 and CCND1, in tandem with 8 common medicines. The study suggests that the annotated medications should be tested in suitable psoriatic cell lines and animal models to optimize the drugs used based on the kind, severity, and related comorbidities of psoriasis. Furthermore, a personalized medicine protocol must be designed for each psoriasis patient that displays different comorbidities.

Screening of Hub Genes in Hepatocellular Carcinoma Based on Network Analysis and Machine Learning

Hepatocellular carcinoma (LIHC) is the fifth common cancer worldwide, and it requires effective diagnosis and treatment to prevent aggressive metastasis. The purpose of this study was to construct a machine learning-based diagnostic model for the diagnosis of liver cancer. Using weighted correlation network analysis (WGCNA), univariate analysis, and Lasso-Cox regression analysis, protein-protein interactions network analysis is used to construct gene networks from transcriptome data of hepatocellular carcinoma patients and find hub genes for machine learning. The five models, including gradient boosting, random forest, support vector machine, logistic regression, and integrated learning, were to identify a multigene prediction model of patients. Immunological assessment, TP53 gene mutation and promoter methylation level analysis, and KEGG pathway analysis were performed on these groups. Potential drug molecular targets for the corresponding hepatocellular carcinomas were obtained by molecular docking for analysis, resulting in the screening of 2 modules that may be relevant to the survival of hepatocellular carcinoma patients, and the construction of 5 diagnostic models and multiple interaction networks. The modes of action of drug-molecule interactions that may be effective against hepatocellular carcinoma core genes CCNA2, CCNB1, and CDK1 were investigated. This study is expected to provide research ideas for early diagnosis of hepatocellular carcinoma.

Custom gene expression panel for evaluation of potential molecular markers in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. It is a highly heterogeneous disease with poor prognosis and limited treatment options, which highlights the need for reliable biomarkers. This study aims to explore molecular markers that allow stratification of HCC and may lead to better prognosis and treatment prediction.

MATERIALS AND METHODS

We studied 20 candidate genes (HCC hub genes, potential drug target genes, predominant somatic mutant genes) retrieved from literature and public databases with potential to be used as the molecular markers. We analysed expression of the genes by RT-qPCR in 30 HCC tumour and adjacent non-tumour paired samples from Vietnamese patients. Fold changes in expression were then determined using the 2-∆∆CT method, and unsupervised hierarchical clustering was generated using Cluster v3.0 software.

RESULTS

Clustering of expression data revealed two subtypes of tumours (proliferative and normal-like) and four clusters for genes. The expression profiles of the genes TOP2A, CDK1, BIRC5, GPC3, IGF2, and AFP were strongly correlated. Proliferative tumours were characterized by high expression of the c-MET, ARID1A, CTNNB1, RAF1, LGR5, and GLUL1 genes. TOP2A, CDK1, and BIRC5 HCC hub genes were highly expressed (> twofold) in 90% (27/30), 83% (25/30), and 83% (24/30) in the tissue samples, respectively. Among the drug target genes, high expression was observed in the GPC3, IGF2 and c-MET genes in 77% (23/30), 63% (19/30), and 37% (11/30), respectively. The somatic mutant Wnt/ß-catenin genes (CTNNB1, GLUL and LGR5) and TERT were highly expressed in 40% and 33% of HCCs, respectively. Among the HCC marker genes, a higher percentage of tumours showed GPC3 expression compared to AFP expression [73% (23/30) vs. 43% (13/30)].

CONCLUSION

The custom panel and molecular markers from this study may be useful for diagnosis, prognosis, biomarker-guided clinical trial design, and prediction of treatment outcomes.

Identification of BRIP1, NSMCE2, ANAPC7, RAD18 and TTL from chromosome segregation gene set associated with hepatocellular carcinoma

INTRODUCTION

Hepatocellular carcinoma is one of the most frequent cancers with high mortality rate worldwide.

METHODS

TCGA LIHC HTseq counts were analyzed. GSEA was performed with GO BP gene sets. GO analysis was performed with differentially expressed genes. The subset of genes contributing most of the enrichment result of GO_BP_CHROMOSOME_SEGREGATION of GSEA were identified. Five genes have been selected in this subset of genes for further analysis. A microarray data set, GSE112790, was analyzed as a validation data set. Survival analysis was performed.

RESULTS

According to GSEA and GO analysis several gene sets and processes related to chromosome segregation were enriched in LIHC. GO_BP_CHROMOSOME_SEGREGATION gene set from GSEA had the highest size of the genes contributing most of the enrichment. Five genes in this gene set; BRIP1, NSMCE2, ANAPC7, RAD18 and TTL, whose expressions and prognostic values have not been studied in hepatocellular carcinoma in detail, have been selected for further analyses. Expression of these five genes were identified as significantly upregulated in LIHC RNA-seq and HCC microarray data set. Survival analysis showed that high expression of the five genes was associated with poor overall survival in HCC patients.

CONCLUSION

Selected genes were upregulated and had prognostic value in HCC.

Construction of novel lncRNA-miRNA-mRNA ceRNA networks associated with prognosis of hepatitis C virus related hepatocellular carcinoma

Background

Hepatitis C virus (HCV) infection contribute to liver fibrosis and cirrhosis, which significantly increases the risk of hepatocellular carcinoma (HCC) development. Previous studies have demonstrated the pivotal role of competitive endogenous RNA (ceRNA) networks in tumorigenesis and cancer progression. Consequently, we herein seek to identify and evaluate the prognostic relevance of a novel ceRNA network associated with HCV-related HCC.

Methods

Differentially expressed genes (DEGs) in GSE140846 dataset from GEO were identified using Network Analyst, and GO, KEGG and Reactome analyses were performed. Furthermore, a protein-protein interaction network was generated, and hub genes were detected. Hub gene expression levels, as well as those of their upstream lncRNAs and miRNAs and associated survival analyses were conducted using appropriate bioinformatics databases. Predicted target relationships were used to establish putative ceRNA networks for HCV-related HCC.

