GTSE1 expression represses apoptotic signaling and confers cisplatin resistance in gastric cancer cells

Subhash, Vinod Vijay; Tan, Shi Hui; Tan, Wei Ching; Yeo, Mei Shi; Xie, Chen; Wong, Foong Ying; Kiat, Zee Ying; Lim, Robert C.; Yong, Wei Peng

doi:10.1186/s12885-015-1550-0

Cited by 47 publications

(52 citation statements)

References 36 publications

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“…Genes with a degree of connection exceeding 15, except for SPAG5 , have been previously linked to the development of gastric cancer. 33 , 34 , 35 , 36 , 37 The result demonstrates the robustness of our bioinformatics analysis methods and reveals a gastric cancer-promoting interaction network associated with cyclin E.…”

Section: Discussionmentioning

confidence: 57%

Analysis of cyclin E co‐expression genes reveals nuclear transcription factor Y subunit alpha is an oncogene in gastric cancer

Bie

et al. 2019

Chronic Diseases and Translational Medicine

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Objective To explore genes potentially co-expressed with cyclin E in gastric cancer and discover possible targets for gastric cancer treatment. Methods The Cancer Genome Atlas (TCGA) stomach adenocarcinoma sequencing data were used to predict genes co-expressed with cyclin E. Co-expression genes predicted by cBioPortal online analysis with Pearson correlation coefficient ≥0.4 were analyzed by gene ontology (GO) enrichment annotation using the PANTHER online platform (Ver. 7). Interactions between proteins encoded by these genes were analyzed using the STRING online platform (Ver. 10.5) and Cytoscape software (Ver. 3.5.1). Genes displaying a high degree of connection were analyzed by transcription factor enrichment prediction using FunRich software (Ver. 3). The significant transcription factor and cyclin E expression levels and their impact on gastric cancer progression were analyzed by Western blotting and Kaplan–Meier survival curve analysis. Results After filtering the co-expression gene prediction results, 78 predicted genes that included 73 protein coding genes and 5 non-coding genes with Pearson correlation coefficient ≥0.4 were selected. The expressions of the genes were considered to be correlated with cyclin E expression. Among the 78 genes co-expressed with cyclin E, 19 genes at the central of the regulatory network associated with cyclin E were discovered. Nuclear transcription factor Y subunit alpha (NF-YA) was identified as a significant transcription factor associated with cyclin E co-expressing genes. Analysis of specimen donors’ clinical records revealed that high expression of NF-YA tended to be associated with increased cyclin E expression. The expression of both was associated with progression of gastric cancer. Western blotting results showed that compared with normal tissues, NF-YA and cyclin E were highly expressed in tumor tissues ( P < 0.001). Survival curve analysis clearly demonstrated relatively poor overall survival of gastric cancer patients with high cyclin E or high NF-YA expression level, compared to patients with low cyclin E or NF-YA expression ( P < 0.05). Conclusions NF-YA may promote gastric cancer progression by increasing the transcription of cyclin E and other cell cycle regulatory genes. NF-YA might be a potential therapeutically useful prognostic factor for gastric cancer.

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Section: Discussionmentioning

confidence: 57%

Analysis of cyclin E co‐expression genes reveals nuclear transcription factor Y subunit alpha is an oncogene in gastric cancer

Bie

et al. 2019

Chronic Diseases and Translational Medicine

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“…Emerging evidence indicated abnormal expression of genes in cell cycle, and apoptosis regulation, like p53, Rb, p27, and TGFβ/IGF2R, is related to etiopathogenesis and prognosis of liver cancer patients [1]. GTSE1 has been demonstrated as a potential biomarker for poor clinical outcome in several types of cancer [10,13,27]. Previous research suggested GTSE1's functional role in hepatocellular carcinoma (HCC) proliferation, colony formation, invasion, and overall survival [9,13].…”

Section: Discussionmentioning

confidence: 99%

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

Zheng

Shi

et al. 2019

Analytical Cellular Pathology

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GTSE1 is well correlated with tumor progression; however, little is known regarding its role in liver cancer prognosis. By analyzing the hepatocellular carcinoma (HCC) datasets in GEO and TCGA databases, we showed that high expression of GTSE1 was correlated with advanced pathologic stage and poor prognosis of HCC patients. To investigate underlying molecular mechanism, we generated GTSE1 knockdown HCC cell line and explored the effects of GTSE1 deficiency in cell growth. Between GTSE1 knockdown and wild-type HCC cells, we identified 979 differentially expressed genes (520 downregulated and 459 upregulated genes) in the analysis of microarray-based gene expression profiling. Functional enrichment analysis of DEGs suggested that S phase was dysregulated without GTSE1 expression, which was further verified from flow cytometry analysis. Moreover, three other DEGs: CDC20, PCNA, and MCM6, were also found contributing to GTSE1-related cell cycle arrest and to be associated with poor overall survival of HCC patients. In conclusion, GTSE1, together with CDC20, PCNA, and MCM6, may synergistically promote adverse prognosis in HCC by activating cell cycle. Genes like GTSE1, CDC20, PCNA, and MCM6 may be promising prognostic molecular biomarkers in liver cancer.

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“…GTSE1 accumulates in the nucleus and binds to p53, resulting in its translocation out of the nucleus and suppression of its apoptosis-inducing ability. In addition, GTSE1 suppresses apoptotic signaling and confers cisplatin resistance in gastric cancer cells (20). Overexpression of GTSE1 has previously been observed in KIRC (21) and may therefore exert a similar function in KIRC.…”

Section: Discussionmentioning

confidence: 99%

Identification of prognostic genes in kidney renal clear cell carcinoma by RNA-seq data analysis

et al. 2017

Molecular Medicine Reports

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The present study aimed to analyze RNA-seq data of kidney renal clear cell carcinoma (KIRC) to identify prognostic genes. RNA-seq data were downloaded from The Cancer Genome Atlas. Feature genes with a coefficient of variation (CV) >0.5 were selected using the genefilter package in R. Gene co-expression networks were constructed with the WGCNA package. Cox regression analysis was performed using the survive package. Furthermore, a functional enrichment analysis was conducted using Database for Annotation, Visualization and Integrated Discovery tools. A total of 533 KIRC samples were collected, from which 6,758 feature genes with a CV >0.5 were obtained for further analysis. The KIRC samples were divided into two sets: The training set (n=319 samples) and the validation set (n=214 samples). Subsequently, gene co-expression networks were constructed for the two sets. A total of 12 modules were identified, and the green module was significantly associated with survival time. Genes from the green module were revealed to be implicated in the cell cycle and p53 signaling pathway. In addition, a total of 11 hub genes were revealed, and 10 of them (CCNA2, CDC20, CDCA8, GTSE1, KIF23, KIF2C, KIF4A, MELK, TOP2A and TPX2) were validated as possessing prognostic value, as determined by conducting a survival analysis on another gene expression dataset. In conclusion, a total of 10 prognostic genes were identified in KIRC. These findings may help to advance the understanding of this disease, and may also provide potential biomarkers for therapeutic development.

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GTSE1 expression represses apoptotic signaling and confers cisplatin resistance in gastric cancer cells

Cited by 47 publications

References 36 publications

Analysis of cyclin E co‐expression genes reveals nuclear transcription factor Y subunit alpha is an oncogene in gastric cancer

Analysis of cyclin E co‐expression genes reveals nuclear transcription factor Y subunit alpha is an oncogene in gastric cancer

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

Identification of prognostic genes in kidney renal clear cell carcinoma by RNA-seq data analysis

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