In this study, we conducted the first-ever examination of the integrated microbiome from stool and saliva samples of colorectal cancer (CRC) patients in comparison to healthy controls (CNs) within the Iranian population, utilizing the 16S rRNA sequencing method. The utilization of microbiota as biomarkers for disease and health has gained significant traction, particularly with the advancements in 16S rRNA sequencing technology.

Our results, as depicted in the demographic table, reveal a noteworthy difference between CPs and CNs concerning occupation, physical activity, and smoking habits. Interestingly, housewives and retired individuals exhibited a higher prevalence of CRC compared to working and non-retired individuals. Furthermore, smoking and a lack of exercise were more prevalent among CP patients compared to CNs.

In general, the incidence of CRC tends to be higher in individuals over 50 years old, whereas those under 50 years old, who typically undergo screening, are generally healthier. This age-related discrepancy is a noteworthy factor contributing to the differences observed between the CP and CN groups. Additionally, the occurrence of CRC in individuals with a family history of the disease and a personal history of other illnesses and surgeries was more prevalent than in CNs. This implies that individuals with a susceptibility marked by a history of other diseases and surgeries are more predisposed to CRC than those without such histories.

The notable observation of distinct microbial profiles between CPs and CNs highlights a significant aspect, suggesting that the microbiome may play a crucial role in the initiation and development of CRC. For instance, certain microbial patterns were found to be significantly more abundant in CRC patients compared to CNs, with specific examples including Chloroflexi, Lactobacillaceae, Rivulariaceae, Calothrix parietina, Rothia dentocariosa, and Rothia mucilaginosa, which exhibited higher abundancy in the saliva of CRC patients but were entirely absent in CN individuals. Similarly, Coprobacillaceae, Enterococcaceae, Neisseriaceae, Streptococcaceae, Bacteroides cellulosilyticus, Coprobacillus cateniformis, Porphyromonas asaccharolytica, Sphingobacterium bambusae, and Streptococcus vestibularis were identified as the most abundant microbes in the feces of CRC patients, whereas they were absent in CN individuals.

While our findings suggest a compelling association between the presence or absence of certain microbes and CRC, it is essential to conduct studies on a larger population to provide more definitive insights. Our results align with the research by Flemer et al. [18], who identified 63 operational taxonomic units (OTU) distinguishing CRC cases from CNs, including 29 oral OTU and 34 stool OTU. Additionally, our findings are consistent with previous studies that have highlighted the ability of specific microbiota to differentiate individuals with CRC or adenoma polyps from healthy individuals.

Notably, research conducted across various geographical regions such as the USA, Canada, Ireland, Spain, China, Colorado, France, and India has explored the increased presence of bacteria in CRC. Despite differences in ethnicity and geography influencing microbial patterns, it is intriguing that many of the microbes identified in these studies closely correlate with those increased in our CRC patients, including Fusobacterium, Porphyromonas, Prevotella, Bacteroides, and Streptococcus [18, 22,23,24,25,26,27,28].

Identifying a group of microbes with higher abundance in CPs than in healthy CNs and demonstrating statistical significance is crucial, as it facilitates the selection of potential biomarker candidates. In our study, we observed an increased number of Fusobacteria in the saliva of CRC patients compared to CNs, as well as a higher abundance of Lachnospiraceae and Prevotellaceae in the stool of CRC patients compared to CNs. Consistent with our findings, Flemer et al. reported differential abundance of certain oral microbiotas between CPs and CNs, including Parvimonas, Haemophilus, Prevotella, Alloprevotella, Neisseria, Lachnoanaerobaculum, and Streptococcus [18].

Furthermore, non-pathogenic microbiota in the human gut or microbiota that produces short-chain fatty acids (SCFA) play a crucial role in human health and disease prevention [29]. In our research, Akkermansia muciniphila showed significantly higher abundance in CNs compared to CPs. Akkermansia muciniphila is an important bacterium that degrades mucin in the gut, and its role is debated regarding whether it is beneficial or harmful [30]. Patients with conditions such as overweight, obesity, type 2 diabetes [31], and inflammatory bowel disease (ulcerative colitis and Crohn's disease) [33, 34] have exhibited reduced levels of Akkermansia muciniphila in their intestines. In contrast to our findings, Wang et al. reported that Akkermansia muciniphila exacerbated the development of colitis-associated CRC in mice [35]. However, similar to our study, Gu et al. concluded that an increased number of Akkermansia muciniphila is associated with protection against inflammatory bowel disease (IBD) and CRC following interventions with nutrients, prebiotics, probiotics, and medications [36]. They noted that despite these therapeutic benefits, some animal studies, such as Wang et al.'s experiment, have reported a negative association with Akkermansia muciniphila [35, 36]. Therefore, it is advisable to consider Akkermansia muciniphila as both a "friend and foe" until additional research and clinical examinations provide further clarity.

A limitation of this study is the small sample size of the cohort, which lacks geographical coverage and broader applicability of the microbiome-based biomarker approach. Validation and confirmation of these findings would benefit from a larger population. Additionally, there is an age difference between the CPs and CNs, which we have attempted to minimize for future studies.

Furthermore, utilizing a combination of selected variable microbiota based on the Mean Decrease GINI model platform, we aimed to enhance the diagnostic ability for the early detection of CRC. For saliva, logistic regression emerged as the optimal model due to its simplicity, boasting an AUC of 91%, sensitivity of 87%, specificity of 80%, PPV of 87%, NPV of 80%, and an ACC of 84%. In contrast, for stool, the support vector machine outperformed other models, achieving the highest AUC of 97%, sensitivity of 92%, specificity of 93%, PPV of 96%, NPV of 87%, and ACC of 90%.

In previous studies, we examined fecal samples of CRC and polyps’ cases versus normal individuals in the Iranian population, employing three models of logistic regression, simple linear combination, and factor with the q-PCR method, ultimately determining specific biomarkers [15]. We identified elevated counts of F. nucleatum, Enterococcus faecalis, Streptococcus bovis, Enterotoxigenic Bacteroides fragilis, and Porphyromonas spp. in CRC stages 0 and I, as well as in adenoma polyps’ cases, specifically in tubular adenomas and notably in villous and tubovillous adenomas. This contrasts with samples from normal, hyperplastic, and sessile serrated adenoma groups.

However, in the current study, we investigated the entire fecal and saliva microbiota of CRC patients and CNs in the Iranian population using the 16S rRNA sequencing technique. Statistical modeling was not limited to stool but extended to saliva as well. Sensitivity and specificity were determined, and biomarker candidates were selected. In parallel with our study, Flemer et al. [18] identified 16 oral microbiota OTUs that distinguished CRC patients from CN individuals with a sensitivity of 53% and specificity of 96%. Their model's sensitivity to using fecal microbiota to distinguish CRC patients was 22% with a specificity of 95%. However, with the combination of oral and stool microbiota, the model's sensitivity increased to 76% for CRC detection.

Furthermore, an identical set of biomarkers between our study and the studies of Yuan et al., Deng et al., and Choi et al. included Bacteroides, Prevotella, Fusobacterium nucleatum, and Veillonella dispar [37,38,39]. By comparing the differences and similarities between our study and these findings, we emphasize the necessity of investigating a large cohort consisting of different geographical populations of CP and CN individuals from Europe, Asia, and America to comprehensively compare the microbiome.