The emerging role of microbiota in lung cancer: a new perspective on lung cancer development and treatment

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide, with limited treatment efficacy and frequent resistance to conventional therapies. Recent advances have uncovered the critical influence of the human microbiota—complex communities of bacteria, viruses, fungi, and other microorganisms—on lung cancer pathogenesis and therapeutic responses. This review synthesizes current knowledge on the compositional and functional roles of microbiota across multiple body sites, including the gut, lung, tumor microenvironment, circulation, and oral cavity, highlighting their contributions to tumor initiation, progression, metastasis, and immune regulation. We emphasize the bidirectional communication between microbial metabolites and host immune pathways, particularly the gut–lung axis, which modulates systemic and local antitumor immunity. Importantly, microbiota composition has been linked to differential responses and toxicities in chemotherapy, radiotherapy, targeted therapy, and immune checkpoint blockade. Microbiota-targeted interventions, such as probiotics, fecal microbiota transplantation, and selective antibiotics, show promising potential to enhance treatment efficacy and mitigate adverse effects. However, challenges remain in clinical translation due to interindividual microbiome variability, mechanistic complexities, and limited longitudinal data. Future research integrating multi-omics, microbial functional profiling, and controlled clinical trials is essential to harness the microbiome as a precision medicine tool in lung cancer management. This review provides a comprehensive overview of the emerging role of microbiota in lung cancer development and therapy, offering new perspectives for innovative therapeutic strategies.

Introduction

Lung cancer remains the leading cause of cancer-related mortality worldwide, accounting for over 1.8 million deaths annually [1]. Despite advances in molecular diagnostics and the advent of targeted therapies and immune checkpoint inhibitors (ICIs), the prognosis for advanced lung cancer remains poor, with low five-year survival rates and frequent therapeutic resistance [2]. These limitations have spurred growing interest in non-genetic, tumor-extrinsic factors that may influence lung cancer onset, progression, and treatment outcomes.

Among these factors, the human microbiota—comprising trillions of bacteria, viruses, fungi, and other microorganisms residing on mucosal surfaces—has emerged as a pivotal modulator of cancer biology [3–6]. Historically, the lungs were considered sterile [7]; however, recent high-throughput sequencing technologies have challenged this dogma, revealing diverse and dynamic microbial communities not only in the gut but also in the lower respiratory tract, tumor microenvironment, bloodstream, and even within tumor tissues. These microbiota interact with host immune, metabolic, and inflammatory pathways, creating a bidirectional network of communication between microbes and tumor cells.

The concept of a gut–lung axis has further expanded our understanding of distant microbial-host interactions [8]. Mounting evidence indicates that gut dysbiosis can influence pulmonary immunity and cancer susceptibility via microbial metabolites, immune cell priming, and cytokine signaling. Moreover, specific taxa within the gut or lung microbiome have been correlated with differential responses to chemotherapy, radiotherapy, and immunotherapy. For instance, Akkermansia muciniphila is increasingly recognized for their roles in enhancing ICI efficacy and promoting anti-tumor T cell responses [9].

In parallel, emerging microbial detection technologies—such as 16 S rRNA sequencing, metagenomic shotgun sequencing, and culturomics—have facilitated the identification of tumor-associated microbiota and their functional contributions [10–12]. These discoveries have led to the conceptual shift that the microbiome is not merely a bystander but an active participant in carcinogenesis and therapeutic modulation. Consequently, therapeutic strategies targeting the microbiota—ranging from probiotics and fecal microbiota transplantation (FMT) to selective antibiotics—are gaining attention as adjuncts to conventional lung cancer treatments [13].

This review aims to synthesize current knowledge on the role of microbiota in lung cancer, with an emphasis on four key areas: (1) compositional and functional characteristics of microbiota across multiple compartments (gut, lung, tumor, circulation, oral); (2) their roles in tumor initiation, progression, metastasis, and immune modulation; (3) interactions with standard therapies including ICIs; (4) microbiota-targeted interventions and future directions. By illuminating this evolving landscape, we hope to provide a new perspective on lung cancer development and treatment through the lens of host–microbe interactions.

The association between microbiota and lung cancer

The human microbiota primarily consists of bacteria, archaea, fungi, and viruses, which influence numerous vital physiological processes within the host, including metabolism homeostasis, immune function, and nutrient production. These microorganisms are intricately linked to the onset and progression of cancer [14]. Previous studies have demonstrated a close association between gut microbiota and gastrointestinal tumor progression [15]. Interestingly, recent research suggests that microbiota derived from the intestines and those colonizing the lungs also play a crucial role in the development and progression of lung cancer (Fig. 1).

Fig. 1.

Microbiota distribution in lung cancer

Gut microbiota

The gut harbors the largest and most complex microbial ecosystem in the human body, with 3.8 × 10¹³ bacteria spanning over 1,000 species, influencing host digestion, metabolism, immune regulation, and inflammation [16–20]. This ecosystem thrives in the colon’s nutrient-rich, anaerobic environment, shaped by inputs from the stomach, liver, gallbladder, and pancreas [21].

In healthy individuals, the dominant microbial phyla include Bacteroidetes, Firmicutes, Ascomycota, and Actinobacteria [22–24].However, in patients with lung cancer, significant gut microbial dysbiosis has been reported. At the class and family levels, notable shifts include reduced Clostridia and Lachnospiraceae [25, 26] which are associated with SCFA production and gut homeostasis [27]and increased Gammaproteobacteria and Enterobacteriaceae [25, 26, 28]linked to pro-inflammatory responses. Genus-level analysis highlights consistent depletion of beneficial genera like Faecalibacterium and Bifidobacterium [26, 29–31]and enrichment of potentially pathogenic genera like Prevotella and Fusobacterium [26, 28, 29, 32]which may modulate therapy response.Importantly, at the species level, key alterations—such as reduced Faecalibacterium prausnitzii and Roseburia intestinalis—suggest impaired anti-inflammatory signaling [32, 33]while enrichment of Ruminococcus gnavus and Fusobacterium mortiferum may drive immune dysregulation [25, 32].

The composition of the gut microbiota also varies according to lung cancer stage and histological subtype. Lactobacillus predominates in the early stages of lung cancer, while Escherichia coli becomes dominant in advanced disease [30, 31].Microbial signatures also differ across histological subtypes. In lung adenocarcinoma, the gut is enriched with Fusicatenibacter and Roseburia. In squamous cell carcinoma, Proteobacteria, Bacteroides, and Enterobacteriaceae show higher abundance across different taxonomic levels [25].In small-cell lung cancer, the relative abundance of the family Lachnospiraceae is significantly reduced [26]. These patterns underscore the potential of gut microbiota as diagnostic, progression-related, or prognostic biomarkers in lung cancer [34].

In addition to bacteria, fungi have also been implicated in the progression of lung cancer. For instance, Candida spp. have been detected in both human subjects and mouse models [35, 36]. However, the specific mechanisms underlying fungal dysbiosis and its role in lung cancer remain poorly understood, and current research in this area is still limited. Table 1.

Table 1.

Gut microbiota alterations detected in lung cancer patients

Level	Taxon	Direction of Change	Sample source	Method	Associated Lung Cancer Subtype(s)	Geographic Origin	References
Phylum	Actinobacteria	↓	Fecal samples	16 S rRNA(V3-V4)	NSCLC; LC	China	[30, 31]
	Bacteroidetes	↑	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China	[29]
	Bacteroidetes	↓	Fecal samples	16 S rRNA(V3-V4)	NSCLC	China	[30]
	Bacteroidota	↑	Fecal samples	16 S rRNA(V1-V2);Shotgun metagenome sequencing	NSCLC	China; Lithuania	[32, 75]
	Cyanobacteria	↑	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China	[29]
	Desulfobacterota	↑	Fecal samples	16 S rRNA(V1-V2)	NSCLC	Lithuania	[75]
	Firmicutes	↓	Fecal samples	16 S rRNA(V1-V2;V3-V4);Shotgun metagenome sequencing	NSCLC	China	[25, 29, 30, 32]
	Fusobacteria	↑	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China	[29]
	Lentisphaerae	↑	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China	[29]
	Planctomycetes	↑	Fecal samples	16 S rRNA	NSCLC	China	[28]
	Proteobacteria	↑	Fecal samples	16 S rRNA(V3-V4)	NSCLC; LUSC	China	[25, 30]
	Spirochaetes	↑	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China	[29]
	Verrucomicrobia	↓	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China	[29]
Class	Clostridia	↓	Fecal samples	16 S rRNA(V3-V4)	NSCLC	China	[25]
	Gammaproteobacteria	↑	Fecal samples	16 S rRNA(V3-V4)	LUSC	China	[25]
Family	Bacteroidaceae	↓	Fecal samples	16 S rRNA(V3-V4)	NSCLC	China	[25]
	Enterobacteriaceae	↑	Fecal samples	16 S rRNA(V3-V4;V4)	NSCLC; LUSC	China	[25, 26, 28]
	Lachnospiraceae	↓	Fecal samples	16 S rRNA(V4)	NSCLC	China	[26]
	Muribaculaceae	↑	Fecal samples	16 S rRNA	NSCLC	China	[274]
Genus	Agathobacter	↑	Fecal samples	16 S rRNA	NSCLC	China	[274]
	Bacteroides	↑	Fecal samples	16 S rRNA(V1-V2;V3-V4);Shotgun metagenome sequencing	NSCLC; LUSC	China; Lithuania	[25, 29, 30, 32, 75]
	Bifidobacterium	↓	Fecal samples	16 S rRNA(V3-V4;V4)	LC; NSCLC	China	[26, 30, 31]
	Blautia	↓	Fecal samples	16 S rRNA(V4)	NSCLC	China	[26]
	Blautia	↑	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China; Lithuania	[75, 274]
	Clostridium	↑	Fecal samples	16 S rRNA	NSCLC	China	[274]
	Coprococcus	↓	Fecal samples	16 S rRNA(V4);Shotgun metagenome sequencing	NSCLC	China	[26, 32]
	Dialister	↓	Fecal samples	16 S rRNA(V1-V2);Shotgun metagenome sequencing	NSCLC	China	[29, 32]
	Dorea	↓	Fecal samples	16 S rRNA and shotgun metagenome sequencing	NSCLC	China	[32]
	Enterobacter	↓	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China	[29]
	Enterococcus	↑	Fecal samples	16 S rRNA(V3-V4);Shotgun metagenome sequencing	LC; LUAD	China	[31, 32]
	Exiguobacterium	↓	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China	[28]
	Faecalibacterium	↓	Fecal samples	16 S rRNA(V1-V2;V3-V4)	NSCLC	China	[29, 30]
	Fusicatenibacter	↑	Fecal samples	16 S rRNA(V1-V2;V3-V4)	NSCLC; LUAD	China; Lithuania	[25, 75]
	Fusobacterium	↑	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China	[29]
	Gardnerella	↓	Fecal samples	16 S rRNA	NSCLC	China	[28]
	Gemmiger	↑	Fecal samples	16 S rRNA	NSCLC	China	[28]
	Kluyvera	↓	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China	[29]
	Lachnospira	↓	Fecal samples	16 S rRNA(V3-V4)	NSCLC	China	[25]
	Microbacterium	↓	Fecal samples	16 S rRNA	NSCLC	China	[28]
	Prevotella	↑	Fecal samples	16 S rRNA(V4);Shotgun metagenome sequencing	NSCLC; LUAD	China	[26, 28, 32]
	Romboutsia	↑	Fecal samples	16 S rRNA(V1-V2)	NSCLC	Lithuania	[75]
	Roseburia	↑	Fecal samples	16 S rRNA(V3-V4)	NSCLC; LUAD	China	[25, 28]
	Roseburia	↓	Fecal samples	16 S rRNA	NSCLC	China	[33]
	Ruminococcus	↑	Fecal samples	16 S rRNA(V3-V4)	NSCLC	China	[30]
	Ruminococcus	↓	Fecal samples	16 S rRNA	NSCLC	China	[33]
	Solimonas	↓	Fecal samples	16 S rRNA	NSCLC	China	[28]
	Streptococcus	↑	Fecal samples	16 S rRNA(V3-V4;V4)	NSCLC	China	[26]
	Streptococcus	↓	Fecal samples	16 S rRNA(V3-V4)	NSCLC	China	[30]
	Veillonella	↑	Fecal samples	16 S rRNA(V1-V2)	NSCLC	China	[29]
	Veillonella	↓	Fecal samples	16 S rRNA(V3-V4)	NSCLC	China	[30]
Species	Bacteroides plebeius	↓	Fecal samples	16 S rRNA and shotgun metagenome sequencing	NSCLC	China	[32]
	Bacteroides thetaiotaomicron	↑	Fecal samples	16 S rRNA and shotgun metagenome sequencing	LUAD	China	[32]
	Clostridium leptum	↓	Fecal samples	16 S rRNA	NSCLC	China	[33]
	Faecalibacterium prausnitzii	↓	Fecal samples	16 S rRNA	NSCLC	China	[33]
	Fusobacterium mortiferum	↑	Fecal samples	16 S rRNA and shotgun metagenome sequencing	LUAD	China	[32]
	Roseburia intestinalis	↓	Fecal samples	16 S rRNA and shotgun metagenome sequencing	NSCLC	China	[32]
	Ruminococcus gnavus	↑	Fecal samples	16 S rRNA(V3-V4)	NSCLC	China	[25]
	Ruminococcus torques	↓	Fecal samples	16 S rRNA and shotgun metagenome sequencing	NSCLC	China	[32]

