Fusobacterium periodonticum promotes colorectal tumorigenesis via decanoic acid-driven neutrophil chemotaxis

Xinmiao Jia; Lingjuan Jiang; Yiyi Gong; Xiaobing Chu; Wei Yu; Juan Du; Jingjia Zhang; Xuesong Shang; Peipei Wang; Jing Wang; Yaqian Li; Zheng Wang; Runing Zhou; Zongping Li; Ying Zhu; Bin Wu; Jingnan Li; Qiwen Yang

doi:10.1038/s41467-026-74591-y

. 2026 Jun 24;17:7911. doi: 10.1038/s41467-026-74591-y

Fusobacterium periodonticum promotes colorectal tumorigenesis via decanoic acid-driven neutrophil chemotaxis

Xinmiao Jia ^1,^2,^#, Lingjuan Jiang ^1,^3,^#, Yiyi Gong ^1,^3,^#, Xiaobing Chu ^1,^4,^#, Wei Yu ^1,^4,^#, Juan Du ^1,^#, Jingjia Zhang ^1,^4,⁵, Xuesong Shang ¹, Peipei Wang ⁶, Jing Wang ⁷, Yaqian Li ⁸, Zheng Wang ⁹, Runing Zhou ⁹, Zongping Li ^1,⁴, Ying Zhu ¹, Bin Wu ^6,^✉, Jingnan Li ^7,^✉, Qiwen Yang ^1,^10,^✉

PMCID: PMC13443604 PMID: 42342666

Abstract

Gut microbiota dysbiosis and immune dysregulation are closely associated with the development of colorectal cancer. Identifying the mechanistic links among specific microbial species, metabolites, and immune responses is crucial for uncovering novel insights into its pathogenesis. Here we show, through metagenomic and metabolomic analyses of clinical cohorts, that Fusobacterium periodonticum is significantly enriched in colorectal cancer patients and strongly correlated with elevated decanoic acid levels. Single-cell transcriptomic results further reveal tissue-specific neutrophil enrichment in colorectal cancer tissues, characterized by high CXCL8 expression and activation of neutrophil-related immune pathways. Cellular experiments demonstrate that decanoic acid induces late apoptosis/necrosis of neutrophils, enhances their chemotaxis through a pertussis toxin-sensitive G-protein-dependent mechanism, and upregulates genes involved in leukocyte migration and tumorigenesis. Mouse models further confirm that F. periodonticum colonization increases intestinal dysplasia and decanoic acid levels, and that decanoic acid intervention promotes tumor progression by facilitating neutrophil infiltration and modulating the local immune microenvironment. Our study reveals an important role of F. periodonticum in colorectal tumorigenesis via decanoic acid-medicated neutrophil chemotaxis, providing mechanistic insights into the pathogenesis of colorectal cancer.

Subject terms: Metagenomics, Colorectal cancer

Here, using a multi-omics approach, the authors reveal that Fusobacterium periodonticum is enriched in colorectal cancer and correlates with elevated decanoic acid, driving neutrophil chemotaxis via a G-protein-dependent mechanism to promote colorectal tumorigenesis.

Introduction

Colorectal cancer (CRC) is the third most common cancer worldwide, accounting for approximately 10% of all cancer cases. It is also the second leading cause of cancer-related deaths worldwide^1,2. In recent years, the incidence of cancer has tended to increase among those under 50 years of age, which poses a grave threat to human life and health³. CRC generally evolves from precancerous lesions such as those seen in colorectal adenoma (CRA) patients^4,5. The pathogenesis of CRC is complex and involves genetic, environmental, lifestyle and immunological factors.

Recent research has focused on the role of the intestinal microbiota in CRC pathogenesis. The gut microbiota plays crucial roles in maintaining intestinal health. Dysbiosis of the gut microbiota has been associated with CRC development^6,7. Studies have shown that putatively procarcinogenic microbial members, including Fusobacterium nucleatum, Escherichia coli, Bacteroides fragilis, and Enterococcus faecalis, have relatively high relative abundances in CRC patients, whereas those of so-called protective genera, including Roseburia, Clostridium, Faecalibacterium and Bifidobacterium, are reduced^8–10. In this context, F. nucleatum has attracted substantial interest because it participates in the mechanism of CRC through promotion of the proliferation and migration of CRC cells, recruitment of infiltrating immune cells, and its effect on the balance of the intestinal microbiota^11–14. Additionally, some microorganisms, including enteropathogenic E. coli with a “pks” gene island, can cause CRC by damaging DNA^15,16. Metabolites of the intestinal microbiota also play a role in CRC development: bile acids can promote CRC growth by suppressing CD8 + T-cell effector functions; formate, a metabolite produced by F. nucleatum, promotes CRC development^17,18.

Moreover, the immune system plays pivotal roles in CRC pathogenesis, maintaining homeostasis and defending against pathogens. Immune dysregulation can trigger chronic inflammation, increasing the risk of CRC¹⁹. Inflammation is a well-established driver of colorectal carcinogenesis. Bacterially induced inflammation has also been shown to be positively correlated with CRC. F. nucleatum can generate a proinflammatory environment conducive to colorectal neoplasia progression by activating the NF-κB pathway and driving myeloid cell infiltration in tumors in Apc^min/+ mice¹². Enterotoxigenic B. fragilis can activate the STAT3 signaling pathway in Apc^min/+ mice, leading to the secretion of large amounts of IL-1, which significantly induces colitis and CRC²⁰. However, the role of crosstalk between the gut microbiome and the host, which shapes the tumor immune environment and modulates the microbiota, remains largely unexplored.

In this study, we performed a multiomics and in-depth investigation of the fecal microbiomes and metabolomes and the single-cell transcriptomes of CRC patients, CRA patients, and healthy controls (HCs), to reveal the occurrence and development of CRC and identified F. periodonticum as a pathogenic bacterium enriched in CRC, whose metabolite decanoic acid can drive the chemotaxis of neutrophils and promote the occurrence of CRC. Through this study, we aim to establish a pathway linking the intestinal microbiota to metabolites and further connecting metabolites to the immune system in an aim to provide clues for exploring the pathogenic mechanisms of CRC.

Results

Gut microbiota analysis reveals F. periodonticum as a significantly enriched species in CRC patients

The structures of the gut microbiota in the CRC, CRA, and HC groups were investigated by metagenomic sequencing (Fig. 1A and Supplementary Data 1). Although no significant differences in α diversity were detected (Supplementary Fig. 1), β diversity significantly differed in terms of community distribution between the CRC/CRA and HC groups (R² = 0.029, P = 0.0098) (Fig. 1B). There were no significant differences in α-diversity or β-diversity between the CRC and CRA groups. A total of 26 species were significantly enriched, and 9 species were depleted in CRC patients compared with HCs (Fig. 1C and Supplementary Data 2). Among the 26 species with increased abundance in CRC patients, 15 exhibited a continuously increasing trend with disease progression (Fig. 1C). Among them, 9 species including Parvimonas micra, Prevotella intermedia, Ruthenibacterium lactatiformans, Dialister pneumosintes, Fusobacterium sp. oral taxon 370, Intestinibacter bartlettii, Clostridium disporicum, F. periodonticum, and F. nucleatum, were significantly enriched in CRC patients compared with those in CRA patients (Supplementary Data 2).

To evaluate the ability of differentially abundant species to distinguish CRC patients from HCs and CRA patients, we built a random forest model. Ultimately, 8 species (P. micra, P. intermedia, R. lactatiformans, D. pneumosintes, I. bartlettii, C. disporicum, F. periodonticum, and F. nucleatum) were selected (Supplementary Fig. 2A), and the area under the receiver operating characteristic curve was 85.23% (Supplementary Fig. 2B). Among these, we confirmed the significant association of several previously reported species (including P. micra, P. intermedia, and F. nucleatum) with CRC. More importantly, our study identified F. periodonticum as being predominantly present in the fecal samples of CRC patients as compared to HC or CRA (Supplementary Fig. 3).

To determine the functional changes in the gut microbiota, a pathway profile was generated. PCoA of pathway abundance revealed no significant differences between CRC patients and CRA patients, but significant differences in overall microbial pathway distribution were found between CRC patients and HCs (R² = 0.062, P = 7e-4) (Fig. 1E). Fifty-two enriched and 64 downregulated pathways were significantly different between CRC patients and HCs (Supplementary Data 3). The top ten enriched pathways in CRC patients were palmitate biosynthesis, ethanolamine utilization, fatty acid elongation-saturated, oleate biosynthesis IV, the sulfate assimilation and cysteine biosynthesis superpathway, palmitoleate biosynthesis I, (5Z)-dodecenoate biosynthesis I, stearate biosynthesis II (bacteria and plants), the L-methionine biosynthesis superpathway, and 8-amino-7-oxononanoate biosynthesis I (Fig. 1F). In addition, 64 pathways were significantly decreased in CRC patients compared with HCs, and the most discriminating pathways were related to UMP biosynthesis (Fig. 1F).

Verification of the gut microbiota with large-scale metagenomic sequencing data confirmed the significant enrichment of F. periodonticum in CRC patients

To confirm the findings in our data, we downloaded 1603 metagenomic sequenced samples (CRC, n = 601; CRA, n = 319; HC, n = 683) (Supplementary Data 4) and analyzed these data via the same pipeline. The results of the species analysis further confirmed the significant enrichment of P. micra, P. intermedia, R. lactatiformans, D. pneumosintes, F. periodonticum, and F. nucleatum in CRC patients compared with that in CRA patients and HCs (Fig. 1D, Supplementary Figs. 4 and 5).

As is well known, F. nucleatum is closely associated with the development of CRC, a conclusion that is also supported by our findings. Our sequencing data indicated that F. nucleatum was significantly enriched in the CRC group, with an LDA of 2.03 and a P value of 0.0002, compared with that in HCs, with an LDA of 2.01 and a P value of 0.0024, compared with that in CRA patients. Across the 1603 public samples, this difference was also obvious (CRC vs. CRA, P < 0.0001; CRC vs. HC, P < 0.0001) (Fig. 1D).

Notably, F. periodonticum is reported to be enriched in CRC patients for the first time. Our sequencing data indicated that F. periodonticum abundance was enriched in the CRC group, with an LDA of 2.13 and a P value of 0.0017, compared with that in HCs, with an LDA of 2.07 and a P value of 0.0076, compared with that in CRA patients. Across the 1603 public samples, this difference was even more obvious (CRC vs. CRA, P < 0.0001; CRC vs. HC, P < 0.0001) (Fig. 1D). We also analyzed the distribution of F. periodonticum in individual cohorts from different geographic origins (Supplementary Fig. 4 and Supplementary Data 4). The results demonstrate that F. periodonticum was significantly more abundant in CRC patients compared to healthy controls (HCs) across six out of the eight analyzed cohorts (excluding the 2016_VogtmannE and 2019_ThomasAM cohorts), suggesting a robust association with colorectal cancer.

We also analyzed the functional changes in the gut microbiota. Interestingly, the microbial fatty acid elongation-saturation pathway was also significantly enriched in the CRC group compared with the HC group (P = 0.00012) (Supplementary Fig. 6).

Global metabolomic profiling revealed the significance of fatty acid metabolism in CRC patient feces

By employing an untargeted liquid chromatography‒tandem mass spectrometry (LC‒MS/MS) methodology, we conducted a thorough analysis of the global metabolic profiles across the three distinct groups.

Sparse partial least squares discriminant analysis (sPLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were executed to characterize the differential profiles between the HC, CRA and CRC groups. The HC group exhibited tight clustering, in contrast to the more dispersed profiles observed in both the CRA and CRC groups (Fig. 2A). This observation implies a greater degree of metabolic heterogeneity in patients. Additionally, there was significant overlap in the metabolomic profiles between the CRC and CRA groups, suggesting a degree of similarity in their metabolic signatures. To enhance the differentiation between these groups, OPLS-DA was employed. The OPLS-DA models demonstrated robust discriminatory power among the three groups, effectively distinguishing the HC group from the CRC group (Fig. 2B, 2C, and Supplementary Figs. 7, 8). However, importantly, there may be some instances of confounding between the CRC and CRA groups or between the CRA and HC groups. The quality of the OPLS-DA models was further validated through permutation tests, and their respective Q2 and R2 values are presented in Supplementary Data 5. The multivariate analysis revealed significant metabolic differences between HC and CRC groups. CRA exhibited transitional alterations, consistent with its precancerous bridging role.

Fig. 2 — A sPLS-DA plots of the CRC, CRA and HC groups. B, C OPLS-DA plots of CRC patients vs. HCs in negative and positive ion modes, respectively. D Volcano plots showing the differences in the abundance of identified metabolites between the CRC and HC groups. P values were analyzed using classic two-sample t-test and adjusted for multiple comparisons using the Benjamini–Hochberg false discovery rate (FDR). E Classification of DAMs by HMDB. Heatmap showing the relative levels of differentially abundance lipids in each group. F Pathway enrichment of DAMs between CRC patients and HCs. Pathway analysis was performed using MetaboAnalyst (version6.0, https://www.metaboanalyst.ca/). P values were directly obtained from the platform outputs using default settings. G Correlation heatmap of DAMs and the microbiota. The color represents the correlation R, and the star symbol represents the significance. *P value < 0.05. H Correlation between the relative expression of decanoic acid and the relative abundance of *F. periodonticum*. I Correlation between the relative expression of palmitic acid and the relative abundance of *F. periodonticum*. J Targeted quantification of potential differentially abundant metabolites in fecal samples. K Targeted quantification of fatty acids in isolated *F. periodonticum* and cultured medium. Box plots showing the concentrations of decanoic acid (L) and palmitic acid (M) in the fecal samples (left plot) (two-tailed Mann–Whitney U test. CRC, n = 20 patients; CRA, n = 20 patients; HC, n = 20 patients) and cultured media (middle plot, unpaired two-tailed t-test, three biological replicates) of *F. periodonticum* and *F. periodonticum* bacterial pellets (right plot, unpaired two-tailed t-test, three biological replicates). H, I Gray shaded bands denote the 95% confidence interval (CI) of the regression line. Correlation was analyzed using the Spearman’s correlation coefficient, with correlation coefficient and P value shown in the plot. For boxplots, the lower and upper hinges correspond to the first and third quartiles, while the upper whisker extends from the hinge to the largest value no further than 1.5 × IQR from the hinge (IQR inter-quartile range). The lower whisker extends from the hinge to the smallest value at most 1.5 × IQR of the hinge. The middle line denotes the median.

