The basic criteria for defining sMPLC is based on the modifications done by Antakli (12, 13);. The particular sampling criteria align with those presented in prior publications (11). The study protocol was reviewed and approved by the Research Ethics Board of the First People’s Hospital of Yunnan Province [Reference No. 20200009]. This study was conducted following the Declaration of Helsinki. Written informed consent was obtained from all participants before participating in the study. All patients received general anesthesia before lung sample collection. Oral cavity microbiota were sampled before intubation. The specific sampling methodology aligns with the description provided in the prior publication (11).

Twenty-seven participants, comprising 8 non-tumor controls, 9 patients with sMPLC, and 10 patients with SPLC, provided self-collected stool samples following detailed printed instructions. Saliva samples were collected from 33 participants, comprising 11 non-tumor controls, 10 patients with sMPLC, and 12 patients with SPLC. To collect saliva samples, participants were instructed to take saliva samples as soon as they woke up without eating or brushing their teeth. The participants were asked to rinse their mouths with water and spit 3 ml of saliva into a 50 ml tube. Saliva samples were stored at −80°C until use (14).

RT-PCR amplification was performed to validate the abundance of each bacterial strain in tumor tissues ( Supplementary Table S3 ). First, the extraction procedure of total RNA from each tissue sample was performed using TRIzol reagent (Invitrogen, USA) following the manufacturer’s protocol. Next, RNA concentration and quality will be assessed using the NanoDrop spectrophotometer and Agilent 2100 Bioanalyzer. The thermal cycling conditions were as follows: hybridization of random primers, 25°C for 10 min; cDNA synthesis, 42°C for 30 min; reverse transcriptase inactivation, 99°C for 5 min; and cooling, 4°C (15).

Real-time quantitative PCR reactions were performed using the Power SYBR Green master mix (ThermoFisher Scientific, Waltham, MA, USA) and an ABI Prism 7900HT Sequence Detection System (Applied Biosystems, ThermoFisher Scientific). The thermal cycling conditions were as follows: initial denaturation step, 95°C for 10 min; 50 cycles, 95°C for 15 s and 65°C for 1 min. The amplification specificity was confirmed by melting curve analysis.

Total genomic DNA was extracted from 121 samples (including 61 BALF samples, 33 saliva samples, and 27 stool samples) using the TGuide S96 Magnetic Soil/Stool DNA Kit (Tiangen Biotech (Beijing) Co., Ltd.) according to the manufacturer’s instructions. The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with the following primer pairs: 338F: 5’- ACTCCTACGGGAGGCAGCA-3’ and 806R: 5’- GGACTACHVGGGTWTCTAAT-3’. PCR products were verified on an agarose gel and purified through the Omega DNA purification kit (Omega Inc., Norcross, GA, USA). The purified PCR products were collected, and the paired ends (2 × 250 bp) were performed on the Illumina Novaseq 6000 platform. Next, the raw data were analyzed and processed with reference to the previously published method (16–20).

Bacteria were obtained from the Guangdong Institute of Microbiology, China, and cell lines were procured from Department of Pulmonary and Critical Care Medicine. Frozen stocks of human lung cancer cells (A549) were rapidly thawed in a 37°C water bath and resuspended in complete culture medium (RPMI1640 with 10% FBS and 1% penicillin-streptomycin for A549). The cells were centrifuged at 1000 rpm for 5 minutes, and the supernatant was discarded. The cells were resuspended and transferred to cell culture flasks. Then cells were incubated at 37°C in a 5% CO₂ atmosphere. For bacterial culture, S. intermedius from a cryopreserved stock was rapidly thawed in a 37°C water bath. A loopful of bacterial culture was inoculated into BHI medium and incubated overnight in an anaerobic incubator at 37°C. Subsequently, in accordance with previous publications (21), the cell cycle and apoptosis following co-incubation of S. intermedius and A549 cell line will be assessed. In brief, co-incubation will be performed using a bacterial cell infection ratio of 1:100 (MOI), and flow cytometry will be utilized for determining the cell cycle and apoptosis.

A549 cell samples will be collected using the aforementioned methods for assessing cell cycle and apoptosis. The control group will consist of A549 + PBS, while the treatment group will include samples co-incubated with S. intermedius and A549. Three replicates will be prepared for both treatment and control groups. The collected samples will be promptly flash-frozen in liquid nitrogen and RNA extraction will be carried out using a kit according to the manufacturer’s instructions, followed by concentration and quality assessment. High-quality RNA samples were used to construct sequencing libraries. The specific method for library construction is consistent with the method described previously (22).

