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Zeng LX, Jiang YC, Wang YQ, Ye F, Ouyang Y, Zhao ZM, Wu QB, Cheng HB. Gut microbiota signatures in colorectal cancer: Evidence synthesis from umbrella review and probiotic intervention studies. World J Gastroenterol 2027; 33(1): 124236 [DOI: 10.3748/wjg.124236]
Li-Xuan Zeng, Yu-Chen Jiang, Yi-Qing Wang, Fei Ye, Yue Ouyang, Zi-Ming Zhao, Qi-Biao Wu, Faculty of Chinese Medicine and State Key Laboratory of Mechanism and Quality of Chinese Medicine, Macau University of Science and Technology, Macau 999078, China
Hai-Bo Cheng, The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China
Hai-Bo Cheng, Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China
Co-corresponding authors: Qi-Biao Wu and Hai-Bo Cheng.
Author contributions: Zeng LX, Jiang YC, and Wang YQ contributed equally to this work, contributed to study conception, literature search, data extraction, quality assessment, statistical analysis, interpretation of results, and manuscript drafting; Ye F and Ouyang Y contributed to literature screening, data curation, and methodological validation; Zhao ZM contributed to study design, methodological guidance, and critical manuscript revision; Wu QB contributed to study supervision, methodological support, interpretation of findings, critical revision of the manuscript, and served as co-corresponding author; Cheng HB contributed to overall study conception, supervision, project administration, interpretation of findings, critical revision of the manuscript, and served as co-corresponding author.
AI contribution statement: The authors declare that no artificial intelligence (AI) tools were used in the preparation of this manuscript.
Supported by National Natural Science Foundation of China, No. 82361168663; and Science and Technology Development Fund, Macau SAR, No. 0048/2023/AFJ, No. 0121/2022/A3, No. 0127/2023/RIA2, No. 0003/2025/NRP and No. 0164/2023/RIA3.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Corresponding author: Hai-Bo Cheng, PhD, Chief Physician, The First Clinical Medical College, Nanjing University of Chinese Medicine, No. 138 Xianlin Road, Nanjing 210023, Jiangsu Province, China. haibocheng@njucm.edu.cn
Received: June 10, 2026 Revised: July 13, 2026 Accepted: October 12, 2026 Published online: January 7, 2027 Processing time: 152 Days and 20.3 Hours
Abstract
BACKGROUND
Aberrant alterations in the gut microbiota have been widely reported in colorectal cancer (CRC), but taxon-level associations remain heterogeneous in direction, magnitude, and reproducibility across studies.
AIM
To conducted an umbrella review with a second-order meta-analysis to identify reproducible CRC-associated taxa and to summarize gut microbiota changes after probiotic or synbiotic interventions.
METHODS
We included eligible meta-analyses of CRC-associated gut microbiota and evaluated them using AMSTAR 2 (a measurement tool to assess systematic reviews). After standardizing effect sizes and deriving missing standard errors, we performed a second-order meta-analysis to estimate pooled effect sizes, 95% confidence intervals (CIs), P values, and heterogeneity. Candidate taxa were identified using predefined criteria, and species-level findings were mapped to genera. We also added original CRC studies on probiotics and synbiotics, assessed bias using RoB 2, and combined the data via standardized mean differences (SMDs), wherever appropriate. Studies without extractable variance estimates were included in the qualitative or percentage-based synthesis.
RESULTS
Eleven meta-analyses of 3569 CRC patients and 3722 healthy controls were included. Frequently reported genera, such as Roseburia, Prevotella, Porphyromonas, Bacteroides, and Streptococcus, showed inconsistent association directions; therefore, reporting frequency alone could not reliably identify stable CRC-associated taxa. Seventeen genera met the predefined significance and reporting-frequency criteria and 8 retained significance in leave-one-out analyses. After heterogeneity-based filtering, Granulicatella and Peptoniphilus remained the most stable genera. Six probiotic/synbiotic intervention studies of 323 participants were included. The quality of the studies was generally low, with some missing outcome data and selective reporting. Pooled analysis revealed that Lactobacillus (SMD = 1.49, 95%CI: 0.67-2.31) and Bifidobacterium (SMD = 1.81, 95%CI: 0.60-3.03) both increased significantly, mainly reflecting supplementation and other taxa remains exploratory. After intervention, single-study SMDs and percentage-based analyses suggested possible increases in Faecalibacterium, Eubacterium, Bacillus, Staphylococcus, and Parabacteroides, and possible decreases in Fusobacterium, Candida, Enterobacteriaceae, Pseudomonas, Porphyromonas, Alloprevotella, Erysipelatoclostridium, Peptostreptococcus, Bacteroides, and Clostridium.
CONCLUSION
This study identifies reproducible CRC-associated microbial candidates and highlights the need to evaluate cross-study stability beyond reporting frequency. Probiotic/synbiotic interventions may alter selected microbial taxa in CRC patients, but larger shotgun metagenomics-based trials are needed to determine whether taxonomic shifts translate into ecological or metabolic restoration.
