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THY1+ cancer stem cells drive metastasis through a pseudohypoxic state shaped by neutrophil-derived mitochondria

Nature Cell Biology volume 28, pages 596–607 (2026)Cite this article

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Abstract

Whether a distinct subset of cancer stem cells (CSCs) is exclusively responsible for metastasis and how this process occurs remain unresolved. Through multi-omics, pan-cancer analysis and multiple tumour-bearing models, we identify THY1⁺ CSCs as the key drivers of metastasis and uncover a previously unrecognized ‘pseudohypoxic’ state (independent of classical hypoxia) as a central regulatory factor. The self-renewal of THY1⁺ CSCs is maintained by IL-6–MYC signalling. Upon encountering neutrophils, THY1⁺ CSCs activate the THY1–Mac1 axis, triggering the Src–Akt/Erk pathway, Rac1 activation and a migrasome-dependent process that induces neutrophils to expel reactive oxygen species-enriched damaged mitochondria. THY1 signalling further enhances macropinocytosis, enabling CSCs to internalize these mitochondria and adopt a pseudohypoxic state, thereby facilitating CSC metastasis. Notably, targeting the IL-6–Myc, THY1–Mac1 or Src–Akt/Erk signalling pathways effectively suppresses pseudohypoxia-driven CSC metastasis. These findings unveil previously unexplored mechanisms by which CSCs undergo metastasis, offering potential strategies to combat tumour metastasis and improve cancer prognosis.

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Fig. 1: THY1 identifies a metastatic subset of cancer stem cells.
Fig. 2: IL-6–Myc signalling regulates the self-renewal of THY1+ CSCs.
Fig. 3: THY1+ CSCs adopt neutrophil-dependent pseudohypoxia to initiate metastasis.
Fig. 4: Neutrophils extrude ROS-enriched mitochondria by THY1 triggering.
Fig. 5: Migrasome assembles ROS-rich mitochondria upon THY1 signals in neutrophils.
Fig. 6: Interfering THY1-elicited macropinocytosis abolished pseudohypoxia-induced metastasis.

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Data availability

All the expression data that support the findings of this study can be obtained from the Gene Expression Omnibus, Mendeley Data or NGDC (GSA for human), and the selected studies are listed below. The RNA sequencing dataset presented in Fig. 3f and Extended Data Fig. 4f, g has been deposited in the Genome Sequence Archive under the accession numer CRA036043. Bulk RNA-seq from the TCGA dataset were obtained from Genomic Data Commons at https://portal.gdc.cancer.gov/. Previously published scRNA-seq data reanalysed here are available under accession codes (1) pan-HCC scRNA-seq data, GSE149614 (ref. 62), PRJCA007744 (ref. 63) and skrx2fz79n (ref. 51); (2) pan-cancer CSC analysis, PRJCA007744 (HCC and intrahepatic cholangiocarcinoma) (ref. 63), GSE131907 (lung cancer) (ref. 64), GSE132465 (colon cancer) (ref. 65), E-MTAB-6149 (lung cancer) (ref. 66), E-MTAB-6653 (lung cancer) (ref. 66), GSE267718 (bladder cancer) (ref. 67), GSE176078 (breast cancer) (ref. 68), GSE154778 (pancreatic cancer) (ref. 69), GSE167297 (gastric cancer) (ref. 70), GSE188711 (colon cancer) (ref. 71) and GSE139829 (melanoma) (ref. 72); (3) pan-cancer neutrophil analysis, PRJCA007744 (HCC and intrahepatic cholangiocarcinoma) (ref. 63), GSE267718 (bladder cancer) (ref. 67), GSE171145 (ref. 73)/GSE127465 (ref. 74) (lung cancer), OEP003254 (pancreatic cancer) (ref. 75) and PRJCA020880 (gastric cancer and colon cancer) (ref. 57); (4) spatial transcriptome data, HRA000437 (ref. 52) and skrx2fz79n (Mendeley Data) (ref. 51); (5) pan-cancer analysis of primary and matched metastatic lesions, GSE149614 (HCC) (ref. 62), GSE197177 (ref. 76)/GSE263733 (ref. 77) (pancreatic cancer), GSE225857 (colon cancer) (ref. 78) and GSE131907 (lung cancer) (ref. 64). All data reported in this paper will be shared by the lead contact upon request. The detailed information of publicly available datasets used in this study is listed in Supplementary Table 1. Source data are provided with this paper.

