Effect of NDC80 in human hepatocellular carcinoma

Role of CENPF and NDC80 in the rehabilitation nursing of hepatocellular carcinoma and cirrhosis: An observational study

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors globally and often develops on the foundation of chronic liver disease or cirrhosis. Cirrhosis is a clinically prevalent chronic progressive liver disease characterized by diffuse liver damage resulting from long-term or repeated actions of 1 or more etiological factors. However, the impact of CENPF and nuclear division cycle 80 (NDC80) genes on rehabilitation nursing of HCC and cirrhosis remains unclear. HCC and cirrhosis datasets GSE63898 and GSE89377 profile files were downloaded from the gene expression omnibus database generated on platforms GPL13667 and GPL6947, respectively. Differentially expressed genes (DEGs) screening, weighted gene co-expression network analysis (WGCNA), construction and analysis of protein-protein interaction (PPI) networks, functional enrichment analysis, gene set enrichment analysis (GSEA), survival analysis, immune infiltration analysis, and comparative toxicogenomics database (CTD) analysis were conducted. Gene expression heatmaps were plotted. miRNAs regulating central DEGs were selected through TargetScan. A total of 626 DEGs were identified. According to gene ontology (GO) analysis, they were primarily enriched in small molecule metabolic processes, drug metabolic processes, binding of identical proteins, and lipid metabolic processes. Kyoto Encyclopedia of Gene and Genome (KEGG) analysis results indicated that the target genes were mainly enriched in metabolic pathways, phagosomes, glycine, serine, and threonine metabolism. The construction and analysis of the PPI network revealed 3 core genes (NDC80, CENPF, RRM2). Gene expression heatmaps showed that core genes (CENPF, NDC80) were highly expressed in HCC and cirrhosis samples. CTD analysis found that 2 genes (CENPF and NDC80) were associated with liver, jaundice, ascites, fever, dyspepsia, and hepatic encephalopathy. CENPF and NDC80 are highly expressed in HCC and cirrhosis, and CENPF and NDC80 might be the biomarkers of rehabilitation nursing of HCC and cirrhosis.

Ubiquitin-conjugating enzyme E2C (UBE2C) is a prognostic indicator for cholangiocarcinoma

Cholangiocarcinoma is the most common malignant bile duct tumor in Southeast Asia. The special location of cholangiocarcinoma leads to it being difficult to diagnose. Currently, the progress in clinical prognosis outcomes remains abysmal owing to the lack of definitive diagnostic criteria. Therefore, uncovering the potential markers for cholangiocarcinoma is a pressing issue. Ubiquitin-conjugating enzyme E2 C (UBE2C) is a critical ubiquitination enzyme; it is involved in the tumorigenesis of various malignancies and affects the patient's prognosis. However, there is currently no relevant literature to indicate whether UBE2C is related to the clinical survival outcome of cholangiocarcinoma patients. In this report, we mined the published cholangiocarcinoma transcriptome data set (GSE26566), compared it with the ubiquitination-associated gene (GO:0016567), and identified that UBE2C was highly expressed in cholangiocarcinoma tumor tissue. Moreover, high expression of UBE2C was markedly correlated with surgical margin, primary tumor, histological variants, and histological grade. More specifically, high expression of UBE2C was negatively associated with overall survival, disease-specific survival, local recurrence-free survival, and metastasis-free survival in patients with cholangiocarcinoma. Our findings demonstrate that UBE2C may provide a potential therapeutic marker and prognostic factor for cholangiocarcinoma patients.

DLAT is a promising prognostic marker and therapeutic target for hepatocellular carcinoma: a comprehensive study based on public databases

Cuproptosis is a new mechanism of cell death that differs from previously identified regulatory cell death mechanisms. Cuproptosis induction holds promise as a new tumour treatment. Therefore, we investigated the value of cuproptosis-related genes in the management of hepatocellular carcinoma (HCC). The cuproptosis-related gene Dihydrolipoamide S-Acetyltransferase (DLAT) were significantly upregulated in liver cancer tissues. High levels of DLAT were an independent prognostic factor for shorter overallsurvival (OS) time. DLAT and its related genes were mainly involved in cell metabolism, tumor progression and immune regulation. DLAT was significantly associated with the level of immune cell infiltration and immune checkpoints in HCC. HCC with high DLAT expression was predicted to be more sensitive to sorafenib treatment. The risk prognostic signature established based on DLAT and its related genes had a good prognostic value. The cuproptosis-related gene DLAT is a promising independent prognostic marker and therapeutic target in HCC. The new prognostic signature can effectively predict the prognosis of HCC patients.

Bioinformatics Identification of Regulatory Genes and Mechanism Related to Hypoxia-Induced PD-L1 Inhibitor Resistance in Hepatocellular Carcinoma

The combination of a PD-L1 inhibitor and an anti-angiogenic agent has become the new reference standard in the first-line treatment of non-excisable hepatocellular carcinoma (HCC) due to the survival advantage, but its objective response rate remains low at 36%. Evidence shows that PD-L1 inhibitor resistance is attributed to hypoxic tumor microenvironment. In this study, we performed bioinformatics analysis to identify genes and the underlying mechanisms that improve the efficacy of PD-L1 inhibition. Two public datasets of gene expression profiles, (1) HCC tumor versus adjacent normal tissue (N = 214) and (2) normoxia versus anoxia of HepG2 cells (N = 6), were collected from Gene Expression Omnibus (GEO) database. We identified HCC-signature and hypoxia-related genes, using differential expression analysis, and their 52 overlapping genes. Of these 52 genes, 14 PD-L1 regulator genes were further identified through the multiple regression analysis of TCGA-LIHC dataset (N = 371), and 10 hub genes were indicated in the protein-protein interaction (PPI) network. It was found that POLE2, GABARAPL1, PIK3R1, NDC80, and TPX2 play critical roles in the response and overall survival in cancer patients under PD-L1 inhibitor treatment. Our study provides new insights and potential biomarkers to enhance the immunotherapeutic role of PD-L1 inhibitors in HCC, which can help in exploring new therapeutic strategies.

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 of core genes as potential biomarkers for predicting progression and prognosis in glioblastoma

Background: Glioblastoma is a common malignant neuroepithelial neoplasm with poor clinical outcomes and limited treatment options. It is extremely important to search and confirm diverse hub genes that are effective in the advance and prediction of glioblastoma.

Methods: We analyzed GSE50161, GSE4290, and GSE68848, the three microarray datasets retrieved from the GEO database. GO function and KEGG pathway enrichment analyses for differentially expressed genes (DEGs) were performed using DAVID. The PPI network of the DEGs was analyzed using the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. Hub genes were identified through the PPI network and a robust rank aggregation method. The Cancer Genome Atlas (TCGA) and the Oncomine database were used to validate the hub genes. In addition, a survival curve analysis was conducted to verify the correlation between the expression of hub genes and patient prognosis. Human glioblastoma cells and normal cells were collected, and then RT-PCR, Western blot, and immunofluorescence were conducted to validate the expression of the NDC80 gene. A cell proliferation assay was used to detect the proliferation of glioma cells. The effects of NDC80 expression on migration and invasion of GBM cell lines were evaluated by conducting scratch and transwell assays.