The metabolites that were pivotal in differentiating the HC group from the CRC group were identified by the S-plot analysis of the OPLS-DA. Metabolites with a variable importance in projection score >1 were selected for further examination through hierarchical clustering. This approach identified a set of metabolites that were markedly distinct between the HC and CRC groups, indicating significant metabolic divergence. Moreover, these differential metabolites could not be effectively clustered in the CRA group, as their expression levels mostly fell within a transitional range between the HC and CRC groups, as shown in Supplementary Fig. 9.

A total of 206 molecules were tentatively identified from the metabolomics data as differentially abundant metabolites (DAMs). Among these, 91 DAMs were significant different between the HC and CRC groups. These findings are visually represented in a volcano plot in Fig. 2D and further details are provided in Supplementary Data 6. The identified molecules were systematically classified according to the taxonomy classes of the Human Metabolome Database (HMDB), as shown in Fig. 2E and Supplementary Data 7. The DAMs were predominantly composed of lipids and lipid-like molecules (26%), organic acids (22%), benzenoids (17%) and organic oxygen compounds (8%). Among these, the differentially abundant lipid molecules, which constitute the most prominent subclass, were subjected to hierarchical clustering analysis across the CRC, CRA, and HC groups. The resulting heatmap clearly demonstrated significant alterations in lipid abundance within the CRC group, suggesting a potential role for lipid metabolism in tumor progression. Pathway enrichment of DAMs revealed that the metabolic pathway β-oxidation of very long-chain fatty acids was significantly enriched in CRC patients (Fig. 2F and Supplementary Data 8). This annotation result suggests that fatty-acid metabolism is altered in CRC patients, offering a lead for further validation.

Association analysis and targeted metabolomics revealed a significant correlation between F. periodonticum and decanoic acid

On the basis of the comprehensive analysis conducted, both the metagenomic pathways and the metabolic pathways of the CRC group were notably enriched in pathways related to fatty acids, with a particular emphasis on medium- to long-chain fatty acids. To investigate the potential linkage and function of metabolites and the gut microbiota, correlation analysis between major DAMs and differentially abundant species was performed. As demonstrated in Fig. 2G-2I, F. periodonticum was the only species that was positively correlated with two medium- to long-chain fatty acids, namely, decanoic acid (R = 0.25, P= 0.0074) and palmitic acid (R = 0.23, P = 0.014). We also observed positive associations between decanoic acid and other bacterial species, including Gemella morbillorum (R = 0.23, P = 0.012) and Bifidobacterium dentium (R = 0.22, P = 0.019). However, the correlation coefficient were lower than those of F. periodonticum. F. periodonticum was also positively correlated with the short-chain fatty acid, hexanoic acid (R = 0.33, P = 0.00032). However, given that multiple bacterial strains showed associations with hexanoic acid, and F. periodonticum was not the most strongly correlated species with this metabolite, we have chosen not to focus on hexanoic acid in our current study. This decision is further supported by our subsequent targeted metabolome (the abundance of hexanoic acid in the bacterial pellet and F. periodonticum culture medium showed no significant difference compared with the control group) and experimental data (no significant relationship between hexanoic acid and neutrophil chemotaxis).

To confirm the observed alterations in the DAMs, we conducted a validation study by measuring 11 significantly differentially abundant metabolites in a cohort of 60 fecal samples. An increase in the abundances of 8 metabolites, including decanoic acid, was observed in the feces of CRC patients (Fig. 2J). The specific concentrations of decanoic acid and palmitic acid are depicted in Fig. 2L and 2M, respectively. Compared with those in HCs, the abundance of decanoic acid was elevated in both the CRC and CRA groups. Notably, CRC patients presented the highest levels of decanoic acid, underscoring its potential role in colorectal tumorigenesis. Given the vital role of median- and long-chain fatty acids in colorectal tumorigenesis, the relationship between the gut microbiota and fatty acids was further investigated. We isolated and cultured F. periodonticum. Taxonomic confirmation of F. periodonticum included average nucleotide identity (ANI) analysis and core-genome phylogenetic tree construction (Supplementary Figs. 10 and 11). By using GC‒MS/MS, we quantified fatty acids in the culture medium and bacterial pellets (for detailed information on the standard curve and ion pairs, refer to Supplementary Data 9 and 10). As shown in Fig. 2K, we detected 35 long-chain fatty acids and 11 short- and medium-chain fatty acids. Compared with those in the control medium, the levels of most fatty acids were significantly greater in the bacterial lysate. Owing to the nature of independent biological experiments, the abundances of some metabolites showed significant variability within groups. Through statistical testing, we ruled out the impact of random fluctuations on the differences in content. Nonetheless, the abundance of decanoic acid was markedly elevated in both the bacterial pellet and F. periodonticum culture medium compared with the control (Fig. 2L, TIC and ion pairs of the standard sample shown in Supplementary Fig. 12). Palmitic acid levels only significantly increased within the bacterial pellets and not in the culture medium (Fig. 2M). These results suggest a more significant correlation between F. periodonticum and decanoic acid than palmitic acid. Additionally, hexanoic acid, a short-chain fatty acid, showed no significant difference in abundance between F. periodonticum and the control group, either in the culture medium or the bacterial pellet (Supplementary Fig. 13).

scRNA-seq reveals the cell types and tissue-specific patterns of neutrophils in CRC patients

To explore the cellular diversity and differences in gene expression in CRC patients, we generated single-cell data for samples collected from CRC and paracancerous tissues. We also collected adenoma tissues as controls (Fig. 1A). After an initial quality control step, we obtained a total of 34,489 single cells from adenoma, paracancerous, and tumor tissues. There was an average of 3971 cells per sample, in which the expression of a median of 1578 genes per cell could be detected (Supplementary Fig. 14 and Supplementary Data 11).

We used unsupervised graph clustering to partition the cells into clusters and visualized the clusters via uniform manifold approximation and projection (UMAP). To determine the cellular identity of each cluster, we generated cluster-specific marker genes via differential gene expression analysis (Supplementary Data 12). The cell types were annotated according to published markers^21–23. To define cancer cells, we calculated large-scale chromosomal copy number variations (CNVs) in each cell type on the basis of average expression patterns across intervals of the genome.

In total, 8 cell types were identified: epithelial cells (EPs), B-cells (BCs), T-cells (TCs), cancer cells (CCs), myeloid cells (MCs), fibroblasts (FIBs), endothelial cells (ECs), and mast cells (MASTs) (Fig. 3A). The two most abundant cell types were EPs (30.51%) and BCs (20.63%) (Fig. 3B). The relative expression of the top 10 genes in each cell type and the markers for each cell type are also displayed (Fig. 3C and 3D). Notably, we found that the percentage of MCs in tumors (15.77%) was significantly greater than that in paracancerous (4.22%) and adenoma tissues (2.59%) (Fig. 4A and Supplementary Fig. 15). Moreover, MCs exhibited unique expression patterns in the CRC group (Fig. 4B). Therefore, we further analyzed the subtypes of MCs and identified 6 subtypes: conventional dendritic cells (cDCs), macrophages, monocells, neutrophils, plasmacytoid dendritic cells (pDCs), and tumor-associated macrophages (TAMs) (Fig. 4C, Supplementary Data 13 and 14). Among these, neutrophils (42.94%, 1290/3004) and macrophages (25.27%, 759/3004) accounted for the greatest proportions, and neutrophils displayed a CRC tissue-specific expression pattern (Fig. 4C and Supplementary Fig. 15). Notably, the proportion of neutrophils in tumor tissues (53.81%) was significantly greater than that in paracancerous (9.8%) and adenoma tissues (4.0%) (Supplementary Fig. 15).

Fig. 3 — A UMAP plots of the annotated cell populations. Each point depicts a single cell, which is colored according to the cell type. B Charting showing the number and percentage of each cell type. C Heatmap showing the relative expression of the top 10 genes in each cell type. D Violin plots displaying the expression of canonical markers for each cell type.

Fig. 4 — A UMAP plots of the cell populations colored according to group. B UMAP plots of the myeloid cell populations colored according to cell subtype. C UMAP plots of the myeloid cell populations colored according to group. Volcano plot showing the DEGs in CRC patients compared with HCs in all cells (D) and myeloid cells (E). P values are analyzed using two-tailed Mann–Whitney U test with Bonferroni correction for multiple comparisons. GO enrichment of upregulated genes in all cells (F) and myeloid cells (G) in CRC patients compared with HCs. Expression levels of focused DEGs in eight cell types (H) and in myeloid cell subtypes (I).

We next defined gene expression changes at the global and cellular levels. We merged all cells and analyzed differential gene expression among tumor, adenoma, and paracancerous tissues at the global level. We identified 226 genes with upregulated expression and 44 genes with downregulated expression in tumor tissues compared with paracancerous tissues (Fig. 4D) and 178 genes with upregulated expression and 89 genes with downregulated expression in tumor tissues compared with adenoma tissues (Supplementary Fig. 16A). Functional enrichment analysis of the upregulated genes revealed that “neutrophil activation” and “neutrophil activation involved in the immune response” were specifically enriched in tumor tissues compared with paracancerous and adenoma tissues (Fig. 4F and Supplementary Fig. 16B). We further analyzed the gene expression of myeloid cells and identified 259 genes with upregulated expression and 306 genes with downregulated expression in tumor tissues compared with paracancerous tissues (Fig. 4E), and 243 genes with upregulated expression and 446 genes with downregulated expression in tumor tissues compared with adenoma tissues (Supplementary Fig. 17A). The functional enrichment of the upregulated genes also revealed enrichment of “neutrophil activation” and “neutrophil activation involved in the immune response” (Fig. 4G and Supplementary Fig. 17B). Among these enriched functional terms, the genes CXCL8, CXCL2, VEGFA, AREG, OSM, PTGS2, and CPT1A showed significantly upregulated expression in tumor tissues. CXCL8, OSM, and PTGS2 were highly expressed in myeloid cells, especially neutrophils (Fig. 4H and 4I).

Additionally, to mitigate potential bias due to limited sample size, we integrated 231 public single-cell datasets (cancer, n = 125; paracancerous, n = 70; HC, n = 36; Supplementary Data 15) and analyzed differential expression patterns of neutrophil-related genes using CRC atlas²⁴. The results demonstrated that neutrophil marker genes (S100A8, S100A9, FCGR3B, and CSF3R) were significantly upregulated in tumor tissues compared to paracancerous and healthy tissues. Furthermore, genes encoding neutrophil-derived chemokines (CXCL8 and CXCL2) and tumor angiogenesis and cell proliferation related genes (VEGFA, OSM, AREG, and PTGS2) were also markedly elevated in cancer tissues (Supplementary Fig. 18).

Decanoic acid promotes neutrophil late apoptosis/necrosis, chemotaxis and facilitates the expression of genes related to leukocyte migration and tumorigenesis

On the basis of our findings from single-cell analysis showing an enrichment of neutrophils in patients with CRC, our study focused on the impact of certain metabolites on neutrophil function in the tumor microenvironment, specifically the effects of decanoic acid and palmitic acid related to F. periodonticum. To investigate this further, we isolated neutrophils from healthy volunteers and treated them with decanoic acid or palmitic acid under both Lithium Heparin and EDTA anticoagulant conditions. Sodium decanoate (DeaNa) increased the proportion of late apoptotic/necrotic neutrophils, as evidenced by higher AnnexinV⁺/PI⁺ cells, while the proportion of early apoptotic cells (AnnexinV⁺/PI⁻) decreased compared with the control group (Fig. 5A‒C). A consistent trend was observed under both anticoagulant conditions, and lithium heparin outperformed EDTA in preserving neutrophil viability (Supplementary Fig. 19). Sodium palmitate (PaNa) further exacerbated late apoptosis/necrosis, showing an even greater effect than DeaNa, accompanied by a reduction in early apoptotic cells (Fig. 5A‒C). In contrast, sodium hexanoate (HexNa) had no significant effect on neutrophil apoptosis (Supplementary Fig. 20). Moreover, DeaNa significantly induced late apoptosis and necrosis (Annexin V⁺PI⁺ cells) in neutrophils at various concentrations below 100 μM, with a trend of increased effects at higher concentrations. However, no significant differences were observed between the low and high concentration groups. Notably, at higher concentrations, the proportion of early apoptotic cells (Annexin V⁺PI⁻) decreased, accompanied by an increase in the viable cell population (Annexin V⁻PI⁻) compared to lower concentrations (Supplementary Fig. 21). These results highlight the significant impact of fatty acid treatment concentrations on neutrophil apoptosis responses. In order to further validate whether the observed effects of fatty acids are consistent in primary human monocytes, freshly isolated human peripheral blood monocytes were treated with indicated concentrations (25, 50, or 100 μM) of HexNa, DeaNa, or PaNa for 24 h. DeaNa did not induce significant apoptosis at any concentration tested, with the proportions of early apoptotic (Annexin V⁺PI⁻) and late apoptotic/necrotic (Annexin V⁺PI⁺) cells remaining comparable to the PBS control group (Supplementary Fig. 22A–C). In contrast, PaNa significantly induced both early apoptosis and late apoptosis/necrosis, with late apoptosis/necrosis at 100 μM exceeding that of the staurosporine (STS) positive control, while early apoptosis was comparable to the STS group (Supplementary Fig. 22A–C). In addition, we examined the regulatory effects of varying fatty acid concentrations on apoptosis in human monocytic THP-1 cells after 18 and 24 h of treatment. Consistent with the 18-h observations, treatment with DeaNa or HexNa for 24 h did not induce notable apoptosis at any concentration tested, with the proportions of early apoptotic (Annexin V⁺PI⁻) and late apoptotic/necrotic (Annexin V⁺PI⁺) cells remaining comparable to the PBS control group (Supplementary Fig. 23A–C). In contrast, PaNa significantly induced both early apoptosis (Annexin V⁺PI⁻) and late apoptosis/necrosis (Annexin V⁺PI⁺), exhibiting a pattern similar to that observed in the positive control (STS) group (Supplementary Fig. 23A–C). These results indicate that the pro-apoptotic effect of DeaNa is specific to neutrophils and does not extend to monocytes.

