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Microbiome dysbiosis, neutrophil recruitment and mesenchymal transition of mesothelial cells promotes peritoneal metastasis of colorectal cancer

Nature Cancer volume 6, pages 493–510 (2025)Cite this article

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Abstract

Peritoneal metastasis (PM) is common in colorectal cancer (CRC), yet its underlying mechanisms are poorly understood. Here, we explored the transcriptional profile of CRC, PM and adjacent tissues revealing key players that facilitate PM. Single-cell analysis of 48 matched samples from 12 patients revealed that remodeling of malignant cells and the tumor microenvironment promotes CRC progression and metastasis. Multiplexed imaging confirmed depletion in PM by enrichment in CRC tissues of neutrophils associated with mucosal immunity disruption, intestinal microbiota dysbiosis and mesenchymal transition of both cancerous and mesothelial cells. Functional analyses in cell lines, organoids and in vivo models demonstrated that dysbiosis promoted inflammation and protumor neutrophil recruitment, while coupled mesenchymal transition of malignant and mesothelial cells disrupted the stromal structure and increased cancer cell invasiveness. Our findings suggest that targeting mesothelial cells and tumor microenvironment remodeling may offer therapeutic strategies for CRC-PM.

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Fig. 1: Study design, single-cell atlas and compositional changes in immune and stromal cell subclusters.
Fig. 2: Transcriptomic differences between cancer cells in peritoneal metastases and those in colorectal cancer.
Fig. 3: The enrichment of neutrophils in CRC is associated with dysbiosis.
Fig. 4: The depletion of neutrophils in PM is attributed to the loss of CXCL14 expression and the diversity of the neutrophils.
Fig. 5: Mesothelial-to-mesenchymal transition.
Fig. 6: Anti-MSLN treatment strategy to inhibit peritoneal metastasis of colorectal cancer.

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Data availability

All raw and processed sequencing data generated as part of this study have been deposited at the Genome Sequence Archive of the BIG Data Center at the Beijing Institution of Genomics, Chinese Academy of Sciences (accession no. HRA003293) and are publicly accessible. The published data used for validation or comparison in this study were retrieved from the NCBI Gene Expression Omnibus database under accession codes GSE17834, GSE132257, GSE132465 and GSE144735. In this study, we used the GRCh38 reference genome for data analysis (https://www.10xgenomics.com/support/software/cell-ranger/downloads). Source data for Figs. 1–6 and Extended Data Figs. 1–6 have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

No algorithm or software was generated for this study. The code for reproducing major figures is available on GitHub (https://github.com/cubense/PM-in-CRC-PROJECT). Any additional information required to reanalyze the data reported in this article is available from the corresponding author upon request.

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Acknowledgements

This work was supported by the grants of Science and Technology Commission of Shanghai Municipality (no. 23XD1400700), the National Natural Science Foundation of China (nos. 82241230, T2125002, 82173133, 82341007, 82103154, 82373090, 82003317, 82103554, 92259303), the National Key Research and Development Program of China (2022YFC2504602), Beijing Natural Science Foundation (Z220014), Shanghai Municipal Health Commission (no. 2022XD012) and the New Cornerstone Science Foundation through the XPLORER PRIZE.

Author information

Author notes

  1. These authors contributed equally: Qingguo Li, Yiwei Xiao, Lingyu Han, Wenqin Luo.

Authors and Affiliations

  1. Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China

    Qingguo Li, Lingyu Han, Wenqin Luo, Weixing Dai, Hongsheng Fang, Renjie Wang, Ye Xu, Sanjun Cai & Guoxiang Cai

  2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

    Qingguo Li, Lingyu Han, Wenqin Luo, Weixing Dai, Hongsheng Fang, Renjie Wang, Ye Xu, Sanjun Cai & Guoxiang Cai

  3. Biomedical Pioneering Innovation Center (BIOPIC), Peking-Tsinghua Center for Life Sciences (CLS), School of Life Sciences, Peking University, Beijing, China

    Yiwei Xiao & Fan Bai

  4. Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope, Biomedical Research Center, Monrovia, CA, USA

