Intratumoral Parvimonas micra promotes esophageal squamous cell carcinoma via p-cresol–induced T_reg differentiation

Abstract

Intratumoral microbiota has emerged as a notable factor influencing cancer initiation and progression. However, its composition and functional impact in esophageal squamous cell carcinoma (ESCC) remain largely unexplored. Here, we performed metagenomic sequencing on 119 paired tumor-normal tissues from patients with ESCC and single-cell RNA sequencing on 45 samples to investigate microbe-host interactions. We identified Parvimonas micra (P. micra), an anaerobic oral-derived bacterium, as significantly enriched in tumor tissues and associated with poor prognosis. Moreover, the abundance of P. micra correlated with increased regulatory T cell (T_reg cell) infiltration in the ESCC tumor microenvironment. Through cellular and animal experiments, we demonstrate that P. micra promotes tumor growth by secreting p-cresol, a metabolite of amino acid fermentation, which elevates reactive oxygen species levels and induces FOXP3⁺ T_reg differentiation, thereby fostering immunosuppression and tumor growth. Our study establishes a mechanistic link between intratumoral microbiota and the immune microenvironment, highlighting the microbial contribution to ESCC progression and prognosis.

Intratumoral Parvimonas micra promotes esophageal cancer via p-cresol–mediated T_reg differentiation and immunosuppression.

INTRODUCTION

Esophageal cancer (EC) ranked as the seventh leading cause of cancer-related mortality worldwide in 2022 (1). Esophageal squamous cell carcinoma (ESCC), the predominant subtype, accounts for ~85% of EC cases globally, with half occurring in China (2). Despite considerable advances in ESCC research over the past decade, diagnosis continues to be constrained by a reliance on endoscopy, while on the therapeutic front, the efficacy of novel treatments like programmed cell death protein 1 (PD-1) inhibitors remains limited (3, 4). These limitations highlight the urgent need for further studies to elucidate the underlying mechanisms. Therefore, deciphering the complexity of tumors and their microenvironment, which encompasses immune cells, stromal cells, extracellular matrix, and microbes, is crucial for advancing the understanding of ESCC pathogenesis.

Growing evidence emphasizes the emerging cross-talk between the microbiome and the immune microenvironment. The microbiome promotes the occurrence and development of cancer through processes such as physical interactions, the production of metabolites, and the generation of carcinogenic toxins (5). These processes can lead to genotoxicity, epigenomic abnormalities, and activation of signaling pathways; drive metabolic reprogramming, disrupt redox homeostasis; induce inflammation; and promote an immunosuppressive tumor microenvironment (6, 7). Existing studies on the gastrointestinal cancer microbiome have primarily focused on colorectal cancer (CRC) and gastric cancer (GC). Specific microorganisms have been implicated in the development and progression of these cancers. For instance, Helicobacter pylori is a well-established risk factor for GC, while Fusobacterium nucleatum (F. nucleatum) has been associated with CRC (8, 9). In contrast, the ESCC microbiome remains understudied. Existing studies, often constrained by small cohort sizes and reliance on 16S ribosomal RNA (rRNA) sequencing, have identified tumor-enriched bacteria such as Fusobacterium, Streptococcus, Lactobacillus, and Staphylococcus (10–13). However, mechanistic research has primarily focused on two pathogens transplanted from studies on other cancers, F. nucleatum and Porphyromonas gingivalis, which have been shown to promote ESCC progression via nuclear factor κB (NF-κB) pathway activation (14, 15). The oncogenic mechanisms of other ESCC-associated bacterial taxa remain largely unexplored, highlighting the need for systematic exploration of the broader microbial landscape linked to ESCC pathogenesis.

In this study, we characterized the metagenomic communities of paired tumor and adjacent normal tissues from 119 patients with ESCC, revealing distinct microbiome profiles between the two tissue types. Parvimonas micra (P. micra) was identified as a prominent species enriched in tumor tissues and associated with poor prognosis. Single-cell RNA sequencing (scRNA-seq) and multiplex immunofluorescence (mIF) staining showed that P. micra abundance positively correlated with regulatory T cell (T_reg cell) infiltration in the tumor microenvironment. Mechanically, we found that P. micra promoted tumor growth by inducing FOXP3⁺ T_reg cells through its metabolite p-cresol, which elevated reactive oxygen species (ROS) levels in CD4⁺ T cells, thereby facilitating T_reg differentiation and creating an immunosuppressive microenvironment.

RESULTS

ESCC exhibits distinct microbiome profiles from adjacent normal tissues

Using a fine-tuned host DNA depletion method, we performed metagenomic sequencing on paired tumor and adjacent normal tissues from 119 patients with ESCC. After applying a series of stringent decontamination filters, 223 samples were retained for subsequent analyses (fig. S1 and Materials and Methods). Their microbiome composition was clearly distinct from that of the negative controls, confirming the effectiveness of the decontamination process (fig. S2A). In addition, scRNA-seq was conducted on 41 ESCC tumor samples and 4 adjacent normal tissue (hereafter referred to as normal tissues) samples from the same cohort (Fig. 1A). For paired tissues from 59 patients, 16S sequencing and internal transcribed spacer (ITS) sequencing were also performed.

Fig. 1. Characterization of the ESCC intratumoral microbiome.

(A) Schematic representation of the overall study design for multiomics data. (B) Intersample variation in the ESCC intratumoral microbiome explained by the indicated factors, estimated using the PERMANOVA method. Colors represent the value of −log₁₀(P value). The Adonis R² value represents the proportion of variance explained. (C) Representative FISH images of bacterial 16S rRNA from the tumor tissues (n = 18) and matched normal tissues (n = 18). (D) Quantification of (C). The P value is determined using a two-tailed paired Wilcoxon rank-sum test. (E) Comparison of normalized bacterial abundance between tumor tissues (n = 59) and matched normal tissues (n = 59) as measured by qPCR. Bacterial abundance was normalized against total DNA concentration using the formula log₁₀(2^−Ct/DNA concentration × 10¹⁴ + 1). The P value is determined using a two-tailed paired Wilcoxon rank-sum test. (F) Proportion of species annotated with aerophilicity among tumor- and normal-enriched species. P values were assessed using the false discovery rate (FDR)–adjusted Fisher’s exact test. For all panels, *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. See also figs. S1 and S2.

A significant correlation was observed in community composition between 16S rRNA gene sequencing and metagenomic sequencing (M² = 0.373, P = 0.001, Procrustes analysis) at the genus level (fig. S2B), and the abundance of microorganisms was strongly correlated between 16S rRNA gene sequencing and metagenomic sequencing (r = 0.60, P < 0.0001, Spearman correlation test), with Treponema, Selenomonas, and Parvimonas exhibiting the highest correlation coefficients (fig. S2C). These results confirmed the reliability of our metagenomic data. Therefore, to achieve a higher resolution at the species level, all subsequent microbiome analyses were based on metagenomic data.

We found that sample type (tumor versus normal) was the most significant factor influencing intratumor community structures at the species level [R² = 0.009, P = 0.001, permutational multivariate analysis of variance (PERMANOVA); Fig. 1B]. Age was identified as the factor associated with the largest variance in microbiome composition (R² = 0.015, P = 0.04, PERMANOVA; Fig. 1B). Fluorescence in situ hybridization (FISH) analysis indicated a higher bacterial load in tumor regions compared to adjacent normal regions (Fig. 1, C and D). This observation was further supported by quantitative polymerase chain reaction (qPCR), which showed a significantly higher ratio of 16S rRNA genes to total DNA in tumor tissues (Fig. 1E). The richness index, but not the Shannon index, was significantly lower in tumor tissues compared to normal tissues (fig. S2D). This finding implies that tumor tissues harbor fewer microbial species but a higher DNA load.

