Streptococcus gallolyticus supernatant induces M2 macrophage polarization and activates IL-17 F signaling in colorectal tumorigenesis
- Open Access
- 01.12.2025
- Research
Erschienen in:
Abstract
Introduction
Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer-related deaths worldwide [1]. Individuals with (IBD) face a 2–3 times higher risk of developing CRC compared to those without IBD [2]. Recent studies have highlighted the critical role of the gut microbiota in driving the progression from colitis to CRC [3, 4]. Dysbiosis is involved in the occurrence and development of CRC through a variety of mechanisms, including chronic inflammation, metabolite-mediated disruption of signal pathways, and DNA damage. In recent years, studies have further revealed the complex roles of host-microbe interactions in modulating energy metabolism, epigenetic regulation, and evasion of antitumor immunity [5‐7]. Growing evidence also suggests that the gut microbiota may influence not only the gastrointestinal tract but also distant systems, such as the nervous and immune systems, either directly or through microbial metabolites entering the circulatory system [7, 8]. Among the various bacterial species present in the gut, species like Streptococcus gallolyticus are found to be associated with CRC [3, 9, 10].
Streptococcus gallolyticus (Sg), also known as Streptococcus bovis biotype I, is a Gram-positive bacterium that inhabits the human gastrointestinal tract as a commensal organism [10]. The association between Sg and CRC was first reported in 1951 [9]. Early observations noted that individuals infected with Sg had a considerably higher risk of developing CRC in the following years despite the absence of any gastrointestinal symptoms during that time. A comprehensive 12-year study revealed that 75% of patients with S. bovis-associated endocarditis were subsequently diagnosed with malignant colonic lesions [11]. Similarly, a 40-year literature review showed that 60% of patients with S. bovis infection had colorectal adenomas or carcinomas [12], and a separate 24-year study demonstrated that 70% of patients with S. bovis bacteremia had colonic tumors, compared to 32% in matched controls [13]. On the other hand, studies have reported a significantly higher detection rate of Sg in clinical samples from patients diagnosed with CRC. Specifically, the incidence of S. bovis fecal carriage was noted to be approximately five times higher in CRC patients compared to those with IBD and three times higher than in healthy controls [14]. Moreover, the presence of Sg was observed to be approximately ten times higher in tumor tissues compared to normal colon tissues [15]. One study found that Sg was present in approximately 74% of tumor tissues and 47% of adjacent normal tissues from CRC patients [16]. Furthermore, a separate investigation detected Sg in the intestinal samples of 62.5% of 99 healthy volunteers [17]. Taken together, these findings point to a strong association between Sg and CRC, raising the possibility that Sg may play a role in colorectal tumorigenesis. Additionally, the seropositivity rates for Sg-specific IgG antibodies were determined to be 68% in CRC patients, 78% in patients with adenomas, and 16.66% in the healthy controls [18]. This observation suggests that Sg may be more prevalent in early adenomatous lesions than in advanced carcinomas, implying a potential role in the initial stages of colorectal tumorigenesis.
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Although the exact mechanisms by which Sg influences CRC remain unclear, the supernatant of Sg cultures (hereafter referred to as Sgsup) might contain specific molecular substances that contribute to CRC development. Bacteria are capable of secreting a wide range of substances, including growth factors, proteases, cytokines, other proteins, and various metabolites, which facilitate complex interactions between the host immune system and cancer cells. Certain metabolites, such as lactate, serve as nutrients for cancer cells and promote cancer progression, while others, such as butyrate, inhibit pro-inflammatory genes and hinder tumor growth [3, 19]. Sgsup contains a complex mixture of bioactive substances, such as proteins, lipids, and metabolites, which may significantly influence CRC progression. However, current research presents conflicting evidence regarding the role of Sgsup in promoting CRC cell proliferation. While some studies suggest that direct contact between Sg and host cells is required for its tumor-promoting effects [16, 20, 21], others posit that components within the Sgsup alone are sufficient to stimulate CRC development [22, 23]. The aim of this study is to investigate the effects of Sgsup on CRC and to figure out its composition and mechanisms of action.
Methods
Clinical data and sample collection
This study involved 46 patients diagnosed with CRC who underwent surgical treatment at the Guangzhou First People’s Hospital between 2012 and 2015. Tissue samples were collected under strict aseptic conditions. For each patient, both tumor tissue and adjacent normal tissue (> 5 cm from the tumor margin) were collected and immediately snap-frozen in liquid nitrogen for rapid preservation. Samples were subsequently stored at -80 °C for further analysis. Clinical data were collected, including age, gender, blood type, tumor size, location, stage, smoking history, alcohol consumption history, 5-year survival, metastasis, and relevant pathological indicators. These data were retrieved through the hospital’s medical record system and telephone follow-up. Informed consent was obtained from all patients. The study was approved by the Ethics Committee of Guangzhou First People’s Hospital, and all procedures were conducted in accordance with the committee’s guidelines to ensure ethical compliance and scientific rigor.
Quantitative real-time PCR (qPCR) for Sg abundance
Genomic DNA (gDNA) was extracted from tissue samples using the Total DNA/RNA/Protein Kit (Omega, USA). The qPCR was performed in a 20 µL reaction mixture containing 10 µL of SYBR Premix Ex Taq II (2×), 1 µL each of forward and reverse primers (10 µM), 2 µL of gDNA, and 6 µL of ddH2O. The qPCR program was set as follows: initial denaturation at 95 °C for 30 s, followed by 40 cycles of denaturation at 95 °C for 5 s, and annealing/extension at 60 °C for 30–60 s. The specific primers for Sg, obtained from the literature [24], were as follows: Forward: 5’-AACGCGAAGAACCTTACCAG-3’, Reverse: 5’-GAGTGCCCAACTGAATGAT G-3’. The β-actin (ACTB) was used as an internal reference gene. Each sample was analyzed in triplicate, and the relative abundance of Sg was determined using the 2(−ΔCT) method.
Quantitative reverse transcription PCR (RT-qPCR)
Mouse colon tissue was collected for RT-qPCR analysis. Total RNA was extracted using the Total DNA/RNA/Protein Kit (Omega, USA), and reverse transcription to cDNA was performed using the PrimeScript RT reagent Kit (Takara, Japan). The qPCR was conducted using the following cycling conditions: initial denaturation at 95 °C for 30 s, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min, then rapid denaturation at 95 °C for 1 s, and a final melting step for 6 s. Each 20 µL reaction mixture contained SYBR Premix Ex Taq II (2X), PCR forward primer (10 μm), PCR reverse primer (10 μm), cDNA solution, and ddH2O. ACTB was used as an internal reference, and relative gene expression was calculated using the 2(−ΔΔCT) method. Analysis of variance (ANOVA) test was applied to the relative expression data. Primer sequences are listed below.
