Tumor-Associated Microbiota in Proximal and Distal Colorectal Cancer and Their Relationships With Clinical Outcomes

Abstract

The proximal and distal subsites of colorectal cancer (CRC) have distinct differences in their embryonic origin, epidemiology, and prognosis. Therefore, they are not considered as the same disease. However, the possible difference in microbial characterization of the two subsites of CRC is still unclear. In this study, we explored tumor microbiota diversity and composition difference in patients with proximal (N = 187) and distal CRCs (N = 142). This was carried out on cancer tissues and adjacent tissues using bacterial 16S rRNA sequencing. The Kaplan–Meier method was used to analyze the correlation between differential flora and overall survival rate of the patients. It was found that there were significant differences in tumor microbial characteristics between the proximal and distal CRC tissues. The microbiota communities were distinctly richer in the proximal colon tumor tissues than in the distal CRC tissues. Microbial diversity and structure were relatively constant in the paracancerous normal tissues of the proximal and distal colorectum. Generally, microbial communities of CRC tumor tissues were composed of Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. Alpha diversity in the proximal and distal CRC tumor tissues was closely related to specific microflora. The abundance of Fusobacteria was associated with age of patient, tumor diameter, and tumor microsatellite instability (MSI) status of the patients. Moreover, Fusobacteria enrichment was associated with poor prognosis especially in patients with proximal colon cancers, but not in patients with distal CRC. In conclusion, proximal and distal subsites of the CRC present distinct microbiota diversity and community structures. The differences indicate that there are different risk factors across anatomical subsites of CRC, which may provide a new strategy for precise prevention and treatment of CRC in the future.

Consumption of Yoghurt and Other Dairy Products and Risk of Colorectal Cancer in Iran: The IROPICAN Study

“…This is also consistent with the evidence of the gut microbiota acting in the early stages of colon carcinogenesis [23], emphasizing the potential benefit of fermented food against cancer development, especially in those sites of the colon where bacteria mostly exert their function [24]. Indeed, different compositions of gut microbiota have been described in relation to different CRC types and outcomes [25,26]. Our data refer to the year before CRC diagnosis/interview, used as a proxy for usual diet of the participants who could have changed their habits once aware of the disease or because of symptoms [27].…”

Gut microbiome: New biomarkers in early screening of colorectal cancer

“…One study analyzed the microbial diversity in the tumor tissues from different intestinal locations and found that the proximal intestinal cancer tissues are significantly richer in intestinal bacteria than the distal intestinal cancer tissues. 39 In 2012, Tjalsma H et al were the first to propose a “driver‐passenger” model to explain the “adenoma‐carcinoma” progression of CRC. 40 The model suggests that driver bacteria promote the colonization of passenger bacteria by altering the intestinal microenvironment, and differences in the types of driver and passenger bacteria are found at different stages of CRC development.…”

“…Moreover, the heterogeneity of the tissue microbiome in different CRC stages indirectly suggests that the fecal microbiome and blood microbiome of CRC stage heterogeneity. One study analyzed the microbial diversity in the tumor tissues from different intestinal locations and found that the proximal intestinal cancer tissues are significantly richer in intestinal bacteria than the distal intestinal cancer tissues 39 . In 2012, Tjalsma H et al were the first to propose a “driver‐passenger” model to explain the “adenoma‐carcinoma” progression of CRC 40 .…”

A novel bi-directional heterogeneous network selection method for disease and microbial association prediction

“…association probability ŷ of original prediction and association probability ŷs of post intervention prediction. Our purpose is that the model can make the best choice between ŷ and ŷs for the final prediction, so as to reduce the impact caused by the local structural difference of nodes [25], which is calculated as follows:…”