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Colorectal polyps serve as precursors to colorectal cancer and pose a growing public health challenge with their increasing incidence. The potential role of gut microbiota (GM) dysbiosis in colorectal polyp pathogenesis has garnered attention, yet existing evidence remains inconsistent. This study aimed to compare gut microbiota differences between colorectal polyp patients and healthy controls using systematic review and meta-analysis using 16S rRNA sequencing data.
Materials and methods
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search was performed across multiple databases (PubMed, Web of Science, Embase, Cochrane Library) up to April 2025. Only studies comparing gut microbiota profiles between colorectal polyp patients and healthy controls were included. Data was independently screened and extracted by two reviewers, and study quality was assessed using the Newcastle–Ottawa Scale. Meta-analyses were conducted with R (version 4.4.1) and Stata (version 18.0), with heterogeneity assessed via the I2 statistic and publication bias through funnel plots, Egger’s test, Begg’s test, and sensitivity analyses.Logit transformation was applied to enhance the accuracy and reproducibility of the analysis. Additionally, KEGG pathway data was utilized to explore the distinct metabolic pathway patterns between polyp patients and healthy controls.
Key findings
Systematic review and meta-analysis were performed by synthesizing 11 independent 16S rRNA-sequenced studies. Our analysis revealed that patients with colorectal polyps exhibited significantly reduced GM diversity, decreased Firmicutes abundance, and increased Fusobacteria abundance. KEGG pathway analysis indicated enrichment of the TCA cycle in polyp patients and more active amino acid metabolism in healthy controls.
Significance
Patients with colorectal polyps have distinct gut microbiota characteristics and specific metabolic shifts. These findings may facilitate the discovery of non-invasive biomarkers, guide personalized prevention strategies, and improve risk stratification for early intervention.
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Introduction
Colorectal cancer (CRC), the third most common cancer globally with approximately 1.926 million new cases annually, is also the second leading cause of cancer deaths, claiming 903,000 lives per year [2]. Meta-analyses have confirmed rising incidence and younger onset trends in CRC [39], Given that 80–95% of CRC cases originate from precancerous polyps [43], understanding the modifiable drivers of polyp pathogenesis is crucial for early interception.
Emerging evidence implicates gut microbiota dysbiosis as a key factor in colorectal diseases, including polyp formation and malignant transformation [26, 28]. Pathogenic bacteria like Fusobacterium nucleatum and Escherichia coli have been linked to colorectal tumorigenesis, while commensal bacteria contribute to gut homeostasis through short-chain fatty acid (SCFA) production [47]. Dysbiosis-induced reduction in SCFA production may compromise epithelial integrity and amplify inflammatory signaling, fostering conditions conducive to polyp formation. Beyond immune and metabolic effects, gut microbiota influence gene expression, cell proliferation, and redox homeostasis in the colonic epithelium [37]. Shifts in key phyla, such as increased Bacteroides and decreased Firmicutes abundance, have been linked to immune dysregulation and carcinogenic progression [44, 46].
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However, existing findings on gut microbiota's role in colorectal polyp pathogenesis are inconsistent and lack generalizability. Many studies fail to consistently identify specific microbial taxa or functional pathways associated with polyp formation, limiting their translational potential in clinical settings [20]. These inconsistencies may stem from methodological heterogeneity, such as differences in sequencing regions, analytical pipelines, and statistical approaches. Furthermore, previous studies often rely on small sample sizes and descriptive comparisons, which hinder quantitative evaluation of reproducible microbial patterns.
The 16S rRNA gene sequencing detects low-abundance taxa with high sensitivity, integrates standardized protocols for clinical compatibility, and allows phylogenetic-based functional inference. These features make it indispensable for diversity analysis, community profiling, and biomarker discovery in microbiome-disease studies. Against this background, the present meta-analysis systematically synthesises evidence from 16S rRNA gene sequencing studies to evaluate gut microbiota alterations associated with colorectal polyps. By consolidating data on taxonomic composition and predicted functional pathways, this work seeks to generate a coherent synthesis of the microbial correlates potentially involved in early colorectal tumorigenesis.
Methods
This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The project has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD42024574958.
Literature search
A PEO search strategy was used, including patients with colorectal polyps (P), gut microbiota exposure, changes, or characteristics (E), and gut microbiota (O). We used a combination of MeSH terms, subject headings, and free-text terms to search PubMed, Web of Science, Embase, Cochrane Library databases and Google Scholar. The search covered from database inception to April 2025. This study does not set restrictions related to language, country and region. Taking Pubmed as an example,.
