Intestinal Lachnospiraceae bacterium-derived propionate inhibits the progression of clear cell renal cell carcinoma

Jun-Yao Xu; Huan Chen; Yao-Yu Yu; Tian-Yi Chen; Yi-Qiu Wang; Wei Xue; Jun-Hua Zheng; Jin-Yao Liu; Wei Zhai

doi:10.1016/j.xcrm.2025.102410

. 2025 Oct 8;6(10):102410. doi: 10.1016/j.xcrm.2025.102410

Intestinal Lachnospiraceae bacterium-derived propionate inhibits the progression of clear cell renal cell carcinoma

Jun-Yao Xu ^1,^3,⁷, Huan Chen ^2,^4,^6,⁷, Yao-Yu Yu ^1,⁷, Tian-Yi Chen ^1,⁷, Yi-Qiu Wang ¹, Wei Xue ¹, Jun-Hua Zheng ^1,^∗, Jin-Yao Liu ^2,^4,^∗∗, Wei Zhai ^1,^5,^8,^∗∗∗

PMCID: PMC12629829 PMID: 41067230

Summary

Gut microbiota has been reported to be associated with the development of various diseases; however, its interaction with clear cell renal cell carcinoma (ccRCC) remains unknown. To investigate the potential relationship between gut microbiota alterations and ccRCC development, we analyze feces from healthy volunteers and ccRCC patients. We realize that ccRCC patients have a lower abundance of Lachnospiraceae bacterium (L. bacterium). Further experiments reveal that L. bacterium and its metabolite, propionate, exert the antitumor effects. Mechanistically, L. bacterium-derived propionate inhibits tumor cell proliferation and migration by downregulating the expression of homeobox D10 (HOXD10) and its downstream interferon-induced transmembrane protein 1 (IFITM1) and then activating JAK1-STAT1/2 pathway. Furthermore, we design a biofilm-coated L. bacterium as a potential probiotic to improve oral delivery and therapeutic efficacy. Finally, the expanded validation cohort confirms that measuring and targeting L. bacterium and its associated pathways will provide valuable insights into clinical management and improve the prognosis of patients with ccRCC.

Keywords: clear cell renal cell carcinoma, microbiome, tumor progression, Lachnospiraceae bacterium, propionate, HOXD10, IFITM1, biofilm-coated

Graphical abstract

Highlights

•
L. bacterium is reduced in patients with clear cell renal cell carcinoma
•
L. bacterium-derived propionate inhibits RCC cell proliferation and migration
•
Propionate suppresses HOXD10-IFITM1 axis and activates JAK1-STAT1/2 signaling
•
Biofilm-coated L. bacterium enhances oral delivery and therapeutic efficacy

Xu et al. report that L. bacterium-derived propionate suppresses clear cell renal cell carcinoma progression by inhibiting the HOXD10-IFITM1 axis and activating JAK-STAT signaling. These findings highlight a microbiota-metabolite-tumor axis in ccRCC and propose a biofilm-based probiotic strategy to enhance therapeutic efficacy.

Introduction

Renal cell carcinoma (RCC), is a frequent malignancy of the urinary system, the specific etiology of which remains to be explored.¹ Clear cell renal cell carcinoma (ccRCC), the most common pathologic type of RCC, accounts for 70% of cases.² Accumulating evidence is currently in favor of the influence of the gut microbiome, as a crucial element of the human body, in oncogenesis³^,⁴ and response to therapy.⁵^,⁶^,⁷ The relationship between the microbiome and the formation of ccRCC is still uncertain, although it has been indicated in the literature that certain gut microbiota and their related active products have relevance to the therapy of ccRCC.⁸^,⁹^,¹⁰ Therefore, it is vital to elucidate the potential effect of the gut microbiome on tumor progression in ccRCC.

Among them, the Lachnospiraceae are abundant as a potential probiotic in the unperturbed adult gut microbiota.¹¹^,¹² As a family of anaerobic bacteria in the Clostridiales order within the Firmicutes phylum, the Lachnospiraceae also include species previously identified as Clostridium cluster XIVa.¹³ The abundance of Lachnospiraceae can be altered by changes in diet.¹⁴ Due to the loss of the multiple beneficial functions performed by members of this family, a decrease in Lachnospiraceae abundance is expected to have adverse health effects.¹² The Lachnospiraceae have been reported to be able to correlate with a variety of tumors through their metabolites, short-chain fatty acids (SCFAs) such as propionate.¹⁵ However, until now, the interaction between Lachnospiraceae metabolites and oncogenesis and the underlying mechanism has not been clarified in ccRCC.

In this study, we performed an integrative analysis of microbiome and metabolomic data from a cohort containing healthy volunteers (HVs) and ccRCC patients and established that a low abundance of Lachnospiraceae bacterium (L. bacterium) in the intestinal tract of ccRCC patients was associated with its development. Further in vitro and in vivo investigations confirmed the hypothesis that L. bacterium, and its metabolite propionate, can inhibit tumor growth and migration. Regarding the underlying mechanism, L. bacterium-derived propionate reduced the expression of interferon-induced transmembrane protein 1 (IFITM1) by downregulating the level of the transcription factor homeobox D10 (HOXD10) in tumor cells, which activated the JAK1-STAT1/2 pathway. Furthermore, to precisely regulate the intestinal microbes, a biofilm encapsulated engineered bacterium was designed in this study to resist the harsh environment of the gastrointestinal tract and to increase its adherence capacity in the intestinal tract in order to enhance the colonization of L. bacterium in the intestine for enhanced oral delivery and treatment.

Results

ccRCC-associated gut dysbiosis correlates with its progression

To explore the link between gut microbiota and ccRCC development, preoperative stool samples were collected from three patients with ccRCC and three age- and sex-matched HVs at Shanghai Renji Hospital (cohort 1, n = 6; Table S1). To assess the impact of gut microbiota on tumor progression, mice were treated with a broad-spectrum antibiotic (ATB) cocktail to deplete endogenous gut microbes, followed by fecal microbiota transplantation (FMT) from HVs or ccRCC donors.⁸ These “humanized” mice were then subcutaneously inoculated with RENCA cells (Figure 1A). Tumor growth, volume, and weight were significantly reduced in mice receiving HV microbiota compared to those receiving ccRCC microbiota (Figures 1B–1D). Additionally, reduced Ki67 expression indicated lower intra-tumoral cell proliferation in the HV group (Figure 1E). Among individual groups, ccRCC1–3 mice showed faster tumor growth, while HV1–3 mice exhibited markedly slower progression (Figure 1F).

ccRCC-associated gut dysbiosis correlates with tumor progression

(A) Experimental workflow: BALB/c mice were treated with ATB to deplete gut microbiota, followed by 14-day FMT from HVs or ccRCC patients, and subcutaneous inoculation with RENCA cells.

(B) Tumor growth kinetics in mice receiving microbiota from HVs or ccRCC patients (n = 6/group).

(C and D) Representative images of excised tumors (C) and tumor weights (D) on day 24 (n = 6/group).

(E) Representative Ki67 immunohistochemistry of tumors from the two groups. Scale bars: 100 μm (overview) and 50 μm (magnified region).

(F) Individual tumor growth curves in mice transplanted with microbiota from three HVs (HV1–3) or ccRCC (ccRCC1–3) donors.

(G) Shannon index of gut microbial diversity in cohort 2 comparing HV and ccRCC.

(H) PCoA based on Bray-Curtis distance of fecal microbiota from HV and ccRCC in cohort 2.

(I and J) Relative abundance of bacterial orders (I) and families (J) in fecal samples from HV and ccRCC in cohort 2.

(K) Shannon index comparing gut microbial diversity between ARCC and LRCC in cohort 2.

(L) Relative abundance of bacterial families in fecal samples from ARCC and LRCC.

(M) LDA with effect size analysis identifying differentially enriched taxa between HV and ccRCC. Only taxa with LDA score >3 are shown.

(N) Relative abundance of *Lachnospiraceae bacterium* in fecal samples from HV and ccRCC in cohort 2.

(O) ROC analysis based on the fecal abundance of *L. bacterium* distinguishing HVs from ccRCC patients.

Data are presented as mean ± SEM. Statistical significance was determined by two-way ANOVA (B), unpaired two-tailed t test (D), one-way ANOVA (E), and Mann-Whitney test (G and N). ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001.

See also Figure S1, and Tables S1 and S2.

To further verify how the disordered gut microbes could affect ccRCC progression, we reanalyzed 16s rRNA gene sequencing results from stool samples in a larger cohort, cohort 2 (n = 107) (Table S2). Through sequencing, we found that there was a significant difference in the α-diversity of gut microbiota between the two groups (Figure 1G; Figure S1A); meanwhile, the principal coordinate analysis (PCoA) showed a significant change in gut microbiota between ccRCC patients and HVs (Figure 1H). At the different levels (Figures 1I and 1J; Figure S1B), both in the ccRCC group and the HV group, Bacteroidaceae in Bacteroidales and Lachnospiraceae in Lachnospirales are important components of the gut microbiota. Meanwhile, we further analyzed the sequencing results from 51 patients with localized RCC (LRCC) and 16 patients with advanced RCC (ARCC) group. Interestingly, despite the absence of significant changes in the α-diversity of gut microbiota between the two groups (Figures S1C and S1D), we discovered that the gut microbiota differed between the two in composition (Figure 1K); meanwhile, Bacteroidaceae in Bacteroidales and Lachnospiraceae in Lachnospirales are also important components of the gut microbiota in both the ARCC and LRCC groups (Figures 1L, S1E, and S1F).

To identify gut microbiota differences between ccRCC patients and HVs, the linear discriminant analysis effect size (LEfSe) analysis was performed,¹⁶ revealing reduced abundance of Prevotellaceae, Lachnospiraceae, and L. bacterium in ccRCC patients (Figures 1M and S1G). Further analysis confirmed significantly lower levels of Lachnospiraceae in ccRCC vs. HV and in ARCC vs. LRCC (Figure S1H), while Prevotellaceae showed no significant difference (Figure S1I). L. bacterium abundance was also markedly reduced in ccRCC compared to HVs (Figure 1N). Subsequently, receiver operating characteristic (ROC) curve analysis showed strong predictive value of these taxa for ccRCC: area under the curves were 0.8606 for L. bacterium (Figure 1O), 0.7780 for Lachnospiraceae (Figure S1J), and 0.6319 for Prevotellaceae (Figure S1K), indicating a strong correlation between Lachnospiraceae, particularly L. bacterium, and ccRCC progression.

Additionally, to further confirm the correlation between differential microbiota and the progression of ccRCC, we analyzed the gut microbiota distribution in patients from cohort 1. The results showed that the relative abundances of Lachnospiraceae and L. bacterium were significantly reduced in ccRCC patients in cohort 1 (Figures S1L and S1M). Moreover, 16S rRNA gene sequencing of fecal samples from FMT-treated mice also indicated a significant decrease in the relative abundance of L. bacterium in the gut of mouse recipients of ccRCC patients’ microbiota (Figure S1N).

In conclusion, compared to gut microbiota from ccRCC, gut microbiota from HVs had an effect in slowing the progression of ccRCC. This suggests the possibility of the presence of effector bacteria, such as L. bacterium in the intestine that may play a dominant role in inhibiting the progression of ccRCC.

L. bacterium protects against ccRCC tumorigenesis through its supernatant

To further verify the role of specific gut bacteria in ccRCC development, RENCA cells were subcutaneously implanted into mice pretreated with ATB to deplete gut microbiota. Mice then received oral PBS (Ctrl), L. bacterium, or Prevotella copri (P. copri) for 2 weeks, followed by tumor growth assessment (Figure 2A). Compared to Ctrl, L. bacterium significantly inhibited tumor growth, whereas P. copri showed no effect (Figures 2B and 2C; Figure S2A). 16S rRNA sequencing of feces from day 12 and day 24 (12 and 24 days after administered with L. bacterium) post-L. bacterium gavage showed a time-dependent increase in its abundance (Figure 2D), along with altered β-diversity (Figure S2B) and gut microbiota composition, including increased commensals such as Lactobacillales (Figure S2C). To test whether eliminating L. bacterium affects tumor suppression, mice were treated with amoxicillin, to which Lachnospiraceae is sensitive (Figure 2E). Notably, amoxicillin reversed the tumor-inhibitory effect of L. bacterium (Figures 2F and 2G; Figure S2D).

To explore whether such antitumor response was attributed to L. bacterium itself or its secreted products, ccRCC cell line 786-O and Caki-1 were co-cultured with L. bacterium and its medium supernatant (SN) in vitro. Firstly, the colony formation assay results revealed that compared with the blank control (PBS and peptone yeast glucose (PYG) modified medium), co-culture with L. bacterium SN (pre-filtered without viable bacteria) had the ability to reduce clone formation (Figure 2H). Secondly, as shown in Figures 2I and S2E, cell viability analysis verified that L. bacterium SN possessed the capacity to inhibit ccRCC cell proliferation. Additionally, this cell-migration effect was further confirmed by the wound healing assay. In conclusion, these results suggested that L. bacterium SN played a role in inhibiting tumor migration (Figures 2J and S2F). The results also implied that the higher the concentration, the stronger the inhibitory effect. Contrarily, tumor suppression effects were not observed after co-culture with PYG medium (Figure S3A) and P. copri SN (Figures S3B–S3D). Furthermore, we observed that different multiplicity of infection (MOI) of L. bacterium (MOI = 10 and 100) had no significant effect on ccRCC cell proliferation and migration through co-culture with 786-O and Caki-1 for 4 h (Figures S3E–S3J).¹⁶ Simultaneously, different MOIs of P. copri had no significant effect on ccRCC cell proliferation and migration (Figures S3K and S3L).

