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.
Highlights
The prognosis estimation of tumor patient is of great significance to guide clinical treatments and facilitate the elucidation of tumorigenesis mechanism
In order to promote the development and evaluation of prognostic biomarkers, some research groups have developed prognosis tools and web servers based on mRNA data by mining the Cancer Genome Atlas (TCGA) and GEO (Gene Expression Omnibus) data and adding complex statistical calculation
This review introduces 14 bioinformatics tools for evaluating cancer prognosis based on mRNA data (Table 1)
Summary
The prognosis estimation of tumor patient is of great significance to guide clinical treatments and facilitate the elucidation of tumorigenesis mechanism. Prognosis is determined by many factors, such as disease stage, clinical performance, treatment experience and understanding of the cancer development. These properties are relative subjective and may lead to inaccurate prognostic estimates, and may even lead to inappropriate anticancer management strategy. Tools for Survival Analysis unprecedented opportunities for many fields such as cancer bioinformatics and precision medicine to improve our understanding in cancer development and treatment [1, 2]. Molecular prognostic biomarkers are the basic components of precision medicine. Accurate clinical estimation using prognostic biomarkers helps determining optimal anti-cancer treatment. The discovery of prognostic biomarkers has become a hot topic in precision medicine
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