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Inclusion criteria and exclusion criteria
Inclusion criteria
(1)
Study Design: Eligible studies include randomized controlled trials, case–control studies, cohort studies, and cross-sectional studies.
(2)
Population: The study population must consist of patients diagnosed with colorectal polyps.
(3)
Methodology: Studies must employ 16S rRNA sequencing technology and provide at least one relevant outcome measure, such as the Chao1 index, Shannon index, or relative abundance of specific bacterial taxa.
(4)
Data Reporting: Studies should clearly report sample sizes, the number and characteristics of both experimental and control groups, as well as the statistical analysis methods employed.
Exclusion criteria
(1)
Non-original Research: Excluded studies include case reports, reviews, abstracts, editorials, research protocols, and conference proceedings.
(2)
Insufficient Data: Studies lacking adequate data for analysis will be excluded. In cases where data extraction is inadequate, attempts will be made to contact authors for raw data. However, inaccessible literature will not be included.
(3)
Methodological Limitations: Studies that do not utilize 16S rRNA sequencing will be excluded.
(4)
Inconsistent Outcome Measures: Studies reporting inconsistent or unclear outcome measures will be omitted from the analysis.
(5)
Sample Size Constraints: Studies with a sample size of five or fewer participants will be excluded to ensure statistical validity.
Literature screening and data extraction
Following the removal of duplicate records, literature screening was conducted in accordance with PRISMA guidelines. The literature screening and data extraction processes were independently performed by two researchers to enhance objectivity. Initial screening involved the assessment of titles and abstracts to determine eligibility for inclusion. Any disagreements between the reviewers were resolved through consultation with a third researcher.
For articles that met the initial inclusion criteria, the full text was thoroughly reviewed to confirm eligibility based on the predefined criteria. Data extraction was systematically organized and included the following key components:
(1)
Basic Information: Details such as the first author’s name, year of publication, and study design were collected.
(2)
Study Population Characteristics: Demographic information, including gender distribution, average age, country of origin, and body mass index (BMI) of participants, was documented.
(3)
Control of Confounding Factors: Information regarding the control of confounding variables in each study was noted to evaluate the robustness of the findings.
(4)
16S rRNA Methodology: A detailed account of the 16S rRNA sequencing methods used in each study was extracted, including the specific amplification regions targeted and the primers employed.
(5)
Outcome Measures: Key outcome measures, such as means and standard deviations for microbial diversity indices and relative abundances of bacterial taxa, were gathered. In instances where necessary data were not reported, authors were contacted to obtain or verify the required information.
Assessment of literature quality
The quality of the included studies was evaluated using the Newcastle–Ottawa Scale (NOS), a widely recognized tool for assessing non-randomized studies [12]. The NOS evaluates three critical dimensions:
(1)
Selection of Study Population (maximum 4 points): This dimension assesses the representativeness of the sample and the methods employed for case definition.
(2)
Comparability Between Groups (maximum 2 points): This dimension focuses on how well confounding factors were controlled.
(3)
Measurement of Exposure or Outcome (maximum 3 points): This dimension involves evaluating the methods of data collection and the integrity of follow-up.
Studies achieving a total score of ≥ 6 were classified as high quality, while those with a score below 6 were categorized as low quality. To ensure the reliability of the quality assessment, two researchers independently scored each study, with any discrepancies addressed through discussion with a third researcher.
Statistical analysis
All statistical analyses were conducted using R version 4.4.1 (utilizing the meta and metafor packages) and Stata version 18.0. Continuous variables were summarized using means and standard deviations, with the combined standardized mean difference (SMD) and corresponding 95% confidence intervals (CIs) calculated using RevMan 5.4.1 software.
Heterogeneity among the results was assessed using the I2 statistic. A fixed-effects model was employed when heterogeneity was low (I2 < 50%), while a random-effects model was applied in cases of high heterogeneity (I2 > 50%). Sensitivity analyses were performed to explore the robustness of the findings, and potential sources of heterogeneity were discussed.
Results were visually presented using forest plots, with a p-value ≤ 0.05 considered statistically significant. Subgroup analyses or additional sensitivity analyses were conducted for studies exhibiting significant heterogeneity to further investigate the sources of variability.