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Gene name | Source | Primer name | Sequence (5’-3’) |
|---|---|---|---|
PTGS2 | mouse | Forward Primer | GCGACATACTCAAGCAGGAGCA |
PTGS2 | mouse | Reverse Primer | AGTGGTAACCGCTCAGGTGTTG |
IL-1β | mouse | Forward Primer | TGGACCTTCCAGGATGAGGACA |
IL-1β | mouse | Reverse Primer | GTTCATCTCGGAGCCTGTAGTG |
IL-8 | mouse | Forward Primer | GGTGATATTCGAGACCATTTACTG |
IL-8 | mouse | Reverse Primer | GCCAACAGTAGCCTTCACCCAT |
IL-6 | mouse | Forward Primer | TACCACTTCACAAGTCGGAGGC |
IL-6 | mouse | Reverse Primer | CTGCAAGTGCATCATCGTTGTTC |
IL-17 A | mouse | Forward Primer | CAGACTACCTCAACCGTTCCAC |
IL-17 A | mouse | Reverse Primer | CTTTCCCTCCGCATTGACAC |
IL-17 F | mouse | Forward Primer | AACCAGGGCATTTCTGTCCCAC |
IL-17 F | mouse | Reverse Primer | GGCATTGATGCAGCCTGAGTGT |
IL-22 | mouse | Forward Primer | GCTTGAGGTGTCCAACTTCCAG |
IL-22 | mouse | Reverse Primer | ACTCCTCGGAACAGTTTCTCCC |
ACTB | mouse | Forward Primer | CATTGCTGACAGGATGCAGAAGG |
ACTB | mouse | Reverse Primer | TGCTGGAAGGTGGACAGTGAGG |
Cell culture
HCT116 and HT29 cells were purchased from ATCC and cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS). The cells were maintained in a humidified incubator at 37 °C with 5% CO₂ to ensure optimal growth conditions. The culture medium was replaced every 48 h to replenish nutrients and remove metabolic waste. When cell confluence reached 70–80%, they were sub-cultured using 0.25% trypsin-EDTA to ensure sustained proliferation and viability for downstream experiments.
Preparation of Sgsup
The Sg strain (DSM16831) was obtained from the BeNa Culture Collection (BNCC). Upon receipt, the bacterial strain was revived and cultured aerobically in the brain heart infusion (BHI) medium (Huankai Microbial Sci & Tech Co., Ltd., China) at 37 °C. When the culture reached the logarithmic growth phase, bacterial cells were pelleted by centrifugation at 3000 rpm for 5 min, and the supernatant was collected. To ensure sterility, the supernatant was filtered through a 0.22 μm membrane and verified to be free of viable bacteria by inoculation culture [25]. The sterile supernatant was freshly prepared prior to each experiment.
Cell proliferation assay
Cell proliferation was assessed using the Cell Counting Kit-8 (CCK-8) assay (Biosharp, China), following the manufacturer’s instructions. A total of 1000 cells in 100 µL of suspension were seeded into each well of a 96-well plate. After the cells adhered to the bottom, 10 uL of Sgsup, BHI medium, or PBS was added to the respective wells. After 8 h of incubation, 10 µL of CCK8 reagent was added to each well and incubated for 1 h in the dark. Absorbance at 450 nm (OD450) was measured using a microplate reader (Thermo Fisher Scientific, USA). Each treatment group included at least three technical replicates. Wells containing medium without cells served as the blank control to correct for background absorbance.
Non-targeted metabolomics
Sgsup and BHI blank medium were subjected to a non-targeted metabolomic analysis conducted by Novogene Co., Ltd. (Beijing, China). For each sample, 1 mL was lyophilized and resuspended in 100 µL of 80% methanol, followed by vortexing, incubation on ice for 5 min, and centrifugation at 15,000 × g at 4 °C for 15 min. The resulting supernatant was diluted to 53% methanol, centrifuged again, and collected for LC-MS analysis. LC-MS was performed using a Hypersil GOLD C18 column at 40 °C with a flow rate of 0.2 mL/min. For positive ion mode, the mobile phases were (A) 0.1% formic acid in water and (B) methanol; for negative ion mode, (A) 5 mM ammonium acetate (pH 9.0) and (B) methanol. The gradient elution was programmed from 2 to 100% B over 10 min, followed by a return to 2% B over 2 min, and held at 100% B. Mass spectrometry was performed in full scan mode with a scan range of m/z 100–1500. ESI source parameters included a spray voltage of 3.5 kV, sheath gas flow at 35 psi, auxiliary gas flow at 10 L/min, capillary temperature of 320 °C, S-lens RF level of 60, and auxiliary gas heater temperature of 350 °C. Raw data were processed using Compound Discoverer 3.1 (CD3.1), and peaks were extracted using a retention time deviation of 0.2 min, mass deviation of 5 ppm, signal intensity variation of 30%, S/N ratio of 3, and a minimum intensity threshold [26]. Metabolite identifications were based on matching molecular ions and fragment spectra against mzCloud, mzVault, and MassList databases, excluding those with a coefficient of variation (CV) greater than 30% in quality control samples. Data processing was performed on CentOS 6.6 using R and Python. Differential metabolites were identified using the following criteria: VIP > 1.0, fold change (FC) > 1.2 or < 0.833, and p-value < 0.05 [27].
Animal modeling
Eight-week-old female Balb/c mice (approximately 25 g) were obtained from the Guangdong Medical Laboratory Animal Center and were kept under specific pathogen-free (SPF) conditions. After one week of acclimatization, a CRC model was established using AOM/DSS. On day 1, mice received an intraperitoneal injection of AOM (Sigma-Aldrich, USA) at a dose of 12.5 mg/kg, followed by 2% DSS (MP Biomedicals, USA) administered in their drinking water for 5 days. This was followed by 14 days of regular water, with the cycle repeated three times. The experiment was concluded on day 80. During the process, mice received 200 µL of the designated intervention via oral gavage every other day. Drinking water was replaced three times per week to ensure freshness. All experimental protocols were approved by the relevant ethical committee and conducted in accordance with established guidelines to ensure humane treatment and minimize animal distress [28].