Results: A total of 716 DEGs were common to all three microarray datasets, which included 188 upregulated DEGs and 528 downregulated DEGs. Furthermore, we found that among the common DEGs, 10 hub genes showed a high degree of connectivity. The expression of the 10 hub genes in TCGA and the Oncomine database was significantly overexpressed in glioblastoma compared with normal genes. Additionally, the survival analysis showed that the patients with low expression of six genes (BIR5C, CDC20, NDC80, CDK1, TOP2A, and MELK) had a significantly favorable prognosis (p < 0.01). We discovered that NDC80, which has been shown to be important in other cancers, also has an important role in malignant gliomas. The RT-PCR, Western blot, and immunofluorescence results showed that the expression level of NDC80 was significantly higher in human glioblastoma cells than in normal cells. Moreover, we identified that NDC80 increased the proliferation and invasion abilities of human glioblastoma cells.

Conclusion: The six genes identified here may be utilized to form a panel of disease progression and predictive biomarkers of glioblastoma for clinical purposes. NDC80, one of the six genes, was discovered to have a potentially important role in GBM, a finding that needs to be further studied.

A TCR mimic CAR T cell specific for NDC80 is broadly reactive with solid tumors and hematologic malignancies

Target identification for chimeric antigen receptor (CAR) T-cell therapies remains challenging due to the limited repertoire of tumor-specific surface proteins. Intracellular proteins presented in the context of cell surface HLA provide a wide pool of potential antigens targetable through T-cell receptor mimic antibodies. Mass spectrometry (MS) of HLA ligands from 8 hematologic and nonhematologic cancer cell lines identified a shared, non-immunogenic, HLA-A*02-restricted ligand (ALNEQIARL) derived from the kinetochore-associated NDC80 gene. CAR T cells directed against the ALNEQIARL:HLA-A*02 complex exhibited high sensitivity and specificity for recognition and killing of multiple cancer types, especially those of hematologic origin, and were efficacious in mouse models against a human leukemia and a solid tumor. In contrast, no toxicities toward resting or activated healthy leukocytes as well as hematopoietic stem cells were observed. This shows how MS can inform the design of broadly reactive therapeutic T-cell receptor mimic CAR T-cell therapies that can target multiple cancer types currently not druggable by small molecules, conventional CAR T cells, T cells, or antibodies.

LINC02154 promotes the proliferation and metastasis of hepatocellular carcinoma by enhancing SPC24 promoter activity and activating the PI3K-AKT signaling pathway

BACKGROUND

Abnormal expression of long non-coding RNAs (lncRNAs) has been associated with the initiation and progression of hepatocellular carcinoma but, as yet, the clinicopathologic significance and potential role of Linc02154 in HCC remains to be determined. Here, we aimed to investigate the potential role and mode of action of Linc02154 in HCC.

METHODS

The expression of Linc02154 in 20 pairs of HCC/normal tissues and 7 HCC cell lines was detected by qRT-PCR. The localization of Linc02154 in HCC cells was detected using fluorescence in situ hybridization and nuclear-plasma separation assays. MTS, EdU incorporation, colony formation, flow cytometry, scratch wound-healing and transwell assays were performed to assess the role of Linc02154 in HCC cell proliferation, migration and invasion in vitro, and BALB/c nude mice xenografts were used to evaluate its role in vivo. RNA sequencing and Western blotting were used to evaluate the regulatory effect of Linc02154 on SPC24 gene expression. A dual-luciferase reporter assay was used to assess a putative interaction of Linc02154 with the SPC24 promoter.

RESULTS

We identified a new lncRNA, Linc02154, that is highly expressed in HCC cells and tissues of patients with a poor overall survival. Functional experiments revealed that exogenous Linc02154 expression in MHCC-97H and SK-Hep1 cells promoted their proliferation, migration and invasion in vitro and their tumorigenesis in vivo. Using a dual luciferase reporter assay we found that Linc02154 can enhance SPC24 promoter (-500 bp ~ -1000 region) activity. Exogenous over-expression of Linc02154 led to up-regulation of SPC24 by activating PI3K/AKT and its downstream signals, including cell cycle progression and EMT-associated gene expression.

CONCLUSION

Our data suggest that Linc02154 may serve as a valuable biomarker of HCC and as a potential therapeutic target.

The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

NDC80 Enhances Cisplatin-resistance in Triple-negative Breast Cancer

BACKGROUNDS

Chemotherapy is a standard systemic treatment option for triple-negative breast cancer (TNBC). Cisplatin has been used to treat TNBC, but frequently leads to cisplatin resistance in patients. The aim of our study was to investigate cisplatin-resistant mechanism in TNBC.

MATERIALS AND METHODS

To identify the potential genes and pathways relative to cisplatin resistance, GSE103115 data were analyzed by the Limma package and Gene set enrichment analysis (GSEA). TNBC data from TCGA, GSE76250 and GSE115275 datasets were used to calculate NDC80 expression. Immunohistochemistry detected NDC80 protein expression in TNBC tissues from patients before and after cisplatin treatment. After expose to cisplatin treatment, the viability and proliferation of TNBC cells were measured by CCK-8 and colony formation assays, respectively.

RESULTS

NDC80 was regarded as a cisplatin-resistant gene because after cisplatin treatment NDC80 was downregulated in cisplatin-sensitive cells but was upregulated in cisplatin-resistant cells. NDC80 was over-expressed in TNBC tissues compared to normal tissues. Furthermore, NDC80 expression in TNBC patients was increased after cisplatin treatment. Cisplatin-sensitive TNBC patients showed lower NDC80 expression than cisplatin-resistant patients. Additionally, NDC80 expression was correlated with clinical stages, tumor size and chemotherapy of TNBC patients. Moreover, NDC80 overexpression promoted the viability and proliferation of TNBC cells and enhanced the cells resistance to cisplatin. The potential pathways relative to cisplatin resistance were obtained, such as p53 signaling pathway and Oxidative phosphorylation.

CONCLUSION

These findings provide new insights for understanding the mechanism of cisplatin resistance in TNBC, and NDC80 may be a potential therapeutic target for TNBC treatment.

Characterization of diagnostic and prognostic significance of cell cycle-linked genes in hepatocellular carcinoma

Background

The high degree of heterogeneity of hepatocellular carcinoma (HCC) imposes a significant challenge to predict the prognosis. Currently, increasing evidence has indicated that cell cycle-linked genes are strongly linked to occurrence and progress of HCC. Herein, we purposed to create a prediction model on the basis of cell cycle-linked genes.

Methods

The transcriptome along with clinicopathological data abstracted from The Cancer Genome Atlas (TCGA) were used as a training cohort. Lasso regression analysis was employed to create a prediction model in TCGA cohort. The data of samples obtained from the International Cancer Genome Consortium (ICGC) data resource were applied in the verification of the model. A series of bioinformatics analyzed the relationship of the risk signature with overall survival (OS), biological function, and clinicopathological features.

Results

Six cell cycle-linked genes (PLK1, CDC20, HSP90AA1, CHEK1, HDAC1, and NDC80) were chosen to create the prognostic model, demonstrating a good prognostic capacity. Further analyses indicated that the model could independently assess the OS of HCC patients. A single-sample gene set enrichment analysis (ssGSEA) indicated that the risk signature was remarkably linked to immune status. Additionally, there was a remarkable association of the risk signature with TP53 mutation frequency, as well as immune checkpoint molecule expression levels.

Conclusions

We created a prediction model using six cell cycle-linked genes to predict HCC prognosis. The six genes are expected to be novel markers for HCC diagnosis, as well as treatment.

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.

