Pan-cancer analysis of microRNAs activity based on network science techniques

• Grant number: 24/14197-5
• Support Opportunities: Scholarships in Brazil - Master
• Start date: May 01, 2025
• End date: February 28, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: André Santanchè
• Grantee: Mylena Roberta dos Santos
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Cancer is recognized as a systems biology disease, requiring an integrated view for its study, such as that provided by network medicine. By applying network science techniques, network medicine has significantly contributed to advances in cancer research, particularly by comparing gene expression profiles across different cancer types. Since the early 2010s, a comparative approach known as pan-cancer analysis has gained prominence, with network medicine analytical tools playing a pivotal role. Additionally, microRNAs are crucial for cancer biology, acting as regulators of gene expression and establishing themselves as important cancer biomarkers and therapeutic targets.However, there are open challenges associated with using complex network-based approaches in pan-cancer research. We argue that, in the comparative analysis of various types of cancer, a central challenge lies in improving strategies that rely on the topological study of networks. From a network design that represents interactions between messenger RNAs and microRNAs, we propose an analytical axis based on topological metrics and structures to systematize the study and comparison of networks from different cancer types. To identify recurring topological patterns, instead of analyzing individual interactions, our analytical axis focuses on organizational and structural characteristics of global nature within networks. This study focuses on the DLK1-DIO3 genomic region, a source of multiple microRNAs identified as tumor suppressors in distinct cancer types. To our knowledge, no pan-cancer analysis grounded in network medicine has specifically targeted a genomic region before.