Bacillota, Pseudomonadota, and Actinomycetota are the predominant phyla in both normal and tumor samples (fig. S2E). At the genus level, Streptococcus, Acinetobacter, and Granulicatella emerged as the most abundant genera in both normal and tumor tissues (fig. S2E). Moreover, Streptococcus mitis, Streptococcus oralis (S. oralis), and Granulicatella adiacens (G. adiacens) were the most abundant species in normal tissues. In contrast, G. adiacens, S. oralis, and P. micra were the most abundant species in the tumor samples (fig. S2E).

We identified 87 species enriched in tumor tissues and 77 species enriched in normal tissues (P < 0.05, ALDEx2 analysis). Notably, most species (70.11%) enriched in tumor tissues were either anaerobic or facultative anaerobes, whereas only 2.60% of the species enriched in normal tissues exhibited similar characteristics (Fig. 1F and table S1). This observation led us to hypothesize that hypoxic tumors may preferentially harbor anaerobic bacteria, as these organisms are well adapted to low-oxygen environments.

Species enriched in tumor tissues correlate with patient prognosis

To identify species associated with overall survival (OS) in patients with ESCC, we performed multivariate Cox regression analysis, adjusting for clinical variables such as age, gender, tumor stage, smoking status, and alcohol consumption. A total of 116 species were significantly associated with ESCC prognosis. Among these, 101 were categorized as poor prognosis microbes [hazard ratio (HR) > 1, P < 0.05], while the remaining 15 species were identified as favorable prognosis microbes (HR < 1, P < 0.05) (Fig. 2A).

Fig. 2. Identification of tumor-enriched bacterial species and their prognostic relevance in ESCC.

(A) Upper Venn diagram: Species enriched in tumors, associated with poor prognosis, and with >60% prevalence in tumors. Lower Venn diagram: Species enriched in normal tissue, associated with favorable prognosis, and with >60% prevalence in normal tissues. The bar plot shows the median relative abundance of the 25 intersection species—tumor abundance for TPPMs and normal-tissue abundance for NFPMs. (B) Correlation of centered log ratio (clr)–transformed median abundance differences (tumor versus matched normal) with log₂ HR from Cox proportional hazards models. Colors indicate species groups, shapes denote aerophilicity, and point sizes reflect median relative abundance. (C) Representative FISH images of P. micra (P. m.) from the tumor (n = 18) and matched normal tissues (n = 18). (D) Quantification of P. micra abundance based on FISH results in (C). (E) Quantification of P. micra abundance in normal and tumor tissues using qPCR. P. micra abundance was normalized against total DNA concentration using the formula log₁₀(2^−Ct/DNA concentration × 10¹⁴ + 1). (F) Box plot showing the relative abundance of P. micra in tumor tissues compared to normal tissues in the GC cohort (n = 85). (G) Box plot showing the relative abundance of P. micra in tumor tissues compared to normal tissues in the CRC cohorts (PRJNA280026: n = 52; PRJNA383606: n = 55; PRJNA861885: n = 292). (H) Abundance of P. micra in responders (R; n = 81) versus nonresponders (NR; n = 254) in a pancancer cohort treated with ICIs. Whiskers represent the median with the interquartile range. For all panels, the P value is determined using a two-tailed Wilcoxon rank-sum test. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. See also fig. S3.

By analyzing the differential species between tumor and normal tissues and their association with prognosis, we identified 17 tumor-enriched poor prognosis microbes (TPPMs) and 8 normal-enriched favorable prognosis microbes (NFPMs) (Fig. 2, A and B, and table S2). The TPPMs were characterized by their higher abundance in tumor tissues, association with poor prognosis, and detection in more than 60% of tumor samples. Conversely, the NFPMs were defined by their enrichment in normal tissues, association with favorable prognosis, and prevalence in more than 60% of normal samples. The top abundant TPPMs included P. micra, Prevotella melaninogenica, and Prevotella intermedia, while Pseudomonas putida, Agrobacterium tumefaciens, and Acinetobacter johnsonii ranked among the top abundant NFPMs.

Notably, among the TPPMs and NFPMs, 12 species had been proposed to be associated with oral infections (Fig. 2A) (16–23). All these bacteria were enriched in tumor tissues and linked to poor prognosis. Metagenomic analysis of paired tumor-saliva samples from an ESCC cohort in Tanzania confirmed that 11 of these TPPMs exhibited significant correlations between paired saliva and esophageal tumor tissues, suggesting a potential oral origin (fig. S3A). These TPPMs might transfer between tumor and normal tissues as their abundance in these two tissues was positively correlated, with P. micra displaying the highest correlation (r = 0.85, P < 0.0001, Spearman correlation test; fig. S3B).

In this study, we focused on P. micra for further investigation, given its recognition as the predominant TPPM. Its enrichment in tumor tissues was confirmed using qPCR on 36 paired non–host-depleted samples and FISH analysis of 18 paired tissue sections (Fig. 2, C to E). In addition, microbiome profiling from an ESCC spatial transcriptomic dataset revealed that P. micra unique molecular identifier (UMI) counts exhibited a progressive increase from normal to low-grade intraepithelial neoplasia to ESCC (fig. S3, C and D). These findings demonstrate that the abundance of P. micra not only elevates in tumor tissues but also correlates with cancer progression, highlighting its potential role in ESCC tumorigenesis. We also observed a significantly higher abundance of P. micra in tumor tissues in GC and CRC using independent datasets (PRJNA280026, PRJNA383606, PRJNA861885, and PRJNA1032279) (Fig. 2, F and G) (24–27). These findings suggest that the enrichment of P. micra in tumor tissues may be a common phenomenon across gastrointestinal cancers. Furthermore, analyses of pancancer immunotherapy cohort data indicated that patients who do not respond to immune checkpoint inhibitors (ICIs) exhibit a higher P. micra abundance compared to responding patients (Fig. 2H).

P. micra shapes the immunosuppressive tumor microenvironment

Previous studies have shown that the intratumoral microbiota can perturb the immune microenvironment (28, 29). To characterize the cellular composition and function of the tumor microenvironment, we performed scRNA-seq on samples from 41 patients (30), identifying six main cell clusters: T cells, B cells, myeloid cells, epithelial cells, fibroblasts, and endothelial cells. These clusters were further subdivided on the basis of established marker genes (figs. S4, A and B, and S5). ESCC tumors were enriched in CD4⁺ T_reg cells (CD4_T_reg) and myofibroblastic cancer–associated fibroblasts (myCAFs), while CD8⁺ tissue-resident memory T cells (CD8_Trm) and CD4⁺ tissue-resident memory T cells (CD4_Trm) were depleted in tumors (Fig. 3A). Survival analysis revealed that a higher proportion of CD4⁺ T_reg cells in tumor tissues was associated with shorter OS times, whereas a higher proportion of CD8⁺ tissue-resident memory T cells was linked to longer OS times (Fig. 3B).

Fig. 3. Correlation between the ESCC microbiome and host cells in the tumor microenvironment.

