Oral microbiome diversity and composition before and after chemotherapy treatment in pediatric oncology patients

doi:10.21203/rs.3.rs-5302958/v1

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Research Article

Oral microbiome diversity and composition before and after chemotherapy treatment in pediatric oncology patients

https://doi.org/10.21203/rs.3.rs-5302958/v1

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Objective

This study explored how anticancer treatment affects the oral microbiome in pediatric patients and its link to oral mucositis (OM). It also examined the effects of different mouth rinses (Caphosol vs. saline solution).

Materials and Methods

Saliva samples were collected from patients before and after chemotherapy in a controlled, double-blind, randomized clinical trial. The trial compared Caphosol and saline solution mouth rinses in patients aged 2 to 17.99 years. Bacterial DNA from saliva samples was analyzed using next-generation sequencing to evaluate 16S rRNA.

Results

We analyzed 110 saliva samples from pediatric cancer patients before and after treatment, focusing on those with and without OM. Significant differences were found in bacterial taxa, including the Bacteroidota phylum, which was more abundant in patients without lesions before treatment. Cancer treatment increased the abundance of the Proteobacteria phylum. Distinct microbiome profiles were associated with OM development, including increased pathogenic species like Rothia mucilaginosa and Fusobacterium periodonticum. Differences in microbiota composition were also found between patients using Caphosol and saline solution mouth rinses.

Conclusions

Changes in the oral microbiota before and after anticancer treatment are linked to OM development, offering potential for identifying high-risk patients and promoting protective bacteria.

Trial registration:

The current trial was registered at Clinical trials.gov (ID: NCT02807337, Date: 20-February-2020).

Oral microbiome

diversity

chemotherapy

randomized

pediatric

mucositis

The oral microbial community is the second most complex in the human body, surpassed only by the gut microbiome (Caselli et al., 2020). Its composition varies significantly between children and adults (Human Microbiome Project Consortium, 2012). A substantial portion of the maturation of a child’s oral microbiome takes place during the first two years of life (Kennedy et al., 2019), and by the age of three, the salivary microbiome becomes more complex, continuing to evolve with age (Crielaard et al., 2011).

The oral microbiota is closely linked to both oral and systemic health. While microbial communities function similarly across individuals, they can exhibit considerable uniqueness and play different roles in healthy versus diseased states (Aggarwal et al., 2023). Children and adolescents undergoing cancer treatment face a high risk of severe side effects, including oral mucositis (OM) (Velten et al., 2017). Over the past decade, research on the oral microbiome in pediatric cancer patients has grown substantially (Rotz & Dandoy, 2020). Nevertheless, OM remains one of the most common and debilitating complications of cancer treatment (Al-Rudayni et al., 2020). In most pediatric patients, progressive mucosal injury and deep ulcerations occur, influenced by the type of cancer treatment, specific protocols, and patient-specific factors (Al-Rudayni et al., 2020). Indeed, recent studies have highlighted a potential causal relationship between the oral microbiome and mucositis in both pediatric and adult cancer patients (Oldenburg et al., 2021; Manzoor et al., 2020; Sonis, 2017; Wang et al., 2015; Laheij & Soet, 2014).

In pediatric cancer patients, oral dysbiosis may arise due to antibiotic use, chemotherapy, and radiotherapy, affecting the mucosal barrier defenses (Hong et al., 2019; Mougeot et al., 2019). Changes to the oral microbiome may significantly influence susceptibility to infections in immunocompromised patients (Laheij et al., 2012; Ye et al., 2013; Stergiotis et al., 2021). However, the exact role of the oral microbiome in the development of OM remains uncertain. Some studies suggest that OM is not a direct consequence of chemotherapy or radiotherapy but rather a response to mucosal injury, which alters the local environment, making it more hostile to resident microbes (Hong et al., 2019; Gupta et al., 2019; Al-Qadami et al., 2022).

The role of the oral microbiome in the pathogenesis of mucositis in pediatric cancer patients is not yet fully understood. The primary objective of our study was to examine the diversity and composition of the oral microbiome before and after chemotherapy in pediatric oncology patients and to identify microbial features associated with the occurrence of OM.