Core Tip: This study synthesizes gut microbiota signatures in colorectal cancer (CRC) through two complementary components: An umbrella review with second-order meta-analysis of CRC-associated taxa, and a synthesis of probiotic or synbiotic intervention evidence. The key innovation is distinguishing frequently reported taxa from truly stable taxa across studies. The findings also suggest that probiotic or synbiotic interventions may partially reshape CRC-related dysbiosis, but higher-quality clinical evidence is still needed.
Citation: Zeng LX, Jiang YC, Wang YQ, Ye F, Ouyang Y, Zhao ZM, Wu QB, Cheng HB. Gut microbiota signatures in colorectal cancer: Evidence synthesis from umbrella review and probiotic intervention studies. World J Gastroenterol 2027; 33(1): 124236
GLOBOCAN 2022 projections estimate that by 2050, the global number of new colorectal cancer (CRC) cases will reach 2.36 million, while the number of CRC-related deaths will increase to 1.11 million[1]. At least 45% of CRC cases may be attributable to poor adherence to healthy lifestyle behaviors[2]. Long-term consumption of a high-fat, high-protein, and low-fibre diet, together with circadian rhythm disruption, may contribute to CRC by disrupting gut microbial homeostasis and promoting intestinal carcinogenesis. As a result, patients with CRC often exhibit marked gut microbial dysbiosis, characterized by a depletion of beneficial commensal taxa and an enrichment of pathogenic or disease-associated microorganisms[3,4].
Advances in microbiome technologies have improved our understanding of CRC-associated microbial communities. Shotgun metagenomic sequencing provides species-level insights into CRC-associated microbial changes[5]. Moreover, microbiota-based strategies, including probiotics, dietary interventions, faecal microbiota transplantation, and other microbiota-targeted approaches, are increasingly being explored for CRC prevention[6].
Despite growing interest in CRC-associated microbiota, reported microbial signatures remain heterogeneous across studies. Such inconsistency may be attributable to differences in both sample type and cohort characteristics[7,8]. Faecal, intestinal mucosal, and tumor tissue samples capture distinct microbial origins and ecological niches. Additionally, host genetic background, geographic setting, dietary habits, and bioinformatics pipelines may vary substantially across cohorts. Therefore, researchers have increasingly synthesised available evidence through systematic reviews and meta-analyses to identify CRC-associated taxa, with several taxa showing relatively consistent associations with CRC across cohorts[9-17]. Building on these findings, the present study performed an umbrella synthesis and second-order meta-analysis of previous meta-analyses to assess further the reproducibility, effect size, and heterogeneity of the candidate microbiota.
Following the umbrella synthesis of CRC-associated microbiota, this study further examined probiotic-induced changes in the gut microbial profiles of CRC patients to identify candidate microbial signatures. Eligible primary studies were synthesised, and taxa suitable for pooling were meta-analyzed to evaluate their response to probiotic treatment. This study aimed to identify microbiota that are stably associated with CRC, assess their potential therapeutic relevance, and provide a basis for subsequent mechanistic studies and the optimization of microbiota-targeted therapeutic strategies.
MATERIALS AND METHODS
Study design
This study comprises two components. The first component was an umbrella review of gut microbiota alterations associated with CRC, followed by a secondary meta-analysis based on its findings. The second component was a systematic review evaluating changes in the gut microbiota of CRC patients following probiotic/synbiotic intervention. Search strategies were developed separately according to the objectives of the two components. A systematic literature search was performed in PubMed, Web of Science, and EMBASE to identify relevant studies published from database inception to March 28, 2026. In addition, the reference lists of all included studies were manually screened to reduce the risk of missing eligible articles.
Literature screening and data extraction
The review protocol was registered in PROSPERO (No. CRD420261405837), and study selection was reported using preferred reporting items for systematic reviews and meta-analyses flow diagrams. Retrieved records were imported into reference management software, screened independently by two reviewers (Jiang YC and Zhao ZM) according to predefined eligibility criteria, and assessed by full text when appropriate; disagreements were resolved by discussion or consultation with a third reviewer (Ye F/Ouyang Y). Methodological quality was independently assessed using AMSTAR 2 (a measurement tool to assess systematic reviews) for the included reviews (Table 1)[10-13,15-21] and RoB 2 for randomized controlled trials, while nonrandomized studies were appraised according to their design characteristics. For AMSTAR 2, each included review was assigned an overall confidence rating based on critical and non-critical weaknesses. For RoB 2, each randomized trial was assessed across five bias domains, followed by an overall risk-of-bias judgement. These assessments helped interpret the findings with appropriate levels of confidence according to study quality. According to the AMSTAR 2 assessment, none of the reviews were rated as having high confidence; 27.3% were rated as having moderate confidence, 45.5% as having low confidence, and 27.3% as having critically low confidence. The RoB 2 assessment revealed that 33.3% of the studies were rated as having a low risk of bias, 50.0% raised some concerns, and 16.7% were rated as high risk. The high-risk judgement was due to concerns in the randomization process.
Table 1 Quality evaluation using AMSTAR 2 (a measurement tool to assess systematic reviews).