Fig. 5 — A Flow cytometry analysis of neutrophil apoptosis after coculture with sodium decanoate (DeaNa), sodium palmitate (PaNa), and PBS. Quantitative graphs showing the proportion of late apoptotic/necrotic (Annexin V⁺/PI⁺) neutrophils (B) and early apoptotic (Annexin V⁺/PI⁻) neutrophils (C) under different treatments (n = 3). D Schematic representation of neutrophil migration in vitro for 3 h at 37 °C. Created in BioRender. Yang, Q. (2026) https://BioRender.com/fm3t1j5. Representative images of migrated neutrophils on the lower membrane stained with DAPI and photographed using the multifunctional enzyme reader. E Numbers of migrating neutrophils (n = 3). F Volcano plot of differential gene expression in sodium decanoate-treated and PBS-treated neutrophils. G Heatmap showing the DEGs identified via RNA-seq in the presence of decanoic acid or PBS. H Quantitative reverse transcription PCR (qRT‒PCR) validation of gene expression changes in neutrophils treated with decanoic acid, palmitic acid and PBS (n = 4). I Cell supernatant CXCL8 expression in neutrophils treated with decanoic acid, palmitic acid and PBS, as determined by ELISA (n = 4). J Simplified schematic illustrating the coculture assay of neutrophils with Caco-2 cells, detailing the experimental setup. Created in BioRender. Yang, Q. (2026) https://BioRender.com/74ys0y4. K qRT‒PCR validation of gene expression changes in Caco-2 cells after coculture with different fatty-acid-treated neutrophils (n = 3). Data are shown as mean ± SEM, with representative results from at least three independent experiments. P values were determined by one-way ANOVA with Holm–Šidák test (B, C), one-way ANOVA with Dunnett’s test (E, K), one-way ANOVA with Tukey test (H–I). DeaNa, sodium decanoate; PaNa, sodium palmitate. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 and ns P > 0.05. Exact P values: B DeaNa vs PBS, P = 0.0465, PaNa vs PBS, P = 0.0002; C DeaNa vs PBS, P < 0.0001, PaNa vs PBS, P < 0.0001; E DeaNa vs PBS, P = 0.0342, PaNa vs PBS, P = 0.4726; H *Cxcl2*, DeaNa vs PBS, P = 0.0015, PaNa vs PBS, P = 0.0001; *Cxcl8*, DeaNa vs PBS, P = 0.0120, PaNa vs PBS, P = 0.0725; *Cpt1a*, DeaNa vs PBS, P = 0.0441, PaNa vs PBS, P < 0.0001; *Osm*, DeaNa vs PBS, P = 0.0078, PaNa vs PBS, P = 0.0019; *Vegfa*, DeaNa vs PBS, P = 0.0319, PaNa vs PBS, P = 0.0009. I DeaNa vs PBS, P = 0.0016, PaNa vs PBS, P = 0.0101. K CXCL8, DeaNa vs PBS, P < 0.0001, PaNa vs PBS, P = 0.9138; OSM, DeaNa vs PBS, P = 0.0865, PaNa vs PBS, P = 0.0031; VEGFA, DeaNa vs PBS, P = 0.0053, PaNa vs PBS, P < 0.0001; AREG, DeaNa vs PBS, P = 0.0451, PaNa vs PBS, P = 0.0391; PTGS2, DeaNa vs PBS, P = 0.0015, PaNa vs PBS, P = 0.0224.

In the process of intestinal inflammation and its progression to cancer, the excessive recruitment and accumulation of neutrophils are associated with increased intestinal permeability, mucosal damage, and tumor promotion. Therefore, we aimed to examine the effect of decanoate on neutrophil migration. To evaluate the effect of decanoate on neutrophil migration, a Transwell assay was conducted. DeaNa or PaNa was added to the lower chamber, while neutrophils were seeded in the upper chamber and incubated for 4 hours to assess their directional migration. The results demonstrated that neutrophils exhibited strong migration toward DeaNa, whereas only minimal migration occurred in PaNa-treated and control groups, indicating that sodium decanoate may enhance neutrophil chemotaxis (Fig. 5D and 5E). Consistently, in the neutrophil Transwell checkerboard assay, under gradient conditions, DeaNa induced significantly greater neutrophil migration compared with PaNa. In contrast, under non-gradient conditions, no statistically significant difference in migration was observed between DeaNa and PaNa, suggesting that the chemotactic effect of sodium decanoate is gradient-dependent (Supplementary Fig. 24).

Activated neutrophils release various chemokines that promote the activation and recruitment of monocytes, macrophages, and other adaptive immune cells, thereby sustaining the inflammatory response. To explore the potential regulatory role of decanoic acid in neutrophil function and phenotype, we performed RNA sequencing (Fig. 5F). Compared with vehicle-treated neutrophils, those treated with sodium decanoate exhibited substantial alterations in gene expression, particularly in genes related to neutrophil-derived chemokines and pro-tumorigenic pathways (Fig. 5G). To validate these transcriptomic findings, we conducted qRT-PCR analysis. Sodium decanoate treatment significantly upregulated the expression of CXCL2 and CXCL8, two key neutrophil-secreted chemokines (Fig. 5H). In addition, sodium decanoate promoted the upregulation of CPT1A, a key enzyme involved in fatty acid oxidation, and OSM, a cytokine implicated in inflammatory signaling and tumor microenvironment modulation (Fig. 5H). Importantly, the expression of VEGFA, a critical regulator of angiogenesis in solid tumor progression, was also markedly increased (Fig. 5H). Consistent with these observations, ELISA analysis of culture supernatants confirmed that decanoic acid significantly enhanced CXCL8 protein production in neutrophils (Fig. 5I). Flow cytometry was also employed to assess CXCL8 secretion by neutrophils. Compared with the control group, sodium decanoate significantly enhanced CXCL8 secretion. Sodium palmitate induced a similar but less pronounced effect, whereas sodium hexanoate (HexNa) had no observable activating effect on CXCL8 secretion (Supplementary Fig. 25). The MFI (median) of CD66b⁺CXCL8⁺ cells was significantly higher in the sodium decanoate group compared with the control group. The sodium palmitate group also showed an increase relative to the control, but the level was lower than that observed in the sodium decanoate group (Supplementary Fig. 25). Collectively, these results demonstrate that decanoic acid modulates neutrophil function by promoting the expression of chemokines and pro-angiogenic factors, potentially contributing to a tumor-promoting microenvironment.

As GPR84 has been identified as a receptor that senses medium-chain fatty acids²⁵, we conducted further analysis of its gene expression involved in this process, and found that the expression of GPR84 was significantly increased in CRC (Supplementary Fig. 26A). Furthermore, transcriptomic analysis of neutrophils stimulated with decanoic acid revealed that GPR84 expression was significantly higher in the decanoic acid group compared to the PBS control group (Supplementary Fig. 26B). Although no statistically significant difference was observed between the decanoic acid and palmitic acid groups, an upward trend in GPR84 expression was noted in the decanoic acid-treated samples.

Given that GPR84 mediates its effects through a Gi-coupled signaling mechanism and that pertussis toxin (PTX) acts as a Gi protein inhibitor, we first optimized the concentration of PTX to investigate whether the effects of decanoic acid on neutrophils are mediated via Gi-coupled signaling. Preliminary ELISA screening revealed that 300 ng/mL of PTX effectively suppressed the decanoic acid-induced increase in CXCL8 secretion (Supplementary Fig. 27A). This concentration was therefore used in all subsequent assays. PTX did not reverse the increase in late apoptosis/necrosis induced by either decanoic acid sodium or palmitic acid sodium, suggesting that the pro-apoptotic effects of these fatty acids are likely independent of PTX-sensitive G-protein signaling (Supplementary Fig. 27B–D). In neutrophil chemotaxis assays, PTX pretreatment significantly reduced neutrophil migration toward decanoic acid, indicating that the chemotactic effect of decanoic acid is dependent on Gi-coupled receptor activity (Supplementary Fig. 27E). Consistently, PTX pretreatment also significantly attenuated decanoic acid-induced neutrophil activation, as evidenced by a reduction in the proportion of CD66b⁺CXCL8⁺ cells and decreased CXCL8 levels in the supernatant (Supplementary Fig. 28). Together, these results support the conclusion that decanoic acid promotes neutrophil chemotaxis through a PTX-sensitive G-protein-dependent mechanism.

The activation of neutrophils leads to the release of chemokines, which further activate and perpetuate inflammation. Notably, sodium decanoate treatment alone did not significantly affect the proliferation of Caco-2 colorectal cancer cells (Supplementary Fig. 29). Transcriptome sequencing of Caco-2 cells treated with sodium decanoate alone revealed no differential expression of chemokine genes (such as CXCL2 and CXCL8), pro-angiogenic genes (VEGFA), or tumor-associated genes (OSM, AREG, and PTGS2) when compared with the control group (Supplementary Fig. 30). However, data from neutrophil and epithelial cell coculture assays revealed that sodium decanoate-treated neutrophils dramatically increased the expression of CXCL8, OSM, VEGFA, AREG and PTGS2 in Caco-2 cells compared to the control group (Figs. 5J and 5K). In contrast, sodium palmitate-treated neutrophils showed no significant effect on CXCL8 and AREG expression in Caco-2 cells, suggesting that its action may not be mediated through neutrophil chemotaxis. These data indicate that decanoic acid might induce a protumor phenotype in neutrophils, which in turn promotes the expression of key pro-inflammatory and tumorigenic factors in Caco-2 cells during colorectal tumorigenesis.

F. periodonticum promotes colorectal tumorigenesis

Given the strong correlation between decanoic acid and F. periodonticum observed in the analysis of microbiota and metabolites, as well as the marked elevation of decanoic acid abundance in both the bacterial pellet and the culture medium of F. periodonticum compared with the control, we further investigated the influence of F. periodonticum on colorectal tumorigenesis.

To investigate the role of F. periodonticum in colorectal tumorigenesis, we employed Apc^min/+ mice, a commonly used transgenic mouse model of spontaneous CRC (Fig. 6A). F. periodonticum was isolated from a male patient. Prior to the oral administration of F. periodonticum, the microbiota of the mice was depleted using a cocktail of broad-spectrum antibiotics. Metagenomic sequencing confirmed microbiota depletion in mouse feces (Fig. 6E). Following microbiota depletion, Apc^min/+ mice were then subjected to daily oral gavage with F. periodonticum, the nonpathogenic E. coli strain MG1655, or a PBS control for 10 weeks (Fig. 6A). After inoculation, there was an increase in the fecal presence of the bacteria administered to each group (Fig. 6E). After 12 weeks, the Apc^min/+ mice were euthanized, and their colons were subjected to both macroscopic and histological examination. Notably, the mice inoculated with F. periodonticum had significantly larger colonic tumors and a lower body weight than those inoculated with E. coli or the PBS control (Fig. 6B–D), although the difference in colonic tumor number did not reach statistical significance relative to the PBS control. Pathologically, Apc^min/+ mice administered F. periodonticum via gavage presented both high-grade dysplasia and low-grade dysplasia, similar to those administered E. coli or PBS control, while the overall F. periodonticum group showed an overall higher incidence of high-grade dysplasia (Fig. 6F, Supplementary Fig. 31, Supplementary Fig. 32A). The adjacent mucosal tissue near the tumors in the F. periodonticum group exhibited more severe inflammation compared to both the E. coli and control groups. This was characterized by increased infiltration of inflammatory cells, partial loss of goblet cells, and architectural disruption of the mucosa (Supplementary Fig. 31).

Fig. 6 — A Schematic diagram showing the experimental design and timeline of the *Apc*^min/+ mouse model (*F. periodonticum* group, n = 10; *E. coli* MG1655 group, n = 8; and PBS control group, n = 9). Created in BioRender. Yang, Q. (2026) https://BioRender.com/ixqx9t1. B Weights of *Apc*^min/+ mice subjected to different treatments (*F. periodonticum* vs Control, P = 0.0012; *F. periodonticum* vs *E. coli*, P = 0.0013; E. coli vs Control, P > 0.9999). C Representative colonic morphologies of mice administered *F. periodonticum*, *E. coli* or PBS control. D Colonic tumor numbers and loads in *Apc*^min/+ mice subjected to different treatments (*F. periodonticum* group, n = 10; *E. coli* MG1655 group, n = 8; and PBS control group, n = 9) (tumor number: *F. periodonticum* vs Control, P = 0.4482; *F. periodonticum* vs *E. coli*, P = 0.0339, *E. coli* vs Control, P = 0.3310; tumor load: *F. periodonticum* vs Control, P = 0.0009, *F. periodonticum* vs *E. coli*, P = 0.0067; *E. coli* vs Control, P = 0.7832.). E Boxplot of α diversity under different treatment conditions (At baseline, 6 weeks and 12 weeks, sample size were n = 10 for *F. periodonticum* group, n = 8 for *E. coli* MG1655 group, and n = 9 for PBS control group. At 2 weeks, sample sizes were n = 5 for *F. periodonticum* group, n = 3 for *E. coli* MG1655 group and n = 3 for PBS control group). The lower and upper hinges correspond to the first and third quartiles, while the upper whisker extends from the hinge to the largest value no further than 1.5 × IQR from the hinge (IQR inter-quartile range). The lower whisker extends from the hinge to the smallest value at most 1.5 × IQR of the hinge. The middle line denotes the median. F Representative histologic images of colon tissues from mice subjected to H&E staining and immunohistochemistry for the distribution of Ly6G expression in colon tissues. Scale bars, 50 μm. G Representative flow cytometry plots showing the proportion of Ly6G + CD11b+ neutrophils within lamina propria mononuclear cells (LPMCs) isolated from mice. H Abundance of decanoic acid in mice administered *F. periodonticum* (n = 10), *E. coli* (n = 8) and control (n = 9) (*F. periodonticum* vs Control, P = 0.0487; *F. periodonticum* vs *E. coli*, P = 0.0194, *E. coli* vs Control, P = 0.9142). The expression change refers to concentration change (deadline - baseline). Data in B, D, and H are analyzed using Ordinary one-way ANOVA with Tukey’s multiple comparisons test (final body weight at the experimental endpoint), Ordinary one-way ANOVA with Tukey’s multiple comparisons test, and unpaired two-tailed t-test, respectively. Data are shown as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant.