Therefore, we hypothesized that L. bacterium-derived metabolites may mediate its antitumor effects. To test this, mice pretreated with antibiotics were orally gavaged with live L. bacterium, its sterile supernatant (SN), or heat-inactivated bacteria (Figure 2K). Both live bacteria and SN significantly suppressed tumor growth compared to PBS control, whereas heat-inactivated bacteria had no effect (Figures 2L and S2G–S2H). To rule out immune involvement, experiments in nude mice yielded consistent results (Figures 2M and S2I–S2K). No significant differences in body weight were observed among groups (Figure S2L), suggesting L. bacterium and its metabolites are non-toxic and may act as antitumor probiotics.

Propionate in the supernatant of L. bacterium exerts the antitumor effect

With recent advances in microbial research, researchers have realized that dozens of microbes could contribute to cancer progression through a variety of mechanisms, such as SCFAs.¹⁷^,¹⁸ The potential of Lachnospiraceae members to produce SCFAs is well documented.¹⁵ We investigated the possibility that increased SCFA production plays a role in the ameliorative effect of L. bacterium on ccRCC. To test this, targeted SCFA metabolomic analysis was conducted on serum samples from a new cohort (cohort 3, n = 20) (Table S3). The results revealed a considerable difference between the two populations’ blood metabolome values (Figure S4A). We found that concentrations of four SCFAs (propionate, butyrate, isobutyrate, and caprylate) were greater in the serum of HVs compared to ccRCC patients (Figure 3A). Analysis of L. bacterium SN also showed elevated acetate and propionate compared to PYG medium (Figures 3B, S4B, and S4C). Venn diagram analysis suggested propionate as a key metabolite linked to ccRCC modulation (Figure 3C). Serum from mice gavaged with L. bacterium similarly showed increased propionate (Figure 3D). In another cohort (cohort 4, n = 30) (Table S4), fecal and serum analysis revealed a positive correlation between L. bacterium abundance and serum propionate (Figure S4D).

To confirm the role of propionate, we performed relevant in vitro experiments. We confirmed that varying concentrations of propionate not only reduced clone formation (Figure 3E), but also inhibited cell proliferation (Figures 3F–3H and S4E), and slowed cell migration (Figures 3I, 3J, and S4F) in the ccRCC cell lines 786-O and Caki-1. Additionally, we observed that the inhibitory effect on these cell lines positively correlated with the concentration of propionate. Moreover, it has been suggested that propionate may act as an inhibitor of histone deacetylase (HDAC), which has an effect on the cell cycle.¹⁹ Treatment with propionate also decreased the proportion of cells in the synthesis (S) phase and increased growth (G0/G1) phase cells in 786-O and Caki-1 (Figures 3K and 3L), which indicates that propionate has an inhibitory effect on the synthesis of cellular genetic information. Further testing was done by exposing nuclear cell extracts to propionate in order to see if it may inhibit the HDAC enzymes produced in the extracts. By treating cells with propionate and trichostatin A (an HDAC inhibitor) for 24 h, we demonstrated that propionate could directly influence the activity of the HDAC enzyme by entering cells (Figure S4G).²⁰ HDAC activity showed a significant and dose-dependently inhibition under the incubation with propionate (Figure S4H).

G protein-coupled receptor 41/43 (GPR41/43) has been previously identified as a propionate receptor.¹⁸ To further investigate whether propionate-induced anticancer effect through GPR41/43, they were blocked by the GPR inhibitor, pertussis toxin (PTX). As expected, the inhibition of tumor cell growth and migration of anti-tumor effects mediated by propionate was blunted by PTX treatment (Figures 3M–3N and S4I–S4K).

L. bacterium-derived propionate downregulates IFITM1 expression to inhibit the tumor progression through activating JAK1-STAT1/2 pathway

To characterize the molecular mechanisms by which L. bacterium-derived propionate impairs the tumor progression, we performed ribonucleic acid sequencing (RNA-seq) analysis between cells treated with or without propionate (Figure S5A) and tumors between mice treated with L. bacterium and Ctrl group. Gene ontology enrichment analysis revealed that the “response to virus” pathway (Figures 4A and S5B) was most significantly altered in propionate-treated cells compared with the group without propionate treatment. We then hypothesized that the significantly changed genes in this pathway might be the key genes and selected the 9 genes with the largest differences for further study. Quantitative real-time PCR (real-time qPCR) confirmed that propionate downregulated OAS3, IFITM1, OAS2, IFI27, USP18, and IFIT7 mRNA levels (Figure 4B). Meanwhile, IFITM1 was not only the most significantly altered among them but it was also significantly different in the mouse tumors from the Ctrl group and mice gavaged with L. bacterium (Figures 4C and S5C–S5E). Given the combined analysis of RNA-seq and 16S rRNA sequencing from mice treated with or without L. bacterium, it was shown that the relative abundance of L. bacterium is indeed negatively correlated with the transcription of IFITM1 and its associated pathways in tumor tissues (Figures S5F and S5G).

*L. bacterium*-derived propionate downregulates the IFITM1 to activate the JAK1-STAT 1/2 pathway to inhibit tumor progression

(A) Gene ontology enrichment analysis of transcriptome profiles in 786-O cells treated with propionate versus control.

(B) RT-qPCR validation of IFITM1 downregulation in 786-O cells treated with propionate.

(C) Schematic showing the integrated analysis of transcriptomic data from propionate-treated 786-O cells and *L. bacterium*-gavaged mouse tumors to identify propionate-regulated targets.

(D) DFS comparison between IFITM1-high and -low ccRCC patients in TCGA cohort.

(E and F) Western blot analysis of IFITM1 (E) and JAK1–STAT1/2 (F) expression in 786-O cells treated with 1 or 10 mM propionate.

(G and H) Western blot analysis of IFITM1 (G) and JAK1–STAT1/2 (H) in 786-O cells transfected with IFITM1-overexpressing plasmids.

(I–K) Functional rescue assays in 786-O cells co-treated with propionate and IFITM1

overexpression: cell viability (I, CCK8 assay), migration (J, transwell assay), and proliferation (K, cell counting). Scale bars: 100 μm.

Data are presented as mean ± SEM. Statistical significance was determined by paired two-tailed t test (B and K), Mann-Whitney test (D), and two-way ANOVA (I). ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001.

See also Figure S5.

Moreover, patients were analyzed for prognosis using The Cancer Genome Atlas (TCGA) database and the results showed that ccRCC patients with higher IFITM1 expression showed a worse prognosis both in disease-free survival (DFS) (Figure 4D) and overall survival (OS) (Figure S5H). To further clarify the immediate effect of L. bacterium metabolite propionate on IFITM1, we performed in vitro analyses in 786-O and Caki-1 cell lines and found that IFITM1 expression was decreased (Figures 4E and S5I).

Meanwhile, in the literature, a close relationship between IFITM1 and JAK1-STAT1/2/p21 is demonstrated.²¹^,²² Western blotting showed propionate induced phosphorylation of JAK1, STAT1/2, increased p21 expression, and no significant changes in total JAK1 and STAT1/2 in 786-O and Caki-1 cell lines. What’s more, no significant changes were observed in total STAT3 and its phosphorylation (Figures 4F and S5J). To further confirm our suspicions, we designed small interfering RNA to reduce the expression of IFITM1 (Figure S5K) and found that phosphorylation of JAK1, STAT1/2, and p21 expression increased as IFITM1 was knocked down (Figure S5L). To investigate whether overexpressing IFITM1 on tumor cells could block the tumoricidal effects mediated by propionate, we utilized lentivirus to induce IFITM1 overexpression in these cells (Figure 4G). We discovered that overexpressing IFITM1 could, to some extent, inhibit the cytotoxic and migratory effects of propionate on tumor cells (Figures 4I–4K). Meanwhile, over-expressed IFITM1 downregulated phosphorylation of JAK1, STAT1/2, and p21 expression, consistent with previous results (Figure 4H). The data indicated that L. bacterium-derived propionate activates the JAK1-STAT1/2 pathway to exert antitumor effects by downregulating IFITM1 expression in ccRCC.

Propionate suppresses IFITM1 expression through downregulating HOXD10

To determine the mechanisms by which the L. bacterium metabolite propionate suppresses IFITM1 expression, we employed a luciferase reporter assay and discovered that the transcriptional activity of IFITM1 promoter was inhibited in ccRCC cells treated with L. bacterium SN (Figure 5A) or propionate (Figure 5B). Then, we used the PROMO genome database to predict genes as potential transcription regulation elements of IFITM1. In the meanwhile, we investigated the RNA-seq gene profiles of cells treated with propionate to find genes that were regulated by propionate. Then, we discovered that HOXD10 was considerably downregulated in propionate-treated cells (p < 0.05 and log₂-fold change > 1.5) (Figure 5C). Furthermore, qPCR and western blotting showed that HOXD10 was downregulated after propionate treatment (Figures 5D, 5E, and S6A). Additionally, the TCGA database was used to examine the patients’ prognoses, and the results revealed that ccRCC patients with increased HOXD10 expression had a worse prognosis both in DFS (Figure 5F) and OS (Figure S6B). Therefore, we hypothesized that HOXD10 might be a transcription regulation element that binds to the promoter region of IFITM1. To confirm this hypothesis, we performed a luciferase reporter assay and observed that the transcriptional activity of the IFITM1 promoter was enhanced after transfection with the HOXD10 plasmid (Figure 5G). Chromatin immunoprecipitation (ChIP) assays were designed and performed with an anti-HOXD10 antibody, showing that HOXD10 was markedly enriched between −1,302 and −1,292 nucleotides (HOXD10 binding site 2, HBS2) in the promoter of IFITM1 (Figures 5H and 5I; Figures S6C and S6E). Furthermore, luciferase report assays demonstrated the luciferase activity markedly decreased after transfection of the plasmid that mutated at the HBS2 position in the promoter of IFITM1 (Figure S6D).

To further confirm the correlation of HOXD10 with propionate and the JAK1-STAT1/2 pathway, we used lentivirus to over-express HOXD10 (Figure 5J). Subsequently, our observations revealed that HOXD10 overexpression not only attenuated the propionate-mediated suppression of cell proliferation and migration (Figures 5K–5M) but also led to the downregulation of the JAK1-STAT1/2 signaling pathway (Figure S6F). Finally, to confirm the relationship between L. bacterium and the expression levels of HOXD10 and IFITM1 in tumors, we analyzed the immunohistochemistry (IHC) staining results of tumors from mice treated with or without L. bacterium. The results indicated that the expression levels of HOXD10 and IFITM1 decreased in the tumors of mice that were administered L. bacterium (Figures S6G and S6H).

Potential methods for future therapeutic models

The previous results showed the anti-tumor effect of L. bacterium. To verify whether L. bacterium can be used as a potential probiotic for modulating therapy, we first tried to change dietary patterns. Among other things, investigations of diet-gut microbiota interactions indicated that high pectin and fiber diets promoted the abundance of Lachnospiraceae at family levels.²³^,²⁴ To examine how dietary modification impacts L. bacterium abundance and ccRCC tumor development, mice were given a normal diet (Ctrl), a low-fiber diet (LFD), or a pectin-rich diet (PRD). After inoculating with RENCA cells, tumor growth was measured, and fecal samples were collected (Figure S7A). PRD increased the relative abundance of Lachnospiraceae and probiotics such as Lactobacillaceae (Figure S7B), and L. bacterium abundance was also higher in the PRD group (Figure S7C). Tumor volume and size were significantly reduced in the PRD group compared to LFD (Figures S7D–S7F). Moreover, dietary changes influenced L. bacterium levels, propionate in serum, and IFITM1 expression in tumors (Figures S7G and S7H).

It is invigorating that a diet rich in pectin and the oral administration of L. bacterium can both inhibit the growth of tumors. Considering that oral probiotics are a more direct method of administration, their effectiveness frequently suffers significantly due to the stringent gastrointestinal conditions and restricted colonization in the intestines. Drawing inspiration from the protective isolation and bio-adhesive interface functions of biofilms, we have endeavored to utilize biofilms to improve the survival of L. bacterium in the stomach and enhance its colonization in the intestines. Firstly, Bacillus subtili (B. subtilis) was cultivated in minimal salts glycerol glutamate (MSgg) medium to obtain B. subtilis grown with biofilms, which were repeatedly blown and washed.²⁵ Next, the obtained material was autoclaved to obtain purified biofilms. Finally, biofilm-coated L. bacterium (BC-L. bacterium) was prepared by dispersing the purified biofilm into the medium of L. bacterium in mixed culture to produce biofilm (Figure 6A). By co-culturing with B. subtilis biofilm, transmission electron microscopy (TEM) showed that L. bacterium formed a robust biofilm and L. bacterium was well encapsulated within the biofilm (Figure 6B). Subsequent quantitative assessment of biofilm production using classical crystal violet staining confirmed that BC-L. bacterium produced approximately 1.3 times more biofilm than that of L. bacterium itself (Figure 6C).²⁶

Potential methods for future therapeutic models

(A) Schematic illustration of the preparation process for BC-*L. bacterium*.

(B) Representative TEM images of native and biofilm-coated *L. bacterium*, with corresponding magnified views. Scale bars: 2 μm (top and middle) and 500 nm (bottom).

(C–E) Quantification of biofilm formation (OD590, crystal violet assay) (C), survival in SGF (pH 2) at 5, 15, and 30 min (D), and in bile salts (0.3 mg/mL) at 1, 2, and 4 h (E).

(F) Adherence of *L. bacterium* and BC-*L. bacterium* to isolated mouse intestinal segments after 30 min incubation and washing.

