SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis

Abstract

Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians. Therefore, to facilitate performance comparisons and validations of survival biomarkers for cancer outcomes, we developed SurvExpress, a cancer-wide gene expression database with clinical outcomes and a web-based tool that provides survival analysis and risk assessment of cancer datasets. The main input of SurvExpress is only the biomarker gene list. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. We show the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer. Compared to other tools, SurvExpress is the largest, most versatile, and quickest free tool available. SurvExpress web can be accessed in http://bioinformatica.mty.itesm.mx/SurvExpress (a tutorial is included). The website was implemented in JSP, JavaScript, MySQL, and R.

Expansion of BCR/ABL1+ cells requires PAK2 but not PAK1

“…We performed in silico analyses of a publicly available database providing expression data of patients that are assigned to a low‐ or a high‐risk group (SurvExpress; http://bioinformatica.mty.itesm.mx:8080/Biomatec/SurvivaX.jsp; Aguirre‐Gamboa et al , 2013). SurvExpress data revealed that levels of PAK2 are significantly elevated in high‐risk groups of patients suffering from Burkitt lymphoma (BL), multiple myeloma (MM), diffuse large B‐cell lymphoma (DLBCL), and mantle cell lymphoma (MCL) (Figs 1A and S1A).…”

“…(A) Expression of PAK 1 and PAK 2 in low‐ and high‐risk patients suffering from Burkitt lymphoma, multiple myeloma, and diffuse large B‐cell lymphoma [SurvExpress database (Aguirre‐Gamboa et al , 2013)]. P ‐values indicate statistical significance in expression between low‐ and high‐risk groups.…”

“…Bioinformatic analyses were performed with the publicly available database SurvExpress (http://bioinformatica.mty.itesm.mx:8080/Biomatec/SurvivaX.jsp; Aguirre‐Gamboa et al , 2013). Patients suffering from different haematological disease entities were divided into low‐ and high‐risk groups according to Aguirre‐Gamboa et al (2013), and levels of PAK1 and PAK2 expression were assessed.…”

“…Patients suffering from different haematological disease entities were divided into low‐ and high‐risk groups according to Aguirre‐Gamboa et al (2013), and levels of PAK1 and PAK2 expression were assessed. Expression of PAK1‐6 was assessed with the publicly available software Genevestigator (https://genevestigator.com/gv/; Hruz et al , 2008).…”

GIANT: an online resource for comprehensive survival analysis in pan-cancer from The Cancer Genome Atlas (Preprint)

“…For instance, SurvExpress, PROGgene, and PrognoScan provide statistical prognostic significance by using mRNA expression data [5][6][7]. However, the survival analysis results are prone to be associated with following limitations: (1) the tools use mRNA expression as a simple categorical value to provide Kaplan-Meier curve in all patients regardless of their characteristics.…”