2.1 Subject selection and clinical outcomes

A total of 45 patients, aged 2 to 17.99 years, with solid or hematological malignancies undergoing chemotherapy, participated in a multicenter, prospective, double-blind, randomized clinical trial (NCT0280733). The clinical outcomes of this trial have been previously reported (Immonen et al., 2020). The chemotherapy regimens included one of the following drugs: high-dose methotrexate (≥ 1g/m²), any anthracycline (doxorubicin, daunorubicin, idarubicin, mitoxantrone), or cisplatin, along with at least two cycles of chemotherapy containing these agents, all of which are known to increase the risk of mucositis. All patients received two 7-day cycles of mouth rinses—one with Caphosol and the other with a saline solution—in a randomized order, with at least a three-week interval between rinse cycles to allow for sufficient mucosal healing between chemotherapy courses. Patients were excluded if they presented with mucositis at the start of chemotherapy, had undergone high-dose chemotherapy followed by stem cell transplantation (SCT), or were receiving induction chemotherapy for acute leukemia. Oral mucositis was assessed using the World Health Organization (WHO) Oral Toxicity Scale, as described in previous studies (Immonen et al., 2020; Qutob et al., 2013; Carreón-Burciaga et al., 2018). Patient-reported outcomes were evaluated daily for two weeks (14 days) using the Children’s International Mucositis Evaluation Scale (ChIMES) (Tomlinson et al., 2009; Jacobs et al., 2013; Immonen et al., 2020).

2.2 Sample collection and preparation

Saliva samples were collected by a dentist, trained nurse, or pediatric oncologist before the start of chemotherapy and upon the patient’s discharge from the hospital at the end of chemotherapy course. In this cross-over design study, four saliva samples from each patient were collected from the mucosa beneath the tongue using a cotton swab. The samples were frozen immediately after collection and stored at -80°C until further processing (Fig. 1). Informed Consent for collecting the samples was obtained from all the patients.

2.3 DNA extraction for microbiome evaluation

Bacterial DNA was extracted from the cotton swab samples using the QIAamp DNA Mini Kit (Qiagen, California, USA) according to the manufacturer’s instructions. The total amount of bacterial DNA was determined by quantitative polymerase chain reaction (PCR) (Ott et al., 2004). The extracted DNA was then stored at -20°C prior to further analyses (Fig. 1).

2.4 Next generation sequencing and data analysis

Bacterial DNA diversity was analyzed using Illumina NovaSeq next-generation sequencing with 250 bp paired-end chemistry, achieving a depth of 100k reads per sample. Primers targeting the V3-V4 hypervariable regions of bacterial 16S rRNA (16S rDNA) were used for amplification (F341, 5ʹ-CCTAYGGGRBGCASCAG-3ʹ and R806, 5ʹ-GGACTACNNGGGTATCTAAT-3ʹ) (Faria et al., 2018). All amplification steps, library preparation, sequencing, and data preprocessing were conducted by Novogene (Cambridge, UK). Before filtering stages, the mean number of raw reads per sample was 119,025 (sd 11,892) and after 99,607 (sd. 11,379), therefore 83.7% of the reads passed the quality, and chimera filtering steps (Figure S1).

Data processing and analysis of the reads was conducted in-house using the R programming language (4.3.1) with RStudio (2023.06.1 + 524) integrated development environment. Sequence quality and length-based filtering, chimera removal and taxonomy assignment were performed using the DADA2 package (1.28.0) (Callahan et al., 2016). The reference taxonomy used was Silva No. 99 v138.1. Phyloseq (1.46.0) (McMurdie & Holmes, 2013) was utilized to connect individual sequences to the most probable phylogeny, while DESeq2 (1.42.1) (Love et al., 2014) was employed for differential abundance analysis between samples. The Benjamini-Hochberg method was applied for multiple test correction; however, volcano plots were created using the plain p-value.

Several R packages were used for data visualization, including microbiomeutilities (1.0.17) (Shetty & Lahti, 2024), which facilitated data formatting for subsequent visualization steps. Alpha diversity plots were created using microbiomeutilities and NMDS plots utilised the vegan package (2.6-6) (Oksanen et al. 2024). EnhancedVolcano (1.20.0) (Blighe et al., 2023), along with in-house R scripts, was used to visualize differential abundance analysis by DESeq2 in volcano plots. A combination bar plot of species-level abundances in the samples was generated using the MicroViz package (0.12.1) (Barnett et al., 2021).