(G) Survival in SIF at 1, 2, and 4 h (n = 3).

(H) Experimental design: RENCA-bearing mice pretreated with ATB were orally administered biofilm, native *L. bacterium*, or BC-*L. bacterium* daily for 14 days (n = 6/group).

(I) Tumor growth kinetics starting (n = 6/group).

(J and K) Representative tumor images (J) and tumor weights (K) on day 24 (n = 6/group).

Data are shown as mean ± SEM. Statistical significance was determined by paired two-tailed t test (C–G), two-way ANOVA (I), and unpaired two-tailed t test (K). ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001.

See also Figures S7 and S8.

To assess the stress resistance of L. bacterium with biofilm coating (BC), in vitro tests were conducted in simulated gastric fluid (SGF), bile salts, and simulated intestinal fluid (SIF). After 30 min in SGF, BC-L. bacterium showed high tolerance with a 44.3% survival rate (Figure 6D). It also remained stable after 4 h in bile acids (Figure 6E). Furthermore, to determine the potential of a biofilm coating in enhancing adhesion to the intestinal mucosa, adhesion assays using mouse intestinal tissue revealed that BC-L. bacterium had a 14.7-fold higher adhesion rate than uncoated L. bacterium (Figure 6F). In SIF, both strains retained substantial viability after 4 h, with survival rates of 70.3% for L. bacterium and 76.9% for BC-L. bacterium (Figure 6G).

To evaluate the in vivo efficacy and safety of BC-L. bacterium, antibiotics-treated mice with subcutaneous tumors were orally administered biofilm, L. bacterium, or BC-L. bacterium for 14 days (Figure 6H). BC-L. bacterium significantly inhibited tumor growth and reduced tumor volume compared to L. bacterium alone, while biofilm alone had no effect (Figures 6I–6K). To enhance translational relevance, we repeated the experiment in mice without antibiotic pretreatment, and results remained consistent (Figures S8A–S8C and S8E). No significant differences in body weight (Figure S8D), liver/kidney function, or organ pathology were observed (Figures S8F–S8G), confirming the safety of BC-L. bacterium.

Levels of L. bacterium, IFITM1, and HOXD10 correlate and predict the outcome of ccRCC patients

To validate the predictive efficacy of L. bacterium for ccRCC, we recollected fecal samples to form a validation cohort, cohort 5 (n = 107, 65ccRCC/42HV) (Table S5) and examined the relative abundance of L. bacterium in them. We found that the relative abundance of L. bacterium was not only significantly decreased in the gut microbes of ccRCC patients compared to HVs (Figure 7A) but also served as a predictor to distinguish between the two (Figure 7B). This situation is the same in the two groups LRCC and ARCC (Figures 7C and 7D). Furthermore, to further elucidate the effects of L. bacterium and its mediated propionate on ccRCC tumor progression both in vitro and in vivo, we employed patient-derived tumor spheres (Figure S8H) and patient-derived xenograft (PDX) models to validate our findings (Figures 7E and S8I).

Subsequently, to assess the correlation between propionate-mediated low expression of IFITM1 and HOXD10 with tumor progression in ccRCC, we examined the levels of IFITM1 and HOXD10 expression using tissue microarrays in cohort 6 (n = 210, 210ccRCC) (Table S6). We found that the higher levels of IFITM1 and HOXD10 were expressed in highly malignant patients (Figures 7F and 7G). Meanwhile, ROC curve analysis was performed utilizing HOXD10 and IFITM1 performance data to forecast the potential ccRCC (Figure 7H). Finally, we assessed the correlation between the IFITM1, HOXD10, and the survival of patients in cohort 6. As shown in Figure 7I, we observed prolonged progression-free survival (PFS) in patients with lower IFITM1 and HOXD10 levels.

Taken together, our data suggest that the L. bacterium-HOXD10-IFITM1 axis may be used as potential prognostic markers for ccRCC. This study demonstrates that propionate produced by L. bacterium activates the propionate receptor GPR41/43, which reduces the expression of HOXD10 and IFITM1 to inhibit tumor development through activating the JAK1-STAT1/2 pathway (Figure 7J).

Discussion

The connection between gut microbiota and ccRCC treatment has been covered in preclinical mouse models and observational cohorts by investigations before.⁸^,²⁷^,²⁸ However, the understanding of ccRCC and gut microbiota is still in its infancy currently. Here, we combined a multi-omics analytical approach, mouse models, and observational cohorts to document that propionate, a metabolite mediated by the ccRCC-negatively associated L. bacterium, inhibits tumor progression, by downregulating the expression of HOXD10 transcription factor and decreasing downstream IFITM1 expression to inhibit the tumor progression through activating JAK1-STAT1/2 pathway.

As a common probiotic, Lachnospiraceae has been shown to have certain correlations with the treatment and development of various tumors. The relative abundance of Lachnospiraceae in the gut of patients is associated with both tumor progression and treatment efficacy.¹⁵^,²⁹^,³⁰^,³¹ Moreover, Lachnospiraceae can impact tumor therapy through the production of metabolic byproducts, such as SCFAs, and by modulating the immune response to tumors.¹¹^,³² But its connection to the advances of ccRCC is yet uncertain. This study correlates L. bacterium with the progression of ccRCC. Meanwhile, during oral gavage of L. bacterium, we observed that fecal L. bacterium abundance was dramatically increased after L. bacterium gavages and sustained throughout the experiment. Additionally, we realized that several beneficial bacteria, such as Lactobacillaceae,¹⁷ increased with gavage of L. bacterium, indicating that gut microbes may not be acted upon by a single bacterial group and that the increase in L. bacterium abundance regulates the abundance of other commensal bacterial groups in a more complex approach, which together affects the product metabolism and tumor progression.³³^,³⁴

Until now, little has been known regarding the role of Lachnospiraceae metabolites in ccRCC. Here, we identified propionate as a microbial metabolite derived from L. bacterium that affected the progression of ccRCC. To further confirm the correlation between L. bacterium and propionate, we observed the relationship between the relative abundance of L. bacterium and propionate levels in both L. bacterium-gavaged mice and patient cohorts, which further corroborated our view. Propionate, as an important component of SCFA, has been documented in other tumor cells to block the cell cycle and inhibit tumor cell migration, which is consistent with our findings.¹⁷^,³⁵^,³⁶ However, the cancer-inhibiting mechanism of propionate has still not been explored in ccRCC models. Furthermore, IFITM1, as a member of the IFN-inducible transmembrane protein family and a multimeric complex engaged in cell adhesion signaling and anti-proliferation signal transduction, has been reported to correlate with the development of malignancies in a variety of tissues, including colorectal, gastric, ovarian, leukemia, and cervical squamous cell carcinoma.²¹^,²²^,³⁷^,³⁸ In addition, the analysis of transcriptome sequencing results combined with 16S rRNA gene sequencing results also suggested a correlation between L. bacterium and IFITM1 expression. Thus, this study advances our understanding of ccRCC by elucidating the relationship between IFITM1 and the gut microbiota. In the present study, we found the propionate-mediated downregulation of IFITM1 expression by co-analyzing the transcriptome results of propionate-treated cells and L. bacterium-gavaged mouse tumors and confirmed that it inhibited the advances in ccRCC by the downstream activation of the JAK1-STAT1/2 pathway, which is consistent with the findings of previous studies. Meanwhile, how IFITM1 is regulated remains unclear, and we have demonstrated that HOXD10 acts as an upstream of IFITM1 to regulate its transcriptional promoter through database prediction and experimental validation. Similarly, our results also revealed that low expression of HOXD10 and IFITM1 in tumor tissues was confirmed to be negatively correlated with tumor progression in multiple clinical cohorts, which agrees with the outcomes of the TCGA database. Thus, we provide an insight into the mechanism of propionate-ccRCC interactions and offer a perspectives on ccRCC therapy and clinical prediction.

To guide clinical patients from the perspective of microbiota, we explore dietary modification and the design of engineered bacteria to affect the population’s gut microbiota in two directions. However, dietary changes, while delaying the development of ccRCC, were not as effective relative to direct gavage L. bacterium, suggesting the need for a more potent and reliable approach for clinical patients. Therefore, we designed BC-L. bacterium as a potential therapeutic strategy. In nature, bacteria grow in a protective manner by producing biofilm with typical physical adhesion and chemical barrier functions to enhance adaptation to environmental stresses.³⁹ The excellent bioadhesion of the biofilm and its impermeability are important for bacteria to respond to external environmental stimuli and survive.⁴⁰ Biofilm’s strong adhesion and impermeability are key to bacterial survival under environmental stress. Unlike B. subtilis, a probiotic capable of robust biofilm production, L. bacterium lacks efficient methods for large-scale biofilm formation.⁴¹^,⁴² To address this, we developed a biofilm-assisted platform to enhance L. bacterium’s gastric survival and intestinal colonization. BC-L. bacterium showed strong resistance to harsh gastrointestinal conditions—including acidic pH, bile acids, and intestinal fluid—and exhibited superior adhesion and survival.⁴³ Its in vivo efficacy and safety were also confirmed. However, further clinical studies are needed to validate its therapeutic potential against ccRCC.

In conclusion, we reveal a role and mechanism for the L. bacterium-derived propionate in ccRCC. The abundance of fecal L. bacterium and the concentration of serum propionate are closely related to tumor progression of ccRCC, and the relative abundance of L. bacterium decreased with increasing tumor grade. Therefore, L. bacterium in fecal matter may serve as noninvasive biomarkers for the early prediction of ccRCC tumor progression. In addition, increasing pectin-rich and high-fiber diets or using engineered bacteria to increase intestinal L. bacterium content may serve as effective strategies to delay the progression of ccRCC development.

Limitations of the study

Because of the complexity in crosstalk between commensal microbiota and the host, L. bacterium is unlikely to be the only gut microbiota that can retard the tumor progression of ccRCC. This is a retrospective, single-center study and is subject to inherent limitations associated with retrospective analyses, such as single race. Another caveat of this study is its relatively small sample capacity. However, the sample capacity was the largest yet applied to these questions. Therefore, future studies need to validate the current findings in multiple centers.

Resource availability

Lead contact

Requests for further information and resources should be directed to and will be fulfilled by the lead contact, Wei Zhai (jacky_zw2002@hotmail.com).

Materials availability

This study did not generate new unique reagents.

Data and code availability

•
Bulk RNA-seq data have been deposited at the NCBI SRA as PRJNA1268942 and PRJNA1268786. 16S rRNA sequencing data have been deposited at the NCBI SRA as PRJNA1269092. The metabolomics data have been deposited to MetaboLights⁴⁴ repository with the study identifier MTBLS12539. All datasets are publicly available as of the date of publication.
•
This paper does not report original code.
•
Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request.

Acknowledgments

This project was supported in part by grants from the National Natural Science Foundation of China (82173214), National Nature Advancement Program of the Academy (RJTJ22-ZD-005), Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine “Clinic Plus” Outstanding Project (2023ZYA001), hospital-pharma Integration Project on Innovative Achievement Translation (SHDC2022CRD022), Hospital-pharma Integration Project on Innovative Achievement Translation (SHDC2022CRD022), Shanghai 2023 “Science and Technology Innovation Action Plan” Medical Innovation Research Special Project/Clinical Research on Diagnosis and Treatment Plans (Strategies) for Key Areas of Disease(23Y21900400), and Shanghai Municipal Health Commission's Health Industry Research Special Project (Excellence Project) (20244Z0004). This study was supported by Shanghai Immune Therapy Institute and the innovative research team of high-level local universities in Shanghai. We thank Prof. Jie Hong for useful advice. We thank Dr. Meng-Chen Xiao for assistance with graphics drawing.

Author contributions

J.-Y.X. and H.C. performed most of the experiments and data analysis. Y.-Y.Y. assisted in the in vivo or in vitro experiments. T.-Y.C. and Y.-Q.W. led the collection of feces, blood, and related clinical data. J.-Y.X., H.C., and W.Z. wrote the original draft. W.X. contributed in experiments guidance. J.-H.Z., J.-Y.L., and W.Z. designed the project and revised and edited the manuscript.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies

Rabbit polyclonal Anti-ACTB	Proteintech	Cat# 81115; RRID:AB_2923704
Rabbit monoclonal Anti-IFITM1	Abcam	Cat# ab233545; RRID:AB_3665846
Rabbit monoclonal Anti-HOXD10	Abcam	Cat# ab138508; RRID: AB_3716462
Mouse monoclonal Anti-JAK1	Proteintech	Cat# 66466; RRID:AB_2881834
Rabbit monoclonal Anti-p-JAK1(Y1034 + Y1035)	Abcam	Cat# ab138005; RRID:AB_3206261
Rabbit polyclonal Anti-STAT1	Proteintech	Cat# 10144; RRID:AB_2286875
Rabbit monoclonal Anti-p-STAT1 (Y701)	Abcam	Cat# ab109457; RRID:AB_10865748
Rabbit polyclonal Anti-STAT2	Proteintech	Cat# 16674; RRID:AB_10644445
Rabbit monoclonal Anti-p-STAT2 (Y690)	Abcam	Cat# ab191601; RRID: AB_3716463
Rabbit monoclonal Anti-STAT3	Cell Signaling Technology	Cat# 12640; RRID:AB_2629499
Rabbit monoclonal Anti-p-STAT3 (Y705)	Abcam	Cat# ab76315; RRID:AB_1658549
Rabbit monoclonal P21	Cell Signaling Technology	Cat# 2947; RRID:AB_823586
Rabbit polyclonal Anti-Ki67	Abcam	Cat# ab15580; RRID:AB_443209