Results

A total of 372 and 360 up- and down-regulated DE-mRNA were identified, which were associated with nuclear division, cell cycle, and ATPase activity. A PPI network containing 704 DE-mRNAs was constructed, and the 6 hub gene with the highest degree of connectivity were selected for subsequent analysis. We discovered that 22 miRNAs and 4 lncRNAs upstream of 11 hub gene were significantly associated with poor prognosis of HCV-related HCC, and used them to constructe a prognostic ceRNA network. Further experiments confirmed the ceRNA-regulatory relationship of BUB1-hsa-miR-193a-3p-MALAT1.

Conclusion

This study provides novel insights into the lncRNA-miRNA-mRNA ceRNA network, and reveals potential lncRNA biomarkers in HCV related HCC.

Identification and validation of real hub genes in hepatocellular carcinoma based on weighted gene co-expression network analysis

BACKGROUND

Hepatocellular Carcinoma (HCC) is one of the most common liver malignancies in the world. With highly invasive biological characteristics and a lack of obvious clinical manifestations, hepatocellular Carcinoma usually has a poor prognosis and ranks fourth in cancer mortality. The etiology and exact molecular mechanism of primary hepatocellular carcinoma are still unclear.

OBJECTIVE

This work aims to help identify biomarkers of early HCC diagnosis or prognosis based on weighted gene co-expression network analysis (WGCNA).

METHODS

Expression data and clinical information of HTSEQ-Counts were downloaded from The Cancer Genome Atlas (TCGA) database, and Gene Expression map GSE121248 was downloaded from Gene Expression Omnibus (GEO). By differentially expressed genes (DEGs) and Weighted Gene co-expression Network Analysis (WGCNA) searched for modules in the two databases that had the same effect on the biological characteristics of HCC, and extracted the module genes with the highest positive correlation with HCC from two databases, and finally obtained overlapping genes. Then, we performed functional enrichment analysis on the overlapping genes to understand their potential biological functions. The top ten hub genes were screened according to MCC through the String database and Cytoscape software and then subjected to survival analysis.

RESULTS

High expression of CDK1, CCNA2, CDC20, KIF11, DLGAP5, KIF20A, ASPM, CEP55, and TPX2 was associated with poorer overall survival (OS) of HCC patients. The DFS curve was plotted using the online website GEPIA2. Finally, based on the enrichment of these genes in the KEGG pathway, real hub genes were screened out, which were CDK1, CCNA2, and CDC20 respectively.

CONCLUSIONS

High expression of these three genes was negatively correlated with survival time in HCC, and the expression of CDK1, CCNA2, and CDC20 were significantly higher in tumor tissues of HCC patients than in normal liver tissues as verified again by the HPA database. All in all, this provides a new feasible target for early and accurate diagnosis of HCC, clinical diagnosis, treatment, and prognosis.

Cell-cycle and apoptosis related and proteomics-based signaling pathways of human hepatoma Huh-7 cells treated by three currently used multi-RTK inhibitors

Sorafenib, lenvatinib and regorafenib, the multi-RTK inhibitors with potent anti-angiogenesis effects, are currently therapeutic drugs generally recommended for the patients with advanced hepatocellular carcinoma (HCC). To date, however, there have been no published studies on the mechanism underling differential effects of the three drugs on HCC cell proliferation, and the proteomic analysis in HCC cell lines treated by regorafenib or lenvatinib. The present study for the first time performed a direct comparison of the cell cycle arrest and apoptosis induction in the Huh-7 cells caused by sorafenib, regorafenib and lenvatinib at respective IC₅₀ using flow cytometry technique, as well as their pharmacological interventions for influencing whole cell proteomics using tandem mass tag-based peptide-labeling coupled with the nLC-HRMS technique. Sorafenib, regorafenib and lenvatinib at respective IC₅₀ drove the remaining surviving Huh-7 cells into a G₀/G₁ arrest, but lenvatinib and regorafenib were much more effective than sorafenib. Lenvatinib produced a much stronger induction of Huh-7 cells into early apoptosis than sorafenib and regorafenib, while necrotic cell proportion induced by regorafenib was 2.4 times as large as that by lenvatinib. The proteomic study revealed 419 proteins downregulated commonly by the three drugs at respective IC₅₀. KEGG pathway analysis of the downregulated proteins indicated the ranking of top six signaling pathways including the spliceosome, DNA replication, cell cycle, mRNA surveillance, P53 and nucleotide excision repair involved in 33 proteins, all of which were directly related to their pharmacological effects on cell cycle and cell apoptosis. Notably, lenvatinib and regorafenib downregulated the proteins of PCNA, Cyclin B1, BCL-xL, TSP1, BUD31, SF3A1 and Mad2 much more strongly than sorafenib. Moreover, most of the proteins in the P53 signaling pathway were downregulated with lenvatinib and regorafenib by more than 36% at least. In conclusion, lenvatinib and regorafenib have much stronger potency against Huh-7 cell proliferation than sorafenib because of their more potent effects on cell cycle arrest and apoptosis induction. The underling mechanism may be at least due to the 33 downregulated proteins centralizing the signal pathways of cell cycle, p53 and DNA synthesis based on the present proteomics study.

Impaired Cdc20 signaling promotes senescence in normal cells and apoptosis in non-small cell lung cancer cells

Cellular senescence is a form of irreversible growth arrest that cancer cells evade. The cell division cycle protein 20 homolog (Cdc20) is a positive regulator of cell division, but how its dysregulation may relate to senescence is unclear. Here, we find that Cdc20 mRNA and protein expression are downregulated in stress-induced premature senescent lung fibroblasts in a p53-dependent manner. Either Cdc20 downregulation or inhibition of anaphase-promoting complex/cyclosome (APC/C) is sufficient to induce premature senescence in lung fibroblasts, while APC/C activation inhibits stress-induced premature senescence. Mechanistically, we show both Cdc20 downregulation and APC/C inhibition induce premature senescence through glycogen synthase kinase (GSK)-3β–mediated phosphorylation and downregulation of securin expression. Interestingly, we determined Cdc20 expression is upregulated in human lung adenocarcinoma. We find that downregulation of Cdc20 in non–small cell lung cancer (NSCLC) cells is sufficient to inhibit cell proliferation and growth in soft agar and to promote apoptosis, but not senescence, in a manner dependent on downregulation of securin following GSK-3β-mediated securin phosphorylation. Similarly, we demonstrate securin expression is downregulated and cell viability is inhibited in NSCLC cells following inhibition of APC/C. Furthermore, we show chemotherapeutic drugs downregulate both Cdc20 and securin protein expression in NSCLC cells. Either Cdc20 downregulation by siRNA or APC/C inhibition sensitize, while securin overexpression inhibits, chemotherapeutic drug-induced NSCLC cell death. Together, our findings provide evidence that Cdc20/APC/C/securin-dependent signaling is a key regulator of cell survival, and its disruption promotes premature senescence in normal lung cells and induces apoptosis in lung cancer cells that have bypassed the senescence barrier.

