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Abstract

Lymph nodes, brain, bone, and liver are recognized as the four most common metastatic sites for lung adenocarcinoma (LUAD). Metastasis to these locations exhibits some common features, such as immune suppression, and distinct tumor microenvironment (TME) heterogeneity involving differentiation of immune cells, impacting treatment efficacy and prognosis. Lymph node metastases are characterized by immune suppression with exhausted CD8+ T cells, expanded regulated T cell (Tregs), M2-polarized macrophages, and high programmed death ligand-1 (PD-L1) expression. Brain metastases display an “immune desert” phenotype due to blood–brain barrier constraints, reduced T-cell infiltration, and microglia-mediated immunosuppression. Bone metastases involve osteoclast activation, RANKL/OPG pathway dysregulation, and metabolic reprogramming, while liver metastases show Kupffer cell-driven PD-L1/ programmed death 1(PD-1) axis suppression and elevated Treg infiltration. Key biomarkers across all types of metastases include PD-L1, cytokine profiles, immune cell ratios, and metabolic markers. Therapeutic strategies focus on combination therapies such as immune checkpoint inhibitors (ICIs) with metabolic modulators, localized drug delivery, and biomarker-guided approaches. Challenges in this field encompass spatial heterogeneity, dynamic TME evolution, and clinical translation barriers. Future research directions highlight spatial transcriptomics, microbiome interactions, and organoid models to optimize personalized immunotherapy. This article aims to provide a comprehensive review of regarding TME alterations across these four main metastatic locations of LUAD. It will also discuss relevant TME biomarkers and their clinical significance on therapeutic response and prognosis. We expect this article to serve as a source of evidence and inspiration for the future development of treatment strategies based on LUAD TME.

Keywords

bone brain liver lung adenocarcinoma lymph node metastasis microenvironment

Introduction

When LUAD metastasizes to lymph nodes, brain, bone, liver, and other organs, the tumor microenvironment (TME) undergoes significant changes in heterogeneity that impact treatment response and prognosis. Different metastatic sites exhibit some common characteristics, including immune suppression, alterations in the quantity and differentiation of immune cells, as well as modifications in various cytokines, immune factors, and metabolic processes.¹ However, they also display distinct characteristic changes. For instance, lymph node metastases are marked by pronounced features of immune suppression characterized by reduced infiltration of CD8+ T cells, an increased ratio of regulatory T cells (Tregs), and elevated expression levels of programmed death ligand-1 (PD-L1). Tumor-associated macrophages (TAMs) tend to polarize toward the M2 phenotype; they secrete IL-10 and TGF-β to inhibit anti-tumor immunity. A low CD8+/FoxP3+ ratio indicates enhanced immune suppression.² The co-inhibitory molecule LAG-3 is highly expressed on exhausted T cells.³ By contrast, brain metastases exhibit “immune desert” characteristics with significantly diminished T-cell infiltration and low drug permeability due to the blood–brain barrier. Microglia facilitate tumor immune evasion through the CXCL12-CXCR4 axis. Elevated levels of IL-6 and vascular endothelial growth factor (VEGF) have been observed in cerebrospinal fluid. An increased proportion of programmed death 1 (PD-1)+ CD8+ T cells within cerebrospinal fluid serves as a predictor for sensitivity to PD-1 inhibitors.⁴ Furthermore, high expression levels of CXCL12 correlate positively with the progression rate of brain metastases (r = 0.62; p < 0.01).⁵ In cases involving bone metastases, osteoclast activation leads to osteolytic destruction while mesenchymal stem cells (MSCs) differentiate into cancer-associated fibroblasts (CAFs), which secrete IL-6 and CCL5 to recruit myeloid-derived suppressor cells (MDSCs).⁶ TRAP5b serves as a marker for bone metabolism; elevated levels (>7.0 U/L) are associated with resistance to zoledronic acid treatment.⁷ A CD38+ plasma cell infiltration ratio exceeding 10% indicates an enhanced response rate to immunotherapy (OR = 2.1).⁸ In the context of liver metastases, there is significant immune suppression characterized by Kupffer cells that inhibit T-cell function via the PD-L1/PD-1 axis. In addition, the Treg ratio in these cases is observed to be 2–3 times higher than that found in primary lesions. The activation of the HGF/c-MET pathway further promotes immune escape mechanisms. Notably, when the proportion of CTLA-4+ CD4+ T cells in the peripheral blood of patients with liver metastasis exceeds 15%, there is a notable extension in survival duration during ipilimumab treatment (mOS: 18.6 months vs 9.3 months).⁹ Furthermore, circulating alpha-fetoprotein (AFP) mRNA levels greater than 100 copies/mL signify a substantial burden of liver metastasis and are associated with resistance to sorafenib (HR=1.89).¹⁰ An infiltration density of CD163+ TAMs greater than 50 per high-power field correlates with early recurrence following resection for liver metastasis (RR = 3.4).¹¹ The considerable heterogeneity present within the TME across different metastatic sites significantly influences treatment strategy selection. A combined assessment involving PD-L1 expression, T-cell subsets, and cytokine profiles can enhance individualized treatment approaches. This article aims to provide a comprehensive review of alterations within the TME at various sites when LUAD metastasizes to lymph nodes, brain, bone, and liver; identify relevant markers linked to treatment outcomes and prognosis; elucidate their clinical significance; and offer reference points for future personalized therapies based on microenvironment-related immune indicators.

The concepts of hot tumor and cold tumor have been used to describe the immune status, including the microenvironment of the tumor.¹² Hot tumors refer to tumor types in which immune cells, especially cytotoxic T lymphocytes with anti-tumor activity, can effectively infiltrate and recognize tumor cells. Their characteristics include high T-cell infiltration, especially a large number of activated CD8+ T cells (cytotoxic T cells), and the presence of immune activation signals, such as the expression of interferon-γ signals, antigen-presenting molecules (such as MHC-I), and co-stimulatory molecules, an inflammatory microenvironment, such as the presence of pro-inflammatory cytokines (such as IFN-γ, TNF-α). Tumors typically exhibit a favorable response to immune checkpoint inhibitors (ICIs) due to the pre-existence and activation of tumor-infiltrating T cells. By blocking immune checkpoint pathways such as PD-1/PD-L1 and CTLA-4, the inhibitory signals that suppress T-cell activity are effectively alleviated, thereby enhancing the anti-tumor immune response. These tumors often display relatively high immunogenicity, which is attributed to the increased expression of neoantigens derived from tumor-specific mutations. This heightened antigenic profile facilitates more efficient recognition of tumor cells as “non-self” by the host immune system.^13–15

Cold tumors, often referred to as “immune-desert” tumors, are characterized by the absence or minimal infiltration of immune cells—particularly cytotoxic T cells—into the tumor microenvironment. Even when T cells are present, they frequently fail to effectively recognize or eliminate tumor cells due to multiple immunosuppressive mechanisms. Key features include low or undetectable levels of CD8+ T cells within tumor tissues and the establishment of an immunosuppressive tumor microenvironment enriched with Tregs, MDSCs, immunosuppressive cytokines (e.g., TGF-β, IL-10, VEGF), and inhibitory molecules. In addition, these tumors often lack essential immune activation signals: tumor cells may downregulate or completely lose MHC class I molecule expression, impairing antigen presentation to T cells; co-stimulatory signals required for full T-cell activation are frequently absent; and defects in the interferon-γ signaling pathway further compromise immune recognition and effector function. Structural barriers also contribute to immune exclusion, including dense fibrotic stroma resulting from activated CAFs and abnormal tumor vasculature, both of which hinder T-cell trafficking into the tumor core. As a consequence, cold tumors typically exhibit poor or no response to ICIs, primarily because there are insufficient pre-existing tumor-reactive T cells to be reactivated. This resistance is further underpinned by low tumor immunogenicity, driven by a low tumor mutation burden and consequently limited neoantigen production, reducing the likelihood that tumor cells will be recognized as “non-self” by the immune system.^13–15

Lymph node metastasis

Lymph node metastasis in LUAD is an important sign of tumor progression and poor prognosis for patients. Lymph nodes, as an important hub of the immune system, undergo complex immune remodeling and immune escape in the TME during the metastasis of LUAD, involving dynamic changes in immune cell subsets, cytokine networks, metabolic characteristics, and immune checkpoint molecules (Figure 1).

Figure 1.

Microenvironment of LUAD lymph node metastasis. The function of T lymphocytes is dysregulated, which is mainly manifested in the exhaustion of CD8+ T cells and the expansion of Tregs, characterized by high expression of inhibitory receptors such as PD-1, TIM-3, and LAG-3, and a decreased ability to secrete IFN-γγ and TNF-α. The proportion of FoxP3+ Tregs in metastatic foci significantly increases. They suppress the function of effector T cells by secreting IL-10, TGF-ββ, and other factors. TAMs mainly present an M2-like phenotype (CD163+, CD206+), promoting angiogenesis and immunosuppression by secreting IL-10, VEGF, and ARG1. MDSCs significantly expand in metastatic lymph nodes, especially PMN-MDSCs. The cytotoxic function of NK cells is impaired, as indicated by the downregulation of activation receptors such as NKG2D and DNAM-1, and the upregulation of inhibitory receptors (such as KIR). TLS may exist in lymph node metastases, where B cells can exert anti-tumor effects through antigen presentation and antibody secretion. However, some B-cell subsets (Bregs) may promote immunosuppression by secreting IL-35.

