Higher Microbial Abundance and Diversity in Bronchus-Associated Lymphoid Tissue Lymphomas Than in Non-cancerous Lung Tissues

Jung Heon Kim; Jae Sik Kim; Noorie Choi; Jiwon Koh; Yoon Kyung Jeon; Ji Hyun Chang; Eung Soo Hwang; Il Han Kim

doi:10.4143/crt.2024.689

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Original Article Higher Microbial Abundance and Diversity in Bronchus-Associated Lymphoid Tissue Lymphomas Than in Non-cancerous Lung Tissues: Jung Heon Kim^1,a), Jae Sik Kim², Noorie Choi^3,b), Jiwon Koh⁴, Yoon Kyung Jeon^4,5, Ji Hyun Chang³, Eung Soo Hwang^1,6,, Il Han Kim^3,; DOI: https://doi.org/10.4143/crt.2024.689
Published online: September 30, 2024

¹Institute of Endemic Diseases, Seoul National University Medical Research Center, Seoul, Korea

²Department of Radiation Oncology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea

³Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Korea

⁴Department of Pathology, Seoul National University College of Medicine, Seoul, Korea

⁵Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea

⁶Department of Microbiology and Immunology, Seoul National University College of Medicine, Seoul, Korea

Correspondence: Eung Soo Hwang, Department of Microbiology and Immunology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea
Tel: 82-2-740-8313 E-mail: hesss@snu.ac.kr

Co-correspondence: Il Han Kim, Department of Radiation Oncology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea
Tel: 82-2-2072-3574 E-mail: ihkim@snu.ac.kr

*Jung Heon Kim and Jae Sik Kim contributed equally to this work.
a)Present address: R&D Division, KogeneBiotech, Seoul, Korea
b)Department of Radiation Oncology, Veterans Health Service Medical Center, Seoul, Korea

• Received: July 24, 2024 • Accepted: September 29, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

Purpose
It is well known that the majority of the extranodal marginal zone lymphomas of mucosa-associated lymphoid tissues (MALT lymphomas) are associated with microbiota, e.g., gastric MALT lymphoma with Helicobacter pylori. In general, they are very sensitive to low-dose radiotherapy and chemotherapeutic agents. The microbiota profile is not clearly elucidated in bronchus-associated lymphoid tissue (BALT) lymphoma, a rare type of MALT lymphoma in the lung. Thus, this study aimed to clarify the intratumor microbiome in BALT lymphoma using the third-generation next-generation sequencing (NGS) method.
Materials and Methods
DNAs were extracted from 12 formalin-fixed paraffin-embedded (FFPE) tumor tissues obtained from BALT lymphoma patients diagnosed between 1990 and 2016. 16S rRNA gene was amplified by polymerase chain reaction. Amplicons were sequenced using a Nanopore platform. Next-generation sequencing analysis was performed to assess microbial profiles. For comparison, FFPE specimens from nine non-cancerous lung tissues were also analyzed.
Results
Specific bacterial families including Burkholderiaceae, Bacillaceae, and Microbacteriaceae were associated with BALT lymphoma by a linear discriminant analysis effect size approach. Although the number of specimens was limited, BALT lymphomas exhibited significantly higher microbial abundance and diversity with distinct microbial composition patterns and correlation networks than non-cancerous lung tissues.
Conclusion
This study provides the first insight into intratumor microbiome in BALT lymphoma using the third-generation NGS method. A distinct microbial composition suggests the presence of a unique tumor microenvironment of BALT lymphoma.
Key words: Bronchus-associated lymphoid tissue (BALT) lymphoma, Extranodal marginal zone B-cell lymphoma, Mucosa-associated lymphoid tissue, Non-Hodgkin lymphoma, Intratumor microbiome

Introduction

Extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue (MALT lymphoma) is a subtype of non-Hodgkin lymphoma that mainly occurs in the mucosa of the stomach, eye, and respiratory tract [1,2]. Chronic inflammation, often resulting from autoimmune diseases or bacterial/viral infections, is a known factor contributing to the development of cancer, including MALT lymphomas [3,4]. Various bacterial infections have been implicated in the development of MALT lymphoma, including Borrelia burgdorferi in the skin, Chlamydophilia psittaci in the ocular adnexa, and Campylobacter jejuni in the small intestine [3]. Epstein-Barr virus, hepatitis B virus, and human immunodeficiency virus are also associated with extranodal lymphomas [5-7].