    Ajay Goel

  5. City of Hope Comprehensive Cancer Center, Duarte, CA, USA

    Ajay Goel

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Contributions

G.C. and F.B. conceived and designed the project. A.G. made suggestions to the frame of the data analysis and manuscript. Q.L., L.H., W.L. and W.D. collected the human samples and clinical information. G.C., Q.L., R.W., Y. Xu and S.C. performed the surgical operation. Q.L. and L.H. performed the scRNA-seq experiments. Y. Xiao performed bioinformatic analyses. Q.L. and L.H. performed IHC and IF experiments. W.L. and L.H. performed FISH and 16S rRNA-seq experiments. H.F. performed functional experiments on neutrophils. L.H. performed functional experiments on mesothelial cells. L.H., W.D., R.W. and W.L. constructed the mouse models and analyzed the in vivo data. Q.L., Y. Xiao, L.H., W.L., W.D., H.F., R.W., Y. Xu, S.C., A.G., F.B. and G.C. discussed and interpreted the data. Y. Xiao wrote the paper with input from all authors. G.C., F.B. and A.G. reviewed and finalized the paper. G.C. and F.B. supervised the project.

Corresponding authors

Correspondence to Ajay Goel, Fan Bai or Guoxiang Cai.

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The authors declare no competing interests.

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Nature Cancer thanks Iain Tan, Louis Vermeulen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 A single-cell landscape of primary tumors and peritoneal metastases in colorectal cancer.

a, Expression levels of signature genes in major cell clusters and subclusters. b, Comparison of the proportions of major cell clusters in normal peritoneal tissues, peritoneal metastases, normal colonic tissues, and CRC tissues from 12 patient. Boxes indicate medians and interquartile ranges (IQRs), and whiskers indicate the minimum and maximum values; two-tailed Wilcoxon test. c, Expression of representative marker genes in four subsets of macrophages within myeloid cells. d, Selected pathway activities in different subsets of macrophages. The pathway activities were assessed per cell using gene set variation analysis (GSVA).e, UMAP visualization of RNA velocity of seven subsets of macrophages and monocytes in peritoneal metastases. f, mIHC validation of macrophage subset Macrophage-FN1 present in peritoneal metastasis tumor tissues. Scale bars, 20 μm. Images are representative of n = 12 experiments. g, Spatial distribution(representative images of n = 12 experiments) of RGS5+ Pericyte cells, ACTA2+ fibroblast cells, CD66+ neutrophils, CD1C + cDC2 cells and TPASB1+ mast cells in CRC tissue and PM tissue. Scale bars, 20 μm. h, Quantitative proportions of RGS5+ Pericytes, TPASB1+ mast cells, ACTA2+ fibroblasts and CD1C + cDC2 cells in CRC tissue vs. PM tissue by quantification mIHC in 10 patients with PM and without liver metastases. For panels a-d and h: CRC, n = 12 patients; PM, n = 12 patients; normal colon, n = 12 patients; normal peritoneal, n = 9 patients. unpaired two-sides t-test. Data are presented as mean values +/- SD.

Source data

Extended Data Fig. 2 Whole-exon sequencing and CNV subclone analysis of malignant cells.

a, Genomic landscape of 24 samples from 11 patients. b, CNV profiles inferred from patient DNA whole-exome sequencing (top) and scRNA-seq (bottom). The heatmap color shows the log2 CNV ratio: red, amplifications; blue, deletions. CNA inference in malignant cells using the subclone mode; rows correspond to malignant cells ordered by tumor and within a clone cluster tree in the left panel for each patient. The legend color shows the tissue types of malignant cells: blue, CRC; red, PM (peritoneal metastases); orange, LM (liver metastasis). c, Phylogenetic trees were constructed based on the mutations observed in each patient. Scale bar represents the number of mutations.For panels a–c: CRC, n = 12 patients; PM, n = 12 patients; normal colon, n = 12 patients; normal peritoneal, n = 9 patients.