Reprogramming of immune cell subsets in lymph node metastasis

In lymph node metastases, the functionality of T lymphocytes is significantly dysregulated, leading to notable alterations in both their infiltration patterns and functional states. This phenomenon is primarily characterized by the exhaustion of CD8+ T cells alongside an expansion of Tregs.¹⁶ Although the number of CD8+ T cells within metastatic lymph nodes may increase, these cells exhibit an exhausted phenotype marked by elevated expression levels of inhibitory receptors such as PD-1, TIM-3, and LAG-3, coupled with a diminished capacity to secrete IFN-γ and TNF-α.¹⁷ Single-cell sequencing studies have indicated that these exhausted CD8+ T cells may enter an irreversible state due to epigenetic reprogramming mediated by the TOX transcription factor. Furthermore, there is a significant increase in the proportion of FoxP3+ Tregs within metastatic foci. These regulatory T cells suppress effector T-cell functions through the secretion of IL-10, TGF-β, and other immunosuppressive factors while fostering an immune-tolerant microenvironment. Research has demonstrated a positive correlation between the degree of Treg infiltration and the burden of lymph node metastasis.¹⁸ In addition to changes in T-cell populations, TAMs also undergo polarization shifts within metastatic lymph nodes. TAMs serve as crucial immune regulators in this microenvironment; however, they predominantly adopt an M2-like phenotype (CD163+, CD206+) during metastasis. In this context, TAMs promote angiogenesis and immunosuppression through their secretion of IL-10, VEGF, and arginase-1 (ARG1).¹⁹ Animal models have demonstrated that targeting the CSF-1/CSF-1R signaling pathway can reverse M2 polarization and inhibit lymph node metastasis.²⁰ Under physiological conditions, CSF-1 can promote the polarization of macrophages into the M2 phenotype, which plays a role in the later stages of tissue repair and maintaining homeostasis. In cancer, the upregulation of CSF-1 promotes the infiltration, survival, and metastasis of TAMs expressing CSF-1R, and blocking the CSF-1/CSF-1R signaling pathway can reduce immunosuppressive TAMs in tumors. Thus, CSF-1/CSF-1R inhibitors may be therapeutic targets for various malignant tumors and have broad application prospects in tumor immunotherapy.²⁰ Recent studies have identified unique metabolic characteristics in TAMs associated with lymph node metastases, such as enhanced fatty acid oxidation; these features may directly inhibit CD8+ T-cell activity through lipid presentation. Furthermore, MDSCs significantly expand in metastatic lymph nodes—particularly polymorphonuclear MDSCs (PMN-MDSCs)—which suppress T-cell proliferation by producing reactive oxygen species (ROS) and arginine metabolites.²¹ Clinical sample analyses indicate that the proportion of MDSCs is negatively correlated with survival rates among patients with lymph node metastasis.²² The cytotoxic function of NK cells in lymph node metastases is compromised, as evidenced by downregulation of activation receptors such as NKG2D and DNAM-1 alongside upregulation of inhibitory receptors like KIR.²³ Mechanistic studies reveal that tumor cells secrete soluble MICA/B to evade NK cell surveillance via a “decoy receptor” mechanism.²⁴ In addition, some research suggests that tertiary lymphoid structures (TLS) may be present within lymph node metastases, where B cells could exert anti-tumor effects through antigen presentation and antibody secretion. However, some B-cell subsets (such as regulatory B cells, Bregs) may promote immunosuppression by secreting IL-35.²⁵ The main components of TME in lymph node metastases are illustrated in Figure 1.

Abnormalities in the network regulation of cytokines and chemokines and dynamic expression of immune checkpoint molecules

First, the levels of immunosuppressive cytokines, including TGF-β, IL-10, and IL-6, are significantly altered. The concentration of TGF-β in metastatic lymph nodes is elevated, which promotes tumor invasion by inducing epithelial–mesenchymal transition (EMT) while suppressing the functions of CD8+ T cells and NK cells.²⁶ In addition, IL-10—secreted by Tregs and TAMs—inhibits the maturation of dendritic cells (DCs) and facilitates the polarization of M2 macrophages. Furthermore, IL-6 mediates immunosuppression through the STAT3 signaling pathway and enhances characteristics associated with tumor stem cells.²⁷ Second, chemokines play a crucial role in mediating immune cell recruitment. CCL18, secreted by M2 macrophages, attracts Tregs and MDSCs, thereby forming an immunosuppressive network. The CXCL12/CXCR4 axis further promotes the homing of tumor cells to lymph nodes while recruiting additional immunosuppressive cell populations.²⁸ Clinical studies suggest that the CCL5-CCR5 pathway may also contribute to alterations within the TME in lymph nodes; notably, CCR5 inhibitors have been shown to block infiltration by immunosuppressive cells in metastatic lymph nodes.²⁹ Third, the dynamic expression of immune checkpoint molecules also changes abnormally. Specifically, PD-L1 expression levels in lymph node metastases are significantly higher than those observed in primary tumors; this phenomenon may be linked to continuous activation of IFN-γ signaling pathways. Clinical investigations indicate that patients exhibiting high PD-L1 expression tend to have a greater response rate to ICIs; however, it is important to note that lymph node metastasis may diminish their efficacy.³⁰ This phenomenon occurs because lymph node metastasis disrupts the functionality of critical tumor-draining lymph nodes by establishing a highly immunosuppressive local microenvironment, characterized by an abundance of Tregs, MDSCs, exhausted T cells, and various inhibitory factors. This condition is frequently accompanied by a systemic immunosuppressive state, which diminishes the efficacy of ICIs. Even when overall PD-L1 expression in the tumor is elevated—typically assessed based on the primary lesion or specific biopsy site—the presence of lymph node metastasis signifies a more intricate immunosuppressive network and potential tumor heterogeneity.CTLA-4 is prominently expressed on the surface of Tregs, while LAG-3 interacts with MHC II molecules to inhibit dendritic cell (DC) function. TIM-3 shows high expression levels in exhausted T cells, with its ligand galectin-9 secreted by tumor cells, thereby creating a negative feedback loop. Furthermore, lymph node metastases exhibit distinct metabolic characteristics; tumor cells generate substantial amounts of lactic acid through the Warburg effect, which inhibits T-cell metabolic activity and fosters Treg expansion.³¹ The upregulation of arginase 1 (ARG1) and inducible nitric oxide synthase (iNOS) results in arginine depletion that directly impedes T-cell proliferation. In addition, free fatty acids released by tumor cells are absorbed by TAMs, promoting their polarization toward the M2 phenotype.³² Given that chemokines and cytokines can be detected in blood using established assays, alterations in their levels may reflect changes in immune status before, during, and after therapy. The integration of these assays could facilitate monitoring therapeutic responses effectively.

Key biomarkers and their clinical significance

First, regarding immune checkpoint molecules, high expression levels of PD-L1 are associated with a shorter survival period; however, it can also serve as a predictive marker for the efficacy of PD-1 inhibitors. The co-expression of LAG-3 and PD-1 indicates T-cell exhaustion, and their combined blockade has been shown to enhance therapeutic efficacy.³ Second, tumor-associated macrophage markers are similarly linked to clinical outcomes. An increase in the density of CD163/CD206 on M2-type TAMs correlates with an elevated risk of postoperative recurrence.³³ Targeting the SIRPα-CD47 axis may enhance macrophage phagocytosis, and its combination with chemotherapy is currently being explored in clinical trials.³³ Third, markers related to T-cell subsets also hold prognostic significance. Patients exhibiting a low CD8+/FOXP3+ ratio (<1.5) demonstrate a 40% reduction in their 5-year survival rate.³⁴ A higher abundance of TCF7+ stem-like T cells is associated with more durable responses to immunotherapy.³⁴ Fourthly, cytokines and chemokines play important roles in both therapy and prognosis. Serum levels of CCL18 show a positive correlation with lymph node metastasis burden (r = 0.65, p < 0.001).³⁵ Finally, metabolism-related markers carry prognostic implications as well. High expression levels of LDHA (lactate dehydrogenase A) correlate with reduced infiltration by CD8+ T cells and poorer prognosis.³⁶ In addition, CD73 (ectonucleotidase), which mediates adenosine generation, is currently under investigation in phase II trials³⁷ when used alongside immunotherapy. All biomarkers along with their clinical significance concerning lymph node metastasis in LUAD are summarized in Table 1.

Table 1.

Biomarkers and their clinical significance for LUAD lymph node metastasis.

Biomarker type	Biomarker name	Biomarker function	Clinical significance	References
Immune checkpoint molecules	PD-L1	Response and prognostic marker	High expression is associated with shorter survival; it serves as a predictive marker for PD-1 inhibitor efficacy.	3
	LAG-3 & PD-1 (co-expression)	Response marker	Indicates T-cell exhaustion; combined blockade improves therapeutic efficacy.	3
Tumor-Associated Macrophages	CD163/CD206 (M2-TAM)	Prognostic marker	Increased density is associated with a higher risk of postoperative recurrence.	33
	SIRPα-CD47 axis	Response marker	Targeting enhances macrophage phagocytosis; combination with chemotherapy shows potential in clinical trials.	33
T-cell Subsets	CD8+/FOXP3+ ratio	Prognostic marker	Low ratio (<1.5) associated with a 40% decrease in 5-year survival rate.	34
	TCF7+ stem-like T cells	Response marker	Higher abundance is associated with a more durable response to immunotherapy.	34
Cytokines/Chemokines	CCL18 (serum)	Prognostic marker	Levels positively correlated with lymph node metastasis burden (r = 0.65, p < 0.001).	35
Metabolism-Related Markers	LDHA	Prognostic marker	High expression is associated with reduced CD8+ T-cell infiltration and poor prognosis.	36
	CD73	Response marker	Mediates adenosine generation; its inhibitors, combined with immunotherapy, are currently in phase II trials.	37

CCL18, chemokine (C-C motif) ligand 18; CD163, cluster of differentiation 163; CD206, cluster of differentiation 206; CD73, cluster of differentiation 73; CD8+/FOXP3+, cluster of differentiation 8+/forkhead box protein P3+; LAG-3, lymphocyte activation gene-3; LDHA, lactate dehydrogenase; LUAD, lung adenocarcinoma; M2-TAM, M2-like tumor-associated macrophage; PD-1, programmed death-1; PD-L1, programmed cell death ligand 1; SIRPα-CD47, signal regulatory protein αalpha α-cluster of differentiation 47; TCF7+, transcription factor 7+.

Treatment strategies and directions, and challenges in translational research

Based on the preceding review, existing treatment strategies can be optimized through four key translational research directions. First, regarding ICIs, combining PD-1 inhibitors with anti-TGF-β antibodies or CD47 inhibitors may effectively counteract the immunosuppression associated with lymph node metastases.^38,39 Furthermore, utilizing nanoparticles for targeted delivery of ICIs to lymph nodes could enhance therapeutic efficacy while minimizing systemic toxicity.⁴⁰ Second, in targeting immunosuppressive cells, reprogramming strategies for TAMs should be considered. This includes the combination of CSF-1R inhibitors with CD40 agonists to promote M1 polarization.⁴¹ In addition, employing CD33-targeted antibodies or STAT3 inhibitors has been shown to reduce the accumulation of MDSCs.²⁷ Third, in terms of metabolic intervention, IDO1 inhibitors such as Epacadostat can reverse abnormal tryptophan metabolism and restore T cells.⁴² Moreover, LDHA inhibitors have demonstrated potential in enhancing anti-tumor immunity by lowering lactate levels.³⁷ Finally, within the realm of personalized immunotherapy, stratified treatment approaches can be developed based on the immune phenotypes observed in lymph node metastases—such as “immune desert,” “immune exclusion,” or “inflammatory.” The integration of neoantigen vaccines with ICIs may further amplify T-cell responses specific to lymph nodes. Challenges and future research directions include the following: first and foremost is addressing spatial heterogeneity; single-cell spatial transcriptomics can elucidate variations within the TME across different regions of lymph nodes—including cortex, paracortex, and medulla. Second is understanding dynamic evolutionary mechanisms; longitudinal studies are essential to clarify how the TME evolves coordinately between primary tumors and metastatic sites. Third, the influence of the microbiome: gut microbiota may modulate the immune status of lymph nodes via the “gut-lung axis,” with related mechanisms yet to be fully elucidated. Finally, a significant challenge in clinical translation remains: overcoming systemic immunosuppression induced by lymph node metastasis.

