Fereshteh Fallah Atanaki - Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
Hesam Montazeri - Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
Kaveh Kavousi - Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

Tumor microenvironments are complex ecosystems comprised of cancer cells, infiltrating immune cells, andother cell types. Diverse cell types interact with each other and collectively create complex networks ofintercellular and intracellular signals. In fact, these networks determine cancer progression as well asresponse to therapy. Recent advances in single-cell transcriptomics have laid the foundations for studyingtumors at the microscopic resolution. This technology provides a compelling strategy to fill gaps inknowledge of human tumors and uncover complex networks between diverse cell types in a tumor ecosystem.In this study, we proposed a framework to explore the molecular mechanisms in different stage of cancer byintegrating single-cell RNA-sequencing (scRNA-seq) data and a multi-layer network that integrateinformation from different levels. The heterogeneous multi-layered network provides a great way to representthe biological system's hierarchy: from gene to cellular function and final phenotype. It allows to uncovernovel relations between biological entities on a microscopic view. The pipeline was applied to gliomascRNA-seq data and complex network analysis (topology and functional analysis) of the different networksfrom different cancer stages revealed important signaling module, important ligand-receptor connection, andmost significant ligand – receptor-TF axis. Differential network analysis between stage III and IV gliomadiscovered the most important nodes and edges in the rewiring of interactions. In addition, hidden hierarchiesin data have been revealed by heterogeneous multi-layered network models.

Tumor microenvironment; complex networks; heterogeneous multi-layered network; scRNA-seq