Impact of KMN network genes on progression and prognosis of non-small cell lung cancer

The Knl1-Mis12-Ndc80 (KMN) network genes (including KNL, MIS12 and NDC80 complexes) encode a highly conserved network of protein complexes that act in cell mitosis. In recent years, multiple studies revealed that KMN network genes also play a vital role in tumor appearance and growth. However, the role of the KMN gene network in non-small cell lung cancer (NSCLC) remains unknown. In this study, we analyzed the effects of KMN genes expression and clinical phenotype in patients with lung adenocarcinoma (LUAD). The expression of KMN network genes and related clinical information was extracted from The Cancer Genome Atlas. The samples were classified into cluster I and II by consistent clustering. We analyzed the gene distribution by principal component analysis, and the potential risk characteristics were analyzed using the least absolute shrinkage and selection operator Cox regression algorithm. Univariate and multivariate Cox regression analyses were used to analyze the clinical information. The Database for Annotation, Visualization, and Integrated Discovery, Gene MANIA and gene set enrichment analysis were used to analyze function and correlation among genes of the KMN network. The expression levels of nine out of ten KMN genes were significantly up-regulated in LUAD and were associated with poor overall survival (OS). Higher expression of NDC80 and KNL1 was related to low OS in both univariate and multivariate analyses. According to two independent prognostic KMN network genes (KNL1 and NDC80), a risk signature was established to predict the prognosis of patients with LUAD. Additionally, the genes NDC80 and KNL1 were considerably enriched in pathways associated with signaling pathways, biological processes, and the cell cycle. The results indicate that KMN network genes are intimately related to lung adenocarcinoma. KMN network genes are involved in the malignant process of LUAD. Assessment of NDC80 and KNL1 might be helpful for prognostic stratification and treatment strategy development.

Bioinformatic Evidence Reveals that Cell Cycle Correlated Genes Drive the Communication between Tumor Cells and the Tumor Microenvironment and Impact the Outcomes of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is an aggressive cancer type with poor prognosis; thus, there is especially necessary and urgent to screen potential prognostic biomarkers for early diagnosis and novel therapeutic targets. In this study, we downloaded target data sets from the GEO database, and obtained codifferentially expressed genes using the limma R package and identified key genes through the protein–protein interaction network and molecular modules, and performed GO and KEGG pathway analyses for key genes via the clusterProfiler package and further determined their correlations with clinicopathological features using the Oncomine database. Survival analysis was completed in the GEPIA and the Kaplan–Meier plotter database. Finally, correlations between key genes, cell types infiltrated in the tumor microenvironment (TME), and hypoxic signatures were explored based on the TIMER database. From the results, 11 key genes related to the cell cycle were determined, and high levels of these key genes' expression were focused on advanced and higher grade status HCC patients, as well as in samples of TP53 mutation and vascular invasion. Besides, the 11 key genes were significantly associated with poor prognosis of HCC and also were positively related to the infiltration level of MDSCs in the TME and the HIF1A and VEGFA of hypoxic signatures, but a negative correlation was found with endothelial cells (ECs) and hematopoietic stem cells. The result determined that 11 key genes (RRM2, NDC80, ECT2, CCNB1, ASPM, CDK1, PRC1, KIF20A, DTL, TOP2A, and PBK) could play a vital role in the pathogenesis of HCC, drive the communication between tumor cells and the TME, and act as probably promising diagnostic, therapeutic, and prognostic biomarkers in HCC patients.

[CDK1, CCNB1 and NDC80 are associated with prognosis and progression of hepatitis B virus-associated hepatocellular carcinoma: a bioinformatic analysis]

OBJECTIVE

To identify the key genes involved in the transformation of hepatitis B virus (HBV) into hepatocellular carcinoma (HCC) and explore the underlying molecular mechanisms.

METHODS

We analyzed the mRNA microarray data of 119 HBV-related HCC tissues and 252 HBV-related non-tumor tissues in GSE55092, GSE84044 and GSE121248 from the GEO database, and the "sva" R package was used to remove the batch effects. Integration analysis was performed to identify the differentially expressed genes (DEGs) in HBV-related liver cancer and liver tissues with HBV infection. The significant DEGs were functionally annotated using GO and KEGG analyses, and the most important modules and hub genes were explored with STRING analysis. Kaplan-Meier and Oncomine databases were used to verify the HCC gene expression data in the TCGA database to explore the correlations of the hub genes with the occurrence, progression and prognosis of HCC. We also examined the expressions of the hub genes in 17 pairs of surgical specimens of HCC and adjacent tissues using RT-qPCR.

RESULTS

We identified a total of 121 DEGs and 3 genetic markers in HCC (P < 0.01). These DEGs included cyclin1 (CDK1), cyclin B1 (CCNB1), and nuclear division cycle 80 (NDC80), which participated in cell cycle, pyrimidine metabolism and DNA replication and were highly correlated (P < 0.05). Analysis of the UALCAN database confirmed high expressions of these 3 genes in HCC tissues, which were correlated with a low survival rate of the patients, as shown by Kaplan-Meier analysis of the prognostic data from the UALCAN database. CDK1, CCNB1 and NDC80 were all correlated with the clinical grading of HCC (P < 0.05). The results of RT-qPCR on the surgical specimens verified significantly higher expressions of CDK1, CCNB1 and NDC80 mRNA in HCC tissues than in the adjacent tissues.

CONCLUSION

CDK1, CCNB1 and NDC80 genes can be used as prognostic markers of HBV-related HCC and may serve as potential targets in preclinical studies and clinical treatment of HCC.

A pharmacophore-based classification better predicts the outcomes of HER2-negative breast cancer patients receiving the anthracycline- and/or taxane-based neoadjuvant chemotherapy

AIMS

Prognosis of patients for human epidermal growth factor receptor 2 (HER2)-negative breast cancer post neoadjuvant chemotherapy is not well understood. The aim of this study was to develop a novel pharmacophore-based signature to better classify and predict the risk of HER2-negative patients after anthracycline-and/or taxane-based neoadjuvant chemotherapy (NACT).

MAIN METHODS

Anthracycline and taxane pharmacophore-based genes were obtained from PharmMapper. Drug-targeted genes (DTG) related clinical and bioinformatic analyses were undertaken in four GEO datasets.

KEY FINDINGS

We used 12 genes from the pharmacophore to develop a DTG score (DTG-S). The DTG-S classification exhibited significant prognostic ability with respect to disease free survival (DFS) for HER2-negative patients who receive at least one type of neoadjuvant chemotherapy that included anthracycline and/or taxane. DTG-S associated with a high predictive ability for pathological complete response (pCR) as well as for prognosis of breast cancer. Using the DTG-S classification in other prediction models may improve the reclassification accuracy for DFS. Combining the DTG-S with other clinicopathological factors may further improve its predictive ability of patients' outcomes. Gene ontology and KEGG pathway analysis showed that the biological processes of DTG-S high group were associated with the cell cycle, cell migration, and cell signal transduction pathways. Targeted drug analysis shows that some CDK inhibitors and PI3K-AKT pathway inhibitors may be useful for high DTG-S patients.

SIGNIFICANCE

The DTG-S classification adds prognostic and predictive information to classical parameters for HER2-negative patients who receive anthracycline-and/or taxane-based NACT, which could improve the patients' risk stratification and may help guide adjuvant treatment.

Construction of Gene Modules and Analysis of Prognostic Biomarkers for Cervical Cancer by Weighted Gene Co-Expression Network Analysis

Background

Despite advances in the understanding of neoplasm, patients with cervical cancer still have a poor prognosis. Identifying prognostic markers of cervical cancer may enable early detection of recurrence and more effective treatment.