Bacterial and virus strains

Lachnospiraceae bacterium	DSMZ	DSM 24404
Prevotella copri	DSMZ	DSM 18205

Biological samples

Fecal and serum samples from ccRCC patients and healthy volunteers	Renji Hospital affiliated with Shanghai Jiao Tong University School of Medicine; See Tables S1–S5	N/A
Human ccRCC tissue microarray	Renji Hospital affiliated with Shanghai Jiao Tong University School of Medicine; See Table S6	N/A

Chemicals, peptides, and recombinant proteins

Pertussis Toxin	Absin	Cat# abs42024900
Sodium propionate	Selleck	Cat# E4022
Metronidazole	MedChemExpress	Cat# HY-B0318
Vancomycin	MedChemExpress	Cat# HY-B0671
Ampicillin	MedChemExpress	Cat# HY-B0522
Neomycin	MedChemExpress	Cat# HY-B0470
Hemin	MedChemExpress	Cat# HY-19424
Vitamin K1	MedChemExpress	Cat# HY-N0684
Pectin	Solarbio	Cat# 9000-69-5

Critical commercial assays

Chromatin Immunoprecipitation Assay Kit	Cell Signaling Technology	Cat# 9003
QIAamp PowerFecal DNA Kit	QIAGEN	Cat# 12830
Dual-LuciferaseÒ Reporter Assay System	Beyotime	Cat# RG029S
Cell Cycle and Apoptosis Analysis Kit	Yeason	Cat# 40301
BCA Protein Assay Kit	Thermo Scientific	Cat# 23225
Amplite® Fluorimetric HDAC Activity Assay Kit ∗Green Fluorescence	AAT Bioquest	Cat# 13601

Deposited data

Bulk RNA sequencing data(786-O cells)	This paper	SRA:PRJNA1268942
Bulk RNA sequencing data(Subcutaneous tumor)	This paper	SRA:PRJNA1268786
16S rRNA sequencing data	This paper	SRA:PRJNA1269092
Metabolomics data	This paper	MTBLS12539

Experimental models: Cell lines

Homo: 786-O	ATCC	CRL-1932
Homo: Caki-1	ATCC	HTB-46
Homo: 293T	ATCC	CRL-3216
Mus: RENCA	ATCC	CRL-2947

Experimental models: Organisms/strains

Mouse: BALB/c mice	Beijing Vital River	N/A
Mouse: BALB/c nude mice	Beijing Vital River	N/A
Mouse: NOD/SCID mice	Beijing Vital River	N/A

Oligonucleotides

Primers and oligonucleotides in Table S7	This paper	N/A
Control siRNA-F: 5′-UUCUCCGAACGUGUCACGUTT-3′	This paper	N/A
Control siRNA-R: 5′-ACGUGACACGUUCGGAGAATT-3′	This paper	N/A
IFITM1 siRNA-F: 5′-CCUCAUGACCAUUGGAUUCAUTT -3′	This paper	N/A
IFITM1 siRNA-R: 5′-AUGAAUCCAAUGGUCAUGAGGTT-3′	This paper	N/A

Recombinant DNA

pEGFP-N1-IFITM1-3×FLAG	This paper	N/A
pGL4.10-IFITM1-promoter(WT)	This paper	N/A
pGL4.10-IFITM1-promoter(MUT)	This paper	N/A
pcDNA3.1(+)-HOXD10-3×FLAG	This paper	N/A

Software and algorithms

ImageJ	National Institutes of Health	https://imagej.nih.gov/ij/
GraphPad Prism	GraphPad Software	www.graphpad.com
R	R Development Core Team	https://www.r-project.org/

Open in a new tab

Experimental model and study participant details

Animal

All animal procedures were approved by the Animal Care Committee of Shanghai Jiao Tong University Affiliated Renji Hospital, and conducted in accordance with institutional and national guidelines for the care and use of laboratory animals. BALB/c mice, BALB/c nude mice, and NOD/SCID mice (female, 4–5 weeks old) were purchased from Vital River (Beijing, China) and housed under SPF conditions with a 12-h light/dark cycle at the Animal Core Facility of Renji Hospital. In all experiments, mice were age-matched and randomly assigned to experimental groups.

Study cohorts

We studied 6 cohorts of ccRCC patients and HV from Renji Hospital affiliated with Shanghai Jiao Tong University School of Medicine between 2007 and 2022. Stool and serum samples were collected before the patient received treatment and stored at −80C immediately.

In Cohort 1, 6 fecal samples were obtained from 3 ccRCC patients and 3 HV matched for their age and sex (Age Mean: 48.5, 4 male and 2 female). Fecal samples were subsequently administered to mice by FMT. The clinical characteristics of the study participants are shown in Table S1. Patients in Cohort 1 are derived from Cohort 2.

In Cohort 2, 107 fecal samples were obtained from 67 ccRCC patients and 40 HV matched for their age and sex (Age Mean: 45.8, 69 male and 38 female). We performed 16s rRNA gene sequencing on fecal samples to assess which bacterium is predominant (and/or different) in ccRCC compared with HV. The clinical characteristics of the study participants are shown in Table S2.

In Cohort 3, 20 serum samples were obtained from 10 ccRCC patients and 10 HV matched for their age and sex (Age Mean: 58.3, 10 male and 10 female). We performed targeted metabolomics of the serum samples to assess which SCFAs make a difference in ccRCC compared with HV. The clinical characteristics of the study participants are shown in Table S3.

In Cohort 4, 30 fecal samples and their corresponding serum samples were obtained from 30 ccRCC patients. We conducted 16S rRNA gene sequencing on fecal samples to evaluate the relative abundance of L. bacterium in feces. Additionally, we conducted targeted metabolomics on serum samples to measure the concentration of propionate in serum. Our aim was to elucidate the correlation between these two factors. The clinical characteristics of the study participants are shown in Table S4.

In Cohort 5, 97 fecal samples were obtained from 55 ccRCC patients and 42 HV matched for their age and sex (Age Mean: 61.8, 57 male and 40 female). We performed qPCR on fecal samples to assess the fecal L. bacterium abundance between ccRCC and HV. The clinical characteristics of the study participants are shown in Table S5.

In Cohort 6, 210 TMA (tissue microarray) tissue samples were obtained from 146 LRCC patients and 64 ARCC matched for their age and sex (Age Mean: 57.3, 140 male and 70 female). We performed IHC on TMAs to assess the expression of IFITM1 and HOXD10 between LRCC and ARCC. The clinical characteristics of the study participants are shown in Table S6.

Sex distribution between comparison groups was assessed using chi-square or Fisher’s exact test in all applicable cohorts. No significant differences were observed. In Cohort 4, which included only ccRCC patients without controls, the sex ratio was balanced.

The study protocol was approved by the Ethics Committees of Renji Hospital (certificate no. KY2020-168 and no. KY2023-049B). Informed consent was obtained from all patients. The research was carried out according to the provisions of the Helsinki Declaration of 1975. Human participants were divided into two groups: a tumor group and a healthy control group. Healthy controls were selected to match the tumor patients with respect to age, sex and BMI. The sample size for each group is reported, and participants were allocated to groups based on these matching criteria to minimize potential confounding factors. Patients with the following conditions will not be enrolled.

(1)
Patients who have received medications that may affect the intestinal flora such as antibiotics within 4 weeks;
(2)
Patients with a history of other malignant tumors;
(3)
Patients with adrenal insufficiency;
(4)
Patients with known HIV or known positive test for AIDS;
(5)
Positive test for hepatitis B virus surface antigen or hepatitis C virus RNA, suggesting active or chronic infection;
(6)
Received any non-tumor vaccination against infectious diseases within 4 weeks, including seasonal (influenza) vaccination, and new coronary vaccinations;
(7)
Systemic hormone therapy and corticosteroid therapy should be discontinued for 2 weeks;
(8)
Any active autoimmune disease or patients with a known history of autoimmune disease;
(9)
Patients with altered hematopoietic or organ function.

Cell lines

The human cell lines 786-O, Caki-1, and 293T, as well as the murine renal carcinoma cell line RENCA, were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). 786-O and RENCA cells were cultured in RPMI-1640 medium, Caki-1 cells in McCoy’s 5A medium, and 293T cells in high-glucose DMEM. All media were supplemented with 10% fetal bovine serum (FBS; Gibco, Shanghai, China) and 1% penicillin-streptomycin. Cells were maintained at 37°C in a humidified atmosphere containing 5% CO₂.

All cell lines used in this study were obtained between 2018 and 2023. They were regularly authenticated by morphologic inspection and short tandem repeat profiling (AuthentiFiler, Invitrogen), and routinely tested negative for mycoplasma contamination using the MycoAlert Detection Kit (Lonza). All cells were used within 20 passages after thawing.

Bacterial culture

Lachnospiraceae bacterium and Prevotella copri (DSM 24404and DSM 18205) were purchased from Deutsche Sammlung von Mikroorganismen und Zellkulturen (DMSZ, Braunschweig, German). L. bacterium were cultured overnight at 37°C under anaerobic conditions in PYG modified medium broth supplemented with hemin, vitamin K1 (MedChemExpress, Shanghai, China). P. copri were cultured overnight at 37°C under anaerobic conditions in Schaedler Broth.

Method details

Tumor models

Antibiotic-pretreated mice received an antibiotic cocktail (1 g/L metronidazole, 0.5 g/L vancomycin, 1 g/L ampicillin, and 1 g/L neomycin) in drinking water for one week before tumor inoculation and randomly divided into groups. RENCA were subcutaneously injected into the right flanks of BALB/c mice on day 0. Tumor volumes were measured every three days by length and width through a digital caliper and calculated by (length∗width²)/2. The tumor volumes are presented as the mean ± SEM. Tumor measurements were performed by an investigator blinded to the group allocation.

For FMT experiment, mice were given drinking water containing an antibiotic cocktail for one week prior to FMT. After a 3-day interval, the prepared fecal suspension was administered to the mice by oral gavage at a dose of 200 μL per mouse. Simultaneously, 100 μL of the same suspension was applied to the fur of each mouse. Oral gavage was performed five times per week for two consecutive weeks to ensure successful colonization of the transplanted microbiota.⁸ For bacterial treatment, two strains of L. bacterium and P. copri were orally administered at a concentration of 5 × 10⁹ live bacteria/mL in a 200 μL volume five times a week during the experiments. For heat-inactivated bacteria, L. bacterium was heated to 95°C for 25 min before oral administration. PYG modified medium was used as a placebo. For antibiotic treatment, amoxicillin (0.5 g/L) was added to drinking water during the experiments. For BC-L. bacterium, L. bacterium and BC-L. bacterium were orally administered at a concentration of 1 × 10⁹ live bacteria/mL in a 200 μL volume three times a week after tumor cell inoculation during the experiments.

Fecal microbiota transplantation experiment

Feces were collected from ccRCC and HV groups. Freshly collected feces were diluted in saline at a ratio of 40 mg feces/mL saline, homogenized, and filtered with a stainless steel sieve (pore size: 0.25 mm). Total bacterial protein concentration was determined by BCA assay to normalize the number of bacterial cells. Fecal mixtures were mixed with 10% autoclaved glycerol, aliquoted, and frozen at −80°C until use. BALB/c mice were administered with 200 mL of the fecal mixture by oral gavage five every week for 2 weeks. Simultaneously, 100 μL of the same suspension was applied to the fur of each mouse. Fecal samples were collected for microbiome analysis.

DNA extraction, 16S rRNA gene amplicon sequencing, and data processing

Total genomic DNA from fecal samples was extracted using the acetyl trimethylammonium bromide (CTAB) method. DNA concentration and purity were monitored using 1% agarose gel. The 16S rRNA V3–V4 region was amplified and sequenced using NovaSeq PE250 (Illumina, CA, United States). Sequencing libraries were generated using the TruSeqDNA PCR-Free Sample Preparation Kit (Illumina, CA, United States) and index codes were added according to the manufacturer’s instructions. Library quality was assessed using the Qubit 2.0 Fluorometer (Thermo Scientific, MA, United States) and the Agilent Bioanalyzer 2100 system (Agilent Technologies Inc., CA, United States). The library was then sequenced on an Illumina NovaSeq platform, with 250 base pair (bp) paired-end reads generated and assigned to each sample based on barcodes and then merged with FLASH (V1.2.7). Quality filtering of raw tags was performed under specific filtering conditions to generate high-quality clean tags according to Quantitative Insights Into Microbial Ecology (QIIME, V1.9.1) quality control processes. Tags were compared with a reference database (Silva) using the UCHIME algorithm to detect and remove chimeric sequences, generating effective tags. Sequence analysis was performed in Uparse software (v7.0.1001). Sequences with ≥97% similarity were assigned to the same operating taxonomic units (OTUs). For each representative sequence, the Silva database was used based on Mothur algorithms to annotate taxonomic information. To study the phylogenetic relationships between different OTUs, and differences in dominant species between different samples (groups), multiple sequence alignments were conducted using MUSCLE software (Version 3.8.311). OTU abundance was normalized using a standard sequence number corresponding to the sample with the least sequences. We calculated α- and β-diversity in QIIME (Version 1.7.0 and 1.9.1, respectively) and displayed data using R version 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria) and GraphPad Prism 6 software (GraphPad Software, Inc., CA, United States). Tax4Fun function predictions were processed using the nearest neighbor method based on minimum 16S rRNA sequence similarity.

Cell treatment

For L. bacterium SN, cells were plated on 6-well plates for 24 h before treatment and then cultured in a medium containing at 100×, 10× dilution of L. bacterium SN or PYG modified medium for 24 h. For propionate, cells were plated on 6-well plates 24 h before treatment and cultured with propionate (0.1 mM, 1 mM, and 10 mM) for 24 h. Then, the cells were subjected to western blotting, qPCR, luciferase assays, and ChIP PCR.

