ZSCAN16 promotes proliferation, migration and invasion of bladder cancer via regulating NF-kB, AKT, mTOR, P38 and other genes

Li, Xuanhao; Shang, Donghao; Shen, Hongliang; Jiang, Song; Hao, Gangyue; Tian, Ye

doi:10.1016/j.biopha.2020.110066

Cited by 8 publications

(7 citation statements)

References 39 publications

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“…However, the molecular mechanisms underlying BC have remained to be fully elucidated. The abnormal expression of the assembly factor for bundle microtubules, TEF transcription factor, PAR bZIP family member, chloride intracellular channel protein 1, zinc finger and the SCAN domain containing 16, c-myc or RAS, p53 or p21 genes, have been reported to be involved in BC (35)(36)(37). Furthermore, the loss of UTX, also known as lysine-specific demethylase 6A, and the activation of receptor tyrosine kinase fibroblast growth factor receptor 3, are reported to be BC-related (38).…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of key biomarkers for bladder cancer

Bai

et al. 2022

Biomed Rep

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Bladder cancer (BC) is one of the most prevalent genitourinary cancers. Despite the growing research interest in BC, the molecular mechanisms underlying its carcinogenesis remain poorly understood. The microarray datasets GSE38264 and GSE61615 obtained from the Gene Expression Omnibus (GEO) database were analyzed and differentially expressed genes (DEGs) were identified, which were then verified using a dataset from The Cancer Genome Atlas (TCGA). By taking the intersection of the two microarray datasets, the common DEGs were identified and these were selected as candidate genes associated with BC. The DEGs were further subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and the protein-protein interaction network was constructed. Further module analysis was performed using STRING and Cytoscape. A total of 362 DEGs were identified, including 13 hub genes, and the GO analysis revealed that these genes were mainly enriched in extracellular matrix organization, positive regulation of cell proliferation, angiogenesis and peptidyl-tyrosine phosphorylation. The expression changes of PTPRC, PDGFRA, CASQ2, TGFBI, KLRD1 and MT1X in the different datasets indicated that these genes were involved in the development of BC. Next, the differential expression of these genes was verified in the TCGA dataset, and ultimately, these 13 genes were determined to be related to the occurrence and development of BC. Finally, the cancer tissues and adjacent tissues of patients with BC were collected and subjected to reverse transcription-quantitative PCR, the results of which were consistent with the bioinformatics prediction. The present findings provide several vital genes for the clinical diagnosis and treatment of BC.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of key biomarkers for bladder cancer

Bai

et al. 2022

Biomed Rep

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“…Among these MAGE proteins, MAGEA2 and MAGEC2 interact with TRIM28 affecting the cell cycle among other processes [25,26]. MAGEC3 also upregulates a stress-related gene, WRNIP1 [27], a DNA replication gene, MCM7 [28], and cancer-related genes, XPO5 [29] and ZSCAN16 [30]. In addition, MAGEC3 downregulates genes that are shown to induce tumor progression, including APLMR [31,32], FBLN5 [33], and FNDC1 [34,35].…”

Section: Magec3 Expression Is Associated With Stress-related Processesmentioning

confidence: 99%

Loss of MAGEC3 Expression Is Associated with Prognosis in Advanced Ovarian Cancers

Ellegate

Mastri

Isenhart

et al. 2022

Cancers

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Rare variants in MAGEC3 are associated with BRCA negative, early-onset ovarian cancers. Given this association, we evaluated the impact of MAGEC3 protein expression on prognosis and transcription. We quantified normal and tumor protein expression of MAGEC3 via immunohistochemistry in n = 394 advanced ovarian cancers, assessed the correlation of these values with clinicopathologic and immunological features and modeled survival using univariate and multivariate models. To extend these results, we quantified MAGEC3 protein expression in n = 180 cancers and used matching RNA sequencing data to determine MAGEC3-associated differentially expressed genes and to build an RNA-based model of MAGEC3 protein levels. This model was tested in a third independent cohort of patients from TCGA’s OV dataset (n = 282). MAGEC3 protein was sporadically lost in ovarian cancers, with half of the cases falling below the 9.5th percentile of normal tissue expression. Cases with MAGEC3 loss demonstrated better progression-free survival [HR = 0.71, p = 0.004], and analyses performed on predicted protein scores were consistent [HR = 0.57 p = 0.002]. MAGEC3 protein was correlated with CD8 protein expression [Pearson’s r = 0.176, p = 0.011], NY-ESO-1 seropositivity, and mRNA expression of tumor antigens at Xq28. Results of gene set enrichment analysis showed that genes associated with MAGEC3 protein expression cluster around G2/M checkpoint (NES = 3.20, FDR < 0.001) and DNA repair (NES = 2.28, FDR < 0.001) hallmark pathways. These results show that MAGEC3 is a prognostic biomarker in ovarian cancer.