Code availability

All analysis and figures were generated using publicly available software packages. No custom code was used in this study.

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (82341014, 32530037, U25C2022 and 82025016) and the Natural Science Foundation of Guangdong Province, China (2023A1515012466 and 2024A1515010549) awarded to D.-M.K., and by grants from the National Natural Science Foundation of China (82322051 and 82271773) awarded to Y.W.

Author information

Author notes

  1. These authors contributed equally: Wen-Hua Wan, Pei-Lin Li, Wen-Jie Cao.

  2. These authors jointly supervised this work: Xiang-Ming Lao, Yuan Wei, Dong-Ming Kuang.

Authors and Affiliations

  1. Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, China

    Wen-Hua Wan, Pei-Lin Li, Wen-Jie Cao, Zheng-Xi Li, Yu-Qi Xin, Limin Zheng, Yuan Wei & Dong-Ming Kuang

  2. State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China

    Jun-Cheng Wang, Muyan Cai & Xiang-Ming Lao

  3. The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China

    Lei Chen

  4. Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

    Lianxin Liu

  5. Sun Yat-sen University Institute of Advanced Studies Hong Kong, Hong Kong, China

    Dong-Ming Kuang

  6. School of Future Biomedical Sciences, Sun Yat-sen University, Guangzhou, China

    Dong-Ming Kuang

  7. Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China

    Dong-Ming Kuang

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Contributions

D.-M.K., Y.W. and X.-M.L. conceived the project. W.-H.W. and P.-L.L. designed experiments. W.-H.W., P.-L.L., W.-J.C., Z.-X.L. and Y.-Q.X. performed most of the experiments and analysed the results. M.C., L.Z. and J.-C.W. provided clinical samples and analysed the related clinical data. L.C. and L.L. made intellectual contributions. D.-M.K., Y.W. and X.-M.L. contributed to study design, supervised the study and contributed to writing the paper.

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Correspondence to Xiang-Ming Lao, Yuan Wei or Dong-Ming Kuang.

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Extended data

Extended Data Fig. 1 Identification of THY1+ cells as a metastatic-related cancer stem cells (CSCs) in HCC.

a–f, Annotations of CSCs in human HCC scRNA datasets. Schematic representation of a modified stemness-related signature approach to derive CSCs from HCC scRNA datasets (a). 3D co-embedding of stemness-related signature and cell entropy score in CSCs and non-CSCs (b). The reliability of the approach to derive CSCs was examined by CytoTRACE and cell entropy score (c). Heatmap displaying marker gene expression across CSC clusters. The top two marker genes of each CSC cluster were listed (d). Proliferation, self-renewal, multipotency, drug resistance, and anti-apoptotic score of CSC clusters (e). Canonical cancer stem cell surface marker expression in defined CSCs (f). g, Univariate (left) and multivariate (right) regression analyses of factors associated with patients’ recurrence in HCC (n = 86 patients). Points represent hazard ratios; error bars indicate 95% confidence intervals. h, Association of THY1⁺ CSC infiltration with patients’ survival utilizing TCGA datasets across nine cancer types. Statistical significance was determined using Cox regression analyses with two-sided tests (g), no adjustment for multiple comparisons was applied, as the analyses were predefined and hypothesis-driven, Kaplan–Meier analysis with log-rank test (h), or unpaired two-tailed Student’s t-test (c). Violin plots show data distribution with median and interquartile range (c). Panel a created with BioRender.com. AFP, α-fetoprotein; CI, confidence interval; HR, hazard ratio.

