Comparative microbiome profiling reveals unique signatures in multiple primary lung cancers

Introduction

Lung cancer is responsible for approximately two million new cases and 1.8 million deaths each year, making it the leading cause of cancer-related fatalities worldwide (1). Despite significant advancements in surgical techniques, chemotherapy, immunotherapy, and targeted therapies, the overall survival rate for lung cancer patients remains dismally low (2,3). Increasing evidence has revealed that the tumor microenvironment (TME) plays a central role in therapeutic resistance, tumor progression, metastasis, and immune evasion (4,5). The TME is a highly dynamic and intricate ecosystem, encompassing neoplastic cells, immune cells, stromal components, blood vessels, the extracellular matrix, and, importantly, a diverse and evolving microbiome (6,7).

Multiple primary lung cancer (MPLC) refers to the presence of two or more primary lung malignancies in a single patient, with each lesion exhibiting an independent genetic basis. With the evolving landscape of lung cancer and the widespread use of spiral computed tomography (CT), the detection rate of MPLC has increased. However, significant challenges persist in its early diagnosis and treatment, particularly for patients with metachronous MPLC (8,9). MPLC is associated with various etiological factors, including smoking, genetic predispositions, environmental and occupational exposures, and chronic lung diseases (10,11). MPLC management is inherently complex, requiring precise diagnostic procedures, individualized treatment strategies, and high recurrence rates as well as immune evasion mechanisms (8,12). Furthermore, the molecular characteristics underlying MPLC remain poorly understood, posing significant challenges to its diagnosis and treatment.

Recent discoveries have highlighted the presence of the intratumoral microbiota, which has provided novel insights into cancer biology, particularly in lung cancer. Microbial communities residing within the TME are not merely passive bystanders but also active participants in tumor progression (13,14). They dynamically interact with tumor cells, immune cells, and therapeutic agents, influencing tumor behavior and patient outcomes (15,16). Studies employing advanced sequencing technologies, such as 16S ribosomal RNA (rRNA) sequencing and metagenomic analysis (17), have identified specific bacteria, including Brevundimonas diminuta, Deinococcus radiodurans, Pseudomonas aeruginosa, and Rhodococcus erythropolis, in the lung cancer microenvironment (18). These microbes have been implicated in modulating inflammatory pathways, promoting immune evasion, and altering tumor metabolism (13). Additionally, microbial metabolites, such as short-chain fatty acids, have been shown to shape the immune microenvironment, further promoting tumorigenesis and disease progression (19,20).

A growing area of interest is the role of the intratumoral microbiome in influencing responses to cancer immunotherapy (21-23). While the influence of the gut microbiome on the efficacy of immune checkpoint inhibitors has been extensively studied (24), it remains unclear whether similar interactions occur within the lung tumor microbiome. Early evidence suggests that specific bacteria within lung tumors may modulate immune responses, including macrophage polarization and T-cell function, potentially affecting the effectiveness of immunotherapy (25,26). Dysbiosis of the lung microbiome has been implicated in cancer initiation and progression through immune modulation and alterations in the TME (27-30).

In this study, we explored the microbial diversity and composition in MPLC and non-MPLC (NMPLC) patients via 16S rRNA sequencing. By comparing microbial profiles between the two groups, we aimed to identify the distinct microbial taxa and composition structures associated with MPLC pathogenesis. Our findings provide new perspectives on the potential functional role of the microbiome in lung cancer development and progression. These insights may facilitate the development of microbiome-based diagnostic, prognostic, and therapeutic strategies. Ultimately, this research could enhance the effectiveness of existing treatment paradigms and contribute to advances in precision oncology.

Methods

Sample collection

This study included a cohort of 39 lung cancer patients who underwent tumor resection at the First People’s Hospital of Yunnan Province between 2022 and 2024. The inclusion criteria were as follows: (I) a histological diagnosis of lung cancer; (II) tumors amenable to surgical resection; and (III) availability of complete clinical data at the time of diagnosis. The exclusion criteria included patients with previous preoperative treatments or a history of other malignancies. All 39 patients satisfied both the inclusion and exclusion criteria and were subsequently included in the analyses. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethics committee of the First People’s Hospital of Yunnan Province (No. KHLL2022-KY159) and informed consent was obtained from all individual participants.

