GTSE1, CDC20, PCNA, and MCM6 Synergistically Affect Regulations in Cell Cycle and Indicate Poor Prognosis in Liver Cancer

Zheng, Yongchang; Shi, Yue; Yu, Si; Han, Yuanyuan; Kang, Kai; Xu, Haifeng; Gu, Huajian; Sang, Xinting; Yang, Chen; Wang, Jingyu

doi:10.1155/2019/1038069

Cited by 25 publications

(23 citation statements)

References 34 publications

(49 reference statements)

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“…Proliferating cell nuclear antigen (PCNA) plays critical roles in many aspects of DNA replication and replication-associated processes, including translesion synthesis, error-free damage bypass, break-induced replication, mismatch repair, and chromatin assembly 48 . Zheng et al analyzed HCC data sets in GEO and TCGA and found that PCNA might be promising prognostic biomarker for HCC 49 . Nucleolar KKE/D repeat proteins NOP56p and NOP58p interact with NOP1p and are required for ribosome biogenesis 50 .…”

Section: Discussionmentioning

confidence: 99%

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

Zhang

Lin

et al. 2021

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

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

Zhang

Lin

et al. 2021

Sci Rep

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“…Its common molecular function is to bind to the tumor suppressor protein p53 and impede the cancer suppressor ability of p53 in cancers [ 8 ]. Some studies further reported that upregulation of GTSE1 was frequently found in several types of human cancers [ 9 , 10 ]. Upregulation of GTSE1 promoted cell proliferation and cell migration and invasion in the progress of hepatocellular carcinoma [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

GTSE1 Facilitates the Malignant Phenotype of Lung Cancer Cells via Activating AKT/mTOR Signaling

Zhang

Meng

Jiang

et al. 2021

Analytical Cellular Pathology

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The expression of G2 and S phase-expressed-1 (GTSE1) was upregulated in human cancer. However, its expression and roles in lung cancer have not been identified yet. In our study, we reported that GTSE1 expression was statistically higher in lung tissues than in the adjacent noncancerous tissues which might be a consequence of hypomethylation of the GTSE1 promoter. The upregulated expression of GTSE1 mRNA predicted the poorer survival of the lung patients. Ectopic expression of GTSE1 in lung cancer cells significantly increased while knockdown of GTSE1 decreased cell proliferation, cell migration, and cell invasion in H460 and A549 cells. Furthermore, knockdown of GTSE1 regulated the cell cycle and promoted cell apoptosis in H460 and A549 cells. Finally, we presented that GTSE1 was able to activate AKT/mTOR signaling in H460 and A549 cells. In conclusion, these results indicated that the overexpressed GTSE1 was involved in the progress of lung cancer by promoting proliferation migration and invasion and inhibiting apoptosis of lung cancer cells via activating AKT/mTOR signaling.

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“…26 GTSE1 regulates p53 by stimulating the relocalization of p53 to the cytoplasm, subsequently inhibiting p53-induced apoptosis. 27 The overexpression of GTSE1 has been reported to be involved in the progression of many cancers, such as gastric cancer, 8 breast cancer, 28 liver cancer, 29 non-small-cell lung cancer, 9 acral melanoma 7 and bladder cancer. 30 However, the role and underlying molecular mechanism of GTSE1 in PCa remain unknown.…”

Section: Discussionmentioning

confidence: 99%

<p>Identification of G2 and S Phase-Expressed-1 as a Potential Biomarker in Patients with Prostate Cancer</p>

Xiong¹,

Zhang²,

Zhang³

2020

CMAR

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Background: This study aimed to predict and explore the possible clinical value and mechanism of genetic markers in prostate cancer (PCa) using a bioinformatics analysis method. Materials and Methods: The RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database to identify the differentially expressed genes (DEGs). The hub genes were screened by building protein-protein interaction (PPI) subnetworks with four topological analysis methods. The overall survival analysis of hub genes was conducted using Kaplan-Meier curves. Furthermore, the bioinformatics results were confirmed in 102 PCa samples collected in our hospital. Gene Set Enrichment Analysis (GSEA) was performed to provide information about the molecular mechanisms underlying PCa. Results: Among 13 hub genes, the high expression of GTSE1 or KIF18B was associated with worse overall survival according to the TCGA samples. Immunoreactive scores for GTSE1 staining were significantly higher in PCa tissues than in paracancerous tissues (P<0.01). The overall survival time of patients with high GTSE1 expression was shorter than that of patients with low GTSE1 expression (P=0.015). GSEA demonstrated that high GTSE1 expression was mainly enriched in the cell cycle (P<0.001), DNA replication (P<0.001), mismatch repair (P<0.001), and p53 signaling pathway (P<0.001). Conclusion: GTSE1 expression was significantly high in PCa and associated with poor prognosis. GTSE1 may serve as a potential biomarker and therapeutic target in PCa patients.

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GTSE1, CDC20, PCNA, and MCM6 Synergistically Affect Regulations in Cell Cycle and Indicate Poor Prognosis in Liver Cancer

Cited by 25 publications

References 34 publications

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

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

GTSE1 Facilitates the Malignant Phenotype of Lung Cancer Cells via Activating AKT/mTOR Signaling

<p>Identification of G2 and S Phase-Expressed-1 as a Potential Biomarker in Patients with Prostate Cancer</p>

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