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Abstract(s)
Cholesterol plays a pivotal role in the progression of tumors, serving as a crucial component for cell membrane formation and the generation of specific proteins and enzymes that stimulate the growth and dissemination of tumor cells. Additionally, cholesterol levels within the tumor microenvironment exert influence over immune responses by hindering the activity of vital components like T-cells and NK-cells, which are indispensable for effective anti-cancer immunity. The primary objective of this research is to investigate whether it is possible to
categorize colon cancer tumors based on disparities in cholesterol-related characteristics and whether these groupings correlate with distinct immune profiles. The Cancer Genome Atlas (TCGA) project is an open-access catalog aiming to comprehensively understand the genomic alterations responsible for various cancer types, by encompassing a vast array of molecular data from thousands of patient samples. One of the pivotal advantages of utilizing TCGA data lies in its sheer scale and diversity. By integrating genomic, transcriptomic, proteomic, and clinical data from a multitude of patients, researchers can identify patterns, mutations, and
biomarkers associated with specific cancers. Taking advantage of this catalog, we selected TCGA RNA-seq dataset from patients with colorectal cancer (480 tumor colon samples and 167 tumor rectum samples). Firstly, we used the Gene Set Enrichment Analysis (GSEA) tool, a powerful tool employed in bioinformatics
and computational biology, to determine the sets of genes and pathways that showed statistically
significance. Upon comparing these samples with their corresponding normal adjacent tissues, notable disparities in lipid metabolism were discerned. While cholesterol-related pathways did not rank as the top differentially regulated pathways, we exclusively observed an upregulation of lipid-related pathways in normal adjacent tissue in comparison to tumor tissue within the colon samples. Subsequently, we conducted in-depth analyses to determine whether colon tumors can be stratified based on differences in cholesterol metabolism
and whether these variations correlate with disparities in the tumor microenvironment.By using the ssGSEA scores of the pathways related to cholesterol metabolism we employed the k-means method to cluster the samples. Remarkably, colon tumor samples naturally segregated into two distinct groups: one characterized by low expression of cholesterol-related genes and the other exhibiting increased expression. Notably, these groupings exhibited disparities in colon sample staging and the prevalence of molecular subtypes within each category. The group displaying enhanced cholesterol metabolism showcased reduced prolifiv eration, underscoring the significance of tumor microenvironment remodeling. Among the top enriched pathways, were pathways associated with modified antigen presentation, cytotoxic immune responses, and remodeling of the extracellular matrix. These observations were consistent with increased infiltration of immune cells driven by the activation of cholesterol metabolism. However, despite the higher quantity of these immune cells, their activation levels were lower in tumors characterized by upregulated cholesterol metabolism. Comparison
of signaling pathways between these groups revealed significant differences in pathways linked to tumor malignancy. In summary, these findings underscore the role of cholesterol metabolism alterations in driving substantial adaptations within the tumor microenvironment. Stratifying colon tumors based on cholesterol metabolism presents a promising avenue, potentially benefiting patients through immunotherapy and cholesterol modulation as adjuvant therapy.
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Keywords
Colorectal cancer Clustering Cholesterol Tumor microenvironment