Interplay between Genome, Metabolome and Microbiome in Colorectal Cancer

Abstract

Background: Colorectal cancer (CRC), a major health concern, is developed depending on environmental, genetic and microbial factors. The microbiome and metabolome have been analyzed to study their role in CRC. However, the interplay of host genetics with those layers in CRC remains unclear. Methods: 120 individuals were sequenced and association analyses were carried out for adenoma and CRC risk, and for selected components of the microbiome and metabolome. The epistasis between genes located in cholesterol pathways was analyzed; modifiable risk factors were studied using Mendelian randomization; and the three omic layers were used to integrate their data and to build risk prediction models. Results: We detected genetic variants that were associated to components of metabolome or microbiome and adenoma or CRC risk (e.g., in LINC01605, PROKR2 and CCSER1 genes). In addition, we found interactions between genes of cholesterol metabolism, and HDL cholesterol levels affected adenoma (p = 0.0448) and CRC (p = 0.0148) risk. The combination of the three omic layers to build risk prediction models reached high AUC values (>0.91). Conclusions: The use of the three omic layers allowed for the finding of biological mechanisms related to the development of adenoma and CRC, and each layer provided complementary information to build risk prediction models.

Performance of the Use of Genetic Information to Assess the Risk of Colorectal Cancer in the Basque Population

“…Various modifiable risk factors have been observed to affect CRC risk [ 11 , 12 , 64 ], but we were not able to find those effects when all CRC patients were analyzed. However, when each location was analyzed, the effect of genetic risk to higher cholesterol levels (general levels or LDL) on left colon cancer and higher BMI on rectal cancer were detected, as has been suggested previously for CRC [ 11 , 12 , 13 , 64 , 65 ]. Although we tried to replicate the results obtained using Mendelian Randomization in previous works [ 12 , 13 ] and the traits and instruments used seem appropriate to replicate them, the results we obtained were limited or were detected only by one method.…”

“…For carrying out Mendelian Randomization (MR) analyses TwoSampleMR (v0.5.6) [ 27 ] and gsmr (v1.0.9) [ 28 ] packages from R language (v4.0.5) were used [ 29 ], as we have used previously to study the effect of modifiable risk factors in CRC risk [ 13 ].…”

“…Moreover, the effect of genetic information on modifiable risk factors (e.g., lipids level) on CRC has been analyzed using Mendelian Randomization analyses [ 11 , 12 ], a method to estimate causal effects if specific assumptions are fulfilled. In addition, it has been detected that some genetic variants involved in the abundance of some microbial groups are related to CRC risk [ 13 ]. Finally, it has been proposed that polygenic risk scores (PRS) derived from different genetic studies are useful to predict the risk of CRC of one individual based on the carriership of risk genetic variants, among other factors [ 14 , 15 ].…”

Colorectal Cancer: A Review of Carcinogenesis, Global Epidemiology, Current Challenges, Risk Factors, Preventive and Treatment Strategies

“…Other effective strategies include identifying and monitoring high-risk populations, including individuals with inflammatory bowel disease, families with hereditary CRC syndrome, individuals whose family history suggests a genetic predisposition to CRC but have no detectable genetic markers, and individuals whose phenotypic appearance indicates high risk. The metabolome helped identify the important biological activities affected by genetic variation [ 12 ]. The most frequently used CRC screening methods are fecal occult blood tests (FOBTs) and lower endoscopy [ 13 ].…”

Rapid and Multifaceted Characterization of the Effects of the Secreted EVome in Pancreatic Cancer: A Global Omics Approach for Screening Putative Biomarkers

“…In recent years, there have also been several studies that show a variety of microorganisms colonized in pancreatic cancer tissues which are now known to be involved in the development of pancreatic cancer regulating in ammation, metabolism, immune changes, and tumor microenvironment changes [25]. In colon cancer, there is an interplay between changes in the genome, metabolome, and the microbiome to effect changes in tumor development and metastasis [26,27]. Here, we show that the cancer cell EVome does induce signi cant changes in the microbiome of experimental mice.…”