Methods

Gene expression profiling data were acquired from the Gene Expression Omnibus database. After data normalization, genes with large variation were screened out. Next, we built co-expression modules by using weighted gene co-expression network analysis to investigate the relationship between the modules and clinical traits related to cervical cancer progression. Functional enrichment analysis was also applied on these co-expressed genes. We integrated the genes into a human protein-protein interaction (PPI) network to expand seed genes and build a co-expression network. For further analysis of the dataset, the Cancer Genome Atlas (TCGA) database was used to identify seed genes and their correlation to cervical cancer prognosis. Verification was further conducted by qPCR and the Human Protein Atlas (HPA) database to measure the expression of hub genes.

Results

Using WGCNA, we identified 25 co-expression modules from 10,016 genes in 128 human cervical cancer samples. After functional enrichment analysis, the magenta, brown, and darkred modules were selected as the three most correlated modules for cancer progression. Additionally, seed genes in the three modules were combined with a PPI network to identify 31 tumor-specific genes. Hierarchical clustering and Gepia results indicated that the expression quantity of hub genes NDC80, TIPIN, MCM3, MCM6, POLA1, and PRC1 may determine the prognosis of cervical cancer. Finally, TIPIN and POLA1 were further filtered by a LASSO model. In addition, their expression was identified by immunohistochemistry in HPA database as well as a biological experiment.

Conclusion

Our research provides a co-expression network of gene modules and identifies TIPIN and POLA1 as stable potential prognostic biomarkers for cervical cancer.

Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer and has poor outcomes. However, the potential molecular biological process underpinning the occurrence and development of HCC is still largely unknown. The purpose of this study was to identify the core genes related to HCC and explore their potential molecular events using bioinformatics methods. HCC-related expression profiles GSE25097 and GSE84005 were selected from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) between 306 HCC tissues and 281 corresponding noncancerous tissues were identified using GEO2R online tools. The protein-protein interaction network (PPIN) was constructed and visualized using the STRING database. Gene Ontology (GO) and KEGG pathway enrichment analyses of the DEGs were carried out using DAVID 6.8 and KOBAS 3.0. Additionally, module analysis and centrality parameter analysis were performed by Cytoscape. The expression differences of key genes in normal hepatocyte cells and HCC cells were verified by quantitative real-time fluorescence polymerase chain reaction (qRT-PCR). Additionally, survival analysis of key genes was performed by GEPIA. Our results showed that a total of 291 DEGs were identified including 99 upregulated genes and 192 downregulated genes. Our results showed that the PPIN of HCC was made up of 287 nodes and 2527 edges. GO analysis showed that these genes were mainly enriched in the molecular function of protein binding. Additionally, KEGG pathway analysis also revealed that DEGs were mainly involved in the metabolic, cell cycle, and chemical carcinogenesis pathways. Interestingly, a significant module with high centrality features including 10 key genes was found. Among these, CDK1, NDC80, HMMR, CDKN3, and PTTG1, which were only upregulated in HCC patients, have attracted much attention. Furthermore, qRT-PCR also confirmed the upregulation of these five key genes in the normal human hepatocyte cell line (HL-7702) and HCC cell lines (SMMC-7721, MHCC-97L, and MHCC-97H); patients with upregulated expression of these five key genes had significantly poorer survival and prognosis. CDK1, NDC80, HMMR, CDKN3, and PTTG1 can be used as molecular markers for HCC. This finding provides potential strategies for clinical diagnosis, accurate treatment, and prognosis analysis of liver cancer.

Three novel circRNAs upregulated in tissue and plasma from hepatocellular carcinoma patients and their regulatory network

Background

The regulatory roles of circular RNAs (circRNAs) in tumorigenesis have attracted increasing attention. However, novel circRNAs with the potential to be used as serum/plasma biomarkers and their regulatory mechanism in the pathogenesis of hepatocellular carcinoma (HCC) remain explored.

Methods

CircRNA expression profiles of tumor tissues and plasma samples from HCC patients were compiled and jointly analyzed. CircRNA–miRNA–mRNA interactions were predicted by bioinformatics tools. The expression of interacting miRNAs and mRNA was verified in independent datasets. Survival analysis and pathway enrichment analysis were conducted on hub genes.

Results

We identified three significantly up-regulated circRNAs (hsa_circ_0009910, hsa_circ_0049783, and hsa_circ_0089172) both in HCC tissues and plasma samples. Two of them were validated to be indeed circular and could be excreted from hepatoma cells. We further revealed four miRNAs (hsa-miR-455-5p, hsa-miR-615-3p, hsa-miR-18a-3p, hsa-miR-4524a-3p) that targeting circRNAs and expressed in human HCC samples, and 95 mRNAs targeted by miRNAs and significantly up-regulated in two HCC cohorts. A protein-protein interaction network revealed 19 hub genes, 12 of them (MCM6, CCNB1, CDC20, NDC80, ZWINT, ASPM, CENPU, MCM3, MCM5, ECT2, CDC7, and DLGAP5) were associated with reduced survival in two HCC cohorts. KEGG, Reactome, and Wikipathway enrichment analysis indicated that the hub genes mainly functioned in DNA replication and cell cycle.

Conclusions

Our study uncovers three novel deregulated circRNAs in tumor and plasma from HCC patients and provides an insight into the pathogenesis from the circRNA–miRNA–mRNA regulatory network.

Involvement of NEK2 and its interaction with NDC80 and CEP250 in hepatocellular carcinoma

BACKGROUND

NEK2 has an established involvement in hepatocellular carcinoma (HCC) but the roles of NEK2 and its interacting proteins in HCC have not been systematically explored.

METHODS

This study examined NEK2 and its interacting proteins in HCC based on multiple databases.

RESULTS

NEK2 mRNA was highly expressed in HCC tissues compared with normal liver tissues. The survival of HCC patients with high NEK2 mRNA expression was shorter than those with low expression. MAD1L1, CEP250, MAPK1, NDC80, PPP1CA, PPP1R2 and NEK11 were the interacting proteins of NEK2. Among them, NDC80 and CEP250 were the key interacting proteins of NEK2. Mitotic prometaphase may be the key pathway that NEK2 and its interacting proteins contributed to HCC pathogenesis. NEK2, NDC80 and CEP250 mRNAs were highly expressed in HCC tissues compared with normal liver tissues. The mRNA levels of NEK2 were positively correlated with those of NDC80 or CEP250. Univariate regression showed that NEK2, NDC80 and CEP250 mRNA expressions were significantly associated with HCC patients' survival. Multivariate regression showed that NDC80 mRNA expression was an independent predictor for HCC patients' survival. Methylations and genetic alterations of NEK2, NDC80 and CEP250 were observed in HCC samples. The alterations of NEK2, NDC80 and CEP250 genes were co-occurrence. Patients with high mRNA expression and genetic alterations of NEK2, NDC80 and CEP250 had poor prognosis.

CONCLUSIONS

NEK2 and its interacting proteins NDC80 and CEP250 play important roles in HCC development and progression and thus may be potentially used as biomarkers and therapeutic targets of HCC.

Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma

Background

The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC).

Methods

The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA).

Results

A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival.

Conclusion

Our study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.

Identification of potential crucial genes associated with the pathogenesis and prognosis of liver hepatocellular carcinoma

AIMS

Liver hepatocellular carcinoma (LIHC) is the main manifestation of primary liver cancer, with low survival rate and poor prognosis. Medical decision-making process of LIHC is so complex that new biomarkers for diagnosis and prognosis have yet to be explored, this study aimed to identify the genes involved in the pathophysiology of LIHC and biomarkers that can be used to predict the prognosis of LIHC.