The full search strategies and predefined eligibility criteria for each component are provided in the Supplementary materials. Ultimately, 11 systematic reviews and meta-analyses met the final inclusion criteria; the selection process is shown in Figure 1, and the extracted study characteristics are summarized in Table 2[10-13,15-21]. For the intervention analysis, six studies were included, with the study selection shown in Figure 2, and the intervention characteristics including probiotic or synbiotic regimens, intervention duration, sample size, outcome measures, and concomitant treatments such as 5-fluorouracil-based chemotherapy are summarized in Table 3[22-27].
Figure 1
Preferred reporting items for systematic reviews and meta-analyses flow diagram of the study selection process for the umbrella meta-analysis.
Figure 2
Preferred reporting items for systematic reviews and meta-analyses flow diagram of the study selection process for the probiotic/synbiotic intervention analysis.
Table 2 Characteristics of the included studies (n = 11).
Austria, Canada, China, Germany, France, India, Italy, Japan, and United States
To compare microbial features between early-onset and late-onset CRC, including virulence factors
Early-onset CRC showed distinct microbial and virulence profiles, including associations with colibactin, Fusobacterium adhesin A, and Fusobacterium nucleatum
Austria, France, Germany, Italy, Denmark, United States, China, and Japan
To identify reproducible CRC-associated biomarkers and explore related functional mechanisms
CRC showed increased microbial richness, reproducible markers such as Fusobacterium nucleatum, and altered amino acid, gluconeogenesis, and choline metabolism
Austria, China, Czech Republic, France, Germany, India, Italy, Japan, Spain, Turkey, and United States
To examine gut microbiome changes across adenoma-carcinoma progression, CRC stage, and tumor location
CRC microbiomes were enriched with oral taxa, and cross-cohort classifiers showed strong predictive performance with reproducible species- and strain-level markers
Data consolidation and effect size harmonization in the umbrella review
To improve comparability across meta-analyses, all reported CRC-associated microbial taxa were systematically consolidated into a harmonized dataset. Taxa with different names across studies were harmonized, while those reported at different taxonomic levels (e.g., genus vs species) were treated as separate entries. Data import, processing, merging, and taxonomic harmonization were performed in R version 4.4.0, primarily using the readxl, dplyr, tidyr, and stringr packages.
Effect directions were first harmonized to address differences in effect measures across meta-analyses, followed by the calibration and standardization of the reported estimates. Missing standard errors were derived from 95% confidence intervals, reported standard errors, P values, or other available statistical information. The direction of association was standardized, with positive values representing enrichment in the CRC group and negative values representing depletion. For the taxa reported in multiple meta-analyses, the reporting frequency (k value) and directional consistency were further summarized, and the median effect size was calculated for descriptive comparison. All procedures for effect size harmonization, statistical summarization, and descriptive analyses were conducted in R version 4.4.0.
Second-order meta-analysis
Based on the umbrella review, the microbial taxa reported in at least two meta-analyses were included in the second-order meta-analysis. Given the clinical and methodological heterogeneity across meta-analyses, a random-effects model was applied. The main summary measures included the pooled effect size, 95% confidence interval, P value, and heterogeneity statistics, including I2, τ2, the Q test, and the P value. All second-order meta-analyses were performed primarily using the meta for package in R version 4.4.0.
To identify relatively robust microbial signals associated with CRC, a two-step screening strategy was applied. First, taxa with P ≤ 0.05 and k ≥ 5 were selected based on statistical significance and evidence coverage. Second, to account for the influence of heterogeneity, an additional criterion of P ≤ 0.05, I2 ≤ 80%, and k ≥ 3 was used to identify taxa that remained significant under relatively low heterogeneity. For the genera that met the screening criteria, species-level data from the relevant primary studies were further examined to determine whether the observed genus-level association was driven by multiple related species.
Meta-analysis of probiotic/synbiotic interventions
A qualitative synthesis was first conducted for the included intervention studies to summarize the study region, study design, intervention type, and major intervention-related changes in the gut microbiota. For taxa with extractable continuous data, studies were included in the quantitative meta-analysis when sufficient data were available to calculate a standardized mean difference (SMD) and its variance, such as the means, variance estimates, and sample sizes. Studies without convertible variance data, including those reporting only relative abundance percentages, significance indicators, or qualitative trends, were excluded from the SMD meta-analysis but retained in the qualitative synthesis. Random-effects models were used to pool the SMDs and 95% confidence intervals for taxa reported in multiple studies, while single-study taxa were summarized descriptively. Park et al[26] were excluded from the primary quantitative meta-analysis because they did not report the variance data required for the SMD calculation and were therefore summarized qualitatively.
Statistical analysis
A leave-one-out sensitivity analysis was conducted for the main taxa to assess the robustness of the results. Heterogeneity was assessed using the I2 statistic, τ2, and the Q test. Data processing, second-order meta-analysis, graphical visualization, and leave-one-out sensitivity analysis were conducted using the meta for package in R version 4.4.0.