Publication bias was assessed using funnel plots, Egger's test, and Begg's test. Asymmetry in the funnel plot indicated potential publication bias, prompting further examination through Egger's and Begg's tests. If the results of these tests yielded p-values greater than 0.05, publication bias was deemed absent Table1.
Table 1
Search strategies and search results of electronic databases
Databases
Results*
PubMed
301
(((((((((((((((((("Colonic Polyps"[MeSH Terms]) OR ("Intestinal Polyps"[MeSH Terms])) OR ("Colonic Polyp*"[Title/Abstract])) OR ("Polyp*,Colonic"[Title/Abstract])) OR ("Colorectal polyp*"[Title/Abstract])) OR ("Colon polyp*"[Title/Abstract])) OR ("Intestinal polyp*"[Title/Abstract])) OR ("Rectal polyp*"[Title/Abstract])) OR ("Adenomatous polyp*"[Title/Abstract])) OR ("Serrated polyp*"[Title/Abstract])) OR ("Hyperplastic polyp*"[Title/Abstract])) OR ("Polyp* development"[Title/Abstract])) OR ("Polyp* formation"[Title/Abstract])) OR ("Precancerous lesions"[Title/Abstract])) OR ("Polyp* recurrence"[Title/Abstract])) OR ("Polyp* progression"[Title/Abstract])) OR ("Adenomatous Polyp*"[Title/Abstract])) OR ("Polyp*, Adenomatous"[Title/Abstract])) AND ((((((((((((((((((((((((((((((((((((((Gastrointestinal Microbiome[MeSH Terms]) OR (Microbiota[MeSH Terms])) OR ("Gut microbio*"[Title/Abstract])) OR ("Intestinal microbio*"[Title/Abstract])) OR ("Colonic microbio*"[Title/Abstract])) OR (Dysbios*[Title/Abstract])) OR ("Microbial diversity"[Title/Abstract])) OR ("Microbial composition"[Title/Abstract])) OR ("Gastrointestinal Microbio*"[Title/Abstract])) OR ("Microbio*, Gastrointestinal"[Title/Abstract])) OR ("Gastrointestinal Microbial Communit*"[Title/Abstract])) OR ("Microbial Communit*, Gastrointestinal"[Title/Abstract])) OR ("Microbio*, Gut"[Title/Abstract])) OR ("Gut Microflora"[Title/Abstract])) OR ("Microflora, Gut"[Title/Abstract])) OR ("Gastrointestinal Microflora"[Title/Abstract])) OR ("Microflora, Gastrointestinal"[Title/Abstract])) OR ("Gastrointestinal Flora"[Title/Abstract])) OR ("Flora, Gastrointestinal"[Title/Abstract])) OR ("Gut Flora"[Title/Abstract])) OR ("Flora, Gut"[Title/Abstract])) OR ("Gastrointestinal Microbiota*"[Title/Abstract])) OR ("Microbiota*, Gastrointestinal"[Title/Abstract])) OR ("Gut Microbiota*"[Title/Abstract])) OR ("Microbiota*, Gut"[Title/Abstract])) OR ("Microbio*, Intestinal"[Title/Abstract])) OR ("Intestinal Flora"[Title/Abstract])) OR ("Flora, Intestinal"[Title/Abstract])) OR ("Intestinal Microbiota*"[Title/Abstract])) OR ("Microbiota*, Intestinal"[Title/Abstract])) OR ("Intestinal Microflora"[Title/Abstract])) OR ("Microflora, Intestinal"[Title/Abstract])) OR ("Enteric Bacteria"[Title/Abstract])) OR ("Bacteria, Enteric"[Title/Abstract])) OR ("Gastric Microbiome*"[Title/Abstract])) OR ("Microbiome*, Gastric"[Title/Abstract])) OR ("Dys-symbios*"[Title/Abstract])) OR (Dysbacterios*[Title/Abstract]))
Embase
465
Web of Science
636
Scopus
331
Cochrane Library
20
EBSCO
1
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Results
Literature search results
A total of 1754 articles were retrieved. After excluding 818 duplicate articles and 135 articles published more than 10 years ago, 662 articles unrelated to the topic were excluded based on titles and abstracts. A preliminary selection of 139 relevant articles was made. Of these, 127 articles did not meet the inclusion criteria after full-text review. These included 34 studies with insufficient sample sizes or disease numbers, 53 studies that did not report 16S rRNA sequencing outcome measures, 6 studies with inconsistent outcome measures, and 10 non-original studies. Finally, 11 studies were included [4, 29, 38, 40, 41, 50‐54, 56‐58]. The PRISMA flow diagram is shown in Fig. 1. All studies used 16S rRNA sequencing to assess the gut microbiota. 6 studies used the V3-V4 region for 16S rRNA measurement [4, 38, 50‐54, 57], 1 studies performed full-length sequencing [58], and four studies measured the V4 region [29, 40, 41, 56]. 7 studies reported microbial relative abundance [4, 29, 50‐54, 57, 58], and 8 studies reported α diversity data [4, 29, 40, 41, 52, 54, 56, 58], all studies reported β diversity.
