Da-Som Kim; Eun Hye Kim; Ji Yong Kim; Dong Ha Kim; Yun Jung Choi; Jaeyi Jeong; Young Hoon Sung; Dong-Cheol Woo; Chong Jai Kim; Jae Cheol Lee; Miyong Yun; Jin-Yong Jeong; Jin Kyung Rho

doi:10.4143/crt.2024.1177

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Original Article Lung and Thoracic cancer The Profile of Gut Microbiota in Carcinogenesis Driven by Mutant EGFR in Non–Small Cell Lung Cancer: Da-Som Kim¹, Eun Hye Kim², Ji Yong Kim³, Dong Ha Kim¹, Yun Jung Choi¹, Jaeyi Jeong¹, Young Hoon Sung⁴, Dong-Cheol Woo⁵, Chong Jai Kim⁶, Jae Cheol Lee⁷, Miyong Yun⁸, Jin-Yong Jeong², Jin Kyung Rho¹; Cancer Research and Treatment : Official Journal of Korean Cancer Association 2026;58(1):115-127.
DOI: https://doi.org/10.4143/crt.2024.1177
Published online: March 4, 2025

¹Department of Biochemistry and Molecular Biology, Brain Korea 21 Project, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

²Department of Microbiology and Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

³Department of Thoracic and Cardiovascular Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

⁴Department of Cell and Genetic Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

⁵MR/CT/US Core Laboratory, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Korea

⁶Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

⁷Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

⁸Lab of Functional Aptamer, Department of Bioindustry and Bioresource Engineering, College of Life Sciences, Sejong University, Seoul, Korea

Correspondence: Jin-Yong Jeong, Department of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center and Department of Microbiology, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea
Tel: 82-2-3010-4105 E-mail: jyjeong@amc.seoul.kr

Co-correspondence: Jin Kyung Rho, Department of Biochemistry and Molecular Biology, Brain Korea 21 Project, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea
Tel: 82-2-3010-2974 E-mail: jkrho@amc.seoul.kr

^*Da-Som Kim and Eun Hye Kim contributed equally to this work.

• Received: December 9, 2024 • Accepted: March 2, 2025

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
Accumulating evidence has clarified that gut dysbiosis is involved in lung cancer development and progression. Although the relationship between tumors and gut microbiota has been extensively studied using clinical samples, no studies have examined the association between mutant epidermal growth factor receptor (EGFR)–induced lung carcinogenesis and dysbiosis in gut microbiota. Therefore, we investigated the gut microbiota profiles in stool samples from human lung-specific conditional EGFR-mutant transgenic mice during lung tumor carcinogenesis.
Materials and Methods
Stool samples were collected before tamoxifen treatment (V1) and at each time point following mutant EGFR expression in lung tissue (V2) and lung tumor appearance (V3). Fecal 16S rRNA taxonomy was analyzed to assess microbial diversity, composition, and dynamic changes at each time point.
Results
We found that microbiota richness and diversity were significantly elevated when tumors developed and grew in the lung. Phylogenetic analysis of the microbial community revealed that Lachnospiraceae, Ruminococcaceae, Porphyromonadaceae, Rhodospirillaceae, Odoribacteraceae, and Desulfovibrionaceae showed a significant increase at the V3 stage compared to the V1 stage at the family level. In contrast, Lactobacillaceae, Bacteroidaceae, Muribaculaceae, Coriobacteriaceae, and Rikenellaceae significantly decreased at the V3 stage compared to the V1 stage. Furthermore, Lactobacillus species, also known as short chain fatty acid-producing bacteria, were relatively abundant at the V1 stage but were depleted with the occurrence of lung tumors at the V3 stage.
Conclusion
Changes in gut microbiota, such as Lactobacillus species, may be a predictive factor for the emergence and progression of tumors in an animal model of lung adenocarcinoma induced by mutant EGFR.
Key words: Epidermal growth factor receptor (EGFR), Carcinogenesis, Mutant EGFR, Transgenic mice, Gastrointestinal microbiome, Lactobacillus species, Non-small-cell lung carcinoma

Introduction

Mutations in the epidermal growth factor receptor (EGFR) have been associated with tumorigenesis in various malignancies, including lung, glioblastoma, and breast cancer [1]. In non–small cell lung cancer (NSCLC), activating mutations in EGFR has ushered in a new treatment era. Among these mutations, exon 19 deletions and a single point mutation in exon 21 (L858R) are the most common, accounting for approximately 90% of all EGFR mutations [2,3]. NSCLC harboring these mutations has shown a dramatic response to EGFR–tyrosine kinase inhibitors (TKIs) [4]. However, the emergence of T790M mutation leads to acquired resistance to EGFR-TKIs [5]. Until now, EGFR-TKIs, such as osimertinib and lazertinib, have been developed into third-generation drugs capable of overcoming the T790M mutation [6,7].