RESULTS
Data collection and umbrella review analysis framework
The included populations were drawn mainly from East Asia, Europe, and North America, with additional cohorts from Australia and India[10-13,15-21]. Several studies also included individuals with non-CRC diseases, such as Crohn’s disease and ulcerative colitis, as reference groups to evaluate the disease specificity of CRC-associated alterations in the gut microbiota. Table 2 summarizes the basic characteristics of the included studies, while Supplementary Table 1 presents the gut microbiota data extracted before the umbrella review. To enable the subsequent meta-analyses, effect sizes were standardized across studies, and standard errors were added. We then applied the umbrella review framework to the integrated analysis of individual microbial taxa (Figure 3). Among the included meta-analyses, Roseburia was the most frequently reported genus, appearing in 11 studies. Prevotella, Porphyromonas, and Bacteroides were also highly reported, appearing in 10 meta-analyses, followed by Streptococcus, Parvimonas, Faecalibacterium, Eubacterium, Clostridium, Bifidobacterium, and Alistipes, which were reported in nine meta-analyses. Overall, highly reported genera were predominantly classified as having a “mixed” consistency type, indicating heterogeneity in the direction of association across meta-analyses. Peptostreptococcus, Escherichia, Bilophila, Anaerotruncus, Hungatella, and Peptoniphilus mainly showed an “all-positive” consistency pattern. In contrast, genera negatively associated with CRC, mainly Acetatifactor, Marvinbryantia, and Anaerobutyricum, were less common.
Figure 3 Umbrella review analysis of gut microbiota genera associated with colorectal cancer.
A: Consistency classification and reporting frequency of microbial genera (k); B: Median effect sizes of the genera reported in the meta-analyses. The error bars represent interquartile ranges, and the bubble size reflects the number of included meta-analyses.
The median effect sizes of different microbial taxa are compared in Figure 3. Among the taxa reported in at least five meta-analyses (k ≥ 5), Parvimonas, Fusobacterium, Gemella, Peptostreptococcus, Escherichia, Prevotella, Porphyromonas, Odoribacter, Granulicatella, and Parabacteroides showed relatively large positive median effect sizes, suggesting that these taxa were not only frequently reported but also more clearly associated with CRC. In contrast, several frequently reported taxa, including Roseburia, Bifidobacterium, Eubacterium, Anaerostipes, Adlercreutzia, Blautia, Lachnospira, and Lachnospiraceae, had median effect sizes close to zero, while Faecalibacterium and Coprococcus showed slight negative shifts. Some less frequently reported taxa, such as Allisonella, Negativibacillus, Criibacterium, Haemophilus, Coprobacter, and Brachyspira, also showed large median effect sizes, but these findings were based on only two meta-analyses and should therefore be interpreted cautiously.
Second-order meta-analysis of CRC-associated microbial taxa
We further performed a second-order meta-analysis on taxa reported in at least two meta-analyses (k ≥ 2) to estimate pooled effect sizes, 95% confidence intervals, P values, and heterogeneity statistics (I2, τ2, and QE test P values), with detailed results shown in Supplementary Table 2. After the second-order meta-analysis (Figure 4), Allisonella had the greatest pooled effect size, followed by Negativibacillus, Criibacterium, Haemophilus, and Parvimonas. Among the positively associated taxa, Parvimonas, Fusobacterium, Porphyromonas, Escherichia, Bacteroides, Alistipes, and Prevotella were supported by a relatively large number of meta-analyses, suggesting more stable CRC-associated enrichment. In contrast, Allisonella, Negativibacillus, Criibacterium, Haemophilus, Enorma, Campylobacter, and Coprobacter also showed strong positive pooled effects, but their wide confidence intervals indicated limited evidence and greater uncertainty. Taxa with pooled effects close to or below zero, including Acetatifactor, Anaerobutyricum, Marvinbryantia, Coprococcus, Bifidobacterium, Clostridiales, and Eubacterium, showed weak or negative associations with CRC.
Figure 4 Forest plot of the second-order meta-analysis for gut microbiota genera associated with colorectal cancer risk.
Pooled effect sizes with 95% confidence intervals are shown for genera that were reported in at least two independent meta-analyses (k ≥ 2). Taxa are ordered by the magnitude of the effect size. The color intensity corresponds to the number of meta-analyses included for each genus.
Identification of statistically significant CRC-associated genera
We further screened significant genera using the predefined criteria of P ≤ 0.05 and k ≥ 5. A total of 17 genera met these criteria (Figure 5 and Table 4). Among them, Parvimonas had the greatest pooled effect size, followed by Fusobacterium and Hungatella (Table 4). The oral-origin genera mainly included Peptostreptococcus, Parvimonas, Gemella, Fusobacterium, and Dialister. Species-level mapping (Figure 5A) revealed that these genera differed in species breadth. Parvimonas, Gemella, Peptostreptococcus, and Dialister were mapped to relatively limited species ranges, including Parvimonas micra, Gemella morbillorum, Gemella sanguinis, Peptostreptococcus stomatis, Peptostreptococcus anaerobius, and Dialister pneumosintes. In contrast, Fusobacterium encompassed a broader range of species, with Fusobacterium nucleatum being the most consistently reported species, followed by Fusobacterium nucleatum subsp. animalis, Fusobacterium periodonticum and Fusobacterium varium.
Figure 5 Species-level taxonomic mapping of gut microbiota genera significantly associated with colorectal cancer (P ≤ 0.05, k ≥ 5).