Given that single-cell data revealed that neutrophils exhibit unique expression patterns in the CRC group, we characterized the neutrophils in the colonic lamina of F. periodonticum-administered Apc^min/+ mice. Compared with that of the control mice, the number of colonic neutrophils (CD11b + Ly6G + ) tended to increase in the F. periodonticum-administered mice compared with the corresponding control mice (Fig. 6G and Supplementary Fig. 33A), although there is no statistically significant difference. Optical density quantification of tumor Ly6G immunohistochemistry indicated significantly higher Ly6G levels in the F. periodonticum group compared to control group (P = 0.0426) (Fig. 6F and Supplementary Fig. 33B).

We further analyzed the changes in the gut microbiota of mice fed F. periodonticum. PCoA of the Bray–Curtis distances based on species-level composition (β diversity) revealed significantly different community distributions among the three groups of mice fed F. periodonticum, E. coli, or the control (R² with the function adonis = 0.02, P = 0.003) (Supplementary Fig. 34). Furthermore, the abundances of several harmful bacteria, including Acholeplasmatales and strains of Lachnospiraceae gradually increase with the colonization of F. periodonticum, which is different from those seen in the groups administered E. coli or the control (Supplementary Fig. 34). Like those in the E. coli group, the abundances of many probiotics, including Lactococcus lactis and Ligilactobacillus murinus, significantly decreased in the F. periodonticum group compared with those in the control group (Supplementary Fig. 35).

Decanoic acid promotes colorectal tumorigenesis

To investigate the role of specific bacteria and metabolites in CRC tumorigenesis, we employed targeted metabolomics to quantify the concentrations of decanoic acid in mice colonized with different strains. Upon feeding the mice with F. periodonticum, an increase in fecal decanoic acid levels was observed. The groups fed E. coli and the control group presented inconsistent changes. Among the three groups, the Apc^min/+ mice that were administered F. periodonticum presented a significantly greater level of decanoic acid than the other two groups did (Fig. 6H, Supplementary Fig. 36).

Consequently, we conducted further interventions with decanoic acid in Apc^min/+ mice (Fig. 7A). Compared with the control group, the intervention group presented reduced body weights and developed greater numbers of larger colonic tumors (Fig. 7B–D). Additionally, compared with control mice, Apc^min/+ mice treated with decanoic acid exhibited a higher tendency to develop high-grade dysplasia (Fig. 7E, Supplementary Fig. 37, Supplementary Fig. 32B). The adjacent mucosal tissue in the sodium decanoate group showed more pronounced inflammation relative to the control group (Supplementary Fig. 37). Ly6G immunohistochemical analysis consistently confirmed increased colonic infiltration of neutrophils in mice treated with decanoic acid (Fig. 7E). Compared with that in control mice, the number of colonic neutrophils (CD11b + Ly6G + ) was significantly greater in the decanoic acid-treated group (P = 0.0357) (Fig. 7F and 7G). Collectively, these findings suggest that decanoic acid contributes to cancer progression by modulating neutrophil infiltration in Apc^min/+ mice.

Discussion

In this study, we performed an in-depth, multiomics investigation of the fecal microbiome and metabolome, as well as analyzing the single-cell transcriptomes of CRC patients, CRA patients, and HCs, to reveal the occurrence and development of CRC and discovered a pathogenic bacterium, F. periodonticum, whose metabolite decanoic acid can drive the chemotaxis of neutrophils and further promote the occurrence of CRC.

Through metagenomic analysis, we found that many procarcinogenic microbial members, including F. nucleatum, E. coli, Klebsiella pneumoniae, Alistipes finegoldii, Flavonifactor plautii, P. micra, and Prevotella intermedia, are enriched in the intestines of CRC patients (Fig. 1), which is consistent with previously published results^9,26,27. Notably, we also discovered the enrichment of other pathogenic bacteria, such as F. periodonticum. F. periodonticum is a member of Fusobacterium spp., a genus of obligate anaerobic filamentous gram-negative rods. The most notorious member of this genus is F. nucleatum, which is associated with various infections and has been reported to potentiate intestinal tumorigenesis and modulate the tumor-immune microenvironment^12,14,28,29. Our research revealed that F. periodonticum also promotes tumor growth, which was confirmed by our animal experiments showing an increase in tumor size. Moreover, experiments on mice have shown that the colonization of F. periodonticum is accompanied by an increase in the abundance of other harmful bacteria. This result indicated that F. periodonticum also affects the balance of the intestinal microbiota.

Additionally, through metagenomic functional analysis, we found that palmitic acid synthesis and saturated fatty acid extension pathways were enriched in the gut microbiota of CRC patients. Metabolomic results further supported the enrichment of saturated fatty acid synthesis pathways, especially for medium- and long-chain fatty acids, such as palmitic acid and decanoic acid, indicating their procarcinogenic function. Reports regarding palmitic acid and CRC have accumulated in recent years, whereas reports on the role of decanoic acid in CRC patients are relatively limited. Palmitic acid, a major component of many foods, has significant effects on cellular processes. Reports have shown that the reprogramming of palmitic acid induced by the dephosphorylation of ACOX1 promotes β-catenin palmitoylation to drive CRC progression³⁰. Additionally, a diet rich in palmitic acid or short-term exposure of tumor cells to palmitic acid can induce a highly metastatic phenotype^9,31,32. Although decanoic acid reportedly has an agonistic effect on calcitriol in controlling inflammatory mediators in colon cancer cells³³, its detailed role in CRC is less well understood. Despite food intake being a key source of saturated fatty acids such as palmitic and decanoic acids, intestinal bacteria also contribute to their production during metabolism; however, the correlation between these acids and the gut microbiota remains unclear. Correlation analysis between DAMs and differentially abundant species revealed that F. periodonticum was positively correlated with the abundances of decanoic acid and palmitic acid (Fig. 2G-I). Targeted metabolomic detection of F. periodonticum cells and supernatants also validated the significant enrichment of decanoic acid in both the supernatant and the cells and of palmitic acid in only the cells (Fig. 2J-M). We speculate that F. periodonticum may play a role in CRC by producing decanoic acid.

Furthermore, our scRNA-seq results revealed a CRC tissue-specific neutrophil expression pattern, with a significantly higher proportion of neutrophils in tumor tissues compared to adjacent and adenoma tissues. Functional enrichment analysis further indicated activation of neutrophil-related pathways and chemotaxis, alongside elevated levels of CXCL8—a key chemokine for neutrophil recruitment. While these findings highlight the prominent presence of neutrophils in the tumor microenvironment, this observation alone does not establish causality between neutrophil activity and tumorigenesis. Nevertheless, our integrated analyses provide biologically relevant insights into the potential role of neutrophils in CRC progression. Although the exact function of neutrophils in tumorigenesis remains debated^34–37, our data show upregulation of VEGFA (vascular endothelial growth factor A), AREG (a member of the epidermal growth factor family), PTGS2 (prostaglandin endoperoxide synthase 2), and OSM (oncostatin M) in CRC patients, which may be associated with neutrophil activity. These factors are known to promote angiogenesis and cell proliferation^38–41, suggesting a plausible link between neutrophil infiltration and tumor progression. Critically, our in vitro and in vivo experiments demonstrate that F. periodonticum and metabolite decanoic acid directly influence neutrophil behavior: (1) decanoic acid promoted neutrophil late apoptosis/necrosis, chemotaxis, and CXCL8 secretion in vitro. It also facilitated the expression of genes related to leukocyte migration and tumorigenesis; (2) both F. periodonticum and decanoic acid increased colonic neutrophil infiltration (Ly6G+ and CD11b + Ly6G+ cells) in Apc^min/+mice. These findings suggest an active host-pathogen crosstalk rather than a passive consequence of neutrophil abundance.

The stimulatory factors driving neutrophil recruitment may include bacterial metabolites. Previous reports have suggested that bacterial metabolites can attract and activate neutrophils, leading to chronic inflammation and a tumor-promoting environment in the colon^42,43. Here, we identified decanoic acid and palmitic acid as enriched metabolites in CRC patients and validated their chemotactic effects on neutrophils, with decanoic acid exhibiting stronger activity. Furthermore, F. periodonticum altered gut microbiota composition, increasing pathogenic taxa (e.g., Acholeplasmatales, Lachnospiraceae) while reducing probiotics (e.g., L. lactis, L. murinus), paralleling increased neutrophil infiltration. These shifts suggest that F. periodonticum and decanoic acid may jointly reshape the gut microbiome-immune interface to favor a tumor-permissive environment. These findings align with broader conceptual frameworks regarding the role of commensal bacteria in cancer immunotherapy⁴⁴, and contribute to the growing understanding of the oncobiome’s functional significance in tumor biology⁴⁵.

Our data provides evidence supporting that decanoic acid promotes neutrophil chemotaxis through a PTX-sensitive G-protein-dependent manner, leading to the recruitment of neutrophils to colorectal cancer cells and the upregulation of genes involved in cell migration and tumorigenesis. However, the investigation into its effects on neutrophil apoptosis revealed a more complex and nuanced picture. The effects of decanoic acid are dependent on both concentration and cell type. In neutrophils, sodium decanoate significantly induced late apoptosis/necrosis across multiple concentrations below 100 μM, and this effect remained evident at 100 μM even in the presence of pertussis toxin, suggesting the potential involvement of receptor-independent pathways. Notably, at 100 μM, the proportion of early apoptotic cells decreased, accompanied by an increase in the viable cell population, indicating that the effect of decanoate on neutrophil fate cannot be readily explained by a simple cytotoxic mechanism. Previous studies have shown that certain fatty acids can modulate membrane properties and influence intrinsic apoptotic pathways through physicochemical interactions with the lipid bilayer^46,47. In this context, our findings raise the possibility that decanoate may regulate neutrophil behavior through distinct mechanisms governing chemotaxis and cell fate. However, the intracellular basis of these effects remains unresolved. Future studies will therefore be needed to determine whether decanoate affects mitochondrial membrane potential, reactive oxygen species production or pro-apoptotic signaling in neutrophils. By contrast, both primary human monocytes and THP-1 cells remained largely insensitive to DeaNa-induced apoptosis after 24 hours of treatment, with early apoptotic (Annexin V⁺PI⁻) and late apoptotic/necrotic (Annexin V⁺PI⁺) populations comparable to those of the PBS control group. This difference indicates that the pro-death effects of decanoate are not uniform across myeloid cell types, but are shaped by cellular context. Together, these results suggest that fatty acid-induced apoptotic responses vary according to fatty acid species, concentration and cell type. A limitation of the present study is that the intracellular basis of these differential responses remains undefined and will require further investigation.

The consistent correlations across models (in vitro, murine, and human data) underscore the biological significance of the F. periodonticum-neutrophil-tumor axis. Future studies should employ more experiments to clarify causal mechanisms. The precise role of neutrophils—whether tumor-promoting or context-dependent—and the therapeutic potential of targeting metabolite-driven neutrophil recruitment remain critical questions for further investigation.

In summary, through the analysis of intestinal metagenomics, metabolomics, single-cell transcriptomics, cell experiments, and animal experiments, we established a pathway linking F. periodonticum to decanoic acid and further connected decanoic acid to neutrophil infiltration, providing insights into the pathogenic mechanisms of CRC.

Methods

Sample collection and study design

A clinical cohort of 53 CRC patients, 35 CRA patients and 35 HCs were recruited from Peking Union Medical College Hospital (PUMCH). Tumor tissues and paracancerous tissues were collected from three CRC patients. Adenoma tissues were also collected as controls.

The diagnosis and prognosis of all lesions are based on endoscopic and/or pathological examination results. CRC patients were diagnosed with CRC through clinical colonoscopy and could be traced back to the clinical gold standard results, such as colonoscopy and/or pathology. CRA patients were diagnosed with CRA by clinical examination and colonoscopy, with no history of previous digestive tract surgery. HC patients had no high-risk factors for colon cancer, normal colonoscopy results, and no first-degree direct relatives with digestive system tumors.

Subjects with any of the following conditions were excluded: subjects who could not undergo colonoscopy due to contraindications; patients with other severe digestive tract diseases, such as Crohn’s disease or ulcerative colitis; individuals diagnosed with, or their spouses or first-degree relatives diagnosed with, hereditary syndromes associated with bowel cancer, such as hereditary nonpolyposis colon cancer syndrome, Peutz–Jeghers syndrome, MYH-associated polyposis, Gardner syndrome, Turcot syndrome, Cowden syndrome, juvenile polyposis, or Cronkhite–Canada’s syndrome, neurofibromatosis, or familial polyposis; patients with serious diseases of the heart, liver, lungs, kidneys, blood, or endocrine system; pregnant women, breastfeeding women or women of childbearing age who are preparing to conceive; patients with other malignant tumors simultaneously; patients who have taken antibiotics in the past three months; subjects who have undergone resection surgery, radiotherapy, or chemotherapy for intestinal cancer and precancerous lesions; and any situations that the investigator determines are not suitable for inclusion. All participants were Chinese.

Fecal sample collection and sequencing

Fecal samples were collected from 53 CRC patients, 35 CRA patients and 35 HCs and stored within 2 h of collection at −80 °C. Aliquots of the feces were sent for DNA extraction and shotgun metagenomic sequencing. Fecal DNA was extracted with a TIANamp Stool DNA Kit (TIANGEN) according to the manufacturer’s instructions. The purity and integrity of the extracted DNA were determined by agarose gel electrophoresis using a Qubit® 2.0 fluorometer (Life Technologies). Qualified DNA with a quantity >1 μg from each sample was used for library construction using a NEBNext® Ultra™ DNA Library Prep Kit for Illumina (New England BioLabs) according to the manufacturer’s instructions. Briefly, the DNA samples were fragmented to a size of 350 bp, end polished, polyA tailed, and ligated with full-length adapters. The library preparations were sequenced on an Illumina NovaSeq platform, and paired-end reads of 150 bp were generated.