Pulmonary MALT lymphoma is rare with a relative proportion of 0.1% in all pulmonary tumors, and bronchus-associated lymphoid tissue (BALT) lymphoma constitutes 70% of them [8,9]. Seoul National University Hospital, one of the largest tertiary institutions in Korea, diagnosed only 23 patients with BALT lymphoma between 1990 and 2016, indicating that it is a very rare disease in Korea. Although it has been suggested that Achromobacter, Chlamydiae, and Mycoplasma infections are associated with BALT lymphomas, their definitive relevance remains inconclusive due to a limited number of cases [10,11]. A European study on Achromobacter xylosoxidans has observed variations in its positivity rate depending on the country, suggesting the potential existence of geographical differences in predominant bacteria involved in BALT lymphomas [10]. Since research on the association of bacteria or viruses with BALT lymphoma in Asian regions is lacking, this study aimed to elucidate intratumor microbiome in BALT lymphoma from 10 patients to deepen our understanding of its tumor microenvironment (TME) using third-generation next-generation sequencing (NGS) method.

Materials and Methods

1. Study population

Medical records of patients diagnosed as BALT lymphoma between 1990 and 2016 at Seoul National University Hospital were reviewed. All patients agreed to future use of the human-derived material for biomedical research since February 2013. We assayed archived formalin-fixed paraffin-embedded (FFPE) lung tissue specimens from patients (n=10; B-1 to B-10) who had not previously received local radiotherapy to the lung. Characteristics of patients are summarized in Table 1.

In addition, nine non-cancerous lung tissue specimens were obtained: three (B-6, B-7, and B-9) from BALT lymphoma cases and other six from non-registered cases including BALT lymphoma (n=1), lung carcinoma (n=2), and inflamed lung biopsied to rule out lung cancer (n=3). A non-cancerous normal lung tissue was confirmed again by two pathologists (J.K. and Y.K.J.). Detailed information regarding tissue specimens is tabulated in S1 Table.

2. DNA preparation

A total of 21 FFPE tissue samples obtained from 16 patients were utilized in this study. FFPE tissue samples were cut into 10 µm sections under aseptic conditions. Five sections of each sample were used for DNA extraction. Genomic DNAs were extracted from FFPE samples using a Qiamap DNA FFPE Tissue Kit (56404, Qiagen). Subsequently, extracted DNAs underwent a repair process utilizing a NEBNext FFPE DNA Repair Mix (M6630L, New England Biolabs).

3. DNA amplification

Polymerase chain reaction (PCR) amplification of the 16S rRNA gene was performed using universal primers. Each PCR was carried out in a total volume of 20.5 µL, containing 2 µL of 10× buffer, 2 µL of dNTP mix, 2 µL of each forward and reverse primer, 0.5 µL of iMax II DNA polymerase, and 14 µL of template DNA. PCR amplification consisted of an initial denaturation step at 95ºC for 3 minutes, followed by 30 cycles of denaturation at 95ºC for 20 seconds, annealing at 55ºC for 30 seconds, and extension at 72ºC for 90 seconds. The amplification was finalized with a 5-minute extension step at 72ºC. All PCR products were purified using a Qiagen PCR Purification Kit (28106, Qiagen).

4. Library preparation

The library for Nanopore sequencing on a MinION platform was prepared using a Ligation Sequencing Kit 1D (SQKLSK108, Oxford Nanopore Technologies) with the following steps: (1) ends of target amplicons were prepared using an NEBNext Ultra II End Repair/dA-Tailing Kit (E7546L, New England Biolabs), which involved repairing and adding a dA-tail to amplicons to facilitate subsequent ligation reactions; (2) prepared amplicons underwent a ligation step using Barcode Adapters from a PCR Barcoding Expansion Pack 1-96 (EXP-PBC096, Oxford Nanopore Technologies) and an NEB Blunt/TA Ligase Master Mix (M0367L, New England Biolabs), which allowed for attachment of barcode adapters to amplicons, enabling downstream sample identification and multiplexing; (3) barcoding PCR was performed using a PCR Barcode primer from the PCR Barcoding Expansion Pack 1-96 (EXP-PBC096, Oxford Nanopore Technologies) and an NEB LongAmp Taq 2× Master Mix (M0287S, New England Biolabs). Amplification conditions for this step involved an initial denaturation step at 95°C for 3 minutes, followed by 19 cycles of 15 seconds at 95°C for denaturation, 15 seconds at 62°C for annealing, and 2 minutes at 65°C for extension. The final extension was performed at 65°C for 10 minutes to ensure complete amplification. Following this protocol, amplicons were appropriately prepared, ligated with barcode adapters, and amplified with barcode primers for subsequent Nanopore sequencing on the MinION platform.