A: Oral-derived genera; B: Gut-derived genera; C: Mixed-derived genera (oral and gut).
Table 4 17 microbial genera were significantly associated with colorectal cancer risk according to second-order meta-analysis (P ≤ 0.05, k ≥ 5).
The gut-derived genera mainly included Bacteroides, Clostridium, Alistipes, Bilophila, Hungatella, Anaerotruncus, Methanobrevibacter, and Parabacteroides. Hungatella was mapped mainly to Hungatella hathewayi (Figure 5B), indicating a positive overall association with CRC and a relatively concentrated genus-to-species pattern. In contrast, Bacteroides, Alistipes, Clostridium, and Parabacteroides encompassed broader species ranges, suggesting greater intragenus heterogeneity. Bilophila, Anaerotruncus, and Methanobrevibacter showed positive pooled effects but covered relatively narrow species ranges, and their species-level evidence remains limited. The mixed-origin genera included Streptococcus, Prevotella, Slackia, and Anaerococcus. Among them, Prevotella and Streptococcus were reported in more meta-analyses and showed positive pooled effects, but their broad species ranges suggested greater intragenus complexity (Figure 5C). In contrast, the species-level distributions of Slackia and Anaerococcus were more limited.
To assess whether individual meta-analyses drove the pooled associations, we performed a leave-one-out sensitivity analysis for the 17 significant CRC-associated genera (Supplementary Figure 1). Peptostreptococcus, Hungatella, Gemella, Prevotella, Anaerococcus, Bacteroides, Clostridium, and Slackia retained statistical significance after the omission of individual meta-analyses, suggesting relatively stable associations. In contrast, the pooled associations for Bilophila, Streptococcus, Fusobacterium, Alistipes, Dialister, Anaerotruncus, Methanobrevibacter, Parabacteroides, and Parvimonas were sensitive to the omission of specific meta-analyses. These changes were mainly related to the omission of Jin et al[18] for Fusobacterium, Methanobrevibacter, Parabacteroides, and Parvimonas; Kharofa et al[13] for Bilophila, Streptococcus, Alistipes, and Anaerotruncus; and Lin et al[19] for Dialister. These findings suggest that for the taxa with significant changes, individual meta-analyses mainly affected the statistical robustness of the pooled associations.
Given the substantial heterogeneity (I2 > 90%) observed for most statistically significant genera, we further screened the pooled results using stricter criteria of P ≤ 0.05, I2 ≤ 80%, and k ≥ 3. This identified two genera (Supplementary Figure 2 and Supplementary Table 3). Granulicatella had a positive pooled effect with no observed heterogeneity, whereas Peptoniphilus had a positive pooled effect with moderate heterogeneity. These findings suggest relatively consistent positive genus-level associations with CRC.
Design and characteristics of probiotic/synbiotic intervention studies
Building on the microbial taxa identified in the umbrella review, we further summarized probiotic- or synbiotic-related changes in the gut microbiota of CRC patients from six original studies. A total of 323 participants were included across studies published between 2007 and 2026 in Ireland, Italy, Sweden, South Korea, and China[22-27]. Most studies were randomized controlled trials, whereas one was a retrospective cohort study. The study population mainly included perioperative CRC patients. Interventions primarily involved Lactobacillus, Bifidobacterium, or their combinations, with some studies additionally incorporating inulin or dietary fibre (Table 3). The risk of bias was assessed using the Cochrane RoB 2 tool (Figure 6). At the domain level, bias in the outcome measurement was generally low, with five studies rated as low risk. In contrast, missing outcome data and the selection of the reported result were the most frequent sources of concern, each with three studies rated as having some concerns. Only one study showed a high risk in the randomisation process, which led to a high overall risk of bias. Overall, the methodological quality of the included randomized studies was generally acceptable.
Figure 6 RoB2 risk of bias summary for the included intervention studies.
A: Domain-level and overall RoB2 judgments for each study; B: Proportion of studies judged at each risk of bias level for each item, presented as percentages across all included studies.
Quantitative meta-analysis and individual study effect sizes of probiotic/synbiotic intervention
For taxa eligible for quantitative meta-analysis (Supplementary Table 4), SMDs were used as effect sizes, and forest plots were constructed (Figure 7). Three taxa were ultimately included in the meta-analysis: Lactobacillus, Bifidobacterium, and Enterococcus. Pooled analysis revealed that probiotic/synbiotic intervention was associated with significantly increased abundances of Lactobacillus (pooled SMD = 1.49, 95% confidence interval: 0.67 to 2.31) and Bifidobacterium (pooled SMD = 1.81, 95% confidence interval: 0.60 to 3.03). Enterococcus also tended to increase after intervention, although the association did not reach statistical significance (SMD = 0.31, 95% confidence interval: -0.03 to 0.65). A leave-one-out sensitivity analysis revealed that the exclusion of any single study did not materially change the direction of the pooled effects for Lactobacillus or Bifidobacterium, supporting the robustness of these findings. The sensitivity analysis for Enterococcus was limited by the small number of included studies (k = 2).