(A) Stick plot showing the proportional difference of the specified cell type between normal and tumor samples. T_reg, regulatory T cells; TAM, tumor-associated macrophage; myCAFs, myofibroblastic cancer–associated fibroblasts; Tex, exhausted T cells; Prolif, proliferating cells; T_H1, T helper 1 cells; iCAFs, inflammatory cancer-associated fibroblasts; TEC, tumor endothelial cells; Tn, naive T cells; RTMs, resident tissue macrophages; GC B, germinal center B cells; Trm, tissue-resident memory T cells; NEC, normal endothelial cell; NFs, normal fibroblasts; NAFs, normal-associated fibroblasts; Tem, effector memory T cells; MD, mucosal defense; Teff, effector T cells; cDC2, type 2 conventional dendritic cells. (B) Kaplan-Meier plot comparing the OS of patients with ESCC stratified by low or high proportions of the specified cell type. HR and 95% CI are calculated by the Cox proportional hazards model. The P value is determined using a log-rank test. AP, antigen presenting; Bmem, memory B cells; HY, hypoxia-related stress; T_FH, T follicular helper cells. (C) Dot plot showing Spearman correlation between the relative abundance of specified bacterial species and the proportion of cell subtypes. Colors indicate the Spearman correlation coefficient (r), while sizes represent the −log₁₀(P) value. (D) Representative multiple immunofluorescence images of KRT6A, CD4, and FOXP3 in tumor tissues (n = 18) and matched normal tissues (n = 18). (E) Quantification of FOXP3⁺CD4⁺ cells in (D). The P value is determined using a two-tailed paired Wilcoxon rank-sum test. (F) Spearman correlation between P. micra abundance and the proportion of FOXP3⁺ CD4⁺ T cells in tumor tissues (n = 18) and matched normal tissues (n = 18). The gray area represents 95% CI. ****P < 0.0001; n.s., not significant. See also figs. S4 and S5.

The 12 putative oral-derived TPPM species exhibited distinct correlations with various cell subtypes (Fig. 3C). Notably, P. micra abundance positively correlated with CD4⁺ T_reg cells (r = 0.38, P = 0.02, Spearman correlation test) and CD4⁺ proliferating T cells (r = 0.45, P = 0.006). Functional characterization revealed that both CD4⁺ T_reg cells and CD4⁺ proliferating T cells displayed robust T_reg transcriptional signatures (fig. S4C). Within the total CD45⁺ immune cell population, the proportion of T_reg cells is significantly higher in ESCC tissues than in normal tissues (fig. S4D). Consistently, our recent spatial transcriptomic study of multistage ESCC progression revealed a progressive increase in T_reg density (31). mIF staining confirmed elevated T_reg cell infiltration in tumors compared to adjacent normal tissues (Fig. 3, D and E). Furthermore, P. micra levels correlated with T_reg cell infiltration in both tumors and adjacent normal tissues (Fig. 3F and fig. S4E). T_reg cell infiltration has been proposed to create an immunosuppressed microenvironment that facilitates tumor immune evasion and progression in multiple cancer types, including ESCC (32–34). Thus, we hypothesized that P. micra may promote ESCC progression by enhancing T_reg cell–mediated suppression of antitumor immunity.

P. micra drives tumor growth by inducing FOXP3⁺ T_reg cells in vivo

To validate the procarcinogenic role of P. micra, we used a subcutaneous tumor mouse model to assess its impact on tumor progression (Fig. 4A). Intratumoral injection of P. micra significantly promoted tumor growth (Fig. 4, B and C), underscoring its potential to exacerbate neoplastic development. Intratumoral colonization of P. micra was corroborated by FISH analysis (Fig. 4, D and E). In addition, mIF staining revealed a significant increase in T_reg cell infiltration within tumor tissues in the P. micra–treated group compared to the phosphate-buffered saline (PBS) control group (Fig. 4, F and G). T_reg cells are proposed to establish an immunosuppressive microenvironment primarily by inducing exhaustion and suppressing the function of CD8⁺ T cells (35). Given this well-established role of T_reg cells, we proceeded to characterize the functional status of CD8⁺ T cells within the tumor tissues. Our experiments verified that in the tumor tissues of the P. micra–treated group, there was a marked decrease in the proportion of cytotoxic GZMB⁺ CD8⁺ T cells, accompanied by a corresponding increase in the proportion of exhausted PD-1⁺ CD8⁺ T cells (Fig. 4, H and I). Collectively, these findings suggest that P. micra may promote ESCC progression by facilitating the infiltration of T_reg cells into the tumor microenvironment, which subsequently suppresses antitumor immune responses.

Fig. 4. Intratumoral injection of P. micra promotes tumor growth and modulates the immune microenvironment.

(A) Experimental design for intratumoral injection of P. micra using a subcutaneous tumor model. (B) Excised tumor images of allografts derived from subcutaneous transplantation of mEC25 cells treated with PBS or P. micra (n = 6 per group). (C) Tumor growth curves of (B). (D) Representative images of FISH of tumor tissues in (B). (E) Quantification of (D). (F) Representative multiple immunofluorescence images of KRT6A, CD4, and FOXP3 of tumor tissues in (B). (G) Quantification of FOXP3⁺CD4⁺ cells in (F). (H) Representative multiple immunofluorescence images of KRT6A, CD8, GZMB, and PD-1 of tumor tissues in (B). (I) Quantification of PD-1⁺CD8⁺ cells and GZMB⁺CD8⁺ cells in (H). For all panels, data are presented as the means ± SD. P values are determined using a two-tailed Student’s t test. ***P < 0.001 and ****P < 0.0001.

P. micra promotes tumor growth by creating an immunosuppressive microenvironment through metabolites

To investigate whether P. micra influences T cell function through its secreted substances, the human T lymphocyte leukemia cell line Jurkat was treated with P. micra–conditioned media (P. micra CM) at different concentrations and heat-killed P. micra at various multiplicities of infection (MOIs) (Fig. 5A). We next assessed the expression of forkhead box P3 (FOXP3), together with transforming growth factor–β1 (TGF-β1) and interleukin-10 (IL-10), two key immunomodulatory cytokines required for T_reg functionality. qPCR and Western blot analysis showed that P. micra CM up-regulated the mRNA levels of FOXP3, TGFB1, and IL10 and increased the protein levels of FOXP3, TGF-β1, and IL-10 in a dose-dependent manner (Fig. 5B and fig. S6A). In contrast, heat-killed P. micra had no significant impact on Jurkat cells (Fig. 5C and fig. S6A). Moreover, we isolated CD4⁺ T cells from the spleens of C57BL/6 mice and treated them with either heat-killed P. micra or P. micra CM. Flow cytometry analysis confirmed that treatment with P. micra CM, but not heat-killed P. micra, resulted in an increased proportion of FOXP3⁺CD25⁺ T_reg cells (Fig. 5, D to G). To better understand the respective contributions of proteins and metabolites in the conditioned medium, we used heat inactivation to denature the secretory proteins present. The results showed that the heat-inactivated P. micra CM had a similar effect to non–heat-inactivated P. micra CM (Fig. 5, H to J, and fig. S6A). These results indicate that small molecular metabolites produced by P. micra, rather than the secretory proteins, are crucial for modulating the T cell function by enhancing the expression of key regulators and effector molecules involved in T_reg cell activity.

Fig. 5. P. micra induces T_reg cell differentiation and promotes tumor growth by secreting metabolites.