5. Sequencing

NGS was conducted on a MinION platform using a FLOMIN106 R9.4 flow cell (Oxford Nanopore Technologies). All sequencing flow cells were primed prior to sequencing to ensure optimal performance following the protocol provided by Oxford Nanopore Technologies. The priming mixture was prepared by diluting 500 µL of Running Buffer Fuel Mix with 500 µL of molecular-grade water.

Each 2D sequencing library was prepared for loading into the flow cell. To do this, 6 µL of each library was mixed with 37.5 µL of Running Buffer Fuel Mix provided by Oxford Nanopore Technologies. Then 31.5 µL of molecular-grade water was added. This step ensured appropriate composition and volume for successful sequencing.

After priming the flow cell and preparing the library, the library mixture was loaded into the Nanopore flow cell for sequencing. The flow cell is a crucial component that houses nanopores and allows the passage of DNA molecules for sequencing. The sequencing process was proceeded by carefully loading the prepared library mixture into the flow cell.

6. NGS analysis

The MinKNOW software was used to assess the status of active nanopores during the sequencing process and default settings. Subsequently, Fast5 files generated from the sequencing run were converted to Fastq files using Poretools. Porechop was used to trim barcode and adapter sequences. Reads obtained were subjected to base-calling using Albacore, a software tool developed by Oxford Nanopore Technologies. Reads were then split based on their barcodes using the EPI2me Desktop Agent (Oxford Nanopore Technologies Metrichor). The “What’s in my pot” tool (WIMP; Oxford Nanopore Technologies) was used to perform taxonomic classification and quantitative analysis of reads derived from 16S rRNA amplicons. This tool facilitated the identification and classification of taxa based on sequencing reads obtained from amplicons.

7. Statistical analysis

Microbiome data were analyzed with a web-based MicobiomeAnalyst (https://www.microbiomeanalyst.ca/) platform following established protocols [12]. Uploaded operational taxonomic unit counts were preprocessed and the total sum scaling method was applied. Microbiota compositions were assessed at the family level, with calculations performed for both actual and relative abundances. The diversity of microbial communities was evaluated by alpha- (Chao1 and t test) and beta-diversity (Bray-Curtis index and permutational multivariate analysis of variance). Alpha-diversity refers to the species diversity within a particular sample, while beta-diversity measures the differences in species composition between samples [13]. Heat tree analysis and linear discriminant analysis (LDA) effect size (LEfSe) approaches were employed to detect microbial taxa with significant differences between groups. Correlation network analysis used sparse correlations for the compositional data algorithm, which revealed potential microbial interactions. A phylogenetic tree was constructed using the ‘phyloseq’ package in R. A p-value of < 0.05 was considered statistically significant.

Results

General characteristics of tumor microbiome in all cases at the family level are depicted in Fig. 1. The microbiome in BALT lymphoma exhibited markedly higher abundance and diversity than that in non-cancerous lung tissues. A phylogenetic tree is presented in S2 Fig.

Principal coordinate analysis was conducted using Bray-Curtis metric distances of beta-diversity, which showed a distinct clustering pattern between groups, indicating phylogenetic proximity of microbial communities within each group (p=0.005) (Fig. 1C). Tumor microbial diversity in each sample was measured by the Chao1 index and compared between groups (Fig. 1D and E). The alpha-diversity of the BALT lymphoma group was significantly higher than that of the non-cancerous lung tissue group (p=0.008).

Phylogenetic heat tree analysis revealed that the Betaproteobacteria community was more abundant in BALT lymphomas than in non-cancerous lung tissues (Fig. 2A). Additionally, a significant presence of Firmicutes phylum was observed in BALT lymphomas.

We used LEfSe to compare the most predominant bacteria between the groups, where a log LDA score of 2 was considered as feature selection criteria, with a false discovery rate-adjusted p-value of < 0.1 (Fig. 2B). Dominant bacterial families in BALT lymphomas were Burkholderiaceae, Bacillaceae, Microbacteriaceae, Thermoanaerobacteraceae, Mycoplasmataceae, Streptococcaceae, Moraxellaceae, Erwiniaceae, Staphylococcaceae, Corynebacteriaceae, Alcaligenaceae, Oxalobacteraceae, and Pasteurellaceae (Table 2). In contrast, non-cancerous lung tissues exhibited a predominance of Propionibacteriaceae and Enterobacteriaceae.