Computational Characterizing Necroptosis Reveals Implications for Immune Infiltration and Immunotherapy of Hepatocellular Carcinoma

Necroptosis is a programmed form of necrotic cell death in regulating cancer ontogenesis, progression, and tumor microenvironment (TME) and could drive tumor-infiltrating cells to release pro-inflammatory cytokines, incurring strong immune responses. Nowadays, there are few identified biomarkers applied in clinical immunotherapy, and it is increasingly recognized that high levels of tumor necroptosis could enhance the response to immunotherapy. However, comprehensive characterization of necroptosis associated with TME and immunotherapy in Hepatocellular carcinoma (HCC) remains unexplored. Here, we computationally characterized necroptosis landscape in HCC samples from TCGA and ICGA cohorts and stratified them into two necroptosis clusters (A or B) with significantly different characteristics in clinical prognosis, immune cell function, and TME-landscapes. Additionally, to further evaluate the necroptosis levels of each sample, we established a novel necroptosis-related gene score (NRGscore). We further investigated the TME, tumor mutational burden (TMB), clinical response to immunotherapy, and chemotherapeutic drug sensitivity of HCC subgroups stratified by the necroptosis landscapes. The NRGscore is robust and highly predictive of HCC clinical outcomes. Further analysis indicated that the high NRGscore group resembles the immune-inflamed phenotype while the low score group is analogous to the immune-exclusion or metabolism phenotype. Additionally, the high NRGscore group is more sensitive to immune checkpoint blockade-based immunotherapy, which was further validated using an external HCC cohort, metastatic melanoma cohort, and advanced urothelial cancer cohort. Besides, the NRGscore was demonstrated as a potential biomarker for chemotherapy, wherein the high NRGscore patients with more tumor stem cell composition could be more sensitive to Cisplatin, Doxorubicin, Paclitaxel-based chemotherapy, and Sorafenib therapy. Collectively, a comprehensive characterization of the necroptosis in HCC suggested its implications for predicting immune infiltration and response to immunotherapy of HCC, providing promising strategies for treatment.

REC8 inhibits proliferation, migration and invasion of breast cancer cells by targeting CDC20

Breast cancer is one of the most types of common malignant tumor in women. REC8 is a known tumor suppressor in several types of cancer; however, the role of REC8 in breast cancer remains unknown. The purpose of the present study was to investigate the effects and underlying mechanism of REC8 on the proliferation, migration and invasion of breast cancer cells. The expression of REC8 in normal and breast cancer cells was detected by reverse transcription‑quantitative PCR and western blotting. Stable REC8‑overexpressing breast cancer cells were constructed to modify the expression of REC8. The expression of cell division cycle 20 (CDC20) in breast cancer cells was altered using the CDC20 inhibitor apcin. Cell viability, proliferation, migration, invasion and apoptosis were determined by Cell Counting Kit‑8, colony formation, wound healing, Transwell and TUNEL assays, respectively. Western blotting was performed to measure the expression of matrix metalloproteinase‑2/9 and apoptosis‑associated proteins [Bcl‑2, caspase‑3, cleaved caspase‑3 and cleaved poly (ADP‑ribose) polymerase]. Compared with normal breast cells, the expression of REC8 was lower in breast cancer cells. Search Tool for the Retrieval of Interacting Genes/Proteins online database was used to predict the interaction between REC8 and CDC20. Overexpression of REC8 significantly inhibited the proliferation, migration and invasion of breast cancer cells in vitro; these changes were reversed by CDC20 overexpression. In conclusion, the present study demonstrated that REC8 decreased proliferation, migration and invasion of breast cancer cells by inhibiting CDC20.

CDC20 in and out of mitosis: a prognostic factor and therapeutic target in hematological malignancies

Cell division cycle 20 homologue (CDC20) is a well-known regulator of cell cycle, as it controls the correct segregation of chromosomes during mitosis. Many studies have focused on the biological role of CDC20 in cancer development, as alterations of its functionality have been linked to genomic instability and evidence demonstrated that high CDC20 expression levels are associated with poor overall survival in solid cancers. More recently, novel CDC20 functions have been demonstrated or suggested, including the regulation of apoptosis and stemness properties and a correlation with immune cell infiltration. Here, we here summarize and discuss the role of CDC20 inside and outside mitosis, starting from its network of interacting proteins. In the last years, CDC20 has also attracted more interest in the blood cancer field, being overexpressed and showing an association with prognosis both in myeloid and lymphoid malignancies. Preclinical findings showed that selective CDC20 and APC/CCDC20/APC/CCDH1 inhibitors, namely Apcin and proTAME, are effective against lymphoma and multiple myeloma cells, resulting in mitotic arrest and apoptosis and synergizing with clinically-relevant drugs. The evidence and hypothesis presented in this review provide the input for further biological and chemical studies aiming to dissect novel potential CDC20 roles and targeting strategies in hematological malignancies.