Metagenomic sequence data processing and analyses

Approximately 30 million paired-end raw reads (30 million forward/reverse) were obtained per sample. KneadData was used for quality control of the raw reads, including host read removal⁴⁸. The human (GRCh38/hg38) reference database was downloaded, and Bowtie2 contained in KneadData was used with Bowtie2 options “--very sensitive --dovetail” for read mapping to the human genome to eliminate human sequences. We trimmed reads with Trimmomatic in KneadData with the Trimmomatic option “SLIDINGWINDOW:4:20 MINLEN:50”.

Metagenomic phylogenetic analysis (MetaPhlAn), which is nested within HUMAnN version 3.6 (the Human Microbiome Project Unified Metabolic Analysis Network)⁴⁸, was used to profile the compositions of microbial communities at the species level from quality-filtered reads. All microbial taxonomic data are reported as relative abundances. HUMAnN version 3.6 was used for metabolic function (gene family and pathway) analysis of the microbial community. The gene families were annotated using UniRef90 identifiers, and pathways were annotated using MetaCyc IDs. The flag “--gap-fill” was set to “on”, allowing us to quantify activation of a pathway even when a small number of its reactions were conspicuously absent. The gene abundance table, functional profiling and pathway reconstruction data are reported. Moreover, all gene family and pathway abundance read per kilobase (RPK) values were normalized to relative abundance using the humann_renorm_table function.

α Diversity was estimated on the basis of the species-level abundance of each sample according to the Shannon index using the vegan “diversity” function in R, and significant differences were determined via Wilcoxon rank-sum tests.

PCoA plots (β diversity) were generated from a Bray–Curtis distance matrix of species-level taxa calculated using the vegan “vegdist” and “cmdscale” functions in R. Sequential multivariate variance analysis was conducted using the vegan “adonis” function in R.

LDA effect size (LEfSe)⁴⁹, an algorithm for high-dimensional biomarker discovery and explanation, was used for the identification of genomic features (taxa, genes, and pathways) that characterize the differences between CRC patients and healthy controls. Both the alpha value for the factorial Kruskal‒Wallis test among classes and the alpha value for the pairwise Wilcoxon test between subclasses were set to 0.05. The threshold on the logarithmic LDA score for discriminative features was 2.0. Visualization of significantly differentially abundant species was achieved with GraPhlAn⁵⁰.

Published fecal metagenomic data download and analysis

We downloaded 1603 (CRC, n = 601; CRA, n = 319; HC, n = 683) metagenomic sequenced samples from 8 published studies for further confirmation of our findings (Supplementary Data 4) and analyzed these data using the unified process described above.

Untargeted metabolomics

Metabolite extraction

Fecal samples were immediately frozen in liquid nitrogen and ground into a fine powder. For metabolite extraction, 1000 μL methanol/acetonitrile/H²O (2:2:1, v/v/v) as a homogenized solution was added. The mixture was subsequently centrifuged at 14,000 × g for 20 min (4 °C). The supernatant was dried in a vacuum centrifuge. For further liquid chromatography-mass spectrometry (LC‒MS) analysis, the samples were redissolved in 100 μL acetonitrile/water (1:1, v/v) and centrifuged at 14,000 × g for 15 min, after which the supernatants were transferred to vials for LC‒MS analysis.

LC‒MS analysis

Analysis was performed via a UHPLC system (1290 Infinity LC, Agilent Technologies, USA) coupled with quadrupole time-of-flight mass spectrometry (AB Sciex TripleTOF 6600). For HILIC separation, samples were analyzed with a 2.1 × 100 mm ACQUITY UPLC BEH amide 1.7 µm column (Waters, Ireland). In both positive and negative modes, mobile phase A contained 25 mM ammonium acetate and 25 mM ammonium hydroxide in water, and phase B was acetonitrile. The separation gradient began with 95% solvent B for 30 s, followed by a linear decrease to 65% B over 6.5 min. It was then further reduced to 40% B within 1 min and maintained at this level for an additional min. The gradient was subsequently ramped to 95% B in 10 s, resulting in a 3-min re-equilibration phase. The ESI source conditions were set as follows: Ion Source Gas1 (Gas1) and Gas2 (Gas2) as 60, curtain gas (CUR) as 30, source temperature: 600 °C, and IonSpray Voltage Floating (ISVF) ± 5500 V. In MS acquisition, the m/z range was 60–1000 Da, and the accumulation time for the TOF MS scan was 0.20 s/spectra. For MS/MS acquisition, the instrument was set to acquire masses in the m/z range of 25–1000 Da, and the accumulation time was set at 0.05 s/spectra. The product ion scan is acquired via information-dependent acquisition (IDA) in high-sensitivity mode. The parameters were set as follows: the collision energy (CE) was fixed at 35 V ± 15 eV; excluding isotopes within 4 Da, the number of candidate ions to monitor per cycle was 10.

Data processing and analysis

The raw MS data were converted to MzXML files using ProteoWizard MSConvert before being imported into XCMS software⁵¹. Collection of Algorithms of MEtabolite pRofile Annotation (CAMERA) was used for annotation of isotopes and adducts. For the extracted ion features, only the variables with more than 50% nonzero measurement values in at least one group were retained. The identification of metabolites was performed by comparing the accuracy of the m/z values (<10 ppm) and MS/MS spectra with an in-house database established with available authentic standards. We performed statistical and bioinformatic evaluations utilizing SIMCA software and the MetaboAnalyst 5.0 platform to elucidate the metabolic signatures associated with each group. The differentially abundant metabolites (DAMs) were determined by applying stringent criteria: either an adjusted P value of less than 0.05 (calculated using the Benjamini‒Hochberg method to control the false discovery rate, FDR) combined with a fold change greater than 2 (two-group comparison), or adjust Anova P value (multi-group comparison) of less than 0.05 (Benjamini‒Hochberg method to control the false discovery rate, FDR) combined with a variable importance in projection (VIP) score greater than 1.

Targeted metabolomics

Sample preparation

The samples were transferred to 2 mL Eppendorf (EP) tubes, and 1 mL of water was added. The mixture was mixed thoroughly by vortexing for 10 s. The mixture was homogenized via a ball mill at 40 Hz for 4 min, followed by ultrasonic treatment for 5 min. The extraction process was repeated three times. The samples were centrifuged at 5000 rpm for 20 min. A 0.8 mL aliquot of the supernatant was transferred to a new 2 mL EP tube. To each tube, 0.1 mL 50% sulfuric acid (H₂SO₄) and 0.8 mL extraction solution (a 25 mg/L stock solution in methyl tert-butyl ether) were added as an internal standard. The mixture was vortexed for 10 s and then allowed to oscillate for 10 min. Subsequently, ultrasonic treatment was performed for 10 min in an ice bath. The samples were subsequently centrifuged at 10,000 rpm for 15 min at 4 °C. The samples were stored at −20 °C for 30 min for phase separation. Finally, the supernatant was transferred into a clean 2 mL glass vial for subsequent gas chromatography‒mass spectrometry (GC‒MS) analysis.

GC‒MS analysis

An gas chromatograph system (7890B, Agilent Technologies, USA) coupled with an Agilent 5977B mass spectrometer was used for GC analysis. A 1 μL aliquot of the analyte was injected in split mode (5:1). Helium was used as the carrier gas, the front inlet purge flow rate was 3 mL/min, and the gas flow rate through the column was 1.2 mL/min. The initial temperature was maintained at 50 °C for 1 min, increased to 200 °C at a rate of 50 °C/min for 15 min, increased to 210 °C at a rate of 2 °C min⁻¹ for 1 min, increased to 230 °C at a rate of 10 °C min⁻¹, and held for 15 min. The injection, transfer line, quad and ion source temperatures were 240 °C, 240 °C, 230 °C and 150 °C, respectively. The energy was −70 eV in electron impact mode. The mass spectrometry data were acquired in Scan/SIM mode after a solvent delay of 7 min.

Tissue sample collection, single-cell transcriptome sequencing and analyses

Sample collection, tissue dissociation and preparation of single-cell suspensions

Tumor tissues and normal tissues adjacent to the tumor (NATs) were collected from three CRC patients. Regarding tissue sampling, we avoided ulcerated areas and instead collected tissue samples from the tumor center, while NATs were obtained from macroscopically unaffected areas located at least 5 cm from the tumor margin. We also collected two adenoma tissues as controls. Freshly isolated tissues were immediately placed in ice-cold DMEM (Wisent, 319-005-CL) supplemented with 1% fetal bovine serum (FBS, Wisent, 086-150) and then transported on ice to preserve viability. Tissues were washed 2‒3 times with PBS, dissected on ice into smaller pieces, and then transferred to 10 mL of digestion medium containing 1 mg/mL collagenase type I (Gibco, 17100‒017), 2 mg/mL dispase II (Sigma‒Aldrich, D4693), 0.5 mg/mL elastase (Solarbio, E8210) and 1 unit/mL DNase I (NEB, M0303S) in PBS with 1% FBS. The tissue was enzymatically digested at 37 °C with shaking at 70 rpm for approximately 60 min. The dissociated cells were collected at intervals of 20 min to increase the cell yield and viability. The cell suspensions were filtered through a 40-μm nylon cell strainer (Corning, 352340) and dissolved in RBC lysis buffer (Invitrogen, 00-4333-57) supplemented with 1 unit/mL DNase I to remove red blood cells. Dissociated cells were washed with PBS containing 0.04% bovine serum albumin (BSA; Sigma‒Aldrich, B2064) and centrifuged at 500 × g for 5 min to obtain cell pellets. Cell viability was determined by Trypan blue (Invitrogen, T10282) staining, and then the cells were suspended in PBS with 0.04% BSA at a density of approximately 1 × 10⁶ cells/mL and kept on ice for single-cell sequencing.

Library construction and sequencing

A Chromium Single-cell 3’ Library and Gel Bead Kit V3.1 (10× Genomics, PN1000268) were used to generate single-cell gel beads in emulsion (GEMs). The captured cells were lysed, and the released RNA was reverse transcribed with primers containing poly-T, barcodes, unique molecular identifiers (UMIs) and the read 1 primer sequence in GEMs. Barcoded cDNA was purified and amplified by PCR. The adapter ligation reaction was performed to add a sample index and read 2 primer sequence. After quality control, the libraries were sequenced on an Illumina NovaSeq 6000 platform in a 150 bp pair-ended manner. The raw data were first processed through primary quality control. The monitored quality assessment parameters were as follows: (1) contained n > 3; (2) the proportion of bases with quality values less than 5 was greater than 20%; and (3) the adapter sequence was included. All the downstream analyses were based on high-quality, clean data. The raw reads were demultiplexed and mapped to the reference genome (GRCh38) via the 10× Genomics Cell Ranger (version 6.0.2) pipeline using default parameters.

Quality control of single-cell data and doublet removal

Raw and filtered feature‒barcode matrices for each sample were generated using 10× Genomics Cell Ranger. To filter low-quality cells and enrich high-quality cells in each dataset, quality control and doublet removal were performed individually for each dataset. First, doublets were removed using Scrublet⁵². The low-quality cells were filtered via the Seurat (version 3.1.5) R package based on each of the following metrics⁵³: nCount_RNA < 400; nCount_RNA > 150000; nFeature_RNA < 200; nFeature_RNA > 10000; percent.mt >= 20.

Dimensional reduction and unsupervised clustering analysis

We sed the Seurat (version 3.1.5) R package⁵³ to perform unsupervised clustering of the single cells using the read count matrix as input. First, the read counts for each cell were divided by the total counts for that cell, multiplied by the scale factor (10,000), and then natural-log transformed. We performed principal component analysis (PCA) on the normalized expression matrix using 2000 highly variable genes identified by the “FindVariableFeatures” function. Following the results of PCA, the top 20 PCs were selected with a resolution parameter equal to 1. Seven types of cells were identified using known markers, including B-cells, T-cells, myeloid cells, mast cells, epithelial cells, endothelial cells, and fibroblasts. For epithelial cells, Infercnv (version 1.6.0)⁵⁴ was used to identify malignant tumor cells and normal epithelial cells. For the clustering of subgroups, the top 12 PCs were selected with a resolution parameter = 1. The cells were projected in 2D space using RunUMAP with the same parameters as above.

Differential analysis

The FindAllMarkers function implemented in Seurat (version 3.1.5) was used to identify differentially expressed genes (DEGs) between different cell types. We set the parameters “only.pos” as “TRUE,” which returned only the genes expressed at a significantly higher level in a given cell type. The Wilcoxon test was performed on each gene (only genes that were detected in a minimum fraction of 0.25 cells in either of the two populations and with a > 0.25-fold difference (log-scale) on average between the two populations of cells were included in the test), and the P value and adjusted P value representing statistical significance were computed.

Gene enrichment analysis

KEGG⁵⁵, GO⁵⁶, and Reactome⁵⁷ enrichment analyses were performed using ClusterProfiler (version 4.0.5)⁵⁸ for the DEGs in each cluster. Adjusted P < 0.05 and |log₂FC | > 0.5 were used as the screening criteria for significant enrichment in all enrichment analyses.

Neutrophil assays

Isolation and incubation of peripheral blood neutrophils

Peripheral blood neutrophils were isolated by recruiting healthy male volunteers, with blood collected using both heparin lithium and EDTA anticoagulant tubes. Specifically, blood was carefully layered over Lymphoprep^TM (Serumwerk, Germany) in 15 ml centrifuge tubes and centrifuged at 500 × g for 50 min, resulting in the separation of the peripheral blood mononuclear cell (PBMC) and polymorphonuclear leukocyte (PMN) layers. The PBMC layer was discarded, and the PMN layer was diluted with PBS before being transferred to a new tube. After a second centrifugation at 400 × g for 10 min, red blood cells were lysed using ACK lysis buffer (Thermo Fisher Scientific, USA), and the mixture was incubated at room temperature for 10 min. A final centrifugation removed the lysed cells, and the PMNs were washed with PBS, resuspended in RPMI 1640 medium (Thermo Fisher Scientific, USA), and counted for subsequent analyses, including transcriptional analysis, qPCR validation, and co-culture with Caco-2 cells. To evaluate the effects of fatty acids on neutrophil viability and function, three distinct culture conditions were employed to assess the influence of medium composition: (1) complete medium (RPMI 1640 containing 2 mM CaCl₂, 20 mM HEPES, 20% heat-inactivated low-endotoxin FBS, and 1% Penicillin/Streptomycin); (2) HEPES medium (RPMI 1640 + 10 mM HEPES); and (3) basic medium (RPMI 1640 without any supplements). Based on the results of preliminary experiments, condition (3) was selected for subsequent formal experiments to eliminate interference from medium components and ensure that the observed effects were directly attributable to fatty acid intervention.