In addition, within the study by Liu and Zhang et al. [29], the cohorts of patients with colorectal polyps and those with adenomas were analyzed as discrete entities, each compared independently against the shared healthy control group. This analytical approach was employed to discern potential stage-specific microbial signatures while upholding methodological rigor by preventing data duplication.
Quality assessment
The basic characteristics and quality assessment of the included studies are shown in Tables 2 and 3.
★indicates 1point, ☆indicates no description and 0 point
α Diversity and relative abundance of microorganisms
Α Diversity is used to measure the diversity of microbial communities within a single sample [11], including indices such as the Chao1 index and the Shannon index. We analyzed the differences in α diversity between colorectal polyp patients and healthy controls. Among the 11 included studies, 5 reported the Chao1 index. A meta-analysis was conducted on these studies, and the results showed significant heterogeneity between the studies (I2 = 81.1%). A random-effects model was applied, and no significant difference was found between the polyp group and the healthy control group in the Chao1 index(SMD = − 0.17, 95% CI − 0.66 to 0.32) (Fig. 2). 9 studies reported the Shannon index. A fixed-effects model was used for analysis (I2 = 48.3%), and the results showed that colorectal polyp patients had a lower Shannon index compared to healthy controls (SMD = − 0.22, 95% CI − 0.44 to − 0.01), indicating reduced microbial diversity in the gut of polyp patients (Fig. 3). Eight studies reported Simpson index data, and a fixed-effects model was used for analysis (I2 = 49.6%) (Fig. 4). The results suggested no difference in the Simpson index between Eastern (SMD = − 0.01, 95% CI − 0.19 to 0.16) and Western populations (SMD = − 0.00, 95% CI − 0.41 to 0.41), as well as overall (SMD = − 0.01, 95% CI − 0.17 to 0.15) (Fig. 5).
Fig. 2
Forest map of alpha diversity differences by Chao index
Relative abundance of microorganisms is compositional data. In performing effect size aggregation, we preprocessed the data using Log Ratio [55], Specifically, we applied a logit transformation [ln(p/(1–p))] to the relative abundance percentages provided by each study, where p is the relative abundance ratio (0–100%). Extreme values of 0% and 100% were replaced with 0.001% and 99.999%, respectively [27]. The difference between groups (logit_diff) and its standard error (SE) were calculated based on the transformed logit values. A random-effects model was applied to aggregate the effect size, addressing the limitations of compositional data analysis.
Four studies reported Firmicutes data, and we used a fixed-effects model to analyze it. The results indicated that the overall Firmicutes abundance in the polyp group was lower than in the healthy control group (Fig. 6), with a statistically significant difference (Logit diff = − 0.58, 95% CI − 1.06 to − 0.11). Four studies provided data on the relative abundance of Bacteroidia, and 3 studies reported Actinobacteriota relative abundance data. We used a fixed-effects model to analyze Bacteroidia and Actinobacteriota. The results showed no significant difference between the polyp patients and healthy controls for Bacteroidia (Logit diff = 0.10, 95% CI − 0.39 to 0.59) and Actinobacteriota (Logit diff =− 0.11, 95% CI − 1.57 to 1.79) (Figs. 7, 8).
Fig. 6
Forest plot of relative abundance of bacteria at Firmicutes
Due to the limited available data on bacterial relative abundance across different species, we conducted subgroup analyses at the Genus level and Order level.
Subgroup analysis
Subgroups with at least two studies were included in the subgroup analysis. When the subgroup effect model showed conflicting results, a random-effects model, which accounts for both between-group and within-group differences, was used for aggregation. We conducted a subgroup analysis of the Chao Index based on polyp type. The forest plot (Fig. 9) indicated that the species richness in patients with Peutz–Jeghers Syndrome (PJS) was lower, but the difference was not statistically significant (SMD = − 0.27, 95% CI − 0.64 to 0.10). The heterogeneity in the Adenoma group was exceptionally high (I2 = 93.9%), which could be related to dietary habits, regional factors, or possibly the classification of Adenoma. As only one group possesses data for the polyp cohort, we are unable to accurately determine the variation in the Chao Index for polyp patients.