METHODS

Six Gene Expression Omnibus (GEO) datasets selected from GEO were screened and integrated to find out the differential expression genes (DEGs) obtained from LIHC and normal hepatic tissues. The Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis of DEGs was implemented by DAVID. The Protein-protein interaction network was performed via STRING. In addition, Cox regression model was used to construct a gene prognostic signature.

RESULTS

We ascertained 10 hub genes, nine of them (CDK1, CDC20, CCNB1, Thymidylate synthetase, Nuclear division cycle80, NUF2, MAD2L1, CCNA2 and BIRC5) as biomarkers of progression in LIHC patients. We also build a six gene prognosis signature (SOCS2, GAS2L3, NLRP5, TAF3, UTP11 and GAGE2A), which can be implemented to predict over survival effectively.

CONCLUSIONS

We revealed promising genes that may participate in the pathophysiology of LIHC, and found available biomarkers for LIHC prognosis prediction, which were significant for researchers to further understand the molecular basis of LIHC and direct the synthesis medicine of LIHC.

Bioinformatic analysis revealing mitotic spindle assembly regulated NDC80 and MAD2L1 as prognostic biomarkers in non-small cell lung cancer development

Background

Lung cancer has been the leading cause of tumor related death, and 80% ~ 85% of it is non-small cell lung cancer (NSCLC). Even with the rising molecular targeted therapies, for example EGFR, ROS1 and ALK, the treatment is still challenging. The study is to identify credible responsible genes during the development of NSCLC using bioinformatic analysis, developing new prognostic biomarkers and potential gene targets to the disease.

Methods

Firstly, three genes expression profiles GSE44077, GSE18842 and GSE33532 were picked from Gene Expression Omnibus (GEO) to analyze the genes with different expression level (GDEs) between NSCLC and normal lung samples, and the cellular location, molecular function and the biology pathways the GDEs enriched in were analyzed. Then, gene function modules of GDEs were explored based on the protein-protein interaction network (PPI), and the top module which contains most genes was identified, followed by containing genes annotation and survival analysis. Moreover, multivariate cox regression analysis was performed in addition to the Kaplan meier survival to narrow down the key genes scale. Further, the clinical pathological features of the picked key genes were explored using TCGA data.

Results

Three GEO profiles shared a total of 664 GDEs, including 232 up-regulated and 432 down-regulated genes. Based on the GDEs PPI network, the top function module containing a total of 69 genes was identified, and 31 of 69 genes were mitotic cell cycle regulation related. And survival analysis of the 31 genes revealed that 17/31 genes statistical significantly related to NSCLC overall survival, including 4 spindle assembly checkpoints, namely NDC80, BUB1B, MAD2L1 and AURKA. Further, multivariate cox regression analysis identified NDC80 and MAD2L1 as independent prognostic indicators in lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) respectively. Interestingly, pearson correlation analysis indicated strong connection between the four genes NDC80, BUB1B, MAD2L1 and AURKA, and their clinical pathological features were addressed.

Conclusions

Using bioinformatic analysis of GEO combined with TCGA data, we revealed two independent prognostic indicators in LUAD and LUSC respectively and analyzed their clinical features. However, more detailed experiments and clinical trials are needed to verify their drug targets role in clinical medical use.

Integrated Bioinformatic Analysis Identifies Networks and Promising Biomarkers for Hepatitis B Virus-Related Hepatocellular Carcinoma

Chronic infection with hepatitis B virus (HBV) has long been recognized as a dominant hazard factor for hepatocellular carcinoma (HCC) and accounts for at least half of HCC instances globally. However, the underlying molecular mechanism of HBV-linked HCC has not been completely elucidated. Here, three microarray datasets, totally containing 170 tumoral samples and 181 adjacent normal tissues from the liver of patients suffering from HBV-related HCC assembled from the Gene Expression Omnibus (GEO) database, were subjected to integrated analysis of differentially expressed genes (DEGs). Subsequently, the analysis of function and pathway enrichment as well as the protein-protein interaction network (PPI) was performed. The ten hub genes screened out from the PPI network were further subjected to expression profile and survival analysis. Overall, 329 DEGs (67 upregulated and 262 downregulated) were identified. Ten DEGs with the highest degree of connectivity included cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), PDZ-binding kinase (PBK), abnormal spindle microtubule assembly (ASPM), nuclear division cycle 80 (NDC80), aurora kinase A (AURKA), targeting protein for xenopus kinesin-like protein 2 (TPX2), kinesin family member 2C (KIF2C), and centromere protein F (CENPF). Kaplan-Meier analysis unveiled that overexpression levels of KIF2C and TPX2 were relevant to both the poor overall survival and relapse-free survival. In summary, the hub genes validated in the present study may provide promising targets for the diagnosis, prognosis, and therapy of HBV-associated HCC. Additionally, our work uncovers various crucial biological components (e.g., extracellular exosome) and signaling pathways that participate in the progression of HCC induced by HBV, serving comprehensive knowledge of the mechanisms regarding HBV-related HCC.

Bioinformatics analysis reveals meaningful markers and outcome predictors in HBV-associated hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of malignant neoplasm of the liver with high morbidity and mortality. Extensive research into the pathology of HCC has been performed; however, the molecular mechanisms underlying the development of hepatitis B virus-associated HCC have remained elusive. Thus, the present study aimed to identify critical genes and pathways associated with the development and progression of HCC. The expression profiles of the GSE121248 dataset were downloaded from the Gene Expression Omnibus database and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses were performed by using the Database for Annotation, Visualization and Integrated Discovery. Subsequently, protein-protein interaction (PPI) networks were constructed for detecting hub genes. In the present study, 1,153 DEGs (777 upregulated and 376 downregulated genes) were identified and the PPI network yielded 15 hub genes. GO analysis revealed that the DEGs were primarily enriched in ‘protein binding’, ‘cytoplasm’ and ‘extracellular exosome’. KEGG analysis indicated that DEGs were accumulated in ‘metabolic pathways’, ‘chemical carcinogenesis’ and ‘fatty acid degradation’. After constructing the PPI network, cyclin-dependent kinase 1, cyclin B1, cyclin A2, mitotic arrest deficient 2 like 1, cyclin B2, DNA topoisomerase IIα, budding uninhibited by benzimidazoles (BUB)1, TTK protein kinase, non-SMC condensin I complex subunit G, NDC80 kinetochore complex component, aurora kinase A, kinesin family member 11, cell division cycle 20, BUB1B and abnormal spindle microtubule assembly were identified as hub genes based on the high degree of connectivity by using Cytoscape software. In addition, overall survival (OS) and disease-free survival (DFS) analyses were performed using the Gene Expression Profiling Interactive Analysis online database, which revealed that the increased expression of all hub genes were associated with poorer OS and DFS outcomes. Receiver operating characteristic curves were constructed using GraphPad prism 7.0 software. The results confirmed that 15 hub genes were able to distinguish HCC form normal tissues. Furthermore, the expression levels of three key genes were analyzed in tumor and normal samples of the Human Protein Atlas database. The present results may provide further insight into the underlying mechanisms of HCC and potential therapeutic targets for the treatment of this disease.

