PBK overexpression promotes metastasis of hepatocellular carcinoma via activating ETV4-uPAR signaling pathway

Yang, Qiuxia; Zhong, Shan; He, Lin; Jia, Xiaojiong; Tang, Hua; Cheng, Sheng-Tao; Ren, Ji-Hua; Yu, Haibo; Zhou, Li; Zhou, Hong-Zhong; Ren, Fang; Hu, Zhong-Wen; Gong, Rui; Huang, Aiping; Chen, Juan

doi:10.1016/j.canlet.2019.03.028

Cited by 78 publications

(77 citation statements)

References 34 publications

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“…Chen et al showed that PRC1 promotes early HCC recurrence and poor patient outcomes in association with the Wnt/ b-catenin signaling pathway 39 . Yang et al demonstrated that PBK promotes the metastasis of hepatocellular carcinoma by activating the ETV4-uPAR signaling pathway 40 . In addition, TOP2A contributes to the early detection and targeted therapy of a variety of cancers [41][42][43] , including HCC 44 .…”

Section: Discussionmentioning

confidence: 99%

Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis

Zhou

Hao

et al. 2019

Cell Transplant

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Hepatocellular carcinoma (HCC) is a widespread, common type of cancer in Asian countries, and the need for biomarker-matched molecularly targeted therapy for HCC has been increasingly recognized. However, the effective treatment for HCC is unclear. Therefore, identifying additional hub genes and pathways as novel prognostic biomarkers for HCC is necessary. In this study, the expression profiles of GSE121248, GSE45267 and GSE84402 were obtained from the Gene Expression Omnibus (GEO), including 132 HCC and 90 noncancerous liver tissues. Differentially expressed genes (DEGs) between HCC and noncancerous samples were identified by GEO2 R and Venn diagrams. In total, 109 DEGs were identified in these datasets, including 24 upregulated genes and 85 downregulated genes. Subsequently, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) preliminary analyses of the DEGs were performed using DAVID. The protein–protein interaction (PPI) network of the DEGs was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized in Cytoscape. Module analysis of the PPI network was performed using MCODE to get hub genes. Moreover, the influence of the hub genes on overall survival was determined with Kaplan–Meier plotter. All hub genes were analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and KEGG. Overall, the hub genes DTL, CDK1, CCNB1, RACGAP1, ECT2, NEK2, BUB1B, PBK, TOP2A, ASPM, HMMR, RRM2, CDKN3, PRC1, and ANLN were upregulated in HCC, and the survival rate was lower for HCC with increased expression of these hub genes. CCNB1, CDK1, and RRM2 were enriched in the p53 signaling pathway, and CCNB1, CDK1, and BUB1B were enriched in the cell cycle. In brief, we screened 15 hub genes and pathways to identify potential prognostic markers for HCC treatment. However, the specific occurrence and development of HCC with expression of the hub genes should be verified in vivo and in vitro.

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Section: Discussionmentioning

confidence: 99%

Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis

Zhou

Hao

et al. 2019

Cell Transplant

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“…Lei et al also reported that PBK overexpression affected the survival times of patients with lung adenocarcinoma [24], and Shih et al found that PBK promoted cell migration by activating the phosphatidylinositol 3-kinase/PTEN/AKT signaling pathway in lung cancer [25]. Moreover, Yang et al showed that PBK can be a potential diagnostic biomarker and therapeutic target for patients with hepatocellular carcinoma and can promote cell migration and invasion by activating the ETV4-uPAR signaling pathway [26]. Finally, Park et al found that PBK acted as a cancer/testis antigen with oncogenic activity in breast cancer [27].…”

Section: Discussionmentioning

confidence: 98%

Identification of Molecular Markers Associated With Ovarian Cancer Prognosis Using Bioinformatics Analysis

