Identification of putative cancer-associated gene clusters and gut-microbiome modules associated with increased susceptibility to GI-tract associated cancers

Abstract

Gastrointestinal (GI)-tract associated cancers remain a significant global health concern due to their high incidence and mortality rates. These complex diseases arise from intricate interactions between genetic factors and environmental influences, including the gut microbiome. Understanding the molecular underpinnings of cancer development is crucial for identifying potential biomarkers and therapeutic targets. This review aims to explore the emerging research on putative cancer-associated gene clusters and gut-microbiome modules associated with increased susceptibility to GI-tract associated cancers. The investigation of cancer-associated gene clusters provides insights into co-expressed or functionally related genes that may contribute to oncogenesis. Integrative genomic approaches have shown promise in uncovering novel candidate genes with implications for cancer risk and progression. Furthermore, recent advancements in bioinformatics and machine learning techniques have enabled the identification of gene regulatory networks and signaling pathways involved in GI cancer development. The gut microbiome, a dynamic community of microorganisms residing in the GI tract, has gained attention as a potential modulator of cancer risk. The microbiome’s influence on the metabolism of dietary components, production of carcinogens, modulation of inflammation, and immune responses may impact the development and progression of GI-tract associated cancers. The characterization of gut-microbiome modules, groups of interacting microorganisms, and their relationship with host factors is key to unraveling the intricate mechanisms by which the microbiome influences cancer susceptibility. This project will use a variety of methods to identify putative cancer-associated gene clusters and gut-microbiome modules associated with increased susceptibility to GI-tract associated cancers. These methods will include genome-wide association studies (GWAS), metagenomic analyses, and microbial interaction network analysis. The identification of these putative cancer-associated gene clusters and gut-microbiome modules will be an important step in understanding the role of the gut microbiome in cancer development. This knowledge could be used to develop new strategies for preventing and treating cancer.