همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

Identification of Driver Genes in Glioblastoma Based on Single-Cell Gene Expression Data Using Integrated Pseudotime and Phylogenetic Analysis

نویسندگان :

Fateme Mirza-Abolhassani¹ Sobhan Ahmadian Moghadam² Fatemeh Zare-Mirakabad³ Kaveh Kavousi⁴

1- دانشگاه صنعتی شریف 2- دانشگاه صنعتی امیرکبیر 3- دانشگاه صنعتی امیرکبیر 4- دانشگاه تهران

کلمات کلیدی :

Glioblastoma،Phylogenetic Trees،Pseudo-Time Analysis،Monocle Algorithm

چکیده :

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 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, employ 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.