Immune-related lincRNA pairs predict prognosis and therapeutic response in hepatocellular carcinoma

Growing evidence has demonstrated the functional relevance of long intergenic noncoding RNAs (lincRNAs) to tumorigenesis and immune response. However, immune-related lincRNAs and their value in predicting the clinical outcomes of patients with liver cancer remain largely unexplored. Herein, we utilized the strategy of iterative gene pairing to construct a tumor-specific immune-related lincRNA pairs signature (IRLPS), which did not require specific expression levels, as an indicator of patient outcomes. The 18-IRLPS we developed was associated with overall survival, tumor progression, and recurrence in liver cancer patients. Multivariate analysis revealed that the risk model was an independent predictive factor. A high IRLPS risk was correlated suppressive immune microenvironment, and IRLPS-high patients might benefit more from CD276 blockade or TMIGD2 agonist. Patients in the high-risk group were associated with elevated tumor mutation, increased sensitivity to dopamine receptor antagonists, cisplatin, doxorubicin, and mitomycin but more resistance to vinblastine. Mechanistically, IRLPS high scores might lead to poor prognosis by promoting cell proliferation and metabolic reprogramming. The prognostic significance of the 18-IRLPS was confirmed in independent cancer datasets. These findings highlighted the robust predictive performances of the 18-IRLPS for prognosis and personalized treatment.

Clinical implications and molecular mechanisms of Cyclin-dependent kinases 4 for patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) was frequently considered as a kind of malignant tumor with a poor prognosis. Cyclin-dependent kinases (CDK) 4 was considered to be cell-cycle-related CDK gene. In this study, we explored the clinical significance of CDK4 in HCC patients.

Methods

Data of HCC patients were obtained from The Cancer Genome Atlas database (TCGA) and the Gene Expression Omnibus (GEO) database. Kaplan–Meier analysis and Cox regression model were performed to calculate median survival time (MST) and the hazard ration (HR), respectively. The joint-effect analysis and prognostic risk score model were constructed to demonstrate significance of prognosis-related genes. The differential expression of prognostic genes was further validated using reverse transcription-quantitative PCR (RT-qPCR) of 58 pairs of HCC samples.

Results

CDK1 and CDK4 were considered prognostic genes in TCGA and GSE14520 cohort. The result of joint-effect model indicated patients in CDK1 and CDK4 low expression groups had a better prognosis in TCGA (adjusted HR = 0.491; adjusted P = 0.003) and GSE14520 cohort (adjusted HR = 0.431; adjusted P = 0.002). Regarding Kaplan–Meier analysis, high expression of CDK1 and CDK4 was related to poor prognosis in both the TCGA (P < 0.001 and = 0.001 for CDK1 and CDK4, respectively) and the GSE14520 cohort (P = 0.006 and = 0.033 for CDK1 and CDK4, respectively). However, only CDK4 (P = 0.042) was validated in RT-qPCR experiment, while CDK1 (P = 0.075) was not.

Conclusion

HCC patients with high CDK4 expression have poor prognosis, and CDK4 could be a potential candidate diagnostic biomarker for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02152-w.

Study on the expression of TOP2A in hepatocellular carcinoma and its relationship with patient prognosis

Background

Hepatocellular carcinoma (HCC) is a primary liver cancer with a high mortality rate. However, the molecular mechanism of HCC formation remains to be explored and studied.

Objective

To investigate the expression of TOP2A in hepatocellular carcinoma (HCC) and its prognosis.

Methods

The data set of hepatocellular carcinoma was downloaded from GEO database for differential gene analysis, and hub gene was identified by Cytoscape. GEPIA was used to verify the expression of HUB gene and evaluate its prognostic value. Then TOP2A was selected as the research object of this paper by combining literature and clinical sample results. Firstly, TIMER database was used to study TOP2A, and the differential expression of TOP2A gene between normal tissues and cancer tissues was analyzed, as well as the correlation between TOP2A gene expression and immune infiltration of HCC cells. Then, the expression of top2a-related antibodies was analyzed using the Human Protein Atlas database, and the differential expression of TOP2A was verified by immunohistochemistry. Then, SRTING database and Cytoscape were used to establish PPI network for TOP2A and protein–protein interaction analysis was performed. The Oncomine database and cBioPortal were used to express and identify TOP2A mutation-related analyses. The expression differences of TOP2A gene were identified by LinkedOmics, and the GO and KEGG pathways were analyzed in combination with related genes. Finally, Kaplan–Meier survival analysis was performed to analyze the clinical and prognosis of HCC patients.

Results

TOP2A may be a new biomarker and therapeutic target for hepatocellular carcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02439-0.

CCNB2, CDC20, AURKA, TOP2A, MELK, NCAPG, KIF20A, UBE2C, PRC1, and ASPM May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis

Background

Hepatocellular carcinoma (HCC) is a highly malignant, recurrent and drug-resistant tumor, and patients often lose the opportunity for surgery when they are diagnosed. Abnormal gene expression is closely related to the occurrence of HCC. The aim of the present study was to identify the differentially expressed genes (DEGs) between tumor tissue and non-tumor tissue of HCC samples in order to investigate the mechanisms of liver cancer.

Methods

The gene expression profile (GSE62232, GSE89377, and GSE112790) was downloaded from the Gene Expression Omnibus (GEO) and analyzed using the online tool GEO2R to identify differentially expressed genes (DEGs). Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery. Protein–protein interaction (PPI) of these DEGs was analyzed based on the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. In addition, we used the online Kaplan–Meier plotter survival analysis tool to evaluate the prognostic value of hub genes expression. HPA database was used to reveal the differences in protein level of hub genes.

Results

A total of 50 upregulated DEGs and 122 downregulated DEGs were identified. Among them, ten hub genes with a high degree of connectivity were picked out. Overexpression of these hub genes was associated with unfavorable prognosis of HCC.

Conclusion

Our study suggests that CCNB2, CDC20, AURKA, TOP2A, MELK, NCAPG, KIF20A, UBE2C, PRC1, and ASPM were overexpressed in HCC compared with normal liver tissue. Overexpression of these genes was an unfavorable prognostic factor of HCC patients. Further study is needed to explore the value of them in the diagnosis and treatment of HCC.