The concept of “oncogene addiction” was first enunciated by Bernard Weinstein to describe the dependency of certain tumor cells on a single activated oncogenic protein or pathway to maintain their malignant properties and survival [8]. A representative example of this concept is lung adenocarcinoma driven by mutant EGFR. This theory has been reinforced by the clinical success of EGFR-targeted therapy and several findings in animal tumor models using genetically engineered models. Some reports have shown that the knock-in of an oncogene, such as activating EGFR mutations and EML4-ALK, efficiently induces lung adenocarcinoma in mice [9,10]. These animal models can be used in various studies, including tumorigenesis, metastasis, and drug development.

The human gut microbiome plays a crucial role in maintaining our health. Dysbiosis within this homeostasis in the gut microbiota has been linked to several disorders [11]. The gut microbiota may also act as a tumor promoter. Dysbiosis affects DNA repair pathways and DNA damage responses [12]. Moreover, they release toxins that cause genomic instability and promote cancer development in predisposed cells [13-15]. Several gut microbiota have been reported to be associated with lung cancer. In particular, the stool of NSCLC patients showed significantly higher levels of Prevotella, Lactobacillus, Rikenellaceae, Streptococcus, Enterobacteriaceae, Oscillospira, and Bacteroides plebeius were compared to healthy controls [16]. Conversely, gut butyrate-producing bacteria such as Clostridium leptum, Faecalibacterium prausnitzii, and Ruminococcus significantly decreased in NSCLC patients [17]. In addition, some studies have shown that the use of probiotics may enhance the efficacy of immunotherapy in NSCLC due to changes in the gut microbiota, whereas the use of antibiotics may attenuate it [18-20]. This suggests that gut microbiota plays an important role in tumor immunotherapy.

Most clinical studies on the gut microbiota in NSCLC patients compare them to healthy controls, regardless of dietary patterns and environmental conditions. This study investigated the gut microbiota profiles in stool samples from human lung-specific conditional EGFR-mutant transgenic mice during lung tumor carcinogenesis induced by mutant EGFR expression. All mice used in the experiments were maintained under the same conditions. Our findings have the advantage of being derived from genetically uniform animals under consistent diet and environmental conditions.

Materials and Methods

1. Experimental mice

Human EGFR^{L858R/T790M/C797S} conditional transgenic mice were generated using CRISPR/Cas9 technology, as previously described [9]. Lung-specific Sftpc-CreER^T2 mice (strain #028054) were obtained from the Jackson Laboratory (Bar Harbor). These two strains were crossbred to generate human lung-specific conditional EGFR^{L858R/T790M/C797S} transgenic mice. All mice used in our experiments were female to maintain consistent conditions.

2. DNA extraction, polymerase chain reaction amplification, and sequencing of 16S rRNA genes

As shown in Fig. 1, stool samples from individual mice were obtained at 4-5 (V1), 11-12 (V2), and 16-17 (V3) weeks and stored at –20°C until use in the laboratory. Metagenomic DNA from stool samples was prepared using the FastDNA SPIN Kit for Soil (MP BIO). Prepared genomic DNA was used for polymerase chain reaction amplification of the V4 hypervariable regions of the 16S ribosomal RNA genes with the primer set of 515F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGCCAGCMGCCGCGGTAA-3′) and 805R (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGG TWTCTAAT-3′). The amplifications were carried out under the following conditions: initial heat activation step at 95°C for 3 minutes, followed by 25 cycles of denaturation at 95°C for 30 seconds, primer annealing at 55°C for 30 seconds, and extension at 72°C for 30 seconds. A final elongation was conducted at 72°C for 5 minutes. Illumina sequencing adapters and dual-index barcodes were then added to the generated amplicons using the Nextera XT DNA index kit (Illumina). The amplified products were normalized and pooled, and the sequencing was carried out at the Asan Institute for Life Science using the Illumina iSeq100 Sequencing system (Illumina) according to the manufacturer’s instructions. All analyses were performed in EzBioCloud 16S-based MTP, which is a bioinformatics cloud platform of CJ Bioscience Inc. [21].