ZWINT: A potential therapeutic biomarker in patients with glioblastoma correlates with cell proliferation and invasion

Glioblastoma (GBM) is the most aggressive primary intracranial tumor in adults. Chemoradiotherapy resistance and recurrence after surgery are the main malignant progression factors, leading to a high mortality rate. Therefore, the exploration of novel biomarkers and molecular mechanisms of GBM is urgent. Differentially expressed genes (DEGs) of GBM were screened in a TCGA dataset. Homo sapiens ZW10 interacting kinetochore protein (ZWINT) was found to be upregulated in GBM, which was confirmed by immunohistochemical staining of a tissue microarray. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. A protein-protein interaction (PPI) network was established by the STRING database, and hub genes were visualized by Cytoscape. The correlation results were verified with the GSE15824 dataset. Bioinformatic analysis confirmed that ZWINT was significantly positively correlated with kinetochore protein NDC80 homolog (NDC80), serine/threonine-protein kinase PLK1 (PLK1) and spindle and kinetochore associated complex subunit 1 (SKA1) and together are involved in regulating mitosis and the cell cycle of GBM. ZWINT expression was knocked down in U251 and U87 MG GBM cells by lentiviral vectors carrying a small hairpin RNA (shRNA) targeting ZWINT. The effect of ZWINT silencing on cell proliferation, invasion and apoptosis was determined by the Celigo assay, MTT assay, Transwell assay, flow cytometry and caspase-3/7 assay in vitro. A subcutaneous xenograft tumor model was established to explore the influence of ZWINT knockdown on GBM growth in vivo. Our preliminary study demonstrated that ZWINT knockdown effectively inhibited proliferation and invasion and induced apoptosis of GBM cells and notably suppressed GBM growth in vivo. Therefore, we speculate that ZWINT may be a potential therapeutic biomarker for GBM, with NDC80 and PLK1 conjointly involved in regulating cell division and the mitotic cell cycle.

Nine hub genes related to the prognosis of HBV-positive hepatocellular carcinoma identified by protein interaction analysis

Background

Hepatocellular carcinoma (HCC) represents the second highest cause of cancer-associated deaths worldwide, and hepatitis B virus (HBV) infection is a major risk factor. Here, we aimed to identify genetic signatures of HBV-positive (HBV⁺) HCC and uncover potential carcinogenic mechanisms.

Methods

Gene expression profiles of 124 HBV-positive samples, including tumor and non-tumor tissues were subjected to bioinformatics analysis. The expression levels of thymidylate synthase (TYMS) and CDC45 in patients’ samples were validated by immunohistochemistry (IHC) and their association with patient survival was assessed by the Kaplan-Meier method.

Results

A total of 666 differentially expressed genes (DEGs) were identified. The 137 upregulated genes were mainly enriched in the cell cycle, P53 signaling pathway, and extracellular matrix-receptor interaction, whereas the 529 downregulated genes were enriched in cytochrome P450 xenobiotic and drug metabolism, and cytokine-cytokine receptor interaction. A total of 15 hub genes were identified from the protein-protein interaction (PPI) network and 10 of them were strongly associated with HBV⁺ HCC. The expression of 9 hub genes (CDK1, NDC80, TYMS, AURKA, FOXM1, CDC45, ZWINT, PBK, and TPX2) was associated with poor overall survival. Validation of TYMS and CDC45 protein expression levels in clinical samples by IHC showed that they were higher in HBV⁺ HCC than in HBV^- HCC or normal tissue and were associated with poor patient survival.

Conclusions

HBV may induce HCC through regulation of host gene expression. Among the hub DEGs identified, 9 key genes could be used as new prognostic biomarkers and treatment targets for HBV⁺ HCC.

Potential therapeutic targets of the nuclear division cycle 80 (NDC80) complexes genes in lung adenocarcinoma

Background: Lung cancer is the most common cancer worldwide, both in terms of the incidence and mortality. NDC80 complex comprising of NDC80, NUF2, SPC24, and SPC25 is a heterotetrameric protein complex located in the outer layer of the kinetochore and plays a critical role in mitosis. This study focuses on the effects of NDC80 complex genes on clinical features and prognosis in lung adenocarcinoma (LUAD). Materials and methods: Expression of NDC80 complex in LUAD and related clinical information was extracted from the TCGA website. NDC80 complex gene functional analysis and correlation analysis was conducted by using DAVID, BiNGO, Gene MANIA, STRING and GSEA. Survival probability was predicted by nomogram. Statistical analysis was used to predict NDC80 complex gene expression on clinical features and prognosis in patients with LUAD. Results: Expression of NDC80, NUF2, SPC24 and SPC25 was significantly elevated in LUAD tumors compared with normal tissues (P < 0.05). These genes showed diagnostic values for LUAD (P < 0.001 for each; area under the curve (AUC), 0.958, 0.968, 0.951, and 0.932 respectively); combinatorial analysis of these genes was more advantageous than single analysis alone (P < 0.001; AUC > 0.900 for each). Expression of both NDC80 and SPC25 correlated with the prognosis of LUAD (P < 0.001; AUC > 0.600 for each). Higher expression of NDC80, NUF2, SPC24 and SPC25 was associated with low overall survival (OS) in univariate analysis. Higher expression of NDC80 and SPC25 was associated with low OS in multivariate analysis. High expression of NDC80 combined with high expression of SPC25 was predictive of poor OS in LUAD in joint analysis. Conclusion: NDC80 complex gene might be an early indicator of diagnosis and prognosis of LUAD. The combined detection of NDC80, NUF2, SPC24 and SPC25 may become a new research direction in LUAD diagnosis and a new target for tumor targeted gene therapy.

Discovery of T-1101 tosylate as a first-in-class clinical candidate for Hec1/Nek2 inhibition in cancer therapy

Highly expressed in cancer 1 (Hec1) plays an essential role in mitosis and is correlated with cancer formation, progression, and survival. Phosphorylation of Hec1 by Nek2 kinase is essential for its mitotic function, thus any disruption of Hec1/Nek2 protein-protein interaction has potential for cancer therapy. We have developed T-1101 tosylate (9j tosylate, 9j formerly known as TAI-95), optimized from 4-aryl-N-pyridinylcarbonyl-2-aminothiazole of scaffold 9 by introducing various C-4' substituents to enhance potency and water solubility, as a first-in-class oral clinical candidate for Hec1 inhibition with potential for cancer therapy. T-1101 has good oral absorption, along with potent in vitro antiproliferative activity (IC₅₀: 14.8-21.5 nM). It can achieve high concentrations in Huh-7 and MDA-MB-231 tumor tissues, and showed promise in antitumor activity in mice bearing human tumor xenografts of liver cancer (Huh-7), as well as of breast cancer (BT474, MDA-MB-231, and MCF7) with oral administration. Oral co-administration of T-1101 halved the dose of sorafenib (25 mg/kg to 12.5 mg/kg) required to exhibit comparable in vivo activity towards Huh-7 xenografts. Cellular events resulting from Hec1/Nek2 inhibition with T-1101 treatment include Nek2 degradation, chromosomal misalignment, and apoptotic cell death. A combination of T-1101 with either of doxorubicin, paclitaxel, and topotecan in select cancer cells also resulted in synergistic effects. Inactivity of T-1101 on non-cancerous cells, a panel of kinases, and hERG demonstrates cancer specificity, target specificity, and cardiac safety, respectively. Subsequent salt screening showed that T-1101 tosylate has good oral AUC (62.5 μM·h), bioavailability (F = 77.4%), and thermal stability. T-1101 tosylate is currently in phase I clinical trials as an orally administered drug for cancer therapy.

Identification of Genes Associated with the Metastasis of Pheochromocytoma/Paraganglioma Based on Weighted Gene Coexpression Network Analysis

Background

Pheochromocytoma/paraganglioma (PCPG) is a benign neuroendocrine neoplasm in most cases, but metastasis and other malignant behaviors can be observed in this tumor. The aim of this study was to identify genes associated with the metastasis of PCPG.