The English language was edited by using a large language model (chatGPT4.0 and Copilot).

3.1 Characteristics of the study population

A total of 45 children and adolescents with a median age of 6.5 years (range 2.1–17.1 years; see Table 1) were randomized in the study (Immonen et al., 2020). Thirteen patients (29%) discontinued the study due to the mouth rinse’s unpleasant taste, severe nausea, vomiting, or overall weakness – six patients (13%) during the Caphosol cycle and seven patients (16%) during the saline solution cycle (Immonen et al., 2020). Thirty-two patients completed the study, each undergoing two full cycles of mouth rinsing. Additionally, two more patients were included, who only underwent one cycle of mouth rinsing, with two saliva samples collected before and after anticancer treatment. In total, 130 saliva samples were collected before and after treatment, and the analysis was performed on 110 samples. The analysis required that each patient have saliva samples collected both before and after at least one treatment cycle. If a sample from either time point was missing, the corresponding paired sample was excluded from the analysis. Following this approach, each patient had either 0, 2, or 4 samples in the final analysis. In the study by Immonen et al. (2020), no severe cases of OM with a WHO score of ≥ 4 were observed. However, mild to moderate OM (WHO scores 1–3) was reported in three patients, with oral symptoms occurring in 13% of participants during the Caphosol cycle and 29% during the saline solution cycle. For further analysis, the saliva samples were categorized into six groups (see Table 2).

3.2. Alpha-diversity and richness of oral microbiota in pediatric cancer patients

Alpha diversity is essential in microbial analysis because it shows the richness and distribution of species within a community, helping to understand its complexity and health implications. It can indicate how treatments, such as chemotherapy, affect microbial communities. A reduction in alpha diversity may signal a loss of beneficial microbial species, potentially leading to conditions like mucositis (Wei et al., 2021)

We analyzed the alpha diversity of oral samples collected before and after anticancer treatment from patients with and without oral lesions. Although in the whole study population there was a trend toward increased alpha diversity following treatment, this increase did not reach statistical significance (p = 0.091) (Fig. 2A). Additionally, the uniformly distributed alpha diversity values suggest that the chemotherapy treatment did not result in significant changes at the overall microbiome level (Fig. 2A). Non-metric multidimensional scaling (NMDS) analysis revealed no clear clustering, a conclusion further corroborated by ANOVA and beta dispersion analysis (Fig. 2D). While there was a subtle increase in deviation among the post-treatment groups, no variables showed significant differentiation. Alpha diversity of oral microbiota did not differ between patients with and without lesions at pre- or post-treatment time points (pre-treatment lesion/no lesion p = 0.93, post-treatment lesion/no lesion p = 0.89) (Fig. 2B). In the post-treatment samples, alpha diversity scores did not differ between the Caphosol and saline solution groups (p = 0.26) (Fig. 2C).

3.3. Abundance differences at phylum level in oral microbiota of patients with and without mucosal lesions

Next, we conducted a comparison of the oral microbiota between pediatric cancer patients with and without oral lesions, both before and after anticancer treatment, across different taxonomical levels (Fig. 3, Table S1). In the pre-treatment samples, we identified significant differences in the relative abundance of one bacterial phylum, with Bacteroidota being more abundant in the group without lesions (p = 0.031). In the post-treatment samples, no significant differences were observed between the patients with and without lesions. However, Proteobacteria phylum was more abundant in the saline solution group compared to the Caphosol group (p = 0.013). Furthermore, our study population showed statistically significant increase in the relative abundance of Proteobacteria phylum during cancer treatment (p = 0.0022) (Fig. 3).

3.4 Abundance differences of OTUs at species level -comparison of patients with and without mucosal lesions

The composition of the oral microbiome typically varies greatly between individuals. Figure 4 presents the 35 most abundant taxa from the samples included in the analysis. The visualization highlights the significant variability in microbiome composition between patients, as well as the changes occurring in the microbiomes of individual patients during treatment. In the pre-treatment samples, we observed statistically significant differences in the relative abundance of ten bacterial taxa when comparing patients with oral lesions to those without (Figs. 4 & 5A, Table S2). Major differences were observed in the mean relative abundance of Bacteroides sp. (p = 0.0030), Faecalibacterium sp. (p = 1.5E-23), Enhydrobacter aerosaccus (p = 0.0030), Methylobacterium-Methylorubrum (p = 0.0019), Capnocytophaga sputigena (p = 0.031), and Lautropia mirabilis (p = 0.046). Interestingly, the mean relative abundance of Bergeyella was higher in the pre-treatment samples from patients without oral lesions (p = 2.6E-5) (Fig. 5A). Overall, these results indicate that the development of oral mucositis during anticancer treatment is associated with distinct oral microbiota profiles present before treatment.