Source data

Extended Data Fig. 2 THY1 signalling regulates the metastasis of CSCs.

a, Percentage of THY1+ cells in Hepa1-6 and H22 cells was analysed by flow cytometry (n = 5). b, Gating strategies for FACS of THY1+ and EpCAM+ CSCs from tumour cells. c,d, Extreme limiting dilution analysis of unsorted, THY1+, or EpCAM+ Hepa1-6 cells (n = 5). e, Survival curves of mice bearing tumours from unsorted, THY1⁺, or EpCAM⁺ Hepa1-6 cells (n = 8). f, Migration potential of unsorted, THY1+, EpCAM+ Hepa1-6 cultured in vitro (n = 5; scale bar, 60 µm). g,h, Indicated B16 cells were intradermally injected at the right flank of C57BL/6 mice. Lung metastases were assessed (n = 5; scale bar, 250 µm). i, Indicated 4T1 cells were inoculated in mammary fat pads of BALB/c mice. Lung metastases were assessed (n = 5; scale bar, 250 µm). j, Indicated CT26 cells were intradermally injected at the right flank of BALB/c mice. Lung metastases were assessed (n = 5; scale bar, 250 µm). k, Efficiency of Thy1-Knockout (sgThy1) in Hepa1-6 cells (n = 5). l, Effects of the αTHY1 antibody on lung metastasis in mice bearing THY1⁺ Hepa1-6 hepatomas (n = 5; scale bar, 250 µm). m, Effects of sgThy1 on survival in mice bearing THY1⁺ Hepa1-6 hepatomas (n = 8). n,o, WT or Epcam-overexpressing (Epcam-OE) THY1+ Hepa1-6 cells were inoculated into mouse livers. Efficiency of Epcam-OE in Hepa1-6 cells (n; n = 5). Lung metastases were analysed (o; n = 5). Data are from three (d,e,m) or five (k,n) independent experiments. Data are presented as mean ± s.e.m. of three (h–j,l,o) or five (a,f) independent experiments. Statistical significance was determined using two-sided likelihood ratio tests (d), one-way ANOVA with Tukey’s post hoc test (f,h–j), unpaired two-tailed Student’s t-test (l,o), or the log-rank (Mantel–Cox) test (e,m). Panel b,c,g,i,j,l,o created with BioRender.com.

Source data

Extended Data Fig. 3 IL-6-Myc signalling is crucial for the self-renewal of THY1+ CSCs.

a, FACS-sorted THY1− human and murine hepatoma cell lines were cultured for different durations in vitro. Percentage of THY1+ cells regenerated from THY1− cells over time (n = 5). b,c, Efficiency of shNANOG, shMYC (b) or MYC-OE (c) in Huh-7 cells (each n = 5). d, Percentage of THY1+ cells generated from unsorted, wild-type THY1− (cultured with control medium), or MYC-OE THY1− (cultured with doxycycline) Huh-7 cells over time (n = 5). The induced group was compared with their corresponding uninduced group. e, GSEA of NANOG or c-Myc signatures (right: M5926) on pseudotime-ordered developmental trajectory from CSC.c4 to CSC.c3 populations. f, Schematic illustration of c-Myc binding sites in the THY1 promoter region. TSS, transcription start site. g, Sequence analysis identifying c-Myc binding sites within the promoter regions of the THY1 gene. h, Schematic diagram illustrating the truncated 5′-flanking regions of the THY1 gene. i,j, Effect of signalling inhibitors treatment (i) or shIL6R (j) on c-Myc expression in Huh-7 cells (each n = 5). k, Effects of IL-6 family cytokines on c-Myc expression in Huh-7 cells. (n = 5). l–n, Wild-type or Il6ra-knockdown EpCAM+ Hepa1-6 cells were inoculated into mouse livers. The efficiency of Il6ra-knockdown was examined (l; n = 5). Gating strategy for FACS analysis of GFP+ tumour cells was shown (m). Tumour volume was measured (n; n = 5; scale bar, 1cm). o, GSEA of c-Myc signatures (left: M5926) or IL-6–STAT3 pathway (right: M5897) in THY1+ versus THY1− CSCs from pan-cancer scRNA datasets. Data are from five (b,c,i,j,k) independent experiments. Data are presented as mean ± s.e.m. of three (l,n) or five (a,d) independent experiments. Statistical significance was determined using two-way ANOVA with Tukey’s post hoc test (d), a one-sided, permutation-based test (e,o), or unpaired two-tailed Student’s t-test (l,n).

