Researchers at the Mount Sinai Center for Transformative Disease Modeling used multi-omics genetic analysis tools to identify 4,749 key gene clusters, termed “prognostic modules,” which they say significantly influence the progression of 32 different types of cancer. The study identified critical genes and their complex relationships that either halt or promote cancer progression. This new understanding opens the door for targeted research and development of future treatments and diagnostic methods for cancers.
They hope that their findings will serve as a comprehensive resource and lay the foundation for developing next-generation cancer treatments and diagnostic markers. Co-authors Bin Zhang, PhD, the Willard T.C. Johnson research professor of neurogenetics and director of the Mount Sinai Center for Transformative Disease Modeling, and Peng Xu, PhD, instructor of genetics and genomic sciences, reported their discoveries in Genome Research, in a paper titled, “Multiscale network modeling reveals the gene regulatory landscape driving cancer prognosis in 32 cancer types.” In their paper, the authors concluded, “Overall, our study provides rich resources of prevalent gene networks and underlying multiscale regulatory mechanisms driving cancer prognosis, which lay a foundation for biomarker discovery and therapeutic target development.”
Despite significant progress in cancer research, understanding the disease’s genetic intricacies remains challenging. Previous research often focused on isolated gene functions in specific cancer types, but as the authors noted, “Cancer is a multifaceted and intricately regulated process that involves the coordinated activity of genes from various pathways … Discovering genes that predict patient survival is crucial for cancer biomarker development, risk assessment, and clinical decision-making.”
And while there are many different cancer types, the authors further pointed out, “… there exists a common framework that governs their development and progression ….” Zhang added, “We aimed to fill this knowledge gap by providing a comprehensive analysis of gene-gene interactions across various forms of cancer.”
For their reported study, Zhang and Xu used a multi-omics approach, incorporating genomic, transcriptomic, and epigenomic data, to carry out a systematic analysis of coexpression networks in 32 different cancer types. They employed advanced systems biology approaches to analyze more than 10,000 patient samples from the Cancer Genome Atlas (TCGA), one of the most comprehensive public cancer databases, and used rigorous network methods to identify and validate the gene clusters that have a significant impact on cancer prognosis. They explained, “.. we used the advanced, uniform, and well-regarded network tool multiscale embedded gene coexpression network analysis (MEGENA) to systematically identify prognostic modules.”
Their analyses identified 4749 prognostic modules that the researchers say play essential roles in cancer regulatory pathways, including cancer cell division, metastasis, and immune microenvironment processes. “The network modules are regulated by multiscale m mechanisms involving the interplay of gene expression, methylation, and chromatin accessibility,” they wrote. The study found that network modules formed preserved module clusters in chromosomal hotspots. “There are also cancer-type-specific prognostic modules that participate in cancers-specific biological processes,” the investigators stated.
“The implications of our findings are profound,” Zhang noted. Added Xu, “Our study goes beyond merely identifying these modules. It also elucidates the multi-scale regulations that govern their functions.”
The team suggests the study serves as a crucial foundation for developing targeted therapies that could lead to improved patient outcomes. “Our findings offer fertile ground for the next wave of cancer research and treatment strategies,” said Zhang. As the authors concluded in their report, “Together, our pan-cancer analysis provides a holistic view of the tumorigenesis of prognostic modules and revealed potential biomarkers for cancer development and progression.” The researchers have shared the networks, modules, prognostic features, and their functional annotations on open-source websites, so scientists can use the data to “ … evaluate and explore a wide range of cancer biology questions.”