Brain metastases

The TME of brain metastases originating from lung cancer (Brain Metastasis TME BrTME) exhibits considerable heterogeneity and is shaped by various factors, including the blood–brain barrier, unique immune regulatory mechanisms within the central nervous system (CNS), and interactions between tumors and neural tissue. Recent advancements in technologies such as single-cell sequencing, spatial transcriptomics, and organoid models have illuminated the dynamic remodeling processes underlying BrTME (Figure 2).

Figure 2.

Microenvironment of LUAD brain metastasis. The blood–brain barrier (BBB) is composed of endothelial cells, pericytes, and astrocyte foot processes. LUAD cells secrete VEGF and MMP-9 to disrupt the integrity of the BBB, and T cells and monocytes can infiltrate the brain parenchyma. CD8+ T cells in brain metastases exhibit a functionally exhausted phenotype (high expression of PD-1 and TIM-3). FoxP3+ regulatory T cells suppress anti-tumor immunity by secreting IL-10 and TGF-β. TAMs are mainly of the M2 type, promoting angiogenesis and immune escape; microglia support tumor growth by releasing IL-6 and CCL2. Activated astrocytes secrete CXCL10 to recruit (Tregs. In brain metastases, TAMs mainly present a CD163+ M2 phenotype, mediating immune escape through PD-L1 expression and adenosine metabolism. CD8+ T-cell infiltration is less and functionally exhausted (PD-1+, TIM-3+), while the proportion of Tregs (FOXP3+) significantly increases, suppressing anti-tumor immunity. PMN-MDSCs inhibit T-cell proliferation through ROS and ARG1, and their number is positively correlated with the burden of brain metastases.

Composition and characteristics of the TME in brain metastases

The TME in brain metastases is primarily composed of three components: the immunomodulatory role of the blood–brain barrier (BBB), the reprogramming of resident immune cells, and the dynamic alterations of infiltrating immune cells. First, the BBB consists of endothelial cells, pericytes, and astrocytic end-feet, which meticulously regulate the entry of immune cells and molecules into the brain parenchyma. LUAD cells secrete factors such as VEGF and MMP-9 that compromise BBB integrity, thereby facilitating metastatic foci formation.⁴³ Following disruption of the BBB, peripheral immune cells—such as T cells and monocytes—can infiltrate brain tissue; however, their functionality is frequently inhibited by local immunosuppressive factors. Although this breach allows for some degree of immune cell infiltration, it is typically characterized by T-cell exhaustion, an accumulation of inhibitory immune cell types, and elevated expression levels of immune checkpoint molecules.⁴⁴ The infiltration level of CD8+ T cells within brain metastases tends to be lower than that observed in primary tumors; these T cells often exhibit a functionally exhausted phenotype marked by high expression levels of PD-1 and TIM-3.⁴⁵ FoxP3+ Tregs suppress anti-tumor immunity through secretion of IL-10 and TGF-β; their density has been shown to correlate positively with disease progression in cases involving brain metastases.⁴⁵ TAMs are predominantly classified as M2-type macrophages that promote angiogenesis and facilitate immune evasion; additionally, microglia contribute to tumor growth via release mechanisms involving IL-6 and CCL2.⁴⁶ Second, the reprogramming of resident immune cells represents a critical event in the context of brain metastases. Microglia, as the primary immune cells within the CNS, initially clear tumor cells through phagocytosis during the early stages of brain metastasis. However, they subsequently transform into a tumor-promoting phenotype (M2 type), characterized by the secretion of IL-10, TGF-β, and arginase-1 (ARG1), which inhibits T-cell activity.⁴⁷ Activated astrocytes convey pro-survival signals to tumor cells via gap junctions and secrete CXCL10 to recruit Tregs.⁴⁸ Third, infiltrating immune cells experience dynamic changes. In brain metastases, TAMs primarily derive from peripheral monocytes and exhibit a CD163+ M2 phenotype. These TAMs facilitate immune evasion through PD-L1 expression and adenosine metabolism.⁴⁹ CD8+ T cells show reduced infiltration and are functionally exhausted, marked by PD-1+ and TIM-3+ expression; conversely, there is a significant increase in the proportion of Tregs (FOXP3+), which suppresses anti-tumor immunity.⁵⁰ Polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) inhibit T-cell proliferation via ROS and ARG1; their numbers correlate positively with the burden of brain metastases.⁵¹ The principal components of the TME in brain metastases are illustrated in Figure 2.

Alterations in cytokines, immune checkpoint molecules, metabolism, and signaling pathways

First, in the context of brain metastases, the chemokine network exerts significant immunosuppressive effects. The CXCL12/CXCR4 axis mediates the directional migration of tumor cells into the brain parenchyma while also recruiting Tregs.⁴ In addition, CCL2/CCR2 facilitates the differentiation of monocytes into M2-type TAMs, with clinical studies indicating that CCR2 antagonists can reduce brain metastases.⁵² Second, immune checkpoint molecules are frequently overexpressed in this setting. Approximately 30% to 50% of brain metastases originating from LUAD express PD-L1 at levels higher than those observed in primary tumors; this phenomenon may be attributed to continuous stimulation by interferon-gamma (IFN-γ).³⁸ The combination of PD-1 inhibitors with radiotherapy has been shown to prolong survival for certain patients. Furthermore, TIM-3 is highly expressed on exhausted T cells, and its ligand Galectin-9 is secreted by astrocytes. Inhibition of TIM-3 can restore T-cell cytotoxicity.⁴⁵ Tumor cells also exhibit high expression levels of CD47, which transmits a “do not eat me” signal through binding to SIRPα on macrophages; consequently, CD47 antibodies have been demonstrated to enhance phagocytosis.⁵³ Third, metabolic remodeling plays a crucial role in immune escape mechanisms. Indoleamine 2,3-dioxygenase (IDO1) is markedly upregulated in brain metastases and catalyzes the conversion of tryptophan into kynurenine—this process inhibits T-cell function while promoting Treg activation.⁵⁴ Tumor cells utilize the Warburg effect to produce lactic acid, which activates GPR81 receptors; this action induces M2 macrophage polarization and suppresses glycolysis within CD8+ T cells.⁵⁵ Moreover, lipid metabolism undergoes reprogramming: free fatty acids present in cerebrospinal fluid are absorbed by TAMs and promote an immunosuppressive phenotype via PPARγ signaling pathways.⁵⁶ Finally, regarding alterations in signaling pathways, TGF-β levels are elevated in brain metastases, which induce EMT in tumor cells and promote M2 polarization of microglia. Inhibition of TGF-β can enhance the infiltration of CD8+ T cells.⁵⁷ Tumor cells secrete IL-6 to activate the STAT3 pathway, thereby promoting the expansion of immunosuppressive cell populations such as MDSCs and Tregs, a process that is associated with resistance to radiotherapy.⁵⁸ It appears that assays measuring cytokines, metabolites, and key proteins may be feasible using cerebrospinal fluid to monitor the status of brain metastasis, particularly for patients undergoing therapies targeting these metastatic lesions.

Key immune biomarkers and their correlation with clinical efficacy and prognosis

First, PD-L1 facilitates immune evasion by binding to PD-1 on T cells. Patients with brain metastases exhibiting high PD-L1 expression (TPS ⩾ 50%) demonstrate a higher response rate to PD-1 inhibitors, such as pembrolizumab (ORR 30%-40%). However, the overall response rate remains lower compared to that observed in primary tumor treatments. The presence of PD-L1 positivity is associated with prolonged intracranial progression-free survival (iPFS), although it should be evaluated in conjunction with T-cell infiltration status.⁵⁹ Second, CD8+ T cells serve as the principal effector cells in anti-tumor immunity. Patients displaying high levels of CD8+ T-cell infiltration within brain metastases experience significantly improved overall survival (OS) (HR = 0.62, 95% CI 0.48–0.79). The co-expression of CD8+ T cells and PD-L1 indicates a more favorable response to immunotherapy.⁶⁰ Third, tumor-infiltrating lymphocytes (TILs) encompass CD4+, CD8+ T cells, and NK cells. A high density of TILs correlates with the synergistic effects of radiotherapy combined with immunotherapy and can enhance local control rates.⁶¹ Fourth, tumor-associated macrophage markers include CD68 and CD163, where CD68 identifies total macrophages while CD163 specifically marks M2-type TAMs. Elevated infiltration of CD163+ TAMs is linked to poor prognosis (OS HR = 1.8, p = 0.003).⁶² Finally, emerging biomarkers comprise exosomes and non-coding RNA molecules. The level of circulating exosomal PD-L1 may reflect the burden of brain metastasis and is associated with resistance to immunotherapy.⁶³ In addition, miR-21 and miR-155 play roles in regulating TAM polarization and T-cell function; notably, high expression levels of miR-21 are indicative of poor prognosis.⁶⁴ In the context of predicting the efficacy and prognosis of immunotherapy, patients exhibiting a high tumor mutational burden (TMB) and PD-L1 positivity demonstrate the most favorable response to ICIs, with an objective response rate (ORR) of 45%.⁶⁵ The upregulation of interferon-gamma (IFN-γ)-related gene expression is indicative of a better therapeutic response to immunotherapy. A scoring model that integrates PD-L1 expression, CD8+ T cell density, and the ratio of TAMs—such as Immunoscore-Brain—can significantly differentiate patient survival stratification, yielding a p-value <0.001).⁶⁶ Furthermore, radiotherapy-induced DNA damage enhances antigen presentation via the STING pathway and correlates with increased infiltration of CD8+ T cells.⁶⁷ In addition, dual inhibition targeting LAG-3 and TIM-3 can effectively reverse T-cell exhaustion following radiotherapy.⁴⁵ All biomarkers along with their clinical significance pertaining to LUAD brain metastasis are summarized in Table 2.

Table 2.

Biomarkers and their clinical significance for LUAD brain metastasis.