(A) Experimental design for coculture systems. h, hours. (B) Western blot analysis of the indicated proteins in Jurkat cells treated with P. micra culture medium (CM) of different concentrations. (C) Western blot analysis of the indicated proteins in Jurkat cells treated with heat-killed P. micra of different MOIs. (D) Flow cytometry of FOXP3 and CD25 in mouse spleen CD4⁺ T cells treated with P. micra CM or BHI (n = 3 biological replicates). (E) Quantification of (D). (F) Flow cytometry of FOXP3 and CD25 in mouse spleen CD4⁺ T cells treated with heat-killed P. micra of different MOIs (n = 3 biological replicates). (G) Quantification of (F). (H) Western blot analysis of the indicated proteins in Jurkat cells treated with heat-inactivated P. micra CM of different concentrations. (I) Flow cytometry of FOXP3 and CD25 in mouse spleen CD4⁺ T cells treated with heat-inactivated P. micra CM or BHI (n = 3 biological replicates). (J) Quantification of (I). (K) Excised tumor images of allografts derived from subcutaneous transplantation of mEC25 cells treated with BHI or P. micra CM (n = 4 per group). (L) Tumor growth curves of (K). (M) Representative multiple immunofluorescence images of KRT6A, CD4, and FOXP3 of tumor tissues in (K). (N) Quantification of FOXP3⁺CD4⁺ cells in (M). (O) Representative multiple immunofluorescence images of KRT6A, CD8, GZMB, and PD-1 of tumor tissues in (K). (P) Quantification of PD-1⁺CD8⁺ cells and GZMB⁺CD8⁺ cells in (O). For all panels, data are presented as the means ± SD. P values are determined using a two-tailed Student’s t test. **P < 0.01, ***P < 0.001, and ****P < 0.0001; n.s., not significant. See also fig. S6.

In vivo, intratumoral injection of P. micra CM significantly promoted tumor growth compared to the brain heart infusion (BHI) control group (Fig. 5, K and L). mIF staining validated that T_reg cell infiltration was significantly higher in the P. micra CM group, which was accompanied by the reduced cytotoxic function of CD8⁺ T cells and increased expression of exhaustion markers (Fig. 5, M to P). Furthermore, in vitro cell proliferation assays demonstrated that P. micra CM did not significantly alter the proliferation of normal esophageal epithelial cells or ESCC cells (fig. S6B). Collectively, these findings suggest that P. micra promotes tumor growth by inducing FOXP3⁺ T_reg differentiation through its metabolites without directly stimulating the proliferation of ESCC cells.

p-Cresol drives T_reg differentiation and tumor progression

To identify microbial metabolites from P. micra involved in tumor progression, we conducted untargeted metabolomic profiling of tumor interstitial fluid from subcutaneous tumor models (Fig. 6, A and B). Tumors injected with P. micra exhibited distinct metabolomic profiles compared to the PBS group, featuring 62 elevated metabolites and 76 reduced metabolites. Among the bacterium-derived metabolites, p-cresol emerged as the most differentially altered (Fig. 6C). In addition, its primary derivative, p-cresol sulfate (PCS), emerged as the most differentially altered metabolite among all metabolites, while another derivative, p-cresol glucuronide, also displayed notable differences (Fig. 6D and fig. S7A). Homology analysis using Blastp of the P. micra reference genome identified sequences with significant similarity to key enzymes involved in the tyrosine–to–p-cresol pathway, including the FldBC (phenyllactate dehydratase) complex, HpdA (hydroxyphenylacetate decarboxylase), and AcdA (acyl-coenzyme A dehydrogenase), suggesting that P. micra has the genetic potential to produce p-cresol (table S3). We then performed mass spectrometric analysis of p-cresol in the culture medium of P. micra and compared it to the control medium. The results showed significantly elevated levels of p-cresol in the culture medium of P. micra, substantiating the capacity of P. micra to generate p-cresol (Fig. 6E). Together, these results suggest that p-cresol could be a potential metabolite mediating the tumor-promoting effects of P. micra.

Fig. 6. Metabolomic analysis reveals altered metabolite profiles in the tumor interstitial fluid of P. micra–treated allografts.

(A) Schematic of metabolomics analysis in the interstitial fluid of tumor following intratumoral injection of P. micra. (B) PCA plot showing overall metabolite patterns in tumor interstitial fluid of the P. micra group and PBS group. (C) Stick plot showing differential levels of bacterium-derived metabolites between the P. micra group and the PBS group. (D) Stick plot showing the top 10 differential metabolite levels in the P. micra group and PBS group. (E) Bar plot showing the quantification of p-cresol in P. micra CM or BHI (n = 3 biological replicates). The P value is determined using a two-tailed Student’s t test. **P < 0.01. See also fig. S7.

Previous studies have shown that p-cresol and its derivative, PCS, could induce oxidative stress by generating ROS across various cell types (36–38). Notably, T_reg cells depend on oxidative phosphorylation–derived ROS to stabilize FOXP3 expression and sustain immunosuppressive stability (39, 40). We therefore hypothesized that p-cresol– and PCS-induced ROS might regulate T_reg differentiation and function. To test this hypothesis, we treated Jurkat cells with varying concentrations of p-cresol. Western blot analysis showed that p-cresol treatment induced a dose-dependent up-regulation of the protein levels of FOXP3, IL-10, and TGF-β1, underscoring its capability to promote the differentiation of T_reg cells (Fig. 7A). In addition, we found that p-cresol enhanced ROS levels in a dose-dependent manner, and this effect could be inhibited by N-acetylcysteine (NAC), a ROS scavenger (Fig. 7, B and C) (41). Furthermore, Western blot analysis, qPCR analysis, and flow cytometry results demonstrated that NAC could abolish p-cresol–driven T_reg differentiation and function (Fig. 7, D to F, and fig. S7B).

Fig. 7. P. micra–derived metabolite p-cresol induces T_reg cell differentiation through ROS-mediated signaling.

(A) Western blot analysis of T_reg-related proteins in Jurkat cells treated with p-cresol of different concentrations. (B) Relative ROS level of Jurkat cells treated with p-cresol of different concentrations (n = 3 biological replicates). (C) Relative ROS level of Jurkat cells treated with p-cresol, NAC, or a combination of p-cresol and NAC (n = 4 biological replicates). (D) Western blot analysis of T_reg-related proteins in Jurkat cells treated with p-cresol, NAC, or a combination of p-cresol and NAC. (E) Flow cytometry of FOXP3 and CD25 in mouse spleen CD4⁺ T cells treated with p-cresol, NAC, or a combination of p-cresol and NAC (n = 3 biological replicates). (F) Quantification of (E). (G) Box plot showing the ROS signature score of CD4⁺ T cells subtypes. Data are presented as the median with 25th to 75th percentiles with a 1.5× quantile range represented by whiskers and outliers. (H) Spearman correlation between the P. micra abundance and the ROS signature score of CD4⁺ T cells. The gray area represents 95% CI. (I) Excised tumor images of allografts derived from subcutaneous transplantation of mEC25 cells treated with PBS, p-cresol, NAC, or a combination of p-cresol and NAC (n = 4 per group). (J) Tumor growth curves of (I). (K) Representative multiple immunofluorescence images of KRT6A, CD4, and FOXP3 of tumor tissues in (I). (L) Quantification of FOXP3⁺CD4⁺ cells in (K). For all panels, data are presented as the means ± SD. P values are determined using a one-way analysis of variance (ANOVA). *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. See also fig. S7.

In addition, we found that T_reg cells exhibited the highest ROS signature scores estimated from scRNA-seq data (Fig. 7G and fig. S7C). ROS signature scores of CD4⁺ T cells positively correlated with the relative abundance of P. micra in tissues (Fig. 7H), supporting the hypothesis that P. micra elevates ROS levels in CD4⁺ T cells. In vivo, intratumoral injection of p-cresol in a subcutaneous tumor mouse model significantly promoted tumor growth and increased the infiltration of T_reg cells while reducing CD8⁺ T cell cytotoxicity and enhancing their exhaustion. Notably, all p-cresol–induced effects were abrogated by the administration of NAC (Fig. 7, I to L, and fig. S7, D and E). Collectively, these findings demonstrate that P. micra exerts a procarcinogenic effect by secreting p-cresol, which elevates ROS levels and induces T_reg differentiation.