Next, RNA-seq paired-end sequencing will be performed using the Illumina platform. Following sequencing, quality control will be conducted on the sequencing data to remove low-quality sequences. Subsequently, these high-quality sequences will be aligned to the genome of S. intermedius. Differential expression genes (DEGs) between the two groups of samples will be identified using the RPKM (reads per kilobase per million mapped reads) method to calculate transcript expression levels. Then, differential expression analysis will be conducted using edgeR (https://bioconductor.org/packages/release/bioc/html/edgeR.html). The criteria for selecting DEGs between the two groups are: log FC (logarithm fold-change) ≥ 2 and FDR (false-discovery rate) ≤ 0.05. Finally, these DEGs will be subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.

Data are presented as mean ± standard deviation (SD). The unpaired Student’s t-test was employed to assess differences between the two groups when applicable. Pathway enrichment analysis was carried out utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Statistical significance was defined as P < 0.05. GraphPad Prism 8.0 software (GraphPad Software, San Diego, CA) was utilized for all analyses.

Sixty-one volunteers were included in the study, comprising 13 non-tumor patients as the control group (3 cases removed due to quality control issues), 25 patients with single primary lung cancer (SPLC), and 23 cases patients with sMPLC patients (6 cases removed due to quality control issues), none of them have the history of smoking ( Figure 1A ). All patients underwent video-assisted thoracoscopic surgery. None of the participants had a history of smoking. Bronchoalveolar lavage (BAL) samples were obtained from all participants, as previously described (11).

To investigate the role of pulmonary microbiota in the initiation and development of sMPLC, BAL samples from all participants were sequenced using 16S rRNA. No significant changes in the α-diversity of the pulmonary microbiota in SPLC and sMPLC patients were detected compared with non-tumor patients ( Figure 1B ). However, the β-diversity of the pulmonary microbiota was significantly different in patients with SPLC and sMPLC compared with non-tumor patients ( Figure 1C , stress < 0.2), suggesting a significant alteration in the pulmonary microbial ecology of SPLC and sMPLC patients. This result suggests that pulmonary microbiota dysregulation may play a crucial role in the occurrence and development of sMPLC.

Venn analysis revealed that SPLC and non-tumor patients had 41 and 38 characteristic genera, respectively, while sMPLC patients had only three characteristic genera ( Supplementary Figure S1 , Supplementary Table S1 ). This result suggests that the composition of pulmonary microbiota is severely imbalanced in patients with sMPLC. Differential abundance analysis demonstrated enrichment of significantly different genera in the three cohorts ( Figure 1D ). Fusobacterium, Parvimonas, and Aggregatibacter were enriched in patients with sMPLC, and the Bacteroides genus was enriched in patients with SPLC compared with non-tumor patients. These genera are known opportunistic pathogens that are closely associated with the development of various diseases (23).

To identify the bacterial genera that only increased in the sMPLC cohort, we screened each genus for specific changes using relative abundance analysis. No significant differences in the relative abundance of Fusobacterium and Streptococcus were detected between non-tumor patients and patients with SPLC; however, the relative abundances of both genera were significantly increased in patients with sMPLC ( Figures 1E, F ). These results suggest that Fusobacterium and Streptococcus may play important roles in the development of sMPLC.

Next, the specific strain of Fusobacterium and Streptococcus that mediates the initiation and development of sMPLC was identified, and the potential differences in microbial abundance between tumor and paracancerous lung tissues were determined. Forty-five volunteers were recruited and categorized into non-tumor, SPLC, and sMPLC groups (15 patients per group) based on clinical indicators. Tissue samples were collected at 0 cm, 1 cm, and 2 cm from the tumor site ( Figure 2A ). In the control group, random normal tissue samples were collected. The 16S rRNA sequencing results revealed an increased relative abundance of bacteria, such as Fusobacterium and Streptococcus. Using RT-PCR, a significant distinction between S. intermedius and Fusobacterium necrophorum within the sMPLC group of conditionally pathogenic bacteria was identified. The relative abundances of F. necrophorum and S. intermedius were significantly higher in tumor tissues from patients with sMPLC than in tumor tissues from patients with SPLC and non-tumor tissues ( Figure 2B, E ). Despite a decrease in relative abundance with increasing distance from the tumor tissue, a similar trend persisted in tissues close (1 cm) to the tumor ( Figures 2C, F ). The relative abundances of F. necrophorum and S. intermedius in tissues 2 cm from the tumor were significantly higher in patients with sMPLC than in patients with SPLC and non-tumor tissues ( Figures 2D, G ). Furthermore, correlation analysis revealed that the closer the distance to the tumor tissue, the higher the relative abundances of S. intermedius and F. necrophorum ( Figures 2H, I ). The significant enrichment of S. intermedius and F. necrophorum in tumor tissues in sMPLC patients indicates their potential role in the development of sMPLC.