Assessment of neutrophil apoptosis

Neutrophils were seeded in 24-well plates at a density of 5 × 10⁵ cells/ml, with 0.5 ml of cell suspension added to each well. Stock solutions of sodium hexanoate (HexNa), sodium decanoate (DeaNa) and sodium palmitate (PaNa) were prepared by accurately weighing the compounds and dissolving them in 5% fatty acid-free BSA in PBS to a final concentration of 50 mM. The control solution consisted solely of 5% fatty acid-free BSA in PBS. Neutrophil apoptosis was evaluated by incubating the neutrophils with sodium hexanoate, sodium decanoate, sodium palmitate, and PBS at a final concentration of 100 μM for 4 hours. To assess the dose-dependent effects of fatty acids on neutrophils, we also established a gradient of concentrations below 100 μM (25 μM, 50 μM, and 100 μM) and evaluated the corresponding cellular responses. After treatment, cells were collected and stained using an Annexin V-FITC/Propidium Iodide (PI)-PE Apoptosis Detection Kit (MedChemExpress, USA). Apoptotic and necrotic cells were distinguished by Annexin V-FITC and PI-PE staining followed by flow cytometry. Cells were characterized by distinct populations: Annexin V⁻/PI⁻ (living cells), Annexin V⁺/PI⁻ (early apoptotic cells), and Annexin V⁺/PI⁺ (late apoptotic/necrotic cells).

Neutrophil chemotaxis assay

The migratory ability of neutrophils was assessed using a 24-well Transwell system (Corning #3415, 3.0 μm pore). In each well, the lower chamber was filled with 500 µL of serum-free RPMI 1640 medium containing a final concentration of 200 µM of different compounds. Neutrophils (5 × 10⁵ cells per chamber) were added to the upper chamber for migration. After a 3-h incubation, the upper chamber was removed, and cells in the lower chamber were centrifuged at 400 × g for 8 min. The supernatant was discarded, and the cell pellet was fixed with 500 µL of formaldehyde for 15 min. To stain the cell nuclei, a DAPI solution was prepared and added to the fixed cells, followed by a 20-minute incubation at 37 °C. After staining, the cells were washed once with PBS. DAPI-stained cells were then photographed and counted using the multifunctional enzyme reader (Cytation 1 Cell Imaging Multi-Mode Reader (Agilent) & BioTek Gen5 Software for Imaging).

Neutrophil transwell checkerboard assay Neutrophil migration was assessed using Transwell inserts with a pore size of 3.0 μm (Corning #3415). Human neutrophils were freshly isolated and suspended in 1640 medium. For the gradient condition, 100 μM or 200 μM compound (DeaNa or PaNa), was added to the lower chamber, while the upper chamber contained neutrophils in buffer without chemoattractant. For the non-gradient condition, equal concentrations (200 μM) of DeaNa or PaNa were added to both the upper and lower chambers to eliminate the concentration gradient. After 3-h incubation, cells that had migrated to the lower chamber were collected and quantified.

Assessment of apoptosis in primary human monocytes and THP-1 cells

For primary human monocytes, peripheral blood mononuclear cells (PBMCs) were first isolated from fresh human blood using Lymphoprep density gradient centrifugation in SepMate tubes (Stemcell Technologies) according to the manufacturer’s instructions. Monocytes were then purified from PBMCs using the EasySep™ Human Monocyte Isolation Kit (Stemcell Technologies) following the manufacturer’s protocol. Purified monocytes were seeded at a density of 5 × 10⁵ cells per well and treated with HexNa, DeaNa, or PaNa at final concentrations of 25, 50, or 100 μM for 24 h. PBS and staurosporine (STS) were included as vehicle and positive controls, respectively. After incubation, cells were harvested, washed with cold PBS, and stained with Annexin V and PI to assess apoptosis.

To evaluate the effects of fatty acids on apoptosis in human monocytic THP-1 cells, cells were seeded at a density of 5 × 10⁵ cells per well and treated with HexNa, DeaNa, or PaNa at final concentrations of 25, 50, or 100 μM. Cells were incubated with the indicated treatments for 18 or 24 h under standard culture conditions. Apoptosis assessment in THP-1 cells was performed using the same procedure as for primary human monocytes.

RNA sequencing (RNA-seq)

Neutrophils were seeded at a density of 1 × 10⁶ cells per well in a 24-well plate. Compounds were added to each well at a final concentration of 100 μM, followed by incubation for 4 h at 37 °C in a 5% CO₂ atmosphere. After incubation, the cells were harvested, resuspended in TRIzol reagent, and lysed thoroughly using a pipette to ensure complete cell disruption and RNA preservation. The lysates were then snap-frozen and stored at −80 °C until RNA extraction. Caco-2 cells were seeded in 12-well plates at 1 × 10⁶ cells per well and treated with 100 μM compounds for 24 h. After incubation, cells were collected and lysed in TRIzol, thoroughly disrupted by pipetting, and the lysates were snap-frozen and stored at −80 °C for subsequent RNA extraction. RNA extraction from the frozen lysates was performed via the TRIzol reagent protocol, which involves phase separation, RNA precipitation, washing, and solubilization. The quality and quantity of the extracted RNA were evaluated before library preparation for sequencing. Standard protocols for RNA-seq were followed to prepare the libraries, which were then sequenced to analyze the transcriptomic changes in neutrophils in response to treatment with the compounds.

Quantitative real-time polymerase chain reaction (qRT‒PCR)

The RNA was extracted from the neutrophils using the RNeasy® Mini Kit (QIAGEN, Germany) according to the manufacturer’s instructions. The extracted RNA was then used for cDNA synthesis via a HiScript IV 1st Strand cDNA Synthesis Kit ( + gDNA wiper) (Novozymes, China). Quantitative real-time PCR (qRT‒PCR) was performed using TB Green Premix DimerEraser™ (TaKaRa, Japan) on a LightCycler 480 II system (Roche, Switzerland) to quantify the expression levels of specific genes. The 2^−ΔΔCt method was used to compare the target gene expression with that of the housekeeping gene β-actin. The primers used in this study are listed in Supplementary Data 16. Statistical significance between two groups was determined by unpaired Student’s t test. Statistical significance among multiple groups was determined by one-way analysis of variance.

Enzyme-linked immunosorbent assay (ELISA)

The levels of CXCL8 produced by neutrophils in response to stimulation with DeaNa, PaNa, and PBS were quantified using a 24-well plate. After incubation for 4 h, the supernatants were carefully collected for analysis. The concentration of CXCL8 in the collected supernatants was determined using a human CXCL8 ELISA kit (MULTI SCIENCES, China).

CXCL8 secretion analysis

Neutrophils were isolated using lithium heparin tubes and treated with 100 μM of different compounds. After 4 h of incubation, surface staining with CD66b (BioLegend, cat. 305106) was performed. Cells were then fixed, permeabilized, and stained intracellularly for CXCL8 (BioLegend, cat. 511410), followed by flow cytometric analysis.

Pertussis toxin inhibition assay

Preliminary experiments were performed using PTX (Pertussis Toxin, Lyophilized, Salt-Free; ListLabs, cat. 181) at concentrations of 0, 50, 100, 200, and 300 ng/mL. Based on these results, 300 ng/mL PTX was selected for all subsequent experiments. Neutrophils were isolated in lithium heparin tubes and incubated in RPMI 1640 medium containing either 300 ng/mL PTX or H₂O for 2 h. The cells were then treated with 100 μM of the indicated compounds for an additional 3 h. After treatment, apoptosis, chemotaxis, the proportion of CD66b⁺CXCL8⁺ cells, and CXCL8 levels were evaluated as described in the experimental Methods section.

Caco-2 cell proliferation assay

The Click-iT™ Plus EdU Flow Cytometry Assay Kit (Thermo Fisher Scientific, cat. C10633) was used to detect Caco-2 cell proliferation. Caco-2 cells were seeded at a density of 2.5 × 10⁵ cells/well in a 24-well plate and treated with compounds (such as DeaNa, PANa, BSA, etc.) at a final concentration of 100 μM. After 24 h of incubation, EdU (10 μM) was added, and cells were incubated for 1.5 h. The cells were then processed for detection, and cell proliferation was assessed using flow cytometry.

Caco-2–neutrophil coculture assay

The Caco-2–neutrophil coculture assay was conducted using a 12-well Transwell system (Corning #3401, 0.4 μm pore size). Caco-2 cells were seeded in the lower chambers at a concentration of 5 × 10⁵ cells/well, and cultured for 24 h to ensure proper adhesion and monolayer formation. The cells were then serum-starved for 12 h to synchronize them into a quiescent state. Subsequently, neutrophils were added to the upper chambers at a density of 5 × 10⁵ cells per chamber, along with the compounds DeaNa and PaNa, each at a final concentration of 100 μM. The coculture was maintained for an additional 24 h. After coculture, the Caco-2 cells in the lower chamber were collected, and total RNA was extracted using the RNeasy® Mini Kit (QIAGEN, Germany). Gene expression in Caco-2 cells following coculture with neutrophils was then analyzed by qRT-PCR.

Isolation, culture and identification of Fusobacterium periodonticum

The Fusobacterium periodonticum strain was isolated from blood samples of a male patient with suspected infection source of gut, admitted to the Medical Intensive Care Unit (MICU) at Peking Union Medical College Hospital in 2022. Peripheral blood specimen was inoculated into BD BACTEC™ anaerobic blood culture bottles and flagged positive after 46 h incubation. Positive cultures were then streaked onto anaerobic blood agar plates and incubated at 35 °C for 48 h in anaerobic jars with AnaeroPack™ systems. To ensure strain purity and stability, the isolate underwent multiple passages ( ≥ 5 generations), and each subculture was verified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with identification accuracy >99.9% (Vitek MS, bioMérieux, France).

The isolated F. periodonticum strain was further subjected to whole-genome sequencing using Illumina NovaSeq platform. For comparative genomic analysis, we retrieved the complete genome sequences of 10 F. periodonticum strains from the NCBI database. Average nucleotide identity (ANI) and core genome-based single-nucleotide polymorphism (SNP) phylogeny were performed to assess the genetic relationship between our isolate and the reference strains. Furthermore, to ensure accurate taxonomic classification, five additional genomes of closely related Fusobacterium nucleatum, Fusobacterium hwasookii, Fusobacterium polymorphum, Fusobacterium vincentii, and Fusobacterium canifelinum (same genus) were included as outgroups in the phylogenetic analysis. Detailed methods can be found in our previous publication⁵⁹.

Moreover, the whole-genome sequencing of F. periodonticum have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA028010) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.

Animal experiments

All mice were housed under specific pathogen free (SPF) conditions. The housing environment was maintained at a temperature of 22 ± 2°C with relative humidity of 55 ± 10%, and a 12 h light/12 h dark cycle. Six-week-old male Apc^min/+ mice were randomly assigned to the F. periodonticum group (n = 10), Escherichia coli (E. coli) MG1655 group (n = 8) or PBS control group (n = 9). The F. periodonticum used in this study was isolated from a male patient admitted to the Medical Intensive Care Unit (MICU) of Peking Union Medical College Hospital in 2022. All mice were fed with Research diet brand feed (catalog number: D12492), and the feeding condition was ad libitum access to the feed. The mice were given a cocktail of broad-spectrum antibiotics (0.2 g/L ampicillin, 0.1 g/L vancomycin, 0.2 g/L neomycin, and 0.2 g/L metronidazole) for 2 weeks to deplete their intestinal microbiota. Bacterial feeding experiments were conducted for a duration of 10 weeks, starting at 8 weeks of age. The mice were orally administered bacteria at a dose of 1 × 10⁸ colony forming units (CFUs) per 200 μl daily. The control group received PBS (200 μl) as the feeding medium. E. coli MG1655 is a nonpathogenic commensal bacterium that naturally resides in the human gut and does not cause dysplasia. After a 12-week period, the mice were humanely euthanized, and their colon tissues were collected for subsequent examination. Fecal samples were obtained from the mice prior to and after antibiotic treatment, one month after oral gavage with the bacterial strain, and immediately before euthanasia.

To investigate the effects of sodium decanoate on Apc^min/+ mice, sodium decanoate (Sigma, 60 mM) was administered via the drinking water to the experimental group (n = 10), and the water was refreshed every 2 days. The control group (n = 7) was provided ad libitum access to regular water. After 10 weeks of treatment, the mice were euthanized, and their colon tissues were collected for subsequent examination.

Colonic tissues were collected from animal models and digested with collagenase VIII at 37 °C for 30 min. The resulting cell suspension was passed through a 70 μm cell strainer to obtain single cells. Isolated lymphocytes were blocked with TruStain FcX™ PLUS anti-mouse CD16/32 antibody (BD Pharmingen, cat. 553141) at 4 °C for 10 min. After blocking, cells were stained with LIVE/DEAD™ dye (BioLegend, cat. 423105) to exclude dead cells, followed by staining with the following fluorochrome-conjugated antibodies for 30 min: AF700 anti-mouse CD45 (BioLegend, cat. 103128), APC anti-mouse CD11c (BioLegend, cat. 117310), PE anti-mouse CD11b (BioLegend, cat. 101208), BV421 anti-mouse MHCII (BioLegend, cat. 107632), and PerCP/Cyanine5.5 anti-mouse Ly-6G (BioLegend, cat. 127616).

The Apc^min/+ mice were purchased from the Shanghai Model Organisms Center and housed within the barrier facility at the Experimental Animal Center of Tsinghua University. All the experiments were conducted in accordance with institutional guidelines.

Histopathology

To evaluate histopathological changes, colon tissues were collected and fixed in 4% paraformaldehyde. Tissue sections were stained with hematoxylin and eosin (H&E) and scored by a pathologist under blinded conditions. High-grade dysplasia and low-grade dysplasia were assessed according to the methods described in the referenced literature⁶⁰.