DISCUSSION

In this study, we demonstrated that the esophageal tumor microbiome exhibits significant differences compared to adjacent normal tissues and identified P. micra as a key bacterium that promotes the progression of ESCC through metabolite-mediated immune suppression. Our findings provide valuable insights into the role of the intratumoral microbiome in ESCC progression.

First, we found that tumor tissues were enriched with anaerobic bacteria, suggesting that the hypoxic tumor microenvironment may selectively support their growth. This observation is consistent with previous pancancer study on 4164 metastatic samples across 26 cancer types, which indicated that hypoxic conditions may promote the enrichment of anaerobic bacteria in tumors (42). Notably, the microbes enriched in tumors and linked to poorer clinical outcomes (i.e., TPPMs) consist primarily of oral microbes associated with periodontal disease, dental caries, and endodontic infections (16–23). This association is further supported by clinical observation that poor oral hygiene is associated with an increased risk of ESCC (43–45). Oral microbiota translocation is increasingly recognized as a pathogenic mechanism across diseases. For example, oral microbes migrating to the intestine exacerbate intestinal inflammation in inflammatory bowel disease (46), while oral-derived Streptococcus anginosus directly interacts with gastric epithelial cells to promote gastric carcinogenesis (47, 48). Our findings suggest that oral-to-esophageal bacterial translocation plays a critical role in ESCC development, mirroring patterns observed in other gastrointestinal malignancies.

In this study, we uncovered the crucial role of P. micra in ESCC tumor progression through multifaceted analyses and experiment validation. P. micra is a nutritionally fastidious, Gram-positive, obligate anaerobe that colonizes mucosal surfaces, predominantly within the oral cavity (49). It is frequently detected in oral infections, particularly in periodontitis, and is known to promote the production of various inflammatory mediators (50). Beyond its role in oral inflammation, the enrichment of P. micra has been documented in oral squamous cell carcinoma (43), GC (47), and CRC (51). However, its mechanistic role in tumor progression remains largely unexplored, with only a few studies conducted in CRC. In CRC, P. micra has been shown to promote cancer cell proliferation via activation of the Wnt/β-catenin signaling pathway, enhance cancer cell adhesion and migration by altering DNA methylation levels (52), and exert protumorigenic effects associated with increased T helper 17 cell infiltration and decreased natural killer cell activity (53, 54). In contrast, our study found that P. micra did not significantly affect the proliferation of normal esophageal epithelial cells or ESCC cells. In addition, scRNA-seq data revealed no association between P. micra and T helper 17 cell infiltration (r = 0.01, P = 0.96, Spearman correlation) or natural killer/natural killer T cell proportion (r = −0.04, P = 0.83, Spearman correlation). Instead, we demonstrated that P. micra significantly shapes the tumor microenvironment by promoting immunosuppression. Specifically, our findings reveal that P. micra colonization leads to increased infiltration of T_reg cells in the tumor microenvironment. This discrepancy highlights the tissue-specific nature of host-microbiome interactions and suggests that P. micra may adopt distinct oncogenic mechanisms depending on the local immune and metabolic contexts.

Metabolites play complex and critical roles in oncogenesis through immune regulation. Microbial-derived short-chain fatty acids like butyrate are known to expand T_reg cells and suppress antitumor immunity, while acetate promotes interferon-γ secretion, enhancing T cell effector functions (55). In pancreatic ductal adenocarcinoma, tryptophan-derived indoles can activate the aryl hydrocarbon receptor in tumor-associated macrophages (TAMs), driving immunosuppression by reducing CD8⁺ T cell infiltration (56). Contrary to these established mechanisms, our metabolomic profiling of ESCC revealed no significant alterations in short-chain fatty acids or tryptophan metabolite levels. Instead, we identified the enrichment of p-cresol and its derivatives. p-Cresol is a phenolic compound primarily generated by gut bacteria and has been implicated in various pathological conditions including chronic kidney diseases and cardiovascular disorders (57–59). In kidney tissue, it activates NADPH [reduced form of NADP⁺ (nicotinamide adenine dinucleotide phosphate)] oxidase–driven ROS production and inflammatory cytokine expression, contributing to renal fibrosis. In the cardiovascular system, p-cresol induces vascular remodeling through direct activation of Rho-kinase signaling. Here, we demonstrate its role in T_reg differentiation and tumor progression. Specifically, we found that p-cresol elevates ROS levels in CD4⁺ T cells, promoting FOXP3⁺ T_reg differentiation and creating an immunosuppressive microenvironment that facilitates tumor growth. This work identified p-cresol as a pivotal microbial metabolite that links microbiome dysbiosis to tumor immune tolerance, enhancing our understanding of how microbiota-derived metabolites influence the tumor microenvironment. Notably, analyses of pancancer immunotherapy cohorts suggested a potential association between P. micra enrichment and reduced immunotherapy responsiveness. Our findings suggest that combining immunotherapy, such as PD-1 inhibitors, with antioxidants such as NAC may represent a promising strategy for the treatment of P. micra–enriched ESCC.

However, several questions remain unresolved. Although we focused on P. micra as a key species, it was not the only microbe enriched in the tumor or linked to poor prognosis. The impact of its synergistic interactions within broader mucosal polymicrobial communities on tumor development also remains unexplored. Mechanistically, while we identified p-cresol secretion and interactions with T_reg cells as critical, other metabolites and cross-talk with different immune or stromal cells may also play a role in ESCC progression. Furthermore, our research only focused on ESCC and normal tissues, lacking precancerous lesion samples. This necessitates further research to elucidate the microbiome’s role across different stages of ESCC development. Emerging research suggests that the intratumoral distribution of microbes is not random but shows distinct spatial preferences (60, 61). As a result, there is a compelling need for spatial omics data to map the organization of these communities and decipher their intricate cross-talk with the tumor microenvironment.

In conclusion, our study revealed that P. micra drives ESCC progression through a critical mechanism of microbial metabolite–mediated immune regulation. This highlights the crucial role of tumor-associated microbes, particularly oral pathobionts, in oncogenesis through diverse mechanisms, warranting further investigation. In addition, their translational potential in tumor risk assessment and therapeutic interventions should be explored.

MATERIALS AND METHODS

Tissue sample collection

ESCC tumor samples and matched adjacent normal samples from the same patients who underwent surgical resection were collected at the Linzhou Esophageal Cancer Hospital (Henan Province, China) between 2018 and 2021. The pathological diagnosis of the tissues was confirmed through histopathological examination. Detailed clinical information for each patient is provided in table S4. This study was approved by the ethics committee of Chinese Academy of Medical Sciences Cancer Hospital (23/305-4047), and written informed consent was obtained from all individuals.

Mouse models

The mouse ESCC cell line mEC25 (5 × 10⁶ cells in 100 μl of PBS) was subcutaneously implanted into 6-week-old C57BL/6 female mice, which were then randomly divided into different groups. For the intratumoral bacterial injection model, PBS or P. micra (10⁸ colony-forming units in 50 μl of PBS) was injected intratumorally every 3 days for a total of four injections. For the intratumoral injection model using bacterial culture media, 50 μl of BHI Broth or P. micra CM were injected intratumorally every 3 days for a total of four injections. For the intratumoral injection model using metabolites, 50 μl of p-cresol (80 μM), NAC (4 mM), or a combination of p-cresol and NAC was injected intratumorally every 3 days for a total of four injections. The tumor volume was assessed from the third day after tumor implantation (before treatment) using the formula length × width²/2. All animal experiments were conducted in accordance with the regulations of the Institutional Animal Care and Use Committee of the Chinese Academy of Medical Science and were approved by the Animal Care Committee of Chinese Academy of Medical Sciences under the reference code NCC2021A271.