Mouse colonoscopy
Colonoscopy was performed on mice using a mouse endoscopic system (KARL STORZ, 26430520-1, Germany) equipped with an IMAGE1 S D3-LINK connection module and a 64,301 AA probe (diameter: 1.9 mm, length: 10 cm), featuring 495 NT fiber optics and a protective probe sleeve (61029 C) with an 8.5 cm working length. The system was connected to a display screen for real-time imaging and photographic documentation. Mice were fasted for 24 h prior to the procedure and anesthetized via intraperitoneal injection of 1% sodium pentobarbital. During the procedure, mice were positioned supine on a heating pad to maintain body temperature. The anus was lubricated with glycerol, and the endoscope tip was coated to minimize mucosal trauma before careful insertion. The scope was adjusted to optimize visualization of the colonic mucosa, and images of the intestinal lumen were recorded. After examination, the endoscope was withdrawn, and the perianal area was cleaned. Mice were closely monitored for signs of pain or complications. All procedures were conducted by an experienced operator to ensure accuracy and consistency in the results.
Flow cytometry
The colon was longitudinally dissected and thoroughly rinsed with cold PBS. Tumor tissue was carefully collected from the colon and enzymatically digested in a shaker at 37 °C for 30 min using a digestion solution composed of 0.5 mg/mL collagenase IV (Roche, Switzerland) and 0.5 mg/mL DNase I (Roche, Switzerland) prepared in calcium- and magnesium-free PBS. The digested cell suspension was filtered through a 70 μm cell strainer and centrifuged at 400 × g for 10 min at 4 °C. The cell pellet was resuspended in 1% bovine serum albumin (BSA) to generate single-cell suspensions. For surface marker staining, 1 µL of the following fluorophore-conjugated antibodies was added to each sample: APC anti-mouse CD45 (BioLegend, 103111, USA), APC/Cy7 anti-mouse CD11b (BioLegend, 101226, USA), PE anti-mouse F4/80 (BioLegend, 123109, USA), FITC anti-mouse CD86 (BioLegend, 105005, USA) and BV421 anti-mouse CD206 (BioLegend, 141717, USA). The mixtures were incubated at 4 °C for 30 min in the dark. Staining was terminated by washing with 1% BSA, followed by centrifugation and resuspension in PBS. Stained cells were then analyzed using a BD FACS Canto II system (BD Biotechnology, USA), and data were analyzed using FlowJo.
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H&E and immunohistochemistry staining
Colonic tissues were isolated, cleaned, embedded in paraffin using the “Swiss roll” technique, and sectioned at a thickness of 4 μm. H&E and immunohistochemistry (IHC) staining were performed as previously described [29, 30]. Microscopic examination was performed, and ImageJ was used for image analysis. Histological scoring was conducted in a blinded manner to evaluate the success of the modeling [31]. Inflammation was assessed using the MCHI scoring system, which consisted of the following components: (1) cupping defects, scored as 0 points for no defects, 1 for < 10%, 2 for 10–50%, and 3 for > 50% of tissue affected; (2) crypt loss, scored as 0 for normal, 1 for < 10% reduction, and 2 for ≥ 10% reduction; (3) hyperplasia, scored as 0 for no hyperplasia, 1 for mild crypt elongation, 2 points for a 2-3-fold increase in crypt length, and 3 for > 3-fold increase; (4) submucosal inflammatory infiltration, scored as 0 for no inflammatory infiltration, 1 for mild, 2 for moderate, and 3 for severe infiltration. The total inflammation score was calculated as follows: Cupping defects score * 1 + crypts score * 2 + hyperplasia score * 2 + submucosal inflammatory infiltration score * 3 [31].
RNA sequencing
The transcriptome analysis of mouse tumor tissue was performed using RNA sequencing (RNA-seq) by PANOMIX Biomedical Tech Co., LTD (Suzhou, China). Total RNA was extracted and tested for concentration and purity, mRNA was then purified and fragmented into 200–300 bp segments, followed by cDNA synthesis, library construction, amplification, and selection of approximately 450 bp fragments. After quality control, Paired-end sequencing was performed using the Illumina HiSeq platform. Raw sequencing data were filtered to obtain clean reads. A reference genome index was constructed using Bowtie2, and the clean reads were aligned to the reference genome using Tophat2. Gene-level read counts were calculated using HTSeq, and transcript expression was normalized using the FPKM. Differentially expressed genes (DEGs) were identified using DESeq, with the thresholds of|log2FoldChange| >1 and P-value < 0.05. GO and KEGG pathway enrichment analyses were subsequently performed to identify the biological processes, molecular functions, and signaling pathways associated with the DEGs. Data visualization of heatmaps, volcano plots, and bubble plots was performed using the ggplot2 package in R [32].
Statistical methods
Data were analyzed using SPSS 25.0 (IBM Corp., USA). Descriptive statistics are presented as means ± standard deviation (χ ± s). For comparisons between two groups, the independent samples t-test was used for normally distributed data, while the Wilcoxon signed-rank test was applied for non-normally distributed data. ANOVA was used for comparisons among multiple groups. Post-hoc tests were conducted following the identification of significant differences using ANOVA. Categorical variables were analyzed using the chi-square test. A P-value of P < 0.05 was considered statistically significant. GraphPad Prism8 (GraphPad Software, USA) and Adobe Illustrator 2020 (Adobe Inc., USA) were used for data analysis and visualization.
Results
High abundance of Sg in CRC tissues correlates with tumor location
Patient demographics and clinical characteristics are shown in Table 1. Sg was detected in 93.5% (43 out of 46) of CRC tissues (both tumor and adjacent normal tissues), while only three patients (6.5%) showed no Sg colonization. Notably, the abundance of Sg in tumor tissues was significantly higher than that in the adjacent normal tissues (P < 0.01) (Fig. 1a).