↑ – Microbiota increases in cases compared to respective controls; ↓ – Microbiota decrease in cases compared to respective controls

Lung microbiota

Traditionally considered sterile, the lungs of healthy individuals actually harbor diverse microbial communities, although the bacterial load remains relatively low [37–39]. Unlike the gut, which is protected by a thick mucus layer, the respiratory tract is lined only with a thin surfactant layer, creating a nutrient-poor environment that may contribute to the lower bacterial density observed in the lungs [7].

The composition of the lung microbiota is influenced by both external influx and internal clearance mechanisms. Microorganisms primarily enter the lungs through microaspiration, inhalation of airborne particles, and mucosal diffusion. Due to the high compositional similarity between the lung and oral microbiota, the oral cavity is considered the main source of healthy lung microbiota [40–43]and microaspiration is regarded as the predominant entry route [7, 39, 44–46].

Recent studies emphasize significant spatial heterogeneity of lung microbial communities across different anatomical regions. Firstly, bacterial load generally decreases progressively from the upper respiratory tract to the lower respiratory tract [47, 48]. Additionally, the microbiota in the right lung lobes more closely resembles that of the upper respiratory tract, which may be related to the relatively straight anatomical structure of the right main bronchus, facilitating microaspiration [44, 49].

High-throughput 16 S rRNA sequencing of bronchoalveolar lavage fluid (BALF) from healthy individuals has revealed that the dominant phyla include Actinobacteria, Firmicutes, Bacteroidetes, and Proteobacteria [50, 51]. In contrast, At the phylum level, Firmicutes and Actinobacteria are consistently enriched in BALF and sputum samples from non-small cell lung cancer (NSCLC) patients, while Bacteroidetes shows bidirectional changes [52, 53]possibly reflecting tumor subtype or anatomical sampling variation.Class and order-level alterations reinforce these trends, with enrichment of Mollicutes and Entomoplasmatales [52]which may contribute to inflammation and local immune suppression.At the genus level, a wide range of taxa—including Streptococcus, Veillonella, Haemophilus, Prevotella, Acinetobacter, and Bacillus—are frequently enriched across tumor subtypes [6, 41, 53–60]. These bacteria are known for immune-modulatory properties and may influence the tumor microenvironment by promoting inflammation, metabolic reprogramming, or immune escape. Some genera (e.g., Prevotella, Capnocytophaga, Rothia) show subtype-specific or bidirectional changes [6, 41, 53, 56, 59–61]suggesting context-dependent roles. At the species level, pathogenic or opportunistic organisms such as Escherichia coli, Candida albicans, Acinetobacter junii, Burkholderia spp., and Streptococcus pneumoniae are enriched in tumor-bearing lungs [62–64]likely reflecting barrier dysfunction and altered host–microbe interactions. Meanwhile, commensals like Lactobacillus plantarum and Prevotella oralis are depleted, possibly indicating loss of protective species [41, 64].

Microbial composition is also closely associated with the aggressiveness of lung cancer. For example, metastatic adenocarcinoma is characterized by a decrease in Streptococcus, and an increase in Veillonella, Legionella, and Rothia. Metastatic squamous cell carcinoma also exhibits elevated levels of Veillonella, Legionella, and Rothia [59, 65]. In addition, increased oxygen levels in lung tumors may promote the colonization of certain aerobic bacteria, thereby facilitating metastasis from other organs and contributing to cancer progression [66] Table 2.

Table 2.

Lung microbiota alterations detected in lung cancer patients

Level	Taxon	Direction of Change	Sample source	Method	Associated Lung Cancer Subtype(s)	Geographic Origin	References
Phylum	Actinobacteria	↑	BALF	16 S rRNA(V3-V4;V5)	NSCLC	China	[275]
	Bacteroidetes	↓	BALF	16 S rRNA(V3-V4)	LC	China	[52]
	Bacteroidetes	↑	BALF	16 S rRNA(V3-V4)	NSCLC	China	[53]
	Firmicutes	↑	BALF; Sputum samples	16 S rRNA(V3-V4)	NSCLC	China; Korea	[41, 53, 58]
	Tenericutes	↑	BALF	16 S rRNA(V3-V4)	LC	China	[52]
Class	Bacteroidia	↓	BALF	16 S rRNA(V3-V4)	LC	China	[52]
	Mollicutes	↑	BALF	16 S rRNA(V3-V4)	LC	China	[52]
Order	Bacteroidales	↓	BALF	16 S rRNA(V3-V4)	LC	China	[52]
	Entomoplasmatales	↑	BALF	16 S rRNA(V3-V4)	LC	China	[52]
Family	Leuconostocaceae	↑	BALF	16 S rRNA(V3-V4)	LC	China	[52]
	Spiroplasmataceae	↑	BALF	16 S rRNA(V3-V4)	LC	China	[52]
Genus	Abiotrophia	↑	Sputum samples	16 S rRNA(V1-V2)	NSCLC	America	[54]
	Achromobacter	↑	BALF	16 S rRNA	LUSC	Portuga	[60]
	Acinetobacter	↑	BALF	16 S rRNA(V3-V6)	ADC	Portuga	[60]
	Actinomyces	↑	BALF; Saliva samples	16 S rRNA(V3-V4)	ADC; MPN	China	[56, 59]
	Arthrobacter	↑	BALF	16 S rRNA(V3-V4)	ADC	China	[59]
	Bacillus	↑	BALF; Sputum samples	16 S rRNA(V3-V4)	NSCLC; ADC	China; Russia	[41, 55]
	Blautia	↑	BALF	16 S rRNA(V3-V4)	LC	China	[61]
	Brevundimonas	↑	BALF	16 S rRNA	ADC	Portuga	[60]
	Brucella	↑	BALF	16 S rRNA	LUSC	China	[41]
	Capnocytophaga	↑	BALF	16 S rRNA(V3-V4)	LC; LUSC	China; Portuga	[60, 61]
	Capnocytophaga	↓	BALF	16 S rRNA(V3-V4)	ADC	China	[59]
	Castellaniella	↑	BALF	16 S rRNA	ADC	China	[41]
	Clostridium	↑	BALF	16 S rRNA(V3-V4)	NSCLC	China	[275]
	Enterobacter	↑	BALF	16 S rRNA	LC; LUSC	France; Portuga	[60, 63]
	Filifactor	↑	BALF; Sputum samples	16 S rRNA(V3-V4)	NSCLC	China	[276]
	Gemella	↑	Sputum samples	16 S rRNA(V3-V4)	LUSC	Russia	[55]
	Gemmiger	↑	BALF	16 S rRNA(V3-V4)	LC	China	[61]
	Granulicatella	↑	Sputum samples	16 S rRNA(V1-V2)	NSCLC	America	[54]
	Haemophilus	↑	Sputum samples	16 S rRNA(V3-V4)	NSCLC	Russia	[55]
	Klebsiella	↑	BALF	16 S rRNA	LUSC	Portuga	[60]
	Kluyvera	↑	BALF	16 S rRNA	LUSC	Portuga	[60]
	Megasphaera	↑	BALF	16 S rRNA(V3-V4)	NSCLC; ADC	China; Korea	[58, 59]
	Morganella	↑	BALF	16 S rRNA	LUSC	Portuga	[60]
	Oscillospira	↑	BALF	16 S rRNA(V3-V4)	LC	China	[61]
	Phenylobacterium	↑	BALF	16 S rRNA	ADC	Portuga	[60]
	Porphyromonas	↑	Saliva samples	16 S rRNA(V3-V4)	MPN	China	[56]
	Prevotella	↑	BALF; PSB; Saliva samples	16 S rRNA(V3-V4;V4)	NSCLC	America; China	[6]; [53]; [56]
	Prevotella	↓	BALF	16 S rRNA	NSCLC	China	[41]
	Propionibacterium	↑	BALF	16 S rRNA	ADC	Portuga	[60]
	Rothia	↓	BALF	16 S rRNA(V3-V4)	ADC	China	[59]
	Rothia	↑	BALF; Saliva samples	16 S rRNA(V3-V4)	LUSC; MPN	China	[56, 59]
	Sediminibacterium	↑	BALF	16 S rRNA(V3-V4)	LC	China	[61]
	Serratia	↑	BALF	16 S rRNA	LUSC	Portuga	[60]
	Spiroplasma	↑	BALF	16 S rRNA(V3-V4)	LC	China	[52]
	Staphylococcus	↑	BALF	16 S rRNA	LC; ADC	France; Portuga	[60, 63]
	Streptococcus	↑	BALF; PSB; Sputum samples; Saliva samples	16 S rRNA(V1-V2;V3-V4;V4)	NSCLC; LUSC; MPN	America; China; Russia	[6, 53–57]
	Streptococcus	↓	BALF	16 S rRNA(V3-V4)	ADC	China	[59]
	Treponema	↑	BALF	16 S rRNA(V3-V4)	NSCLC	China	[276]
	Veillonella	↑	BALF; PSB; Saliva samples	16 S rRNA(V3-V4;V4)	NSCLC; ADC; LUSC; MPN	America; Korea; China	[6, 53, 56, 58, 59]
	Weissella	↑	BALF	16 S rRNA(V3-V4)	LC	China	[52]
Species	Acinetobacter junii	↑	Sputum samples	16 S rRNA	LC	United Kingdom	[62]
	Aspergillus fumigatus	↑	BALF	16 S rRNA	LC	France	[63]
	Burkholderia cenocepacia	↑	BALF	metagenomic NGS	LC	China	[64]
	Burkholderia latens	↑	BALF	metagenomic NGS	LC	China	[64]
	Burkholderia oklahomensis	↑	BALF	metagenomic NGS	LC	China	[64]
	Candida albicans	↑	BALF	16 S rRNA	LC	France	[63]
	Corynebacterium accolens	↑	BALF	metagenomic NGS	LC	China	[64]
	Escherichia coli	↑	BALF; Sputum samples	16 S rRNA	LC	France; United Kingdom	[62]; [63]
	Gemella sanguinis	↑	BALF	16 S rRNA	NSCLC	China	[41]
	Granulicatella adiacens	↑	Sputum samples	16 S rRNA	LC	United Kingdom	[62]
	Haemophilus influenzae	↑	BALF	16 S rRNA	LC	France	[63]
	Lactobacillus plantarum	↓	BALF	metagenomic NGS	LC	China	[64]
	Porphyromonas somerae	↑	BALF	metagenomic NGS	LC	China	[64]
	Prevotella oralis	↓	BALF	16 S rRNA	NSCLC	China	[41]
	Streptococcus intermedius	↑	BALF; Sputum samples	16 S rRNA	LC; NSCLC	China; United Kingdom	[41, 62]
	Streptococcus mitis	↑	BALF	metagenomic NGS	LC	China	[64]
	Streptococcus pneumoniae	↑	BALF	16 S rRNA	LC	France	[63]
	Streptococcus viridans	↑	Sputum samples	16 S rRNA	LC	United Kingdom	[62]