Source data

Extended Data Fig. 3 Representative malignant programs and the depletion of two mucosal immune components in CRC.

a, Spatial distribution(representative images of n = 12 experiments) of the expression of COL1A2 and SPP1 in PM and CRC cancer cells. Scale bars, 20 μm. Images are representative of n = 12 patients experiments. b,Violin plots displaying the expression of two cell cycle programs in three cell cycle phases identified by Seurat. c, Expression of 12 patient- or sample-specific programs in UMAPs of malignant cells. d, Ridgeline plots showing the expression of four colon lineage cluster programs in subsets of normal epithelial cells from two scRNA-seq cohort datasets of CRC. e, Density plot showing the distribution of plasma and B cell clusters (left panel) and T/NK clusters (right panel) in different tissue types. f, Proportions of IGHA plasma cells (top) and IEL_GDT cells (bottom) in normal colonic tissue vs CRCs in two external scRNA-seq datasets.Unpaired two-sides Wilcoxon test. Boxes indicate medians and interquartile ranges (IQRs), and whiskers indicate the minimum and maximum values. g, The top 10 genera identified by 16S rRNA sequencing in colorectal cancer and normal colon tissues. h, The paired comparison between cancer and normal tissues of 26 external validation of CRC patients reveals significant differences in three genera among the top 10 identified genera by 16S rRNA sequencing. Boxes indicate medians and interquartile ranges (IQRs), and whiskers indicate the minimum and maximum values. p values were calculated using actual genera counts by paired Wilcoxon rank-sum tests. i, Correlations across neutrophils, IEL_GDT, IgA plasma cell abundance and bacteria counts in CRC samples. Correlations were calculated using Pearson’s r. (CRC, n = 26 patients; normal colon, n = 26 patients). P < 0.0001.

Source data

Extended Data Fig. 4 Dysbiosis of intestinal bacteria and the recruitment of neutrophils in normal peritoneal tissue.

a, Dot plot depicting the selected ligand–receptor interactions between neutrophils and other cells in PM vs normal peritoneal tissue. Communication probability and p values were calculated using CellChat.All p value < 0.0001. The significance of each ligand–receptor interaction score is assessed using a random permutation test. b, Expression of CXCL14 in stromal cells across different tissues. c, Migration of dHL-60 cells in a chemotaxis assay; the y axis represents the ratio of cell counts in the supernatant of the CXCL14 group in comparison with the PBS group; p values were calculated using an unpaired two-side t-test, biological replicate=3. Boxes indicate medians and interquartile ranges (IQRs), and whiskers indicate the minimum and maximum valuesd, UMAP showing the expression levels of selected signature genes in subsets of neutrophils. e, Selected pathway activities among the different neutrophil subsets. Pathway activities were scored per cell using gene set variation analysis (GSVA). Data are presented as mean values +/- SD. f, Functional enrichment (−log10 of FDR-adjusted p value, two-sides hypergeometric test) of differential genes in neutrophils in normal peritoneal tissues and CRC tissues using Metascape, with the selected pathways labeled. g, Top 10 differentially active transcription factors in each tissue.For panels a-g: CRC, n = 12 patients; PM, n = 12 patients; normal colon, n = 12 patients; normal peritoneal, n = 9 patients.

Source data

Extended Data Fig. 5 Heterogeneity of neutrophils and validation using in vitro models.

