OSskcm: an online survival analysis webserver for skin cutaneous melanoma based on 1085 transcriptomic profiles
OSskcm: an online survival analysis webserver for skin cutaneous melanoma based on 1085 transcriptomic profiles
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DOI：
10.1186/s12935-020-01262-3
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发表时间：
2020-05-19
影响因子：
5.8
通讯作者：
Guo, Xiangqian
中科院分区：
文献类型：
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作者：
Zhang, Lu;Wang, Qiang;Guo, Xiangqian
皮肤黑色素瘤是最具侵袭性和致死性的皮肤癌之一。因此，寻找预后生物标志物对指导临床治疗具有重要意义。然而，对于未经训练的研究人员来说，处理高维谱数据并在谱数据集中识别潜在的预后基因在技术上具有挑战性。方法在本研究中，我们开发了一个网络服务器，利用TCGA和GEO数据库中的数据分析皮肤黑色素瘤基因的预后价值。该网络服务器被命名为皮肤黑色素瘤在线共识生存网络服务器（OSskcm），其中包括1085个临床黑色素瘤样本。OSskcm托管在一个Windows Tomcat服务器上。服务器端脚本是用Java脚本开发的。数据库系统由SQL Server管理，它集成了基因表达数据和临床数据。单因素考克斯回归分析计算Kaplan-Meier（KM）生存曲线、危险比（HR）和95%可信区间（95%CI）。结果在OSskcm中，用户通过输入官方基因符号并选择适当的选项，可以在输出网页上获得具有对数秩P值和HR的KM生存图。此外，包括种族、分期、性别、年龄和治疗类型在内的临床特征也可作为混杂因素纳入预后分析，以限制黑色素瘤患者亚组的分析。结论OSskcm对生物学家和临床医生评估和验证新的或感兴趣的黑色素瘤预后生物标志物具有很高的价值。OSskcm可在线访问：http://bioinfo.henu.edu.cn/Melanoma/MelanomaList.jsp。
Background Cutaneous melanoma is one of the most aggressive and lethal skin cancers. It is greatly important to identify prognostic biomarkers to guide the clinical management. However, it is technically challenging for untrained researchers to process high dimensional profiling data and identify potential prognostic genes in profiling datasets. Methods In this study, we developed a webserver to analyze the prognostic values of genes in cutaneous melanoma using data from TCGA and GEO databases. The webserver is named Online consensus Survival webserver for Skin Cutaneous Melanoma (OSskcm) which includes 1085 clinical melanoma samples. The OSskcm is hosted in a windows tomcat server. Server-side scripts were developed in Java script. The database system is managed by a SQL Server, which integrates gene expression data and clinical data. The Kaplan-Meier (KM) survival curves, Hazard ratio (HR) and 95% confidence interval (95%CI) were calculated in a univariate Cox regression analysis. Results In OSskcm, by inputting official gene symbol and selecting proper options, users could obtain KM survival plot with log-rank P value and HR on the output web page. In addition, clinical characters including race, stage, gender, age and type of therapy could also be included in the prognosis analysis as confounding factors to constrain the analysis in a subgroup of melanoma patients. Conclusion The OSskcm is highly valuable for biologists and clinicians to perform the assessment and validation of new or interested prognostic biomarkers for melanoma. OSskcm can be accessed online at: http://bioinfo.henu.edu.cn/Melanoma/MelanomaList.jsp.