↑ – Microbiota increases in cases compared to respective controls; ↓ – Microbiota decrease in cases compared to respective controls

Intratumoral microbiota

Although sputum, bronchial brushings, and BALF are commonly used for microbiota analysis, direct sequencing of intratumoral microbiota using surgical specimens provides a more accurate representation of microbial composition within the tumor. Intratumoral microbiota refers to microorganisms present inside lung tumors, primarily residing within tumor cells and immune cells, constituting part of the tumor microenvironment (TME). Notably, the bacterial load within tumor cells is often substantially higher than that found in immune or stromal cells [5, 67].

Where do these intratumoral microbes originate? Several mechanisms have been proposed to explain microbial colonization in tumors: (1) the hypoxic tumor environment favors the survival of anaerobic bacteria; (2) the immunosuppressive TME allows persistent microbial colonization; and (3) abnormal tumor vasculature may facilitate microbial infiltration from the circulatory system [68–70].

Recent studies using 16 S rRNA sequencing and whole-genome metagenomics have uncovered distinct microbial signatures within tumor tissues of lung cancer patients (Table 3). Although sampling methods and geographic origins vary, several patterns are consistent.At the phylum level, Proteobacteria, Actinobacteria, and Firmicutes show inconsistent shifts—some enriched, others depleted—likely reflecting tumor heterogeneity and technical variation [3, 71–73]. Aenigmarchaeota, a rarely reported phylum, was notably enriched in Chinese cohorts, suggesting niche colonization [73].At the genus level, Streptococcus, Prevotella, Veillonella, Corynebacterium, Neisseria, and Acidovorax were repeatedly enriched across diverse lung cancer subtypes [3, 69, 74–78]. These genera are often associated with inflammation, biofilm formation, or immune modulation. Some commensals (e.g., Dialister, Fusobacterium, Staphylococcus) were depleted [3, 69, 76]potentially indicating loss of microbial diversity.Species-level resolution remains limited, but findings like Acidovorax temporans of lung squamous cell carcinoma(LUSC) and Akkermansia muciniphila of lung adenocarcinoma(LUAD) suggest subtype-specific microbial colonization with possible functional impact on mucin metabolism and immune regulation [3, 77].

Table 3.

Intratumoral microbiota alterations detected in lung cancer patients

Level	Taxon	Direction of Change	Sample source	Method	Associated Lung Cancer Subtype(s)	Geographic Origin	References
Phylum	Actinobacteria	↑	Tissue samples	16 S rRNA(V3-V4)	LUAD	China	[71]
	Proteobacteria	↑	Tissue samples	16 S rRNA(V3-V4;V3-V5)	NSCLC; LUAD	Amercia; China	[3, 71, 72]
	Bacteroidetes	↓	Tissue samples	16 S rRNA(V3-V4)	NSCLC; LUAD	China	[71, 72]
	Firmicutes	↓	Tissue samples	16 S rRNA(V3-V4;V3-V5)	LC; LUAD	Amercia; China	[3, 71–73]
	Actinobacteria	↓	Tissue samples	16 S rRNA	NSCLC	China	[72]
	Bacteroidota	↑	Tissue samples	16 S rRNA(V1-V2)	NSCLC	Lithuania	[75]
	Firmicutes	↑	Tissue samples	16 S rRNA(V1-V2)	NSCLC	Lithuania	[75]
	Proteobacteria	↓	Tissue samples	16 S rRNA(V1-V2;V3-V4)	LC; NSCLC; LUAD	China; Chile; Lithuania	[73, 75, 77]
	Aenigmarchaeota	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[73]
	Bacteroidetes	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[73]
Order	Bacteroidales	↑	Tissue samples	16 S rRNA	LUAD	Chile	[77]
	Parvibaculales	↑	Tissue samples	16 S rRNA(V3-V4)	LUAD	China	[71]
	Neisseriales	↑	Tissue samples	16 S rRNA(All length)	LC	China	[78]
Family	Parvibaculaceae	↑	Tissue samples	16 S rRNA(V3-V4)	LUAD	China	[71]
	Enterobacteriaceae	↓	Tissue samples	16 S rRNA(V4)	LUAD	America	[277]
	Methylobacteriaceae	↑	Tissue samples	16 S rRNA(V4)	LUAD	America	[277]
	Lactobacillaceae	↑	Tissue samples	16 S rRNA(All length)	LC	China	[78]
	Streptococcaceae	↑	Tissue samples	16 S rRNA(All length)	LC	China	[78]
Genus	Corynebacterium	↑	Tissue samples	16 S rRNA; WGS	LC; LUAD	America; Chile	[74, 77]
	Ralstonia	↑	Tissue samples	16 S rRNA(V3-V4)	LUSC	Italy	[65]
	Thermus	↑	Tissue samples	16 S rRNA(V3-V4)	LUAD	Italy	[65]
	Staphylococcus	↓	Tissue samples	16 S rRNA(V3-V4)	NSCLC	China	[69, 76]
	Streptococcus	↑	Tissue samples	16 S rRNA; WGS	NSCLC; LUSC	America; China	[69, 74]
	Neisseria	↑	Tissue samples	16 S rRNA	NSCLC	China	[69]
	Dialister	↓	Tissue samples	16 S rRNA	NSCLC	China	[69]
	Acidovorax	↑	Tissue samples	16 S rRNA(V3-V5)	LUSC	America	[3]
	Anaerococcus	↑	Tissue samples	16 S rRNA(V3-V5)	LUSC	Amercia	[3]
	Comamonas	↑	Tissue samples	16 S rRNA(V3-V5)	LUSC	Amercia	[3]
	Fusobacterium	↓	Tissue samples	16 S rRNA(V3-V5)	NSCLC	Amercia	[3]
	Klebsiella	↑	Tissue samples	16 S rRNA(V3-V5)	LUSC	Amercia	[3]
	Leptothrix	↑	Tissue samples	16 S rRNA(V3-V5)	LUSC	Amercia	[3]
	Rhodoferax	↑	Tissue samples	16 S rRNA(V3-V5)	LUSC	Amercia	[3]
	Tepidimonas	↑	Tissue samples	16 S rRNA(V3-V5)	LUSC	Amercia	[3]
	Propionibacterium	↓	Tissue samples	16 S rRNA(V3-V4)	NSCLC	China	[278]
	Ancylobacter	↑	Tissue samples	16 S rRNA(V3-V4)	LUAD	China	[71]
	Lactobacillus	↓	Tissue samples	16 S rRNA(V3-V4)	LUAD	China	[71]
	Parvibaculum	↑	Tissue samples	16 S rRNA(V3-V4)	LUAD	China	[71]
	Renibacterium	↑	Tissue samples	16 S rRNA(V3-V4)	LUAD	China	[71]
	Anaerococcus	↓	Tissue samples	16 S rRNA(V3-V4)	NSCLC	China	[279]
	Brevibacillus	↓	Tissue samples	16 S rRNA(V3-V4)	NSCLC	China	[279]
	Cupriavidus	↓	Tissue samples	16 S rRNA(V3-V4)	NSCLC	China	[279]
	Massilia	↑	Tissue samples	16 S rRNA(V3-V4)	NSCLC	China	[279]
	Phenylobacterium	↑	Tissue samples	16 S rRNA(V3-V4)	NSCLC	China	[279]
	Pseudoxanthomonas	↑	Tissue samples	16 S rRNA(V3-V4)	NSCLC	China	[279]
	Ruminococcus	↑	Tissue samples	16 S rRNA(V3-V4)	LUSC	China	[279]
	Polaromonas	↑	Tissue samples	16 S rRNA(V3-V4;V3-V5)	LUSC	Amercia; China	[3, 279]
	Haemophilus	↑	Tissue samples	16 S rRNA	LUAD	China	[72]
	Brevundimonas	↑	Tissue samples	16 S rRNA(V3-V4;V3-V5)	NSCLC; LUSC	Amercia; China	[3, 72, 279]
	Alloprevotella	↑	Tissue samples	16 S rRNA(V1-V2)	NSCLC	Lithuania	[75]
	Cutibacterium	↑	Tissue samples	16 S rRNA(V1-V2)	NSCLC	Lithuania	[75]
	Leptotrichia	↑	Tissue samples	16 S rRNA(V1-V2)	NSCLC	Lithuania	[75]
	Pseudomonas	↓	Tissue samples	16 S rRNA(V1-V2;V3-V4)	NSCLC	China; Lithuania	[75, 76]
	Dialister	↑	Tissue samples	16 S rRNA(V4)	LUAD	America	[277]
	Methylobacterium	↓	Tissue samples	16 S rRNA(V4)	LUAD	America	[277]
	Sphingomonas	↓	Tissue samples	16 S rRNA(V4)	NSCLC; LUAD	America; China	[72, 277]
	Aerococcus	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Akkermansia	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Anaerovorax	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Campylobacter	↓	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Cloacibacterium	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Donghicola	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Dubosiella	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Lachnospira	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Lactobacillus	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Marivivens	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Methylobacterium	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Paenibacillus	↑	Tissue samples	16 S rRNA(V3-V4)	LC	China	[280]
	Faecalibacterium	↑	Tissue samples	16 S rRNA(V3-V4)	LUAD	China	[174]
	Fusobacterium	↑	Tissue samples	16 S rRNA(V3-V4)	LUAD	China	[174]
	Veillonella	↑	Tissue samples	16 S rRNA(V3-V4)	NSCLC	China	[76]
	Prevotella	↑	Tissue samples	16 S rRNA(V1-V2;V3-V4);WGS	NSCLC	America; China; Lithuania	[74–76]
Species	Akkermansia muciniphila	↑	Tissue samples	16 S rRNA	LUAD	Chile	[77]
	Acidovorax temporans	↑	Tissue samples	16 S rRNA(V3-V5)	LUSC	America	[3]
	Haemophilus influenzae	↑	Tissue samples	16 S rRNA(V4)	LUAD	America	[277]