3. Phylogenetic diversity of the gut microbiome

The generation of taxonomic composition charts for all taxonomic ranks from phylum to species was performed using the EzBioCloud 16S rRNA database. We measured microbial diversity and dynamic changes at each time point by comparing alpha-diversity (ACE, Chao1, Shannon, and Simpson indices) and beta-diversity (principal coordinate analysis [PCoA]) analyses. The linear discriminant analysis effect size (LEfSe v1.0) analysis was applied to identify differential taxonomy at each time point. The logarithmic linear discriminant analysis scores were evaluated with a threshold of 3.0 or more. The phylogenetic relationships of the microbiomes of each of the three groups of mice were visualized using a cladogram and microbiome heatmap. All the analytics mentioned above were performed by in-house programs of CJ Bioscience Inc. (EzBioCloud) [21].

4. Statistical analysis

Statistical analyses were conducted using GraphPad Prism 9.4.1 software (GraphPad Software Inc.). Statistically, group differences at each time point were determined using the Kruskal-Wallis test and two-way ANOVA for continuous variables. A p-value of less than 0.05 was deemed statistically significant.

Results

1. Characteristics of mutant EGFR transgenic mice

As previously described, human lung-specific conditional EGFR^{L858R/T790M/C797S} transgenic mice can develop lung cancer through mutant EGFR expression when treated with tamoxifen, as shown in Fig. 1. To evaluate changes in the gut microbiome during the carcinogenesis of oncogene-addicted tumors, stool samples were collected at each stage (V1: normal; V2: mutant EGFR expression in lung tissue; V3: emergence of lung tumor). Subsequently, 16S rRNA gene sequencing was performed on the stool samples harvested from the experimental mice.

2. Phylogenetic diversity of gut microbiome

To determine whether mutant EGFR-driven lung carcinogenesis affects gut dysbiosis, we studied gut metagenomic profiles in the pre- and post-treatment periods (V1 vs. V2 vs. V3) with tamoxifen. Alpha- and beta-diversity were compared to assess the microbial diversity of these three groups. Alpha diversity was analyzed by calculating the ACE and Chao1 indices (a proxy for richness) and Shannon and Simpson indices (a proxy for diversity). The ACE and Chao1 indices revealed higher richness in the V2 and V3 groups compared to the V1 group (Fig. 2A). However, the Shannon and Simpson indices showed a tendency for microbial diversity to decrease in the V2 group compared to the V1 group, with a significant recovery trend in the V3 group (Fig. 2B). These results indicate that the emergence of mutant EGFR-driven lung tumors significantly impacts the diversity and richness of the gut microbiome.

Beta-diversity within these three groups was analyzed using Bray-Curtis (a qualitative measure) and UniFrac distances (a quantitative measure) (Fig. 2C). A significant separation in bacterial community composition was observed between these three groups, indicating that alterations in the gut microbiota could be attributed to changes occurring during the carcinogenesis of oncogene-addicted tumors.

3. Gut microbiota profile at each time point

The bacterial taxa discriminating the gut microbiota of the three groups were calculated. At the phylum level (Fig. 3A), Bacteroidetes were the most abundant fecal microbiota at the V1 stage, followed by Firmicutes and Proteobacteria. During the mutant EGFR expression (V2 stage) period, the gut microbiota changed, with a decrease in Firmicutes and an increase in Bacteroidetes. Firmicutes recovered again 15 weeks later, during the emergence of a lung tumor (V3 stage) (Fig. 3A and B). Firmicutes and Bacteroidetes are two major phyla of the domain bacteria and are dominant in gut microbiota. In this study, a decrease in the ratio of Firmicutes to Bacteroidetes was observed in the V2 stage, which recovered again in the V3 stage (Fig. 3C), suggesting that the Firmicutes to Bacteroidetes ratio may be correlated with disease.

At the family level (Fig. 3D), gut microbiota showed significant differences among the three groups. In particular, Lactobacillaceae was significantly decreased in the V3 stage compared to the V1 stage (Fig. 3E). The Lactobacillaceae family contains many Lactobacillus species that are important components of the human and animal microbiota and have beneficial effects on human health. Conversely, Lachnospiraceae and Ruminococcaceae significantly increased in the V3 stage (Fig. 3F and G).

