Novel lipid metabolism-related gene signature associated with clinical and immune features in lung adenocarcinoma patients experiencing lymph node metastasis

Introduction

Lung cancer is a highly malignant tumor worldwide, ranking first in morbidity and mortality (1). And more than 40% of non-small cell lung cancers (NSCLCs) are lung adenocarcinoma (LUAD) (2,3). Despite diagnostic and therapeutic improvements, the 5-year survival rate of metastatic LUAD is approximately 15% (4,5). The lymphatic system is the main route for lung cancer metastasis, and lymphatic metastasis is a vital factor affecting the stage and prognosis of lung cancer (6,7). Experimental studies have shown that cancer cells spreading to lymph nodes can invade blood vessels in lymph nodes and thus trigger tumor growth in distant organs (8,9). Therefore, lymph node metastasis (LNM) is closely related to distant metastasis of tumors, systemic spread, postoperative recurrence, and survival of LUAD patients (10-12). Thus, further investigations are required to identify LNM-associated pathological genes and the underlying molecular mechanisms.

In recent years, accumulating evidence has indicated that lipid metabolic reprogramming contributes to the metastasis cascade of the lymphatic system, subsequent distant organs, and further malignant progression (13-15). Through comparative transcriptomic and metabolomic analysis of primary and LNM tumor cells in melanoma mouse models, Lee et al. found that LNM was associated with lipid metabolism disorders involving a metabolic shift toward fatty acid oxidation (16). Moreover, a study on cervical cancer also showed that the lipid metabolism-related gene (LMRG) FABP5 could promote the tumor epithelial-mesenchymal transition (EMT), lymphangiogenesis, and LNM by reprogramming the fatty acid metabolism (17). Collectively, the studies mentioned above suggest a close relationship between LNM and lipid metabolism disorders. Currently, few studies exist on the relationship between LNM and lipid metabolism levels in LUAD. Given this deficiency, we employed bioinformatic data-mining to identify LMRGs linked to LNM in LUAD, aiming to elucidate their role in LUAD prognosis and underlying mechanisms. Our findings underscore the critical importance of lipid metabolic reprogramming in LUAD progression and its potential as a therapeutic target. We present this article in accordance with the TRIPOD reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1476/rc).

Methods

LUAD data acquisition and processing

Clinicopathological information and RNA-sequence data regarding LUAD patients were collected from The Cancer Genome Atlas (TCGA, https://www.tcga.org/) database. Patients were stratified into two groups based on LNM status: the LNM-positive (LN+) group, consisting of 164 patients with confirmed LNM, and the LNM-negative (LN−) group, comprising 318 patients without evidence of lymph node involvement. This cohort of 482 patients served as the training cohort for our analysis, enabling the identification of LMRGs associated with LNM. The GSE50081 dataset was downloaded from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database, with 127 LUAD samples included and set as the verification cohort. This independent cohort allowed us to assess the robustness and generalizability of the LMRG signature identified in the TCGA training cohort.

Identification of LMRGs in nodal staging-specific LUAD

Seven lipid metabolism-related pathways, including 189 LMRGs, were extracted from the Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp/blastkoala/). The “limma” package (https://www.bioconductor.org/packages/limma.html) (18) was performed to identify differentially expressed genes (DEGs) for the subgroups of LNM (w/wo LNM of LUAD). A log₂ [fold change (FC)] >1, a P value <0.05, and a false discovery rate (FDR) <0.05 were considered as cut-off values.

Cox regression and survival analyses of LMRGs in LUAD

Univariate Cox regression was conducted using the “survival” package in R for LMRGs, and genes with a P value <0.01 were considered prognostic-associated genes. Each LMRG from the Cox model was further plotted using the Kaplan-Meier survival curve (19).

Least absolute shrinkage and selection operator (LASSO) regression construction and verification in LUAD

Subsequently, a LASSO-Cox regression model was constructed using the R package “glmnet” (20). To establish a prognostic LMRG signature, genes filtered through LASSO regression were further analyzed using multivariate Cox regression. The resulting risk score was calculated as follows: Risk score = GPD1L × (−0.259) + SPHK1 × 0.087 + ST3GAL4 × 0.168, where negative coefficients (e.g., GPD1L) denote protective effects, and positive coefficients (e.g., SPHK1, ST3GAL4) indicate risk-enhancing roles. Patients were stratified into high- and low-risk subgroups based on the median risk score threshold (median =0.98), which was objectively determined to optimize survival discrimination. The prognostic accuracy of this signature was rigorously validated using time-dependent receiver operating characteristic (ROC) curves, which demonstrated its robust performance in predicting both pathological N-stage progression (pN1–pN3) and survival outcomes at 12, 24, and 36 months. The above model was validated internally with TCGA and externally with GSE50081 from the GEO dataset. Additionally, the clinical features and LMRG-based nomogram (https://cran.r-project.org/web/packages/rms/index.html) were plotted.

