Colorectal cancer (CRC) has become the third most common malignant tumor in the world . The American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system is the standard for determining the prognosis of patients with CRC and is highly correlated with 5-year overall survival. According to the TNM staging system, It has not spread to nearby lymph nodes (N0) or 1 to 3 nearby lymph nodes (N1) .
The microbiome is a complex ecosystem of microorganisms that live in and on the human body, particularly in the gut . The gut microbiome plays a crucial role in human health, including digestion, vitamin synthesis, and immune system modulation . The gut microbiome can modulate the immune response to cancer cells . The microbiome can promote an inflammatory response that promotes the growth of cancer cells or stimulate an immune response that helps eliminate cancer cells . Several studies demonstrated that gut microbiome dysbiosis is associated with tumorigenesis and/or tumor growth across CRCs [5,7]. Individuals with CRC have a less diverse microbiome, meaning there are fewer types of bacteria present. There is a higher abundance of specific bacterial species in individuals with CRC, such as
Internal organs were known to aseptic, but recent investigations have unveiled the existence of microorganisms, encompassing bacteria, viruses, and fungi, residing within tumors [13,14]. It is noteworthy that the phenomenon of microbiome invasion in the tumor microenvironment remains an actively researched area, with numerous aspects concerning its impact on tumor biology and response to treatment yet to be comprehensively elucidated. Nevertheless, exploration of the microbiome's role within the tumor microenvironment holds considerable potential for advancing our understanding of cancer biology and facilitating the development of innovative therapeutic strategies.
Recent studies suggest that the composition of the microbiome may influence the effectiveness of cancer treatments . The gut microbiome plays a role in the efficacy of immunotherapy, a type of cancer treatment that harnesses the immune system to target cancer cells [15-17]. Changes in the gut microbiome may contribute to the development and progression of CRC and may play a role in the efficacy of cancer treatment . Thus, in this study, we understand the underlying molecular differences among the microbiome and tumor-infiltrating immune cells of CRC according to the lymph node metastasis stages.
We downloaded Kraken-TCGA (The Cancer Genome Analysis)-Raw Data (n=18,116) from microbiome count data . We selected data whose sample_type is a primary tumor, investigation is TCGA-COAD (colon adenocarcinoma) and experimental_strategy is RNA sequencing. For this study, we also downloaded Metadata-TCGA (n=18,116) and Clinical data (n=7,579) to obtain patient data . We screened samples whose pathologic N lable is N0, N1, N1a, N1b, and N1c and merged three files using sample ID, case uuid, and case-submitter-ID (Fig. 1). Then, samples obtained from one person with the same case-submitter-ID were integrated using the mean. In duplicated samples, we selected preferentially clinical data including the TCGA barcode from “file name”. In this study, processing and producing graphs were used with the R program (version 4.2.0.).
To compare N0, and N1 groups and identify significantly different bacteria between them, we processed the merged dataset and inputted it into LEfSe via the Huttenhower Lab Galaxy Server. LEfSe computes effect sizes, enabling the quantification of the magnitude with which a specific microbe is linked to a particular group. This analytical approach extends beyond the mere identification of differences, providing insight into the significance and strength of these observed distinctions . We used the LEfSe and adjusted the logarithmic LDA score cutoff to 2.0. Virus samples were excluded before input.
According to pathologic groups, we drew a boxplot graph of the genus screened by LEfSe . A genus with excessive outlier value that might affect the overall comparative study and a genus whose median is 0 was excluded from the boxplot. But a genus with 0 medians was included in the survival analysis. Boxplot was drawn with data in the range of 0.01 to 0.97 quantile of count data.
To identify the genus that significantly affects the survival of patients, we drew the Kaplan-Meyer survival curve and represented the significance level (
Correlation analysis between tumor infiltrate immune cells with microbiome was performed through TCGA query. We retrieved RNA expression data using the R package “TCGA bio links” . Barcode was used preferentially if there was a detailed case submitter code in the file name, and if not, case-submitter-ID was used. In the case of duplicated samples because of different plate numbers, data was used with average values. Gene ensembles were converted to
We obtained a final 368 samples that met our study criteria. Of these, 266 samples were classified as N0 stages, and 102 samples were classified as N1 stages (Table 1). Overall, there was no significant difference in patient status between the two groups, as determined by population numbers. However, the N0 group had significantly younger patients than the N1 group (N0: 68.58647 years, and N1: 64.74510 years,
To assess microbial diversity between the two groups, alpha diversity, and beta diversity analyses were conducted (Fig. 2). Alpha diversity was evaluated using the observed method, which measures microbial richness, as well as the Shannon and Simpson indices, which account for evenness. None of the three methods revealed any significant difference in alpha diversity between the two groups (Fig. 2A). Principal coordinate analysis of the Bray-Curtis dissimilarity index further demonstrated that there was no statistically significant variation in the microbial composition between the two groups (Fig. 2B).
As a result of the boxplot that depicts genera screened by LEfSe (Fig. 3A), 18 genera in the N1 group, and 3 genera in the N0 group were found, excluding genera with a median 0 and those with a large influence of outlier (Fig. 3B and C). Although excluded in the boxplot, genera with 0 medians are analyzed in survival analysis.
To identify the genera that significantly affect survival in CRC, we performed survival analysis using the Kaplan-Meier method on the output data from the LEfSe analysis (Fig. 4). Based on a significance level of 0.1, we found that the genera
To identify mechanisms of action
In this study, we first demonstrated microbiomes are associated with the survival of lymph node metastasis in CRC patients. Microbiomes communicate with gut mucosa and regulate gut immune function [15-17]. During the cancer progression, changes in pH and metabolism in the gut seem to occur and good microbiomes also disappear .
We observed no significant variation in the alpha and beta diversity of microorganisms between the N0 and N1 groups. However, we identified specific strains that exhibited statistically significant differences between the two groups. Notably, a greater number of bacteria with significantly higher abundance were found in the N1 group compared to the N0 group. Among the strains exhibiting differences between N0 and N1, the majority of strains showing higher abundance in N1 were identified as gram-negative bacteria.
In our survival analysis, interestingly,
Microbiome and immune cell co-occurrence network analysis unveiled a heightened number of significant correlations within the N1 group. Notably, increased intercellular interaction among immune cells and a stronger correlation with
Also, tumor-infiltrating lymphocyte affects the prognosis of patients and accuracy is proportional to stage level  and we could apply a new score system that reflects the diagnosis and predicts future prognosis associated with the microbiome in CRC. It would help to understand the survival and therapy for CRC patients.
This work was supported by grants from the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2021R1A2C4001466, 2022R1A5A2027161, and 2021R1A6A3A01086785).
DL and YHK initiated the study and guided the work. DH and YY collected and normalized the data. DH, YY, and HK analyzed the experimental data and interpreted the data. All authors wrote the manuscript with input from all co-authors.
Demographic of sample
|Black or African American
|Pathologic N stage
|Days to last follow-up (day)
Values are presented as mean (range) or number only.
N0, not spread to nearby lymph nodes; N1, 1 to 3 nearby lymph nodes.
-, not available.