We collected paired lung tumor tissues and adjacent non-tumor tissues from all patients immediately after surgical resection. Tumor tissues were sampled from both the central and peripheral regions to ensure representativeness, whereas adjacent non-tumor tissues were taken at least 2 cm from the tumor margin to minimize contamination. For eight patients diagnosed with MPLC, samples were obtained from multiple lesions. All tissue samples were collected via sterile surgical instruments to prevent external contamination, flash-frozen in liquid nitrogen, and stored at −80 ℃ until further processing.

Genomic DNA extraction and bacterial 16S rRNA sequencing

Total genomic DNA was extracted with a Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) according to the manufacturer’s protocol and stored at −20 ℃ for subsequent analyses. The DNA concentration and purity were quantified with a NanoDrop (Thermo Fisher Scientific, Waltham, MA, USA), and its quality was verified via agarose gel electrophoresis.

16S rRNA gene amplicon sequencing

The bacterial 16S rRNA gene was amplified via PCR by targeting the V3–V4 region, according to a previously established protocol (31). The PCR products were purified with VAHTS™ DNA Clean Beads (Vazyme, Nanjing, China) and quantified with a Qsep1 instrument (Bioptic, New Taipei City). Paired-end 250 bp sequencing was then conducted on the Illumina MiSeq platform at Biolinker Technology Co., Ltd. (Kunming, China).

Bioinformatics analysis

The raw sequencing data were processed using QIIME2 to obtain clean reads. Amplicon sequence variants (ASVs) were inferred using the DADA2 plugin, which includes quality filtering, denoising, chimera removal, and merging of paired-end reads. Representative sequences were extracted. Data analysis was conducted in QIIME2 and R (v4.2.2). Alpha diversity indices (Chao1, observed species, Shannon, and Simpson indices) were calculated at the ASV level and visualized as box plots. Beta diversity was analyzed via Jaccard, Bray-Curtis, and UniFrac metrics, with principal coordinate analysis (PCoA) for visualization. Group differences in microbial structure were assessed via PERMANOVA, ANOSIM, and Permdisp in QIIME2. Taxonomic compositions and abundances were visualized with GraPhlAn, and ASV-level differential abundances were analyzed via ZicoSeq. Orthogonal partial least squares discriminant analysis (OPLS-DA) (via “MetaboAnalyst”) and random forest analysis (“randomForest”) were applied to explore and classify microbiota variations. Relative abundance histograms were generated via R scripts, and t-tests revealed significant group differences (P<0.05, |R|>0.65). Functional predictions were performed via PICRUSt2 with the MetaCyc and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Sequencing and bioinformatics services were provided by Biolinker Technology Co., Ltd. (Kunming, China).

Statistical analyses

The demographic and clinical characteristics of the patients were compared via the Chi-squared test. Fisher’s exact test was used to examine associations between categorical variables, whereas the Wilcoxon rank-sum test was used to compare continuous variables between two groups. Additionally, the Wilcoxon rank-sum test was employed to assess alpha diversity and Bray-Curtis dissimilarities between different groups. A P value of less than 0.05 was considered statistically significant. Statistical significance was defined as P<0.05.

Results

Characteristics of the study cohort

To characterize the microbial communities across different tissues, we performed taxonomic profiling of the V3–V5 region of the 16S rRNA gene. Following stringent quality control procedures and contaminant removal, high-quality sequences were retained for downstream analysis. To explore the potential role of the microbiome in lung cancer progression, we collected paired tissue samples (tumor tissue and tumor-adjacent normal tissue) from lung cancer patients(n=39) who underwent surgical resection. The clinical details of the enrolled patients are listed in Table 1.

Patients who had used antibiotics or probiotics within 1 month before surgery or neoadjuvant therapy were excluded on the basis of postoperative follow-up data and patient prescription records. Bacterial DNA was extracted from both the tumor and adjacent normal lung tissues of the 39 paired samples. The patients in the MPLC and NMPLC groups were matched according to age, sex, smoking history, clinical stage, tumor differentiation, tumor diameter, and pathology (Table S1).

Microbial diversity is not associated with cancer tissue or normal tissue

We first proceeded to assess the microbial diversity of the tumor microbiome via several well-established metrics, including the observed species, Shannon, and Simpson diversity indices. The analysis revealed no significant differences in alpha diversity within tumor tissues, as indicated by both the Shannon (P>0.05) and Simpson indices (P>0.05) (Figure 1A), suggesting a relatively homogeneous microbial composition. Furthermore, evaluation of beta diversity using PCoA showed no significant differences in microbial community structure between tumor and adjacent normal tissues (Figure 1B).