Figure 7 Forest plots and leave-one-out sensitivity analyses of probiotic/synbiotic effects on the gut microbiota in colorectal cancer patients.
Forest plots show pooled standardized mean differences with 95% confidence intervals. A: Forest plots of eligible quantitative meta-analyses by taxon; B: Leave-one-out sensitivity analysis. SMD: Standardized mean differences; CI: Confidence interval.
For the remaining taxa, pooled analysis was not possible because each taxon was reported in only one intervention study; therefore, individual-study SMD estimates are presented as exploratory evidence (k = 1; Figure 8 and Supplementary Table 4). Faecalibacterium showed the largest positive SMD, indicating that the abundance of Faecalibacterium was greater in the probiotic group than in the control group. Eubacterium, Bacillus, and Staphylococcus also showed positive but smaller SMDs. In contrast, negative SMDs were observed for Fusobacterium, Candida, Enterobacteriaceae, Pseudomonas, Porphyromonas, Prevotella, Peptostreptococcus, Bacteroides, and Clostridium perfringens, with Fusobacterium showing the greatest absolute effect.
Figure 8 Individual-study standardized mean differences for microbial taxa reported in single intervention studies.
Standardized mean differences with 95% confidence intervals are shown for taxa reported in single studies (k = 1). SMD: Standardized mean differences; CI: Confidence interval.
The study by Park et al[26] was excluded from the primary meta-analysis because only relative abundance percentages were reported, without the variance estimates required for the SMD calculation. We thus calculated absolute differences and effect direction between the probiotic and control groups as an exploratory sensitivity analysis. As shown in Figure 9 and Supplementary Table 5, the probiotic group presented relatively high relative abundances of Akkermansia, Bifidobacterium, Parabacteroides, Prevotella, and Veillonella and relatively low relative abundances of Alloprevotella, Erysipelatoclostridium, Fusobacterium, Lactobacillus, and Porphyromonas. Significant increases were observed for Bifidobacterium, Parabacteroides, and Prevotella, whereas significant decreases were observed for Alloprevotella, Erysipelatoclostridium, and Porphyromonas. The greatest positive differences were observed for Akkermansia (P > 0.05), and the greatest negative differences were observed for Porphyromonas.
Figure 9 Qualitative summary of microbiota changes in the study of Park et al[26].
Red and blue indicate increased and decreased relative abundance, respectively. NS: Not significant.
DISCUSSION
This study synthesized secondary evidence on CRC-associated gut microbiota through an umbrella review and second-order meta-analysis of 11 meta-analyses. Reporting frequency alone was insufficient to define robust CRC-associated signals, as Roseburia, Eubacterium, and Bifidobacterium were frequently reported but showed weaker or directionally unstable associations. The leave-one-out analysis also revealed that several well-studied genera, such as Parvimonas, Fusobacterium, and Porphyromonas, remained heterogeneous across studies, mainly because of differences in effect size magnitude among meta-analyses. Given that the included studies used faecal/stool metagenomic data, the heterogeneity was unlikely to be due to sample type or sequencing platform. Instead, there may be differences in disease stage, geographic background, and genus-level species complexity[10-13,15-21].
For example, Fusobacterium and Gemella seemed to have the strongest signals in the German cohort. In the Wirbel et al’s dataset[20], Germany reported the most substantial genus-level fold changes for Fusobacterium (fold change = 2.88) and Gemella (fold change = 1.62). Similarly, in the Liu et al’s dataset[11], the greatest species-level log fold changes (log FC) were detected for Fusobacterium nucleatum and Gemella morbillorum (log FC = 2.56 and 3.36, respectively) in Germany. Disease stage could also affect the degree of CRC-associated microbial changes. Liu et al[11] reported a clear stepwise increase in Parvimonas micra from controls to early and then to advanced CRC patients, and Gemella morbillorum and Streptococcus anginosus tended to be more abundant in advanced CRC patients. Fusobacterium nucleatum is already enriched in CRC, but the separation between early-stage disease and advanced-stage disease is unclear[11]. A similar pattern was also present in the Piccinno et al[15] dataset. Several CRC-associated taxa, including Parvimonas, Fusobacterium, Gemella, Peptostreptococcus, Porphyromonas, and Granulicatella, were enriched in CRC patients compared with those in both controls and adenoma patients. Furthermore, Parvimonas, Porphyromonas, Streptococcus, and Hungatella were enriched in advanced-stage CRC, whereas Faecalibacterium was depleted in stage IV disease.
However, the findings should be interpreted as exploratory. Formal subgroup analysis was not feasible because taxon-specific effect estimates with standard errors or confidence intervals were not consistently reported across the included meta-analyses for geographic region, disease stage, or clinical covariates. As a result, we were unable to evaluate whether factors such as body mass index, age, diet, or antibiotic exposure contributed to the observed heterogeneity. Future studies would benefit from more standardized reporting of cohort-level covariates. Such improvements would enable a more robust assessment of the heterogeneity we observed and its potential microbial drivers, including regional, clinical, or technical differences.
