Abstract
‘Breast cancer gene-expression miner’ (bc-GenExMiner) is a breast cancer–associated web portal (http://bcgenex.ico.unicancer.fr). Here, we describe the development of a new statistical mining module, which permits several differential gene expression analyses, i.e. ‘Expression’ module. Sixty-two breast cancer cohorts and one healthy breast cohort with their corresponding clinicopathological information are included in bc-GenExMiner v4.5 version. Analyses are based on microarray or RNAseq transcriptomic data. Thirty-nine differential gene expression analyses, grouped into 13 categories, according to clinicopathological and molecular characteristics (‘Targeted’ and ‘Exhaustive’) and gene expression (‘Customized’), have been developed. Output results are visualized in four forms of plots. This new statistical mining module offers, among other things, the possibility to compare gene expression in healthy (cancer-free), tumour-adjacent and tumour tissues at once and in three triple-negative breast cancer subtypes (i.e. C1: molecular apocrine tumours; C2: basal-like tumours infiltrated by immune suppressive cells and C3: basal-like tumours triggering an ineffective immune response). Several validation tests showed that bioinformatics process did not alter the pathobiological information contained in the source data. In this work, we developed and demonstrated that bc-GenExMiner ‘Expression’ module can be used for exploratory and validation purposes. Database URL: http://bcgenex.ico.unicancer.fr
Highlights
High-throughput gene expression data associated with their clinicopathological features represent a treasure trove of information for medical research
As no gold standard exists, we offered the possibility to explore gene expression according to IHC, gene expression signatures (GESs) and sequencing modes of p53 status determination [6, 7]
Bc-genExMiner development is guided by one principle: to offer the most easy-to-use, reliable, complete, and biologically and clinically relevant web-based tool to breast cancer researchers and clinicians
Summary
High-throughput gene expression data associated with their clinicopathological features represent a treasure trove of information for medical research. Complex diseases such as cancer could benefit from this wealth of information. Before it can benefit the widest possible range of researchers, these data and statistical mining functions need to be automated by bioinformatics experts, for instance in the form of integrated easy-to-use web-based tools. In 2013, bc-GenExMiner v3.0 database included 3237 patient genomic data from 21 microarray studies, and two analysis modules were described: ‘Prognostic’ and ‘Correlation’. Output results are visualized in the form of four types of plots: box and whisker, beeswarm, violin and raincloud
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