↑ – Microbiota increases in cases compared to respective controls; ↓ – Microbiota decrease in cases compared to respective controls

Although the role of fungi in cancer has been less explored, emerging evidence suggests their potential involvement in shaping antitumor immunity [79]. For instance, has been detected in lung cancer tissues, with CARD9 implicated in antifungal and antitumor responses [80]. has been identified as the fungus most strongly associated with NSCLC [81]. Additionally, has been linked to lung adenocarcinoma, while Blastomyces dermatitidis/gilchristii has been associated with squamous cell carcinoma [82–84].

Ma et al. analyzed six independent lung cancer cohorts and selected the top seven most abundant fungal species for correlation analysis with lung cancer. These included Pichia kudriavzevii, Saccharomyces paradoxus, Eremothecium sinecaudum, and Schizosaccharomyces pombe, among others [85]. These findings collectively suggest that the lung mycobiome may play a yet underexplored role in lung carcinogenesis.

Following the COVID-19 pandemic, the human virome has drawn increasing attention in cancer research. Several DNA viruses—such as HHV-6B, Parvovirus B19V, Epstein–Barr virus (EBV), and torque teno virus (TTV)—have been widely detected across various human tissues [86]. Some viruses, including JC polyomavirus (JCPyV), human papillomavirus (HPV), EBV, human T-lymphotropic virus (HTLV), and Y53 sarcoma virus, have been implicated in lung cancer pathogenesis [87–94]particularly in lung adenocarcinoma and squamous cell carcinoma. Although the oncogenic mechanisms of viruses in lung cancer remain unclear, accumulating evidence indicates that viral infection may influence lung cancer subtype distribution and patient prognosis [92].

Despite the increased risk of severe COVID-19 outcomes in lung cancer patients, current research on the relationship between SARS-CoV-2 infection and lung cancer initiation remains limited and warrants further investigation [95].

In addition, Chlamydia has been shown to promote lung tumor progression by stimulating the production of bone morphogenetic protein 2 (BMP2) in bronchial epithelial and cancer cells [96]. These findings highlight the potential role of non-canonical pathogens in lung cancer pathogenesis.

Overall, microbial taxa may play context-dependent roles—protective or pathogenic—across different niches. In healthy lungs, microbiota are shaped by microbial migration, growth, and clearance. Disruption of this balance is implicated in chronic respiratory diseases and may influence systemic conditions. Lung and gut microbiota likely interact to regulate lung cancer progression, modulated by immune, microbial, and environmental factors Table 3.

Circulating microbial DNA

In recent years, the detection of circulating microbial DNA (cmDNA) in the bloodstream has offered new perspectives for cancer prediction, diagnosis, and the study of immune responses. Multiple studies have identified microbial-derived fragments, including genetic signatures of bacteria, fungi, and even viruses, in the plasma cell-free DNA of cancer patients [97–99]. In lung cancer patients, microbial genera such as Pseudomonas, Acinetobacter, Cutibacterium, Comamonas, Staphylococcus, Helicobacter, Gardnerella, Klebsiella, Massilia, and Microbulbifer have been detected at relatively high abundance in the blood [100, 101].

Currently, the precise origins of cmDNA remain unclear. Prevailing hypotheses suggest that it may arise from: (1) translocation of gut microbiota across the intestinal barrier; (2) leakage of microbial components during antigen presentation by immune cells; and (3) entry of intratumoral microbes into the circulation during tumor metastasis [98, 102, 103]. Moreover, the presence of cmDNA is thought to be closely associated with host immune dysfunction [104]. While it is still uncertain whether viable microbes directly enter the bloodstream, some studies indicate that cmDNA can partially reflect the microbial composition associated with tumors. For example, Chen et al. reported that cmDNA closely mirrors the microbial profiles found within tumor tissues [100]while Dohlman et al. found fungal elements from gastrointestinal tumors present in peripheral blood, suggesting that local microbes may escape tissue barriers under certain conditions [84]. These findings support the notion that cell-free microbial DNA may either originate from microbes colonizing tumor tissues or from gut microbiota that actively translocate or passively leak into the circulation following increased barrier permeability [105].

At present, cmDNA is increasingly being applied in the early detection and staging of various cancers, including lung, liver, and colorectal cancers [99, 100, 106]. In studies of NSCLC, researchers have developed the Microbial Abundance Prognostic Score model, which integrates microbial abundance with clinical features to predict overall survival (OS) and assess its potential as a non-invasive biomarker for preoperative recurrence prediction [101, 107]. This emerging field holds great promise for advancing precision medicine in NSCLC.

Oral microbiota

Lower oral microbial α-diversity has been associated with an increased risk of lung cancer. Specifically, higher abundances of Spirochaetia and Bacteroidetes are correlated with a reduced risk, whereas elevated levels of Bacilli and the order Lactobacillales are linked to an increased risk [108]. Furthermore, Liu et al. reported that Porphyromonas gingivalis can colonize lung cancer tissues via oral or hematogenous routes. Under poor oral hygiene conditions, this pathogen promotes tumor progression and is closely associated with metastasis, staging, and poor prognosis in lung cancer patients [109]. In addition, patients with synchronous multiple primary lung cancers (sMPLC) exhibit similar microbial compositions between the oral cavity and lung tissues, along with significantly reduced microbial diversity. This suggests that oral microbes may originate from the lungs and contribute to disease pathogenesis, potentially serving as diagnostic and therapeutic targets for sMPLC [110].

Taken together, these findings indicate that the composition and diversity of the oral microbiota may influence lung cancer development, underscoring the potential role of the microbiome in lung cancer etiology. The concept of an oral–lung axis may thus represent a promising avenue for future research.

Detection of the microbiome: challenges and advances in lung cancer research

Detecting the pulmonary microbiome presents numerous challenges, primarily due to the high risk of contamination from microorganisms in the upper respiratory tract and oral cavity during sample collection. Furthermore, healthy lung tissue is difficult to obtain, and appropriate control samples are extremely scarce, which limits the depth of lung microbiome research. Commonly used pulmonary and related sample types include sputum, BALF, exhaled breath condensate, saliva, and blood.

Sputum collection is non-invasive and convenient but is often contaminated by oral and upper airway flora, resulting in low specificity. In contrast, BALF sampling is more invasive but provides a more accurate representation of the lung microbiota with reduced contamination and is therefore widely employed in pulmonary microbiome studies. EBC offers a non-invasive, easy-to-perform method suitable for long-term follow-up across age groups and has shown potential in early lung cancer detection [111, 112]. Saliva and blood samples, due to their non- or minimally invasive nature, are also increasingly used in lung cancer detection and biomarker research.

On the molecular level, 16 S rRNA gene sequencing remains the most widely used technique for bacterial classification and diversity analysis. It enables taxonomic profiling at the genus level but has limited resolution for species or strain-level identification and does not directly reveal microbial functions [113]. Notably, different studies often target various hypervariable regions of the 16 S gene—such as V1–V2, V3–V4, etc.—for amplification [12, 114, 115]. Sequence variability and conservation across regions differ, leading to technical bias in interpreting microbial community structure. For example, over half of the amplicons from the V4 region may fail to map accurately to the source species, whereas full-length 16 S sequencing significantly improves taxonomic resolution. Moreover, specific regions exhibit differential recognition efficiency for various taxa—e.g., the V1–V2 region performs poorly in classifying, while V3–V5 underperforms for Actinobacteria [116]. Such variability complicates cross-study comparisons and data integration, affecting the consistency and reliability of results [117]. Therefore, it is recommended that studies adopt standardized amplification regions or employ multi-region sequencing with bioinformatic correction strategies to enhance comparability and accuracy.