In accordance with our predefined criteria (P ≤ 0.05 and k ≥ 5), 17 significant genera were identified. These taxa included oral-associated, gut-associated, and mixed-origin genera, suggesting that CRC-related dysbiosis may reflect a shared oral–gut microbial niche. Oral-associated microbes may reach the intestine and contribute to CRC through colonization, biofilm formation, inflammation, immune modulation, and metabolic alterations[28].
Our genus-species mapping revealed several oral-associated CRC-related species. Parvimonas micra may promote epithelial proliferation by altering DNA methylation and tumor-related gene expression[29], whereas Solobacterium moorei may activate the integrin α2/β1-focal adhesion kinase-c-Myc pathway and promote tumor cell proliferation[30]. Fusobacterium nucleatum is associated with poor epithelial adhesion, tumor immune evasion, and a reduced response to therapy[31-33]. Recently, some studies have shown that Fusobacterium nucleatum subsp. animalis, particularly the Fna C2 Lineage, is highly abundant in CRC tumor tissue and invasive biofilms[34]. Fusobacterium nucleatum may bridge oral dysbiosis and the intestinal tumor microenvironment. Gut-associated taxa may also alter the microbial balance. Bacteroides fragilis can damage the epithelial barrier and cause toxin-mediated damage[35,36], and Hungatella hathewayi can reduce nutrient levels in the mucosa by degrading host-derived glycosaminoglycans, affecting the growth of other bacteria[37,38].
After the selection criteria were applied, both Granulicatella and Peptoniphilus, which are gram-positive anaerobic cocci, were still consistently associated. Granulicatella adiacens is located mainly in the oral cavity, contributes to biofilm formation, and is part of a multispecies microbial community[39]. As a proteolytic anaerobe, Peptoniphilus ferments amino acids and proteins into short-chain fatty acids (SCFAs), ammonia, and other inflammation-related metabolites[40,41]. These metabolic characteristics may help them survive in inflamed or necrotic mucosal tissues that occur during the progression of tumors. On the basis of several independent CRC studies, Granulicatella adiacens, Peptoniphilus asaccharolyticus, Peptoniphilus harei, and Peptoniphilus lacrimalis have been repeatedly identified in the microbiota of faeces, oral cavities, and tumors, and they frequently co-occur with Fusobacterium nucleatum, Parvimonas micra, Streptococcus, and Peptostreptococcus species[10]. Although the direct carcinogenic mechanisms of Granulicatella and Peptoniphilus are not known, they are thought to preferentially colonise low-oxygen, inflamed, and protein-rich regions in tumors on the basis of their ecological characteristics[42]. This finding may support their value as relatively stable CRC-associated microbial biomarkers.
Some meta-analyses have included other microbial groups, such as fungi and archaea, and have reported cross-kingdom interactions between them in CRC. Liu et al[11] reported that fungal taxa, such as Aspergillus rambellii and Trichophyton mentagrophytes, were selected as features for CRC-based diagnostic models. Aspergillus rambellii and Fusobacterium nucleatum often occur together, but the reasons for this remain unclear. Talaromyces islandicus is associated with Clostridium saccharobutylicum, and these two organisms may functionally complement carbohydrate fermentation and SCFA-producing consortia[43,44]. Candida albicans has also been shown to be altered in CRC; it can directly interact with Fusobacterium nucleatum to enhance mucosal colonization and promote tumor progression[45]. The gut archaeome also shows consistent restructuring in CRC, characterized by the enrichment of methanogenic archaea such as Methanobrevibacter A smithii and Methanosphaera stadtmanae[19]. Altered archaeal communities are often associated with the abundance of SCFA-producing bacteria such as Faecalibacterium, Roseburia, and Eubacterium. Methanogenic archaea may act as hydrogen-scavenging organisms that stabilise bacterial fermentation and thus indirectly reduce SCFA production in the intestine[46-48]. Collectively, the above findings support a cross-kingdom ecological framework for CRC that may affect host responses through coordinated changes in microbial metabolites.
We next examined alterations in the gut microbiota of patients with CRC following probiotic/synbiotic intervention. On the basis of the six original studies, the abundance of Lactobacillus and Bifidobacterium increased after intervention, which is consistent with the fact that most of the included interventions contained these genera. In our second-order meta-analysis, Lactobacillus was relatively highly reported and was consistently linked to an increased risk of CRC (pooled effect = 0.342). Species-level mapping revealed that this signal is driven mainly by taxa such as Lactobacillus/Ligilactobacillus ruminis, Lactobacillus paragasseri, Lactobacillus apis, and Latilactobacillus curvatus, which are both gut commensals and bacteria associated with fermented foods or nontypical human sources[22-27]. Therefore, the positive CRC risk association is likely a composite signal rather than a uniform procarcinogenic effect of all Lactobacillus species. Bifidobacterium was also frequently mentioned in second-order meta-analyses, but it had a relatively small effect size. Together, these results suggest that these two genera are unlikely to serve as direct probiotic targets alone and that their effects may instead depend on broader microbial interactions.