A network was constructed to investigate interactions among these distinct bacteria at the family level (Fig. 2C). Among dominant families identified in the LEfSe analysis, Burkholderiaceae, Bacillaceae, Thermoanaerobacteraceae, Erwiniaceae, Staphylococcaceae, Corynebacteriaceae, Oxalobacteraceae, and Pasteurellaceae demonstrated positive correlations with each other (S3 Table). However, a negative correlation was observed between Streptococcaceae and Staphylococcaceae, with a correlation coefficient of –0.4962. Additionally, Thermoanaerobacteraceae showed a negative correlation with primary bacterial taxa Propionibacteriaceae and Enterobacteriaceae predominantly found in non-cancerous lung tissues, suggesting that an increase in abundance of Thermoanaerobacteraceae might lead to decreases of these two bacterial families. The microbial dysbiosis index was 0.570.

Discussion

This study found notable disparities in microbial compositions between BALT lymphomas and non-cancerous lung tissues. Particularly, BALT lymphomas exhibited substantially elevated microbial diversity and abundance compared to non-cancerous lung tissues, prominently characterized by significant prevalences of Betaproteobacteria and Firmicutes. These findings offer valuable insights into the potential role of the tumor microbiome in the pathogenesis and progression of BALT lymphomas. Moreover, these findings might suggest novel diagnostic and therapeutic approaches for treating BALT lymphomas by modulating the microbiome.

Intratumor microbiome in BALT lymphomas was confirmed by Nanopore sequencing, an innovative third-generation NGS method. A common limitation of second-generation NGS techniques was that they could only analyze up to approximately 450 bp of 16S rRNA hypervariable regions [14]. This constraint might lead to difficulties in distinguishing between phylogenetically closely related microbes [14]. However, Oxford Nanopore Technologies sequencing enables rapid and real-time long-read sequencing with precision technology, making it more suitable for microbiome analysis [15].

Recently, there has been a surge in interest in tumor microbiome, which refers to the microbial community residing in the TME. Emerging studies have shed light on its crucial role in cancer development and progression [16]. It has long been known that tumors caused by specific viruses and bacteria, such as human papillomavirus–positive oropharyngeal cancer or Helicobacter pylori associated stomach cancer, tend to have a more favorable prognosis [17,18]. Our research demonstrated a noteworthy discovery in this context as we observed significantly higher abundance and diversity in BALT lymphomas compared to non-cancerous lung tissues. This heightened microbial diversity in BALT lymphoma suggests potential involvement of microbiota in the pathogenesis of BALT lymphoma. Given the ability of gut microbiome metabolites to modulate the immune microenvironment and radiosensitivity [19], it may be plausible to explain that tumor microbiome of BALT lymphoma can play a role in a markedly inflammatory TME, thus increasing its radiosensitivity.

Tumor microbiome communities in BALT lymphomas significantly differed from those in non-cancerous lung tissues. Phylogenetic analysis demonstrated a clear separation of microbial communities between groups. Proteobacteria, especially Burkholderiaceae, was more abundant in BALT lymphomas, indicating their potentially causative role in the development of BALT lymphomas. Proteobacteria are known to induce host DNA or chromosomal damage through genotoxins, which can lead to tumor formation [20,21]. Among them, the Burkholderiaceae family showing the highest LDA score in BALT lymphoma samples is known for its ecological diversity, with one of its members being a highly pathogenic organism that can cause pneumonia [22]. In addition, the Burkholderia cepacia complex is an opportunistic pathogen in cystic fibrosis patients, causing a decline in lung function and fatal necrotizing pneumonia [23,24]. Although the presence of multiple antibiotic resistance mechanisms poses challenges for its eradication, extracytoplasmic proteins have been recently suggested as potential targets for infection treatment [25]. They might also be considered for BALT lymphoma therapy.

Although the LDA score for Alcaligenaceae is relatively low in our study, it aligns with previous research suggesting its possible involvement in the lymphomagenesis of BALT lymphoma [26]. Adam et al. [26] reported that in a study of nine BALT lymphoma patients, bacteria from the Alcaligenaceae family—specifically Alcaligenes, Achromobacter, and AKIW733—were detected in eight patients, while these bacterial clades were absent in all control cases. Our finding further supports the prospective role of Alcaligenaceae as a pathogen affiliated with BALT lymphoma.