Compared with 16 S sequencing, shotgun metagenomic sequencing covers the entire genomic DNA within a sample, allowing for high-resolution taxonomic identification, functional gene profiling, and detection of antibiotic resistance genes. It also offers improved sensitivity for low-abundance or rare microorganisms [11, 118]. While tools like PICRUSt can infer microbial functions from 16 S data, their predictive accuracy remains limited. Recent integrative multi-omics approaches—combining genomics, proteomics, and metabolomics—have provided deeper insights into host–microbiome interactions in the lung [119].

The combination of traditional culture techniques with modern molecular methods offers a more comprehensive view of the lung microbiota, especially aiding in the diagnosis of culture-negative infections. However, molecular amplification techniques are susceptible to amplification bias and false positives, making the use of multiple complementary methods critical for ensuring diagnostic accuracy and reliability.

Importantly, due to the inherently low biomass of lung microbiota, sequencing results are highly susceptible to contamination from reagents, instruments, and the environment, potentially leading to spurious signals and background noise. Stringent experimental design, appropriate negative controls, and rigorous contamination control protocols are essential to ensure the validity and reproducibility of findings in lung microbiome studies [120, 121].

Distinct alterations in the microbiome of lung cancer patients offer new perspectives for disease diagnosis and biomarker discovery. Non-invasive sample types such as EBC and sputum show great promise for early lung cancer detection. Moreover, the integration of multi-omics technologies further reveals microbe-related molecular mechanisms within the tumor microenvironment, offering novel targets and research directions for precision therapies.

Co-evolution of the microbiota and host environment

The microbiota and the host environment have established a complex and dynamic interactive relationship through long-term co-evolution. While the host provides the microbiota with a habitat and nutritional support, microbes participate extensively in various physiological processes, including metabolism, immune regulation, and barrier maintenance. Alterations in the host environment—particularly in the lungs, which are open and sensitive to external exposures—can significantly affect local microbial homeostasis. For instance, smoking, air pollution, asbestos exposure, and particulate matter (PM2.5) are not only established risk factors for lung cancer but also profoundly influence the composition of the lung microbiota [122, 123]. Cigarette smoke can induce airway epithelial injury, excessive mucus secretion, and local hypoxia, thereby facilitating colonization by opportunistic pathogens, which may provoke chronic inflammation and promote tumor development [5, 7]. Smoking is also associated with a dysbiotic state characterized by a reduction in beneficial microbes and enrichment of potentially harmful species—an imbalance considered a key contributor to lung carcinogenesis [122].

Environmental factors also impact the gut microbiota, which in turn can influence pulmonary health through the “gut–lung axis.” Broad-spectrum antibiotic use can significantly reduce gut microbial diversity, leading to immune dysregulation and increased susceptibility to allergic airway diseases and lung cancer [108, 124, 125]. Animal studies have shown that modulation of lung microbiota by antibiotic treatment can suppress melanoma lung metastasis [126]and in human cohorts, antibiotic exposure has been linked to reduced efficacy of ICI [127]. Additionally, proton pump inhibitors (PPIs), commonly prescribed for gastric mucosal protection, may indirectly alter microbial composition by affecting gastric acid secretion and intestinal pH. They may also exert systemic effects by interfering with nutrient absorption, hormone metabolism, and immune regulation [128–130]. A study by Baek et al. further reported that PPI use was associated with a significantly increased risk of all-cause mortality in advanced NSCLC patients [131].

Collectively, these findings highlight a close and functionally relevant co-evolutionary relationship between host environmental factors and the microbiota. A deeper understanding of these interactions may offer novel perspectives for the prevention and treatment of lung cancer.

The microbial blueprint of lung cancer: mechanisms that matter

Although the gastrointestinal tract and the respiratory tract are anatomically separate, they share a common embryonic origin and exhibit significant structural similarities. Both are integral components of the mucosal immune system. These features suggest the possibility of bidirectional interactions between the gastrointestinal tract and the respiratory tract, underpinned by similar mechanisms involving epithelial cells, immune cells, and diverse microbial communities [132, 133]. The main mechanisms can be summarized as follows: epithelial cells are capable of sensing and recognizing microbial signals and transmitting them to immune cells in the lamina propria, thereby modulating the immune microenvironment and its dynamic interactions in both the gut and lungs. In addition, interactions between microbes and epithelial cells are mediated through bacterial metabolites, structural components, and the microbes themselves, which in turn influence mucosal immune homeostasis and inflammatory responses [134].

Following the rise of the “gut–brain axis” as a research focus, emerging experimental and epidemiological evidence has confirmed a critical connection between the gut and the lungs, leading to the emergence of the “gut–lung axis” concept [8]. With continuous advancements in sequencing and detection technologies, uncovering the functional differences between gut and lung microbiota—and addressing the current gaps in tumor-associated microbial metabolism research within the context of the GLA—has become a pressing and essential direction for future investigation.

The bidirectional interaction of the gut-lung axis

The gut–lung axis represents a complex and dynamic ecosystem in which microbiota from the gastrointestinal and respiratory tracts engage in both direct and indirect bidirectional interactions. Direct routes include the aspiration of gastrointestinal secretions into the respiratory tract or the swallowing of sputum into the gastrointestinal tracts [8, 40, 135]. Indirect communication occurs through microbial components, metabolites, or immune-mediated signaling via the lymphatic and circulatory systems, allowing gut microbiota to remotely regulate pulmonary immune status [136]. Whiteson et al. introduced the island biogeography theory, proposing that the gut and lungs represent distinct microbial “islands” shaped by the order and nature of microbial immigration [137]. However, this independence appears to be relative—disruption of epithelial barriers and immune homeostasis can lead to bacterial translocation and microbiota dysbiosis, ultimately affecting distal organs such as the lungs [138].

The lung microbiota plays a pivotal role in both innate and adaptive immune responses by interacting with epithelial and immune cells [139]. Dysregulation of this ecosystem has been associated with diseases such as asthma and chronic obstructive pulmonary disease (COPD) [140]. Conversely, pulmonary infections—such as methicillin-resistant Staphylococcus aureus (MRSA) pneumonia—can induce intestinal epithelial cell apoptosis, underscoring the bidirectional influence of lung microbes on gut health [141]. Moreover, environmental and infectious exposures—such as lipopolysaccharides (LPS) or Influenza A virus—can significantly alter gut microbial composition and mucosal integrity [142–144]. These findings further highlight the reciprocal nature of the gut–lung axis.

Numerous studies have demonstrated that gut microbiota can influence pulmonary homeostasis through various mechanisms, thereby affecting susceptibility to lung diseases [145–147]. Alterations in intestinal permeability have been linked to changes in lung microbial composition [135]. Recent evidence suggests that Akkermansia may translocate from the gut to lung tumors via the systemic circulation, potentially reshaping the intratumoral microbiota and exerting antitumor effects [148]. In contrast, segmented filamentous bacteria can induce the migration of intestinal Th17 cells to the lungs and activate dual TCR autoreactive T cells, contributing to autoimmune pulmonary pathology. This finding reveals a mechanism by which gut microbiota remotely modulate pulmonary immune responses along the gut–lung axis via Th17 chemotaxis [149].

Despite these insights, many critical questions remain unresolved regarding the mechanisms underlying gut–lung microbial interactions, their roles in cancer progression, and whether modulation of the gut microbiota can reshape tumor-infiltrating microbiota and the host immune microenvironment (Fig 2).

Fig. 2.

The bidirectional interaction of the gut-lung axis: The gut-lung axis refers to the interaction between microbial communities and their metabolites between the intestines and lungs, and their impact on host health. This axis emphasizes the interplay between gut and lung microbiota, including microbial migration pathways and potential regulatory mechanisms. Under healthy conditions, this interaction contributes to immune system balance and maintenance of defense functions. However, in disease states, particularly during lung cancer development, the gut-lung axis may play a significant role, where dysbiosis of the microbiota could influence tumor onset, progression, and therapeutic outcomes

Mechanisms of intrinsic microbiota in lung cancer

Microorganisms increase susceptibility to carcinogenesis at multiple levels, including dysbiosis of the microbiota, translocation and communication, inflammation, immune responses, and the effects of virulence and metabolic changes (Fig 3).

Fig. 3.

Mechanisms of microbial influence on lung cancer: Microbes increase susceptibility to carcinogenesis through various mechanisms including dysbiosis, translocation and communication, inflammation, immune responses, virulence factors, and metabolic changes

Chronic inflammation

Chronic inflammation is a well-established risk factor for the development of NSCLC [150]. Emerging evidence highlights the pivotal role of the lung microbiota in driving inflammatory processes. Microorganisms stimulate inflammatory responses by binding pathogen-associated molecular patterns (PAMPs) to pattern recognition receptors (PRRs), leading to the recruitment of inflammatory cells and cytokines—critical components of the tumor microenvironment. Epidemiological studies reveal that over half of NSCLC patients have a recent history of bacterial pneumonia or other pulmonary infections, suggesting a strong link between inflammation and tumorigenesis [151, 152].

COPD, a condition marked by chronic inflammation and dysregulated lung microbiota, is closely associated with an elevated risk of lung cancer. Notably, non-typeable Haemophilus influenzae (NTHi) is frequently detected in the lungs of COPD patients, where it induces IL-17 C expression, promotes neutrophil infiltration into tumor tissues, and drives inflammatory responses, thereby accelerating lung cancer initiation and progression [153]. Oral-origin bacteria can enter the lower respiratory tract via microaspiration, inducing Th17- and γδ T cell-mediated inflammatory responses and impairing alveolar macrophage TLR4 signaling, thereby modulating basal mucosal immunity in the lung [43, 154]. Consequently, IL-17 has emerged as a key mediator of tumor-associated inflammation and lung cancer progression. Moreover, various microorganisms—including Escherichia coli—can promote inflammatory cascades by activating host Toll-like receptors (TLR2 and TLR4), thereby enhancing tumor cell adhesion and metastatic potential, which contributes to lung cancer invasiveness and affects patient prognosis [155–157].

Interestingly, Mycobacterium tuberculosis (TB) has also been implicated in lung cancer pathogenesis, possibly due to persistent inflammation induced by tuberculosis [158, 159]although direct evidence remains limited. Furthermore, gut microbiota, via the GLA, may contribute to bacterial pneumonia, thereby causing neutrophil infiltration and creating a pro-inflammatory pulmonary microenvironment [160, 161].