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Identification of the critical genes and miRNAs in hepatocellular carcinoma by integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is a global health problem with complex etiology and pathogenesis. Microarray data are increasingly being used as a novel and effective method for cancer pathogenesis analysis. An integrative analysis of genes and miRNA for HCC was conducted to unravel the potential prognosis of HCC. Two gene microarray datasets (GSE89377 and GSE101685) and two miRNA expression profiles (GSE112264 and GSE113740) were obtained from Gene Expression Omnibus database. A total of 177 differently expressed genes (DEGs) and 80 differently expressed miRNAs (DEMs) were screened out. Functional enrichment of DEGs was proceeded by Clue GO and these genes were significantly enriched in the chemical carcinogenesis pathway. A protein-protein interaction network was then established on the STRING platform, and ten hub genes (CDC20, TOP2A, ASPM, NCAPG, AURKA, CYP2E1, HMMR, PRC1, TYMS, and CYP4A11) were visualized via Cytoscape software. Then, a miRNA-target network was established to identify the hub dysregulated miRNA. A key miRNA (hsa-miR-124-3p) was filtered. Finally, the miRNA-target-transcription factor network was constructed for hsa-miR-124-3p. The network for hsa-miR-124-3p included two transcription factors (TFs) and five targets. These identified DEGs and DEMs, TFs, targets, and regulatory networks may help advance our understanding of the underlying pathogenesis of HCC.

Identification of Differentially Expressed Genes Associated with the Prognosis and Diagnosis of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

OBJECTIVE

The goal of this study was to understand the possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis.

METHODS

GEO contains datasets of gene expression, miRNA, and methylation patterns of diseased and healthy/control patients. The GSE62232 dataset was selected by employing the server Gene Expression Omnibus. A total of 91 samples were collected, including 81 HCC and 10 healthy samples as control. GSE62232 was analysed through GEO2R, and Functional Enrichment Analysis was performed to extract rational information from a set of DEGs. The Protein-Protein Relationship Networking search method has been used for extracting the interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analysed using GEPIA to estimate the effect of their differential expression on cancer progression.

RESULTS

We identified the top 10 hub genes through CytoHubba plugin. These included BUB1, BUB1B, CCNB1, CCNA2, CCNB2, CDC20, CDK1 and MAD2L1, NCAPG, and NDC80. NCAPG and NDC80 reported for the first time in this study while the remaining from a recently reported literature. The pathogenesis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for future prognostic and predictive biomarkers studies that could promote selective molecular therapy for HCC.

Identification and Validation of Hub Genes Predicting Prognosis of Hepatocellular Carcinoma

INTRODUCTION

The aim of this study is selecting the hub genes associated with hepatocellular carcinoma (HCC) to construct a Cox regression model for predicting prognosis in HCC patients.

METHODS

Using HCC patient data from the ICGC and TCGA databases, screened for 40 core genes highly correlated with histological grade of HCC. Univariate and multivariate Cox regression analyses were performed on the genes highly associated with HCC prognosis, and the model was established. The expression of those genes was measured by immunohistochemistry in 110 HCC patients who underwent the surgery in the First Affiliated Hospital of Wenzhou Medical University. The survival of HCC patients was analyzed by the Kaplan-Meier method.

RESULTS

Eight genes (CDC45, CENPA, MCM10, MELK, CDC20, ASF1B, FANCD2, and NCAPH) were correlated with prognosis, and the same result was observed in 110 HCC patients. Using the regression model, the HCC patients in the training set were classified as high- and low-risk groups. The overall survival of patients in the high-risk group was shorter than that in the low-risk group, and the same results were obtained in the verification set.

CONCLUSION

This study found that the risk model according to these 8 genes can be used as a predictor of prognosis in HCC. These genes may become alternative biomarkers and therapeutic targets and provide new therapeutic strategies for HCC.

The effect of key DNA methylation in different regions on gene expression in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common cancer with high morbidity and mortality. As we all know, the alteration of DNA methylation has a crucial impact on the occurrence of HCC. However, the mechanism of the effect of DNA methylation in different regions on gene expression is still unclear. Here, by computing and analyzing the distribution of differential methylation in 12 different regions in HCC tissues and adjacent normal tissues, not only the hypermethylation of CpG islands and global hypomethylation were found, but also a stable distribution pattern of differential methylation in HCC was found. Then the correlations between DNA methylations in different regions and gene expressions were calculated, and the diversity of correlations in different regions was determined. The key genes of differential methylation and differential expression related to the survival of HCC patients were obtained by using Cox regression analysis, a four-gene prognostic risk scoring model was constructed, and the prognostic performance was well verified. The regions of the differentially methylated CpG sites corresponding to the four key genes were located and their influences on the expression were analyzed. The results indicate that the promoter, first exon, 5'UTR, sixth exon, N_Shore, and S_Shore hypomethylation promotes the expression of key oncogenes, which together lead to the occurrence of HCC. These results might help to study the role of DNA methylation in HCC and provide potential biomarkers for the diagnosis of HCC.

CCNB1 promotes the development of hepatocellular carcinoma by mediating DNA replication in the cell cycle

In our studies, cyclin B1 (CCNB1) mRNA and protein were overexpressed in hepatocellular carcinoma (HCC) tissues compared with non-HCC tissues. Moreover, CCNB1 was overexpressed in the serum of HCC patients. The expression of CCNB1 was associated with several crucial clinicopathologic characteristics, and the HCC patients with overexpressed CCNB1 had worse overall survival outcomes. In the screening of interactional genes, a total of 266 upregulated co-expression genes, which were positively associated with CCNB1, were selected from the datasets, and 67 downregulated co-expression genes, which were negatively associated with CCNB1, were identified. The key genes might be functionally enriched in DNA replication and the cell cycle pathways. CDC20, CCNA2, PLK1, and FTCD were selected for further research because they were highly connected in the protein-protein interaction networks. Upregulated CDC20, CCNA2, and PLK1 and downregulated FTCD might result in undesirable overall survival outcomes for HCC patients. The univariate Cox analysis results showed that CDC20 and PLK1 might be two independent risk factors, while FTCD might be protective in HCC. Therefore, CCNB1 may participate in the cell cycle of HCC by regulating DNA replication, and CCNB1 may provide a direction for the diagnosis of early-stage HCC and targeted HCC therapy.