Biomarker type	Biomarker name	Biomarker function	Clinical significance	References
Immune checkpoint molecules	PD-L1	Response and prognostic marker	High expression (TPS ⩾ 50%) in brain metastases associates with:	59
			• Higher response rate to PD-1 inhibitors (ORR 30%–40%)
			• Longer intracranial progression-free survival (iPFS)
			• Requires combined evaluation with T cell infiltration
	Exosomal PD-L1	Response marker	Circulating levels:	63
			• Reflect brain metastasis burden
			• Associated with immunotherapy resistance
	High TMB + PD-L1+	Response marker	Predicts best response to ICIs	65
T-cell subsets	CD8+ T cells	Response and prognostic marker	High infiltration in brain metastases:	60
			• Significantly prolonged overall survival
			• Co-expression with PD-L1 indicates better response to immunotherapy
	TILs	Response marker	High density (includes CD4+, CD8+ T cells, NK cells):	61
			• Associates with synergistic effect of radiotherapy + immunotherapy
			• Improves local control rate
	Immunoscore-Brain	Prognostic marker	Composite model significantly distinguishes patient survival stratification (p < 0.001)	66
	(PD-L1, CD8+, TAMs ratio)
Tumor-associated macrophages	CD68+ TAMs	Prognostic marker	Marker for total macrophages (prognostic role not specified in text)	62
	CD163+ TAMs	Prognostic marker	High infiltration (M2-type TAMs):	62
			• Associated with poor prognosis
Cytokines/Chemokines/other molecules	IFN-γ-related genes	Response marker	Upregulation predicts better response to immunotherapy	65
	LAG-3 / TIM-3	Response marker	Dual immune checkpoint inhibition reverses T cell exhaustion post-radiotherapy	45
	STING pathway	Response marker	Radiotherapy-induced DNA damage enhances antigen presentation via this pathway; associated with increased CD8+ T cell infiltration	67
	miR-21	Prognostic marker	High expression:	64
			• Indicates poor prognosis
			• Regulates TAMs polarization and T cell function
	miR-155	Prognostic marker	Regulates TAMs polarization and T cell function (prognostic role not specified)	64

CD163, cluster of differentiation 163; CD68, cluster of differentiation 68; CD8+, cluster of differentiation 8+; DNA, deoxyribonucleic acid; ICI, immune checkpoint inhibitor; LAG-3, lymphocyte activation gene-3; LUAD, lung adenocarcinoma; miR, micro RNA; ORR, objective response rate; PD-1, programmed death-1; PD-L1, programmed cell death ligand 1; STING, stimulator of interferon genes; TAM, tumor-associated macrophage; TMB, tumor mutational burden; TPS, tumor proportion score.

Therapeutic strategies, clinical challenges, and future research directions

First, regarding ICIs, the PD-1/PD-L1 inhibitor pembrolizumab demonstrates an objective response rate (ORR) of 29% in patients with PD-L1-positive brain metastases; however, it is important to note the associated risk of immune-related encephalitis.⁶⁸ The combination of CTLA-4 inhibitor ipilimumab with a PD-1 inhibitor has been shown to enhance efficacy but also increases toxicity.⁶⁹ Second, a phase II trial investigating the IDO1 inhibitor epacadostat in conjunction with pembrolizumab revealed limited efficacy, potentially due to the activation of compensatory tryptophan metabolic pathways.⁷⁰ The CSF-1R inhibitor PLX3397 can effectively reduce M2-type TAMs, and preclinical models indicate a synergistic effect when combined with radiotherapy.⁷¹ Third, local treatment strategies and drug delivery methods may contribute to improved therapeutic outcomes. Focused ultrasound techniques that temporarily disrupt the BBB can enhance the delivery efficiency of chemotherapy agents and antibody therapies into the brain.⁷² In addition, drug-loaded nanoparticles within nanoparticle delivery systems have shown promise in targeting brain metastases while minimizing systemic toxicity.⁷² Finally, personalized neoantigen vaccines represent an innovative treatment option. These vaccines are designed based on TMB and have demonstrated potential benefits by activating T-cell responses within the brain during early clinical trials.⁷³ Future research directions primarily encompass several key areas. First, spatial transcriptomics technology could be employed to elucidate immune differences between the core and peripheral regions of brain metastases. Second, investigations into microbiome interventions may reveal how gut microbiota regulates brain immunological microenvironments through the gut–brain axis.⁷⁴ Third, organoid models derived from patient samples can be developed to simulate tumor microenvironments associated with brain metastasis. Finally, regarding the development of biomarkers, predictive models for efficacy can be established based on circulating tumor DNA (ctDNA) and the immune repertoire found in cerebrospinal fluid.

Bone metastasis

Bone metastasis represents a significant cause of mortality in patients with advanced lung cancer, exhibiting a high incidence rate of approximately 30%–40%. This condition profoundly impacts both the quality of life and prognosis for affected individuals. The TME associated with bone metastases is highly heterogeneous, comprising tumor cells, osteoblasts, osteoclasts, mesenchymal cells, and immune cells. Recent studies have indicated that the immunosuppressive characteristics of the TME are critical driving factors in the progression of bone metastasis in LUAD. Furthermore, dynamic alterations within this environment are closely linked to mechanisms of immune evasion, disruptions in bone remodeling processes, and resistance to treatment (Figure 3).

Figure 3.

Microenvironment of LUAD bone metastasis. The TME of bone metastases from LUAD exhibits a significant immunosuppressive state, characterized by the enrichment of Tregs, TAMs, and MDSCs. In LUAD bone metastases, CD8⁺ T-cell infiltration is reduced, and they present an exhausted phenotype (PD-1⁺, TIM-3⁺, LAG-3⁺), while the proportion of Tregs significantly increases. The excessive secretion of TGF-β and IL-10 in bone metastases promotes the expansion of Tregs. The clonal diversity of T cells in bone metastases is significantly reduced, indicating restricted immune responses. Macrophage polarization is imbalanced. TAMs are the main components of the TME, and the proportion of M2-type macrophages significantly increases, promoting osteoclast differentiation by secreting IL-6 and CCL2, while inhibiting the activity of CD8⁺ T cells. miR-21 carried by tumor cell exosomes can induce macrophage polarization to M2 type through TLR7/8 signaling. MDSCs are significantly enriched and inhibit T-cell proliferation through Arg-1 and iNOS-mediated metabolic reprogramming.

Functional remodeling of immune cell subsets

The TME of bone metastases is characterized by a pronounced immunosuppressive state, primarily marked by the accumulation of Tregs, TAMs, and MDSCs. First, T-cell exhaustion and functional impairment are observed in LUAD bone metastases. In the bone metastatic lesions associated with LUAD, there is a notable reduction in CD8⁺ T-cell infiltration, which exhibits an exhausted phenotype characterized by PD-1⁺, TIM-3⁺, and LAG-3⁺ expression. Concurrently, the proportion of Tregs significantly increases while the secretion capacity for granzyme B (GZMB) and IFN-γ diminishes.⁷⁵ Studies have demonstrated that excessive secretion of TGF-β and IL-10 within these bone metastases promotes the expansion of Tregs via activation of the STAT3 pathway while concurrently inhibiting effector T-cell function.⁷⁶ In addition, RANKL released from the bone matrix can induce apoptosis in T cells through activation of the NF-κB signaling pathway.⁷⁷ Single-cell sequencing studies have revealed a significant reduction in clonal diversity among T cells present in bone metastases, indicating restricted immune responses.⁷⁸ Second, an imbalance in macrophage polarization is evident within the tumor microenvironment. TAMs represent a dominant immune cell population in this context, and bone metastases exhibit a marked skewing toward M2-polarized macrophages. These M2-type TAMs promote osteoclast differentiation through the secretion of IL-6 and CCL2, while concurrently suppressing the effector functions of CD8⁺ T cells, thereby contributing to both immunosuppression and tumor-induced bone destruction.⁷⁹ Furthermore, research has demonstrated that miR-21 carried by tumor cell exosomes can induce macrophage polarization toward the M2 phenotype via TLR7/8 signaling.⁸⁰ Third, MDSCs are expanded. MDSCs are markedly enriched in both peripheral blood and bone marrow of patients with LUAD exhibiting bone metastases, where they inhibit T-cell proliferation through metabolic reprogramming mediated by arginase-1 (Arg-1) and inducible nitric oxide synthase (iNOS). Animal models have indicated that blocking CXCR2 can impede the recruitment of MDSCs to bone metastases.⁸¹ The primary components of the TME in bone metastases are illustrated in Figure 3.

Immunosuppressive molecular network

An immunosuppressive molecular network is established in the bone metastases of LUAD. First, in the context of chemokine and cytokine signaling, tumor-derived CCL2 plays a pivotal role by recruiting CCR2⁺ monocytes to bone metastatic sites, which subsequently differentiate into osteoclasts and contribute to both bone resorption and the establishment of an immunosuppressive microenvironment. The CXCL12/CXCR4 axis mediates the homing of tumor cells to the bone marrow while activating the PI3K/AKT pathway, which aids in immune evasion.⁸² Second, concerning immune checkpoint molecules, the expression level of PD-L1 in bone metastases is significantly elevated compared to that in primary tumors; this upregulation is driven by hypoxia-inducible factor HIF-1α and epigenetic modifications such as histone deacetylation mediated by HDAC3).⁸³ In addition, co-expression of novel immune checkpoints like LAG-3 and TIGIT on T cells indicates a need for combined blocking strategies. Third, metabolic reprogramming occurs within LUAD bone metastases. The accumulation of lactate and adenosine within the TME inhibits NK cell function while promoting T regulatory cell differentiation, thus exacerbating immunosuppression.⁸⁴ Studies have demonstrated that the inhibition of lactate dehydrogenase (LDHA) can effectively reverse T-cell exhaustion. The elevated levels of lactate in the bone metastasis microenvironment, resulting from tumor cell glycolysis, promote macrophage polarization toward the M2 phenotype and suppress T-cell functionality.⁸⁵ Furthermore, the hypoxic conditions within this environment enhance PD-L1 expression via HIF-1α, thereby exacerbating immunosuppression.⁸⁶ In addition, TGF-β facilitates EMT in tumor cells and promotes osteoclast differentiation while concurrently inhibiting natural killer (NK) cell activity. The IL-6/STAT3 signaling pathway activates JAK2/STAT3 signaling in tumor cells and upregulates the expression of the anti-apoptotic protein Bcl-2.⁸⁷ Characterizing the immune molecular network through biopsy analysis of bone metastases may provide valuable insights into immune status and aid in developing personalized therapeutic strategies targeting bone metastasis.