4. Gut microbial composition and differentially abundant taxa

To confirm the phylogenetic relationship of the microbial community, it was visualized using a cladogram (Fig. 4A). The microbial communities from the family level to the genus level showed significant differences among the three groups. Specifically, Porphyromonadaceae, Rhodospirillaceae, Odoribacteraceae, and Desulfovibrionaceae showed an increasing trend at the V3 stage compared to the V1 stage at the family level (Fig. 4B). Conversely, Bacteroidaceae, Muribaculaceae, Coriobacteriaceae, and Rikenellaceae showed a tendency to decrease at the V3 stage compared to the V1 stage (Fig. 4C).

Moreover, LEfSe and microbiome heatmap analyses were performed to determine species-level differences in microbial composition between the three groups (Fig. 5A and B). Lactobacillus species, known short chain fatty acid (SCFA)-producing bacteria, were relatively abundant in the V1 stage, but these were depleted with the appearance of lung tumors at the V3 stage (Fig. 6A-D). SCFAs are an important energy source for the intestinal mucosa and play a crucial role in regulating intestinal immune responses and tumorigenesis [16,17,22]. Therefore, the abundance of Lactobacillus species at the V1 stage and their disappearance at the V3 stage may be associated with lung carcinogenesis due to mutant EGFR expression.

Species AB599946, belonging to the Bacteroidaceae family, showed a significant decrease in the V3 stage compared to the V1 stage (Fig. 6E). Conversely, several bacteria species, including PAC001188, PAC001105, PAC001783, PAC001079, and PAC000185, showed a significant increase in the V3 group compared to the V1 group (Fig. 6F-J).

Discussion

In the present study, we have investigated gut microbiota profiles in stool samples from human lung-specific conditional EGFR^{L858R/T790M/C797S} transgenic mice during lung tumor carcinogenesis induced by mutant EGFR expression. This is the first metagenomics study to explore the correlation between lung adenocarcinoma carcinogenesis and dynamic changes in gut microbiota. Considering that most clinical data show significant differences between patients, our data can help determine a direct correlation between the occurrence of lung cancer and changes in the gut microbiome under controlled dietary and environmental conditions.

Our results showed that mutant EGFR-driven lung carcinogenesis and progression significantly impact the diversity and abundance of the gut microbial community. In addition, distinct gut microbiome compositions were identified at each time point during lung tumor carcinogenesis through mutant EGFR expression. The diversity of the gut microbiome is defined as the number and abundance distribution of distinct types of microorganisms living within the gut. Abnormal diversity in the gut (gut dysbiosis) has been linked to human diseases such as cancer, obesity, inflammatory bowel disease, and more [23-25]. In our study, alpha diversity and beta diversity at the V2 and V3 stages dramatically differed from the V1 stage. A higher alpha diversity index, represented by both the Shannon index and inverse Simpson index in the V3 stage, suggests that more bacterial species are harbored in the gut, consistent with the differential composition of the gut microbiome between the V1 and V3 stages. Beta diversity, visualized through PCoA analysis of 16S sequencing, also displays a significant separation in bacterial community composition based on the pre- and post-treatment periods with tamoxifen. Therefore, our results indicate that changes in the gut microbiota could be linked to alterations during the development of oncogene-dependent tumors.

We found that the gut microbiota showed significant differences during mutant EGFR-driven lung carcinogenesis at each time point. In particular, Lactobacillus species, including Lactobacillus gasseri, Lactobacillus reuteri, Lactobacillus intestinalis, and Lactobacillus_uc, were relatively abundant in the V1 stage but were depleted with the appearance of lung tumors at the V3 stage. Lactobacillus species are among the bacterial species that produce SCFAs. SCFAs are an important energy source for the intestinal mucosa and play a crucial role in regulating intestinal immune responses and tumorigenesis [16,17]. They also have beneficial effects on human health and are used in fermenting dairy products or as probiotic supplements [26]. Therefore, they exert an immunomodulatory effect and maintain intestinal homeostasis. In this regard, the abundance of Lactobacillus species at the V1 stage and their disappearance at the V3 stage might be a potential biomarker to predict lung carcinogenesis driven by mutant EGFR expression.

Some Lactobacillus species inhibited the cell growth and viability of HT-29 and Caco-2 cancer cells via the downregulation of ErbB-2 and ErbB-3 gene expression [27]. Previous in vitro studies using the colorectal cancer cell line HT-29 demonstrated that exopolysaccharides from Lactobacillus strains induced apoptosis and G0/G1 cell cycle arrest [28]. Moreover, Lactobacillus species function as immunomodulators in lung diseases. They exhibit anti-inflammatory effects by reducing pulmonary inflammation in chronic obstructive pulmonary disease mouse models [29]. Some Lactobacillus species have been shown to enhance the inhibitory effect of gemcitabine on tumor growth in pancreatic cancer, which may be synergistically enhanced by the intervention of probiotic compositions [30]. Therefore, Lactobacillus species show potential for inhibiting tumor growth and alleviating lung diseases.