Fig. 9
Forest plot of Chao index of age subgroup analysis
We also performed a subgroup analysis of gut microbiota abundance at the Genus and Order levels for polyp patients and healthy controls (Figs. 10, 11). Five studies reported data at the Genus level, and there was no significant difference between the two groups at this level. Four studies reported data on Escherichia-Shigella, and although the abundance of Escherichia-Shigella was higher in polyp patients compared to healthy controls, the difference was not statistically significant. A total of three studies reported Bacteroides relative abundance, and although polyp patients had slightly higher relative abundance of Bacteroides compared to healthy controls, the difference was not statistically significant. Four studies reported Faecalibacterium relative abundance data, and while Faecalibacterium abundance was lower in polyp patients, the difference was not statistically significant.
Fig. 10
Forest plot of relative abundance of bacteria at the Genus level
Although order-level differences were explored, only Fusobacteriales was reported by a sufficient number of studies to permit quantitative pooling. For other orders, including Enterobacterales, the data were too sparse to support meaningful meta-analysis. Likewise, phylum-level data were insufficiently reported across included studies. These analyses were therefore omitted to maintain methodological rigor and to avoid overinterpretation of limited evidence. At the Order level, five studies reported Fusobacteriales relative abundance. The relative abundance of Fusobacteriales was higher in polyp patients compared to healthy controls, with a statistically significant difference (Logit diff = 1.35, 95% CI 0.09 to 2.61). This may be related to its role in promoting inflammatory responses and altering the structure and function of the gut microbiota.
Differences in sample sources (fecal vs. mucosal) may influence microbial community profiles due to ecological niche specificity [45]. Fecal specimens predominantly capture luminal microbiota influenced by intestinal transit dynamics and dietary substrates, while mucosal biopsies selectively preserve mucosa-associated microbial communities that directly interact with epithelial surfaces [49]. To account for potential confounding effects introduced by these inherent sample-type differences, we performed stratified subgroup analyses for key outcomes.
Regarding α-diversity (Shannon index), the fecal subgroup analysis (n = 7 studies) revealed a statistically significant reduction in microbial diversity among colorectal polyp patients compared to healthy controls (SMD = − 0.24, 95% CI − 0.47 to 0.00), with moderate heterogeneity (I2 = 48%, p = 0.092) (Fig. 12). In contrast, the mucosal subgroup (n = 2 studies) showed a comparable but non-significant effect size (SMD = − 0.13, 95% CI − 0.88 to 0.61) with substantially higher heterogeneity (I2 = 78.2%, p = 0.032). Importantly, formal testing for subgroup differences indicated no statistically significant effect modification by sample type (p = 0.776). The overall pooled analysis confirmed a significant reduction in microbial diversity among polyp patients (SMD = − 0.22, 95% CI − 0.44 to − 0.01) with moderate heterogeneity (I2 = 48.3%), supporting the biological consistency of this finding across different sampling methodologies.
Fig. 12
Forest plot of Shannon Index of sample sources subgroup analysis
To systematically assess potential confounding effects of sample type on Fusobacteriales abundance analysis, we conducted a stratified subgroup analysis by sample source (Fig. 13). The fecal sample subgroup analysis (n = 7 studies) demonstrated a non-significant elevation in Fusobacteriales abundance (SMD = 1.68, 95% CI − 1.23 to 4.60), paralleled by similar non-significant findings in the mucosal sample subgroup (SMD = 1.27, 95% CI − 0.13 to 2.68). Both subgroups exhibited concordant positive effect directions with low heterogeneity (I2 < 30%). Formal testing revealed no statistically significant between-subgroup difference (p = 0.805), indicating minimal effect modification by sample type.
Fig. 13
Forest plot of Fusobacteriales of sample sources subgroup analysis
Notably, the overall pooled analysis across all studies showed statistically significant Fusobacteriales enrichment in colorectal polyp patients (SMD = 1.35, 95% CI 0.09–2.61, p < 0.05), with this aggregated effect potentially reflecting enhanced statistical power compared to individual subgroup analyses.