We also examined differences in the abundance of oral microbiota after anticancer treatment. Our comparison of species-level operational taxonomic units (OTUs) revealed statistically significant differences in the abundance of twelve OTUs (Figs. 4 & 5B, Table S2). We identified several taxa that were more abundant in the patients with oral lesions. These taxa included Rothia mucilaginosa (p = 0.013), Fusobacterium periodonticum (p = 0.021), Leptotrichia sp. (p = 1.6E-4), Serratia sp. (p = 0.0041), Capnocytophaga sputigena (p = 1.4E-5), Sphingomonas sp. (p = 1.6E-4), Parapusillimonas sp. (p = 1.6E-4), Staphylococcus sp. (p = 0.021) and Turicibacter sp. (p = 0.021) (Fig. 5B). Conversely, in patients without lesions, the relative abundance of Haemophilus (p = 0.0040) and Alloprevotella (p = 0.038) species was higher (Fig. 5B).

Our study revealed significant changes in the composition of oral microbiota at the species level among patients undergoing anticancer treatment, observed not only in those who developed mucositis but also in those whose oral mucosa remained healthy after treatment (Table S2). Interestingly, the abundance of seven bacterial species, including Haemophilus (p = 0.035) and Alloprevotella sp. (p = 0.00085) increased significantly during anticancer treatment in patients with no lesions (Table S2). These genera are part of the protective normal microbiome of the oral cavity in healthy children (Könönen et al., 2022; Kosikowska et al., 2015).

As a whole, our study population showed statistically significant increase in the relative abundance of Leptotrichia species, Rothia dentocariosa, Peptococcus species and Actinomyces pacaensi during anticancer treatment (Fig. 5F).

3.5 Abundance differences of oral microbiota between the Caphosol and saline solution oral rinse groups

From the post-treatment samples, we analyzed the abundance differences in OTUs aggregated down to species level between the Caphosol and saline solution groups. In the Caphosol group, the mean relative abundance of Atopobium parvulum (p = 0.0014), Burkholderia-Caballeronia-Paraburkholderia (p = 0.014) and Capnocytophaga sputigena (p = 0.032) was higher compared to the saline solution group (Fig. 5D). Additionally, the most significant differences in taxon abundance between the mouth rinse groups were observed in the relative abundance of Streptococcus pseudopneumoniae (p = 3e-4) in the saline solution group, and Cardiobacterium hominis (p = 0.0049) and Mucispirillum sp. (p = 0.023) in the Caphosol group (Fig. 5E).

In this follow-up to a previously published randomized, controlled, double-blind trial, we analysed 110 saliva samples to evaluate the impact of chemotherapy on the oral microbiota. Our findings reveal that paediatric cancer treatment alters the oral microbiome at both the species and phylum levels, leading to notable changes in its composition and diversity. Specifically, we found that the development of oral mucositis during anticancer treatment was associated with distinct oral microbiota profiles present before treatment, as well as shifts in the microbiota following treatment. These observations suggest that dysbiosis, both pre- and post-treatment, may play a critical role in the development of oral lesions.

Previous research has documented reductions in microbial diversity and observed compositional changes in several oral taxa during anticancer treatment (Oldenburg et al., 2021; Sonis, 2017; Wang et al., 2015). Once dysbiosis occurs, it disrupts the homeostasis of the oral epithelial barriers, impairing the epithelial response and accelerating pathological processes. Earlier studies have proposed that specific bacterial changes may promote the development of oral mucositis and hinder the healing of pre-existing ulcers (Bruno et al., 2023; Min et al., 2023). Despite the significant clinical impact of OM, its underlying mechanisms remain poorly understood.

