Title:Differential Gene Expression in Cancer: An Overrated Analysis?
Volume: 17
Issue: 5
Author(s): Jessica Carballido*Rocío Cecchini
Affiliation:
- Department of CS and Engineering - Institute for CS and Engineering, CONICET – UNS, Bahía Blanca, Bs. As. Argentina
Keywords:
KEGG, Differential expression analysis (DE), RNA sequencing expression, cancer expression data, TCGA, amigo.
Abstract: The search for marker genes associated with different pathologies traditionally begins with
some form of differential expression analysis. This step is essential in most functional genomics' works
that analyze gene expression data. In the present article, we present a different analysis, starting from
the known biological significance of different groups of genes and then assessing the proportion of differentially
expressed genes. The analysis is performed in the context of cancer expression data to unveil
the true importance of differential expression, approaching it from different research objectives. Firstly,
it was seen that the percentage of differentially expressed genes is generally low concerning gene sets
annotated in KEGG. On the other hand, it was observed that in the training and prediction process of
both statistical and machine learning models, the fact of using differentially expressed genes sustainably
improves their results.
