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Slow Skeletal Muscle Troponin T, Titin and Myosin Light Chain 3 Are Candidate Prognostic Biomarkers for Ewing's Sarcoma

Overview
Journal Oncol Lett
Specialty Oncology
Date 2019 Dec 7
PMID 31807166
Citations 4
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Abstract

Ewing's sarcoma (ES) is a common malignant bone tumor in children and adolescents. Although great efforts have been made to understand the pathogenesis and development of ES, the underlying molecular mechanism remains unclear. The present study aimed to identify new key genes as potential biomarkers for the diagnosis, targeted therapy or prognosis of ES. mRNA expression profile chip data sets GSE17674, GSE17679 and GSE45544 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened using the R software limma package, and functional and pathway enrichment analyses were performed using the enrichplot package and GSEA software. The NetworkAnalyst online tool, as well as Cytoscape and its plug-ins cytoHubba and NetworkAnalyzer, were used to construct a protein-protein interaction network (PPI) and conduct module analysis to screen key (hub) genes. LABSO COX regression and overall survival (OS) analysis of the Hub genes were performed. A total of 211 DEGs were obtained by integrating and analyzing the three data sets. The functions and pathways of the DEGs were mainly associated with the regulation of small-molecule metabolic processes, cofactor-binding, amino acid, proteasome and ribosome biosynthesis in eukaryotes, as well as the Rac1, cell cycle and P53 signaling pathways. A total of one important module and 20 hub genes were screened from the PPI network using the Maximum Correlation Criteria algorithm of cytoHubba. LASSO COX regression results revealed that titin (), fast skeletal muscle troponin T, skeletal muscle actin α-actin, nebulin, troponin C type 2 (fast), myosin light-chain 3 (), slow skeletal muscle troponin T (), myosin-binding protein C1 slow-type, tropomyosin 3 and myosin heavy-chain 7 were associated with prognosis in patients with ES. The Kaplan-Meier curves demonstrated that high mRNA expression levels of (P<0.001), (P=0.049), titin-cap (P=0.04), tropomodulin 1 (P=0.011), troponin I2 fast skeletal type (P=0.021) and (P=0.017) were associated with poor OS in patients with ES. In conclusion, the DEGs identified in the present study may be key genes in the pathogenesis of ES, three of which, namely and , may be potential prognostic biomarkers for ES.

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References
1.
Agelopoulos K, Richter G, Schmidt E, Dirksen U, von Heyking K, Moser B . Deep Sequencing in Conjunction with Expression and Functional Analyses Reveals Activation of FGFR1 in Ewing Sarcoma. Clin Cancer Res. 2015; 21(21):4935-46. DOI: 10.1158/1078-0432.CCR-14-2744. View

2.
Smoot M, Ono K, Ruscheinski J, Wang P, Ideker T . Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2010; 27(3):431-2. PMC: 3031041. DOI: 10.1093/bioinformatics/btq675. View

3.
Sawaki K, Kanda M, Miwa T, Umeda S, Tanaka H, Tanaka C . Troponin I2 as a Specific Biomarker for Prediction of Peritoneal Metastasis in Gastric Cancer. Ann Surg Oncol. 2018; 25(7):2083-2090. DOI: 10.1245/s10434-018-6480-z. View

4.
Yang C, Yu T, Han C, Qin W, Liao X, Yu L . Genome-Wide Association Study of MKI67 Expression and its Clinical Implications in HBV-Related Hepatocellular Carcinoma in Southern China. Cell Physiol Biochem. 2017; 42(4):1342-1357. DOI: 10.1159/000478963. View

5.
Dalan A, Gulluoglu S, Tuysuz E, Kuskucu A, Yaltirik C, Ozturk O . Simultaneous analysis of miRNA-mRNA in human meningiomas by integrating transcriptome: A relationship between PTX3 and miR-29c. BMC Cancer. 2017; 17(1):207. PMC: 5361823. DOI: 10.1186/s12885-017-3198-4. View