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Review
. 2020 Feb 5:10:68.
doi: 10.3389/fonc.2020.00068. eCollection 2020.

Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis

Hong Zheng  1 , Guosen Zhang  1 , Lu Zhang  1 , Qiang Wang  1 , Huimin Li  1 , Yali Han  1 , Longxiang Xie  1 , Zhongyi Yan  1 , Yongqiang Li  1 , Yang An  1 , Huan Dong  1 , Wan Zhu  2 , Xiangqian Guo  1
Affiliations
Review

Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis

Hong Zheng et al. Front Oncol. .

Abstract

Prognostic biomarkers are of great significance to predict the outcome of patients with cancer, to guide the clinical treatments, to elucidate tumorigenesis mechanisms, and offer the opportunity of identifying therapeutic targets. To screen and develop prognostic biomarkers, high throughput profiling methods including gene microarray and next-generation sequencing have been widely applied and shown great success. However, due to the lack of independent validation, only very few prognostic biomarkers have been applied for clinical practice. In order to cross-validate the reliability of potential prognostic biomarkers, some groups have collected the omics datasets (i.e., epigenetics/transcriptome/proteome) with relative follow-up data (such as OS/DSS/PFS) of clinical samples from different cohorts, and developed the easy-to-use online bioinformatics tools and web servers to assist the biomarker screening and validation. These tools and web servers provide great convenience for the development of prognostic biomarkers, for the study of molecular mechanisms of tumorigenesis and progression, and even for the discovery of important therapeutic targets. Aim to help researchers to get a quick learning and understand the function of these tools, the current review delves into the introduction of the usage, characteristics and algorithms of tools, and web servers, such as LOGpc, KM plotter, GEPIA, TCPA, OncoLnc, PrognoScan, MethSurv, SurvExpress, UALCAN, etc., and further help researchers to select more suitable tools for their own research. In addition, all the tools introduced in this review can be reached at http://bioinfo.henu.edu.cn/WebServiceList.html.

Keywords: cancer; prognosis; survival; tool; web server.

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Figures

Figure 1
Figure 1
Search flowchart: prognostic web servers for cancers included and excluded in each step.
Figure 2
Figure 2
The time axis for the publication of prognostic web servers.
Figure 3
Figure 3
Distribution of cancer types in web servers. (A) LOGpc (mRNA level); (B) PROGmiRV2 (miRNA level); (C) OncoLnc (lncRNA level); (D) CaPSSA (mutation level); (E) GSCALite (methylation level); (F) TCPAv3.0 (protein level).
See this image and copyright information in PMC

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