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Extended Data Fig. 4 Pseudohypoxia drives the metastatic potential of THY1+ CSCs.

a, Schematic overview of datasets used in pan-cancer scRNA analysis. b–d, Metastasis score (b) and hypoxia signature score (c) of THY1+ CSCs in pan-cancer scRNA datasets. The correlation of average metastasis score and hypoxia signature score across different cancer types (d). e, Distribution of hypoxic area (stained by hypoxyprobe) and HIF1α+ cells in tumours of Thy1-OE Hepa1-6 hepatoma-bearing mice (n = 11; scale bar, 75 µm). The numbers of HIF1α+ cells in normoxic and hypoxic areas were calculated. f,g, Venn diagram showing genes related to pseudohypoxia in Hepa1-6 hepatoma, defined as those upregulated in Thy1-OE versus WT mouse hepatoma, as well as in Hif1a-competent versus Hif1a-knockdown Thy1-OE groups (f). Cancer hallmark enrichment analysis of these genes was performed (g) using bulk RNA-seq data (n = 3; CRA036043). The red line indicates adjusted P < 0.05. h, Comparative analysis of metastasis-related gene expression profiles among THY1− CSCs, HIF-signaturehigh THY1+ CSCs, and HIF-signaturelow THY1+ CSCs in human HCC. i,j, Expression of hypoxia-related proteins (i) and EMT-related proteins (j) of WT and Thy1-OE Hepa1-6 cells was analysed (each n = 5). k, Migration potential of WT and Thy1-OE Hepa1-6 cells cultured in vitro was estimated (n = 5; scale bar, 60 µm). l, Effects of Thy1-OE on survival in mice bearing Hepa1-6 hepatomas (n = 8). Data are from three (f,g) or five (i,j) independent experiments. Data are presented as mean ±  s.e.m. of three (e,l) or five (k) independent experiments. Statistical significance was determined using linear regression with two-sided t-tests for regression coefficients (d), over-representation analysis based on the hypergeometric test, with false discovery rate correction applied for multiple comparisons (g,h), unpaired two-tailed Student’s t-test (e,k) or the log-rank (Mantel–Cox) test (l). Violin plots show data distribution with median and interquartile range (b,c). Panel a created with BioRender.com. ERI, enabling replicative immortality; REM, reprogramming energy metabolism; RCD, resisting cell death; TPI, tumour-promoting inflammation; TIM, tissue invasion and metastasis; IA, inducing angiogenesis; AID, avoiding immune destruction; EGS, evading growth suppressors; GI, genome instability and mutation; SPS, sustaining proliferative signalling.

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Extended Data Fig. 5 Neutrophil-triggered pseudohypoxia is required for the metastasis property of THY1+ CSCs.

a, GO enrichment analysis of notably upregulated genes (log2FC > 1, p.adj < 0.01) in HIF-signaturelow THY1+ CSC versus HIF-signaturelow THY1− tumour cell-related regions. b, Heatmap of neutrophil infiltration indices in TCGA cancer tissues with low or high THY1+ CSC hypoxia signature score, calculated by GSVA using specific markers (log2FC > 1, min.pct > 0.2) defining THY1⁺ CSC-related hypoxic regions in the spatial transcriptome analysis. c, Efficiency of THY1-knockdown (shTHY1) or overexpression (THY1-OE) in Huh-7 cells (n = 5). d, Efficiency of HIF1A-GFP-overexpression (HIF1α-GFP) in Huh-7 cells (n = 3; scale bar, 10 µm), wild-type GFP was used as control. e, Metastasis score of THY1+ CSCs in patients with HCC with high versus low neutrophil infiltration (n = 94 patients). f, Gating strategies for FACS of neutrophils from mouse hepatoma tumours. g, Depleting efficiency of αLy6G antibody on neutrophils in mouse hepatoma model (n = 5). Data are from three (d) or five (c) independent experiments. Data are presented as mean ± s.e.m. of three (g) independent experiments. Statistical significance was determined using over-representation analysis based on the hypergeometric test, with false discovery rate correction applied for multiple comparisons (a), the Mann–Whitney U test (b,e), with false discovery rate correction applied for multiple comparisons (b), or two-way ANOVA with Tukey’s post hoc test (g). Violin plots show data distribution with median and interquartile range (e).