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Table 1
Characteristics of the study population
Variables | Statistics (%) | Variables | Statistics (%) |
|---|---|---|---|
Age | AJCCstage | ||
≥ 60 | 30(65.2) | 1 | 6(13.0) |
<60 | 16(34.8) | 2 | 22(47.8) |
Sex | 3 | 4(8.7) | |
Male | 27(58.7) | 4 | 14(30.4) |
Female | 19(41.3) | Smoking | |
Blood type | Yes | 9(19.6) | |
A | 11(23.9) | no | 37(80.4) |
B | 16(34.8) | Alcohol use | |
O | 17(37.0) | Yes | 5(10.9) |
AB | 2(4.3) | No | 41(89.1) |
Tumor location | CEA | ||
Rectum | 21(45.7) | Positive | 41(93.2) |
Sigmoid colon | 13(28.3) | Negtive | 3(6.8) |
Left hemicolon | 3(6.5) | P53 | |
Right hemicolon | 9(19.6) | Positive | 25(58.1) |
Tumor size | Negative | 18(41.9) | |
≥ 4 cm | 29(63.0) | Ki67 | |
<4 cm | 17(37.0) | ≥ 65% | 25(54.3) |
TNMstage | < 65% | 21(45.7) | |
T | 5-year survival | ||
1 | 0(0) | Live | 19(52.8) |
2 | 7(15.2) | Dead | 17(47.2) |
3 | 12(26.1) | Recurrence | |
4 | 27(58.7) | Yes | 13(36.1) |
N | No | 23(63.9) | |
0 | 31(67.4) | Sg in tumor | |
1 | 9(19.6) | Positive | 22(47.8) |
2 | 6(13.0) | Negative | 24(52.2) |
M | Sg in peritumor | ||
0 | 32(69.6) | Positive | 14(30.4) |
1 | 14(30.4) | Negative | 32(69.6) |
Fig. 1
Increased Sg abundance in tumor tissues and the proliferative effect of Sgsup on colon cancer cells, along with metabolite analysis. (a) Sg abundance in tumor tissues (n = 43) is significantly higher than that in adjacent normal tissues (n = 43). (b) Sgsup promotes proliferation of HCT116 (n = 4) and HT29 (n = 3). (c) Volcano plot showing differential metabolites between BHI medium (n = 3) and Sgsup (n = 3). (d-g) inosine-5’-monophosphate (IMP), methionine, uridine, and creatine levels (n = 3). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant
Based on ΔCT values, the samples were divided into two groups: Sg-positive (high abundance) and Sg-negative (low abundance or test-negative). In tumor tissues, 22 cases (47.8%) were Sg-positive, while 24 were negative. In adjacent normal tissues, 14 cases (30.4%) were Sg-positive, and 32 were negative. Chi-square analysis revealed a significant correlation between Sg abundance in tumors and adjacent normal tissues (P < 0.001), as well as a significant association with the tumor location (P < 0.01). However, no significant associations were observed between Sg abundance and other factors, such as age, gender, blood type, tumor size, stage, smoking, alcohol consumption, CEA, P53, Ki67 positivity, five-year survival, or recurrence (P > 0.05) (Table 2).
Table 2
Correlation analysis of Sg abundance and clinical information in tumor and adjacent normal tissues of CRC patients
Variable | Sg-negative in tumor (n = 24) | Sg-positive in tumor (n = 22) | P value | Sg-negative in adjacent normal tissues (n = 32) | Sg-positive in adjacent normal tissues (n = 14) | P-value |
|---|---|---|---|---|---|---|
Age | 0.404 | 0.447 | ||||
≥ 60 | 17 | 13 | 22 | 8 | ||
<60 | 7 | 9 | 10 | 6 | ||
Sex | 0.958 | 0.611 | ||||
Male | 14 | 13 | 18 | 9 | ||
Female | 10 | 9 | 14 | 5 | ||
Blood type | 0.385 | 0.391 | ||||
A | 7 | 4 | 9 | 2 | ||
B | 7 | 9 | 9 | 7 | ||
O | 8 | 9 | 12 | 5 | ||
AB | 2 | 0 | 2 | 0 | ||
Tumor location | 0.007 | 0.203 | ||||
Rectum | 11 | 10 | 13 | 8 | ||
Sigmoid colon | 11 | 2 | 12 | 1 | ||
Left hemicolon | 1 | 2 | 2 | 1 | ||
Right hemicolon | 1 | 8 | 5 | 4 | ||
Tumor size | 0.193 | 0.436 | ||||
≥ 4 cm | 13 | 16 | 19 | 10 | ||
<4 cm | 11 | 6 | 13 | 4 | ||
TNM stage | ||||||
T | 0.422 | 0.867 | ||||
1 | 0 | 0 | 0 | 0 | ||
2 | 4 | 3 | 5 | 2 | ||
3 | 8 | 4 | 9 | 3 | ||
4 | 12 | 15 | 18 | 9 | ||
N | 0.696 | 0.733 | ||||
0 | 15 | 16 | 21 | 10 | ||
1 | 5 | 4 | 6 | 3 | ||
2 | 4 | 2 | 5 | 1 | ||
M | 0.845 | 0.607 | ||||
0 | 17 | 15 | 23 | 9 | ||
1 | 7 | 7 | 9 | 5 | ||
AJCCstage | 0.251 | 0.577 | ||||
1 | 3 | 3 | 4 | 2 | ||
2 | 10 | 12 | 15 | 7 | ||
3 | 4 | 0 | 4 | 0 | ||
4 | 7 | 7 | 9 | 5 | ||
Smoking | 0.605 | 0.308 | ||||
Yes | 4 | 5 | 5 | 4 | ||
no | 20 | 17 | 27 | 10 | ||
Alcohol use | 0.564 | 0.591 | ||||
Yes | 2 | 3 | 4 | 1 | ||
No | 22 | 19 | 28 | 13 | ||
CEA | 0.496 | 0.882 | ||||
Positive | 22 | 19 | 29 | 12 | ||
Negative | 1 | 2 | 2 | 1 | ||
P53 | 0.697 | 0.987 | ||||
Positive | 14 | 11 | 18 | 7 | ||
Negative | 9 | 9 | 13 | 5 | ||
Ki67 | 0.080 | 0.301 | ||||
≥ 65% | 16 | 9 | 19 | 6 | ||
< 65% | 8 | 13 | 13 | 8 | ||
5-year survival | 0.463 | 0.847 | ||||
Live | 10 | 9 | 14 | 5 | ||
Dead | 11 | 6 | 13 | 4 | ||
Recurrence | 0.587 | 0.636 | ||||
Yes | 8 | 5 | 10 | 3 | ||
No | 12 | 11 | 16 | 7 | ||
Sg in tumor | - | < 0.0001 | ||||
Positive | - | - | 8 | 14 | ||
Negative | - | - | 24 | 0 | ||
Sg in adjacent normal tissues | < 0.0001 | - | ||||
Positive | 0 | 14 | - | - | ||
Negative | 24 | 8 | - | - |
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Sgsup promotes CRC cell proliferation and component analysis
In order to explore the potential pathogenic effects of Sg on CRC, Sgsup was extracted for further study. Sgsup was applied to HCT116 and HT29 cells to evaluate its impact on cell proliferation. Results indicated that neither the PBS group nor the BHI (the medium used for cultivating Sg) group had a significant impact on cell proliferation in either HCT116 or HT29 cells (Fig. 1b). In contrast, treatment with Sgsup significantly enhanced cell proliferation compared to the PBS control group in both cell lines (Fig. 1b).