In summary, chronic inflammation—regardless of its origin—can compromise epithelial barriers, fostering microbial dysbiosis and colonization. These changes may play a crucial role in the non-heritable progression of lung cancer, driving both tumor initiation and progression through sustained inflammatory signaling.

Immune response

The MIS refers to a set of immune defense systems distributed on the surfaces of the body’s mucosae. Its primary function is to protect mucosal surfaces from invasion by pathogens and other external stimuli. The mucosal immune system includes a series of immune cells, mucous layers, antimicrobial proteins, and other defensive substances. It is not only a physiological barrier but also has immunological characteristics. Despite preventing immune cells residing in the intestinal mucosa from recognizing the commensal microbial community in the gut lumen [134]it is capable of recognizing and eliciting innate or adaptive immune responses to combat various pathogens [162].

The gut microbiota and immune regulation

The symbiotic relationship between the gut microbiota and the immune system is essential for maintaining homeostasis and normal physiological functions. The absence of microbiota has been shown to impair immune competence [163]. PAMPs, such as LPS, flagellin, and peptidoglycan (PGN), expressed by microbes, are recognized by PRRs located on intestinal epithelial cells and innate immune cells. This recognition triggers inflammatory responses that initiate adaptive immunity by recruiting dendritic cells and macrophages, activating B and T cells, and promoting IgA secretion. Effector cells such as Th17 and regulatory T cells (Treg) collaboratively regulate immune responses within gut-associated lymphoid tissue (GALT), maintaining mucosal barrier integrity and immune homeostasis. Moreover, regulatory and effector T cells can migrate from the GALT to lymph nodes and enter the bloodstream, thereby orchestrating systemic immune responses [133, 164–168].For example, Enterocloster spp. in the gut have been shown to suppress MAdCAM-1 expression, promoting the migration of immunosuppressive α4β7⁺ Th17 cells to lung tumor tissue, which compromises the efficacy of PD-1 immunotherapy and accelerates tumor progression [169, 170]. In contrast, gut dysbiosis has been linked to widespread immunosuppression in the lung associated with altered hematopoiesis, including significant reductions in γδ-T cells, natural killer (NK) cells, macrophages, dendritic cells (DCs), monocytes, and neutrophils, highlighting its pivotal role in dampening pulmonary immune responses [171, 172].

In summary, gut microbiota influences immune responses locally and systemically, contributing to tumor development by affecting the immune landscape. However, more research is needed to understand the detailed communication between the gut and other organs, including the lungs, in the context of cancer immunity.

The lung microbiota and immune regulation

The development of lung cancer may be influenced by interactions between the local airway microbiota, tumor cells, and the immune system. Streptococcus have been shown to increase Th1 and Th17 cell infiltration in lung cancer [173]. In addition, pulmonary bacteria can stimulate macrophages and neutrophils to secrete MyD88-dependent IL-1β and IL-23, which in turn activate the proliferation and activation of γδ T cells, leading to IL-17 A production that promotes inflammatory responses and tumor cell proliferation [154].

Moreover, intratumoral Aspergillus sydowii promotes the maturation and activation of myeloid-derived suppressor cells (MDSCs) via β-glucan-mediated activation of the Dectin-1/CARD9 signaling pathway. MDSCs, in turn, contribute to tumor progression, metastasis, and immune evasion within the tumor microenvironment [83].

Continuous stimulation by bacterial antigens in the tumor microenvironment leads to increased expression of the inhibitory receptor TIGIT on NK cells and its ligand CD155 on tumor cells. The binding of CD155 on tumor cells to TIGIT on NK cells results in NK cell exhaustion. Concurrently, the secretion of IL-2 and IFN-γ is reduced, further impairing NK cell proliferation and activation, ultimately facilitating tumor growth [174].

Microbes also influence macrophage polarization within the tumor microenvironment. The pulmonary fungus Talaromyces marneffei induces M2 macrophage polarization by activating the arginine–ornithine metabolic pathway, thereby promoting non-small cell lung cancer growth [175]. Additionally, the upper respiratory tract commensal Staphylococcus aureus promotes M2 alveolar macrophage polarization through TLR2-mediated upregulation of CD206 and Dectin-1 [176]. These M2-polarized alveolar macrophages suppress immune responses by secreting anti-inflammatory cytokines and downregulating the expression of costimulatory ligands.

Aberrant activation of signal transduction pathways

Emerging evidence suggests that the development and progression of lung cancer are influenced not only by host genetic mutations and environmental exposures but also by tumor-associated microorganisms, which participate through modulation of key signaling pathways.

Among viruses, HPV, Merkel cell polyomavirus (MCPyV), and EBV have been shown to disrupt cellular homeostasis and promote lung tumorigenesis by altering chromatin architecture and interfering with DNA repair pathways [177]. Notably, the oncogenic proteins E6 and E7 encoded by HPV can mediate degradation of p53 and inactivation of the Rb pathway, respectively, thereby compromising cell cycle regulation and inducing malignant transformation [178, 179].

In advanced lung cancer patients, immune dysfunction is common, predisposing them to opportunistic infections. These pathogens can activate TLR signaling pathways, triggering inflammatory immune responses that contribute to a pro-tumorigenic microenvironment and tumor progression. For instance, is significantly enriched in sMPLC, and has been shown to promote tumorigenesis by inhibiting apoptosis, activating cell cycle progression, and stimulating inflammatory and immune-related signaling pathways such as TNF, IL-17, NF-κB, and Th17 differentiation, ultimately reshaping the immune microenvironment to favor tumor development [110].

Bacteria may contribute to lung cancer via diverse signaling mechanisms. Common airway genera, including Prevotella, Streptococcus, and Veillonella, have been shown to activate ERK and PI3K signaling in pulmonary epithelial cells, promoting abnormal proliferation and resistance to apoptosis, thereby facilitating carcinogenesis [6, 43]. Moreover, the gut microbiota can modulate lung cancer via the gut–lung axis. LPS produced by gut bacteria can trigger systemic inflammation and oxidative stress through the TLR4/NF-κB pathway, creating a pro-tumorigenic milieu for lung cancer initiation [180]. In addition, certain gut microbes may enhance lung cancer stemness and metastatic potential by regulating IL-11 expression through the circRNA/miRNA/SOX9 axis, a process associated with poor prognosis [181]. has also been reported to promote lung cancer cell migration and invasion by upregulating DDIT4, thereby inhibiting the mTORC2/AKT signaling pathway [182].

Importantly, a recent study revealed that Staphylococcus nepalensis and Staphylococcus capitis are selectively enriched in metastatic lung cancer lesions and can secrete D-/L-lactate, leading to upregulation of the lactate transporter MCT1 and enhanced lactate uptake by tumor cells. This process activates HIF-1α-mediated pseudo-hypoxic signaling, thereby promoting tumor cell migration and distant metastasis [183]. These findings suggest that certain bacterial species may play critical roles in lung cancer metastasis by reprogramming metabolic pathways.

In summary, various microorganisms participate in lung cancer initiation, progression, and metastasis by modulating intracellular signaling networks. Targeting microbiota-associated pathways may represent a promising therapeutic strategy for lung cancer.

Bacterial components and metabolic products

Bacterial components and their metabolic products are key mediators through which bacteria influence lung cancer, playing vital roles in modulating immune responses, inflammatory states, and the development and progression of the disease.

LPS, a major component of the cell wall of Gram-negative bacteria, can enter the bloodstream under conditions of gut microbiota dysbiosis or bacterial infection, and has been detected in lung cancer tissues [5]. LPS activates the NLRP3 inflammasome, inducing the release of proinflammatory cytokines such as IL-1β, thereby enhancing systemic inflammation and promoting tumor cell proliferation and distant metastasis [184, 185]. Moreover, Helicobacter pylori secretes VacA toxin, which can damage the airway epithelial barrier and induce the expression of cytokines such as IL-6 and IL-8, establishing a chronically inflamed pulmonary microenvironment conducive to tumor initiation [186].

Among bacterial metabolites, short-chain fatty acids (SCFAs)—including acetate, propionate, and butyrate—have essential anti-tumor and immunoregulatory functions. These SCFAs are generated by gut microbiota fermenting dietary fiber and can enhance intestinal barrier integrity, suppress inflammation, and exert anticancer effects by inducing tumor cell apoptosis and differentiation [187–189]. In cancer patients, SCFA-producing bacteria (e.g., members of the Firmicutes and Actinobacteria phyla) are often depleted, resulting in reduced SCFA levels and increased gut permeability, which may facilitate the entry of carcinogenic factors into the circulation [190–192].

SCFAs can modulate immune homeostasis by activating G protein-coupled receptors (GPR109A, GPR41, GPR43) and inhibiting histone deacetylases (HDACs). For instance, they promote the differentiation of IL-10⁺ and Foxp3⁺ Tregs, contribute to NLRP3 inflammasome activation, and support tissue repair [193–196]. SCFAs can also induce p21 expression, thereby suppressing the proliferation of lung cancer cells and inducing apoptosis [197–200].

However, the effects of SCFAs are not universally beneficial. Their metabolic products may also exert adverse effects. For example, propionate can be metabolized to methylmalonic acid (MMA), which enhances cancer cell invasiveness [201]. Additionally, SCFAs can induce polarization of macrophages toward the M2 phenotype, which may support tumor invasion and immune evasion under certain conditions [202]. Elevated circulating SCFA levels can reduce IL-6 and IL-8 expression in lung tissue, potentially alleviating inflammation-associated lung cancer progression [203–205]. In patients with NSCLC, reduced abundance of butyrate-producing bacteria (e.g., Roseburia) and Helicobacter pylori suggests that gut microbial composition is closely linked to lung cancer prognosis [33].

Notably, Akkermansia may influence host immune responses through propionate production, while tryptophan metabolites, such as aryl hydrocarbon receptor (AhR) ligands, can induce IL-22 expression, thereby modulating pulmonary mucosal immunity and barrier integrity [206, 207].

Although SCFAs are widely regarded as having anti-inflammatory and anti-tumor properties, their biological effects may be concentration-dependent and bidirectional. At low concentrations, butyrate can upregulate the oncogenic long non-coding RNA H19 and activate M2 macrophage activity, potentially promoting tumor growth and immune suppression [101, 170, 208, 209]. Decreased SCFA levels are also associated with cancer cachexia, suggesting a potential role in tumor-related nutrition and metabolism [210].