Figure 1 Microbiome characteristics in tumor and tumor-adjacent normal lung tissues from all patients. (A) Alpha diversity of the lung microbiome in tumor and adjacent normal lung tissues, as assessed via the observed species, Simpson, Chao1, and Shannon indices. (B) NMDS analysis of the normal lung microbiome in tumor and adjacent normal lung tissues, which is based on Bray-Curtis distances, was used to assess beta diversity. (C) The top 10 microbial taxa at the phylum level for each individual. (D) The top 10 microbial taxa at the phylum level for both the tumor and adjacent normal lung tissue groups. (E) The top 10 microbial taxa at the genus level for each individual. (F) The top 10 microbial taxa at the genus level for both the tumor and adjacent normal lung tissue groups. CA, cancer (tumor tissues); N, normal (tumor-adjacent tissues); NMDS, non-metric multi-dimensional scaling.

To investigate the microbial communities within lung cancer tumor tissues, we then conducted a comprehensive annotation of the detected microbial taxa. Our results revealed that the dominant phyla present in both tumor and adjacent tissues included Proteobacteria, Firmicutes_D, Bacteroidetes, Firmicutes_A, Actinobacteria, Acidobacteria, Cyanobacteria, Chloroflexi, Bacillaceae, and Verrucomicrobiota, among others (Figure 1C,1D). At the genus level, the most abundant species in both tumor and tumor-adjacent tissues included Gottfriedia, Mycoplasma, Massilia, Neobacillus, JC017, Methylophilus, SIO2C1, Prevotella, Methylobacterium, Clostridium_T, and Gottfriedia (Figure 1E,1F).

Correlations between intratumoral microbial signatures and clinical characteristics

We further investigated the associations between key clinical characteristics, including smoking status, tumor differentiation, tumor size, and regional distribution (specifically Xuanwei, Yunnan Province, a high-incidence area for lung cancer), and microbial profiles were further analyzed. The results revealed no significant differences in alpha or beta diversity concerning area (Figure 2A,2B), smoking status (Figure 2C,2D), tumor size (over 2 cm or not) (Figure 2E,2F), or tumor differentiation (Figure 2G,2H). Although factors such as visceral pleural invasion (VPI), vascular invasion (VI), spread through air spaces (STAS), and pathological characteristics in patients with lung cancer are considered important, variations in intratumoral microbial diversity are minimal across these groups. Similarly, patients with different imaging features, including pure ground-glass opacity (GGO), part-solid (partSol), solid, and honeycomb (Hnycmb), exhibited comparable intratumoral microbial diversity (Figure S1A-S1H).

Figure 2 Correlations between the intratumoural microbiome and clinical characteristics. (A,B) α diversity and β diversity analyses of tumor tissues stratified by patient’s area (Xuanwei or not) (A,B), smoking or not (C,D), tumor size (E,F), and tumor differentiation (G,H). (I) Heatmap showing the correlations between differentially abundant genera in tumor tissues and clinical indices. N_smorker, never-smoker patients; N_XW, non-Xuanwei patients (control group); PCoA, principal coordinate analysis; Xuanwei, patients from a high-incidence lung cancer region in Yunnan Province, China.

Although microbial diversity did not significantly differ across various clinical features, differences in microbial composition were observed among the clinical categories. Correlation analysis revealed associations between specific microbial taxa and distinct clinical features in the differential analysis of each clinical characteristic (Figure 2I). These findings suggest that variations in microbial species composition may be linked to different clinical characteristics.

Associations of intratumoral microbiome diversity with MPLC in lung cancer patients

To investigate the associations between MPLC and NMPLC, we conducted a comprehensive analysis of tumor and tumor-adjacent tissues from both patient groups. We first assessed and compared the microbial diversity of tumor tissues between the MPLC and NMPLC groups. Alpha diversity was initially assessed using the Observed species (Figure 3A), Simpson (Figure 3B), and Shannon (Figure 3C) indices to compare the MPLC and NMPLC groups. The MPLC group exhibited significantly higher microbial diversity, as indicated by the Simpson index (P=0.02). Additionally, beta diversity analysis of tumor tissues revealed notable differences between the MPLC and NMPLC groups (Figure 3D). OPLS-DA further demonstrated that the two groups could be distinctly separated at both the phylum and genus levels (Figure 3E,3F). These results underscore the significant differences in microbial diversity between the tumor tissues of patients with MPLC and those with NMPLC. However, in tumor-adjacent tissues, analyses of both alpha and beta diversity revealed no significant differences between the MPLC and NMPLC groups (Figure S2A,S2B), indicating that MPLC does not substantially alter the richness or evenness of the microbiome in adjacent normal tissues. These results suggest that the microbiota might play an important role in the multiple primary processes of tumors and are expected to provide potential powerful microbial biomarkers for the diagnosis and treatment of MPLC types.