Methods

The Cancer Genome Atlas (TCGA) expression profile data and clinical information were downloaded from the cbioportal, and the weighted gene coexpression network analysis (WGCNA) was conducted. The gene coexpression modules were extracted from the network through the WGCNA package of R software. We further extracted metastasis-related modules of PCPG. Enrichment analysis of Biological Process of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes was carried out for important modules, and survival analysis of hub genes in the modules was performed.

Results

A total of 168 PCPG samples were included in this study. The weighted gene coexpression network was constructed with 5125 genes of the top 25% variance among the 20501 genes obtained from the database. We identified 11 coexpression modules, among which the salmon module was associated with the age of PCPG patients at diagnosis, metastasis, and malignancy of the tumors.

Conclusion

WGCNA was performed to identify the gene coexpression modules and hub genes in the metastasis-related gene module of PCPG. The findings in this study provide a new clue for further study of the mechanisms underlying the PCPG metastasis.

RANK/RANKL Acts as a Protective Factor by Targeting Cholangiocytes in Primary Biliary Cholangitis

BACKGROUND

Primary biliary cholangitis (PBC) is an autoimmune liver disease characterized by the highly selective autoimmune injury of small intrahepatic bile ducts. Studies reported that the cholangiocytes from PBC patients expressed significantly higher levels of both receptor activator of nuclear factor-kappa B (RANK) and its ligand RANKL. However, the accurate role of RANK/RANKL axis in PBC remains unclear.

METHODS

Forty patients with PBC were enrolled according to the inclusion criteria. The biochemical parameters (alkaline phosphatase, ALP; gamma-glutamyltransferase, GGT; alanine aminotransferase, ALT; aspartate transaminase, AST; total bilirubin, TB) were collected at baseline and followed-up after 6 months of treatment with ursodeoxycholic acid (UDCA, 15 mg/kg d). Stages of PBC were diagnosed based on liver biopsy histopathology according to Nakanuma's criteria. RANK expression in hepatic tissues was detected by immunohistochemistry. The cellular immunofluorescence method was used to locate the distribution of RANK in the human intrahepatic biliary epithelial cells (HIBECs) cultured in vitro. HIBECs were treated with RANKL at a concentration of 100 ng/ml or transfected with RANK-overexpressing lentivirus (LV-RANK). CCK-8 assay and cell cycle assay were used to detect the cell proliferation. Real-time PCR was used to detect the expression of IL-6, E-cadherin, VCAM, ICAM-1, TNF-α, and CD80.

RESULTS

RANK expression in liver biopsies from early PBC patients (stage I + stage II) was significantly lower than that from advanced PBC patients (stage III + stage IV) (1.7 ± 0.63 vs. 2.3 ± 0.45 scores, P < 0.05). High-RANK patients seemed to have better response to UDCA than low-RANK patients (88.9% vs. 40.9%, P < 0.05). The baseline biochemical parameters between the two groups were comparable. The decline percentages of ALP and GGT after UDCA treatment were more obvious in high-RANK patients than those in low-RANK patients (53.90% ± 9.82% vs. 23.93% ± 6.24%, P < 0.05; 74.11% ± 7.18% vs. 48.00% ± 8.17%, P < 0.05, respectively). HIBECs proliferation was significantly inhibited after treatment of RANKL or transfection with LV-RANK. Increased expression of IL-6 and E-cadherin was observed in HIBECs treated with RANKL or LV-RANK.

CONCLUSION

The overall hepatic RANK expression was associated with disease severity and biochemical response in PBC patients. Activation of RANK/RANKL signaling pathway inhibited cholangiocytes proliferation in vitro. Our study suggested that RANK/RANKL pathway might be a potential target of immunotherapy of PBC based on its involvement in the occurrence and development of the disease.

Bioinformatics Analysis based on Multiple Databases Identifies Hub Genes Associated with Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common liver cancer and the mechanisms of hepatocarcinogenesis remain elusive.

Objective

This study aims to mine hub genes associated with HCC using multiple databases.

Methods

Data sets GSE45267, GSE60502, GSE74656 were downloaded from GEO database. Differentially expressed genes (DEGs) between HCC and control in each set were identified by limma software. The GO term and KEGG pathway enrichment of the DEGs aggregated in the datasets (aggregated DEGs) were analyzed using DAVID and KOBAS 3.0 databases. Protein-protein interaction (PPI) network of the aggregated DEGs was constructed using STRING database. GSEA software was used to verify the biological process. Association between hub genes and HCC prognosis was analyzed using patients' information from TCGA database by survminer R package.

Results

From GSE45267, GSE60502 and GSE74656, 7583, 2349, and 553 DEGs were identified respectively. A total of 221 aggregated DEGs, which were mainly enriched in 109 GO terms and 29 KEGG pathways, were identified. Cell cycle phase, mitotic cell cycle, cell division, nuclear division and mitosis were the most significant GO terms. Metabolic pathways, cell cycle, chemical carcinogenesis, retinol metabolism and fatty acid degradation were the main KEGG pathways. Nine hub genes (TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK) were selected by PPI network and all of them were associated with prognosis of HCC patients.

Conclusion

TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK were hub genes in HCC, which may be potential biomarkers of HCC and targets of HCC therapy.

Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

This study aimed to select the feature genes of hepatocellular carcinoma (HCC) with the Fisher score algorithm and to identify hub genes with the Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to examine the enrichment of terms. Gene set enrichment analysis (GSEA) was used to identify the classes of genes that are overrepresented. Following the construction of a protein-protein interaction network with the feature genes, hub genes were identified with the MCC algorithm. The Kaplan–Meier plotter was utilized to assess the prognosis of patients based on expression of the hub genes. The feature genes were closely associated with cancer and the cell cycle, as revealed by GO, KEGG and GSEA enrichment analyses. Survival analysis showed that the overexpression of the Fisher score–selected hub genes was associated with decreased survival time (P < 0.05). Weighted gene co-expression network analysis (WGCNA), Lasso, ReliefF and random forest were used for comparison with the Fisher score algorithm. The comparison among these approaches showed that the Fisher score algorithm is superior to the Lasso and ReliefF algorithms in terms of hub gene identification and has similar performance to the WGCNA and random forest algorithms. Our results demonstrated that the Fisher score followed by the application of the MCC algorithm can accurately identify hub genes in HCC.

Identification of candidate genes associated with the pathogenesis of small cell lung cancer via integrated bioinformatics analysis

The pathogenesis of small cell lung cancer (SCLC), a highly metastatic malignant tumor, remains unclear. In the present study, important genes and pathways that are involved in the pathogenesis of SCLC were identified. The following four datasets were downloaded from the Gene Expression Omnibus: GSE60052, GSE43346, GSE15240 and GSE6044. The differentially expressed genes (DEGs) between the SCLC samples and the normal samples were analyzed using R software. The limma package was used for every dataset. The RobustRankAggreg package was used to integrate the DEGs from the four datasets. Functional and pathway enrichment analyses were conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases with FunRich software and R software, respectively. In addition, the protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database and Cytoscape software. Hub genes and significant modules were identified using Molecular Complex Detection in Cytoscape software. Finally, the expression values of hub genes were determined using the Oncomine online database. In total, 412 DEGs were identified following the integration of the four datasets, with 146 upregulated genes and 266 downregulated genes. The upregulated DEGs were primarily enriched in the cell cycle, cell division and microtubule binding. The downregulated DEGs were primarily enriched in the complement and coagulation cascades, the cytokine-mediated signaling pathway and protein binding. Eight hub genes and 1 significant module correlated to the cell cycle pathway were identified based on a subset of the PPI network. Finally, five hub genes were identified as highly expressed in SCLC tissue compared with normal tissue. The cell cycle pathway may be the pathway most closely associated with the pathogenesis of SCLC. NDC80, BUB1B, PLK1, CDC20 and MAD2L1 should be the focus of follow-up studies regarding the diagnosis and treatment of SCLC.

