Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

Abstract

The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called “differential expression analysis” approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.

Genetic Optimization in Uncovering Biologically Meaningful Gene Biomarkers for Glioblastoma Subtypes

“…High-variability genes (HVGs), for instance, may not always reflect true biological variation but could be influenced by technical noise, such as dropout events, potentially leading to false conclusions about cellular heterogeneity [16]. Similarly, DEGs may not fully capture the complexity of gene regulation dynamics, as they might overlook subtle but biologically relevant changes in gene expression [17]. Therefore, a balanced approach that considers both DEGs and HVGs, along with additional validation methods, is crucial for accurate scRNA-seq data interpretation.…”