Furthermore, untargeted metabolomic analysis of the supernatant revealed substantial differences in metabolite profiles compared to the blank medium. A total of 851 metabolites showed significant alterations, including 67 that were upregulated and 13 that were downregulated (Fig. 1c). Among the upregulated metabolites, inosine-5’-monophosphate (IMP), methionine, uridine, and creatine were significantly elevated in Sgsup (Fig. 1d-g).
Sgsup promotes colonic tumor progression, exacerbates inflammation, and induces macrophage M2 polarisation in AOM/DSS mice
Colonic tumor growth was monitored in vivo using mouse endoscopy. Healthy mice exhibited a smooth, circular colonic lumen with pale pink mucosa, intace vascular architecture, and a translucent mucosal surface. In contrast, AOM/DSS-induced CRC mice displayed irregular masses of varying sizes, reduced mucosal translucency, and disrupted vascular structures (Fig. 2a). At study termination, gross examination of the dissected colons revealed visible masses, confirmed as tumors by H&E staining, in all experimental groups except the normal controls. Mice treated with Sgsup exhibited a significant increase in both the number and size of colonic tumors compared to the AOM/DSS model group (Fig. 2b). Statistical analysis showed a significant increase in tumor count (Fig. 2e) and tumor burden (Fig. 2f) after Sgsup treatment. Body weight in the normal group gradually increased over time, whereas AOM/DSS-treated mice showed progressive weight loss, with the Sgsup group exhibiting a significant reduction during the first week of DSS exposure (Fig. 2c). The length of the colon was significantly shortened after the Sgsup treatment (Fig. 2g and h).
Fig. 2
Sgsup promotes tumor growth and analysis of macronomic parameters in AOM/DSS mice. (a) Mouse colonoscopy. (b) Tumor condition of mouse colon. (c) Body weight during the first week of DSS administration. (d) AOM/DSS-induced mice develop different degrees of tumor development. (e) Mouse colon tumor count. (f) Tumor burden in the colon. (g) Colon length. (h) Images of mouse colons. NORNAL group (n = 10), AOM + DSS group (n = 6), AOM + DSS + Sgsup group (n = 7); *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant
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H&E staining revealed the typical histological architecture of the normal mouse colon, composed of mucosal, submucosal, muscular, and serosal layers. In the normal group, crypts were well-organized, with intact glandular structures and clearly defined goblet cells at the base. In contrast, colons from AOM/DSS-treated mice exhibited varying degrees of dysplasia. Mild dysplasia was characterized by disordered glandular arrangement, nuclear hyperchromasia, and cellular heterogeneity. Low-grade dysplasia (early adenomas) showed pseudostratified nuclei and cellular crowding without invasion into deeper layers. High-grade dysplasia (carcinoma in situ) demonstrated fused or sieve-like crypts and a complete loss of nuclear polarity. Invasive carcinoma was defined by neoplastic cells breaching the basement membrane and infiltrating the submucosa (Fig. 2a). Regarding inflammatory conditions, the disease activity index (DAI) scores, recorded on the day of maximum weight loss, were significantly higher in the Sgsup group compared to the model group (Fig. 3b). Histologically, the colonic epithelium in the normal group remained intact, and there was no evidence of ulceration or submucosal inflammatory infiltration. However, the model group exhibited increased infiltration of chronic inflammatory cells and lymphoid follicular hyperplasia. The Sgsup group showed irregularities in subepithelial crypts and visible ulcerated lesions (Fig. 3a). Histological inflammation scoring demonstrated increased inflammation in the Sgsup group compared to the model group (Fig. 3c). Furthermore, RT-qPCR analysis of colon tumor tissues revealed upregulation of pro-inflammatory markers, including PTGS2 (Fig. 3d), IL-6 (Fig. 3e), IL-1β (Fig. 3f), and IL-8 (Fig. 3g), after Sgsup treatment as compared to the model group.
Fig. 3
Sgsup exacerbates inflammation levels in AOM/DSS mice. (a) H&E-stained colon sections; (b) DAI score on the day of maximal weight loss; (c) Inflammation scores based on H&E staining; (d-g) Relative mRNA expression of inflammatory markers PTGS2 (d), IL-6 (e), IL-1β (f), IL-8 (g) in colonic tumor tissues, as determined by qPCR. NORNAL group (n = 10), AOM + DSS group (n = 6), AOM + DSS + Sgsup group (n = 7); *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant
IHC staining revealed that Sgsup significantly increased the expression of the macrophage marker F4/80 in tumor tissues compared to the model group. Notably, the M2 polarization marker CD206 was significantly upregulated, while the M1 marker CD86 showed comparatively lower expression (Fig. 4a). These findings were further supported by flow cytometry analysis, which demonstrated an elevated proportion of tumor-infiltrating macrophages following Sgsup treatment, with a pronounced shift toward M2 polarization and a corresponding reduction in the M1/M2 ratio relative to the model group (Fig. 4b and g).