Figure 3 Tumor microbial community differences between MPLC and NMPLC cohorts. Alpha diversity of the microbiome as indicated by (A) observed species, (B) Simpson, and (C) Shannon indices. (D) PCoA analysis of the normal lung microbiome in tumor and tumor-adjacent tissues via Bray-Curtis metric distances of beta diversity. (E) OPLS-DA analysis at the phylum level. (F) OPLS-DA analysis at the genus level. MPLC, multiple primary lung cancer; NMPLC, non-multiple primary lung cancer; OPLS-DA, orthogonal partial least squares discriminant analysis; PC, principal component; PCoA, principal coordinate analysis.

Intratumoral microbiome communities are significantly different between MPLC and NMPLC patients

To further elucidate microbial differences, we analyzed the relative abundances of bacterial taxa in patients with MPLC and NMPLC. In tumor tissues, two cohorts shared most of the predominant microbial taxa observed in both groups at the phylum (Figure 4A) and genus levels (Figure 4B). At the phylum level, there was an expansion of Bacteroidota and Firmicutes_A, accompanied by a reduced abundance of Proteobacteria and Firmicutes_D in MPLC cohorts (Figure 4C). At the genus level, MPLC patients presented higher abundances of SIO2C1 and Prevotella but lower abundances of Massilia and Neobacillus, compared to NMPLC patients (Figure 4D). In the tumor-adjacent normal tissues, the fraction of Firmicutes_A in MPLC was lower than that in NMPLC, whereas the Acidobacteriota showed the opposite trend at the phylum level (Figure S2C). Additionally, the abundance of JC017 was significantly higher in MPLC compared to NMPLC tissues (Figure S2D). These findings indicate that the tumor and adjacent normal microbiota in MPLC differ significantly from those in NMPLC patients.

Figure 4 Intratumor microbiome communities are significantly different between MPLC and NMPLC patients. Venn diagram of microbial co-abundance communities at the phylum (A) and genus levels (B) between the two groups. Bar plots of the phylum (C) and genus (D) taxonomic levels in MPLC and NMPLC patients. Relative abundance was used. (E) Microbial-based differential analysis of metabolic pathways. PICRUSt2 combined with the KEGG database was used to predict the function of the bacterial microbiota in tissues. (F) Genara contribution analysis of differential metabolic pathways. KEGG, Kyoto Encyclopedia of Genes and Genomes; LDA, linear discriminant analysis; MPLC, multiple primary lung cancer; NMPLC, non-multiple primary lung cancer.

To further explore the metabolic pathways potentially influenced by different microbial taxa, functional predictions were performed using PICRUSt2 combined with the KEGG database. We observed that pathways related to tetracycline biosynthesis and naphthalene degradation were upregulated in MPLC, whereas riboflavin metabolism was downregulated in NMPLC (Figure 4E). Key microbial contributors, such as Burkholderia, Pseudochrobactrum, and Paracoccus, were identified as major participants in these metabolic pathways (Figure 4F). These findings suggest that microorganisms associated with tetracycline biosynthesis and naphthalene degradation might play a significant role in the pathogenesis of MPLC.

Potential powerful microbial biomarkers for multi-primary lung cancer

To further identify microorganisms potentially involved in MPLC progression, we applied a random forest model to distinguish MPLC from NMPLC based on microbial signatures. Random forest analysis identified distinct microbial signatures at the genus level that effectively differentiated MPLC from NMPLC in both tumor and adjacent normal tissues. In tumor samples, 11 genera were found to be significantly differentially abundant between the two groups (Figure 5A), with their relative abundances varying markedly between MPLC and NMPLC (Figure 5B). In contrast, 12 genera exhibited significant differences in relative abundance within adjacent normal tissues (Figure 6A). Notably, comparative analysis of tumor and adjacent normal tissues from both MPLC and NMPLC patients identified two genera, Faecalusia and Burkholderia, that were consistently enriched in both tissue types (Figure 6B). The relative abundances of these genera were also significantly higher in MPLC than in NMPLC within the tumor-adjacent tissues (Figure 6C). These findings suggest that adjacent normal tissues can also distinguish between MPLC and NMPLC. Faecalusia and Burkholderia may serve as potential microbial biomarkers for MPLC and could play important roles in its pathogenesis.