a, Colony formation assay, in which control dHL-60 cells, NFKB1-overexpressing dHL-60 cells, and STAT4-overexpressing dHL-60 cells were cultured in the upper chamber, and SW480 (left upper) or HC116 cells (left bottom) were cultured in the lower chamber. Images are representative of n = 3 experiments.Bar plot showing the number of SW480 cells (right upper) or HC116 cells (right bottom) in the colony formation assay. p values were calculated using an unpaired two-sides t-test, n=3 biological replicate.b, Migration assay, SW480 cells or HCT116 cells co-cultured with control dHL-60 cells, NFKB1-overexpressing dHL-60 cells, and STAT4-overexpressing dHL-60 cells for 3 days. Their migration capacity was assessed using the Transwell assay. Images are representative of n = 3 experiments. p values were calculated using an unpaired two-sides t-test, biological replicate=3. c, Cytokine assays showing the relative concentration of cytokines in cancer cells co-cultured with NFKB1-overexpressing dHL-60 cells or STAT4-overexpressing dHL-60 cells (biological replicate=3) error bars represent mean +/- SD; P values were determined using an unpaired two-sided Wilcoxon test. d, Their migration capacity was assessed using the migration assay. HCT116 cells co-cultured with control neutrophils, NFKB1-overexpressing neutrophils, and STAT4-overexpressing neutrophils for 3 days. Images are representative of n = 3 experiments. Bar plots showing the migration cells number of HCT116 cells (biological replicate=3). Error bars represent mean +/- SD; P values were determined using an unpaired two-sided t-test. e, Colony formation assay, in which control neutrophils, NFKB1-overexpressing neutrophils, and STAT4-overexpressing neutrophils were cultured in the upper chamber, and HCT116 cells were cultured in the lower chamber. Images are representative of n = 3 experiments.(Empty vector VS copGFP-NFKB1 p = 0.0097; Empty vector VS copGFP-STAT4 p = 0.0353) p values were calculated using an unpaired two-sides t-test, biological replicate=3.f, The gene expression of NLRP3 and TNFSF10 in neutrophils in scRNA-seq analysis. g, qPCR for assessing the activation status of two transcription factors. Relative expression of NFKB1 and STAT4 in two sorted neutrophils and control neutrophils without sorted(biological replicate=4); Error bars represent mean +/- SD; P values were determined using an unpaired two-sided t-test. h, Top panel showing HCT116 cells (left panel) and SW480 cells (right panel) co-cultured with control neutrophils, TNFSF10 + NLRP3- neutrophils, and NLRP3 + TNFSF10- neutrophils for 3 days. Their migration capacity was assessed using the Transwell assay (middle panel). Images are representative of n = 3 experiments. p values were calculated using an unpaired two-sides t-test, biological replicate=3.Bar plot showing the comparison of number of cell migration and colonies among three groups (bottom panel). Plots represent 10 validations, and error bars represent mean +/- SD. p values were calculated using an unpaired two-sides t-test. Data are presented as mean values +/- SD.

Source data

Extended Data Fig. 6 Mesothelial-to-mesenchymal transition.

a, Spatial distribution(representative images of n = 12 experiments) of PM and normal peritoneum stained for MSLN on pathological slides. b, Representative image of fluorescent staining showing the morphology change in MET-5A cells (with GFP) in control and patient-derived CRC and PM tumor organoids co-cultured with RKO cells (with Red CMTPX). c, Spatial distribution(representative images of n = 12 experiments) of fibroblasts (αSMA), mesothelial cells (MSLN), in normal peritoneal tissue by mIHC. Scale bars, 20 μm. d, Spatial distribution(representative images of n = 12 experiments) of fibroblasts (αSMA), mesothelial cells (MSLN), in PMs by mIHC. Scale bars, 20 μm. Images are representative of n = 12 experiments e, Metabolic reaction activities related to glycolysis and fatty acid oxidation were compared in mesothelial cells using the Compass software between normal peritoneal and PM tissues (left). The metabolic reaction activities related to glycolysis and fatty acid oxidation were compared in MET-5A cells between control and after co-cultured with RKO cells using LC/MS metabolomics. p values were calculated using unpaired two-sides Wilcoxon test. f, Representative images of the tumor load as examined by luminescence at 3 weeks in an in vivo model by intraperitoneal injection. g, Flowchart of the mouse drug administration experiment. h, The Western blot results demonstrate the successful knockout of MSLN in MC38 cell line.(n = 3 biological replicate) i, No differences of the weight of colorectal tumors was found between the anti-MSLN group and control group(biological replicate=3). ns, not significantly, unpaired two-sides t-test. Data are presented as mean values +/- SD. j, The peritoneal weight in the anti-MSLN group was significant decreased compared to control group(biological replicate=3); unpaired two-sides t-test. Data are presented as mean values +/- SD. k, The peritoneal metastasis node size in the anti-MSLN group was significant decreased compared to control group (n = 3 biological replicate). ns, not significantly; unpaired two-sides t-test. Data are presented as mean values +/- SD. l, Representative photos of colonic and peritoneal tissue in the anti-MSLN and control groups. The minimum unit of the ruler is millimeter.

Source data

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Source Data Fig. 6d,e and Extended Data Fig. 6h

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Li, Q., Xiao, Y., Han, L. et al. Microbiome dysbiosis, neutrophil recruitment and mesenchymal transition of mesothelial cells promotes peritoneal metastasis of colorectal cancer. Nat Cancer 6, 493–510 (2025). https://doi.org/10.1038/s43018-025-00910-9

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