Fig. 4
Sgsup intervention increases TAM infiltration and promotes M2 polarization in colonic tumor tissues. M2-like macrophages were defined as CD45⁺CD11b⁺F4/80⁺CD206⁺ cells. Polarization status was primarily assessed using these surface markers. (a) IHC of colon tissues (x100). (b) Flow cytometric analysis of colonic tumor tissues. (c-f) Statistical analysis results of TAMs (n = 3) (c), M1-like macrophages (d), M2-like macrophages (e), M1/M2 ratio (f). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant
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Sgsup promotes CRC progression via activation of the IL-17 pathway
In order to investigate the underlying mechanism by which Sgsup promotes CRC progression, RNA-seq analysis was performed on tumor tissues from treated mice. The results revealed significant transcriptional alterations, with 68 genes upregulated, and 47 genes downregulated in the Sgsup group compared to the model group (Fig. 5a and b). Additionally, KEGG pathway enrichment analysis identified the IL-17 signaling pathway as the most significant enriched pathway (Fig. 5c). To further validate the RNA-seq findings, the expression of key cytokines associated with the IL-17 pathway was examined. While no significant differences were observed in IL-17 A expression among the group (Fig. 5d), Sgsup treatment led to a significant upregulation of IL-17 F (Fig. 5e) and IL-22 (Fig. 5f) compared to the model group.
Fig. 5
Sgsup promotes CRC progression through activation of the IL-17 signaling pathway. (a) Volcano plot of DEGs. (b) Heatmap of clustered DEGs. (c) KEGG pathway enrichment analysis of DEGs. (d–f) Expression levels of IL-17 A (d), IL-17 F (e), and IL-22 (f) in colonic tumor tissues, as determined by qPCR (n = 6). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant
Discussion
Numerous studies have established a strong association between Sg infection and CRC [33‐35]. As a commensal bacterium predominantly residing in the ileal region of the healthy gut, Sg significantly influences host metabolism and immunity [36, 37]. However, dysbiosis due to bacterial overgrowth may modulate disease progression [38]. Sg detection in colonic tissues and feces tends to increase during gut inflammation. Prior cross-regional studies have reported varying Sg positivity rates in tumor tissues (3.2–74%) [12, 15, 16, 34, 35, 39‐41], possibly due to diverse sampling populations and detection methods. This study utilized Sg-specific qPCR to assess Sg colonization in tumors and adjacent normal tissues from 46 CRC patients, identifying Sg colonization in 93.5% of cases. Tumor tissues exhibited notably higher Sg abundance than adjacent normal tissues, consistent with earlier findings. Additionally, this study identified a correlation between Sg abundance and tumor location, whereas no significant associations were observed with age, gender, blood type, tumor size, stage, smoking, alcohol, survival, CEA, P53, Ki67, or recurrence.
Earlier studies demonstrated that Sg promotes proliferation in certain CRC cell lines, including HCT116, HT29, and LoVo. In contrast, no significant proliferative effect was observed in CRC cell lines SW480 and SW1116, normal human colon epithelial cells (FHC and CCD 841 CoN), human renal epithelial cells (HEK293), or the lung cancer cell line A549 [16]. Some studies suggested that Sgsup does not promote CRC cell proliferation, implying that direct bacterial contact with cells may be required for its effect [16]. However, other studies indicated that Sgsup can promote the proliferation and migration of CRC cells [22, 23]. This study revealed that Sgsup promoted the proliferation of CRC cell lines HCT116 and HT29, underscoring Sg’s capacity to secrete specific bioactive substances that facilitate CRC progression. To explore these specific pathogenic components, Sgsup was analyzed via non-targeted metabolomics, revealing significant increases in IMP, methionine, uridine, and creatine compared to blank medium. Increased IMP enhances the availability of purine nucleotides, supporting rapid DNA and RNA synthesis critical for tumor cell proliferation [42]. Increased uridine supports the synthesis of RNA, essential for protein production and cell growth, particularly in cancers with upregulated RNA synthesis [43]. Elevated methionine boosts S-adenosylmethionine (SAM), the primary methyl donor, potentially leading to DNA and histone hypermethylation, which can silence tumor suppressor genes or activate oncogenes, thereby promoting cancer progression [44]. Creatine, a naturally occurring nitrogenous organic acid, plays key roles in thermogenesis, immune function, and cancer cell survival [45]. Though once considered an oncogenic metabolite, recent evidence suggested that creatine has a promoting effect on tumor progression [46]. Creatine could promote invasion and metastasis in pancreatic, colorectal, and breast cancers [47]. Creatine acts as an energy buffer during high ATP demand by phosphorylating ADP to ATP through phosphocreatine, thus mitigating transient increases in energy expenditure [48‐50]. CRC cells secrete brain-type creatine kinase, which, together with hepatocyte-derived creatine, generates extracellular phosphocreatine. This phosphocreatine is then taken up by CRC cells, supporting metastatic survival in the liver [48]. Moreover, the knockdown of Slc6a8, a creatine transporter in macrophages, reduces creatine uptake and abundance, impairing M2-like macrophage effector functions in vivo [51]. Creatine supplementation modulates macrophage polarization by inhibiting the IFNγ-JAK-STAT1-iNOS pathway (M1-like) and promoting the IL-4-STAT6-ARG1 pathway (M2-like), thus favoring M2 polarizatio [45]. Additionally, creatine endocytosis is heightened in M2-like compared to M1-like macrophages, suggesting a feed-forward mechanism where creatine promotes macrophage homeostasis toward an M2-like phenotype [45].
Numerous studies have established a strong association between Sg infection and CRC [33‐35]. As a commensal bacterium predominantly residing in the ileal region of the healthy gut, Sg significantly influences host metabolism and immunity [36, 37]. However, dysbiosis due to bacterial overgrowth may modulate disease progression [38]. Sg detection in colonic tissues and feces tends to increase during gut inflammation. Prior cross-regional studies have reported varying Sg positivity rates in tumor tissues (3.2–74%) [12, 15, 16, 34, 35, 39‐41], possibly due to diverse sampling populations and detection methods. This study utilized Sg-specific qPCR to assess Sg colonization in tumors and adjacent normal tissues from 46 CRC patients, identifying Sg colonization in 93.5% of cases. Tumor tissues exhibited notably higher Sg abundance than adjacent normal tissues, consistent with earlier findings. Additionally, this study identified a correlation between Sg abundance and tumor location, whereas no significant associations were observed with age, gender, blood type, tumor size, stage, smoking, alcohol, survival, CEA, P53, Ki67, or recurrence.