Regarding Firmicutes phylum analysis, all available data (n = 4 studies) exclusively derived from fecal samples, consequently precluding meaningful mucosal-fecal comparative assessments. This methodological constraint necessitated exclusion of Firmicutes from formal subgroup analyses by sample type.
Linear discriminant analysis (LDA) is a supervised dimensionality reduction and classification method designed to identify and extract discriminatory features between different groups. It is commonly used in microbial diversity studies to select and visualize significant microbial differences [25]. Among the 11 studies included, 7 provided LDA-related data. After summarizing the LDA data from these 8 studies, we found that biomarkers in patients with colorectal polyps include Escherichia, Shigella, Enterobacter, and Lachnoclostridium. In contrast, Blautia, Lachnospirales, Bacteroides, and Firmicutes were significantly higher in the healthy control group. This suggests that the levels of these bacteria are significantly lower in the gut microbiota of polyp patients compared to healthy controls.
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The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a large bioinformatics database that contains genomic, chemical, and system functional information, widely used in the study of gene and metabolic pathways. In microbiome research, it is commonly used for functional prediction analysis by annotating microbial communities from 16S rRNA sequencing data to corresponding metabolic pathways, to infer their potential roles in energy metabolism, lipopolysaccharide synthesis, and amino acid metabolism [18, 19]. KEGG pathway analysis helps reveal differences in microbial ecosystem functions between different groups, leading to a deeper understanding of the mechanisms through which the gut microbiota contributes to disease progression [1, 24]. Three studies performed KEGG analysis on sequencing data. After integrating the reported KEGG data, we found that the gut microbiota in polyp patients plays a potential role in pathways such as the Arnon Buchanan cycle, Membrane Transport, and TCA cycle. Two studies indicated that Amino Acid Metabolism is significantly enriched in the healthy control group. Additionally, energy metabolism and gluconeogenesis were relatively enriched in the healthy control group.
β Diversity is used to measure the structural differences in microbial communities between different samples [17], After summarizing the β diversity data from the included studies, we found no significant difference in β diversity between the two groups.
Sensitivity analysis
Due to the high heterogeneity in the Chao Indedx results, we used the Baujat plot to identify studies contributing to the heterogeneity. The Baujat plot results (Fig. 14) showed that Chen and Niu et al. [4], contributed significantly to the overall heterogeneity. Sensitivity analysis using the leave-one-out method indicated that after excluding this study, the heterogeneity significantly decreased (I2 = 81.1% to 0%) (Fig. 15). This heterogeneity may be related to dietary habits or regional factors.
Funnel plot analysis was used to assess publication bias (S1), which indicated some publication bias in the relative abundance of Actinobacteriota and at the Order level. This suggests that studies with no significant differences may not have been fully published, leading to a certain bias in our understanding of changes in bacterial community abundance.
Discussion
Accumulating evidence implicates gut microbiota dysbiosis as a key contributor to the pathogenesis of colorectal polyps. A disrupted microbial ecosystem, characterized by the overrepresentation of pathogenic taxa and depletion of beneficial commensals, may impair mucosal immunity and compromise epithelial barrier integrity, thereby promoting chronic inflammation and neoplastic initiation [50, 59]. Microbial diversity indices such as Chao1, Shannon, and Simpson provide critical insights into the structure and stability of gut microbial communities. Specifically, the Chao1 index reflects species richness, the Shannon index incorporates both richness and evenness, while the Simpson index emphasizes the dominance of specific taxa within the community [23].
This meta-analysis, encompassing 11 studies with various designs (3 cross-sectional, 3 cohort, and 5 case–control), found no significant differences in Chao1 and Simpson indices between individuals with polyps and healthy controls. However, substantial heterogeneity in Chao1 values, predominantly influenced by the study conducted by Chen and Niu et al. [4], hindered conclusive interpretation. Subgroup analyses suggested a non-significant reduction in microbial richness among patients with Peutz-Jeghers syndrome (PJS), raising the possibility that hereditary polyposis syndromes may be associated with distinct microbial signatures. For adenomas, which represent the most common type of precancerous polyps, the lack of detailed subclassification across the included studies limited further stratified analysis [8, 10, 15, 31].