RNA sequencing and data analysis

Total RNA was isolated from cells and tumor tissues using the TRIzol reagent (Invitrogen, USA). The amount and quality of total RNA samples were determined using a NanoDrop 2000 spectrophotometer (Thermo, USA). High-quality RNA with a 260/280 absorbance ratio of 1.8–2.2 was used for library construction and sequencing. The Illumina HiSeq library construction was performed according to the manufacturer’s instructions (Illumina, USA). Oligo-dT primers were used to reverse transcribe mRNA into cDNA (APExBIO, China). Amplify the cDNA for synthesis of the second cDNA chain. The cDNA products were purified using magnetic beads. After library construction, the library fragments were enriched by PCR amplification and selected according to a fragment size of 350–550 bp. Library quality was assessed using an Agilent 2100 Bioanalyzer (Agilent, USA). The library was sequenced using the Illumina NovaSeq 6000 sequencing platform to generate raw reads. Raw paired-end fastq reads were filtered using TrimGalore to discard adapters and low-quality bases by calling the Cutadapt tool. The clean reads obtained were aligned to the hg19 human genome using HISAT2, followed by reference genome-guided transcriptome assembly and gene expression quantification using StringTie. Differentially expressed genes (DEGs) were identified using DEseq2 with a cutoff value of log₂|fold-change|≥0.25, p < 0.05. ClusterProfiler was used to perform functional enrichment analysis for the annotated significant DEGs and potential genes in the identified modules based on gene ontology and KEGG pathway categories. Functional categories with p-values <0.05 were considered statistically significant. Gene set enrichment analysis was performed using the ClusterProfiler package with a gene list sorted by log₂fold change.

Targeted metabolomic analysis

Serum samples from HV and ccRCC patients were subjected to identification and quantification of the metabolites using an ultra-performance liquid chromatography (UPLC) system and a high-resolution tandem mass spectrometer Xevo G2 XS QTOF (Waters, UK). Reverse-phase chromatography was employed, using both positive and negative electrospray ionization modes (ESI, RP+, and RP−). A 10 μL of the sample solution was injected into an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm, 1.8μm, Waters, UK). The column oven was maintained at 50°C. The flow rate was 0.4 mL per minute and the mobile phase consisted of solvent A (water +0.1% formic acid) and solvent B (acetonitrile +0.1% formic acid). Gradient elution conditions were set as follows: 0–2 min, 100% phase A; 2–11 min, 0%–100% B; 11–13 min, 100% B; 13–15 min, 0%–100% A. ESI source was operated using the following conditions: for the positive ion mode, the capillary and sampling cone voltages were set at 3.0 kV and 40.0 V, respectively. For the negative ion mode, the capillary and sampling cone voltages were set at 2.0 kV and 40.0 V, respectively. The mass spectrometry data were acquired in Centroid MSE mode. The TOF mass range was from 50 to 1200 Da, and the scan time was 0.2 s. For the MS/MS detection, all precursors were fragmented using 20–40 eV, and the scan time was 0.2 s. During acquisition, the LE signal was taken every 3 s to calibrate the mass accuracy. Samples were analyzed in one batch with a randomized injection order. The stability and functionality of the system were monitored throughout all the instrumental analyses using quality controls, i.e., the pooling of all samples acquired at the beginning of the analytical sequence and after every ten injections. Data preprocessing was performed using Progenesis QI (version 2.2). A support vector regression-based normalization was performed to minimize unwanted variations in feature intensities. P-values for fold change were adjusted for multiple testing using the Benjamini–Hochberg false discovery rate. Principal component analysis was performed on auto-scaled intensities (mean = 0, standard deviation = 1) of all quantified metabolite features detected in RP+ and RP-, respectively using the R package “mixOmics”. Metabolite identification was carried out based on accurate mass and production spectrum matching against online databases and literature.

Transwell assays

Cells were resuspended in FBS-free medium. 600 μL of culture medium supplemented with 5% FBS was added to the lower chamber, and 300 μL of suspension containing 2 × 10ˆ5 cells was seeded into the top chamber (24-well insert, 8 μm pore size, Corning, USA). The chambers were incubated at 37°C in a humidified environment for 12 h. After fixation in 4% paraformaldehyde for 30 min, cells were stained with crystal violet for 20 min. Images were captured using an inverted microscope, and invading cells were quantified from three random fields using ImageJ software.

Wound healing assays

786-O or Caki-1 were cultured in 6-well plates and grown to 80% confluence. A straight scratch was made using a sterile 200 μL pipette tip, and the plate was then incubated in a 37°C incubator for 24 h in a humidified environment. Images were captured at 0 and 24 h using an inverted microscope (e.g., Nikon Eclipse 80i, Nikon, Japan) to evaluate the wound closure. The wound area was measured by ImageJ software to quantify the migration ability of the cells.

Cell viability analysis

Approximately 2000 cells per well were seeded in a 96-well plate with 100 μL of complete culture medium. After 24 h of incubation, the medium was removed and replaced with 100 μL of fresh complete culture medium containing 10 μL of CCK-8 solution (Cell Counting Kit-8, Beyotime, China). The absorbance of each well was measured at 450 nm using a microplate reader (Thermo Scientific, Pittsburgh, PA, USA) to determine cell viability. The absorbance values of each group were recorded and used to calculate relative cell viability.

Colony formation assays

Approximately 2000 cells per well were seeded in 6-well plates with 2 mL of complete culture medium. After 24 h of incubation, cells were treated with 100× or 10× dilution of L. bacterium SN, P. copri SN or different concentrations of propionate (0.1 mM, 1 mM, and 10 mM) for 24 h. Following treatment, the medium was replaced with fresh complete culture medium, and cells were cultured for an additional 14 days to allow colony formation. Colonies were then fixed with 4% paraformaldehyde and photographed.

Cell cycle assay

786-O and Caki-1 cells were cultured in medium containing propionate for 48 h. Subsequently, cell cycle analysis was performed using the Cell Cycle and Apoptosis Analysis Kit (Yeasen, Shanghai, China), according to the manufacturer’s instructions. Flow cytometry was carried out on a BD LSRFortessa (BD Biosciences, San Jose, CA, USA) at an excitation wavelength of 488 nm.

5-Ethynyl-2′-deoxyuridine (EdU) assay

786-O and Caki-1 cells were cultured in medium containing propionate for 48 h or co-cultured with L. bacterium for 4 h. Subsequently, cells were subjected to a 5-ethynyl-2′-deoxyuridine (EdU) incorporation assay using the Cell-Light EdU Apollo 567 In Vitro Kit (Ribobio, Shanghai, China), following the manufacturer’s instructions. Briefly, EdU was added to the culture medium for 2 h, followed by cell fixation, permeabilization, and fluorescent staining with Apollo567 and Hoechst 33342. Fluorescence signals were analyzed to evaluate cell proliferation.

Histone deacetylase (HDAC) activity assay

HDAC activity was measured using the Amplite Fluorimetric HDAC Activity Assay Kit (AAT Bioquest, USA). The nuclear extract was diluted to 1:40 and added to microplate wells. Then, 20 μL of HDAC Green substrate and 100 μL of signal enhancer were added, followed by 10 μL of the test compound (HDAC inhibitor Trichostatin A). The plate was incubated at 37°C for 20 min, followed by the addition of 50 μL of HDAC Green substrate solution, and incubated for an additional 60 min. Finally, fluorescence intensity was measured at 490/525 nm using a microplate reader, and HDAC activity was determined based on changes in fluorescence.

RNA extraction and qRT-PCR assay

Extractions of total RNA from samples were conducted through the TRIZOL Reagent (Life Technologies). Under manufacturers’ instructions, RNA was reversely transcribed into cDNA with Color Reverse Transcription Mix (EZBioscience), which was further quantified by qPCR assays to determine the relative expression level of the target gene with Color SYBR Green qPCR Master Mix (EZBioscience). The 2^-ΔΔCT method was conducted to evaluate the relative expression among genes. GAPDH was determined as an internal control. All the primers used were listed in the key resources table.

Western blot

Cell extracts were collected and quantified with a BCA Protein Assay Kit (Thermo Fisher Scientific). Protein samples (40μg) were resolved in 10% sodium dodecyl sulfate (SDS) polyacrylamide gels and transferred to polyvinylidene difluoride (PVDF) membranes. After blocking non-specific binding sites for 60 min with 5% BSA, membranes were incubated with primary antibodies overnight at 4°C. After washing, the membranes were labeled with HRP-conjugated secondary antibodies and detected using enhanced chemiluminescence (ECL) (Pierce Biotech). A b-actin antibody was used as a control. Information on all antibodies is listed in the key resources table.

Chromatin immunoprecipitation PCR

ChIP was performed according to the manufacturer’s protocol (Cell Signaling Technology). Chromatin was sonicated and immunoprecipitated with different primary antibodies at 4°C overnight. After immunoprecipitation, the protein‒DNA cross-links were reversed, and the DNA was purified. Then, real-time PCR was performed in a 10 μL reaction system using SYBR Green qPCR Master Mix (EZBioscience) and immunoprecipitated DNA as the template. The ChIP‒qPCR primers are listed in the key resources table.

Construction of small interfering RNAs and transfection of cells

SiRNAs were designed and synthesized by Tsingke Biological Technology (Beijing, China). Cells were seeded in 6-well plates, and 24 h later, when they reached 60–70% confluence, specific small interfering RNA (siRNA) (100 nM) or control siRNA (100 nM) was transfected using Lipofectamine 3000 (Thermo Fisher Scientific) following the manufacturer’s instructions. All siRNA sequences used are listed in the key resources table.

Immunohistochemistry analysis

IHC was performed to detect protein expression in mouse tumor tissues and tissue microarrays (TMAs). Briefly, paraffin-embedded sections were incubated at 55°C–60°C for 2 h, followed by deparaffinization in xylene and rehydration through a graded ethanol series (95%, 85%, and 75%). Antigen retrieval was carried out by heating the sections in EDTA buffer using a microwave for 6 min at medium-high power and 15 min at medium-low power. Endogenous peroxidase activity was blocked by incubation with 3% hydrogen peroxide for 15 min. Non-specific binding was blocked with goat serum for 30 min at room temperature (25°C). Sections were then incubated with primary antibodies overnight at 4°C, followed by incubation with secondary antibodies for 30–60 min at room temperature. Visualization was achieved using 3,3′-diaminobenzidine (DAB), and nuclei were counterstained with hematoxylin. Images were acquired using a Nikon Eclipse 80i microscope (Nikon, Tokyo, Japan).

Dual-luciferase reporter assays

Dual-luciferase reporter assays were performed to investigate the mechanism by which HOXD10 regulates IFITM1 transcription. Cells were co-transfected with HOXD10 overexpression plasmid, IFITM1 promoter luciferase reporter vector, negative control plasmid, and 100 ng of pRL-TK Renilla luciferase plasmid using Lipofectamine 3000 (Thermo Fisher Scientific). After 48 h, luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Beyotime, Shanghai, China) according to the manufacturer’s protocol. Firefly luciferase activity was normalized to Renilla luciferase activity, and the results were expressed as fold changes relative to the control group.

Preparation of biofilm-coated L. bacterium (BC- L. bacterium)

First, clinically used Bacillus subtilis (B. subtilis, Hanmi Pharma-ceutical Co. Ltd.) is inoculated into 5 mL of Luria-Bertani (LB) medium and cultured overnight at 37°C. Then, resuspend 10 μL of the seed culture and inoculate into 5 mL of MSgg (Coolaber science & technology), culturing at 30°C for 3 days to obtain B. subtilis with a biofilm. Next, B. subtilis with biofilm is sterilized under high pressure (120°C, 30 min) to remove live bacteria, and the biofilm is collected by centrifugation. Finally, the obtained biofilm (10 μL) is co-cultured with L. bacterium for 3 days and resuspended in (phosphate buffered saline) PBS to prepare bacteria individually coated with biofilm (BC- L. bacterium).

Characterization of BC- L. bacterium

The L. bacterium and BC-L. bacterium surface morphology were studied with a JEM-2100 transmission electron microscope and a 200 kV field emission transmission electron microscope (JEOLJEM-2100F, Hitachi, Japan). The absorbance of the plates was read at the corresponding wavelength using a multimode reader (Multiskan FC, Thermo Fisher Instrument Co., Ltd., US).

Quantitative detection of biofilms by crystal violet staining

In a 96-well plate, L. bacterium (10⁵ CFUs), 10 μL of B. subtilis biofilm, and L. bacterium + B. subtilis biofilm (10 μL) combination were inoculated, and five replicate wells were set up for each sample. The medium without any inoculated strain was used as a blank control, and a 36 h static incubation at 37°C was conducted. After cultivation, the medium was gently discarded, and three rinses with PBS were performed to remove planktonic bacteria and impurities. Each well was added with 200 μL methanol to fix the biofilm. After 15 min, the methanol was discarded, and upon complete evaporation, 200 μL of 0.1% crystal violet was added for a 5-min staining. Excess dye was then washed away using PBS, and the samples were allowed to dry. In the final step, each well was added with 200 μL of 33% acetic acid and agitated for 10 min. The absorbance at a 590 nm wavelength (OD 590) was then measured using a multi-functional enzyme reader. Among them, L. bacterium = OD_{L. bacterium}-OD_blank, BC-L. bacterium = OD_{BC-L. bacterium}-OD_{B. subtilis} _biofilms.

