Identification of driver genes in glioblastoma based on single-cell gene expression data using integrated pseudotime and phylogenetic analysis
Abstract
Cancer encompasses a group of diverse diseases characterized by uncontrolled cell growth that can invade nearby tissues. Despite extensive advancements in medicine, treating cancer remains a significant challenge, therefore humankind is still searching for a simpler method of treating cancer. One critical question in this pursuit is identifying the origin of cancer and also understanding the pathways of cancer growth, particularly in mitotic processes. Glioblastoma (GBM), the most aggressive primary brain tumor, exemplifies this challenge, exhibits notable intratumoral heterogeneity and genetic complexity, which make effective treatment highly obscure. Recent researches have approached this subject from two distinct perspectives: gene expression profiles and genetic mutations. While each has provided valuable insights, integrating these viewpoints may offer a more comprehensive understanding of GBM progression. In this study, we combine two models: phylogenetic trees using the neighbor-joining algorithm to map mutation propagation, and the Monocle algorithm to generate pseudo-time paths. Our findings reveal that these two approaches are interconnected, demonstrating that mutations not only drive cancer progression but also provide deeper insights into the pathways underlying GBM evolution.
Fatemeh Zare-Mirakabad
Associate Professor
My research interests include bioinformatics, computational biology and artificial intelligence.