Identification of 5 Hub Genes Related to the Early Diagnosis, Tumour Stage, and Poor Outcomes of Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis

BACKGROUND

The majority of primary liver cancers in adults worldwide are hepatocellular carcinomas (HCCs, or hepatomas). Thus, a deep understanding of the underlying mechanisms for the pathogenesis and carcinogenesis of HCC at the molecular level could facilitate the development of novel early diagnostic and therapeutic treatments to improve the approaches and prognosis for HCC patients. Our study elucidates the underlying molecular mechanisms of HBV-HCC development and progression and identifies important genes related to the early diagnosis, tumour stage, and poor outcomes of HCC.

METHODS

GSE55092 and GSE121248 gene expression profiling data were downloaded from the Gene Expression Omnibus (GEO) database. There were 119 HCC samples and 128 nontumour tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). Volcano plots and Venn diagrams were drawn by using the ggplot2 package in R. A heat map was generated by using Heatmapper. By using the clusterProfiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, KM survival curves and ROC curves were generated to validate hub gene expression.

RESULTS

By GO enrichment analysis, 694 DEGs were enriched in the following GO terms: organic acid catabolic process, carboxylic acid catabolic process, carboxylic acid biosynthetic process, collagen-containing extracellular matrix, blood microparticle, condensed chromosome kinetochore, arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. In the KEGG pathway enrichment analysis, DEGs were enriched in arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. By PPI network construction and analysis of hub genes, we selected the top 10 genes, including CDK1, CCNB2, CDC20, BUB1, BUB1B, CCNB1, NDC80, CENPF, MAD2L1, and NUF2. By using TCGA and THPA databases, we found five genes, CDK1, CDC20, CCNB1, CENPF, and MAD2L1, that were related to the early diagnosis, tumour stage, and poor outcomes of HBV-HCC.

CONCLUSIONS

Five abnormally expressed hub genes of HBV-HCC are informative for early diagnosis, tumour stage determination, and poor outcome prediction.

Low expression of KIF20A suppresses cell proliferation, promotes chemosensitivity and is associated with better prognosis in HCC

This study analysed the microarray datasets from Gene Expression Omnibus (GEO) database, and aimed to identify novel potential hub genes associated with the progression of HCC via bioinformatics analysis and experimental validation. The common differentially expressed genes (DEGs) from five GEO datasets were screened using GEO2R tool. The expression and survival analysis of hub genes in HCC were performed using Gene Expression Profiling Interactive Analysis, UALCAN and Kaplan-Meier plotter tools. In vitro functional assays were used to determine the caspase-3, -9, cell proliferation and chemo-sensitivity of HCC cells. A total of 177 common DEGs were identified between normal liver and HCC tissues among these datasets. Functional enrichment and PPI network analysis identified 22 hub genes from the common DEGs. The mRNA expression of 22 hub genes was all significantly up-regulated in HCC tissues compared to that in normal liver tissues. Further survival analysis showed that 10 hub genes predicted poor prognosis of patients with HCC. More importantly, the in vitro functional studies demonstrated that KIF20A knockdown suppressed the HCC cell proliferation and promoted the chemosensitivity of HCC cells to cisplatin and sorafenib. In conclusion, the present study identified a total of 177 common DEGs among 5 GEO microarray datasets and found that 10 hub genes could predict the poor prognosis of patients with HCC using the comprehensive bioinformatics analysis. Furthermore, KIF20A silence suppressed cell proliferation and enhanced chemosensitivity in HCC cells. Further studies may be required to determine the mechanistic role of these hub genes in HCC progression.

Identification of CDC20 as a Novel Biomarker in Diagnosis and Treatment of Wilms Tumor

Objective: Wilms tumor (WT) is a common malignant solid tumor in children. Many tumor biomarkers have been reported; however, there are poorly targetable molecular mechanisms which have been defined in WT. This study aimed to identify the oncogene in WT and explore the potential mechanisms. Methods: Differentially expressed genes (DEGs) in three independent RNA-seq datasets were downloaded from The Cancer Genome Atlas data portal and the Gene Expression Omnibus database (GSE66405 and GSE73209). The common DEGs were then subjected to Gene Ontology enrichment analysis, protein-protein interaction (PPI) network analysis, and gene set enrichment analysis. The protein expression levels of the hub gene were analyzed by immunohistochemical analysis and Western blotting in a 60 WT sample. The univariate Kaplan-Meier analysis for overall survival was performed, and the log-rank test was utilized. A small interfering RNA targeting cell division cycle 20 (CDC20) was transfected into G401 and SK-NEP-1 cell lines. The Cell Counting Kit-8 assay and wound healing assay were used to observe the changes in cell proliferation and migration after transfection. Flow cytometry was used to detect the effect on the cell cycle. Western blot was conducted to study the changes of related functional proteins. Results: We commonly identified 44 upregulation and 272 downregulation differentially expressed genes in three independent RNA-seq datasets. Gene and pathway enrichment analyses of the regulatory networks involving hub genes suggested that cell cycle changes are crucial in WT. The top 15 highly connected genes were found by PPI network analysis. Furthermore, we demonstrated that one candidate biomarker, CDC20, for the diagnosis of WT was detected, and its high expression predicted poor prognosis of WT patients. Moreover, the area under the curve value obtained by receiver operating characteristic curve analysis from paired WT samples was 0.9181. Finally, we found that the suppression of CDC20 inhibited proliferation and migration and resulted in G2/M phase arrest in WT cells. The mechanism may be involved in increasing the protein level of securin, cyclin B1, and cyclin A Conclusion: Our results suggest that CDC20 could serve as a candidate diagnostic and prognostic biomarker for WT, and suppression of CDC20 may be a potential approach for the prevention and treatment of WT.

Identification of Multiple Hub Genes and Pathways in Hepatocellular Carcinoma: A Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.