On the other hand, our results showed species PAC001188, PAC001105, PAC001783, PAC001079, and PAC000185 significantly increased at the V3 stage compared to the V1 stage. Unfortunately, no studies have yet linked these species to lung cancer. Although further studies are necessary to investigate the association between these species and their clinical implications, changes in gut microbiota may serve as predictive indicators for lung cancer.

Our study had some limitations. First, our findings were not interpreted for clinical significance using appropriate specimens. Second, our results do not clarify whether this is applicable beyond mutant EGFR-driven lung cancer. Thus, further studies using clinical samples and other lung cancer animal models are necessary.

In conclusion, the gut microbiome exhibited dynamic changes at each time point during lung tumor carcinogenesis driven by mutant EGFR expression. Lung cancer formation and progression induced by mutant EGFR expression significantly impact the alpha and beta diversity of the gut microbial community. SCFAs-producing bacteria, such as Lactobacillus species, were depleted and altered with the emergence of lung tumors, accompanied by dynamic changes in gut microbial diversity. Therefore, the gut microbiome, including Lactobacillus species, may be a predictive factor for mutant EGFR-driven lung carcinogenesis.

NOTES

Ethical Statement

Animal experiments complied with the Korean Ministry of Food and Drug Safety (MFDS) guidelines and were approved by the Institutional Animal Care and Use Committee of the Asan Institute for Life Sciences (2023-30-273).

Author Contributions

Conceived and designed the analysis: Kim DS, Kim EH, Jeong JY, Rho JK.

Collected the data: Kim DS, Kim EH, Kim DH, Choi YJ, Jeong J, Sung YH, Woo DC.

Contributed data or analysis tools: Kim DH, Kim JY, Kim CJ, Lee JC, Yun M, Rho JK.

Performed the analysis: Kim DH, Kim DS, Choi YJ, Kim EH, Jeong J, Sung YH, Woo DC, Kim CJ, Lee JC, Yun M, Jeong JY, Rho JK.

Wrote the paper: Kim DS, Jeong JY, Rho JK.

Conflicts of Interest

Conflict of interest relevant to this article was not reported.

Funding

This study was supported by a grant (2022IP0029 to J.K. Rho) from the Asan Institute for Life Science, Seoul, South Korea, and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2023-00261982 and HR21C0198).

Acknowledgments

We thank the Laboratory of Animal Research core facilities and the MR/CT core facility at the Convergence Medicine Research Center (CREDIT) at Asan Medical Center, as well as the Human Microbiome Research Center at the University College of Medicine, for providing access to their shared equipment, services, and expertise.

Fig. 1.

Characterization of human lung-specific conditional EGFR^{L858R/T790M/C797S} transgenic mice and preparation of stool from experimental mice. (A) Study design. EGFR^{L858R/T790M/C797S}/CreER^T2 mice were treated with tamoxifen (75 mg/kg) at 6 weeks. Human mutant EGFR expression and the occurrence of lung tumors were confirmed using an in vivo imaging system and magnetic resonance imaging at the indicated times. (B) Sample groups. F-mEGFR-sV1 (normal, V1); F-mEGFR-sV2 (mutant EGFR expression in lung tissue, V2); F-mEGFRsV3 (emergence of lung tumors, V3); V, stool collection.

Fig. 2.

Alpha and beta diversity measurements of the stool microbiome. Alpha-diversity analysis based on ACE and Chao1 (A) and Shannon and Simpson indexes (B) in the studied populations. Beta-diversity analysis based on Bray-Curtis distance and UniFrac distance (C). NS, not significant; *p < 0.05.

Fig. 3.

Comparison of the relative abundance of bacterial phyla and family levels. (A) Relative abundance of total gut microbiota at the phylum level. ETC, under 1.0% on average. (B) Average relative abundance of the Firmicutes phylum and (C) Firmicutes to Bacteroidetes ratio in the pre- and post-treatment periods (V1 vs. V2 vs. V3) with tamoxifen. (D) Relative abundance of total gut microbiota at the family level. (E-G) Average relative abundance of predominant microbiota at the family level pre- and post-treatment with tamoxifen. NS, not significant; *p < 0.05.