In terms of Thermoanaerobacteraceae, bacteria from this family are primarily found in high-temperature environments such as hot springs [27]. There is little evidence of their pathogenicity in humans [28]. In these respects, the predominance of Thermoanaerobacteraceae in lung samples is highly interesting, necessitating further research to investigate whether there might be an association between BALT lymphoma and environmental factors such as high temperature. Furthermore, Thermoanaerobacteraceae appears to interfere with both Propionibacteriaceae and Enterobacteriaceae, implying that it can suppress normal lung flora and consequently induce dysbiosis.

There have been reports linking other bacterial families to lung cancer and respiratory conditions. Bacillaceae and Streptococcaceae have been associated with lung cancer [29,30], whereas Corynebacteriaceae has been shown to decrease in lung cancer patients, according to a meta-analysis [31]. Microbacteriaceae has been found to increase in pneumonia patients, contributing to changes in microbial composition [32]. Mycoplasmataceae and Staphylococcaceae are well-known pathogens of the respiratory system [33], and Moraxellaceae is a newly recognized human-specific pathogen linked to both upper and lower respiratory tract infections [34]. In contrast to these, Pasteurellaceae is associated with respiratory tract infections in various animals [35,36]. Oxalobacteraceae, more prevalent in smokers, has been connected to airway hyperreactivity [37,38]. The role of Erwiniaceae, however, has not yet been reported in the literature.

Achromobacter xylosoxidans has been suggested as the causative pathogen of pulmonary MALT lymphoma based on its high prevalence in this lymphoma [10]. Of interest, Achromobacter xylosoxidans belongs to the same order of Burkholderiales as Burkholderiaceae, which had the highest LDA score in our study. There could be several reasons for the difference observed at the family level compared to the previous study [10]. First, the previous research only focused on investigating the presence of Achromobacter xylosoxidans based on 16S rRNA analysis [10]. It is reasonable to observe discrepancies at the family level, given that the prevalence of specific bacteria varies significantly depending on the country and even among different cohorts within the same country [10,39,40]. Moreover, Achromobacter xylosoxidans is notably isolated in cystic fibrosis patients. Considering the rare incidence of cystic fibrosis in Asian population [41], race and ethnic differences might have influenced our results. For instance, the unique distribution of autoimmune diseases in Korea compared to Western countries has led to distinct patterns of autoimmune disease-associated lymphoma [42]. Thus, further in-depth exploratory studies are warranted.

Results of correlation network analysis are intriguing. A microbial dysbiosis index of 0.570 suggested that there was a slight increase in microbial dysbiosis in BALT lymphomas compared to non-cancerous tissues. This resulted in a significant increase in the abundance of certain bacteria in BALT lymphomas compared to non-cancerous lung tissues. These correlations are particularly important, as they may suggest potential interactions between bacterial taxa that could shape the TME and possibley influence disease progression. Understanding these microbial interactions may reveal new biomarkers or therapeutic targets, potentially leading to better management of BALT lymphomas.

We analyzed seven lung carcinoma samples as different controls. However, it was difficult to draw significant conclusions due to results being obtained from four different histopathological types of lung cancer tissues (data not shown). However, compared to BALT lymphomas, both abundance and diversity were lower in these samples, with alpha-diversity showing no significant difference from non-cancerous lung tissues. In the heat tree analysis of lung carcinomas and non-cancerous lung tissues, similar to BALT lymphomas, lung carcinoma samples showed a higher abundance level of the Firmicutes phylum. In the LEfSe analysis using all samples of BALT lymphomas, lung carcinomas, and noncancerous lung tissues, Burkholderiaceae, Bacillaceae, Microbacteriaceae, Erwiniaceae, and Pasteurellaceae were predominant in BALT lymphomas in our results. Additionally, Thermoanaerobacteraceae was identified in lung carcinomas. These findings suggest that Thermoanaerobacteraceae family might play a primary role in tumor formation acorss various cancer types.

Since most samples were obtained via percutaneous needle biopsy, the probability of including bronchus tissue with a relatively higher microbial presence was low. This was indicated by the lower abundance and diversity in the group of lung carcinoma patients who underwent percutaneous needle biopsy compared to those who underwent bronchoscopy biopsy (data not shown).