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“…Among these MAGE proteins, MAGEA2 and MAGEC2 interact with TRIM28 affecting the cell cycle among other processes [14,15]. MAGEC3 also upregulates a stress related gene, WRNIP1 [16]; a DNA replication gene, MCM7 [17]; and cancer related genes, XPO5 [18] and ZSCAN16 [19]. In addition, MAGEC3 downregulates genes that are shown to induce tumor progression including APLMR [20,21], FBLN5 [22], and FNDC1 [23,24].…”

Section: Magec3 Protein Expression Across Multiple Disease Sites Ffpe Blocks Were Available Frommentioning

confidence: 99%

MAGEC3 is a prognostic biomarker in ovarian and kidney cancers

Ellegate

Mastri

Isenhart

et al. 2021

Preprint

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Rare variants in MAGEC3, members of the melanoma antigen gene family, are associated with BRCA-independent early onset ovarian cancers, while somatic mutations of this gene have been associated with kidney cancers. In this report, we quantified normal and tumor protein expression of MAGEC3 via immunohistochemistry in N=394 ovarian cancers and N=220 renal cell carcinomas. MAGEC3 protein levels fell into two categories – normal MAGEC3 and MAGEC3 loss – characterized by expression equivalent to normal tissue or significantly lower than normal tissue, respectively. Interestingly, cases with MAGEC3 loss demonstrated better overall survival in both ovarian cancers and renal cell carcinomas, which resembles patient outcomes with BRCA2 loss. MAGEC3 protein expression was associated with upregulation of pathways regulating G2/M checkpoint (NES: 4.13, FDR<0.001) and mitotic spindle formation (NES: 2.84, FDR<0.001). Increased CD8+ cell infiltration, coordinate expression of other cancer testis antigens, and tumor mutational burden were also associated with MAGEC3 expression. To emphasize the impact of these results, we built a prognostic RNA-based model using N=180 cancers of an independent cohort with matching transcriptomic data and tested its performance in two large public cohorts (N=282 ovary and N=606 kidney). Results based on predicted protein scores within these patients validated those discovered in patients with directly measured MAGEC3 protein. The RNA model was reproduced in independent cohorts implying a broader potential for MAGEC3-driven disease etiology and relevance to potential treatment selection.STATEMENT OF TRANSLATIONAL RELEVANCEMAGEC3 protein is expressed in multiple tissues and is dysregulated in cancer. In this work, we show that ovarian and kidney cancer patients with loss of MAGEC3 protein have favorable prognosis, indicating that MAGEC3 protein level may be used as a prognostic biomarker. Integrative genomic analysis of patients spanning more than nine cancer types showed an association between MAGEC3 protein and genes affecting stress response, including those involved in cell cycle and DNA damage repair. Additionally, it is correlated with tumor mutational burden in patients with mutated oncogenes. These associations suggest that MAGEC3 protein levels may be used to identify patients with deficient DNA damage repair mechanisms that can be targeted by PARP inhibitors. To operationalize this idea, we use machine learning to predict MAGEC3 protein levels from RNA sequencing data which can facilitate the identification of patients for treatment stratification according to their MAGEC3 status.

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ZSCAN16 promotes proliferation, migration and invasion of bladder cancer via regulating NF-kB, AKT, mTOR, P38 and other genes

Cited by 8 publications

References 39 publications

Bioinformatics analysis of key biomarkers for bladder cancer

Bioinformatics analysis of key biomarkers for bladder cancer

Loss of MAGEC3 Expression Is Associated with Prognosis in Advanced Ovarian Cancers

MAGEC3 is a prognostic biomarker in ovarian and kidney cancers

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