Our study did not reveal significant differences in alpha diversity in oral samples collected before and after anticancer treatment among patients who developed mucosal lesions. Similarly, no substantial changes in oral microbiota richness were observed before and after treatment in pediatric cancer patients. It is possible that these patients had already experienced changes in their oral microbiome compared to healthy children, as they had undergone at least one chemotherapy cycle prior to the first oral sample collection. Additionally, the presence of cancer itself could have already influenced oral microbiome diversity, which might explain why we did not observe significant differences in alpha diversity. Laheij et al. (2022) conducted a longitudinal analysis of the oral microbiome in 50 recipients of allogeneic stem cell transplantation (SCT), examining samples collected before and up to 18 months after SCT. Their study found a marked reduction in alpha diversity one week post-SCT. Furthermore, reduced alpha diversity was linked to a decreased abundance of health-associated genera such as Streptococcus and Veillonella, and an increased abundance of disease-associated genera like Staphylococcus and Mycobacterium (Laheij et al., 2022). Wang et al. (2014) examined oral health and microbiota in 13 pediatric patients diagnosed with acute lymphoblastic leukemia (ALL) compared to 12 healthy controls, revealing that children with ALL experienced dysbiosis characterized by reduced richness and diversity (Wang et al., 2014). Similarly, Ye et al. (2013) found that pediatric cancer patients who later developed oral mucositis exhibited higher microbial diversity at diagnosis and more significant bacterial community changes following chemotherapy before mucositis onset.

Another important finding from our study was the lower mean relative abundance of Bergeyella in patients who developed oral lesions compared to those who did not. Bergeyella, a Gram-negative, rod-shaped bacterium, is a key component of the oral microbiota (Sharma et al., 2019). Bruno et al. (2023) suggested that Bergeyella could be a potential target for oral mucositis prevention. Our results, combined with their findings, suggest that Bergeyella may have a protective role against oral mucositis and microbiome dysbiosis, though further research is needed to confirm this.

Additionally, we observed an increased abundance of Rothia mucilaginosa and Fusobacterium periodonticum in patients who developed oral lesions during anticancer treatment. Rothia mucilaginosa, is a Gram-positive coccus commonly found in the oral cavity and is an opportunistic pathogen in immunocompromised individuals (Robertson et al., 2021; Getzenberg et al., 2021). Fusobacterium, an anaerobic Gram-negative bacterium, is also an opportunistic pathogen, and studies have shown elevated levels in the saliva of children with cancer (Longo et al., 2023; Wang et al., 2021). Laheij et al. (2012) identified that Fusobacterium nucleatum and other oral bacteria increased the risk of ulcerative mucositis in hematopoietic stem cell transplant patients.

Post-chemotherapy, we also noted higher mean relative abundances of Capnocytophaga and Leptotrichia species in patients who developed oral lesions. These Gram-negative bacteria have been associated with oral mucositis in children with neutropenia or malignancy (Laheij & de Soet, 2014; Vasconcelos et al., 2016; Ye et al., 2013). Leptotrichia species, typically found in the oral cavity, have been linked to mucositis and other pathologies, particularly in immunocompromised individuals (Eribe & Olsen, 2017).

Conversely, patients without oral lesions showed significantly higher mean relative abundances of Haemophilus and Alloprevotella species in their post-treatment samples. Haemophilus species are commonly found in the oral cavity and respiratory tract of healthy children (Kosikowska et al., 2015). However, our results differ from those of Vesty et al. (2020), who found that a higher abundance of Haemophilus on the buccal mucosa was associated with an increased susceptibility to oral mucositis. One potential explanation for this discrepancy is the different sampling locations in the oral cavity. Alloprevotella rava, identified in patients with oral dysbiosis, may also play a role, though its precise function remains unclear (Manzoor et al., 2020; Oldenburg et al., 2021).

Furthermore, we observed an increased relative abundance of Parapusillimonas, Faecalibaculum, and Turicibacter in patients who developed oral lesions post-treatment. These bacteria are not typically found in the oral cavity and may be indicative of bacterial dysbiosis (Otálora-Otálora et al., 2023; Zagato et al., 2020; Lynch et al., 2023). Dysbiosis often enables the colonization of non-native bacteria, contributing to the development of oral mucositis (Hong et al., 2019).