Key immune markers and their clinical significance

First, the expression of PD-L1 in bone metastases is often lower than that observed in primary tumors, which may be attributed to TGF-β-mediated immune evasion. Patients exhibiting high levels of PD-L1 expression tend to have a higher response rate to ICIs. However, patients with bone metastases characterized by low PD-L1 expression and an immunosuppressive microenvironment demonstrate limited efficacy when treated with ICIs alone.⁸⁸ Second, the density of tumor-infiltrating CD8+ T cells shows a positive correlation with progression-free survival (PFS) among patients with bone metastasis; conversely, elevated levels of exhaustion phenotype markers such as TIM-3 indicate diminished therapeutic effectiveness.⁸⁹ Third, the proportion of CD163+ TAMs correlates positively with the burden of bone metastasis and serves as an independent prognostic factor (HR = 1.72, 95% CI 1.21–2.45).⁹⁰ In addition, RANKL (receptor activator of nuclear factor κB ligand), secreted by tumor cells, facilitates osteoclast differentiation. An increased RANKL/OPG (osteoprotegerin) ratio signifies a heightened risk for developing bone metastasis (HR = 2.1) and is associated with resistance to zoledronic acid treatment.⁹¹ Finally, elevated serum levels of IL-6 and IL-8 are linked to the progression of bone metastasis, demonstrating an area under the curve (AUC) value of 0.82 for predictive accuracy.⁹² Furthermore, PIK3CA mutations detected in circulating tumor DNA are associated with resistance to EGFR-TKI therapy in patients suffering from bone metastases.⁹³ A comprehensive summary of all biomarkers along with their clinical significance pertaining to LUAD-related bone metastasis is found in Table 3.

Table 3.

Biomarkers and their clinical significance for LUAD bone metastasis.

Biomarker type	Biomarker name	Biomarker function	Clinical significance	References
Immune checkpoint molecules	PD-L1 expression	Response and prognostic marker	Lower in bone metastases vs primary tumors (TGF-β-mediated immune escape). High expression correlates with better ICI response; low expression in an immunosuppressive microenvironment limits ICI efficacy.	88
T-cell Subsets	Tumor-infiltrating CD8+ T cells	Response and prognostic marker	Density positively correlates with PFS in bone metastasis patients. High exhaustion markers (e.g., TIM-3) indicate reduced efficacy.	89
Tumor-associated macrophages	CD163+ TAMs proportion	Response and prognostic marker	Positively correlated with bone metastasis burden; an independent prognostic factor	90
Cytokines/Chemokines	Serum IL-6 and IL-8 levels	Prognostic marker	Elevated levels are associated with bone metastasis progression	92
Others	RANKL/OPG ratio	Response and prognostic marker	An elevated ratio indicates increased bone metastasis risk and resistance to zoledronic acid. RANKL promotes osteoclast differentiation.	91

CD163, cluster of differentiation 163; CD8, cluster of differentiation 8; ICI, immune checkpoint inhibitor; IL, interleukin; OPG, osteoprotegerin; PD-L1, programmed cell death ligand 1; PFS, progression-free survival; RANKL, receptor activator of nuclear factor KB ligand; TAM, tumor-associated macrophage; TGF-B, transforming growth factor-β.

Therapeutic strategies targeting the TME and future prospects

First, regarding ICIs, clinical data indicate that the response rate to monotherapy with anti-PD-1/PD-L1 agents in patients with bone metastases is less than 15%. This low efficacy may be attributed to the highly immunosuppressive characteristics of the TME.⁸⁸ Combination therapy regimens, such as PD-1 inhibitors combined with zoledronic acid, have demonstrated a synergistic effect in inhibiting osteoclast activity while enhancing T-cell infiltration. Furthermore, the combination of anti-PD-1 antibodies and RANKL inhibitors (e.g., Denosumab) can effectively inhibit bone destruction and bolster anti-tumor immunity.⁹⁴ In addition, TGF-β inhibitors like Galunisertib have shown potential in reversing Treg-mediated immunosuppression.⁸⁸ Second, CAR-T cells targeting bone metastasis-specific antigens—such as PSMA or CD44v6—have exhibited promising anti-tumor activity in murine models; however, they must overcome physical barriers presented by the TME, including dense bone matrix structures.⁹⁵ Moreover, CAR-T cells directed against tumor-associated antigens like MUC1 have significantly inhibited bone metastases in preclinical studies.⁹⁵ Third, IDO1 inhibitors such as Epacadostat and A2AR antagonists like CPI-444 are capable of reversing immunosuppression observed in preclinical models and are currently undergoing phase II clinical trials. LDHA inhibitors restore T-cell functionality by reducing lactate concentrations within the microenvironment.⁸⁵

Liver metastasis

Liver metastasis from LUAD accounts for 15% to 20% of cases.⁹⁶ It is one of the primary causes of mortality in patients with advanced lung cancer, characterized by a notably poor prognosis. The 5-year survival rate for patients with liver metastases is less than 5%, and the efficacy of traditional chemotherapy and targeted therapies remains limited.⁹⁶ As an immune-privileged organ, the liver’s unique TME plays a crucial role in the process of tumor metastasis. Recent studies have identified that this distinctive TME becomes a significant driving factor for liver metastasis in LUAD by suppressing anti-tumor immune responses and facilitating immune escape.⁹⁷ Research into the TME has unveiled specific immunosuppressive characteristics associated with liver metastases, which not only influence disease progression but are also closely linked to resistance against immunotherapy. Understanding the composition and function of this microenvironment holds great significance for developing novel immunotherapeutic strategies (Figure 4).

Figure 4.

Microenvironment of LUAD liver metastasis. In liver metastases, the infiltration of CD8+ T cells is reduced, while the proportion of Tregs is significantly increased, which inhibits the function of effector T cells. The infiltration level of FoxP3+ Tregs in liver metastases is significantly higher than that in primary tumors, and they suppress the function of effector T cells by secreting IL-10 and TGF-β. Kupffer cells and TAMs induce an immunosuppressive microenvironment by secreting cytokines such as IL-10 and TGF-β. The proportion of CD163+ or CD206+ M2-type TAMs increases, and they secrete IL-6 and CCL18 to promote angiogenesis and immune escape. MDSCs inhibit T-cell activity through ARG1 and NOS2. NK cells are exhausted due to the upregulation of PD-1 expression. The CXCL12/CXCR4 axis drives tumor cells to the liver. HSCs secrete FN1 and collagen to build a fibrotic matrix that promotes metastasis. The expression of PD-L1, CTLA-4, TIM-3, and LAG-3 is significantly enhanced in liver metastases. HIF-1α drives PD-L1 expression and promotes lactate accumulation, inhibiting T-cell metabolic activity.

Composition of the liver TME and its changes in the TME of lung cancer liver metastases

The liver TME comprises innate immune cells, adaptive immune cells, stromal cells, and extracellular matrix components, exhibiting distinct characteristics of immune tolerance. The primary liver immune cell types include T cells, macrophages, MDSCs, and NK cells. In cases of LUAD with liver metastases, the TME demonstrates a pronounced immunosuppressive state. First, there is a notable reduction in CD8+ T-cell infiltration within liver metastases; conversely, the proportion of Tregs significantly increases, thereby inhibiting effector T-cell function.⁸⁹ The infiltration levels of FoxP3+ Tregs are markedly higher in liver metastases compared to those observed in primary tumors. These Tregs exert their inhibitory effects on effector T-cell activity through the secretion of IL-10 and transforming growth factor-beta (TGF-β). A study involving 45 patients with LUAD liver metastases revealed that the density of Tregs negatively correlates with OS outcomes (HR = 2.1; 95% CI: 1.3–3.4).⁸⁹ Second, resident Kupffer cells and TAMs contribute to an immunosuppressive microenvironment by secreting cytokines such as IL-10 and TGF-β that promote immunosuppression.⁹⁸ Within liver metastases, there is an increased proportion of CD163+ or CD206+ M2-type TAMs; these macrophages secrete IL-6 and CCL18, which facilitate angiogenesis and enable immune evasion. High levels of M2 TAM infiltration correlate with shorter OS durations (p = 0.008) (11). Third, MDSCs proliferate both in peripheral blood and within the livers of patients suffering from metastatic disease while inhibiting T-cell activity via mechanisms involving arginase-1 (ARG1) and nitric oxide synthase 2 (NOS2) pathways.⁹⁹ Finally, although NK cells are abundant in the liver environment, their functionality is compromised due to elevated expression levels of PD-1 associated with metastatic conditions.¹⁰⁰

In addition to the aforementioned alterations in immune cell populations, there have also been significant changes in cytokines, immune checkpoint molecules, and the metabolic microenvironment. Cytokines and chemokines play a crucial role in liver metastasis. Transforming growth factor-beta (TGF-β), interleukin-6 (IL-6), and interleukin-10 (IL-10) contribute to the establishment of an immunosuppressive microenvironment. The CXCL12/CXCR4 axis facilitates the homing of tumor cells to the liver.¹⁰¹ Hepatic stellate cells (HSCs) secrete fibronectin (FN1) and collagen, thereby constructing a fibrotic matrix that promotes metastatic spread.¹⁰² The expression levels of programmed death-ligand 1 (PD-L1), cytotoxic T lymphocyte-associated protein 4 (CTLA-4), T-cell immunoglobulin mucin receptor 3 (TIM-3), and lymphocyte activation gene 3 (LAG-3) are significantly elevated in liver metastases.¹⁰³ Liver metastases exhibit a hypoxic and acidic microenvironment. Hypoxia-inducible factor 1-alpha (HIF-1α) drives PD-L1 expression while promoting lactate accumulation, which inhibits T-cell metabolic activity.¹⁰⁴ The primary components of the TME associated with liver metastases are illustrated in Figure 4. Assessing immune cell constituents as well as cytokine and chemokine profiles is instrumental for understanding the microenvironment of liver metastasis; this knowledge may inform therapeutic strategies, particularly regarding the potential for cellular therapies.

