TO THE EDITOR
We read with great interest the study exploring the differences in diversity and composition of the colonic mucosa-associated microbiota between patients with and without colorectal cancer (CRC) in Indonesia[1]. This study investigated the spatiotemporal atlas of colonic mucosal microbial niche reconstruction in patients with CRC, with the expectation of providing new biomarkers and intervention targets for early diagnosis and personalized treatment of CRC in this region.
CRC is one of the most prevalent malignant tumors globally and the second leading cause of cancer-related deaths[2]. Its etiology and pathogenesis are highly complex[3]. The gut microbiota, as the key to decoding the “dark matter” of the gut, is a crucial tool for determining disease mechanisms[4]. In recent years, the role of the gut microbiota in CRC development and progression has become a research hotspot. The gut microbiota influences the pathological processes of CRC through various mechanisms, including modulating host immune responses, generating metabolic products, and affecting gut barrier function[5,6]. Studies have shown that certain specific bacterial communities (e.g., Fusobacterium nucleatum and Bacteroides fragilis) are significantly enriched in patients with CRC and contribute to tumor development and progression by promoting tumor stem cell neogenesis, regulating cell proliferation and cell cycles, and inducing chemotherapy-resistance responses[7-10]. Additionally, short-chain fatty acids (such as butyrate) produced by metabolism of the gut microbiota play an important role in maintaining gut homeostasis and inhibiting tumor development, whereas some microbial metabolites (such as secondary bile acids) may have procarcinogenic effects[11-14]. Although numerous studies have revealed associations between the gut microbiota and CRC, microbial compositions differ significantly among populations from different regions, which may lead to heterogeneity in the CRC pathogenesis and biomarkers across populations[15]. For example, Pesoa et al[16] found that β-diversity differed significantly among populations from the United States of America, United Kingdom and Argentina and may be closely related to the geographic region of individuals. Therefore, research on the gut mucosal microbiota in specific populations (such as the Indonesian population) holds important scientific and clinical significance.
A comparison of colonic mucosal samples revealed significant differences in microbial diversity and composition between patients with and without CRC in Indonesia. The main findings were as follows: (1) Differences in microbial diversity: For α-diversity, the median Shannon index was higher in the CRC group than in the non-CRC group (3.28 vs 2.82, P > 0.05), indicating a tendency for increased richness and evenness of the microbial community in the CRC group, although the difference was not statistically significant. The Simpson index showed the opposite trend (0.050 vs 0.060, P > 0.05), also without statistical significance. For β-diversity, the two groups differed significantly at the genus (P = 0.002) and species (P = 0.001) levels, indicating that the overall microbial community structure in the colonic mucosa of CRC patients differed distinctly from that of non-CRC patients; (2) Differences in microbial composition: 38 phyla were identified, with Firmicutes, Proteobacteria, Bacteroidetes, and Fusobacteria being dominant. Although the relative abundance of each phylum did not significantly differ between the two groups, Fusobacteria and Proteobacteria were more abundant in the CRC group. 188 genera were identified, among which, Fusobacterium, Faecalibacterium, Citrobacter, Prevotella, and Bacteroides were the most common. The relative abundances of Bacteroides, Campylobacter, Peptostreptococcus, and Parvimonas were significantly higher in the CRC group, whereas the relative abundances of Faecalibacterium, Haemophilus, and Phocaeicola were higher in the non-CRC group. At the species level, the relative abundances of Fusobacterium nucleatum, Bacteroides fragilis, Parvimonas micra, Peptostreptococcus stomatis, Enterococcus faecalis, and Campylobacter hominis were significantly higher in the CRC group. Conversely, Faecalibacterium prausnitzii, Haemophilus parainfluenzae, and Prevotella copri were more common in the non-CRC group; (3) Exploration of potential diagnostic biomarkers: The potentials of Fusobacterium nucleatum and Bacteroides fragilis, both individually and in combination, were evaluated for diagnosing CRC. The areas under the curve were 0.727 for Fusobacterium nucleatum [95% confidence interval (CI): 0.600-0.853] and 0.735 for Bacteroides fragilis (95%CI: 0.607-0.862). The combined area under the curve was 0.786 (95%CI: 0.671-0.900). The combined diagnostic sensitivity was 82.8%, the specificity was 50%, the positive predictive value was 70.7%, and the negative predictive value was 66.7%; and (4) Other aspects: In the CRC group, the median age was higher (61 years vs 47 years, P = 0.002), and the proportion of men was higher (62.9% vs 41.7%) than in the non-CRC group. Regarding diet, 81.4% of participants consumed at least one serving of vegetables and fruits daily, and 78.2% had meat in their diet, but only 8.5% consumed 100-500 g of red meat daily.