Figure 5 Random forest analysis of tumor tissues between MPLC and NMPLC. (A) Bar plot showing the top genera that discriminate tumors from normal tissues identified through random forest analysis. (B) Comparison of the relative abundance of genera between MPLC and NMPLC. *, P<0.05; **, P<0.05. MPLC, multiple primary lung cancer; NMPLC, non-multiple primary lung cancer.

Figure 6 Random forest analysis of tumor-adjacent tissues between MPLC and NMPLC. (A) Bar plot of the top genera capable of discriminating MPLC from NMPLC in tumor-adjacent tissues, as determined by random forest analysis at the genus level. (B) Venn diagram showing the overlapping differential genera between MPLC and NMPLC in both tumor and tumor-adjacent tissues. (C) Comparison of the relative abundance of the two overlapping genera in tumor-adjacent tissues. *, P<0.05; **, P<0.05. MPLC, multiple primary lung cancer; NMPLC, non-multiple primary lung cancer.

Discussion

Advancements in early diagnosis have led to the identification of an increasing number of patients with MPLC. Emerging evidence indicates that TME microbiota actively influence cancer biology rather than acting as passive components (10). Intratumor bacteria primarily reside in cells, including cancer cells and immune cells. Specifically, the intratumoral microbiome can alter the immune landscape within the TME, potentially enhancing or suppressing antitumor immune responses and thereby influencing tumor progression and therapeutic efficacy (32-34). However, the progression of MPLC involves complex changes in the intratumoral microbiome that remain largely unknown (13,23). Our study characterizes the intratumoral microbial distribution in MPLC, revealing altered microbial communities. In this study, we performed 16S rRNA sequencing on tumor tissues and adjacent noncancerous tissues from 39 patients with lung cancer. Our results provide new insights into the microbial characteristics of lung cancer, highlighting the potential role of the intratumoral microbiota in tumor heterogeneity and pathogenesis.

Our results revealed that there was no significant difference in microbial diversity between tumor tissues and adjacent tissues. By analyzing the microorganisms within the tumor and their different clinical characteristics, we found that there was no significant difference in microbial diversity within the tumor, including lung cancer risk factors such as smoking, degree of lung cancer differentiation, and tumor size, which was consistent with findings from previous studies (35,36). Microbial diversity differed significantly between MPLC and NMPLC groups within both tumor and paired adjacent normal tissues.

One of the key findings of this study is the increased α diversity observed in MPLC tissues compared with that in NMPLC tissues, despite the lack of significant differences in diversity between tumor and nontumor tissues within each group. Increased microbial diversity has been linked to tumor progression and poor prognosis in various cancers, including lung cancer, suggesting that an increased diverse microbiome might facilitate a more tumor-promoting environment (37,38).

At the phylum level, MPLC tissues were enriched in Bacteroidota and Firmicutes_A but reduced Proteobacteria and Firmicutes_D. These disturbances may be reflective of functional changes in the microbiome that influence MPLC progression. For example, Bacteroides enrichment has been reported in several cancers and is often associated with the production of proinflammatory metabolites, such as lipopolysaccharides, that may promote tumor formation and immune crosstalk (38,39). In contrast, Proteobacteria, a predominant phylum in healthy lung tissues, is often depleted in cancerous tissues, and its loss has been associated with impaired microbial homeostasis and reduced anti-inflammatory properties (40). These phylum-level changes suggest that the intratumoral microbiome in MPLC may exhibit a tumor-supportive imbalance that warrants further investigation.

At the genus level, we identified Faecousia and Burkholderia as key genera with significantly higher abundance in MPLC compared to NMPLC tissues. Importantly, these genera were consistently enriched in both tumor and adjacent nontumor tissues in MPLC, highlighting their potential role as biomarkers for MPLC. While Faecousia has rarely been studied in cancer (41), its association with metabolic pathways such as those involved in tetracycline biosynthesis raises intriguing possibilities. Tetracycline biosynthesis pathways are often associated with antibiotic resistance, which may enable the microbiota to persist in the inflammatory and hypoxic TME. Moreover, tetracycline derivatives have been shown to affect immune responses and the TME in certain cancers, suggesting that Faecousia-associated metabolic reprogramming could influence MPLC progression through similar mechanisms. Burkholderia, on the other hand, is well documented as an opportunistic pathogen with biofilm-forming capabilities and resistance to environmental stressors. Its enrichment in MPLC tissues may suggest a role in creating a tumor-permissive niche, possibly by modulating immune responses or contributing to metabolic dysregulation. Notably, Burkholderia has been implicated in the production of reactive oxygen species and inflammatory mediators, both of which can promote DNA damage, genomic instability, and tumor formation (42,43). The consistent presence of Burkholderia in MPLC tissues across different microenvironments (tumor and adjacent tissues) indicates that it might be part of a broader microbiota-tumor interaction network unique to MPLC. Our current study highlights their potential diagnostic value in distinguishing MPLC from NMPLC cases. Future research will be necessary to validate these candidates and assess their clinical utility in early identification and classification of MPLC.

