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ORIGINAL ARTICLE
Year : 2019  |  Volume : 5  |  Issue : 3  |  Page : 47-49

OSMCC: An online survival analysis tool for Merkel cell carcinoma


1 Department of Preventive Medicine, Joint National Laboratory for Antibody Drug Engineering, Cell Signal Transduction Laboratory, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
2 Department of Preventive Medicine, Joint National Laboratory for Antibody Drug Engineering, Cell Signal Transduction Laboratory, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng; Department of Thoracic Surgery, The Affiliated Nanshi Hospital of Henan University, Nanyang, China
3 Department of Anesthesia, Stanford University, Pasteur Drive Stanford, CA, USA

Correspondence Address:
Prof. Xinying Ji
Department of Preventive Medicine, Joint National Laboratory for Antibody Drug Engineering, Cell Signal Transduction Laboratory, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ctm.ctm_19_19

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Aims: To develop a free accessible online tool to identify the prognostic markers for Merkel cell carcinoma (MCC) and to estimate the significance of interested gene in a cohort of clinical patients. Settings and Design: R package is used to calculate and plot the Kaplan–Meier survival curve. Subjects and Methods: An online search engine was developed by combining MCC datasets with available anatomoclinical data in Gene Expression Omnibus. In current study, genomic expression profile of thirty patients comprising 42985 probes and 21651 genes was evaluated. Patients were divided into first quartile, second quartile, and third quartile. Information about different cancer patients of varying stages (Stage I–IV) was stored using median survival scale of 14.5 months. Data were stored in SQL Server database and hosted on Windows Server 2008 using Apache Tomcat application server. Statistical Analysis Used: Log-rank test was applied and P < 0.05 was considered statistically significant. Results: An Online Survival analysis tool for MCC abbreviating as OSMCC was developed, which can assess the expression level relevance of various genes on the clinical outcome in MCC patients. By OSMCC, the survival curve could be displayed, and the hazard ratio with 95% confidence intervals and log-rank P value can also be calculated. Conclusions: The study demonstrated the ability of OSMCC to identify and analyze transcriptome and clinical datasets for MCC through prognosis significance analysis. So far, OSMCC is the first advanced and specific tool for the prognostic measurement of MCC. Furthermore, OSMCC can prove to be a highly valuable database for the preliminary assessment and identification of potential MCC prognostic biomarkers. OSMCC is accessible at http://bioinfo.henu.edu.cn/MCC/MCCList.jsp.


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