Beyond SCFAs, other microbial metabolites can also influence lung cancer progression. For instance, microcystins produced by cyanobacteria may regulate inflammation by downregulating CD36, a molecule involved in PARP1 signaling, thereby affecting lung cancer-related pathways [211, 212]. In addition, intratumoral bacteria can secrete methionine, which supports the proliferation of tumor-promoting microbes by inhibiting degradation of its metabolite S-adenosylmethionine (SAM), reinforcing a positive feedback loop between microbes and tumors [213]. It is also noteworthy that the Mycoplasma-associated metabolite D-phenylalanine can induce epithelial–mesenchymal transition (EMT), promoting metastasis in NSCLC [214].

In summary, bacterial components (e.g., LPS) and metabolites (e.g., SCFAs) have profound effects on the pulmonary immune landscape and tumor progression by modulating immune cell polarization, cytokine networks, and epithelial barrier function. Depending on their type, concentration, and the host’s microbial context, these molecules may exert either pro-tumor or anti-tumor effects, underscoring their complex and context-dependent roles in lung cancer development.

The role of the microbiota in lung cancer therapy

The relationship between microbiota and chemotherapy and radiotherapy

The gut microbiota plays a crucial regulatory role during radiotherapy and chemotherapy in lung cancer, capable of both enhancing antitumor immunity and influencing treatment tolerance and toxicity (Fig 4).

Fig. 4.

Role of the microbiota in lung cancer therapy

Common chemotherapeutic agents such as cyclophosphamide (CTX) and cisplatin have been shown to enhance anticancer efficacy through modulation of gut microbiota composition. CTX promotes the translocation of Gram-negative bacteria to secondary lymphoid organs, inducing pathogenic Th17 cell subsets and Th1 memory responses, thereby enhancing immune-mediated antitumor effects [215]. Conversely, the absence of Gram-positive bacteria significantly weakens the therapeutic efficacy of cisplatin [216]. In a mouse lung cancer model, cisplatin combined with Lactobacillus acidophilus significantly reduced tumor volume [217]. Furthermore, gut microbes can influence DNA damage induced by platinum and anthracycline drugs and modulate subsequent immune responses, thus affecting chemotherapy sensitivity and efficacy [218]. For instance, docetaxel treatment in NSCLC disrupts colonic microbiota, leading to reduced anticancer effects [219]. Some studies suggest that specific bacteria can restore drug sensitivity under dysbiotic conditions—for example, Eubacterium hirae enhances the antitumor effect of CTX, promotes Th1 and cytotoxic T lymphocyte (CTL) responses, and improves both local and systemic immunity [220]. Akkermansia muciniphila has also demonstrated potential to augment cisplatin efficacy. In a study by Chen et al., combining Akkermansia with cisplatin significantly improved treatment outcomes in a Lewis lung carcinoma mouse model [221]. This effect may involve multiple key signaling pathways, including cytokine–cytokine receptor interaction, Th17 differentiation, FOXO, PI3K-Akt, and JAK-STAT signaling. Mechanistically, the combination therapy downregulated the expression of proliferation- and apoptosis-related proteins such as Ki-67, p53, and FasL, while upregulating Fas expression and reducing the accumulation of immunosuppressive Treg cells (CD4⁺CD25⁺Foxp3⁺).

Radiotherapy is a conventional approach for various solid tumors, yet its efficacy is often constrained by local tumor hypoxia. Studies have found that several anaerobic and facultative anaerobic bacteria (e.g., Escherichia coli, Bifidobacterium, Clostridium) can selectively colonize hypoxic tumor regions, alleviating hypoxia and sensitizing tumors to radiation, thereby enhancing therapeutic outcomes [222]. In addition, bacterial toxins such as CDT and ClyA can arrest tumor cells in the G2/M phase—when they are most sensitive to radiation—thus increasing radiotherapeutic efficacy [223–225]. Gut microbial metabolites may also mitigate radiation-induced toxicities [226]. In mouse models exposed to total body irradiation, FMT significantly prevented radiation pneumonitis, suggesting the potential value of the gut–lung axis in managing radiotherapy toxicity [226]. In NSCLC animal models, sustained FMT improved lung function and alleviated post-radiation pulmonary inflammation [227, 228].

Notably, certain bacterial species may contribute to radio- and chemoresistance. Lactobacillus iners, for instance, has been shown to enhance resistance to both chemotherapy and radiotherapy across various cancer cell lines, and is associated with shorter relapse-free survival in NSCLC patients [229]. Potential mechanisms include suppression of DNA damage response pathways, disruption of cell cycle checkpoints, induction of oxidative stress, and promotion of EMT, ultimately favoring tumor cell survival and therapeutic resistance [230].

Clinical studies further support the microbiota’s influence on radiochemotherapy efficacy. In NSCLC patients undergoing chemoradiotherapy, increased abundance of the Clostridia in the gut was significantly associated with improved survival outcomes [231]. Although these findings provide preliminary insight into the microbiota’s role in lung cancer radiochemotherapy, further mechanistic studies are warranted.

In summary, the gut microbiota affects lung cancer responses to chemotherapy and radiotherapy through immune modulation, metabolic activity, and pathway interference, and may also participate in the development of treatment-related toxicity and resistance. A deeper understanding of these microbiota–therapy interactions may aid in the development of novel microbiota-targeted strategies to enhance lung cancer treatment efficacy.

The relationship between microbiota and targeted therapy

Jiang et al. demonstrated that changes in the gut microbiota mediated the anti-tumor efficacy of probiotics and antibiotics used in combination therapy with gefitinib in tumor-bearing mice. This initial finding elucidates the causal role of gut microbiota in modulating the anti-cancer effects of gefitinib [232]. Xihuang Pill (XHW), by modulating the composition of gut microbiota to increase the proportion of Bacteroidales and Muribaculaceae, enhances the anti-cancer effects of anlotinib through the tumor angiogenesis pathway [233]. Currently, the specific mechanisms by which microbiota influence targeted therapies remain limited. While some associations between microbiota and treatment outcomes have been identified, further research and validation are necessary to understand the precise mechanisms through which microbiota modulate the efficacy of targeted therapies.

The relationship between microbiota and immunotherapy

In recent years, numerous studies have emphasized the critical role of the gut microbiota in modulating the efficacy of immune checkpoint blockade therapy. In patients with NSCLC, greater gut microbial diversity is generally associated with a better response to anti-PD-1 treatment. This diversity enhances the function of memory T cells and NK cells, thereby eliciting a more effective antitumor immune response.

Certain beneficial bacteria, such as Akkermansia muciniphila and Enterococcus hirae, have been shown to enhance PD-1 blockade efficacy through IL-12–dependent pathways by promoting the accumulation of IFN-γ⁺ CCR9⁺ CXCR3⁺ CD4⁺ T cells at tumor sites, even reversing immune resistance [234–238]. A. muciniphila can also activate immune responses by suppressing the CAF–neutrophil–CXCL3–PD-L1 signaling axis, alleviating CD8⁺ T cell exhaustion, and further enhancing responsiveness to PD-1 therapy in lung cancer patients.Metagenomic analyses support these findings, suggesting that the relative abundance of A. muciniphila may serve as a biomarker for predicting ICB treatment efficacy and survival in NSCLC patients [9]. Moreover, Jin et al. found that responders to immunotherapy had a gut microbiota enriched with Gram-negative bacteria, which was associated with treatment outcomes.

Some microbiota-targeted interventions have also shown therapeutic potential. For example, ginseng polysaccharides (GP) combined with PD-1 inhibitors can improve treatment efficacy by reshaping the gut microbiota to enhance CD8⁺ T cell function [239, 240]. Additionally, microbial metabolites such as SCFAs, tryptophan derivatives, lysine, and niacin can modulate the effects of PD-1 blockade [192].

Antibiotic use has been shown to impair immune therapy responses. Potential mechanisms include reduced expression of key adhesion molecules in the gut, enhanced colonization by Enterocloster, and increased migration of Treg cells to tumor sites in the lungs, thereby weakening immune activation [169, 170]. Antibiotics also blunt the antitumor effects of CTLA-4 blockade, whereas beneficial bacteria like Bacteroides fragilis can enhance treatment by promoting Th1 responses [241].

Bifidobacterium is a genus with dual roles. Some studies indicate it enhances antitumor immunity by modulating CD47 expression, inducing dendritic cell maturation, and increasing CD8⁺ T cell infiltration. Its derived exosomes can also upregulate PD-L1 expression on tumor cells via the TLR4–NF-κB pathway, synergizing with anti-PD-1 therapy [242].

However, the effects of Bifidobacterium are context-dependent; certain strains may suppress PD-1 therapy efficacy under specific conditions [243–247]highlighting the need for cautious clinical evaluation.

The Clostridium butyricum MIYAIRI 588 strain (CBM588) has shown promise in enhancing the efficacy of immunochemotherapy in NSCLC patients while helping restore gut microbiota diversity [248–251].

In addition to the gut microbiota, the lung microbiome has also emerged as a key player in immunotherapy. In NSCLC patients with high PD-L1 expression and good treatment response, Firmicutes and Actinobacteria are more abundant. Some species, such as Moraxella, can promote PD-L1 expression and recruit Th17 cells, creating a pro-inflammatory environment. Conversely, Veillonella dispar correlates positively with high PD-L1 levels, while Haemophilus influenzae and Neisseria perflava are associated with low PD-L1 expression [252–254]. Certain bacteria influence treatment response by modulating metabolites or suppressing CD8⁺ T cell and M1 macrophage infiltration [255, 256].

The microbiome has also been linked to immune-related adverse events (irAEs), which often occur in microbe-rich areas like the skin and gastrointestinal tract, suggesting microbial involvement in their pathogenesis. Antibiotic use, particularly depending on timing and spectrum, is associated with increased irAE risk [257, 258]. Studies show that microbiota composition and functional pathways differ significantly in patients with irAEs, especially in those induced by ICIs [259, 260].

Moreover, microbiota changes are linked to extraintestinal irAEs affecting the lungs, liver, kidneys, and blood. For example, antibiotic use in lung cancer patients significantly increases the risk of multi-organ irAEs [258]. Some irAE-associated bacteria, such as Prevotella copri, Bacteroides dorei, Blautia glucerasea, and Streptococcus viridans, may encode autoimmune-mimicking peptides that trigger autoimmune responses against organs like the skin or thyroid [261].