Earlier studies demonstrated that Sg promotes proliferation in certain CRC cell lines, including HCT116, HT29, and LoVo. In contrast, no significant proliferative effect was observed in CRC cell lines SW480 and SW1116, normal human colon epithelial cells (FHC and CCD 841 CoN), human renal epithelial cells (HEK293), or the lung cancer cell line A549 [16]. Some studies suggested that Sgsup does not promote CRC cell proliferation, implying that direct bacterial contact with cells may be required for its effect [16]. However, other studies indicated that Sgsup can promote the proliferation and migration of CRC cells [22, 23]. This study revealed that Sgsup promoted the proliferation of CRC cell lines HCT116 and HT29, underscoring Sg’s capacity to secrete specific bioactive substances that facilitate CRC progression. To explore these specific pathogenic components, Sgsup was analyzed via non-targeted metabolomics, revealing significant increases in IMP, methionine, uridine, and creatine compared to blank medium. Increased IMP enhances the availability of purine nucleotides, supporting rapid DNA and RNA synthesis critical for tumor cell proliferation [42]. Increased uridine supports the synthesis of RNA, essential for protein production and cell growth, particularly in cancers with upregulated RNA synthesis [43]. Elevated methionine boosts S-adenosylmethionine (SAM), the primary methyl donor, potentially leading to DNA and histone hypermethylation, which can silence tumor suppressor genes or activate oncogenes, thereby promoting cancer progression [44]. Creatine, a naturally occurring nitrogenous organic acid, plays key roles in thermogenesis, immune function, and cancer cell survival [45]. Though once considered an oncogenic metabolite, recent evidence suggested that creatine has a promoting effect on tumor progression [46]. Creatine could promote invasion and metastasis in pancreatic, colorectal, and breast cancers [47]. Creatine acts as an energy buffer during high ATP demand by phosphorylating ADP to ATP through phosphocreatine, thus mitigating transient increases in energy expenditure [48‐50]. CRC cells secrete brain-type creatine kinase, which, together with hepatocyte-derived creatine, generates extracellular phosphocreatine. This phosphocreatine is then taken up by CRC cells, supporting metastatic survival in the liver [48]. Moreover, the knockdown of Slc6a8, a creatine transporter in macrophages, reduces creatine uptake and abundance, impairing M2-like macrophage effector functions in vivo [51]. Creatine supplementation modulates macrophage polarization by inhibiting the IFNγ-JAK-STAT1-iNOS pathway (M1-like) and promoting the IL-4-STAT6-ARG1 pathway (M2-like), thus favoring M2 polarizatio [45]. Additionally, creatine endocytosis is heightened in M2-like compared to M1-like macrophages, suggesting a feed-forward mechanism where creatine promotes macrophage homeostasis toward an M2-like phenotype [45].
Based on these findings, this study conducted in vivo experiments using mouse endoscopy to evaluate intestinal changes. In the normal group, the intestinal lumen appeared smooth and translucent pink, whereas the model group exhibited flatter intestinal lesions, potentially indicative of early-stage tumorigenesis. Notably, treatment of Sgsup resulted in significantly larger and irregularly raised intestinal tumors, suggesting progression to more advanced stages. The observed increase in colorectal tumor number, tumor burden, and inflammation following Sgsup treatment supports a role for Sgsup in promoting tumorigenesis and exacerbating inflammation in AOM/DSS mice. The gastrointestinal tract harbors the largest population of macrophages. On the one hand, these macrophages recruit regulatory T cells (Tregs) via chemokine secretion, thus fostering an immunosuppressive tumor microenvironment [52‐54]. On the other hand, tumor-associated macrophages (TAMs) interact with the microbiota through different metabolic pathways. A dynamic interplay between macrophages, gut bacteria, and tumor promotion has been observed. For example, gut microbiota can incite chemokine production via lipopolysaccharide (LPS), promoting the accumulation of monocyte-like macrophages and generating an inflammatory environment that supports colitis-associated tumorigenesis [55]. Importantly, the depletion of macrophages completely abolishes the pro-tumorigenic effects of dysbiotic gut bacterial communities, underscoring the symbiotic interdependence between bacteria and macrophages for tumor development [56]. Furthermore, macrophages drive alterations in the CRC-associated microbiota. For instance, Fusobacterium nucleatum promotes the recruitment of M2 macrophages and myeloid-derived suppressor cells (MDSCs), establishing an immunosuppressive tumor microenvironment that facilitates tumor progression [57]. CRC often begins within an inflamed epithelial stroma characterized by the presence of pro-inflammatory M1 macrophages, which produce reactive oxygen species (ROS) that can induce oncogenic mutations in neighboring epithelial cells [58]. As tumorigenesis progresses, additional bone marrow-derived monocytes are recruited to the tumor microenvironment, where they secrete a variety of growth factors and chemokines, including CCL2, CCL5, VEGF, and TGF-β, that promote the polarization of TAMs toward the M2 phenotype. This shift supports tumor growth, angiogenesis, and immunosuppression, thereby accelerating CRC progression. Sg has been shown to exhibit prolonged intracellular survival within macrophages compared to other bacterial species, triggering specific cytokine expression and minimizing macrophage lysis [59]. Furthermore, Sg selectively recruits tumor-infiltrating myeloid cells, encompassing MDSCs, TAMs, and dendritic cells [24]. This study unveiled increased infiltration of M2-polarized macrophages in mouse tumor tissues after Sgsup treatment, suggesting that Sgsup may promote tumor progression by enhancing immunosuppression through the recruitment and polarization of TAMs.