Key biomarkers and their clinical significance

First, the expression level of PD-L1 serves as a crucial biomarker. Although PD-L1 expression in liver metastases is typically lower than that observed in primary tumors (with positive rates approximately 15% vs 30%), its expression still holds prognostic value. A meta-analysis indicated that the ORR for patients with liver metastasis exhibiting PD-L1 positivity (TPS ⩾ 1%) following immunotherapy was 24%, significantly higher than the 8% observed in negative patients (p = 0.02).¹⁰⁵ However, the predictive efficacy of PD-L1 as a standalone biomarker is limited and should be considered alongside other indicators. Second, TMB in liver metastases generally presents at lower levels compared to primary tumors (median TMB of 5.2 vs 7.1 mut/Mb). Nevertheless, high TMB (⩾10 mut/Mb) remains associated with extended PFS, demonstrating a hazard ratio of HR = 0.52 and p = 0.03).¹⁰⁶ The integration of TMB and PD-L1 can enhance predictive accuracy. Third, the profile of circulating immune cells in peripheral blood represents another significant biomarker. In patients with liver metastasis, there is an increased proportion of CTLA-4+ CD4+ T cells and PD-1+ CD8+ T cells within peripheral blood; dynamic monitoring reveals that a reduction in these cell populations post-treatment correlates with prolonged PFS (p = 0.01).⁸⁹ Finally, characteristics related to the intestinal microbiome have emerged as newly identified biomarkers over recent years. Recent studies indicate a decrease in Akkermansia muciniphila abundance among liver metastasis patients’ intestines while observing an increase in Bacteroides fragilis; notably, this latter bacterium has been linked to resistance against anti-PD-1 treatment.¹⁰⁷ All biomarkers along with their clinical significance pertaining to LUAD liver metastasis are summarized comprehensively in Table 4.

Table 4.

Biomarkers and their clinical significance for LUAD liver metastasis.

Biomarker name	Biomarker function	Clinical significance	Reference
PD-L1 Expression	Response and prognostic marker	Lower in liver metastasis vs primary tumor. Prognostic: PD-L1+ (TPS ⩾1%) liver mets pts had higher ORR vs PD-L1 after immunotherapy. Limited predictive efficacy alone; needs combination.	105
Tumor Mutational Burden (TMB)	Response and prognostic marker	Lower in liver metastasis vs primary tumor. Prognostic: High TMB (⩾10 mut/Mb) associated with longer PFS. Combining TMB with PD-L1 improves predictive accuracy.	106
Peripheral Blood Immune Cell Profile	Prognostic marker	Increased proportion of CTLA-4+ CD4+ T cells and PD-1+ CD8+ T cells in liver mets pts. Dynamic monitoring: Reduction of these cells post-treatment associated with prolonged PFS.	89
Gut Microbiome Characteristics	Response marker	Decreased abundance of Akkermansia muciniphila and increased abundance of Bacteroides fragilis in liver mets pts. Increased Bacteroides fragilis associated with resistance to anti-PD-1 therapy.	107

CD4, cluster of differentiation 4; CTLA-4, cytotoxic T lymphocyte-associated antigen 4; ORR, objective response rate; PD-1, programmed death-1; PD-L1, programmed cell death ligand 1; PFS, progression-free survival; TMB, tumor mutational burden.

The association between the TME and treatment response and prognosis

First, regarding the efficacy of ICIs, patients with liver metastases generally exhibit a low response rate to ICIs, with an overall response rate (ORR) of approximately 10%–15%. This limited efficacy may be attributed to the immunosuppressive nature of the TME. A retrospective study indicated that the median PFS for liver metastasis patients treated with ICIs was merely 2.8 months, which is significantly lower than that observed in non-metastatic patients (5.1 months; p = 0.001).¹⁰⁸ Although PD-1/PD-L1 inhibitors, such as pembrolizumab, have shown effectiveness in certain liver metastasis cases, their response rate remains low at around 15%, potentially due to an increased presence of MDSCs within the liver.¹⁰⁹ Combination therapies—such as ICI plus anti-angiogenic agents—may partially overcome drug resistance by normalizing blood vessels and enhancing T-cell infiltration.¹¹⁰ Furthermore, local radiotherapy can release tumor antigens and improve ICI efficacy.⁶⁷ Second, targeting immunosuppressive cells may yield therapeutic benefits. CCR2 inhibitors like PF-04136309 impede MDSC migration to the liver.¹¹¹ In addition, CSF-1R inhibitors such as PLX3397 facilitate the conversion of M2-type TAMs into pro-inflammatory phenotypes,¹¹² thereby promoting TAM reprogramming. Third, concerning prognostic markers, patients exhibiting a CD8+ T cell/Treg ratio greater than 2 had a median OS of 18 months—significantly longer than those with a ratio ⩽ 2 who had an OS of only 9 months (p = 0.004).¹¹² Moreover, high levels of IFN-γ signaling correlate with extended OS outcomes [hazard ratio = 0.61; p = 0.03], underscoring the significance of Th1-type immune responses in this context.¹¹

Future research directions and challenges

First, regarding the analysis of spatial heterogeneity, significant differences exist in the distribution of immune cells across various regions of liver metastases. This necessitates the application of multi-region sequencing or mass cytometry to effectively address spatial heterogeneity. Second, in terms of metabolic intervention strategies, targeting IDO1 or lactate dehydrogenase (LDH) may have the potential to reverse immunosuppression. Third, in the development of novel biomarkers, PD-L1 detection using exosome-derived sources or dynamic monitoring of circulating tumor DNA (ctDNA) holds promise as a more sensitive approach for predicting therapeutic response.

Conclusion

The TME associated with LUAD metastases exhibits site-specific heterogeneity that significantly influences therapeutic responses. Metastases in lymph nodes, brain, bone, and liver each display unique immunosuppressive characteristics that require tailored treatment strategies. Biomarkers such as PD-L1 expression levels, immune cell ratios, and cytokine concentrations serve as important indicators for prognosis and treatment planning. The combination of ICIs with metabolic or stromal-targeted therapies shows considerable promise; however, challenges related to overcoming TME complexity persist. Future research directions should focus on advanced spatial profiling techniques, microbiome modulation approaches, and organoid models aimed at refining personalized immunotherapy protocols to enhance clinical outcomes.

Footnotes

Acknowledgements

None.

Declarations

ORCID iDs

Lele Song

Li Yu

References

Altorki

Markowitz

Gao

, et al. The lung microenvironment: an important regulator of tumour growth and metastasis. Nat Rev Cancer 2019; 19(1): 9–31.

Chen

Zhao

Wang

, et al. Human leukocyte antigen class II-based immune risk model for recurrence evaluation in stage I-III small cell lung cancer. J Immunother Cancer 2021; 9(8): e002554.

Hald

Rakaee

Martinez

, et al. LAG-3 in non-small-cell lung cancer: expression in primary tumors and metastatic lymph nodes is associated with improved survival. Clin Lung Cancer 2018; 19(3): 249–259.e2.

Lüke

Blazquez

Yamaci

, et al. Isolated metastasis of an EGFR-L858R-mutated NSCLC of the meninges: the potential impact of CXCL12/CXCR4 axis in EGFRmut NSCLC in diagnosis, follow-up and treatment. Oncotarget 2018; 9(27): 18844–18857.

Smaldone

Di Matteo

Castelluccio

, et al. Targeting the CXCR4/CXCL12 axis in cancer therapy: analysis of recent advances in the development of potential anticancer agents. Molecules 2025; 30(6): 1380.

Ban

Mai

, et al. Targeting autocrine CCL5-CCR5 Axis reprograms immunosuppressive myeloid cells and reinvigorates antitumor immunity. Cancer Res 2017;77(11):2857-2868.

Zhang

Wang

LYJGJZYPM

. Zoledronic acid combined with chemotherapy in non-small cell lung cancer with bone metastasis. J Buon 2021; 26(3): 830–836.

Lin

Chang

Hong

, et al. STAT3 phosphorylation at Ser727 and Tyr705 differentially regulates the EMT-MET switch and cancer metastasis. Oncogene 2021; 40(4): 791–805.

Yao

Liu

Feng

, et al. Unveiling the role of HGF/c-met signaling in non-small cell lung cancer tumor microenvironment. Int J Mol Sci 2024; 25(16): 9101.

10.

Wang

Liao

Wang

, et al. Tribbles pseudokinase 3 converts sorafenib therapy to neutrophil-mediated lung metastasis in hepatocellular carcinoma. Adv Sci (Weinh) 2025; 12(13): e2413682.

11.

Sanchez-Moral

Paul

Martori

, et al. Macrophage CD5L is a target for cancer immunotherapy. EBioMedicine 2023; 91: 104555.

12.

Teng

Ngiow

Ribas

, et al. Classifying cancers based on T-cell infiltration and PD-L1. Cancer Res 2015;75(11):2139-2145.

13.

Chen

Mellman

Elements of cancer immunity and the cancer-immune set point. Nature 2017; 541(7637): 321–330.

14.

Binnewies

Roberts

Kersten

, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med 2018; 24(5): 541–550.

15.

Smyth

Ngiow

Ribas

, et al. Combination cancer immunotherapies tailored to the tumour microenvironment. Nat Rev Clin Oncol 2016; 13(3): 143–158. doi:10.1038/nrclinonc.2015.209

16.

Raniszewska

Vroman

Dumoulin

, et al. PD-L1⁺ lung cancer stem cells modify the metastatic lymph-node immunomicroenvironment in nsclc patients. Cancer Immunol Immunother 2021; 70(2): 453–461.

17.

Qin

Dong

, et al. Prognostic values of TIM-3 expression in patients with solid tumors: a meta-analysis and database evaluation. Front Oncol 2020; 10: 1288.

18.

Yang

Cai

, et al. Functional status and spatial architecture of tumor-infiltrating CD8+ T cells are associated with lymph node metastases in non-small cell lung cancer. J Transl Med 2023; 21(1): 320.

19.

Larionova

Tuguzbaeva

Ponomaryova

, et al. Tumor-associated macrophages in human breast, colorectal, lung, ovarian and prostate cancers. Front Oncol 2020; 10: 566511.

20.

Zhang

Yang

, et al. Integrated analysis of long noncoding RNA expression profiles in lymph node metastasis of hepatocellular carcinoma. Gene 2018; 676: 47–55.

21.

Kim

Cha

Park

, et al. Reactive oxygen species modulator 1 expression predicts lymph node metastasis and survival in early-stage non-small cell lung cancer. PLoS One 2020; 15(12): e0239670.

22.

Bockorny

Semenisty

Macarulla

, et al. BL-8040, a CXCR4 antagonist, in combination with pembrolizumab and chemotherapy for pancreatic cancer: the COMBAT trial. Nat Med 2020; 26(6): 878–885.

23.

Nakajima

Murakawa

Fukami

, et al. A phase I study of adoptive immunotherapy for recurrent non-small-cell lung cancer patients with autologous gammadelta T cells. Eur J Cardiothorac Surg 2010; 37(5): 1191–1197.

24.

Lin

Lalani

Harding

, et al. Inhibition of lymphogenous metastasis using adeno-associated virus-mediated gene transfer of a soluble VEGFR-3 decoy receptor. Cancer Res 2005; 65(15): 6901–6909.