Application of Molecular Nanoprobes in the Analysis of Differentially Expressed Genes and Prognostic Models of Primary Hepatocellular Carcinoma

Analyzing hub genes related to tumorigenesis based on biological big data has recently become a hotspot in biomedicine. Nanoprobes, nanobodies and theranostic molecules targeting hub genes delivered by nanocarriers have been widely applied in tumor theranostics. Hepatocellular carcinoma (HCC) is one of the most common cancers, with a poor prognosis and high mortality. Identifying hub genes according to the gene expression levels and constructing prognostic signatures related to the onset and outcome of HCC will be of great significance. In this study, the expression profiles of HCC and normal tissue were obtained from the GEO database and analyzed by GEO₂R to identify DEGs. GO terms and KEGG pathways were enriched in DAVID software. The STRING database was consulted to find protein-protein interactions between proteins encoded by the DEGs, which were visualized by Cytoscape. Then, overall survival associated with the hub genes was calculated by the Kaplan-Meier plotter online tool, and verification of the results was carried out on TCGA samples and their corresponding clinical information. A total of 603 DEGs were obtained, of which 479 were upregulated and 124 were downregulated. PPI networks including 603 DEGs and 18 clusters were constructed, of which 7 clusters with MCODE score ≥3 and nodes ≥5 were selected. The 5 genes with the highest degrees of connectivity were identified as hub genes, and a prognostic model was constructed. The expression and prognostic potential of this model was validated on TCGA clinical data. In conclusion, a five-gene signature (TOP2A, PCNA, AURKA, CDC20, CCNB2) overexpressed inHCC was identified, and a prognostic model was constructed. This gene signature may act as a prognostic model for HCC and provide potential targets of nanotechnology.

Genomic Analysis of Vascular Invasion in HCC Reveals Molecular Drivers and Predictive Biomarkers

BACKGROUND AND AIMS

Vascular invasion (VI) is a critical risk factor for HCC recurrence and poor survival. The molecular drivers of vascular invasion in HCC are open for investigation. Deciphering the molecular landscape of invasive HCC will help identify therapeutic targets and noninvasive biomarkers.

APPROACH AND RESULTS

To this end, we undertook this study to evaluate the genomic, transcriptomic, and proteomic profile of tumors with VI using the multiplatform cancer genome atlas (The Cancer Genome Atlas; TCGA) data (n = 373). In the TCGA Liver Hepatocellular Carcinoma cohort, macrovascular invasion was present in 5% (n = 17) of tumors and microvascular invasion in 25% (n = 94) of tumors. Functional pathway analysis revealed that the MYC oncogene was a common upstream regulator of the mRNA, miRNA, and proteomic changes in VI. We performed comparative proteomic analyses of invasive human HCC and MYC-driven murine HCC and identified fibronectin to be a proteomic biomarker of invasive HCC (mouse fibronectin 1 [Fn1], P = 1.7 × 10^-11 ; human FN1, P = 1.5 × 10^-4 ) conserved across the two species. Mechanistically, we show that FN1 promotes the migratory and invasive phenotype of HCC cancer cells. We demonstrate tissue overexpression of fibronectin in human HCC using a large independent cohort of human HCC tissue microarray (n = 153; P < 0.001). Lastly, we showed that plasma fibronectin levels were significantly elevated in patients with HCC (n = 35; mean = 307.7 μg/mL; SEM = 35.9) when compared to cirrhosis (n = 10; mean = 41.8 μg/mL; SEM = 13.3; P < 0.0001).

CONCLUSIONS

Our study evaluates the molecular landscape of tumors with VI, identifying distinct transcriptional, epigenetic, and proteomic changes driven by the MYC oncogene. We show that MYC up-regulates fibronectin expression, which promotes HCC invasiveness. In addition, we identify fibronectin to be a promising noninvasive proteomic biomarker of VI in HCC.

Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

Objective

The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC).

Methods

Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes.

Results

We identified 11 hub genes (CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway.

Conclusions

In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

Identification of CDC20 as an immune infiltration-correlated prognostic biomarker in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a malignancy with a poor prognosis. E3 ubiquitin-protein ligases play essential roles in HCC, such as regulating progression, migration, and metastasis. We aimed to explore a hub E3 ubiquitin-protein ligase gene and verify its association with prognosis and immune cell infiltration in HCC. Cell division cycle 20 (CDC20) was identified as a hub E3 ubiquitin-protein ligase in HCC by determining the intersecting genes in a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) using HCC data from the International Cancer Genome Consortium (ICGC) and the gene list of 919 E3 ubiquitin-protein ligases. DEGs and their correlations with clinicopathological features were explored in The Cancer Genome Atlas (TCGA), ICGC, and Gene Expression Omnibus (GEO) databases via the Wilcoxon signed-rank test. The prognostic value of CDC20 was illustrated by Kaplan-Meier (K-M) curves and Cox regression analyses. Subsequently, the correlation between CDC20 and immune infiltration was demonstrated via the Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA). CDC20 expression was significantly higher in HCC than in normal tissues (all P < 0.05). High CDC20 expression predicted a poor prognosis and might be an independent risk factor in HCC (P < 0.05). Additionally, CDC20 was correlated with the immune infiltration of CD8 + T cells, T cells (general), monocytes, and exhausted T cells. This study reveals the potential prognostic value of CDC20 in HCC and demonstrates that CDC20 may be an immune-associated therapeutic target in HCC because of its correlation with immune infiltration.

Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC.

Methods

The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort.

Results

We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

Connection Between CDC20 Expression and Hepatocellular Carcinoma Prognosis

Background

Hepatocellular carcinoma (HCC) occurs frequently in China, with high morbidity and mortality. Cell division cycle 20 homolog (CDC20) is reportedly related to many cancers. In this study, we discuss a potential link of CDC20 expression to HCC patients’ prognoses.

Material/Methods

Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to assess CDC20 expression in HCC and the paired noncancerous tissues. Chi-square analysis was used to assess potential association of CDC20 expression with clinicopathologic profiles among HCC patients. The overall survival for HCC patients with different CDC20 expressions was assessed using the Kaplan-Meier method. To evaluate the prognostic value for HCC patients, Cox regression analyses were performed.