A potential pathway enriched by RNA-seq is the IL-17 signaling pathway. The IL-17 pathway is activated by members of the IL-17 cytokine family, which are secreted by various immune cells, such as T helper 17 (Th17), γδ T cells, natural killer (NK) cells, and innate lymphoid cells (ILCs) [60]. IL-17 exerts its biological role by binding to the IL-17 receptor (IL-17R), which is expressed in multiple cell types, including epithelial cells, endothelial cells, fibroblasts, and immune cells. Upon ligand binding, downstream signaling cascades, primarily NF-κB and MAPK pathways, are activated, promoting the expression of pro-inflammatory cytokines and chemokines that mediate immune cell recruitment to sites of infection or inflammation [61]. IL-17 signaling induces the expression of various pro-inflammatory cytokines, including IL-6, IL-1β, IL-8, and PTGS2. These cytokines not only contribute to the inflammatory microenvironment but also enhance the activation and recruitment of IL-17-producing cells, thereby amplifying IL-17 pathway signaling. This establishes a positive feedback loop that sustains chronic inflammation and facilitates tumor initiation and progression in CRC [62]. Studies have shown the presence of IL-17 and Th17 cells in a wide range of tumors, promoting tumor growth and metastasis through diverse mechanisms [62‐64]. The IL-17 pathway has been demonstrated to promote tumor angiogenesis, inhibit antitumor immunity, and enhance the survival and proliferation of tumor cells [65]. Among the IL-17 family, IL-17 F plays a key role in regulating intestinal commensal microbiota. It is constitutively expressed in the gut and stimulates the production of antimicrobial peptides to maintain mucosal homeostasis [66]. Fusobacterium nucleatum, known for its association with chronic inflammation and cancer, exacerbates intestinal inflammation in mice by enhancing the IL-17 F / NF-κB pathway [67]. In this study, after Sgsup treatment, both IL-17 F and IL-22 exhibited significant upregulation, while IL-17 A showed a downward trend that did not reach statistical significance. It is worth noting that IL-17 A has been reported to maintain intestinal barrier integrity and protect against colitis, whereas IL-17 F exerts stronger pro-inflammatory effects [67]. This observation is supported by human studies showing elevated IL-17 F mRNA expression in colonic biopsies from patients with ulcerative colitis, contributing to a pro-inflammatory microenvironment in conjunction with cytokines such as IL-6 [67]. Microbiota-induced IL-17 A has been linked to the pathogenesis of various malignancies, including colon, breast, pancreatic, and ovarian cancers and multiple myeloma [68, 69]. However, the precise role of IL-17 A in different cancers remains to be fully understood. For instance, in melanoma and ovarian cancer, Th17 cells activate antitumor cytotoxic T-cell responses [70], but they exhibit pro-tumorigenic properties in various mouse models of CRC [71, 72], hepatocellular carcinoma [73, 74], and pancreatic cancer [75]. These findings suggest that Sgsup may contribute to heightened inflammatory responses and facilitate CRC progression via activation of the IL-17 signaling pathway. Recently, studies have shed light on the interaction between the IL-17 pathway and macrophage polarization, particularly in promoting the M2-like phenotype. Research indicates that IL-17 could promote macrophage M2 polarization [76]. IL-17 A has been shown to activate macrophages directly [77]. In colitis, IL-17 has been shown to induce the expansion of M2-like macrophage subpopulations, which contribute to protection against the progression of severe inflammation [78]. IL-17 A is also proposed to act as a key stimulus for the pathogenic polarization of macrophages toward the M2 phenotype, potentially through its initial effects on endometriotic lesions [79]. In addition, IL-17 can indirectly promote M2 macrophage polarization by activating the COX-2/PGE2 signaling pathway in cancer cells, thereby contributing to the modulation of the tumor immune microenvironment [80].
Our findings on Sg are consistent with established paradigms of microbe-induced carcinogenesis observed in other oncogenic bacteria. Similar to Helicobacter pylori, Sgsup: (i) induces chronic inflammation, as evidenced by elevated inflammatory scores and upregulation of inflammatory cytokine); (ii) activates pro-tumorigenic signaling pathways, such as the IL-17 pathway, potentially involving downstream NF-κB signaling; and (iii) promotes an immunosuppressive microenvironment exemplified by M2 macrophage polarization, analogous to the Treg recruitment seen in H. pylori infections. Furthermore, similar to Pseudomonas aeruginosa, Sgsup contains secreted factors capable of inducing tissue damage and is enriched in metabolites (e.g., creatine) that exhibit potential immunomodulatory properties. This convergence highlights core mechanisms, i.e., chronic inflammation, immune evasion, and direct/metabolic manipulation, employed by diverse bacteria to foster oncogenesis [81]. While individual microbial species exhibit niche-specific adaptations, targeting these shared pathogenic hubs holds therapeutic promise.
This study investigated Sg colonization in tumors and adjacent normal tissues from CRC patients, revealing a significant correlation between Sg abundance and tumor location. Functional assays demonstrated that Sgsup promotes CRC progression, enhances tumor-associated macrophage infiltration, and induces M2 polarization. Transcriptomic analysis suggested that these effects may be mediated through activation of the IL-17 signaling pathway. However, several limitations should be noted. First, the patient cohort was limited to a small, single-center sample, underscoring the need for broader, multicenter validation. Second, although metabolomic profiling identified significant enrichment of creatine in Sgsup, its direct role in CRC progression or immune modulation was not experimentally validated—for instance, through gain- or loss-of-function studies involving creatine supplementation, analogs, inhibitors, or silencing of creatine transporters. Extending these findings to larger clinical cohorts and examining the interactions within complex microbial communities may provide valuable insights for the development of innovative microbiome-targeted interventions in CRC [82, 83]. Moreover, the characterization of M2 macrophages in this study relied primarily on the surface marker CD206, along with CD86, for M1 identification. While CD206 is a well-established M2 marker, future studies should incorporate additional functional markers (e.g., Arg1, Ym1) and cytokine profiling (e.g., IL-10, TGF-β) to enhance the robustness of macrophage phenotyping. Finally, although creatine was found to be significantly elevated in Sgsup through metabolomic analysis, its direct role in CRC progression was not confirmed by intervention experiments, such as supplementation with creatine or its analogs, use of creatine synthesis inhibitors, or genetic silencing of creatine transporters.
In conclusion, this study underscores the potential of Sgsup as a promotive factor in CRC progression, presenting it as a potential target for CRC prevention and treatment. Further investigation is needed to study the precise molecular interactions between Sg-derived metabolites (e.g., creatine) and host signaling pathways (particularly those involved in M2 macrophage polarization and the IL-17/IL-22 pathways) in CRC pathogenesis (Fig. 6). A critical next step involves validating the functional roles of specific metabolites in modulating tumor metabolism and immune remodeling. In addition, investigating therapeutic strategies aimed at targeting Sg-associated virulence factors or key host inflammatory pathways is essential.
Fig. 6
Proposed mechanism by which Sgsup promotes CRC. Sgsup induces M2 macrophage polarization and activates IL-17 F/IL-22 signaling pathways, contributing to colorectal tumorigenesis
Declarations
Ethics approval and consent to participate
All methods were carried out in accordance with the principles of the Declaration of Helsinki. The study was approved by the Medical Ethics Committee of Guangzhou First People’s Hospital (K-2019-146-01). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. The animal study was reviewed and approved by the Guangdong Medical Laboratory Animal Center (GDMLAC; Certificate number SYXK 2022-0002). All subsequent studies were performed in accordance with the guidelines approved by the Animal Ethics Committee of GDMLAC.
Competing interests
The authors declare no competing interests.
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