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Extended Data Fig. 6 THY1 signalling promote ROS-enriched mitochondria extrusion by neutrophils.

a, THY1+ Huh-7 cells were left untreated, co-cultured with neutrophils directly or in a transwell system. HIF1α protein level was analysed (n = 5). b,c, THY1+ Huh-7 cells were left untreated, treated with Co-CM or THY1-CM in the absence (b,c) or presence (c) of the proteasome inhibitor MG132. mRNA (b) and protein (c) level of HIF1α were analysed (each n = 5). d, Schematic overview of achieving different Co-CM fractions. e, Effects of blocking antibodies on mitochondrial proteins in the 18,000g pellet from Co-CM derived from cocultures of THY1+ Huh-7 cells and neutrophils (n = 5). f, Effects of THY1-Fc on mitochondrial proteins in the 18,000g pellet from neutrophil-conditioned medium (n = 5). g,h, Mitochondrial content (g) and mitochondrial membrane potential (h) in untreated or THY1-Fc-treated neutrophils were analysed (each n = 5). i, Schematic representation of isolating neutrophil mitochondria. j, Intracellular mitochondria were extracted from fresh, 12-hour-cultured untreated or THY1-Fc-treated neutrophils. Mitochondrial membrane potential of these mitochondria was analysed (n = 5). k, Selection of stable mitochondrial content indicator genes for Fig. 4n, o. The selected genes are highlighted in red, with variation coefficient less than 1.0 in both blood and tumour samples from patients with HCC. l, Gating strategies for FACS of neutrophils from HCC tumours. m, Mitochondrial content in neutrophils from HCC tumour and paired blood was analysed in the original HCC cohort by flow cytometry (n = 17 patients). Data are from five (a,c,e,f,j) independent experiments. Data are presented as mean ± s.e.m. of five independent experiments (b,g,h,m). Statistical significance was determined using unpaired two-tailed Student’s t-test (g,h), paired two-tailed Student’s t-test (m), or one-way ANOVA with Tukey’s post hoc test (b). Panels d,i and l created with BioRender.com.

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Extended Data Fig. 7 THY1 signalling facilitate neutrophil unhealthy mitochondria extrusion via migrasome.

a, Schematic representation of the protocol used for MEP sorting. Mitochondrial proteins from each indicated layer were analysed by immunoblotting (n = 5). b, Schematic overview of the isolation of different THY1-CM fractions. Marker proteins for migrasomes, exosomes, and apoptotic bodies in the purified THY1-CM fractions obtained from the same centrifugation process were analysed by immunoblotting. Samples were normalized to total protein concentration determined by the bicinchoninic acid (BCA) assay (n = 5). c, Time-lapse imaging of untreated neutrophils. The white dashed line outlined the cells. (n = 3; scale bar, 5 µm). The cell membrane was labelled with DIL. d, Membrane potential in excreted and intracellular mitochondria from untreated or THY1-Fc -treated neutrophils was analysed using Mitotracker Deep Red staining. (n = 3; scale bar, 5 µm). Representative images of THY1-Fc-treated neutrophils were shown. CFSE staining was employed to outline the cells. e, Lysosome-, endoplasmic reticulum-, and Golgi-related proteins in migrasomes from THY1-Fc-treated neutrophils were analysed (n = 5). f, Dot plot of ITGAM expression in each cell type, with dot size representing the fraction of expressing cells and colour indicating mean expression intensity, based on a publicly available scRNA dataset (skrx2fz79n). g, Monocytes were left untreated or treated with THY1-Fc. Migrasomal and mitochondrial proteins in MEP derived from CM were determined (n = 5). Data are from five (a,b,e,g) independent experiments. Data are presented as mean ± s.e.m. of over 100 cells (c,d) across three independent experiments. Statistical significance was determined using unpaired two-tailed Student’s t-test (d) or the two-sided Wilcoxon rank-sum test, and false discovery rate correction was applied for multiple comparisons across cell types (f). Violin plots show data distribution with median and interquartile range (d). Panel b created with BioRender.com.