The functional analysis of the differential microbiota revealed that the tetracycline biosynthesis and naphthalene degradation pathways were upregulated in MPLC, whereas riboflavin metabolism was downregulated in NMPLC tissues. These findings suggest that the metabolic activities of the microbiota may differ significantly between MPLC and NMPLC, potentially contributing to their distinct pathogenesis. The upregulation of naphthalene degradation pathways in MPLC tissues is particularly noteworthy, as naphthalene metabolites are known to generate ROS, which can exacerbate DNA damage and promote tumorigenesis (44). Moreover, the downregulation of riboflavin metabolism in NMPLC tissues may reflect a reduced capacity for antioxidant defense, which has been shown to play a role in regulating tumor growth and immune responses, and riboflavin deficiency has been regarded as a risk factor for cancer (45,46). These functional differences underscore the importance of microbial metabolism in shaping the TME and influencing lung cancer outcomes.

Despite the strengths of this study, several limitations should be noted. First, the sample size was relatively small, which may restrict the generalizability of the findings. For example, in our current cohort, females accounted for the majority of MPLC cases, which may have contributed to the observed microbial differences. Larger, multicenter studies are needed to validate these microbial patterns and assess their clinical relevance. Second, while functional analyses based on 16S rRNA sequencing provide valuable insights, they lack the resolution of metagenomic or metatranscriptomic approaches, which could confirm the functional roles of specific taxa and pathways. Finally, experimental validation of the roles of Faecousia and Burkholderia in MPLC pathogenesis is lacking. Moreover, while microbial differences between MPLC and NMPLC samples were identified, these findings do not clarify whether these variations play a causative role in tumorigenesis or merely reflect secondary consequences of the disease. Future studies incorporating in vitro and in vivo models are needed to elucidate their mechanistic roles.

Conclusions

In conclusion, this study identified distinct microbial compositions and metabolic activities in MPLC compared with those in NMPLC, with Faecousia and Burkholderia emerging as promising biomarkers for MPLC. These findings reveal distinct intratumoral microbiome compositions between MPLC and NMPLC patients, suggesting potential diagnostic value for enhancing precision in distinguishing these cancer subtypes. This could enable clinicians to make timely, tailored therapeutic decisions.

Acknowledgments

We are grateful to the patients involved in this study.

Footnote

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-926/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-926/prf

