Charting the metabolic biogeography of the colorectum in cancer: challenging the right sided versus left sided classification

Abstract

Objective: Colorectal cancer (CRC) is conventionally classified as right sided, left sided, and rectal cancer. Clinicopathological, molecular features and risk factors do not change abruptly along the colorectum, and variations exist even within the refined subsites, which may contribute to inconsistencies in the identification of clinically relevant CRC biomarkers. We generated a CRC metabolome map to describe the association between metabolites, diagnostic and survival heterogeneity in cancers of different subsites of the colorectum.

Design: Utilizing 372 patient-matched tumor and normal mucosa tissues, liquid chromatography-mass spectrometry was applied to examine metabolomic profiles along seven subsites of the colorectum: cecum (n = 63), ascending colon (n = 44), transverse colon (n = 32), descending colon (n = 28), sigmoid colon (n = 75), rectosigmoid colon (n = 38), and rectum (n = 92).

Results: 39 and 70 significantly altered metabolites (including bile acids, lysophosphatidylcholines and lysophosphatidylethanolamines) among tumors and normal mucosa, respectively, showed inter-subsite metabolic heterogeneity between CRC subsites. Gradual changes in metabolite abundances with significantly linear trends from cecum to rectum were observed: 23 tumor-specific metabolites, 30 normal mucosa-specific metabolites, and 15 metabolites in both tumor and normal mucosa, had concentration gradients across the colorectum, and is disease status dependent. The metabolites that showed a linear trend included bile acids, amino acids, lysophosphatidylcholines, and lysophosphatidylethanolamines. Comparison of tumors to patient-matched normal mucosa revealed metabolite changes exclusive to each subsite, thereby further highlighting differences in cancer metabolism across the 7 subsites of the colorectum. Furthermore, metabolites associated with survival were different and unique to each subsite. Finally, an interactive and publicly accessible CRC metabolome database was designed to enable access and utilization of this rich data resource ( https://colorectal-cancer-metabolome.com/yale-university ).

Conclusions: Gradual changes exist in metabolite abundances from the cecum to the rectum. The association between patient survival and distinct metabolites with anatomic subsite of the colorectum, reveals differences between cancers across the colorectum. These inter-subsite metabolic heterogeneities enrich the current understanding and substantiate previous studies that have challenged the conventional classification of right-sided, left-sided, and rectal cancers, by identifying specific metabolites that offer new biological insights into CRC subsite heterogeneity. The database designed in this study will enable researchers to delve into granular information on the CRC metabolome, which until now has not been available.

Keywords: Biomarkers; Colorectal cancer; Metabolome database; Metabolomics; Subsite heterogeneity.

Fig. 1

A Workflow to investigate diagnostic and survival heterogeneity among cancers of different subsites of the colorectum. C-Cecum, A-Ascending, T-Transverse, D-Descending, S-Sigmoid, RS-Rectosigmoid, R-Rectum. B Unique metabolites altered in tumor tissue compared to normal mucosa tissues in each specific subsite of the colorectum. C Metabolites uniquely altered in the left (descending, sigmoid, rectosigmoid and rectum) and right (cecum, ascending) subsites of the colorectum. FDR-corrected p-values with log₂ fold changes in brackets are shown for statistically significant metabolites. The significant changes (FDR-corrected p < 0.05) are highlighted in blue. Only FDR-corrected p-values and not fold changes are shown for non-significant metabolites

Fig. 2

A Hierarchical clustering analysis of metabolite abundances from colorectum tissue samples. Abundances for metabolites significantly differentiating in at least one pairwise comparison between colorectum subsites are shown across seven colorectum subsites in tumors (39 metabolites). Abundances were averaged from each subsite. Metabolites highlighted with blue asterisk (19 metabolites) were commonly altered in both normal and tumor tissues. B Hierarchical clustering analysis of metabolite abundances from colorectum tissue samples. Abundances for metabolites significantly differentiating in at least one pairwise comparison between three major regions are shown across the left side colon, right side colon, and rectum in tumors. Abundances were averaged from each subsite. Metabolites highlighted with blue asterisk (25 metabolites) were commonly altered in both normal and tumor tissues. C Distribution of metabolite abundances showing concentration gradient exist from cecum to rectum in both tumors and normal mucosa. Linear regression analysis was performed between metabolite abundances and seven subsites. Three representative metabolites showing significantly linear trend (P_linear<0.05) from cecum to rectum. The concentration gradient is similar in both normal colon and colon tumor (glycoursodeoxycholic acid), concentration gradient is different between normal colon and colon tumor (N-acetylneuraminic acid and adenosine diphosphate). Each subsite is presented in a unique color. Red-Cecum, green-Ascending, blue-Transverse, cyan- Descending, light violet-Sigmoid, yellow-Rectosigmoid and dark violet-Rectum. D Subsite specific unique metabolites markers significantly (p < 0.05) associated with CRC prognosis. Hazard ratio (HR) for each metabolite (log₂-transformed abundance) was calculated by Cox proportional hazard regression analysis between log₂ abundances of individual metabolites and 5-year overall survival in each subsite, adjusting for age, sex, chemotherapy and stage. Error bars represent 95% confidence intervals (CIs). The unique survival metabolites for each subsite are color coded differently. Cecum-blue, Ascending-pink, Transverse-green, Descending-yellow, Sigmoid-purple, Rectosigmoid-grey, Rectum-ice blue. GDCA - Glycodeoxycholic acid, GUDCA -Glycoursodeoxycholic acid, 5-HIAA - 5-Hydroxyindole-3-acetic acid, N-acetylneuraminic acid, PAA - Phenylacetylglycine, PAGln - Phenylacetylglutamine, NMDA - N-Methyl-D-aspartic acid, E2-3G - Estradiol-3-glucuronide, gamma-Glu-Glu - Gamma-glutamylglutamate, g-Glu-epsilon-lys - Epsilon-(gamma-Glutamyl)lysine, SAM - S-Adenosyl-methionine, NAAG - N-Acetylaspartylglutamic acid