Resistance assessment in vitro

For the preparation of SGF, 3.84 mL of HCl (Shanghai Guoyao Group Chemical Reagent Co., Ltd) and 10 g of pepsin (1:15000, Shanghai Titan Technology Co., Ltd) were added to every 1000 mL. For the bile salt solution preparation, bile salts (Adamas Reagent, Ltd.) were weighed and dissolved in water to achieve a concentration of 0.3 m/mL. For the preparation of SIF (SIG), the pH of 100 mL of a potassium dihydrogen phosphate solution (1.34% wt) was adjusted to 6.8 using a 0.1 M NaOH solution. Subsequently, 2 g of pancreatin (1:250, Shanghai Titan Technology Co., Ltd) was added, and the volume was made up to 200 mL with water. L. bacterium and BC-L. bacterium were separately resuspended in 1 mL SGF (pH 2.0), bile salt solution, and SIF, and were then incubated at room temperature. At the predetermined time points, the bacteria were washed three times with PBS, collected, and then dispersed in 1 mL PBS for later use. A sample of the bacterial suspension (100 μL) was spread onto sheep blood agar plates, which were then incubated overnight at 37°C for bacterial counting.

Evaluation of intestinal adhesion performance

BALB/c mice (6 weeks old) were fasted for 24 h. After the mice were euthanized, their intestines were obtained. Intestinal segments (approximately 1 cm) were excised equidistantly from the same position of the intestines. These segments were then thoroughly washed with PBS to remove any existing bacteria. L. bacterium and BC-L. bacterium (10¹⁰ CFUs) were separately added (10 μL each) to the intestinal segments, which were then incubated at 37°C for 30 min. Subsequently, a large amount of PBS (6 mL) was used to wash away the unbound bacterial fluid. The intestinal segments were homogenized and then diluted with 1 mL of PBS. The bacterial suspension obtained was spread onto sheep blood agar plates, which were then incubated overnight at 37°C. Subsequently, the number of colonies was recorded.

Detection of L. bacterium

Genomic DNA was extracted by QIAamp DNA Stool Mini Kit (200 mg feces/sample) according to the manufacturer’s instructions. The sample DNA concentration was measured with Nanodrop 2000 and loaded at 40 ng (feces)/well for the qPCR assay. The average cycle threshold (Ct) value was calculated from triplicates. Duplicates with >2 cycle differences were excluded from the analysis. The relative abundance of L. bacterium based on the ΔCt value was defined as Ct (L. bacterium) -Ct (16S ribosome genes).

Patient-derived xenograft mouse model

PDX tumors were established using freshly isolated human ccRCC specimens. Tumor tissues were minced into approximately 1 mm³ fragments and subcutaneously implanted into the right flank of 6-week-old male NOD/SCID mice. When the tumor diameter reached approximately 1 cm, the subcutaneous tumors were harvested and dissected into uniform 5 × 3 × 2 mm³ fragments. These fragments were loaded into a trocar and re-implanted subcutaneously into the flanks of new 6-week-old NOD/SCID mice. After approximately 4 weeks of tumor growth, mice were pretreated with an antibiotic cocktail administered in drinking water for 5 days. Subsequently, mice received oral gavage with either L. bacterium (1 × 10⁸ CFU in 200 μL PBS) or PBS alone five times a week during the experiments. All animal experiments were conducted in accordance with institutional ethical guidelines.⁴⁵^,⁴⁶ Tumor tissue samples were obtained from three patients with ccRCC, and used to generate the PDX model as described. Mice were randomly assigned to experimental groups upon tumor re-implantation. The clinical characteristics of the patient donor are provided in Table S7.

In vitro culture and evaluation of patient-derived tumor spheres

Fresh tumor specimens were collected and stored in tissue storage solution (Miltenyi, Germany) within 24 h post-surgery.⁴⁷ The tissue was washed with PBS, minced into fragments smaller than 1 mm³, and enzymatically digested for 40 min using a solution containing 1640 medium (Gibco, USA), 10% FBS (Wisent, Canada), 2 mg/mL collagenase IV (Sigma, USA), and 150 μg/mL DNase I (Roche, Germany). After filtration through 70 μm sieves and lysis of erythrocytes with ACK lysing buffer (Gibco, USA), the remaining cells were cultured as non-adherent spheroids in ultra-low attachment 6-well plates (3Dsphearo, Jet Biofil, China) in Advanced DMEM/F12 (Gibco, USA), supplemented with 1× Neural-27 without Vitamin A (Meiluncell, China), 20 ng/mL EGF (Novoprotein, China), 20 ng/mL bFGF (Novoprotein, China), 1× GlutaMax (Gibco, USA), 1× Antibiotic-Antimycotic (Wisent, Canada), and 4 μg/mL Heparin sodium (Selleck, USA). Once the spheroids reached a diameter of approximately 700–1000 μm, or the cultures were confluent, the spheroids were dissociated with TrypLE Express (Gibco, USA) and filtered through 70 μm sieves (Falcon, USA). Cells were then seeded in ultra-low attachment 96 U-Well Microplates (Thermo Scientific, USA) at 4000 cells per well and centrifuged at 300g for 5 min. The cells were subsequently co-cultured with 0.1 mM propionate. The treatment effects were quantified at 0, 3, 6, and 9 days post-seeding by measuring metabolic activity using the CellCounting-Lite 3D Luminescent Cell Viability Assay (Vazyme, China) according to the manufacturer’s instructions.

Quantification and statistical analysis

Statistical analysis

Three biological replicates were performed in vitro. All data are expressed as the mean ± SEM. Normality of the data was assessed using the one-sample Kolmogorov-Smirnov test. For normally distributed data, comparisons between two groups were made using independent sample t-tests, and comparisons among three or more groups were performed using one-way ANOVA. If data were not normally distributed, the nonparametric Mann-Whitney test was used for group comparisons. Pearson correlation was used to assess the relationship between variables. PFS was defined as the time from randomization to the first recurrence or progression. Patients without recurrence or progression were censored at the last follow-up or at death. Kaplan-Meier survival curves were used to estimate PFS, and the log rank test was applied for comparisons. All statistical analyses were performed using GraphPad Prism version 9.0 for Windows (GraphPad Software, Boston, MA, USA, www.graphpad.com). A p value <0.05 was considered statistically significant, with significance denoted as ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001.

Additional resources

The clinical trial associated with this study was registered at the Chinese Clinical Trial Registry (ChiCTR) under the registration number: ChiCTR2100044215. The trial information is available at: https://www.chictr.org.cn/showproj.html?proj=122937.

Published: October 8, 2025

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.xcrm.2025.102410.

Contributor Information

Jun-Hua Zheng, Email: zhengjh0471@sina.com.

Jin-Yao Liu, Email: jyliu@sjtu.edu.cn.

Wei Zhai, Email: jacky_zw2002@hotmail.com.

Supplemental information

Document S1. Figures S1–S8, and Tables S1–S5, S7, and S8

mmc1.pdf^{(2.2MB, pdf)}

Table S6. Clinical and laboratory characteristics of the 146 LRCC patients and 64 ARCC patients in cohort 6