Funding: This work was supported by the Joint Foundation of Science & Technology Department of Yunnan Province and Kunming Medical University (Nos. 202401AY070001-257 to Z.X. and 202301AY070001-231 to Y.W.) and the Open Project of Clinical Medical Center for Thoracic Diseases, the First People’s Hospital of Yunnan Province (No. 2022LCZXKF-XB01 to Y.C.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-926/coif). Y.C. reports that this research was supported by the Open Project of Clinical Medical Center for Thoracic Diseases, the First People’s Hospital of Yunnan Province (No. 2022LCZXKF-XB01). Y.W. reports that this research was supported by the Joint Foundation of Science & Technology Department of Yunnan Province and Kunming Medical University (No. 202301AY070001-231). Z.X. reports that this research was supported by the Joint Foundation of Science & Technology Department of Yunnan Province and Kunming Medical University (No. 202401AY070001-257). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethics committee of the First People’s Hospital of Yunnan Province (No. KHLL2022-KY159) and informed consent was obtained from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023. CA Cancer J Clin 2023;73:17-48. [Crossref] [PubMed]
2. Denning DW. Global incidence and mortality of severe fungal disease. Lancet Infect Dis 2024;24:e428-38. [Crossref] [PubMed]
3. Rina A, Maffeo D, Minnai F, et al. The Genetic Analysis and Clinical Therapy in Lung Cancer: Current Advances and Future Directions. Cancers (Basel) 2024;16:2882. [Crossref] [PubMed]
4. Desai P, Takahashi N, Kumar R, et al. Microenvironment shapes small-cell lung cancer neuroendocrine states and presents therapeutic opportunities. Cell Rep Med 2024;5:101610. [Crossref] [PubMed]
5. Zhang Y, Hu Q, Pei Y, et al. A patient-specific lung cancer assembloid model with heterogeneous tumor microenvironments. Nat Commun 2024;15:3382. [Crossref] [PubMed]
6. Xiao Y, Yu D. Tumor microenvironment as a therapeutic target in cancer. Pharmacol Ther 2021;221:107753. [Crossref] [PubMed]
7. Wang L, Zhang L, Zhang Z, et al. Advances in targeting tumor microenvironment for immunotherapy. Front Immunol 2024;15:1472772. [Crossref] [PubMed]
8. Dong H, Tian Y, Xin S, et al. Diagnosis and management of multiple primary lung cancer. Front Oncol 2024;14:1392969. [Crossref] [PubMed]
9. Voulaz E, Novellis P, Rossetti F, et al. Distinguishing multiple lung primaries from intra-pulmonary metastases and treatment implications. Expert Rev Anticancer Ther 2020;20:985-95. [Crossref] [PubMed]
10. Nejman D, Livyatan I, Fuks G, et al. The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science 2020;368:973-80. [Crossref] [PubMed]
11. Wang Z, Zhang Q, Wang C, et al. Multiple primary lung cancer: Updates and perspectives. Int J Cancer 2024;155:785-99. [Crossref] [PubMed]
12. Tian H, Bai G, Yang Z, et al. Multiple primary lung cancer: Updates of clinical management and genomic features. Front Oncol 2023;13:1034752. [Crossref] [PubMed]
13. Cai J, Zhang W, Zhu S, et al. Gut and Intratumoral microbiota: Key to lung Cancer development and immunotherapy. Int Immunopharmacol 2025;156:114677. [Crossref] [PubMed]
14. Ochi T, Fujiki R, Fukuyo M, et al. Association of Intratumoral Bacterial Abundance With Lung Cancer Prognosis in Chiba University Hospital Cohort. Cancer Sci 2025;116:2040-6. [Crossref] [PubMed]
15. Xu J, Cheng M, Liu J, et al. Research progress on the impact of intratumoral microbiota on the immune microenvironment of malignant tumors and its role in immunotherapy. Front Immunol 2024;15:1389446. [Crossref] [PubMed]
16. Shi S, Chu Y, Liu H, et al. Predictable regulation of survival by intratumoral microbe-immune crosstalk in patients with lung adenocarcinoma. Microb Cell 2024;11:29-40. [Crossref] [PubMed]
17. Chai X, Wang J, Li H, et al. Intratumor microbiome features reveal antitumor potentials of intrahepatic cholangiocarcinoma. Gut Microbes 2023;15:2156255. [Crossref] [PubMed]
18. Chang YS, Hsu MH, Tu SJ, et al. Metatranscriptomic Analysis of Human Lung Metagenomes from Patients with Lung Cancer. Genes (Basel) 2021;12:1458. [Crossref] [PubMed]
19. Li S, Duan Y, Luo S, et al. Short-chain fatty acids and cancer. Trends Cancer 2025;11:154-68. [Crossref] [PubMed]
20. Thome CD, Tausche P, Hohenberger K, et al. Short-chain fatty acids induced lung tumor cell death and increased peripheral blood CD4+ T cells in NSCLC and control patients ex vivo. Front Immunol 2024;15:1328263. [Crossref] [PubMed]