Lactobacillus may reduce pathogens in the microenvironment through multiple mechanisms[49]. Through the production of organic acids, such as lactic acid, Lactobacillus creates an unfavorable environment for acid-sensitive pathogens, such as Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecalis, and Clostridium perfringens[50]. In addition, Lactobacillus-produced antimicrobial metabolites, including bacteriocins, biosurfactants, and cyclic dipeptides, may further inhibit virulence expression and adhesion in pathogens such as Porphyromonas gingivalis and Prevotella intermedia[51]. Therefore, Lactobacillus supplementation may reduce the ecological fitness of anaerobic pathobionts, such as Fusobacterium, Porphyromonas, Prevotella, and Bacteroides[49,52,53]. Furthermore, Bifidobacterium may exert its effects through competitive colonization and metabolism-mediated barrier protection[54,55]. Bifidobacterium can compete with potential pathogens, such as Fusobacterium, for epithelial adhesion sites and nutrients, while acetate production may contribute to epithelial barrier integrity[56]. A pilot intervention study revealed that a probiotic formula containing Bifidobacterium adolescentis, Bifidobacterium longum, and Bifidobacterium bifidum inhibited the growth of Fusobacterium nucleatum in vitro. Some Bifidobacterium strains have also been shown to antagonize biofilm formation by Actinomyces and Porphyromonas gingivalis[57,58].
Notably, our results revealed an increasing trend in the abundance of Enterococcus after probiotic intervention. Probiotic intervention may remodel microbial metabolism, thereby altering microbial competition and creating a favorable niche for commensal members of Enterococcus. However, the functional role of Enterococcus in this context remains unclear, as some Enterococcus species may act as opportunistic pathogens. This uncertainty is consistent with our review findings: Three meta-analyses reported the presence of Enterococcus, but the direction of its association with CRC was inconsistent. Therefore, Enterococcus should be regarded as a candidate taxon for further investigation rather than being directly interpreted as either beneficial or detrimental.
We should acknowledge that evidence for probiotic supplementation in CRC remains limited, mainly from animal studies and a few exploratory clinical studies. Most clinical studies do not primarily assess probiotic-induced changes in CRC-associated microbiota, and available microbial analyses are often limited to 16S rRNA sequencing. This limitation results in limited species-level resolution and prevents the direct characterization of probiotic-induced microbial changes in metabolic pathways or functional activities. Therefore, whether the observed taxonomic shifts reflect the restoration of microbial metabolic functions, such as SCFA production and bile acid metabolism, remains unclear. Future clinical trials should incorporate shotgun metagenomic sequencing and metabolomic analyses to provide direct evidence. Additionally, although probiotic supplementation may alter CRC-associated microbiota, it remains unclear whether differences in dose, duration, or formulation led to distinct changes in the relative abundance of CRC-associated bacteria. Higher-dose or longer-duration probiotic supplementation may be associated with more evident modulatory effects on the CRC-associated dysbiotic microbiota. Hibberd et al[25] administered Bifidobacterium animalis subsp. lactis Bl-04 and Lactobacillus acidophilus NCFM to CRC patients at a relatively high daily dose for 31 ± 28 days and reported decreased abundances of CRC-enriched taxa such as Fusobacterium and Peptostreptococcus, along with increased abundances of Faecalibacterium and other butyrate-associated bacteria[25]. Similarly, Liu et al[24] used a total daily dose of 2.6 × 1014 colony-forming units and reported reductions in Enterobacteriaceae, Pseudomonas, and Candida[24]. These findings should be interpreted as preliminary observations rather than direct evidence supporting a dose- or duration-dependent relationship. Therefore, future standardized clinical studies with species-level analyses are needed to determine whether more intensive probiotic interventions can consistently suppress CRC-associated dysbiosis.
CONCLUSION
This umbrella review and second-order meta-analysis provides an integrated overview of gut microbial alterations associated with CRC and identifies 17 genera that met the predefined significance and evidence thresholds. The coexistence of oral-associated, gut-associated, and mixed-origin taxa supports the concept that CRC-related dysbiosis reflects a coordinated oral-gut microbial ecosystem rather than the action of a single pathogen. Nevertheless, reporting frequency did not necessarily indicate a robust association, and several widely studied genera, including Fusobacterium, Parvimonas, Porphyromonas, Gemella, and Streptococcus showed some degree of heterogeneity across the included meta-analyses, potentially related to geographic background, disease stage, and species-level complexity within genera. Preliminary evidence further suggests that probiotic or synbiotic interventions may increase Lactobacillus and Bifidobacterium while reducing the ecological fitness of several CRC-associated pathobionts. However, the available intervention evidence remains limited and does not establish causal effects or clinical efficacy. Future studies should adopt standardized reporting of clinical covariates and incorporate shotgun metagenomics, species- or strain-level analyses, and metabolomics. Adequately powered clinical trials with clearly defined probiotic formulations, doses, and intervention durations are also needed to determine whether microbiota modulation can consistently restore microbial functions and improve CRC prevention.
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Footnotes
Peer review: Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific quality: Grade B, Grade B
Novelty: Grade C, Grade C
Creativity or innovation: Grade C, Grade C
Scientific significance: Grade B, Grade C
P-Reviewer: Shrivastav D, Assistant Professor, PhD, India; Zhou HX, China S-Editor: Fan M L-Editor: A P-Editor: Zhang YL
