OSMCC:默克尔细胞癌的在线生存分析工具

U. Saddozai, Qiang Wang, Xiaoxiao Sun, Yifang Dang, Jiajia Lv, Junfang Xin, Wan Zhu, Yongqiang Li, Xinying Ji, Xiangqian Guo
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引用次数: 0

摘要

目的:开发一个免费的在线工具来识别默克尔细胞癌(MCC)的预后标志物,并估计感兴趣基因在临床患者队列中的意义。设置与设计:使用R软件包计算和绘制Kaplan-Meier生存曲线。对象和方法:将MCC数据集与基因表达Omnibus中可用的解剖临床数据相结合,开发了一个在线搜索引擎。本研究评估了30例患者的基因组表达谱,包括42985个探针和21651个基因。患者被分为第一四分位数、第二四分位数和第三四分位数。采用14.5个月的中位生存期来存储不同分期(I-IV期)的不同癌症患者的信息。数据存储在SQL Server数据库中,使用Apache Tomcat应用服务器托管在Windows Server 2008上。方法:采用Log-rank检验,以P < 0.05为差异有统计学意义。结果:开发了MCC在线生存分析工具(简称OSMCC),可以评估MCC患者中各种基因的表达水平与临床预后的相关性。通过OSMCC可以显示生存曲线,并可以计算95%置信区间的风险比和log-rank P值。结论:本研究通过预后意义分析证明了OSMCC能够识别和分析MCC的转录组和临床数据集。到目前为止,OSMCC是首个用于MCC预后测量的先进和专用工具。此外,OSMCC可以被证明是一个非常有价值的数据库,用于初步评估和鉴定潜在的MCC预后生物标志物。OSMCC可通过http://bioinfo.henu.edu.cn/MCC/MCCList.jsp访问。
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OSMCC: An online survival analysis tool for Merkel cell carcinoma
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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