Cell culture and treatment

The human ESCC cell lines KYSE70 and KYSE150 were provided by Y. Shimada of Hyogo College of Medicine, Japan. The normal human esophagus epithelial cell line Het-1A was purchased from American Type Culture Collection. The mouse ESCC cell line mEC25 was provided by L. Fu of Shenzhen University International Cancer Center. These cell lines were cultured in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. The human T lymphocyte leukemia cell line Jurkat, clone E6-1, was purchased from Wuhan Pricella Biotechnology and cultured in RPMI 1640 medium supplemented with 10% FBS and 1% penicillin-streptomycin. For further experiments, Jurkat cells were treated with P. micra CM, heat-inactivated P. micra CM, heat-killed P. micra, p-cresol (10 to 80 μM), or NAC (2 mM) for 24 hours. All cells were cultured at 37°C in a humidified incubator with 5% CO₂, routinely authenticated by short-tandem repeat analysis, and regularly treated with a mycoplasma-removing agent.

Bacteria culture

P. micra was purchased from China General Microbiological Culture Collection Center (China). P. micra was cultured on BHI agar and grown in BHI broth medium under anaerobic conditions created by AnaeroPack (Mitsubishi Gas Chemica) at 37°C in a humidified incubator. The bacterial conditioned medium was collected by centrifugation at 5000g for 10 min, followed by filtration through a 0.22-μm filter. The conditioned medium was incubated at 56°C for 1 hour to prepare heat-inactivated conditioned medium. Heat-killed bacteria were prepared using the pasteurization method.

Metagenome sequencing and analysis

Sample preparation and host DNA depletion

Paired tumor and adjacent normal tissues from 119 patients with ESCC were divided into ~50 mg of tissue fragments under sterile conditions using a clean bench. These tissue fragments were carefully washed three times with PBS to remove surface contaminants. To minimize batch effects, ~12 tumor tissues and their corresponding normal tissues from the same patient were processed within the same batch. For each batch, one or two sterile water samples were incorporated as negative controls and processed following the same experimental procedures. Host nucleic acids were efficiently removed using an optimized, previously published bacterial DNA isolation method (62, 63). Esophageal tumor tissues were cut into small pieces and digested in PBS containing collagenase IV (2 mg/ml; Sigma-Aldrich), hyaluronidase (0.5 mg/ml; Sigma-Aldrich), and 20 units of TURBO DNase (deoxyribonuclease; Invitrogen) for 30 min at 37°C. The digested cell suspension was subsequently filtered through a 70-μm cell strainer. Subsequently, the remaining samples were centrifuged at 15,000g for 10 min at 4°C, and the pellet was resuspended with a final concentration of 0.025% saponin (Sigma-Aldrich) and incubated at room temperature for 10 min. After incubation, 14 units of TURBO DNase (Invitrogen) and 50 units of benzonase (Merck Millipore) were added to each sample, and the mixture was incubated at 37°C for 3 hours. Afterward, the nucleases were quenched by adding 20 μl of proteinase K (Qiagen) and incubating at 56°C for 10 min.

DNA extraction and metagenomic sequencing

Nucleic acids were extracted using the DNeasy PowerSoil Pro Kit (Qiagen) according to the manufacturer’s instructions. Host DNA–depleted samples were transferred to PowerBead Pro Tubes, followed by mechanical lysis using a bead-beating system to disrupt microbial cells. DNA was captured on a silica membrane within a spin column, subsequently washed, and eluted from the membrane, preparing it ready for further analysis. Metagenomic libraries were constructed with the QIAseq FX DNA Library UDI Kit (Qiagen) according to the manufacturer’s instructions and sequenced on the DNBSEQ-T7 platform with 150–base pair paired-end reads, generating ~28 million reads for each sample.

Microbial profiling pipeline

Raw reads were processed with fastp (version 0.20.0) for adapter trimming, removal of low-quality sequences, and merging of paired-end reads (64). Low-complexity reads were filtered using komplexity (version 0.3.6) with parameters -F -k -t 0.2 (65). Human and vector sequences were removed using BMTagger by using a database consisting of UniVec vector sequences and the GRCh38 human genome. Given that reads from the mouse genome were detected, likely due to batch sequencing contamination, reads were mapped to the GRCm39 mouse genome using BWA (66), and hit reads were removed from subsequent analyses. Taxonomic classification of the filtered reads was performed using Kraken2 (67) with a subset of the National Center for Biotechnology Information (NCBI) nt database (May 2023), which contains extensive human genomic sequences (including the GRCh38 and CHM13 genomes) along with bacterial, archaeal, fungal, and viral sequences. Species-level abundances were further refined using Bracken, which applies a Bayesian model to reassign reads classifications (68).

Contamination assessment

False-positive microbial detections resulting from contamination from the environment, reagents, and human sources have been a major issue in cancer microbiome studies (69–71). Here, we have developed a stringent pipeline to mitigate these challenges.

First, the proportion of microbial reads in our samples ranged from 0.15 to 92.17%, with a median of 15.5%, which was notably higher than the typical tissue microbiome read proportion of less than 1% because of the implementation of the host depletion step (fig. S2F). To minimize the potential misidentification of human DNA as microbial sequences, we included the human reference genome in our Kraken2 database.

Then, four sequential filters were applied to remove potential contaminant species:

1) Decontam filter: False positives were identified by Decontam (version 1.10.0; frequency mode), which identifies contaminants on the basis of the negative correlation between species abundance and DNA input amount (72).

2) Quantile filter: A microbial species was classified as a contaminant if its abundance in the negative control exceeded the 95th percentile of the distribution in samples, as determined by a one-sample quantile test.

3) Correlation filter: A microbial species was classified as a contaminant if it exhibited a strong correlation (r > 0.7, Spearman correlation test) with any contaminant identified by Decontam or the Quantile filter.

4) Abundance filter: After steps 1 to 3, we separated the microbial components into archaea, bacteria, fungi, and viruses; relative abundances were recalculated within each category; and those with a maximum relative abundance below 0.001 across all samples were removed.

Last, only samples with more than 50,000 bacterial reads were used for downstream analyses (223). Among these, 210 paired samples were derived from 105 patients.

Microbial analysis for metagenomic data

Procrustes analysis was performed to evaluate the association between 16S and metagenome data using the vegan R package. Alpha diversity (Shannon and richness indices) was calculated using the vegan R package. PERMANOVA was conducted with the adonis2 function. Differential species between tumor and paired normal tissues were identified using the ALDEx2 R package, with a significance threshold based on the expected P < 0.05 from the Wilcoxon rank-sum test (73). Survival analysis was performed using Cox regression models to determine HRs and 95% confidence intervals (CIs), with adjustments made for covariates including age, gender, tumor stage, smoking status, and alcohol consumption. Bacterial aerophilicity annotations were generated using bugphyzz (version 0.99.3), with only those annotations scoring greater than 0.5 being retained.

For the analysis of paired saliva-tumor data for ESCC, only paired-end sequencing samples with bacterial reads greater than 10,000 were retained, following the original paper’s recommendations (74). For the pancancer ICI cohort, samples that received immunotherapy and had available response data were included. Complete response and partial response were classified as the responding group, while progressive disease and stable disease were categorized as the nonresponding group (42).