Fig. 4.

Differences in gut microbial communities in the three stages. (A) A cladogram showing the phylogenetic relationship of the microbial community. (B) Based on the phylogenetic tree, the average relative abundance of microbiota tended to increase at stage V3. (C) Based on the phylogenetic tree, the average relative abundance of microbiota shows a tendency to decrease at stage V3. NS, not significant; *p < 0.05.

Fig. 5.

Comparison of bacterial taxa using the linear discriminant analysis (LDA) effect size (LEfSe) algorithm and Heat map. (A) LEfSe analysis results for each of the three groups of mice. The effect size threshold for LEfSe analysis is 3.0 or more. (B) Heat map visualization of gut microbiota results for each of the three groups of mice.

Fig. 6.

Average relative abundance of predominant gut microbiota during lung tumor carcinogenesis through mutant epidermal growth factor receptor (EGFR) expression. (A-E) The average relative abundance of microbiota tended to decrease at stage V3 at the species level. (F-J) The average relative abundance of microbiota tended to increase at stage V3 at the species level. f, family; g, genus; s, species. NS, not significant; *p < 0.05.

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The Profile of Gut Microbiota in Carcinogenesis Driven by Mutant EGFR in Non–Small Cell Lung Cancer

Cancer Res Treat. 2026;58(1):115-127. Published online March 4, 2025

DOI: https://doi.org/10.4143/crt.2024.1177

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Abstract
Introduction
Materials and Methods
Results
Discussion
NOTES
REFERENCES

The Profile of Gut Microbiota in Carcinogenesis Driven by Mutant EGFR in Non–Small Cell Lung Cancer

Fig. 1. Characterization of human lung-specific conditional EGFRL858R/T790M/C797S transgenic mice and preparation of stool from experimental mice. (A) Study design. EGFRL858R/T790M/C797S/CreERT2 mice were treated with tamoxifen (75 mg/kg) at 6 weeks. Human mutant EGFR expression and the occurrence of lung tumors were confirmed using an in vivo imaging system and magnetic resonance imaging at the indicated times. (B) Sample groups. F-mEGFR-sV1 (normal, V1); F-mEGFR-sV2 (mutant EGFR expression in lung tissue, V2); F-mEGFRsV3 (emergence of lung tumors, V3); V, stool collection.

Fig. 2. Alpha and beta diversity measurements of the stool microbiome. Alpha-diversity analysis based on ACE and Chao1 (A) and Shannon and Simpson indexes (B) in the studied populations. Beta-diversity analysis based on Bray-Curtis distance and UniFrac distance (C). NS, not significant; *p < 0.05.

Fig. 3. Comparison of the relative abundance of bacterial phyla and family levels. (A) Relative abundance of total gut microbiota at the phylum level. ETC, under 1.0% on average. (B) Average relative abundance of the Firmicutes phylum and (C) Firmicutes to Bacteroidetes ratio in the pre- and post-treatment periods (V1 vs. V2 vs. V3) with tamoxifen. (D) Relative abundance of total gut microbiota at the family level. (E-G) Average relative abundance of predominant microbiota at the family level pre- and post-treatment with tamoxifen. NS, not significant; *p < 0.05.

Fig. 4. Differences in gut microbial communities in the three stages. (A) A cladogram showing the phylogenetic relationship of the microbial community. (B) Based on the phylogenetic tree, the average relative abundance of microbiota tended to increase at stage V3. (C) Based on the phylogenetic tree, the average relative abundance of microbiota shows a tendency to decrease at stage V3. NS, not significant; *p < 0.05.

Fig. 5. Comparison of bacterial taxa using the linear discriminant analysis (LDA) effect size (LEfSe) algorithm and Heat map. (A) LEfSe analysis results for each of the three groups of mice. The effect size threshold for LEfSe analysis is 3.0 or more. (B) Heat map visualization of gut microbiota results for each of the three groups of mice.

Fig. 6. Average relative abundance of predominant gut microbiota during lung tumor carcinogenesis through mutant epidermal growth factor receptor (EGFR) expression. (A-E) The average relative abundance of microbiota tended to decrease at stage V3 at the species level. (F-J) The average relative abundance of microbiota tended to increase at stage V3 at the species level. f, family; g, genus; s, species. NS, not significant; *p < 0.05.

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The Profile of Gut Microbiota in Carcinogenesis Driven by Mutant EGFR in Non–Small Cell Lung Cancer