mmc2.xlsx^{(21.3KB, xlsx)}

Document S2. Article plus supplemental information

mmc3.pdf^{(33.5MB, pdf)}

References

1.Rini B.I., Campbell S.C., Escudier B. Renal cell carcinoma. Lancet. 2009;373:1119–1132. doi: 10.1016/s0140-6736(09)60229-4. [DOI] [PubMed] [Google Scholar]
2.Curti B.D. Renal cell carcinoma. JAMA. 2004;292:97–100. doi: 10.1001/jama.292.1.97. [DOI] [PubMed] [Google Scholar]
3.Cullin N., Azevedo Antunes C., Straussman R., Stein-Thoeringer C.K., Elinav E. Microbiome and cancer. Cancer Cell. 2021;39:1317–1341. doi: 10.1016/j.ccell.2021.08.006. [DOI] [PubMed] [Google Scholar]
4.Li Q., Hu W., Liu W.-X., Zhao L.-Y., Huang D., Liu X.-D., Chan H., Zhang Y., Zeng J.-D., Coker O.O., et al. Streptococcus thermophilus Inhibits Colorectal Tumorigenesis Through Secreting β-Galactosidase. Gastroenterology. 2021;160:1179–1193.e14. doi: 10.1053/j.gastro.2020.09.003. [DOI] [PubMed] [Google Scholar]
5.Jiang S.-S., Xie Y.-L., Xiao X.-Y., Kang Z.-R., Lin X.-L., Zhang L., Li C.-S., Qian Y., Xu P.-P., Leng X.-X., et al. Fusobacterium nucleatum-derived succinic acid induces tumor resistance to immunotherapy in colorectal cancer. Cell Host Microbe. 2023;31:781–797.e9. doi: 10.1016/j.chom.2023.04.010. [DOI] [PubMed] [Google Scholar]
6.Routy B., Lenehan J.G., Miller W.H., Jamal R., Messaoudene M., Daisley B.A., Hes C., Al K.F., Martinez-Gili L., Punčochář M., et al. Fecal microbiota transplantation plus anti-PD-1 immunotherapy in advanced melanoma: a phase I trial. Nat. Med. 2023;29:2121–2132. doi: 10.1038/s41591-023-02453-x. [DOI] [PubMed] [Google Scholar]
7.Derosa L., Hellmann M.D., Spaziano M., Halpenny D., Fidelle M., Rizvi H., Long N., Plodkowski A.J., Arbour K.C., Chaft J.E., et al. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann. Oncol. 2018;29:1437–1444. doi: 10.1093/annonc/mdy103. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Routy B., Le Chatelier E., Derosa L., Duong C.P.M., Alou M.T., Daillère R., Fluckiger A., Messaoudene M., Rauber C., Roberti M.P., et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science. 2018;359:91–97. doi: 10.1126/science.aan3706. [DOI] [PubMed] [Google Scholar]
9.Derosa L., Routy B., Fidelle M., Iebba V., Alla L., Pasolli E., Segata N., Desnoyer A., Pietrantonio F., Ferrere G., et al. Gut Bacteria Composition Drives Primary Resistance to Cancer Immunotherapy in Renal Cell Carcinoma Patients. Eur. Urol. 2020;78:195–206. doi: 10.1016/j.eururo.2020.04.044. [DOI] [PubMed] [Google Scholar]
10.Dizman N., Meza L., Bergerot P., Alcantara M., Dorff T., Lyou Y., Frankel P., Cui Y., Mira V., Llamas M., et al. Nivolumab plus ipilimumab with or without live bacterial supplementation in metastatic renal cell carcinoma: a randomized phase 1 trial. Nat. Med. 2022;28:704–712. doi: 10.1038/s41591-022-01694-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Zhang X., Yu D., Wu D., Gao X., Shao F., Zhao M., Wang J., Ma J., Wang W., Qin X., et al. Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance. Cell Host Microbe. 2023;31:418–432.e8. doi: 10.1016/j.chom.2023.01.013. [DOI] [PubMed] [Google Scholar]
12.Sorbara M.T., Littmann E.R., Fontana E., Moody T.U., Kohout C.E., Gjonbalaj M., Eaton V., Seok R., Leiner I.M., Pamer E.G. Functional and Genomic Variation between Human-Derived Isolates of Lachnospiraceae Reveals Inter- and Intra-Species Diversity. Cell Host Microbe. 2020;28:134–146.e4. doi: 10.1016/j.chom.2020.05.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Lopetuso L.R., Scaldaferri F., Petito V., Gasbarrini A. Commensal Clostridia: leading players in the maintenance of gut homeostasis. Gut Pathog. 2013;5:23. doi: 10.1186/1757-4749-5-23. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.David L.A., Maurice C.F., Carmody R.N., Gootenberg D.B., Button J.E., Wolfe B.E., Ling A.V., Devlin A.S., Varma Y., Fischbach M.A., et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–563. doi: 10.1038/nature12820. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Guo H., Chou W.-C., Lai Y., Liang K., Tam J.W., Brickey W.J., Chen L., Montgomery N.D., Li X., Bohannon L.M., et al. Multi-omics analyses of radiation survivors identify radioprotective microbes and metabolites. Science. 2020;370 doi: 10.1126/science.aay9097. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Fu A., Yao B., Dong T., Chen Y., Yao J., Liu Y., Li H., Bai H., Liu X., Zhang Y., et al. Tumor-resident intracellular microbiota promotes metastatic colonization in breast cancer. Cell. 2022;185:1356–1372.e26. doi: 10.1016/j.cell.2022.02.027. [DOI] [PubMed] [Google Scholar]
17.Bindels L.B., Porporato P., Dewulf E.M., Verrax J., Neyrinck A.M., Martin J.C., Scott K.P., Buc Calderon P., Feron O., Muccioli G.G., et al. Gut microbiota-derived propionate reduces cancer cell proliferation in the liver. Br. J. Cancer. 2012;107:1337–1344. doi: 10.1038/bjc.2012.409. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Koh A., De Vadder F., Kovatcheva-Datchary P., Bäckhed F. From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites. Cell. 2016;165:1332–1345. doi: 10.1016/j.cell.2016.05.041. [DOI] [PubMed] [Google Scholar]
19.Sivaprakasam S., Prasad P.D., Singh N. Benefits of short-chain fatty acids and their receptors in inflammation and carcinogenesis. Pharmacol. Ther. 2016;164:144–151. doi: 10.1016/j.pharmthera.2016.04.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Xiong H., Guo B., Gan Z., Song D., Lu Z., Yi H., Wu Y., Wang Y., Du H. Butyrate upregulates endogenous host defense peptides to enhance disease resistance in piglets via histone deacetylase inhibition. Sci. Rep. 2016;6 doi: 10.1038/srep27070. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Yang J., Li L., Xi Y., Sun R., Wang H., Ren Y., Zhao L., Wang X., Li X. Combination of IFITM1 knockdown and radiotherapy inhibits the growth of oral cancer. Cancer Sci. 2018;109:3115–3128. doi: 10.1111/cas.13640. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Lui A.J., Geanes E.S., Ogony J., Behbod F., Marquess J., Valdez K., Jewell W., Tawfik O., Lewis-Wambi J. IFITM1 suppression blocks proliferation and invasion of aromatase inhibitor-resistant breast cancer in vivo by JAK/STAT-mediated induction of p21. Cancer Lett. 2017;399:29–43. doi: 10.1016/j.canlet.2017.04.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Tian L., Scholte J., Borewicz K., van den Bogert B., Smidt H., Scheurink A.J.W., Gruppen H., Schols H.A. Effects of pectin supplementation on the fermentation patterns of different structural carbohydrates in rats. Mol. Nutr. Food Res. 2016;60:2256–2266. doi: 10.1002/mnfr.201600149. [DOI] [PubMed] [Google Scholar]
24.Pascale N., Gu F., Larsen N., Jespersen L., Respondek F. The Potential of Pectins to Modulate the Human Gut Microbiota Evaluated by In Vitro Fermentation: A Systematic Review. Nutrients. 2022;14 doi: 10.3390/nu14173629. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Wang X., Cao Z., Zhang M., Meng L., Ming Z., Liu J. Bioinspired oral delivery of gut microbiota by self-coating with biofilms. Sci. Adv. 2020;6 doi: 10.1126/sciadv.abb1952. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Li X., Yan Z., Xu J. Quantitative variation of biofilms among strains in natural populations of Candida albicans. Microbiology (Read.) 2003;149:353–362. doi: 10.1099/mic.0.25932-0. [DOI] [PubMed] [Google Scholar]
27.Pal S.K., Li S.M., Wu X., Qin H., Kortylewski M., Hsu J., Carmichael C., Frankel P. Stool Bacteriomic Profiling in Patients with Metastatic Renal Cell Carcinoma Receiving Vascular Endothelial Growth Factor-Tyrosine Kinase Inhibitors. Clin. Cancer Res. 2015;21:5286–5293. doi: 10.1158/1078-0432.Ccr-15-0724. [DOI] [PubMed] [Google Scholar]
28.Dai G., Chen X., He Y. The Gut Microbiota Activates AhR Through the Tryptophan Metabolite Kyn to Mediate Renal Cell Carcinoma Metastasis. Front. Nutr. 2021;8 doi: 10.3389/fnut.2021.712327. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Peng Z., Cheng S., Kou Y., Wang Z., Jin R., Hu H., Zhang X., Gong J.F., Li J., Lu M., et al. The Gut Microbiome Is Associated with Clinical Response to Anti-PD-1/PD-L1 Immunotherapy in Gastrointestinal Cancer. Cancer Immunol. Res. 2020;8:1251–1261. doi: 10.1158/2326-6066.Cir-19-1014. [DOI] [PubMed] [Google Scholar]
30.Di Modica M., Gargari G., Regondi V., Bonizzi A., Arioli S., Belmonte B., De Cecco L., Fasano E., Bianchi F., Bertolotti A., et al. Gut Microbiota Condition the Therapeutic Efficacy of Trastuzumab in HER2-Positive Breast Cancer. Cancer Res. 2021;81:2195–2206. doi: 10.1158/0008-5472.Can-20-1659. [DOI] [PubMed] [Google Scholar]
31.Peters B.A., Dominianni C., Shapiro J.A., Church T.R., Wu J., Miller G., Yuen E., Freiman H., Lustbader I., Salik J., et al. The gut microbiota in conventional and serrated precursors of colorectal cancer. Microbiome. 2016;4:69. doi: 10.1186/s40168-016-0218-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Atarashi K., Tanoue T., Oshima K., Suda W., Nagano Y., Nishikawa H., Fukuda S., Saito T., Narushima S., Hase K., et al. Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota. Nature. 2013;500:232–236. doi: 10.1038/nature12331. [DOI] [PubMed] [Google Scholar]
33.Bender M.J., McPherson A.C., Phelps C.M., Pandey S.P., Laughlin C.R., Shapira J.H., Medina Sanchez L., Rana M., Richie T.G., Mims T.S., et al. Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment. Cell. 2023;186:1846–1862.e26. doi: 10.1016/j.cell.2023.03.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Schwarzer M., Makki K., Storelli G., Machuca-Gayet I., Srutkova D., Hermanova P., Martino M.E., Balmand S., Hudcovic T., Heddi A., et al. Lactobacillus plantarum strain maintains growth of infant mice during chronic undernutrition. Science. 2016;351:854–857. doi: 10.1126/science.aad8588. [DOI] [PubMed] [Google Scholar]
35.Jeng J.H., Chan C.P., Ho Y.S., Lan W.H., Hsieh C.C., Chang M.C. Effects of butyrate and propionate on the adhesion, growth, cell cycle kinetics, and protein synthesis of cultured human gingival fibroblasts. J. Periodontol. 1999;70:1435–1442. doi: 10.1902/jop.1999.70.12.1435. [DOI] [PubMed] [Google Scholar]
36.Tang Y., Chen Y., Jiang H., Robbins G.T., Nie D. G-protein-coupled receptor for short-chain fatty acids suppresses colon cancer. Int. J. Cancer. 2011;128:847–856. doi: 10.1002/ijc.25638. [DOI] [PubMed] [Google Scholar]
37.Cheon H., Wang Y., Wightman S.M., Jackson M.W., Stark G.R. How cancer cells make and respond to interferon-I. Trends Cancer. 2023;9:83–92. doi: 10.1016/j.trecan.2022.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Ivashkiv L.B., Donlin L.T. Regulation of type I interferon responses. Nat. Rev. Immunol. 2014;14:36–49. doi: 10.1038/nri3581. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Flemming H.C., Wingender J., Szewzyk U., Steinberg P., Rice S.A., Kjelleberg S. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 2016;14:563–575. doi: 10.1038/nrmicro.2016.94. [DOI] [PubMed] [Google Scholar]
40.Dunne W.M., Jr. Bacterial adhesion: seen any good biofilms lately? Clin. Microbiol. Rev. 2002;15:155–166. doi: 10.1128/cmr.15.2.155-166.2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Huang J., Liu S., Zhang C., Wang X., Pu J., Ba F., Xue S., Ye H., Zhao T., Li K., et al. Programmable and printable Bacillus subtilis biofilms as engineered living materials. Nat Chem Biol. 2019;15:34–41. doi: 10.1038/s41589-018-0169-2. [DOI] [PubMed] [Google Scholar]
42.Berne C., Ellison C.K., Ducret A., Brun Y.V. Bacterial adhesion at the single-cell level. Nat Rev Microbiol. 2018;16:616–627. doi: 10.1038/s41579-018-0057-5. [DOI] [PubMed] [Google Scholar]
43.Lin S., Mukherjee S., Li J., Hou W., Pan C., Liu J. Mucosal immunity-mediated modulation of the gut microbiome by oral delivery of probiotics into Peyer’s patches. Sci Adv. 2021;7 doi: 10.1126/sciadv.abf0677. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Yurekten O., Payne T., Tejera N., Amaladoss F.X., Martin C., Williams M., O'Donovan C. MetaboLights: open data repository for metabolomics. Nucleic Acids Res. 2024;52:D640–D646. doi: 10.1093/nar/gkad1045. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Qin W., Li J., Gao N., Kong X., Guo L., Chen Y., Huang L., Chen X., Qi F. Multiomics-based molecular subtyping based on the commensal microbiome predicts molecular characteristics and the therapeutic response in breast cancer. Mol. Cancer. 2024;23:99. doi: 10.1186/s12943-024-02017-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Chen S., Zhang L., Li M., Zhang Y., Sun M., Wang L., Lin J., Cui Y., Chen Q., Jin C., et al. Fusobacterium nucleatum reduces METTL3-mediated m(6)A modification and contributes to colorectal cancer metastasis. Nat. Commun. 2022;13:1248. doi: 10.1038/s41467-022-28913-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Minami J.K., Morrow D., Bayley N.A., Fernandez E.G., Salinas J.J., Tse C., Zhu H., Su B., Plawat R., Jones A., et al. CDKN2A deletion remodels lipid metabolism to prime glioblastoma for ferroptosis. Cancer Cell. 2023;41:1048–1060.e9. doi: 10.1016/j.ccell.2023.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Document S1. Figures S1–S8, and Tables S1–S5, S7, and S8

mmc1.pdf^{(2.2MB, pdf)}

Table S6. Clinical and laboratory characteristics of the 146 LRCC patients and 64 ARCC patients in cohort 6

mmc2.xlsx^{(21.3KB, xlsx)}

Document S2. Article plus supplemental information

mmc3.pdf^{(33.5MB, pdf)}

Data Availability Statement

•
Bulk RNA-seq data have been deposited at the NCBI SRA as PRJNA1268942 and PRJNA1268786. 16S rRNA sequencing data have been deposited at the NCBI SRA as PRJNA1269092. The metabolomics data have been deposited to MetaboLights⁴⁴ repository with the study identifier MTBLS12539. All datasets are publicly available as of the date of publication.
•
This paper does not report original code.
•
Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request.

[bib1] 1.Rini B.I., Campbell S.C., Escudier B. Renal cell carcinoma. Lancet. 2009;373:1119–1132. doi: 10.1016/s0140-6736(09)60229-4. [DOI] [PubMed] [Google Scholar]

[bib2] 2.Curti B.D. Renal cell carcinoma. JAMA. 2004;292:97–100. doi: 10.1001/jama.292.1.97. [DOI] [PubMed] [Google Scholar]

[bib3] 3.Cullin N., Azevedo Antunes C., Straussman R., Stein-Thoeringer C.K., Elinav E. Microbiome and cancer. Cancer Cell. 2021;39:1317–1341. doi: 10.1016/j.ccell.2021.08.006. [DOI] [PubMed] [Google Scholar]

[bib4] 4.Li Q., Hu W., Liu W.-X., Zhao L.-Y., Huang D., Liu X.-D., Chan H., Zhang Y., Zeng J.-D., Coker O.O., et al. Streptococcus thermophilus Inhibits Colorectal Tumorigenesis Through Secreting β-Galactosidase. Gastroenterology. 2021;160:1179–1193.e14. doi: 10.1053/j.gastro.2020.09.003. [DOI] [PubMed] [Google Scholar]

[bib5] 5.Jiang S.-S., Xie Y.-L., Xiao X.-Y., Kang Z.-R., Lin X.-L., Zhang L., Li C.-S., Qian Y., Xu P.-P., Leng X.-X., et al. Fusobacterium nucleatum-derived succinic acid induces tumor resistance to immunotherapy in colorectal cancer. Cell Host Microbe. 2023;31:781–797.e9. doi: 10.1016/j.chom.2023.04.010. [DOI] [PubMed] [Google Scholar]

[bib6] 6.Routy B., Lenehan J.G., Miller W.H., Jamal R., Messaoudene M., Daisley B.A., Hes C., Al K.F., Martinez-Gili L., Punčochář M., et al. Fecal microbiota transplantation plus anti-PD-1 immunotherapy in advanced melanoma: a phase I trial. Nat. Med. 2023;29:2121–2132. doi: 10.1038/s41591-023-02453-x. [DOI] [PubMed] [Google Scholar]

[bib7] 7.Derosa L., Hellmann M.D., Spaziano M., Halpenny D., Fidelle M., Rizvi H., Long N., Plodkowski A.J., Arbour K.C., Chaft J.E., et al. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann. Oncol. 2018;29:1437–1444. doi: 10.1093/annonc/mdy103. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib8] 8.Routy B., Le Chatelier E., Derosa L., Duong C.P.M., Alou M.T., Daillère R., Fluckiger A., Messaoudene M., Rauber C., Roberti M.P., et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science. 2018;359:91–97. doi: 10.1126/science.aan3706. [DOI] [PubMed] [Google Scholar]

[bib9] 9.Derosa L., Routy B., Fidelle M., Iebba V., Alla L., Pasolli E., Segata N., Desnoyer A., Pietrantonio F., Ferrere G., et al. Gut Bacteria Composition Drives Primary Resistance to Cancer Immunotherapy in Renal Cell Carcinoma Patients. Eur. Urol. 2020;78:195–206. doi: 10.1016/j.eururo.2020.04.044. [DOI] [PubMed] [Google Scholar]

[bib10] 10.Dizman N., Meza L., Bergerot P., Alcantara M., Dorff T., Lyou Y., Frankel P., Cui Y., Mira V., Llamas M., et al. Nivolumab plus ipilimumab with or without live bacterial supplementation in metastatic renal cell carcinoma: a randomized phase 1 trial. Nat. Med. 2022;28:704–712. doi: 10.1038/s41591-022-01694-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib11] 11.Zhang X., Yu D., Wu D., Gao X., Shao F., Zhao M., Wang J., Ma J., Wang W., Qin X., et al. Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance. Cell Host Microbe. 2023;31:418–432.e8. doi: 10.1016/j.chom.2023.01.013. [DOI] [PubMed] [Google Scholar]