Our study had several limitations. The retrospective nature of this study and the limited number of samples were significant constraints. Although validation might be necessary using a large cohort of patients, the uniqueness of this disease underscores that our study stands as an optimal outcome, considering its rarity. The analysis only provided a broad spectrum of pathogens at the family level. Therefore, it is necessary to use a larger number of samples to identify more specific bacteria and their interactions in the development and progression of BALT lymphomas. This study lacked information on other factors such as antibiotic usage that could influence lung microbiome. Moreover, our study focused on microbial communities at a specific time point. Longitudinal studies may provide insight into dynamic changes in the tumor microbiome during disease progression and treatment. Lastly, we acknowledge that our study did not directly establish the identified bacteria as causative agents of BALT lymphoma. Further studies, including mechanistic research, are necessary to determine whether these bacteria play a direct role in the development of BALT lymphoma. However, the significance of our study lies in providing a foundation for future research into the microbial influence on BALT lymphoma development and progression.

Despite these limitations, this study yielded significant implications for specific microbial signatures of BALT lymphomas and added to existing knowledge on the etiology of BALT lymphomas. Understanding the pathogen spectrum and microbial communities associated with BALT lymphomas can pave the way for the development of tailored eradication therapies.

In conclusion, our research underscores the significance of tumor microbiome in BALT lymphomas. The unique tumor microbe microenvironment and varying abundance of specific bacterial families suggest a potential role of microbiota in the pathogenesis of this tumor. Particularly, Proteobacteria, with a notable emphasis on Burkholderiaceae, appeared to play a significant role in BALT lymphoma development. Moreover, our study highlights the influence of geographical location and ethnicity on the spectrum of microbiological agents responsible for BALT lymphoma. Future studies should focus on elucidating mechanistic links between tumor microbiome and disease development, aiming to translate these findings into improved diagnostic and therapeutic strategies for patients with BALT lymphomas.

Electronic Supplementary Material

Supplementary materials are available at Cancer Research and Treatment website (https://www.e-crt.org).

crt-2024-689_S1_Table.pdf

crt-2024-689_S2_Fig.pdf

crt-2024-689_S3_Table.pdf

NOTES

Ethical Statement

All procedures in this study involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee as well as the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the Institutional Review Board of Seoul National University Hospital (No. C-1706-127-861). The requirement for informed consent was waived due to its retrospective design.

Author Contributions

Conceived and designed the analysis: Hwang ES, Kim IH.

Collected the data: Choi N, Koh J, Jeon YK.

Contributed data or analysis tools: Kim JH, Kim JS, Chang JH, Hwang ES, Kim IH.

Performed the analysis: Kim JH, Kim JS, Chang JH.

Wrote the paper: Kim JH, Kim JS, Chang JH.

Writing-review and editing: Kim JH, Kim JS, Choi N, Koh J, Jeon YK, Chang JH, Hwang ES, Kim IH.

Conflict of Interest

Conflict of interest relevant to this article was not reported.

Funding

This work was partially supported by the Soonchunhyang University Research Fund to Jae Sik Kim.

Fig. 1.

Phylogenetic and microbial community diversity profiles of samples obtained from patients with bronchus-associated lymphoid tissue (BALT) lymphomas and non-cancerous lung tissues. (A) Actual abundance. (B) Relative abundance. (C) Beta-diversity. (D) Alpha-diversity of each sample. (E) Alpha-diversity of groups.

Fig. 2.

(A) Heat tree analysis of bronchus-associated lymphoid tissue (BALT) lymphomas and non-cancerous lung tissues. (B) Linear discriminant analysis (LDA) of effect size. (C) Correlation network analysis. Nodes (taxa) are interconnected when there is a statistically significant correlation between them. The size of each node reflects the number of its connections, while the thickness of a connection denotes the strength of that correlation.

Table 1.

Characteristics of bronchus-associated lymphoid tissue lymphoma cases

Characteristic	No. of cases (%) (n=10)
Age (yr)	62 (45-81)
Sex
Male	4 (40.0)
Female	6 (60.0)
ECOG performance status
0	9 (90.0)
1	1 (10.0)
Smoking history
No	7 (70.0)
Yes	3 (30.0)
Ann Arbor stage
1	8 (80.0)
2	0
3	0
4	2 (20.0)

Values are presented as median (range) or number (%). ECOG, Eastern Cooperative Oncology Group.

Table 2.