These results provide a new perspective on the pathogenesis of CRC and offer important references for future diagnosis and treatment. The following advantages of the study strongly support the reliability and scientific nature of the results and lay a solid foundation for subsequent clinical applications and further research. (1) Rationality of the study design: The researchers used a case-control design, comparing the microbiota compositions of patients with and without CRC. This design effectively controls for confounding variables and provides more reliable results. In selecting the samples, the researchers excluded patients who had used antibiotics or probiotics or had special diets, ensuring the homogeneity of the samples; (2) Advanced technological application: In addition to 16S rDNA sequencing, the researchers used the Oxford nanopore technologies platform for sequencing. This third-generation sequencing technology generates longer sequence fragments, allowing more accurate species-level classification; (3) Focus on the Indonesian population: The results provide unique microbiota data for the Indonesian region, which is of great significance for understanding the differences in microbiotas across geographical regions and their relationship with CRC. Diet and lifestyle in Indonesia differ significantly from those of Western countries, which endow the study results with unique scientific value; (4) Scientific significance of the results: Microbiota compositions differed significantly between patients with and without CRC, especially at the genus and species levels, thus offering potential biomarkers for early diagnosis and prognosis assessment of CRC, with important clinical application value; and (5) Preliminary exploration of microbial functions: The study explored the potential mechanisms of certain microbes (such as Fusobacterium nucleatum and Bacteroides fragilis) in CRC. For example, Fusobacterium nucleatum may induce inflammatory responses and DNA damage by secreting the FadA protein, thereby promoting carcinogenesis, which provides direction for future functional studies.
Although this study yielded significant results by exploring the relationship between CRC and the colonic mucosal microbiota, providing valuable data support for subsequent research and clinical applications, the limitations in its research process must be addressed as they may impact the interpretation and application of the results. Thus, the process must be thoroughly analyzed and improved in future studies to further enhance the scientific nature and reliability of the research. The main bottlenecks that must be resolved are as follows: (1) Limitations in sample selection: The samples from the non-CRC group were derived from patients with gastrointestinal symptoms but no obvious tumors. This may have led to selection bias as other gastrointestinal diseases (e.g., irritable bowel syndrome or microscopic colitis) may affect the microbiota composition. Moreover, an ideal control group should be healthy, asymptomatic individuals to exclude interference from other diseases with the microbiota; (2) Insufficient diagnostic criteria for non-CRC patients: The diagnostic criteria for the non-CRC group may have some subjectivity. For example, some patients may have had undetected early cancerous lesions, which may affect the accuracy of the results; (3) Insufficient sample size: Although a sample size of 59 was statistically sufficient, it was still relatively small, which may affect the universality and representativeness of the results. A larger sample size would enhance the reliability and applicability of the results; (4) Lack of longitudinal studies: This study was a cross-sectional study; thus, it could reveal no causal relationship between microbiota alterations and CRC progression[17]. Longitudinal studies allow better understanding the dynamic changes in the microbiota during cancer development; (5) Failure to consider other influencing factors: The study did not fully consider the impact of diet, antibiotic drugs, lifestyle, genetic factors, and other factors on the microbiota. These factors can significantly affect the microbiota composition, thereby influencing the research results. For example, the Indonesian diet is characterized by high fiber and low red meat content, which may impact the microbiota composition; (6) Complexity of the statistical analysis: Although appropriate statistical models were used, analysis of microbiota data is typically complex and often requires more refined statistical methods to address multiple comparisons and non-normal data distributions. Moreover, some results (e.g., differences in the Shannon and Simpson indices) did not reach statistical significance, likely owing to the small sample size or large data variability; and (7) Lack of in-depth exploration of microbial functions: This study mainly focused on microorganism composition and did not investigate microbial functions and mechanisms of action in CRC. Future studies should consider combining techniques such as metabolomics and transcriptomics to further reveal the functional characteristics of the microbiota.
To create more rigorous evidence and promote clinical translation, a systematic improvement plan must be developed to address the existing bottlenecks. Future research should focus on optimizing the following six dimensions: (1) Increasing sample size and long-term follow-up: Future studies should expand the samples to include more CRC patients and non-CRC control populations. Long-term follow-up study designs should also be implemented to conduct regular microbiota testing for CRC patients to understand the trends in microbiota changes at different CRC stages; (2) Expanding study populations and disease controls: Similar studies should be conducted in different regions, incorporating populations with diverse ethnic backgrounds and lifestyles. Additionally, populations with other gastrointestinal diseases (e.g., inflammatory bowel disease and intestinal infectious diseases) should be included as controls to more accurately assess the specificity of the CRC-associated microbiota; (3) In-depth functional analysis and mechanistic studies: Metagenomics techniques, in-depth sequencing and functional analysis of samples should be combined to comprehensively understand the functional characteristics of the microbiota. Additionally, in vitro experiments and animal models should be used to investigate interaction mechanisms between the microbiota and host immune system as well as intestinal barrier function; (4) Optimizing control group selection: As much as possible, healthy individuals without gastrointestinal symptoms should be selected as controls to minimize the impact of microbiota disturbances in the control group on the study results; (5) Detailed assessment of diet and other factors: More refined dietary survey methods should be used to quantify participants’ dietary structures, and detailed information on medication history and lifestyle habits should be collected to assess the impact of these factors on the microbiota; and (6) In-depth exploration of diagnostic biomarkers: By combining multiple types of microbiotas, more effective diagnostic models should be constructed to improve the diagnostic sensitivity and specificity, and the current findings should also be compared with similar studies to contextualize the novelty of the proposed biomarkers. Moreover, biomarkers derived from non-invasive specimens such as the fecal microbiota, urine and blood should also be explored to enhance the convenience of specimen collection and diagnosis.
In summary, this study unveiled the spatiotemporal atlas of colonic mucosal microbial niche reconstruction, which serves as the microbial geography within the neoplastic microenvironment. It provided important foundational data for microbiota research in patients with CRC in Indonesia. Despite some limitations, the findings hold significant clinical implications and offer direction for future research. Future studies should further investigate the functions of the microbiota and their mechanisms of action in CRC based on local populations to provide new insights for early diagnosis and treatment of CRC.