Studies also suggest that ICB treatment itself can reshape the tumor-associated microbiome. After treatment, responders often exhibit reduced abundance of Bacteroidetes and Bifidobacterium, potentially influencing antigen presentation and HLA peptide generation [262, 263]. Additionally, changes in the oral microbiome and its metabolites have been linked to ICB responsiveness in NSCLC, though mechanistic insights remain limited [264].

In conclusion, the microbiome exhibits significant regulatory potential in cancer immunotherapy—capable of enhancing treatment response or contributing to toxicity. However, its effects are highly complex and individualized, posing challenges for safe and effective clinical translation. Future research should aim to elucidate microbial mechanisms and optimize microbiota-targeted interventions to enable more precise and effective cancer immunotherapies.

New strategies for microbiota modulation

Microbiota modulation, as an emerging strategy, is gradually demonstrating significant value in the prevention and treatment of lung cancer. This approach primarily involves interventions such as probiotics, prebiotics, postbiotics, synbiotics, FMT, and even antibiotics.

Probiotics and related interventions

Probiotics—live microorganisms that confer health benefits when administered in appropriate amounts—are at the forefront of this strategy [265]. Alongside them, prebiotics (non-digestible substrates that promote beneficial microbes), postbiotics (their metabolites), and synbiotics (combined formulations) have shown promise in modulating host immunity [266, 267]. These agents can regulate humoral, cellular, and innate immune responses, thereby enhancing anti-tumor defenses [268].

Accumulating evidence suggests that gut microbiota composition significantly influences lung cancer patients’ responses to immunotherapy, and may serve as a predictive biomarker [269]. Beneficial taxa such as Akkermansia muciniphila, Faecalibacterium prausnitzii, Bifidobacterium spp., and Bacteroides fragilis have been associated with improved ICI efficacy, possibly by lowering the host’s immune checkpoint and enhancing tumor antigen presentation. For example, oral administration of Clostridium butyricum MIYAIRI 588 has been associated with prolonged progression-free and overall survival in NSCLC patients receiving immunotherapy [270].

However, the direct evidence on how these microbial formulations impact lung cancer-associated microbiota remains limited. Issues such as gastrointestinal degradation, first-pass metabolism, and low bioavailability of oral formulations limit therapeutic potential. More prospective, mechanistic clinical studies are needed to validate their efficacy.

Fecal microbiota transplantation

FMT has attracted increasing attention as a means to restore gut microbial homeostasis. Preclinical studies have shown that transplanting feces from immunotherapy responders into germ-free or antibiotic-treated mice enhances CD8⁺ T cell activity and improves tumor control [271]. Early human trials further suggest that FMT may restore sensitivity to ICIs, possibly by reducing IL-8 expression, decreasing immunosuppressive cell populations, and reshaping gut-lung immune crosstalk [272].

To systematically evaluate the current evidence, we reviewed ongoing and completed clinical trials (see Table 4), which reveal a growing number of phase 1/2 studies investigating FMT, alone or combined with immunotherapy/chemotherapy, in advanced or treatment-refractory NSCLC (e.g., NCT04951583, NCT05286294, NCT05669846). These trials aim to improve objective response rate (ORR) and progression-free survival (PFS) by restoring beneficial microbial profiles. However, most are early-phase with limited sample sizes and heterogeneous designs. Importantly, few trials integrate longitudinal microbiome sequencing, limiting mechanistic insight and patient stratification.

Table 4.

Clinical trials on microbiota modulation in lung cancer patients

Item	NCT number	Phase	Enrollment	Status	Agents	Primary endpoint
Fecal Microbiota transplantation	NCT04951583	Phase 2	45	Active, not recruiting	FMT + ICI	ORR
	NCT05286294	Phase 2	20	Active, not recruiting	FMT	Safety; ORR
	NCT05502913	Phase 2	80	Recruiting	FMT + Chemotherapy + Immunotherapy	PFS
	NCT05669846	Phase 2	26	Recruiting	FMT + Pembrolizumab	ORR
	NCT06403111	Phase 2	62	Recruiting	FMT + Chemotherapy + Immunotherapy	PFS
	NCT04521075	Phase 1,2	42	Unknown status	FMT + Nivolumab	Safety; ORR
	NCT05008861	Phase 1	20	Unknown status	FMT + anti-PD-1/PD-L1 treatment	Safety
	NCT05286294	Phase 2	20	Active, not recruiting	FMT	Safety; ORR
	NCT03819296	Phase 1	800	Recruiting	FMT + prednisone + infliximab/vedolizumab	Difference in stool microbiome pattern; Safety
	NCT04924374	Not Applicable	25	Completed	FMT + anti-PD-1 treatment	Safety
Probiotics	NCT02771470	Phase 1	41	Completed	Probiotics	Composition of Microorganisms in stool
	NCT06943235	Phase 2	60	Not yet recruiting	Probiotics + Radiotherapy	PFS
	NCT05094167	Not Applicable	46	Unknown status	Probiotic	ORR
	NCT01465802	Phase 2	236	Completed	Probiotic + Dacomitinib + Alclometasone cream	Safety
	NCT04857697	Phase 1	6	Completed	Probiotic	Length and adherence of probiotics; Percentage of CD8+, CD4+, and T-reg cells; Cytokine counts
	NCT04699721	Phase 2	60	Active, not recruiting	Probiotic + Nivolumab + Paclitaxel + Carboplatin	pCR
	NCT06428422	Not Applicable	100	Recruiting	Probiotic	ORR; PFS; OS
	NCT03642548	Phase 3	180	Unknown status	Probiotic (Bifico) plus platinum-based doublet chemotherapy vs. placebo plus chemotherapy	ORR; PFS
Prebiotics	NCT05303493	Phase 1	45	Recruiting	Camu Camu Capsules + ICI	Safety
Antibiotic	NCT05777603	Phase 1	23	Recruiting	Aztreonam + Vancomycin + Pembrolizumab	Safety
	NCT03546829	Phase 1	10	Recruiting	Vancomycin + Radiotherapy	Th1 immune response; Safety
	NCT07001618	Phase 2	162	Not yet recruiting	MaaT033 capsule	ORR

Antibiotics and other strategies

Antibiotics can profoundly impact microbiota composition and function. Evidence suggests that systemic antibiotic use impairs ICI efficacy and increases the risk of irAEs [258]. In contrast, localized antibiotic delivery (e.g., pulmonary nebulization) may reduce Tregs, enhance T cell and NK cell function, and inhibit tumor progression in animal models [126, 273]. Nonetheless, indiscriminate antibiotic use may eliminate both pathogenic and beneficial taxa, with unpredictable consequences. Therefore, individualized antibiotic strategies and targeted antimicrobial development are crucial to minimize off-target microbiota disruption.

Systematic clinical evaluation and future directions

A systematic summary of clinical trials involving microbiota modulation in lung cancer reveals a field gaining momentum, yet still in early stages of clinical translation. Trials investigating probiotics (e.g., NCT02771470, NCT04699721), prebiotics (e.g., NCT05303493), FMT (e.g., NCT05502913, NCT04924374), and antibiotics (e.g., NCT05777603, NCT07001618) have shown encouraging early-phase results, particularly in the context of combination with ICIs.

Despite this, challenges persist. Many studies suffer from small sample sizes, non-standardized interventions (e.g., FMT donor variability, probiotic strain selection), and inconsistent outcome measures. Furthermore, most trials lack integrated microbiome sequencing or immune profiling, limiting mechanistic understanding. High-quality, randomized controlled trials with biomarker stratification, long-term endpoints (OS, PFS), and defined responder phenotypes are urgently needed to realize the clinical potential of microbiota-based interventions in lung cancer.

Abbreviations

AhR

Aryl hydrocarbon receptor

BALF

Bronchoalveolar lavage fluid

BMP2

Bone morphogenetic protein 2

cmDNA

Circulating microbial DNA

COPD

Chronic obstructive pulmonary disease

CTL

Cytotoxic T lymphocyte

CTX

Cyclophosphamide

DCs

Dendritic cells

EBV

Epstein–Barr virus

EMT

Epithelial–mesenchymal transition

FMT

Fecal microbiota transplantation

GALT

Gut-associated lymphoid tissue

GP

Ginseng polysaccharides

GPR

G protein-coupled receptors

HDACs

Histone deacetylases

HPV

Human papillomavirus

HTLV

Human T-lymphotropic virus

ICIs

Immune checkpoint inhibitors

irAEs

Immune-related adverse events

JCPyV

JC polyomavirus

LC

Lung cancer

LPS

Lipopolysaccharides

LUAD

Lung adenocarcinoma

LUSC

Lung squamous cell carcinoma

MCPyV

Merkel cell polyomavirus

MDSCs

Myeloid-derived suppressor cells

MMA

Methylmalonic acid

MRSA

Methicillin-resistant Staphylococcus aureus

NGS

Next-generation sequencing

NK

Natural killer

NSCLC

Non-small cell lung cancer

ORR

Objective response rate

OS

Overall survival

PAMPs

Pathogen-associated molecular patterns

PFS

Progression-free survival

PGN

Peptidoglycan

PPIs

Proton pump inhibitors

PRRs

Pattern recognition receptors

SAM

S-adenosylmethionine

sMPLC

Synchronous multiple primary lung cancers

SCFAs

Short-chain fatty acids

TB

Tuberculosis

TME

Tumor microenvironment

TLR

Toll-like receptors

Treg

Regulatory T cells

TTV

Torque teno virus

XHW

Xihuang Pill

Author contributions

C.Y: Responsible for the conceptualization and structural design of the review, leading the literature search and selection, drafting the initial manuscript, and conducting thorough manuscript editing.Y.C: Conducted data curation and analysis, provided key literature and informational support, and participated in drafting and revising the manuscript.Y.T, S.H, H.W, X.Z, Q.C: Responsible for organizing and validating data, providing additional research resources and technical support, and participating in the review and revision of the manuscript.W.S, S.H: Supervised the overall progress of the review, provided project management and funding support, and ensured the manuscript met publication standards.

Funding

This research was supported by the National Natural Science Foundation of China under Grant Number 62131009 and the Beijing Xisike Clinical Oncology Research Foundation(No. Y-2023AZMETQN-0066). The funding agencies played a role in the conceptualization and design of the review. They also contributed to the decision to publish and provided support during the preparation of the manuscript. We would like to acknowledge their contribution to this work.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethical approval and consent to participate

Not applicable.

Consent for publication

All authors read and approved the final version of the manuscript for publication.

Competing interests

The authors declare no competing interests.