Chuang¹,

Lyu²,

Liu³

2020

Preprint

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Background Ovarian cancer is associated with a high mortality rate worldwide. However, the pathogenesis, clinicopathological features, and genetic mechanisms of ovarian cancer are still unclear, and it is essential to identify prognostic markers for its clinical diagnosis and treatment. Here, we utilized bioinformatic analysis to identify potential genes involved in mediating BRCA1 expression to elucidate the potential mechanisms underlying ovarian cancer. Methods Gene expression profiling (GSE14407) was performed to identify differentially expressed genes (DEGs) and analyze the weighted gene co-expression network. We selected the key module that was significantly associated with BRCA1 expression and performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for genes in the hub module. We then screened the hub genes utilizing the Search Tool for the Retrieval of Interacting Genes Database (STRING) and Molecular Complex Detection (MCODE) plug-in in Cytoscape. We validated gene expression levels through The Cancer Genome Atlas and GTEx databases for hub genes and screened genes that were related to overall survival in patients with ovarian cancer using the Kaplan-Meier plotter database. Results In total, 3124 DEGs were detected, including 433 upregulated genes and 2691 downregulated genes. We selected the brown module, which was the most significantly associated with BRCA1 expression. GO analysis showed that the hub module genes were significantly enriched in biological processes, including the mitotic cell cycle process, chromosome segregation, and cell division. KEGG analysis showed that the hub module genes were particularly enriched in the cell cycle, p53 signaling pathway, and small cell lung cancer. We selected 30 hub genes from the protein-protein interaction network, which had 88 nodes and 721 edges. Further analyses identified PBK as a prognosis-associated hub gene. Notably, PBK expression was significantly increased in ovarian cancer tissues, as demonstrated by immunohistochemistry analysis using samples from the Human Protein Atlas database.Conclusion PBK was found to be associated with overall survival in patients with ovarian cancer. Our results provide insights into our understanding of the molecular mechanisms and molecular diagnosis of ovarian cancer.

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“…STC2 overexpression can promote proliferation of HCC, ovarian cancer, and stem cells (31)(32)(33). Similarly, PBK promotes metastasis of HCC by activating the ETV4-uPAR signaling pathway (34). PTTG1 has been reported to be associated with poor prognosis and angiogenesis in HCC (35).…”

Section: Discussionmentioning

confidence: 99%

Development of a Prognostic Gene Signature For Hepatocellular Carcinoma

Luo

et al. 2020

Preprint

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Background: Accurate prediction of overall survival is important for prognosis and the assignment of appropriate personalized clinical treatment in hepatocellular carcinoma (HCC) patients. The aim of the present study was to establish an optimal gene model for the independent prediction of prognosis associated with common clinical patterns.Methods: Gene expression profiles and the corresponding clinical information of the LIHC cohort were obtained from The Cancer Genome Atlas. Differentially expressed genes were found using the R package “limma”. Subsequently, a prognostic gene signature was developed using the LASSO Cox regression model. Kaplan–Meier, log-rank, and receiver operating characteristic (ROC) analyses were performed to verify the predictive accuracy of the prognostic model. Finally, a nomogram and calibration plot were created using the “rms” package.Results: Differentially expressed genes were screened with threshold criteria (FDR < 0.01 and |log FC|>3) and 563 differentially expressed genes were obtained, including 448 downregulated and 115 upregulated genes. Using the LASSO Cox regression model, a prognostic gene signature was developed based on nine genes,IQGAP3, BIRC5, PTTG1, STC2, CDKN3, PBK, EXO1, NEIL3, and HOXD9, the expression levels of which were quantitated using RT-qPCR. According to the risk scores, patients were separated into high-risk and low-risk groups. Patients with lower risk scores generally had a better survival rate than those with higher risk scores. The mortality rate in the high-risk group was 42.02%, while that in the low-risk group was 12.50%. Results of the log-rank test showed significant differences in mortality between the two groups (HR: 4.86; 95% CI: 2.72–8.69; P = 1.01E-08). Subsequently, we assessed the prognostic accuracy of the gene signature using an ROC curve and the results show good sensitivity and specificity, with an average area under the curve (AUC) of 0.81 at 5 years (P < 0.01). Following multivariate adjustment for conventional clinical patterns, the prognostic gene signature remained a powerful and independent factor (HR: 4.70; 95% CI: 2.61–8.38; P = 2.06E-07), confirming its robust predictive ability of overall survival in HCC patients. Finally, a nomogram was established based on the gene signature and four clinicopathological features, which demonstrated an advantageous discriminating ability with the potential to facilitate clinical decision-making in HCC.Conclusion: Our prognostic gene signature can be used as a combined biomarker for the independent prediction of overall survival in HCC patients. Moreover, we created a nomogram that can be used to infer prognosis and aid individualized decisions regarding treatment and surveillance.

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PBK overexpression promotes metastasis of hepatocellular carcinoma via activating ETV4-uPAR signaling pathway

Cited by 78 publications

References 34 publications

Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis

Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis

Identification of Molecular Markers Associated With Ovarian Cancer Prognosis Using Bioinformatics Analysis

Development of a Prognostic Gene Signature For Hepatocellular Carcinoma

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