Lastly, we found that patients in the Caphosol group had a higher abundance of Atopobium parvulum, Burkholderia-Caballeronia-Paraburkholderia, and Capnocytophaga sputigena compared to those in the saline solution group. Atopobium species have been linked to dental caries and oral mucositis (Holgerson et al., 2015; Shouval et al., 2020; Faraci et al., 2024), while Burkholderia and Capnocytophaga are potential human pathogens (Mendes et al., 2020; Chen et al., 2023). In contrast, Streptococcus pseudopneumoniae was more abundant in the saline solution group. Streptococcus pseudopneumoniae, a member of the viridans group, can cause infections in immunocompromised individuals (Teles et al., 2011). These findings align with previous studies, such as that by Immonen et al. (2020), which found differences in bacterial richness between treatment groups but did not show a reduction in oral mucositis incidence with Caphosol.

Our study has several limitations. The most notable is the small sample size, which may have affected the precision of our estimates. Additionally, all participants had undergone at least one cycle of chemotherapy before their initial oral sample was collected, and a healthy control group was not included for comparison.

Our results suggest that a higher risk of OM is associated with a decreased relative abundance of Alloprevotella and Haemophilus species in pre-treatment saliva, and an increased abundance of Capnocytophaga and Leptotrichia species post-treatment. These findings indicate that anticancer therapy, alongside a dysbiotic oral microbiome, may significantly influence the incidence, severity, and management of mucositis. Identifying patients at high risk for OM could enable improved treatment strategies by fostering protective bacterial species and reducing harmful ones. Further research is needed to clarify the microbiome characteristics that heighten the risk of mucositis, particularly in paediatric cancer patients.

HSCT	Haematopoietic stem cell transplantation
ALL	Acute lymphoblastic leukemia
DNA	Deoxyribonucleic acid
NGS	Next generation sequencing method
rRNA	Ribosomal ribonucleic acid
rDNA	Recombinant deoxyribonucleic acid
PCR	Polymerase chain reaction
OTUs	Operational taxonomic units
CVCs	Central venous catheters
RAS	Recurrent aphthous stomatitis
VGS	Viridans group streptococci
WHO	World Health Organization

ETHICS STATEMENT

The study was approved by The Regional Ethics Committee of Tampere University Hospital. All patients and/or their parents provided written informed consent prior to participation. Every subject was given a study code number, and analyses were carried out without personal identification data.

CONSENT FOR PUBLICATION

Informed Consent for collecting the samples was obtained from all the patients.

COMPETING INTERESTS

The author(s) declare no competing interests.

FUNDING

This work was supported by grants from the Competitive State Research Financing of the Expert Responsibility Area of Tampere University Hospital (EI, OL 9V033), the Apollonia Foundation (E.I.), and The Pediatric Cancer Foundation Väre.

Author Contribution

E.I., L. P. and M. P. conceived the study and drafted the manuscript. E. I and M.P. collected saliva samples from patients. L.P. carried out next generation sequencing and data analysis. H.P. extracted DNA from saliva samples. A.N., L.A., T.P. and O.L. contributed by reviewing and editing the manuscript. All authors have provided their final approval and agree to be accountable for all aspects of the work.

ACKNOWLEDGEMENTS

Apollonia Foundation (E.I.), and The Pediatric Cancer Foundation Väre.

AVAILABILITY OF DATA AND MATERIALS

All data generated or analyzed during this study are included in this published article and its supplementary information files.

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Tables 1 and 2 are available in the Supplementary Files section.

No competing interests reported.

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Oral microbiome diversity and composition before and after chemotherapy treatment in pediatric oncology patients

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Abstract

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1. INTRODUCTION

2. MATERIALS AND METHODS

2.1 Subject selection and clinical outcomes

2.2 Sample collection and preparation

2.3 DNA extraction for microbiome evaluation

2.4 Next generation sequencing and data analysis

3. RESULTS

3.1 Characteristics of the study population

3.2. Alpha-diversity and richness of oral microbiota in pediatric cancer patients

3.3. Abundance differences at phylum level in oral microbiota of patients with and without mucosal lesions

3.4 Abundance differences of OTUs at species level -comparison of patients with and without mucosal lesions

3.5 Abundance differences of oral microbiota between the Caphosol and saline solution oral rinse groups

4. DISCUSSION

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