25.

Wakasu

Tagawa

Haratake

, et al. Preventive effect of tertiary lymphoid structures on lymph node metastasis of lung adenocarcinoma. Cancer Immunol Immunother 2023; 72(6): 1823–1834.

26.

Salvo

Garasa

Dotor

, et al. Combined targeting of TGF-β1 and integrin β3 impairs lymph node metastasis in a mouse model of non-small-cell lung cancer. Mol Cancer 2014; 13: 112.

27.

Tong

Wang

Jiang

, et al. Correlation between p-STAT3 overexpression and prognosis in lung cancer: a systematic review and meta-analysis. PLoS One 2017; 12(8): e0182282.

28.

Shi

Zhang

Sun

, et al. CC chemokine ligand 18(CCL18) promotes migration and invasion of lung cancer cells by binding to Nir1 through Nir1-ELMO1/DOC180 signaling pathway. Mol Carcinog 2016; 55(12): 2051–2062.

29.

Kalpana

Figy

Yeung

, et al. Reduced RhoA expression enhances breast cancer metastasis with a concomitant increase in CCR5 and CXCR4 chemokines signaling. Sci Rep 2019; 9(1): 16351.

30.

Akosman

Kamle

, et al. CHI3L1 regulates PD-L1 and anti-CHI3L1-PD-1 antibody elicits synergistic antitumor responses. J Clin Invest 2021; 131(21): e137750.

31.

Zhang

, et al. Increased TIM-3 expression in tumor-associated macrophages predicts a poorer prognosis in non-small cell lung cancer: a retrospective cohort study. J Thorac Dis 2023; 15(3): 1433–1444.

32.

Zhang

Liu

Wang

, et al. Upregulation of cytoskeleton protein and extracellular matrix protein induced by stromal-derived nitric oxide promotes lung cancer invasion and metastasis. Curr Mol Med 2014; 14(6): 762–771.

33.

Katopodi

Petanidis

Domvri

, et al. Kras-driven intratumoral heterogeneity triggers infiltration of M2 polarized macrophages via the circHIPK3/PTK2 immunosuppressive circuit. Sci Rep 2021;11(1):15455.

34.

Nomura

Nakai

Nishina

, et al. 18F-fluorodeoxyglucose uptake in PET is associated with the tumor microenvironment in metastatic lymph nodes and prognosis in N2 lung adenocarcinoma. Cancer Sci 2022; 113(4): 1488–1496.

35.

Schmid

Haager

, et al. Local Concentrations of CC-chemokine-ligand 18 Correlate with Tumor size in non-small cell lung cancer and are elevated in lymph node-positive disease. Anticancer Res 2016; 36(9): 4667–4671.

36.

Yang

Zhang

Cao

, et al. Overexpression of lncRNA IGFBP4-1 reprograms energy metabolism to promote lung cancer progression. Mol Cancer 2017; 16(1): 154.

37.

Inoue

Yoshimura

Kurabe

, et al. Prognostic impact of CD73 and A2A adenosine receptor expression in non-small-cell lung cancer. Oncotarget 2017; 8(5): 8738–8751.

38.

Cheng

Ding

Chen

, et al. Anti-PD-L1/TGF-βR fusion protein (SHR-1701) overcomes disrupted lymphocyte recovery-induced resistance to PD-1/PD-L1 inhibitors in lung cancer. Cancer Commun (Lond) 2022; 42(1): 17–36.

39.

Lian

Xie

, et al. Simultaneous blocking of CD47 and PD-L1 increases innate and adaptive cancer immune responses and cytokine release. EBioMedicine 2019; 42: 281–295.

40.

Kato

Lee

Huang

, et al. Personalized siRNA-nanoparticle systemic therapy using metastatic lymph node specimens obtained with EBUS-TBNA in lung cancer. Mol Cancer Res 2018;16(1):47–57.

41.

Pai

Hellmann

Sauter

, et al. Lineage tracing reveals clonal progenitors and long-term persistence of tumor-specific T cells during immune checkpoint blockade. Cancer Cell 2023; 41(4): 776–790.e7.

42.

Boyer

Hui

Urban

, et al. Pembrolizumab with platinum-based chemotherapy with or without epacadostat as first-line treatment for metastatic non-small cell lung cancer: a randomized, partially double-blind, placebo-controlled phase II study. BMC Cancer 2024;23(Suppl 1):1250.

43.

Hassan

MHU

Shahbaz

Momal

, et al. Exploring punicalagin potential against cancers: a comprehensive review. Food Sci Nutr 2025; 13(3): e70072.

44.

Jiang

Guo

Biological mechanisms and immunotherapy of brain metastases in non-small cell lung cancer. Biochim Biophys Acta Rev Cancer 2025; 1880(3): 189320.

45.

Gupta

Aguirre-Ducler

, et al. Spatially resolved analysis of the T cell immune contexture in lung cancer-associated brain metastases. J Immunother Cancer 2021; 9(10): e002684.

46.

Harrison

, et al. Targeting brain lesions of non-small cell lung cancer by enhancing CCL2-mediated CAR-T cell migration. Nat Commun 2022;13(1):2154.

47.

Monteiro

Miarka

Perea-García

, et al. Stratification of radiosensitive brain metastases based on an actionable S100A9/RAGE resistance mechanism. Nat Med 2022; 28(4): 752–765.

48.

Ilhan-Mutlu

Siehs

Berghoff

, et al. Expression profiling of angiogenesis-related genes in brain metastases of lung cancer and melanoma. Tumour Biol 2016; 37(1): 1173–1182.

49.

Álvarez-Prado

ÁF

Maas

Soukup

, et al. Immunogenomic analysis of human brain metastases reveals diverse immune landscapes across genetically distinct tumors. Cell Rep Med 2023; 4(1): 100900.

50.

Kleinberger

Çifçi

Paiato

, et al. Density and entropy of immune cells within the tumor microenvironment of primary tumors and matched brain metastases. Acta Neuropathol Commun 2025; 13(1): 34.

51.

Lamano

, et al. Tumor-induced peripheral immunosuppression promotes brain metastasis in patients with non-small cell lung cancer. Cancer Immunol Immunother 2019; 68(9): 1501–1513.

52.

Sedighzadeh

Khoshbin

Razi

, et al. A narrative review of tumor-associated macrophages in lung cancer: regulation of macrophage polarization and therapeutic implications. Transl Lung Cancer Res 2021; 10(4): 1889–1916.

53.

Theruvath

Menard

Smith

BAH

, et al. Anti-GD2 synergizes with CD47 blockade to mediate tumor eradication. Nat Med 2022; 28(2): 333–344.

54.

Zhai

Spranger

Binder

, et al. Molecular pathways: Targeting IDO1 and other tryptophan dioxygenases for cancer immunotherapy. Clin Cancer Res 2015; 21(24): 5427–5433.

55.

Gong

, et al. Dual roles of lactate in EGFR-TKI-resistant lung cancer by targeting GPR81 and MCT1. J Oncol 2022; 2022: 3425841.

56.

Wei

Wang

Yang

, et al. MicroRNA-550a-3-5p controls the brain metastasis of lung cancer by directly targeting YAP1. Cancer Cell Int 2021; 21(1): 491.

57.

Deng

, et al. TGF-β1-mediated exosomal lnc-MMP2-2 increases blood-brain barrier permeability via the miRNA-1207-5p/EPB41L5 axis to promote non-small cell lung cancer brain metastasis [published correction appears in Cell Death Dis. 2021 Oct 7;12(10):916.]. Cell Death Dis 2021; 12(8): 721.

58.

Jin

Kang

Wang

, et al. Targeting polarized phenotype of microglia via IL6/JAK2/STAT3 signaling to reduce NSCLC brain metastasis. Signal Transduct Target Ther 2022; 7(1): 52.

59.

Nannini

Guisier

Curcio

, et al. Outcomes of patients with non-small cell lung cancer and brain metastases treated with the upfront single agent pembrolizumab: a retrospective and multicentric study of the ESCKEYP GFPC cohort. Curr Oncol 2024; 31(3): 1656–1666.

60.

Tang

Wang

, et al. Brain Metastasis from EGFR-Mutated Non-Small Cell Lung Cancer: Secretion of IL11 from Astrocytes Up-Regulates PDL1 and Promotes Immune Escape. Adv Sci (Weinh) 2024; 11(26): e2306348.

61.

Hendry

Salgado

Gevaert

, et al. Assessing tumor-infiltrating lymphocytes in solid tumors: a practical review for pathologists and proposal for a standardized method from the international immuno-oncology biomarkers working group: Part 2: TILs in melanoma, gastrointestinal tract carcinomas, non-small cell lung carcinoma and mesothelioma, endometrial and ovarian carcinomas, squamous cell carcinoma of the head and neck, genitourinary carcinomas, and primary brain tumors. Adv Anat Pathol 2017; 24(6): 311–335.

62.

Restle

Dux

, et al. Organ-specific heterogeneity in tumor-infiltrating immune cells and cancer antigen expression in primary and autologous metastatic lung adenocarcinoma. J Immunother Cancer 2023; 11(6): e006609.

63.

Jiang

Chu

, et al. Anti-EGFR ScFv functionalized exosomes delivering LPCAT1 specific siRNAs for inhibition of lung cancer brain metastases. J Nanobiotechnology 2024; 22(1): 159.

64.

Zhu

, et al. Effect of whole-brain and intensity-modulated radiotherapy on serum levels of miR-21 and prognosis for lung cancer metastatic to the brain. Med Sci Monit 2020; 26: e924640.

65.

Stein

Pandey

Xiu

, et al. Tumor mutational burden is site specific in non-small-cell lung cancer and is highest in lung adenocarcinoma brain metastases. JCO Precis Oncol 2019; 3: 1–13.

66.

Randrian

Desette

Emambux

, et al. New Artificial intelligence score and immune infiltrates as prognostic factors in colorectal cancer with brain metastases. Front Immunol 2021; 12: 750407.

67.

Long

Guo

Chen

, et al. GPR162 activates STING dependent DNA damage pathway as a novel tumor suppressor and radiation sensitizer [published correction appears in Signal Transduct Target Ther. 2023 Jun 30;8(1):259.]. Signal Transduct Target Ther 2023; 8(1): 48.

68.

Goto

Hayashi

, et al. HERTHENA-Lung01, a Phase ii trial of patritumab deruxtecan (HER3-DXd) in epidermal growth factor receptor-mutated non-small-cell lung cancer after epidermal growth factor receptor tyrosine kinase inhibitor therapy and platinum-based chemotherapy. J Clin Oncol 2023; 41(35): 5363–5375.