16S rRNA sequencing and analysis

After constructing metagenomic libraries for all 119 patients, only 59 paired tissue samples retained sufficient nucleic acids for further 16S rRNA and ITS sequencing. The total genomic DNA from these tissues, without depletion of host DNA, was extracted using the DNeasy PowerSoil Pro Kit (Qiagen) according to the manufacturer’s instructions. Total DNA concentrations were determined using a Qubit fluorometer (Thermo Fisher Scientific). The V4 region of the bacterial 16S rRNA gene was amplified using the 515F primer (5′-GTGYCAGCMGCCGCGGTAA-3′) and the 806R primer (5′-GGACTACNVGGGTWTCTAAT-3′). Illumina sequencing adapters were then added using the VAHTS Universal DNA Library Prep Kit for Illumina V3 (Vazyme). The final libraries were sequenced on the Illumina HiSeq platform with 250–base pair paired-end reads. Demultiplexing and quality filtering of 16S data were performed on the raw sequence data using the q2-demux plug-in in Qiime2 (version 2024.2) (75), and primers were trimmed with cutadapt (76). Amplicon sequence variants (ASVs) were counted after denoising by DADA2 (77). Taxonomy was assigned to ASVs using a pretrained Naive Bayes classifier, trained on the Silva V.138 reference sequence database.

Validation of P. micra in other gastrointestinal cancers was conducted by reanalyzing publicly available 16S cohort data. The raw sequences (PRJNA280026, PRJNA861885, and PRJNA383606 for CRC and PRJNA1032279 for GC) were downloaded and processed using the above pipeline. ASVs classified under the genus Parvimonas were extracted and realigned to the NCBI 16S rRNA database using Megablast (78). Only ASVs with the highest match scores identified as P. micra were retained for further analysis.

ITS sequencing and analysis

The fungal ITS regions were amplified from the DNA of the 59 paired tumor and adjacent normal tissue samples. Amplification was performed using the ITS1F primer (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and the ITS2 primer (5′-GCTGCGTTCTTCATCGATGC-3′). Following amplification, ITS libraries were prepared and sequenced following the protocol previously detailed for the 16S pipeline. Sequencing data were analyzed using the QIIME 2 pipeline. Taxonomic classification of the fungal ASVs was performed using the UNITE version 9 reference database.

Procrustes analysis demonstrated inconsistency between the fungal communities derived from ITS and metagenomic data. Further examination of the ITS data revealed irregularities in the fungal communities of most samples. Specifically, 50 samples displayed an overabundance of Rhizophydiales gen. Incertae sedis, with a relative abundance exceeding 0.99. The remaining samples presented Candida abundances ranging from 0.53 to 0.98. Consequently, we decided not to include the ITS results in this study. Future studies with optimized protocols may better characterize the ESCC-associated mycobiome.

Microbiome detection from spatial transcriptomic data

The 10x Genomics Visium spatial transcriptomic sequencing from five patients were previously described (79). Raw sequencing data were aligned and quantified using the SpaceRanger workflow (version 1.0.0). The bam files generated by SpaceRanger Count were processed to remove human reads using bowtie2 against a database comprising UniVec sequences and the GRCh38 human genome. The reads were then aligned against the nonredundant nucleotide database (2021.07) from NCBI using Megablast (78). The taxonomic classification was performed using MEGAN (version 6.20.14), which uses the lowest common ancestor algorithm (80). Sample P32 was retained for further analysis as the reads from P. micra exceeded 100. The microbial annotation results were aligned with corrected 10× barcodes (identifying the capture spot of origin) and UMIs (unique transcripts) from the SpaceRanger output on the basis of the read names. Species-level microbial UMI counts for each spot barcode were subsequently integrated into the corresponding Visium sample using Seurat’s AddMetadata function.

qPCR analysis of bacterial DNA

The quantification of total bacterial 16S DNA was conducted using the Femto Bacterial DNA Quantification Kit (Zymo Research), and the quantification of P. micra 16S DNA was conducted using the SYBR Premix Ex Taq II Kits (TaKaRa). The relative quantification results for both total bacteria and P. micra 16S DNA were normalized against total DNA concentration using the formula log₁₀(2^−Ct/DNA concentration × 10¹⁴ + 1). The qPCR primers used in this study are listed in table S5.

scRNA-seq data analysis

scRNA-seq data from 41 tumor and 4 adjacent normal tissues, also used in the present study, have been previously described (30). In this study, the Seurat package (version 4.2.0) (81) was used for data processing, and the Harmony package (version 0.1.1) (82) was used for batch effect correction. Cell types were annotated on the basis of the expression of known markers, as shown in fig. S4. Signature scoring of CD4⁺ T cells was calculated using the AddModuleScore function in the Seurat package, with gene sets from a previous article (83). ROS signature scoring of CD4⁺ T cells was calculated using the same function, with the HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY gene set from the Molecular Signatures Database (www.gsea-msigdb.org/gsea/msigdb).

Survival analysis

Survival analysis was performed using the Kaplan-Meier method, and differences between groups were assessed by the log-rank test. Optimal cutoff values for dividing the groups were determined using the surv_cutpoint function in the survminer package (version 0.4.9) to obtain the most minimal P values of log-rank test. The Cox proportional hazards model was used to calculate the HR and 95% CI, adjusting for age, gender, tumor stage, smoking status, and alcohol consumption as covariates.

Fluorescence in situ hybridization

Formalin-fixed, paraffin-embedded samples were sectioned into 4-μm-thick sections. After deparaffinization and rehydration, the sections were treated with the tissue autofluorescence quenching agent for 30 min. FISH assays were performed using the Fluorescent in situ Hybridization Kit (BersinBio). Briefly, the sections were blocked with a prehybridization solution at 37°C for 30 min. Subsequently, the probe was mixed with a hybridization solution at a 1:40 ratio and applied to the sections. The sections were incubated at 73°C for 5 to 8 min and then quickly transferred to 37°C for overnight hybridization. After DAPI (4′,6-diamidino-2-phenylindole) staining, the sections were mounted and scanned for analysis. The probes used in this study are listed in table S6.

The bacterial abundance in each tissue section was quantified using ImageJ software. Briefly, three nonoverlapping fields of view were randomly selected for each section, and the percentage of positively stained area was estimated for each field. The mean percentage across the three fields was then calculated to represent the bacterial abundance for the section.

Immunofluorescence staining

Paraffin-embedded sections were deparaffinized and hydrated, fixed with 4% paraformaldehyde, and blocked with a peroxidase blocking solution. Subsequently, the sections were placed in a tris-EDTA (pH = 9.0) antigen retrieval solution and heated to boiling using a microwave oven for antigen retrieval. Following the manufacturer’s instructions, the sections were blocked with a sheep serum blocking solution and incubated with primary antibodies, secondary antibodies, and fluorescent dyes using the Opal 5-Color Manual IHC Kit (PANOVUE). Before staining for nuclear proteins, the sections were permeabilized with 0.1% Triton X-100 at room temperature for 30 min. The dilution ratios for each antibody were as follows: keratin 6A (KRT6A; 1:800), CD4 (1:500), FOXP3 (1:250), CD8 (1:1000), PD-1 (1:500), and granzyme B (GZMB; 1:200). After DAPI staining, the sections were mounted and scanned for analysis. The primary antibodies used in this study are listed in table S6.

For quantification, three nonoverlapping fields of view were randomly selected for each section, and the percentage of positively stained cells was estimated for each field. The mean percentage across the three fields was then calculated to represent the overall positive cell percentage for the section.