Single-cell transcriptomics reveals the landscape of intra-tumoral heterogeneity and stemness-related subpopulations in liver cancer

Hepatocellular carcinoma (HCC) is heterogeneous, rendering its current curative treatments ineffective. The emergence of single-cell genomics represents a powerful strategy in delineating the complex molecular landscapes of cancers. In this study, we demonstrated the feasibility and merit of using single-cell RNA sequencing to dissect the intra-tumoral heterogeneity and analyze the single-cell transcriptomic landscape to detect rare cell subpopulations of significance. Exploration of the inter-relationship among liver cancer stem cell markers showed two distinct major cell populations according to EPCAM expression, and the EPCAM⁺ cells had upregulated expression of multiple oncogenes. We also identified a CD24⁺/CD44⁺-enriched cell subpopulation within the EPCAM⁺ cells which had specific signature genes and might indicate a novel stemness-related cell subclone in HCC. Notably, knockdown of signature gene CTSE for CD24⁺/CD44⁺ cells significantly reduced self-renewal ability on HCC cells in vitro and the stemness-related role of CTSE was further confirmed by in vivo tumorigenicity assays in nude mice. In summary, single-cell genomics is a useful tool to delineate HCC intratumoral heterogeneity at better resolution. It can identify rare but important cell subpopulations, and may guide better precision medicine in the long run.

Nuclear division cycle 80 complex is associated with malignancy and predicts poor survival of hepatocellular carcinoma

The NDC80 (nuclear division cycle 80) complex takes part in chromosome segregation by forming an outer kinetochore and providing a platform for the interaction between chromosomes and microtubules, thus impacting the progression of mitosis and the cell cycle. The clinical significance of its components, NDC80, nuf2, spc24, and spc25, were widely explored in various malignancies respectively, yet seldom were they studied from the perspective of a complex. This paper explores the clinical importance of the NDC80 kinetochore complex components in terms of their expression level, prognostic value, and therapeutic potential in HCC (hepatocellular carcinoma) patients. With the data from several paired HCC samples from Nanfang Hospital, HCC patients from the TCGA database and other cases from GSE89377, we analyzed the expression levels of the NDC80 complex components, NDC80/nuf2/spc24/spc25, along with the survival data as well as other clinical features using statistical methods and GSEA. The study found that a high expression of NDC80 complex predicts poor survival, and these components have the potential to be used as therapeutic targets.

Downregulation of nucleolar and spindle-associated protein 1 expression suppresses liver cancer cell function

The aim of the present study was to determine the role of nucleolar and spindle-associated protein 1 (NuSAP1) in human liver cancer. NuSAP1 expression was determined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR), western blotting and immunohistochemistry in hepatocellular carcinoma (HCC) and adjacent tissues. The expression of NuSAP1 gene was detected by RT-qPCR in liver cancer cell lines. Expression information for NuSAP1 was determined using the UALCAN and Oncomine databases. The Kaplan-Meier plotter and The Cancer Genome Atlas databases were used to obtain overall survival data for liver cancer. Liver cancer cell lines HepG2 and Huh-7 were transfected with lentiviral particles to silence the endogenous NuSAP1 gene expression. RT-qPCR and western blotting were performed to confirm the silencing efficiency. Cell Counting Kit-8 was used to estimate the effects of NuSAP1 silencing on HepG2 and Huh-7 cell proliferation. Cell cycle and apoptosis analyses were performed using flow cytometry. Cell invasion was assessed using the Transwell assay with microscopy imaging. The results revealed that the NuSAP1 expression levels in HCC tissues were significantly increased compared with the adjacent tissues. The survival time of patients with HCC with a high NuSAP1 expression was markedly decreased compared with that of patients with HCC with a low expression level of NuSAP1. Functional studies revealed that NuSAP1 silencing significantly reduced HepG2 and Huh-7 cell proliferation and invasion. Increased apoptosis and cell cycle arrest at the G1 phase were observed following NuSAP1 knockdown. NuSAP1 silencing significantly inhibited the mRNA expression of DNA methyltransferase but not glioma-associated oncogene. NuSAP1 contributed to liver cancer development by reducing apoptosis and promoting cell cycle progression. The abnormal expression level of NuSAP1 may serve a role in promoting liver cancer progression.

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.

High expression of ubiquitin-conjugating enzyme E2A predicts poor prognosis in hepatocellular carcinoma

The present study aimed to illustrate the association of the expression of ubiquitin-conjugating enzyme E2A (UBE2A) with the clinicopathological parameters and prognosis in hepatocellular carcinoma (HCC). The expression levels of UBE2A mRNA and protein in a total of 276 HCC tissues and six liver cell lines was detected by fluorescent quantitative polymerase chain reaction, western blotting and immunohistochemistry. Statistical analysis was also performed to assess the association of the expression of UBE2A with the clinicopathological parameters and prognosis by the GraphPad Prism and SPSS version 21.0 software. UBE2A mRNA and protein were highly expressed in HCC tissues compared with those in the adjacent normal tissue. Immunohistochemical analysis revealed that UBE2A protein was more strongly stained in the 276 paraffin-embedded HCC tissues as compared with the 63 adjacent normal tissue. Statistical analysis also demonstrated that UBE2A expression was significantly associated with histological differentiation, TNM stage and vascular invasion of HCC (P<0.05). Notably, HCC patients with a high expression of UBE2A had a shorter survival time as compared with those with a low expression of UBE2A. There results suggested that UBE2A may be involved in the pathogenesis of HCC and may serve as an important prognostic marker. Further exploration of the involvement of UBE2A in HCC development may provide novel therapeutic targets.

Identification of Driver Genes and Key Pathways of Glioblastoma Shows JNJ-7706621 as a Novel Antiglioblastoma Drug

OBJECTIVE

The aim of this study is to identify novel targets of diagnosis, therapy, and prognosis for glioblastoma, as well as to verify the therapeutic effect of JNJ-7706621 regarding glioblastoma.

METHODS

The gene expression profiles of GSE42656, GSE50161, and GSE86574 were obtained respectively from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified with comparison between gene expression profiles of the glioblastoma tissues and normal tissues. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and protein-protein interaction (PPI) network analyses were performed. Quantitative reverse transcription polymerase chain reaction and survival curve analysis were also conducted to verify the correlation between expression of hub genes and prognosis. Moreover, in vitro, MTT assay, colony-forming assay, the scratch assay, and flow cytometry were performed to verify the therapeutic effect of JNJ-7706621.

RESULTS

AURKA, NDC80, KIF4A, and NUSAP1 were identified as hub genes after PPI network analysis. Differential expression of those genes was detected between human normal glial cells and glioblastoma cells by quantitative reverse transcription polymerase chain reaction (P < 0.05), and the survival curve analysis showed that the patients with low expression of gene AURKA, NDC80, KIF4A, and NUSAP1 had a significant favorable prognosis (P < 0.05). In vitro assays showed that JNJ-7706621 inhibited glioblastoma cellular viability, proliferation, and migration via inducing glioblastoma cells apoptosis.

CONCLUSIONS

AURKA, NDC80, KIF4A, and NUSAP1 were significantly more highly expressed in glioblastoma cells than in human normal glial cell. Patients with low expression of those 4 genes had a favorable prognosis. JNJ-7706621 was a potential drug in treatment of patients with glioblastoma.