Predominant families and their taxonomy in bronchus-associated lymphoid tissue lymphomas

Phylum	Class	Order	Family
Proteobacteria	Betaproteobacteria	Burkholderiales	Burkholderiaceae
Firmicutes	Bacilli	Bacillales	Bacillaceae
Actinobacteria	Actinomycetia	Micrococcales	Microbacteriaceae
Firmicutes	Clostridia	Thermoanaerobacterales	Thermoanaerobacteraceae
Tenericutes	Mollicutes	Mycoplasmatales	Mycoplasmataceae
Firmicutes	Bacilli	Lactobacillales	Streptococcaceae
Proteobacteria	Gammaproteobacteria	Pseudomonadales	Moraxellaceae
Proteobacteria	Gammaproteobacteria	Enterobacterales	Erwiniaceae
Firmicutes	Bacilli	Bacillales	Staphylococcaceae
Actinobacteria	Actinomycetia	Corynebacteriales	Corynebacteriaceae
Proteobacteria	Betaproteobacteria	Burkholderiales	Alcaligenaceae
Proteobacteria	Betaproteobacteria	Burkholderiales	Oxalobacteracea
Proteobacteria	Gammaproteobacteria	Pasteurellales	Pasteurellaceae

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Higher Microbial Abundance and Diversity in Bronchus-Associated Lymphoid Tissue Lymphomas Than in Non-cancerous Lung Tissues

DOI: https://doi.org/10.4143/crt.2024.689

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Abstract
Introduction
Materials and Methods
Results
Discussion
Electronic Supplementary Material
NOTES
REFERENCES

Higher Microbial Abundance and Diversity in Bronchus-Associated Lymphoid Tissue Lymphomas Than in Non-cancerous Lung Tissues

Fig. 1. Phylogenetic and microbial community diversity profiles of samples obtained from patients with bronchus-associated lymphoid tissue (BALT) lymphomas and non-cancerous lung tissues. (A) Actual abundance. (B) Relative abundance. (C) Beta-diversity. (D) Alpha-diversity of each sample. (E) Alpha-diversity of groups.

Fig. 2. (A) Heat tree analysis of bronchus-associated lymphoid tissue (BALT) lymphomas and non-cancerous lung tissues. (B) Linear discriminant analysis (LDA) of effect size. (C) Correlation network analysis. Nodes (taxa) are interconnected when there is a statistically significant correlation between them. The size of each node reflects the number of its connections, while the thickness of a connection denotes the strength of that correlation.

Fig. 1.

Fig. 2.

Higher Microbial Abundance and Diversity in Bronchus-Associated Lymphoid Tissue Lymphomas Than in Non-cancerous Lung Tissues

Characteristic	No. of cases (%) (n=10)
Age (yr)	62 (45-81)
Sex
Male	4 (40.0)
Female	6 (60.0)
ECOG performance status
0	9 (90.0)
1	1 (10.0)
Smoking history
No	7 (70.0)
Yes	3 (30.0)
Ann Arbor stage
1	8 (80.0)
2	0
3	0
4	2 (20.0)

Phylum	Class	Order	Family
Proteobacteria	Betaproteobacteria	Burkholderiales	Burkholderiaceae
Firmicutes	Bacilli	Bacillales	Bacillaceae
Actinobacteria	Actinomycetia	Micrococcales	Microbacteriaceae
Firmicutes	Clostridia	Thermoanaerobacterales	Thermoanaerobacteraceae
Tenericutes	Mollicutes	Mycoplasmatales	Mycoplasmataceae
Firmicutes	Bacilli	Lactobacillales	Streptococcaceae
Proteobacteria	Gammaproteobacteria	Pseudomonadales	Moraxellaceae
Proteobacteria	Gammaproteobacteria	Enterobacterales	Erwiniaceae
Firmicutes	Bacilli	Bacillales	Staphylococcaceae
Actinobacteria	Actinomycetia	Corynebacteriales	Corynebacteriaceae
Proteobacteria	Betaproteobacteria	Burkholderiales	Alcaligenaceae
Proteobacteria	Betaproteobacteria	Burkholderiales	Oxalobacteracea
Proteobacteria	Gammaproteobacteria	Pasteurellales	Pasteurellaceae

Table 1. Characteristics of bronchus-associated lymphoid tissue lymphoma cases

Values are presented as median (range) or number (%). ECOG, Eastern Cooperative Oncology Group.

Table 2. Predominant families and their taxonomy in bronchus-associated lymphoid tissue lymphomas