Results

The expression of CDC20 was elevated among HCC specimens compared with adjacent noncancerous ones (P<0.05). The expression of CDC20 was significantly related to differentiation (P<0.001), tumor node metastasis stage (P<0.001), and lymphatic metastasis (P<0.001). Moreover, HCC patients with high CDC20 expression had dismal overall survival rates compared with low CDC20 expression (P<0.05). CDC20 alone could forecast HCC prognoses according to multivariable Cox regression analysis (hazard ratio=2.354, 95% confidence interval=1.177–4.709, P=0.016).

Conclusions

Overexpressed CDC20 may act as a reliable biomarker for dismal prognoses among HCC patients.

A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma

To further improve the effect of gene modules identification, combining the Newman algorithm in community detection and K-means algorithm framework, a new method of gene module identification, GCNA-Kpca algorithm, was proposed. The core idea of the algorithm was to build a gene co-expression network (GCN) based on gene expression data firstly; Then the Newman algorithm was used to initially identify gene modules based on the topology of GCN, and the number of clusters and clustering centers were determined; Finally the number of clusters and clustering centers were input into the K-means algorithm framework, and the secondary clustering was performed based on the gene expression profile to obtain the final gene modules. The algorithm took into account the role of modularity in the clustering process, and could find the optimal membership module for each gene through multiple iterations. Experimental results showed that the algorithm proposed in this paper had the best performance in error rate, biological significance and CNN classification indicators (Precision, Recall and F-score). The gene module obtained by GCNA-Kpca was used for the task of key gene identification, and these key genes had the highest prognostic significance. Moreover, GCNA-Kpca algorithm was used to identify 10 key genes in hepatocellular carcinoma (HCC): CDC20, CCNB1, EIF4A3, H2AFX, NOP56, RFC4, NOP58, AURKA, PCNA, and FEN1. According to the validation, it was reasonable to speculate that these 10 key genes could be biomarkers for HCC. And NOP56 and NOP58 are key genes for HCC that we discovered for the first time.

Identification of the hub gene BUB1B in hepatocellular carcinoma via bioinformatic analysis and in vitro experiments

Background

Hepatocellular carcinoma (HCC) is one of the most commonly diagnosed cancers and the fourth leading cause of cancer-related deaths in the world. Although the treatment of HCC has made great progress in recent years, the therapeutic effects on HCC are still unsatisfactory due to difficulty in early diagnosis, chemoresistance and high recurrence rate post-surgery.

Methods

In this study, we identified differentially expressed genes (DEGs) based on four Gene Expression Omnibus (GEO) datasets (GSE45267, GSE98383, GSE101685 and GSE112790) between HCC and normal hepatic tissues. A protein–protein interaction (PPI) network was established to identify the central nodes associated with HCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the central nodes were conducted to find the hub genes. The expression levels of the hub genes were validated based on the ONCOMINE and Gene Expression Profiling Interactive Analysis (GEPIA) databases. Additionally, the genetic alterations of the hub genes were evaluated by cBioPortal. The role of the hub genes on the overall survival (OS) and relapse survival (RFS) of HCC patients was evaluated by Kaplan-Meier plotter. At last, the mechanistic role of the hub genes was illustrated by in vitro experiments.

Results

We found the following seven hub genes: BUB1B, CCNB1, CCNB2, CDC20, CDK1, MAD2L1 and RRM2 using integrated bioinformatics analysis. All of the hub genes were significantly upregulated in HCC tissues. And the seven hub genes were associated with the OS and RFS of HCC patients. Finally, in vitro experiments indicated that BUB1B played roles in HCC cell proliferation, migration, invasion, apoptosis and cell cycle by partially affecting mitochondrial functions.

Conclusions

In summary, we identified seven hub genes that were associated with the expression and prognosis of HCC. The mechanistic oncogenic role of BUB1B in HCC was first illustrated. BUB1B might play an important role in HCC and could be potential therapeutic targets for HCC.

Gene networks and transcriptional regulators associated with liver cancer development and progression

BACKGROUND

Treatment options for hepatocellular carcinoma (HCC) are limited, and overall survival is poor. Despite the high frequency of this malignoma, its basic disease mechanisms are poorly understood. Therefore, the aim of this study was to use different methodological approaches and combine the results to improve our knowledge on the development and progression of HCC.

METHODS

Twenty-three HCC samples were characterized by histological, morphometric and cytogenetic analyses, as well as comparative genomic hybridization (aCGH) and genome-wide gene expression followed by a bioinformatic search for potential transcriptional regulators and master regulatory molecules of gene networks.

RESULTS

Histological evaluation revealed low, intermediate and high-grade HCCs, and gene expression analysis split them into two main sets: GE1-HCC and GE2-HCC, with a low and high proliferation gene expression signature, respectively. Array-based comparative genomic hybridization demonstrated a high level of chromosomal instability, with recurrent chromosomal gains of 1q, 6p, 7q, 8q, 11q, 17q, 19p/q and 20q in both HCC groups and losses of 1p, 4q, 6q, 13q and 18q characteristic for GE2-HCC. Gene expression and bioinformatics analyses revealed that different genes and gene regulatory networks underlie the distinct biological features observed in GE1-HCC and GE2-HCC. Besides previously reported dysregulated genes, the current study identified new candidate genes with a putative role in liver cancer, e.g. C1orf35, PAFAH1B3, ZNF219 and others.

CONCLUSION

Analysis of our findings, in accordance with the available published data, argues in favour of the notion that the activated E2F1 signalling pathway, which can be responsible for both inappropriate cell proliferation and initial chromosomal instability, plays a pivotal role in HCC development and progression. A dedifferentiation switch that manifests in exaggerated gene expression changes might be due to turning on transcriptional co-regulators with broad impact on gene expression, e.g. POU2F1 (OCT1) and NFY, as a response to accumulating cell stress during malignant development. Our findings point towards the necessity of different approaches for the treatment of HCC forms with low and high proliferation signatures and provide new candidates for developing appropriate HCC therapies.