In contrast, the Shannon index was significantly lower in the polyp group, indicating a reduction in microbial diversity. This finding supports the hypothesis that decreased diversity may serve as a potential early biomarker for polyp detection. The unchanged Simpson index, however, suggests that dominant taxa remained relatively stable in abundance, implying that the decline in diversity may stem from the loss of low-abundance, potentially beneficial species rather than from wholesale shifts in the dominant microbial populations. Subgroup analyses revealed distinct patterns between sample types. Fecal samples consistently showed statistically significant reductions in Shannon diversity among colorectal polyp patients with stable effect estimates across studies. The mucosal subgroup, while displaying a comparable directional trend, failed to achieve statistical significance and exhibited greater heterogeneity. Several factors likely contribute to this observation: the limited number of available studies, smaller sample sizes in this subgroup, and the known spatial variability of mucosal microenvironments. Importantly, the parallel trends observed in both sample types support the biological validity of reduced microbial diversity in polyp pathogenesis, independent of sampling methodology.
Taxonomic analysis revealed a significant decrease in Firmicutes in individuals with polyps, while no consistent differences were observed for Bacteroidia or Actinobacteriota. Firmicutes, a key phylum involved in short-chain fatty acid (SCFA) production and immune homeostasis, includes important genera such as Clostridium, Lactobacillus, Faecalibacterium, and Ruminococcus [14, 16, 21, 30, 35, 44, 46]. The depletion of Firmicutes may signal a disruption of metabolic and immunological functions critical for maintaining intestinal homeostasis [9, 48]. Notably, Fusobacteriales demonstrated a consistent pattern of elevated abundance across all sample types examined. The observed low inter-study heterogeneity, coupled with robust pooled effect estimates, substantiates its potential utility as a microbial biomarker for colorectal polyp detection. Nevertheless, these preliminary findings warrant validation through larger-scale, prospective cohort studies to establish definitive clinical relevance.
At finer taxonomic levels, Escherichia-Shigella and Bacteroides exhibited increased relative abundance in polyp patients, albeit without statistical significance. Conversely, Faecalibacterium, a major SCFA producer with anti-inflammatory effects, was markedly reduced. A significant increase in the abundance of Fusobacteria at the order level further supports its established role in promoting mucosal inflammation and tumorigenesis [3, 61]. Linear discriminant analysis (LDA) identified Escherichia, Shigella, Enterobacter, and Lachnoclostridium as enriched in polyp patients, whereas Blautia, Lachnospirales, Bacteroides, and Firmicutes were more prevalent in healthy controls. Blautia, a beneficial SCFA-producing anaerobe with immunomodulatory properties, has been inversely associated with adenoma development and may represent a protective microbial signature [13, 22, 32].
It is worth noting that changes in the composition of the gut microbiota may be influenced by dietary habits or lifestyle [6]. Dietary patterns, such as high-fiber diets and the Mediterranean diet, are typically associated with increased gut microbiota diversity and the enrichment of bacterial genera associated with anti-inflammatory and anti-cancer properties [34]. Conversely, high-fat, high-red-meat diets may lead to reduced microbiota diversity and increased enrichment of pro-inflammatory bacterial genera [42]. Since most of the literature included in this study did not provide detailed dietary and lifestyle data, we are unable to assess the potential confounding effects of these factors on gut microbiota differences or determine whether the observed microbiota changes were influenced by behavioral factors. Therefore, future studies should systematically collect these key confounding variables during the design phase to enhance the clinical interpretability and translational value of the results.
Although existing evidence suggests that colorectal polyps in different anatomical locations may exhibit systematic differences in microbiological characteristics [7], particularly with right-sided (proximal) lesions often associated with microbiota remodeling linked to inflammation, metabolic abnormalities, and immune responses, and correlated with poorer clinical outcomes [33], this study was unable to perform subgroup analyses based on polyp anatomical location. This was due to the lack of clear lesion localization information in most included studies, therefore, some associations may be masked or diluted. Notably, the anatomical segment-dependent spatial distribution of microbiota in the intestine suggests that future microbiota studies should not only report overall intestinal status but also conduct stratified analyses based on polyp location to identify spatially distinct microbial biomarkers, thereby advancing the development of early disease screening and intervention targets.
Functional predictions based on KEGG pathway analyses, reported in three of the included studies, revealed enrichment of pathways related to the tricarboxylic acid (TCA) cycle, membrane transport, and the Arnon-Buchanan cycle in polyp patients. In contrast, pathways associated with amino acid metabolism were predominantly enriched in healthy controls. Upregulation of the TCA cycle, which is central to microbial energy production and epithelial cellular function, may reflect a metabolic adaptation that favors pro-inflammatory states and epithelial proliferation [5, 36]. Conversely, the enhancement of amino acid metabolism in controls suggests more efficient nutrient utilization and immune regulation [51]. Together, these results highlight the metabolic and compositional shifts associated with dysbiosis and underscore its potential mechanistic contribution to early colorectal polyp formation and progression.