[bib12] 12.Sorbara M.T., Littmann E.R., Fontana E., Moody T.U., Kohout C.E., Gjonbalaj M., Eaton V., Seok R., Leiner I.M., Pamer E.G. Functional and Genomic Variation between Human-Derived Isolates of Lachnospiraceae Reveals Inter- and Intra-Species Diversity. Cell Host Microbe. 2020;28:134–146.e4. doi: 10.1016/j.chom.2020.05.005. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib13] 13.Lopetuso L.R., Scaldaferri F., Petito V., Gasbarrini A. Commensal Clostridia: leading players in the maintenance of gut homeostasis. Gut Pathog. 2013;5:23. doi: 10.1186/1757-4749-5-23. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib14] 14.David L.A., Maurice C.F., Carmody R.N., Gootenberg D.B., Button J.E., Wolfe B.E., Ling A.V., Devlin A.S., Varma Y., Fischbach M.A., et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–563. doi: 10.1038/nature12820. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib15] 15.Guo H., Chou W.-C., Lai Y., Liang K., Tam J.W., Brickey W.J., Chen L., Montgomery N.D., Li X., Bohannon L.M., et al. Multi-omics analyses of radiation survivors identify radioprotective microbes and metabolites. Science. 2020;370 doi: 10.1126/science.aay9097. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib16] 16.Fu A., Yao B., Dong T., Chen Y., Yao J., Liu Y., Li H., Bai H., Liu X., Zhang Y., et al. Tumor-resident intracellular microbiota promotes metastatic colonization in breast cancer. Cell. 2022;185:1356–1372.e26. doi: 10.1016/j.cell.2022.02.027. [DOI] [PubMed] [Google Scholar]

[bib17] 17.Bindels L.B., Porporato P., Dewulf E.M., Verrax J., Neyrinck A.M., Martin J.C., Scott K.P., Buc Calderon P., Feron O., Muccioli G.G., et al. Gut microbiota-derived propionate reduces cancer cell proliferation in the liver. Br. J. Cancer. 2012;107:1337–1344. doi: 10.1038/bjc.2012.409. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib18] 18.Koh A., De Vadder F., Kovatcheva-Datchary P., Bäckhed F. From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites. Cell. 2016;165:1332–1345. doi: 10.1016/j.cell.2016.05.041. [DOI] [PubMed] [Google Scholar]

[bib19] 19.Sivaprakasam S., Prasad P.D., Singh N. Benefits of short-chain fatty acids and their receptors in inflammation and carcinogenesis. Pharmacol. Ther. 2016;164:144–151. doi: 10.1016/j.pharmthera.2016.04.007. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib20] 20.Xiong H., Guo B., Gan Z., Song D., Lu Z., Yi H., Wu Y., Wang Y., Du H. Butyrate upregulates endogenous host defense peptides to enhance disease resistance in piglets via histone deacetylase inhibition. Sci. Rep. 2016;6 doi: 10.1038/srep27070. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib21] 21.Yang J., Li L., Xi Y., Sun R., Wang H., Ren Y., Zhao L., Wang X., Li X. Combination of IFITM1 knockdown and radiotherapy inhibits the growth of oral cancer. Cancer Sci. 2018;109:3115–3128. doi: 10.1111/cas.13640. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib22] 22.Lui A.J., Geanes E.S., Ogony J., Behbod F., Marquess J., Valdez K., Jewell W., Tawfik O., Lewis-Wambi J. IFITM1 suppression blocks proliferation and invasion of aromatase inhibitor-resistant breast cancer in vivo by JAK/STAT-mediated induction of p21. Cancer Lett. 2017;399:29–43. doi: 10.1016/j.canlet.2017.04.005. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib23] 23.Tian L., Scholte J., Borewicz K., van den Bogert B., Smidt H., Scheurink A.J.W., Gruppen H., Schols H.A. Effects of pectin supplementation on the fermentation patterns of different structural carbohydrates in rats. Mol. Nutr. Food Res. 2016;60:2256–2266. doi: 10.1002/mnfr.201600149. [DOI] [PubMed] [Google Scholar]

[bib24] 24.Pascale N., Gu F., Larsen N., Jespersen L., Respondek F. The Potential of Pectins to Modulate the Human Gut Microbiota Evaluated by In Vitro Fermentation: A Systematic Review. Nutrients. 2022;14 doi: 10.3390/nu14173629. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib25] 25.Wang X., Cao Z., Zhang M., Meng L., Ming Z., Liu J. Bioinspired oral delivery of gut microbiota by self-coating with biofilms. Sci. Adv. 2020;6 doi: 10.1126/sciadv.abb1952. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib26] 26.Li X., Yan Z., Xu J. Quantitative variation of biofilms among strains in natural populations of Candida albicans. Microbiology (Read.) 2003;149:353–362. doi: 10.1099/mic.0.25932-0. [DOI] [PubMed] [Google Scholar]

[bib27] 27.Pal S.K., Li S.M., Wu X., Qin H., Kortylewski M., Hsu J., Carmichael C., Frankel P. Stool Bacteriomic Profiling in Patients with Metastatic Renal Cell Carcinoma Receiving Vascular Endothelial Growth Factor-Tyrosine Kinase Inhibitors. Clin. Cancer Res. 2015;21:5286–5293. doi: 10.1158/1078-0432.Ccr-15-0724. [DOI] [PubMed] [Google Scholar]

[bib28] 28.Dai G., Chen X., He Y. The Gut Microbiota Activates AhR Through the Tryptophan Metabolite Kyn to Mediate Renal Cell Carcinoma Metastasis. Front. Nutr. 2021;8 doi: 10.3389/fnut.2021.712327. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib29] 29.Peng Z., Cheng S., Kou Y., Wang Z., Jin R., Hu H., Zhang X., Gong J.F., Li J., Lu M., et al. The Gut Microbiome Is Associated with Clinical Response to Anti-PD-1/PD-L1 Immunotherapy in Gastrointestinal Cancer. Cancer Immunol. Res. 2020;8:1251–1261. doi: 10.1158/2326-6066.Cir-19-1014. [DOI] [PubMed] [Google Scholar]

[bib30] 30.Di Modica M., Gargari G., Regondi V., Bonizzi A., Arioli S., Belmonte B., De Cecco L., Fasano E., Bianchi F., Bertolotti A., et al. Gut Microbiota Condition the Therapeutic Efficacy of Trastuzumab in HER2-Positive Breast Cancer. Cancer Res. 2021;81:2195–2206. doi: 10.1158/0008-5472.Can-20-1659. [DOI] [PubMed] [Google Scholar]

[bib31] 31.Peters B.A., Dominianni C., Shapiro J.A., Church T.R., Wu J., Miller G., Yuen E., Freiman H., Lustbader I., Salik J., et al. The gut microbiota in conventional and serrated precursors of colorectal cancer. Microbiome. 2016;4:69. doi: 10.1186/s40168-016-0218-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib32] 32.Atarashi K., Tanoue T., Oshima K., Suda W., Nagano Y., Nishikawa H., Fukuda S., Saito T., Narushima S., Hase K., et al. Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota. Nature. 2013;500:232–236. doi: 10.1038/nature12331. [DOI] [PubMed] [Google Scholar]

[bib33] 33.Bender M.J., McPherson A.C., Phelps C.M., Pandey S.P., Laughlin C.R., Shapira J.H., Medina Sanchez L., Rana M., Richie T.G., Mims T.S., et al. Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment. Cell. 2023;186:1846–1862.e26. doi: 10.1016/j.cell.2023.03.011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib34] 34.Schwarzer M., Makki K., Storelli G., Machuca-Gayet I., Srutkova D., Hermanova P., Martino M.E., Balmand S., Hudcovic T., Heddi A., et al. Lactobacillus plantarum strain maintains growth of infant mice during chronic undernutrition. Science. 2016;351:854–857. doi: 10.1126/science.aad8588. [DOI] [PubMed] [Google Scholar]

[bib35] 35.Jeng J.H., Chan C.P., Ho Y.S., Lan W.H., Hsieh C.C., Chang M.C. Effects of butyrate and propionate on the adhesion, growth, cell cycle kinetics, and protein synthesis of cultured human gingival fibroblasts. J. Periodontol. 1999;70:1435–1442. doi: 10.1902/jop.1999.70.12.1435. [DOI] [PubMed] [Google Scholar]

[bib36] 36.Tang Y., Chen Y., Jiang H., Robbins G.T., Nie D. G-protein-coupled receptor for short-chain fatty acids suppresses colon cancer. Int. J. Cancer. 2011;128:847–856. doi: 10.1002/ijc.25638. [DOI] [PubMed] [Google Scholar]

[bib37] 37.Cheon H., Wang Y., Wightman S.M., Jackson M.W., Stark G.R. How cancer cells make and respond to interferon-I. Trends Cancer. 2023;9:83–92. doi: 10.1016/j.trecan.2022.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib38] 38.Ivashkiv L.B., Donlin L.T. Regulation of type I interferon responses. Nat. Rev. Immunol. 2014;14:36–49. doi: 10.1038/nri3581. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib39] 39.Flemming H.C., Wingender J., Szewzyk U., Steinberg P., Rice S.A., Kjelleberg S. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 2016;14:563–575. doi: 10.1038/nrmicro.2016.94. [DOI] [PubMed] [Google Scholar]

[bib40] 40.Dunne W.M., Jr. Bacterial adhesion: seen any good biofilms lately? Clin. Microbiol. Rev. 2002;15:155–166. doi: 10.1128/cmr.15.2.155-166.2002. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib45] 41.Huang J., Liu S., Zhang C., Wang X., Pu J., Ba F., Xue S., Ye H., Zhao T., Li K., et al. Programmable and printable Bacillus subtilis biofilms as engineered living materials. Nat Chem Biol. 2019;15:34–41. doi: 10.1038/s41589-018-0169-2. [DOI] [PubMed] [Google Scholar]

[bib46] 42.Berne C., Ellison C.K., Ducret A., Brun Y.V. Bacterial adhesion at the single-cell level. Nat Rev Microbiol. 2018;16:616–627. doi: 10.1038/s41579-018-0057-5. [DOI] [PubMed] [Google Scholar]

[bib47] 43.Lin S., Mukherjee S., Li J., Hou W., Pan C., Liu J. Mucosal immunity-mediated modulation of the gut microbiome by oral delivery of probiotics into Peyer’s patches. Sci Adv. 2021;7 doi: 10.1126/sciadv.abf0677. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib44] 44.Yurekten O., Payne T., Tejera N., Amaladoss F.X., Martin C., Williams M., O'Donovan C. MetaboLights: open data repository for metabolomics. Nucleic Acids Res. 2024;52:D640–D646. doi: 10.1093/nar/gkad1045. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib41] 45.Qin W., Li J., Gao N., Kong X., Guo L., Chen Y., Huang L., Chen X., Qi F. Multiomics-based molecular subtyping based on the commensal microbiome predicts molecular characteristics and the therapeutic response in breast cancer. Mol. Cancer. 2024;23:99. doi: 10.1186/s12943-024-02017-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib42] 46.Chen S., Zhang L., Li M., Zhang Y., Sun M., Wang L., Lin J., Cui Y., Chen Q., Jin C., et al. Fusobacterium nucleatum reduces METTL3-mediated m(6)A modification and contributes to colorectal cancer metastasis. Nat. Commun. 2022;13:1248. doi: 10.1038/s41467-022-28913-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib43] 47.Minami J.K., Morrow D., Bayley N.A., Fernandez E.G., Salinas J.J., Tse C., Zhu H., Su B., Plawat R., Jones A., et al. CDKN2A deletion remodels lipid metabolism to prime glioblastoma for ferroptosis. Cancer Cell. 2023;41:1048–1060.e9. doi: 10.1016/j.ccell.2023.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Intestinal Lachnospiraceae bacterium-derived propionate inhibits the progression of clear cell renal cell carcinoma

Jun-Yao Xu

Huan Chen

Yao-Yu Yu

Tian-Yi Chen

Yi-Qiu Wang

Wei Xue

Jun-Hua Zheng

Jin-Yao Liu

Wei Zhai

Summary

Graphical abstract

Highlights

Introduction

Results

ccRCC-associated gut dysbiosis correlates with its progression

Figure 1.

L. bacterium protects against ccRCC tumorigenesis through its supernatant

Figure 2.

Propionate in the supernatant of L. bacterium exerts the antitumor effect

Figure 3.

L. bacterium-derived propionate downregulates IFITM1 expression to inhibit the tumor progression through activating JAK1-STAT1/2 pathway

Figure 4.

Propionate suppresses IFITM1 expression through downregulating HOXD10

Figure 5.

Potential methods for future therapeutic models

Figure 6.

Levels of L. bacterium, IFITM1, and HOXD10 correlate and predict the outcome of ccRCC patients

Figure 7.

Discussion

Limitations of the study

Resource availability

Lead contact

Materials availability

Data and code availability

Acknowledgments

Author contributions

Declaration of interests

STAR★Methods

Key resources table

Experimental model and study participant details

Animal

Study cohorts

Cell lines

Bacterial culture

Method details

Tumor models

Fecal microbiota transplantation experiment

DNA extraction, 16S rRNA gene amplicon sequencing, and data processing

Cell treatment

RNA sequencing and data analysis

Targeted metabolomic analysis

Transwell assays

Wound healing assays

Cell viability analysis

Colony formation assays

Cell cycle assay

5-Ethynyl-2′-deoxyuridine (EdU) assay

Histone deacetylase (HDAC) activity assay

RNA extraction and qRT-PCR assay

Western blot

Chromatin immunoprecipitation PCR

Construction of small interfering RNAs and transfection of cells

Immunohistochemistry analysis

Dual-luciferase reporter assays

Preparation of biofilm-coated L. bacterium (BC- L. bacterium)

Characterization of BC- L. bacterium

Quantitative detection of biofilms by crystal violet staining

Resistance assessment in vitro

Evaluation of intestinal adhesion performance

Detection of L. bacterium

Patient-derived xenograft mouse model

In vitro culture and evaluation of patient-derived tumor spheres

Quantification and statistical analysis

Statistical analysis

Additional resources

Footnotes

Contributor Information

Supplemental information