21. Xue C, Chu Q, Zheng Q, et al. Current understanding of the intratumoral microbiome in various tumors. Cell Rep Med 2023;4:100884. [Crossref] [PubMed]
22. Fu A, Yao B, Dong T, et al. Tumor-resident intracellular microbiota promotes metastatic colonization in breast cancer. Cell 2022;185:1356-1372.e26. [Crossref] [PubMed]
23. Gao F, Yu B, Rao B, et al. The effect of the intratumoral microbiome on tumor occurrence, progression, prognosis and treatment. Front Immunol 2022;13:1051987. [Crossref] [PubMed]
24. Hu M, Lin X, Sun T, et al. Gut microbiome for predicting immune checkpoint blockade-associated adverse events. Genome Med 2024;16:16. [Crossref] [PubMed]
25. Apopa PL, Alley L, Penney RB, et al. PARP1 Is Up-Regulated in Non-small Cell Lung Cancer Tissues in the Presence of the Cyanobacterial Toxin Microcystin. Front Microbiol 2018;9:1757. [Crossref] [PubMed]
26. Altorki NK, Markowitz GJ, Gao D, et al. The lung microenvironment: an important regulator of tumour growth and metastasis. Nat Rev Cancer 2019;19:9-31. [Crossref] [PubMed]
27. Mao Q, Jiang F, Yin R, et al. Interplay between the lung microbiome and lung cancer. Cancer Lett 2018;415:40-8. [Crossref] [PubMed]
28. Jin C, Lagoudas GK, Zhao C, et al. Commensal Microbiota Promote Lung Cancer Development via γδ T Cells. Cell 2019;176:998-1013.e16. [Crossref] [PubMed]
29. Ma Y, Chen H, Li H, et al. Intratumor microbiome-derived butyrate promotes lung cancer metastasis. Cell Rep Med 2024;5:101488. [Crossref] [PubMed]
30. Wong-Rolle A, Dong Q, Zhu Y, et al. Spatial meta-transcriptomics reveal associations of intratumor bacteria burden with lung cancer cells showing a distinct oncogenic signature. J Immunother Cancer 2022;10:e004698. [Crossref] [PubMed]
31. Riquelme E, Zhang Y, Zhang L, et al. Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes. Cell 2019;178:795-806.e12. [Crossref] [PubMed]
32. Yang L, Li A, Wang Y, et al. Intratumoral microbiota: roles in cancer initiation, development and therapeutic efficacy. Signal Transduct Target Ther 2023;8:35. [Crossref] [PubMed]
33. Kyriazi AA, Karaglani M, Agelaki S, et al. Intratumoral Microbiome: Foe or Friend in Reshaping the Tumor Microenvironment Landscape? Cells 2024;13:1279. [Crossref] [PubMed]
34. Liao K, Wen J, Liu Z, et al. The role of intratumoral microbiome in the occurrence, proliferation, metastasis of colorectal cancer and its underlying therapeutic strategies. Ageing Res Rev 2025;111:102820. [Crossref] [PubMed]
35. Xia X, Chen J, Cheng Y, et al. Comparative analysis of the lung microbiota in patients with respiratory infections, tuberculosis, and lung cancer: A preliminary study. Front Cell Infect Microbiol 2022;12:1024867. [Crossref] [PubMed]
36. Kim OH, Choi BY, Kim DK, et al. The microbiome of lung cancer tissue and its association with pathological and clinical parameters. Am J Cancer Res 2022;12:2350-62.
37. Garrett WS. Cancer and the microbiota. Science 2015;348:80-6. [Crossref] [PubMed]
38. Greathouse KL, White JR, Vargas AJ, et al. Interaction between the microbiome and TP53 in human lung cancer. Genome Biol 2018;19:123. [Crossref] [PubMed]
39. Cheng C, Wang Z, Wang J, et al. Characterization of the lung microbiome and exploration of potential bacterial biomarkers for lung cancer. Transl Lung Cancer Res 2020;9:693-704. [Crossref] [PubMed]
40. Dickson RP, Erb-Downward JR, Martinez FJ, et al. The Microbiome and the Respiratory Tract. Annu Rev Physiol 2016;78:481-504. [Crossref] [PubMed]
41. Wortelboer K, de Jonge PA, Scheithauer TPM, et al. Phage-microbe dynamics after sterile faecal filtrate transplantation in individuals with metabolic syndrome: a double-blind, randomised, placebo-controlled clinical trial assessing efficacy and safety. Nat Commun 2023;14:5600. [Crossref] [PubMed]
42. Sousa SA, Ramos CG, Leitão JH. Burkholderia cepacia Complex: Emerging Multihost Pathogens Equipped with a Wide Range of Virulence Factors and Determinants. Int J Microbiol 2011;2011:607575. [Crossref] [PubMed]
43. Mann T, Ben-David D, Zlotkin A, et al. An outbreak of Burkholderia cenocepacia bacteremia in immunocompromised oncology patients. Infection 2010;38:187-94. [Crossref] [PubMed]
44. Griffin MO, Fricovsky E, Ceballos G, et al. Tetracyclines: a pleitropic family of compounds with promising therapeutic properties. Review of the literature. Am J Physiol Cell Physiol 2010;299:C539-48. [Crossref] [PubMed]
45. Powers HJ. Riboflavin (vitamin B-2) and health. Am J Clin Nutr 2003;77:1352-60. [Crossref] [PubMed]
46. McNulty H, Pentieva K, Ward M. Causes and Clinical Sequelae of Riboflavin Deficiency. Annu Rev Nutr 2023;43:101-22. [Crossref] [PubMed]