To understand how S. intermedius and F. necrophorum contribute to the development of sMPLC, the effects of the bacteria on cell cycle distribution in A549 lung cancer cells were determined using flow cytometry ( Figure 3A ). The cell cycle of A549 cells was significantly shorter in the presence of S. intermedius but not F. necrophorum ( Figure 3A ), and S. intermedius significantly reduced apoptosis in A549 ( Figure 3B ). These results show that S. intermedius promotes the growth of lung cancer cells by inhibiting apoptosis and activating the cell cycle process.

To determine the molecular pathways involved in the promotion of sMPLC by S. intermedius, transcriptome sequencing of A549 lung cancer cells was performed in the presence or absence of S. intermedius. S. intermedius induced the upregulation of 190 genes and the downregulation of 142 genes compared with untreated cells ( Figure 3C ). Importantly, pathways directly associated with lung tumors, such as small-cell lung cancer, were also significantly enriched ( Figure 3D ), supporting the role of S. intermedius in the development of lung tumors. KEGG enrichment analysis of the differentially regulated genes revealed the significant enrichment of 64 pathways, including the apoptosis pathway ( Supplemtary Table S2 ), which confirms the effect of S. intermedius on apoptosis. In addition to apoptosis, several pathways related to inflammation and immune function were enriched, including the tumor necrosis factor (TNF), IL-17, and NF-kappa B signaling pathways and Th17 cell differentiation ( Figure 3D ). Thus, S. intermedius may affect the development of sMPLC by inducing changes in the immune microenvironment.

As dysregulation of lung microbiota contributes to the development of sMPLC, the sources of lung microbiota are important for the diagnosis and treatment of sMPLC. Thus, saliva and fecal samples collected from non-tumor patients and patients with SPLC and sMPLC were subjected to 16S rRNA sequencing. The α-diversity of oral microbiota in non-tumor patients, SPLC, and sMPLC did not change significantly ( Figure 4A ). However, the β-diversity of oral microbiota was significantly different in the sMPLC group compared with the non-tumor group ( Figure 4B ), indicating a substantial difference in the composition of oral microbiome profiles. Furthermore, linear discriminant analysis effect size revealed enrichment of Streptococcus, Actinomyces, and Mobiluncus in the oral microbiota of sMPLC, SPLC, and non-tumor patients, respectively ( Figure 4C ), and Streptococcus was significantly enriched in both the oral cavity and the lung tissue of patients with sMPLC as mentioned above. An intersection analysis revealed that the oral microbiota was similar to the lung microbiota in patients with sMPLC. As shown in Figure 4D , patients with sMPLC had fewer characteristic genera (only 27), whereas SPLC and non-tumor patients had 154 and 101 characteristic genera, respectively, indicating a significantly lower diversity of microbiota in the sMPLC group. These results highlight the close connection between the oral and lung microbiota.

Similar to the oral microbiota, the α-diversities of the gut microbiota were not significantly different between the three groups ( Figure 4E ), while the β-diversity showed significant differences ( Figure 4F ). However, we did not observe changes in Streptococcus in fecal microbiota. In addition, intersection analysis revealed that relatively few characteristic genera were detected in the fecal microbiota of patients with sMPLC, but the distribution of distinct characteristic microbiota between the groups was different from the oral and lung microbiota ( Supplementary Figure S2A ). Intersection analysis revealed 453, 137, and 83 characteristic genera in the gut, oral, and lung microbiota, respectively ( Supplementary Figure S2B ), and more microorganisms intersected in the oral-lung axis than in the lung-gut axis ( Supplementary Figure S2C ). In summary, these results suggest that Streptococcus, that lead to the development of sMPLC, may originate from the oral cavity.