Statistical analysis

Statistical analysis was performed using GraphPad Prism software (version 10.5.0.774). Mann–Whitney U test or unpaired t-test was used for two-group comparisons. One-way ANOVA was used for comparisons among three or more groups. Specific statistical tests are detailed in figure legends. P < 0.05 was considered statistically significant. The numbers of biological and experimental replicates are stated in figure legends. Data are presented as means ± SD or SEM. SD standard deviation; SEM standard error of the mean.

Ethics

Our study was approved by the Human Research Ethics Committee of the Institutional Review Board (IRB) of PUMCH (Approval number HS-2869), and all participants provided written informed consent. Our study was also approved by the Ethical Inspection in Animal Experimentation of Tsinghua University (Approval number 23-ZJR1).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary Information^{(4.5MB, pdf)}

41467_2026_74591_MOESM2_ESM.pdf^{(231.2KB, pdf)}

Description of Additional Supplementary Files

Supplementary Data 1-16^{(5MB, xlsx)}

Reporting Summary^{(138.9KB, pdf)}

Transparent Peer Review file^{(3.9MB, pdf)}

Source data

Source Data^{(38.6KB, xlsx)}

Acknowledgements

We acknowledge the Department of Pathology at Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, for providing pathological images and related textual materials used in this study. This work was supported by the High Performance Computing Cluster of the Center for Bioinformatics, National Infrastructures for Translational Medicine (Peking Union Medical College Hospital), through computing resources and technical services.

Author contributions

Q.W.Y., X.M.J., J.N.L., B.W. and J.D. conceived and designed the study. X.M.J. performed bioinformatics analyses and visualized the data for figures. Y.Y.G. performed metabolome analysis. L.J.J., X.B.C., W.Y., Z.P.L., Y.Z. and Y.Q.L. performed cellular and animal experiments. J.J.Z., X.S.S., P.P.W. and J.W. recruited patients. Z.W. and R.N.Z. performed the histopathological evaluations. X.M.J., L.J.J., Y.Y.G., X.B.C., W.Y. and Q.W.Y. wrote the manuscript.

Peer review

Peer review information

Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

Funding

This work was supported by CAMS Innovation Fund for Medical Sciences (CIFMS) (2025-I2M- XHJC-004), CAMS Innovation Fund for Medical Sciences (2025-I2M-KJ-001), National Science and Technology Major Project (2025ZD01903400 and 2024ZD0532804), the Peking Union Medical College Hospital Talent Cultivation Program (Category D) (UHB12054 and UHB11899), the Fundamental Research Funds for the Central Universities, Peking Union Medical College (3332024203), CAMS Innovation Fund for Medical Sciences (CIFMS) (2025-I2M-XHXX-003 and 2025-I2M-XHCL-013). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data availability

The metagenomics raw data generated in this study have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Science under accession code CRA016703. The raw genome sequencing data of Fusobacterium periodonticum generated in this study have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Science under accession code CRA028010. The single-cell RNA-sequencing raw data generated in this study have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Science under accession code HRA017133. Metabolomics raw data generated in this study have been deposited in the MetaboLights repository under accession code MTBLS14505. The result files generated in this study are provided in the Supplementary Information/ Supplementary Data files. Source data are provided with this paper.

Code availability

Analysis codes are available on Github at https://github.com/yang-lab-2026/CRC.git⁶¹.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Xinmiao Jia, Lingjuan Jiang, Yiyi Gong, Xiaobing Chu, Wei Yu, Juan Du.

These authors jointly supervised this work: Bin Wu, Jingnan Li, Qiwen Yang.

Contributor Information

Bin Wu, Email: wubin0279@hotmail.com.

Jingnan Li, Email: lijn2008@126.com.

Qiwen Yang, Email: yangqiwen81@vip.163.com.

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-026-74591-y.

References

1.Siegel, R. L., Wagle, N. S., Cercek, A., Smith, R. A. & Jemal, A. Colorectal cancer statistics, 2023. Ca. Cancer J. Clin.73, 233–254 (2023). [DOI] [PubMed] [Google Scholar]
2.Keum, N. & Giovannucci, E. Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies. Nat. Rev. Gastroenterol. Hepatol.16, 713–732 (2019). [DOI] [PubMed] [Google Scholar]
3.Siegel, R. L., Giaquinto, A. N. & Jemal, A. Cancer statistics, 2024. Ca. Cancer J. Clin.74, 12–49 (2024). [DOI] [PubMed] [Google Scholar]
4.Roshandel, G., Ghasemi-Kebria, F. & Malekzadeh, R. Colorectal cancer: epidemiology, risk factors, and prevention. Cancers16, 1530 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Strum, W. B. Colorectal adenomas. N. Engl. J. Med.374, 1065–1075 (2016). [DOI] [PubMed] [Google Scholar]
6.Janney, A., Powrie, F. & Mann, E. H. Host-microbiota maladaptation in colorectal cancer. Nature585, 509–517 (2020). [DOI] [PubMed] [Google Scholar]
7.Ahn, J. et al. Human gut microbiome and risk for colorectal cancer. J. Natl. Cancer Inst.105, 1907–1911 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Garrett, W. S. The gut microbiota and colon cancer. Science364, 1133–1135 (2019). [DOI] [PubMed] [Google Scholar]
9.Wong, S. H. & Yu, J. Gut microbiota in colorectal cancer: mechanisms of action and clinical applications. Nat. Rev. Gastroenterol. Hepatol.16, 690–704 (2019). [DOI] [PubMed] [Google Scholar]
10.Ternes, D. et al. Microbiome in colorectal cancer: How to get from meta-omics to mechanism? Trends Microbiol.28, 401–423 (2020). [DOI] [PubMed] [Google Scholar]
11.Kong, C. et al. Fusobacterium Nucleatum promotes the development of colorectal cancer by activating a cytochrome P450/epoxyoctadecenoic acid axis via TLR4/Keap1/NRF2 signaling. Cancer Res.81, 4485–4498 (2021). [DOI] [PubMed] [Google Scholar]
12.Kostic, A. D. et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe. 14, 207–215 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Yang, Y. et al. Fusobacterium nucleatum increases proliferation of colorectal cancer cells and tumor development in mice by activating Toll-like receptor 4 signaling to nuclear factor−κB, and up-regulating expression of microRNA-21. Gastroenterology152, 851–866.e24 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Yu, T. et al. Fusobacterium nucleatum promotes chemoresistance to colorectal cancer by modulating autophagy. Cell170, 548–563.e16 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Cuevas-Ramos, G. et al. Escherichia coli induces DNA damage in vivo and triggers genomic instability in mammalian cells. Proc. Natl. Acad. Sci. USA. 107, 11537–11542 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Huycke, M. M., Abrams, V. & Moore, D. R. Enterococcus faecalis produces extracellular superoxide and hydrogen peroxide that damages colonic epithelial cell DNA. Carcinogenesis23, 529–536 (2002). [DOI] [PubMed] [Google Scholar]
17.Ternes, D. et al. The gut microbial metabolite formate exacerbates colorectal cancer progression. Nat. Metab.4, 458–475 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Cong, J. et al. Bile acids modified by the intestinal microbiota promote colorectal cancer growth by suppressing CD8+ T cell effector functions. Immunity57, 876–889.e11 (2024). [DOI] [PubMed] [Google Scholar]
19.Schmitt, M. & Greten, F. R. The inflammatory pathogenesis of colorectal cancer. Nat. Rev. Immunol.21, 653–667 (2021). [DOI] [PubMed] [Google Scholar]
20.Wick, E. C. et al. Stat3 activation in murine colitis induced by enterotoxigenic Bacteroides fragilis. Inflamm. Bowel Dis.20, 821–834 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Qian, J. et al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. 30, 745–762 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Li, H. et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet.49, 708–718 (2017). [DOI] [PubMed] [Google Scholar]
23.Pelka, K. et al. Spatially organized multicellular immune hubs in human colorectal cancer. Cell184, 4734–4752.e20 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Chu, X. et al. Integrative single-cell analysis of human colorectal cancer reveals patient stratification with distinct immune evasion mechanisms. Nat. Cancer5, 1409–1426 (2024). [DOI] [PubMed] [Google Scholar]
25.Liu, H. et al. Structural insights into ligand recognition and activation of the medium-chain fatty acid-sensing receptor GPR84. Nat. Commun.14, 3271 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Yu, J. et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut. 66, 70–78 (2017). [DOI] [PubMed] [Google Scholar]
27.Si, H. et al. Colorectal cancer occurrence and treatment based on changes in intestinal flora. Semin. Cancer Biol.70, 3–10 (2021). [DOI] [PubMed] [Google Scholar]
28.Flanagan, L. et al. Fusobacterium nucleatum associates with stages of colorectal neoplasia development, colorectal cancer and disease outcome. Eur. J. Clin. Microbiol. Infect. Dis.33, 1381–1390 (2014). [DOI] [PubMed] [Google Scholar]
29.Mima, K. et al. Fusobacterium nucleatum in colorectal carcinoma tissue and patient prognosis. Gut. 65, 1973–1980 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Zhang, Q. et al. Reprogramming of palmitic acid induced by dephosphorylation of ACOX1 promotes β-catenin palmitoylation to drive colorectal cancer progression. Cell Discov.9, 26 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Pascual, G. et al. Dietary palmitic acid promotes a prometastatic memory via Schwann cells. Nature599, 485–490 (2021). [DOI] [PubMed] [Google Scholar]
32.Fatima, S. et al. High-fat diet feeding and palmitic acid increase CRC growth in β2AR-dependent manner. Cell Death Dis.10, 711 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Negm, A., Sedky, A. & Elsawy, H. Capric acid behaves agonistic effect on calcitriol to control inflammatory mediators in colon cancer cells. Mol. Basel Switz.27, 6624 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Hedrick, C. C. & Malanchi, I. Neutrophils in cancer: heterogeneous and multifaceted. Nat. Rev. Immunol.22, 173–187 (2022). [DOI] [PubMed] [Google Scholar]
35.Ocana, A., Nieto-Jiménez, C., Pandiella, A. & Templeton, A. J. Neutrophils in cancer: prognostic role and therapeutic strategies. Mol. Cancer16, 137 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Parkos, C. A. Neutrophil-epithelial interactions: a double-edged sword. Am. J. Pathol.186, 1404–1416 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Cheung, E. C. & Vousden, K. H. The role of ROS in tumour development and progression. Nat. Rev. Cancer22, 280–297 (2022). [DOI] [PubMed] [Google Scholar]
38.Claesson-Welsh, L. & Welsh, M. VEGFA and tumour angiogenesis. J. Intern. Med.273, 114–127 (2013). [DOI] [PubMed] [Google Scholar]
39.Berasain, C. & Avila, M. A. Amphiregulin. Semin. Cell Dev. Biol.28, 31–41 (2014). [DOI] [PubMed] [Google Scholar]
40.Kunzmann, A. T. et al. PTGS2 (Cyclooxygenase-2) expression and survival among colorectal cancer patients: a systematic review. Cancer Epidemiol. Biomark. Prev.22, 1490–1497 (2013). [DOI] [PubMed] [Google Scholar]
41.Wolf, C. L., Pruett, C., Lighter, D. & Jorcyk, C. L. The clinical relevance of OSM in inflammatory diseases: a comprehensive review. Front. Immunol.14, 1239732 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Zhang, D. & Frenette, P. S. Cross talk between neutrophils and the microbiota. Blood133, 2168–2177 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Li, G. et al. Microbiota metabolite butyrate constrains neutrophil functions and ameliorates mucosal inflammation in inflammatory bowel disease. Gut. Microbes13, 1968257 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Zhang, X., Qin, X. & Wang, S. Commensal bacteria and cancer immunotherapy: strategy and opportunity. Life Med. 2, lnad024 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Abbas, M. & Tangney, M. The oncobiome; what, so what, now what? Microbiome Res. Rep.4, 16 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Peng, S. et al. Metabolomics reveals that CAF-derived lipids promote colorectal cancer peritoneal metastasis by enhancing membrane fluidity. Int. J. Biol. Sci.18, 1912–1932 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Fujimoto, K., Iwasaki, C., Kawaguchi, H., Yasugi, E. & Oshima, M. Cell membrane dynamics and the induction of apoptosis by lipid compounds. FEBS Lett.446, 113–116 (1999). [DOI] [PubMed] [Google Scholar]
48.Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife10, e65088 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol.12, R60 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C. & Segata, N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ3, e1029 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Domingo-Almenara, X. & Siuzdak, G. Metabolomics data processing using XCMS. Methods Mol. Biol.2104, 11–24 (2020). [DOI] [PubMed] [Google Scholar]
52.Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst.8, 281–291.e9 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Stuart, T. et al. Comprehensive integration of single-cell data. Cell177, 1888–1902.e21 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science344, 1396–1401 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res.28, 27–30 (2000). [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet.25, 25–29 (2000). [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Gillespie, M. et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res.50, D687–D692 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics J. Integr. Biol.16, 284–287 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Jia, X. et al. The key role of iroBCDN-lacking pLVPK-like plasmid in the evolution of the most prevalent hypervirulent carbapenem-resistant ST11-KL64 Klebsiella pneumoniae in China. Drug Resist. Updat.77, 101137 (2024). [DOI] [PubMed] [Google Scholar]
60.Boivin, G. P. et al. Pathology of mouse models of intestinal cancer: consensus report and recommendations. Gastroenterology124, 762–777 (2003). [DOI] [PubMed] [Google Scholar]
61.Jia, X. M. et al. Fusobacterium periodonticum promotes colorectal tumorigenesis via decanoic acid-driven neutrophil chemotaxis. Github Reposit10.5281/zenodo.20278634 (2026). [DOI] [PMC free article] [PubMed]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information^{(4.5MB, pdf)}

41467_2026_74591_MOESM2_ESM.pdf^{(231.2KB, pdf)}

Description of Additional Supplementary Files

Supplementary Data 1-16^{(5MB, xlsx)}

Reporting Summary^{(138.9KB, pdf)}

Transparent Peer Review file^{(3.9MB, pdf)}

Source Data^{(38.6KB, xlsx)}

Data Availability Statement

Analysis codes are available on Github at https://github.com/yang-lab-2026/CRC.git⁶¹.