Novelty
This innovative meta-analysis on intestinal flora and polyps stands out in several ways. First, unlike prior underpowered individual studies, it synthesizes 11 independent 16S rRNA-sequenced studies via systematic review and meta-analysis. This approach spans diverse countries, ethnicities, and study designs, enhancing result breadth and stability while minimizing heterogeneity bias.
On the analytical front, we introduced logit transformation for microbial data preprocessing, improving analysis accuracy and reproducibility. Our comprehensive analysis framework goes beyond conventional alpha diversity metrics. It incorporates phylum and genus-level abundance comparisons, intestinal polyp subtype subgroup analyses, beta diversity assessments, and sensitivity analyses, adding analytical depth.
Functionally, this study breaks new ground by integrating functional prediction analysis. By consolidating KEGG pathway data, we reveal distinct metabolic pathway patterns between polyp patients and healthy controls, moving beyond mere compositional differences.
In biomarker discovery, we compiled key genera from eight LDA-based studies. Polyp-associated genera like Escherichia and Shigella were identified, alongside health-linked genera such as Blautia and Bacteroides. These findings present promising non-invasive diagnostic candidates.
Clinically, the analysis elucidates intestinal flora's role in polyp formation mechanisms, including metabolic reprogramming and immune regulation. Despite 16S rRNA sequencing limitations, our systematic data integration clarifies key flora and functional trends, paving the way for future multi-omics research.
Overall, this study marks a significant step in advancing intestinal flora research from descriptive comparisons toward mechanistic understanding and clinical translation through methodological refinement and multi-dimensional analysis.
Limitations
Despite methodological rigor and adherence to PRISMA guidelines, this meta-analysis has several limitations. The included observational studies were subject to high heterogeneity, stemming from differences in participant populations, sequencing protocols (e.g., DNA extraction, sequencing depth, and platform), and microbial annotation pipelines. The classification of adenomas was inconsistently reported, precluding stratified analyses based on histologic subtype, which may influence microbial patterns (e.g., tubular vs. villous adenomas). Additionally, while 16S rRNA sequencing provides valuable compositional insights, its resolution is insufficient for precise species-level identification or functional prediction. Future multi-omics integration (metagenomics/metabolomics) is critical to resolve functional pathways and host-microbe interactions.
In addition, existing studies have confirmed that dietary habits and lifestyle have a significant impact on the gut microbiota. However, due to the lack of standardized reporting of this information in the original studies, we are unable to make consistent adjustments or conduct further subgroup analyses. This omission may have led to a certain degree of confounding bias, and future studies need to systematically record these factors in their design. Similarly, this study was unable to comprehensively control for or evaluate participants' medication history prior to the study, particularly antibiotics, proton pump inhibitors (PPIs) [62], and other medications known to influence gut microbiota composition [60]. Although some original studies mentioned recent antibiotic use in their exclusion criteria, the reporting lacked systematic quantification and consistent operational definitions. Given that medication use can significantly alter gut microbiota structure over both short- and medium-term periods, this factor may introduce confounding effects that could impact the accurate estimation of associations between microbiota characteristics and colorectal polyps. Future investigations should incorporate detailed medication history as a potential confounding variable in analytical models to minimize its impact on gut microbiota analysis outcomes.
Conclusion
This PRISMA-compliant synthesis identifies reproducible microbial alterations associated with colorectal polyps, characterized by reduced Shannon diversity, decreased Firmicutes, and enrichment of Fusobacteriales. These compositional changes may reflect impaired short-chain fatty acid (SCFA)–mediated mucosal integrity. The enrichment of Fusobacteria-related taxa further suggests potential involvement of TCA cycle–related redox imbalance, offering testable hypotheses for future mechanistic research.
Declarations
Ethics approval and consent to participate
Each of the included studies was reviewed and approved by an institutional ethics committee or review board, in accordance with established ethical guidelines. Participant information was fully anonymized to maintain confidentiality, and all research activities adhered to relevant data protection laws to safeguard individual privacy and data security.
Competing interests
The authors declare no competing interests.
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