69.

Chu

Niu

Xiao

, et al. The long-term and short-term efficacy of immunotherapy in non-small cell lung cancer patients with brain metastases: a systematic review and meta-analysis. Front Immunol 2022; 13: 875488.

70.

Mitchell

Hamid

Smith

, et al. Epacadostat plus pembrolizumab in patients with advanced solid tumors: phase i results from a multicenter, open-label phase I/II trial (ECHO-202/KEYNOTE-037). J Clin Oncol 2018; 36(32): 3223–3230.

71.

Shang

Zhang

Lei

, et al. Insights into CSF-1/CSF-1R signaling: the role of macrophage in radiotherapy. Front Immunol 2025; 16: 1530890.

72.

Meng

Hao

, et al. “On/off”-switchable crosslinked PTX-nanoformulation with improved precise delivery for NSCLC brain metastases and restrained adverse reaction over nab-PTX. Biomaterials 2024; 307: 122537.

73.

McGrail

Pilié

Rashid

, et al. High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. Ann Oncol 2021; 32(5): 661–672.

74.

Liu

Lin

Xin

, et al. Metagenomics and non-targeted metabolomics reveal the role of gut microbiota and its metabolites in brain metastasis of non-small cell lung cancer. Thorac Cancer 2025; 16(8): e70068.

75.

Skurikhin

Pershina

Ermakova

, et al. Reprogrammed CD8⁺ T-lymphocytes isolated from bone marrow have anticancer potential in lung cancer. Biomedicines 2022; 10(6): 1450.

76.

Zhang

Sai

Wang

, et al. Hypoxic BMSC-derived exosomal miRNAs promote metastasis of lung cancer cells via STAT3-induced EMT. Mol Cancer 2019; 18(1): 40.

77.

Wang

Chao

Chen

, et al. Thrombospondin enhances RANKL-dependent osteoclastogenesis and facilitates lung cancer bone metastasis. Biochem Pharmacol 2019; 166: 23–32.

78.

Saijo

Goto

Nakano

, et al. Bone marrow-derived fibrocytes promote stem cell-like properties of lung cancer cells. Cancer Lett 2018; 421: 17–27.

79.

Tariq

Zhang

Liang

, et al. Gefitinib inhibits M2-like polarization of tumor-associated macrophages in Lewis lung cancer by targeting the STAT6 signaling pathway. Acta Pharmacol Sin 2017; 38(11): 1501–1511.

80.

Hayashi

Takabayashi

, et al. Immunotherapeutic activity of a conjugate of a Toll-like receptor 7 ligand. Proc Natl Acad Sci USA 2007; 104(10): 3990–3995.

81.

Sai

Dai

Fan

, et al. Cancer-educated mesenchymal stem cells promote the survival of cancer cells at primary and distant metastatic sites via the expansion of bone marrow-derived-PMN-MDSCs. Cell Death Dis 2019; 10(12): 941.

82.

Xie

Zeng

Fan

, et al. Effect of CXCL12/CXCR4 on increasing the metastatic potential of non-small cell lung cancer in vitro is inhibited through the downregulation of CXCR4 chemokine receptor expression. Oncol Lett 2014; 7(4): 941–947.

83.

Chen

Sun

Shen

, et al. Sema4D expression and secretion are increased by HIF-1α and inhibit osteogenesis in bone metastases of lung cancer. Clin Exp Metastasis 2019; 36(1): 39–56.

84.

Mai

, et al. Immunotherapeutic treatment of lung cancer and bone metastasis with a mPLA/mRNA tumor vaccine. Acta Biomater 2023; 169: 489–499.

85.

Faubert

Cai

, et al. Lactate Metabolism in Human Lung Tumors. Cell 2017; 171(2): 358–371.e9.

86.

Johnson

Schipani

Giaccia

AJ.

HIF targets in bone remodeling and metastatic disease. Pharmacol Ther 2015; 150: 169–177.

87.

Shi

Feng

Xie

, et al. Targeted blockade of TGF-β and IL-6/JAK2/STAT3 pathways inhibits lung cancer growth promoted by bone marrow-derived myofibroblasts. Sci Rep 2017; 7(1): 8660.

88.

Dong

Lan

Gao

, et al. Prognostic significance of bone metastasis and clinical value of bone radiotherapy in metastatic non-small cell lung cancer receiving PD-1/PD-L1 inhibitors: results from a multicenter, prospective, observational study. Transl Lung Cancer Res 2024; 13(10): 2603–2616.

89.

Yang

Sun

, et al. Galectin-9 interacts with PD-1 and TIM-3 to regulate T cell death and is a target for cancer immunotherapy. Nat Commun 2021; 12(1): 832.

90.

Wang

Pan

Chen

, et al. SIRP-alpha-IL-6 axis induces immunosuppressive macrophages in non-small-cell lung cancer. Biochem Biophys Res Commun 2023; 682: 386–396.

91.

Brouns

AJWM

Hendriks

LEL

Robbesom-van den Berge

, et al. Association of RANKL and EGFR gene expression with bone metastases in patients with metastatic non-small cell lung cancer. Front Oncol 2023; 13: 1145001.

92.

Hernández

Moreno

Zandueta

, et al. Novel alternatively spliced ADAM8 isoforms contribute to the aggressive bone metastatic phenotype of lung cancer. Oncogene 2010;29(26):3758–3769.

93.

Zhao

Han

, et al. Prognostic value of KRAS/TP53/PIK3CA in non-small cell lung cancer. Oncol Lett 2019; 17(3): 3233–3240.

94.

Casimiro

Vilhais

Gomes

, et al. The Roadmap of RANKL/RANK pathway in Cancer. Cells 2021; 10(8): 1978.

95.

Wang

Sun

YL.

Therapeutic targets and biomarkers of tumor immunotherapy: response versus non-response. Signal Transduct Target Ther 2022; 7(1): 331.

96.

Lengel

Mastrogiacomo

Connolly

, et al. Genomic mapping of metastatic organotropism in lung adenocarcinoma. Cancer Cell 2023; 41(5): 970–985.e3.

97.

Yanagawa

Tran

Salehi-Rad

, et al. Single-cell characterization of pulmonary nodules implicates suppression of immunosurveillance across early stages of lung adenocarcinoma. Cancer Res 2023; 83(19): 3305–3319.

98.

Huang

Zhang

, et al. miR-7/TGF-β2 axis sustains acidic tumor microenvironment-induced lung cancer metastasis. Acta Pharm Sin B 2022; 12(2): 821–837.

99.

Ishikawa

Yoshida

Higashihara

, et al. Different effects of constitutive nitric oxide synthase and heme oxygenase on pulmonary or liver metastasis of colon cancer in mice. Clin Exp Metastasis 2003; 20(5): 445–450.

100.

Schmidt

Eskiocak

Kohn

, et al. Enhanced adaptive immune responses in lung adenocarcinoma through natural killer cell stimulation. Proc Natl Acad Sci USA 2019; 116(35): 17460–17469.

101.

Liu

Cheng

, et al. Overexpression of CXCR7 accelerates tumor growth and metastasis of lung cancer cells. Respir Res 2020; 21(1): 287.

102.

Wang

Zhang

Meng

, et al. Long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1 in extracellular vesicles promotes hepatic stellate cell activation, liver fibrosis and β-Catenin signaling pathway. Front Physiol 2022; 13: 792182.

103.

Peng

Chen

, et al. MIR155HG is a prognostic biomarker and associated with immune infiltration and immune checkpoint molecules expression in multiple cancers. Cancer Med 2019; 8(17): 7161–7173.

104.

Jan

Lai

Yang

, et al. Adenylate kinase 4 modulates oxidative stress and stabilizes HIF-1α to drive lung adenocarcinoma metastasis. J Hematol Oncol 2019; 12(1): 12.

105.

Qiu

Wang

, et al. The debatable role of immune checkpoint blockade therapy in lung adenocarcinoma-oriented liver metastatic malignant lesions. J Cancer Res Clin Oncol 2023; 149(9): 5791–5802.

106.

Gao

Wang

Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data. Front Immunol 2023; 14: 1090838.

107.

Tan

Yang

, et al. Akkermansia muciniphila outer membrane protein regulates recruitment of CD8⁺ T cells in lung adenocarcinoma and through JAK-STAT signalling pathway. Microb Biotechnol 2024; 17(7): e14522.

108.

Zhang

, et al. Real-world evidence of advanced non-small cell lung carcinoma treated with an immune checkpoint inhibitor plus chemotherapy. Oncol Lett 2024; 28(3): 405.

109.

Hashimoto

Sarker

Reebye

, et al. Upregulation of C/EBPα inhibits suppressive activity of myeloid cells and potentiates antitumor response in mice and patients with cancer. Clin Cancer Res 2021; 27(21): 5961–5978.

110.

Hack

Zhu

Wang

Augmenting anticancer immunity through combined targeting of angiogenic and PD-1/PD-L1 pathways: challenges and opportunities. Front Immunol 2020; 11: 598877.

111.

Mittal

Wang

Akimova

, et al. The CCR2/MCP-1 Chemokine Pathway and Lung Adenocarcinoma. Cancers (Basel) 2020; 12(12): 3723.

112.

Zhang

Jiang

Zou

, et al. Pexidartinib synergize PD-1 antibody through inhibiting treg infiltration by reducing TAM-derived CCL22 in lung adenocarcinoma. Front Pharmacol 2023; 14: 1092767.

Unraveling the alterations and biomarkers in the tumor microenvironment in lung adenocarcinoma metastases and their indications for therapeutic response and prognosis

Abstract

Keywords

Introduction

Lymph node metastasis

Reprogramming of immune cell subsets in lymph node metastasis

Abnormalities in the network regulation of cytokines and chemokines and dynamic expression of immune checkpoint molecules

Key biomarkers and their clinical significance

Treatment strategies and directions, and challenges in translational research

Brain metastases

Composition and characteristics of the TME in brain metastases

Alterations in cytokines, immune checkpoint molecules, metabolism, and signaling pathways

Key immune biomarkers and their correlation with clinical efficacy and prognosis

Therapeutic strategies, clinical challenges, and future research directions

Bone metastasis

Functional remodeling of immune cell subsets

Immunosuppressive molecular network

Key immune markers and their clinical significance

Therapeutic strategies targeting the TME and future prospects

Liver metastasis

Composition of the liver TME and its changes in the TME of lung cancer liver metastases

Key biomarkers and their clinical significance

The association between the TME and treatment response and prognosis

Future research directions and challenges

Conclusion

Footnotes

Acknowledgements

Declarations

ORCID iDs

References