Western blot analysis

The total protein was extracted from cell lysates, and the protein concentration was quantified using the BCA kit (Thermo Fisher Scientific). Lysates containing 10 to 15 μg of protein were separated by SDS–polyacrylamide gel electrophoresis and transferred to the polyvinylidene difluoride membrane (Millipore). The membrane was blocked with 5% skim milk at room temperature for 1 hour, incubated with primary antibodies overnight at 4°C, and then incubated with secondary antibodies for 1 hour at room temperature. The signal of the target protein bands was detected using the Chemiluminescent Substrate kit (Thermo Fisher Scientific) and visualized on the Amersham Imager 600 instrument. The primary and secondary antibodies used in this study are listed in table S6. The expression levels of all target proteins were quantified by normalizing the grayscale intensity values to those of the housekeeping protein. All Western blot analyses were performed with three biological replicates.

RNA extraction and qPCR analysis

The total RNA of the cells was extracted using the RNA-Quick Purification Kit (ES Science), followed by reverse transcription using the PrimeScript RT reagent kit (TaKaRa). The quantification of target mRNA expression levels was performed using SYBR Premix Ex Taq II kits (TaKaRa), and the relative quantification results of the target mRNA were expressed as ΔΔCt values normalized to the housekeeping gene ACTB. The qPCR primers used in this study are listed in table S5.

Mouse CD4⁺ T cell isolation and in vitro assays

CD4⁺ T cells were isolated from mouse spleens using the EasySep Mouse CD4⁺ T Cell Isolation Kit (STEMCELL). The cells were then cultured in Advanced RPMI 1640 medium supplemented with 10% FBS, 1× penicillin-streptomycin, 1× GlutaMAX, and IL-2 (20 ng/ml). Following activation with anti-mouse CD3/CD28 dynabeads (Gibco), the CD4⁺ T cells were treated with P. micra CM, heat-inactivated P. micra CM, heat-killed P. micra, p-cresol (40 μM), or NAC (2 mM) for 24 hours.

Flow cytometry

The prepared cells were stained with Fixable Viability Dye 780 (Thermo Fisher Scientific) to identify dead cells. Subsequently, the cells were stained with antibodies targeting surface markers. After fixation and permeabilization using the True-Nuclear Transcription Factor Buffer Set (BioLegend), the cells were stained with antibodies targeting intracellular proteins. The samples were then analyzed using the flow cytometer. The data were processed and analyzed using FlowJo software.

Cell viability assays

HET-1A, KYSE70, and KYSE150 cells were seeded in 96-well plates. Twenty-four hours later, cell viability was assessed using the Cell Counting Kit-8 (DOJINDO). Subsequently, the cells were treated with P. micra CM at various concentrations. Cell viability was then assessed again at three or four time points following the treatment.

Metabolomics and analysis

Tumor interstitial fluid was extracted from three randomly selected tumors per group (intratumoral injection of P. micra or PBS) as previously described (84). Briefly, tumor tissues were cut into 3- to 5-mm³ fragments and placed onto a 70-μm cell strainer. The strainer was mounted on a 50-ml centrifuge tube and centrifuged at 400g for 15 min at 4°C. The collected fluid was then passed through a 0.22-μm filter. The filtered interstitial fluid was stored at −80°C for subsequent metabolomic analysis.

The sample stored at −80°C was thawed on ice and vortexed for 10 s. A 150-μl extract solution [acetonitrile (ACN):methanol = 1:4, v/v] containing an internal standard was added into 50 μl of sample. Then, the sample was vortexed for 3 min and centrifuged at 12,000 rpm for 10 min at 4°C. One hundred fifty microliters of aliquots of the supernatant was collected and placed at −20°C for 30 min and then centrifuged at 12,000 rpm for 3 min at 4°C. One hundred twenty microliters of aliquots of the supernatant was transferred for liquid chromatography–mass spectrometry (LC-MS) analysis.

All samples were for three LC-MS methods. One aliquot was analyzed using positive ion conditions and was eluted from a T3 column (Waters ACQUITY UPLC HSS T3 C18 1.8 μm, 2.1 mm by 100 mm). Another aliquot was analyzed using negative ion conditions. The third aliquot was analyzed via negative ionization and was eluted from a HILIC (hydrophilic interaction liquid chromatography) column (Waters ACQUITY UPLC BEH HILIC Column, 1 mm by 150 mm, 1.7 μm). Data acquisition was operated using the information dependent acquisition mode using Analyst TF 1.7.1 Software (Sciex, Concord, ON, Canada). LIT and triple quadrupole (QQQ) scans were acquired on a triple quadrupole-linear ion trap mass spectrometer (QTRAP), QTRAP LC-MS/MS System, equipped with an ESI Turbo Ion-Spray interface, operating in positive and negative ion modes and controlled by Analyst 1.6.3 software (Sciex).

Unsupervised principal components analysis (PCA) was performed using the prcomp function in R. The data underwent unit variance scaling before PCA. For OPLS-DA (orthogonal partial least squares discriminant analysis), the data were log₂ transformed and mean centered before analysis using MetaboAnalystR (version 1.0.1) (85). To avoid overfitting, a permutation test (200 permutations) was performed. Differential metabolites were determined by VIP > 1 (OPLS-DA) and P < 0.05 (Student’s t test). The origin of metabolites was annotated using MetOrigin (86).

Homology analysis of tyrosine–to–p-cresol pathway genes in P. micra

To investigate the potential of P. micra to metabolize tyrosine to p-cresol, we conducted a bioinformatic analysis of key enzymes involved in this metabolic pathway. Reference amino acid sequences of key enzymes in tyrosine–to–p-cresol conversion, including tyrosine lyase (ThiH; accession no. NP_418417.1); FldBC complex activator, medium subunit, and small subunit (accession nos. EDU39254.1, EDU39255.1, and EDU39256.1, respectively); AcdA (accession no. EDU39257.1); HpdA (accession no. CAD65891.1); HpdB (accession no. CAD65889.1); and pyruvate:ferredoxin oxidoreductase A (accession No. EDU39094.1), were collected from previously characterized p-cresol–producing bacteria on the basis of the published literature (87).

These reference sequences were used as queries in a protein BLAST (blastp) analysis against the annotated protein sequences from the P. micra American Type Culture Collection 33270 reference genome (GCF_000154405.1) obtained from the NCBI database. The blastp parameters were set to default values. Sequence similarities were evaluated on the basis of the percent identity and E-value. For multiple results, select the result with the highest bit score.

p-Cresol quantification

A UPLC-MS/MS (ultraperformance LC-tandem MS) method was developed for the absolute quantification of p-cresol in bacterial culture supernatants. Four hundred microliters of the supernatant and 1600 μl of ACN were mixed, vortexed for 1 min, and centrifuged at 15,000 rpm for 5 min at 4°C. Then, 1800 μl of the supernatant was transferred to a clean tube for vacuum concentration at 1400 r/min and 0°C. The dried samples were reconstituted in 50 μl of 90% ACN, vortexed for 1 min, and centrifuged at 15,000 rpm for 5 min at 4°C. Forty microliters of the supernatant was transferred to mass spectrometry sample vials for analysis.

ROS assays

The cells were incubated with the fluorescent probe DCFH-DA (2′,7′-dichlorodihydrofluorescein diacetate; Beyotime) at 37°C for 20 min and subsequently washed three times with serum-free culture medium. The cell suspension was then transferred to a black 96-well plate. The ROS levels were quantified by measuring the fluorescence intensity using a microplate reader.

Quantification and statistical analysis

Statistical details and methods are described in the figure legends, main text, or Materials and Methods. All statistical analyses were performed using R version 4.1.2 and GraphPad Prism 9 software.

Supplementary Materials

The PDF file includes:

Figs. S1 to S7

Tables S4 to S6

Legends for tables S1 to S3

Other Supplementary Material for this manuscript includes the following:

Tables S1 to S3