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Extended Data Fig. 8 THY1 signalling enhanced macropinocytosis to initiate tumour pseudohypoxia.

a, Three-dimensional reconstruction and positional mapping of TOM20-GFP-loaded migrasomes in a THY1⁺ Huh-7 cell (n = 30 cells from three independent experiments; Scale bar, 10 µm). The cell membrane was labelled with DIL. b, Macropinocytosis of dextran in Carmil1 wild-type (Carmil1-WT) and Carmil1 mutant (Carmil1-AA) Thy1-overexpressing (Thy1-OE) Hepa1-6 cells (n = 3; scale bar, 10 µm). c, Carmil1-WT and Carmil1-AA Thy1-OE Hepa1-6 cells were inoculated into mice livers. The total number of metastatic foci of 30 lung sections were assessed (n = 5; scale bar, 250 µm). d, GSEA of the macropinocytosis signature in THY1⁺ CSCs versus THY1⁻ cancer cells in HCC single-cell transcriptomic datasets. e, Uptake of migrasomes from TOM20-GFP–expressing HL60 cells by THY1-overexpressing Huh-7 cells in the presence of recombinant Mac1-Fc protein (n = 3; scale bar, 10 µm). f, Effect of THY1-overexpression (THY1-OE) on signalling pathway activation in Huh-7 cells (n = 5). g,h, Effects of pathway inhibitors on migrasome uptake (g; n = 3) and HIF1α expression (h; n = 5) in THY1⁺ Huh-7 cells. i, Specificity of TthIII digestion on C57BL/6-specific mitochondrial DNA fraction was assessed (n = 5). j, UMAP visualization of the cell clusters, patient annotations, and NMT states of tumour cell from 11 patients with HCC with marked NMT. Data are from three (a) or five (f,h,i) independent experiments. Data are presented as mean ± s.e.m. of five mice (c) or over 100 cells (b,e,g) across three independent experiments. Statistical significance was determined using unpaired two-tailed Student’s t-test (b,c,e), a one-sided, permutation-based test (d), one-way ANOVA with Tukey’s post hoc test (g). Violin plots show data distribution with median and interquartile range (b,e,g). Panel i created with BioRender.com.

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Supplementary information

Reporting Summary (download PDF )

Supplementary Table 1 (download XLSX )

Information on pan-cancer samples from publicly available datasets.

Supplementary Table 2 (download XLSX )

Clinical characteristics of the 135 patients with HCC.

Supplementary Table 3 (download XLSX )

Clinical characteristics of 22 patients with untreated HCC with paired PT and PVTT tissues.

Supplementary Table 4 (download XLSX )

Clinical characteristics of 86 patients with untreated HCC.

Supplementary Table 5 (download XLSX )

Clinical characteristics of ten patients with untreated HCC with fresh blood.

Supplementary Table 6 (download XLSX )

List of CSC transcription factors and ChIP-seq analysis of transcription factors binding the THY1 gene.

Supplementary Table 7 (download XLSX )

Clinical characteristics of 17 patients with untreated HCC with fresh tissues and blood.

Supplementary Table 8 (download XLSX )

Summary of cell lines.

Supplementary Table 9 (download XLSX )

Summary of primers.

Supplementary Table 10 (download XLSX )

Summary of plasmids.

Supplementary Table 11 (download XLSX )

Summary of shRNA, guideRNA.

Supplementary Table 12 (download XLSX )

Summary of antibodies.

Supplementary Table 13 (download XLSX )

Summary of critical kits.

Supplementary Table 14 (download XLSX )

Gene sets used in GSEA and GSVA.

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Wan, WH., Li, PL., Cao, WJ. et al. THY1+ cancer stem cells drive metastasis through a pseudohypoxic state shaped by neutrophil-derived mitochondria. Nat Cell Biol 28, 596–607 (2026). https://doi.org/10.1038/s41556-026-01876-1

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