[CR1] 1.Siegel, R. L., Wagle, N. S., Cercek, A., Smith, R. A. & Jemal, A. Colorectal cancer statistics, 2023. Ca. Cancer J. Clin.73, 233–254 (2023). [DOI] [PubMed] [Google Scholar]

[CR2] 2.Keum, N. & Giovannucci, E. Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies. Nat. Rev. Gastroenterol. Hepatol.16, 713–732 (2019). [DOI] [PubMed] [Google Scholar]

[CR3] 3.Siegel, R. L., Giaquinto, A. N. & Jemal, A. Cancer statistics, 2024. Ca. Cancer J. Clin.74, 12–49 (2024). [DOI] [PubMed] [Google Scholar]

[CR4] 4.Roshandel, G., Ghasemi-Kebria, F. & Malekzadeh, R. Colorectal cancer: epidemiology, risk factors, and prevention. Cancers16, 1530 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR5] 5.Strum, W. B. Colorectal adenomas. N. Engl. J. Med.374, 1065–1075 (2016). [DOI] [PubMed] [Google Scholar]

[CR6] 6.Janney, A., Powrie, F. & Mann, E. H. Host-microbiota maladaptation in colorectal cancer. Nature585, 509–517 (2020). [DOI] [PubMed] [Google Scholar]

[CR7] 7.Ahn, J. et al. Human gut microbiome and risk for colorectal cancer. J. Natl. Cancer Inst.105, 1907–1911 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR8] 8.Garrett, W. S. The gut microbiota and colon cancer. Science364, 1133–1135 (2019). [DOI] [PubMed] [Google Scholar]

[CR9] 9.Wong, S. H. & Yu, J. Gut microbiota in colorectal cancer: mechanisms of action and clinical applications. Nat. Rev. Gastroenterol. Hepatol.16, 690–704 (2019). [DOI] [PubMed] [Google Scholar]

[CR10] 10.Ternes, D. et al. Microbiome in colorectal cancer: How to get from meta-omics to mechanism? Trends Microbiol.28, 401–423 (2020). [DOI] [PubMed] [Google Scholar]

[CR11] 11.Kong, C. et al. Fusobacterium Nucleatum promotes the development of colorectal cancer by activating a cytochrome P450/epoxyoctadecenoic acid axis via TLR4/Keap1/NRF2 signaling. Cancer Res.81, 4485–4498 (2021). [DOI] [PubMed] [Google Scholar]

[CR12] 12.Kostic, A. D. et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe. 14, 207–215 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR13] 13.Yang, Y. et al. Fusobacterium nucleatum increases proliferation of colorectal cancer cells and tumor development in mice by activating Toll-like receptor 4 signaling to nuclear factor−κB, and up-regulating expression of microRNA-21. Gastroenterology152, 851–866.e24 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR14] 14.Yu, T. et al. Fusobacterium nucleatum promotes chemoresistance to colorectal cancer by modulating autophagy. Cell170, 548–563.e16 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR15] 15.Cuevas-Ramos, G. et al. Escherichia coli induces DNA damage in vivo and triggers genomic instability in mammalian cells. Proc. Natl. Acad. Sci. USA. 107, 11537–11542 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR16] 16.Huycke, M. M., Abrams, V. & Moore, D. R. Enterococcus faecalis produces extracellular superoxide and hydrogen peroxide that damages colonic epithelial cell DNA. Carcinogenesis23, 529–536 (2002). [DOI] [PubMed] [Google Scholar]

[CR17] 17.Ternes, D. et al. The gut microbial metabolite formate exacerbates colorectal cancer progression. Nat. Metab.4, 458–475 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR18] 18.Cong, J. et al. Bile acids modified by the intestinal microbiota promote colorectal cancer growth by suppressing CD8+ T cell effector functions. Immunity57, 876–889.e11 (2024). [DOI] [PubMed] [Google Scholar]

[CR19] 19.Schmitt, M. & Greten, F. R. The inflammatory pathogenesis of colorectal cancer. Nat. Rev. Immunol.21, 653–667 (2021). [DOI] [PubMed] [Google Scholar]

[CR20] 20.Wick, E. C. et al. Stat3 activation in murine colitis induced by enterotoxigenic Bacteroides fragilis. Inflamm. Bowel Dis.20, 821–834 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR21] 21.Qian, J. et al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. 30, 745–762 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR22] 22.Li, H. et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet.49, 708–718 (2017). [DOI] [PubMed] [Google Scholar]

[CR23] 23.Pelka, K. et al. Spatially organized multicellular immune hubs in human colorectal cancer. Cell184, 4734–4752.e20 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR24] 24.Chu, X. et al. Integrative single-cell analysis of human colorectal cancer reveals patient stratification with distinct immune evasion mechanisms. Nat. Cancer5, 1409–1426 (2024). [DOI] [PubMed] [Google Scholar]

[CR25] 25.Liu, H. et al. Structural insights into ligand recognition and activation of the medium-chain fatty acid-sensing receptor GPR84. Nat. Commun.14, 3271 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR26] 26.Yu, J. et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut. 66, 70–78 (2017). [DOI] [PubMed] [Google Scholar]

[CR27] 27.Si, H. et al. Colorectal cancer occurrence and treatment based on changes in intestinal flora. Semin. Cancer Biol.70, 3–10 (2021). [DOI] [PubMed] [Google Scholar]

[CR28] 28.Flanagan, L. et al. Fusobacterium nucleatum associates with stages of colorectal neoplasia development, colorectal cancer and disease outcome. Eur. J. Clin. Microbiol. Infect. Dis.33, 1381–1390 (2014). [DOI] [PubMed] [Google Scholar]

[CR29] 29.Mima, K. et al. Fusobacterium nucleatum in colorectal carcinoma tissue and patient prognosis. Gut. 65, 1973–1980 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR30] 30.Zhang, Q. et al. Reprogramming of palmitic acid induced by dephosphorylation of ACOX1 promotes β-catenin palmitoylation to drive colorectal cancer progression. Cell Discov.9, 26 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR31] 31.Pascual, G. et al. Dietary palmitic acid promotes a prometastatic memory via Schwann cells. Nature599, 485–490 (2021). [DOI] [PubMed] [Google Scholar]

[CR32] 32.Fatima, S. et al. High-fat diet feeding and palmitic acid increase CRC growth in β2AR-dependent manner. Cell Death Dis.10, 711 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR33] 33.Negm, A., Sedky, A. & Elsawy, H. Capric acid behaves agonistic effect on calcitriol to control inflammatory mediators in colon cancer cells. Mol. Basel Switz.27, 6624 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR34] 34.Hedrick, C. C. & Malanchi, I. Neutrophils in cancer: heterogeneous and multifaceted. Nat. Rev. Immunol.22, 173–187 (2022). [DOI] [PubMed] [Google Scholar]

[CR35] 35.Ocana, A., Nieto-Jiménez, C., Pandiella, A. & Templeton, A. J. Neutrophils in cancer: prognostic role and therapeutic strategies. Mol. Cancer16, 137 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR36] 36.Parkos, C. A. Neutrophil-epithelial interactions: a double-edged sword. Am. J. Pathol.186, 1404–1416 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR37] 37.Cheung, E. C. & Vousden, K. H. The role of ROS in tumour development and progression. Nat. Rev. Cancer22, 280–297 (2022). [DOI] [PubMed] [Google Scholar]

[CR38] 38.Claesson-Welsh, L. & Welsh, M. VEGFA and tumour angiogenesis. J. Intern. Med.273, 114–127 (2013). [DOI] [PubMed] [Google Scholar]

[CR39] 39.Berasain, C. & Avila, M. A. Amphiregulin. Semin. Cell Dev. Biol.28, 31–41 (2014). [DOI] [PubMed] [Google Scholar]

[CR40] 40.Kunzmann, A. T. et al. PTGS2 (Cyclooxygenase-2) expression and survival among colorectal cancer patients: a systematic review. Cancer Epidemiol. Biomark. Prev.22, 1490–1497 (2013). [DOI] [PubMed] [Google Scholar]

[CR41] 41.Wolf, C. L., Pruett, C., Lighter, D. & Jorcyk, C. L. The clinical relevance of OSM in inflammatory diseases: a comprehensive review. Front. Immunol.14, 1239732 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR42] 42.Zhang, D. & Frenette, P. S. Cross talk between neutrophils and the microbiota. Blood133, 2168–2177 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR43] 43.Li, G. et al. Microbiota metabolite butyrate constrains neutrophil functions and ameliorates mucosal inflammation in inflammatory bowel disease. Gut. Microbes13, 1968257 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR44] 44.Zhang, X., Qin, X. & Wang, S. Commensal bacteria and cancer immunotherapy: strategy and opportunity. Life Med. 2, lnad024 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR45] 45.Abbas, M. & Tangney, M. The oncobiome; what, so what, now what? Microbiome Res. Rep.4, 16 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR46] 46.Peng, S. et al. Metabolomics reveals that CAF-derived lipids promote colorectal cancer peritoneal metastasis by enhancing membrane fluidity. Int. J. Biol. Sci.18, 1912–1932 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR47] 47.Fujimoto, K., Iwasaki, C., Kawaguchi, H., Yasugi, E. & Oshima, M. Cell membrane dynamics and the induction of apoptosis by lipid compounds. FEBS Lett.446, 113–116 (1999). [DOI] [PubMed] [Google Scholar]

[CR48] 48.Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife10, e65088 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR49] 49.Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol.12, R60 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR50] 50.Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C. & Segata, N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ3, e1029 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR51] 51.Domingo-Almenara, X. & Siuzdak, G. Metabolomics data processing using XCMS. Methods Mol. Biol.2104, 11–24 (2020). [DOI] [PubMed] [Google Scholar]

[CR52] 52.Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst.8, 281–291.e9 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR53] 53.Stuart, T. et al. Comprehensive integration of single-cell data. Cell177, 1888–1902.e21 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR54] 54.Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science344, 1396–1401 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR55] 55.Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res.28, 27–30 (2000). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR56] 56.Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet.25, 25–29 (2000). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR57] 57.Gillespie, M. et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res.50, D687–D692 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR58] 58.Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics J. Integr. Biol.16, 284–287 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR59] 59.Jia, X. et al. The key role of iroBCDN-lacking pLVPK-like plasmid in the evolution of the most prevalent hypervirulent carbapenem-resistant ST11-KL64 Klebsiella pneumoniae in China. Drug Resist. Updat.77, 101137 (2024). [DOI] [PubMed] [Google Scholar]

[CR60] 60.Boivin, G. P. et al. Pathology of mouse models of intestinal cancer: consensus report and recommendations. Gastroenterology124, 762–777 (2003). [DOI] [PubMed] [Google Scholar]

[CR61] 61.Jia, X. M. et al. Fusobacterium periodonticum promotes colorectal tumorigenesis via decanoic acid-driven neutrophil chemotaxis. Github Reposit10.5281/zenodo.20278634 (2026). [DOI] [PMC free article] [PubMed]

PERMALINK

Fusobacterium periodonticum promotes colorectal tumorigenesis via decanoic acid-driven neutrophil chemotaxis

Xinmiao Jia

Lingjuan Jiang

Yiyi Gong

Xiaobing Chu

Wei Yu

Juan Du

Jingjia Zhang

Xuesong Shang

Peipei Wang

Jing Wang

Yaqian Li

Zheng Wang

Runing Zhou

Zongping Li

Ying Zhu

Bin Wu

Jingnan Li

Qiwen Yang

Abstract

Introduction

Results

Gut microbiota analysis reveals F. periodonticum as a significantly enriched species in CRC patients

Fig. 1. Taxonomic and functional profiles of the fecal microbiota in CRC patients.

Verification of the gut microbiota with large-scale metagenomic sequencing data confirmed the significant enrichment of F. periodonticum in CRC patients

Global metabolomic profiling revealed the significance of fatty acid metabolism in CRC patient feces

Fig. 2. Global metabolome profiling.

Association analysis and targeted metabolomics revealed a significant correlation between F. periodonticum and decanoic acid

scRNA-seq reveals the cell types and tissue-specific patterns of neutrophils in CRC patients

Fig. 3. Diverse cell types delineated by scRNA-seq analysis.

Fig. 4. Tissue-specific patterns of neutrophils and differentially expressed genes in CRC patients.

Decanoic acid promotes neutrophil late apoptosis/necrosis, chemotaxis and facilitates the expression of genes related to leukocyte migration and tumorigenesis

Fig. 5. Decanoic acid promotes neutrophil migration and a protumor phenotype.

F. periodonticum promotes colorectal tumorigenesis

Fig. 6. F. periodonticum promotes colorectal tumorigenesis.

Decanoic acid promotes colorectal tumorigenesis

Fig. 7. Decanoic acid promotes colorectal tumorigenesis.

Discussion

Methods

Sample collection and study design

Fecal sample collection and sequencing

Metagenomic sequence data processing and analyses

Published fecal metagenomic data download and analysis

Untargeted metabolomics

Metabolite extraction

LC‒MS analysis

Data processing and analysis

Targeted metabolomics

Sample preparation

GC‒MS analysis

Tissue sample collection, single-cell transcriptome sequencing and analyses

Sample collection, tissue dissociation and preparation of single-cell suspensions

Library construction and sequencing

Quality control of single-cell data and doublet removal

Dimensional reduction and unsupervised clustering analysis

Differential analysis

Gene enrichment analysis

Neutrophil assays

Isolation and incubation of peripheral blood neutrophils

Assessment of neutrophil apoptosis

Neutrophil chemotaxis assay

Assessment of apoptosis in primary human monocytes and THP-1 cells

RNA sequencing (RNA-seq)

Quantitative real-time polymerase chain reaction (qRT‒PCR)

Enzyme-linked immunosorbent assay (ELISA)

CXCL8 secretion analysis

Pertussis toxin inhibition assay

Caco-2 cell proliferation assay

Caco-2–neutrophil coculture assay

Isolation, culture and identification of Fusobacterium periodonticum

Animal experiments

Histopathology

Statistical analysis

Ethics

Reporting summary

Supplementary information

Source data

Acknowledgements

Author contributions