Immunohistochemical (IHC) examination of the LMRGs

To examine three LMRG expressions at the protein level, we obtained the IHC images from the Human Protein Atlas (HPA) database and validated them on a commercial tissue microarray (TMA). The protein expressions of SPHK1 and ST3GAL4 were checked using the HPA database (21,22). As no staining data of GPD1L were found in the HPA database, we performed the IHC analyses of GPD1L on a TMA of LUAD (Cat #LUC1601, Shanghai Zhuohao Medical Science and Technology Co., Ltd., Shanghai, China) (23) containing 75 LUAD tumors and matched adjacent normal tissue with clinical and prognostic information. The TMA slide was incubated with a rabbit polyclonal antibody against the GPD1L protein (1:100 dilution; Abcepta, AP10723a-400). Quantification was based on the percentage of positive cells and the staining intensity. Briefly, the GPD1L positive staining was scored as follows: 0, <5% of the tumor cells in the lesions; 1, 5–30% of the tumor cells; 2, 31–60% of the tumor cells; and 3, ≥61% of the tumor cells. The intensity was graded as follows: 0, negative; 1+, weak; 2+, moderate; and 3+, strong. A final score between 0 and 12 was calculated by multiplying the positive rate and intensity scores (24). The X-Tile package (Yale University, USA) was performed to calculate the best cut-off points (cut-off =6) for overall survival (OS). A final staining index was recorded, in which a score of 0 was considered the negative expression, scores of 1–6 were considered the low expression, and ≥6 was considered the high expression.

Genomic alteration analysis of LMRGs and corresponding survival analysis

The cBioPortal (https://www.cbioportal.org/) is an online database for exploring cancer genomics, including somatic mutations, DNA copy number, and mRNA expression. Here, we used it to analyze the correlation between mutations (OncoPrint display of gene mutation) and LMRG subgroups of LUAD (TCGA, Firehose Legacy) patients. The survival analysis for LMRG subgroups was investigated using a Kaplan-Meier plot for several genes (TP53, KRAS, and EGFR) frequently mutated in LUAD.

Estimation of immune infiltration and correlation analysis between LMRG signature and tumor microenvironment scores

CIBERSORT (R Bioconductor package, http://cibersort.stanford.edu/) is a versatile computational method for quantifying cell compositions from gene expression profiles (25). In this study, we used it to estimate the relationship between the LMRG signature and immune cell infiltrates in the TCGA-LUAD dataset. Subsequently, the ESTIMATE algorithm (R Bioconductor package, https://bioinformatics.mdanderson.org/estimate/) was applied to calculate the microenvironment scores based on gene set enrichment analysis (GSEA) (26). The immune, stromal, and estimate scores were automatically output to estimate the infiltrating stromal and immune cells for two LMRG signature subgroups.

Prediction of immunotherapeutic response and evaluation of drug sensitivity

The Tumor Immune Dysfunction and Exclusion (TIDE, http://tide.dfci.harvard.edu/) algorithm (27) and subclass mapping (28) were used to predict the response to immunotherapy in LMRG subgroups. Moreover, the tumor mutation burden (TMB), microsatellite instability (MSI), and tumor neoantigen scores were also assessed in the TCGA-LUAD cohort (29). Additionally, we used the Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/) database (30) to explore the association between the LMRG subgroup and clinical anti-LUAD drugs, including tyrosine kinase inhibitor (TKI) drugs (e.g., erlotinib, afatinib, selumetinib), chemotherapy drugs (e.g., cisplatin, docetaxel, paclitaxel), and VEGF receptor inhibitors. The half-maximal inhibitory concentration (IC₅₀) of related anticancer drugs in lung cancer cell lines was obtained using the GDSC database.

Functional and pathway enrichment analyses of LMRGs

To further explore the potential biological processes and molecular characteristics for LMRG subsets, we conducted GSEA (http://www.broadinstitute.org/gsea/) (31) to identify the related biological function enrichment in the LMRG high-risk group. Meanwhile, gene set variation analysis (GSVA) was conducted using the “GSVA” R package (32) to reveal the specific signaling pathways involved in DEGs for the LMRG high- and low-risk groups.

Statistical analysis

Statistical analyses were conducted in R v4.2.1. Group comparisons used Wilcoxon rank-sum (continuous) and Chi-squared (categorical) tests. Survival analysis employed Kaplan-Meier/log-rank test and Cox regression [reporting HRs (95% CIs)]. Time-dependent ROC curves and Spearman correlation assessed predictive accuracy and variable associations. P<0.05 indicated statistical significance.

Data availability

The profile data analyzed in this study were obtained from TCGA database at TCGA-LUAD dataset, GEO at GSE50081, the TIDE database, cBioPortal (https://www.cbioportal.org/), GDSC database and HPA database. The IHC data of the commercial TMA are available upon reasonable request from the corresponding author. Other data generated in this study are available within the article and its supplementary data files.

Ethical statement

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Results

Identification of LMRGs associated with LNM in LUAD and evaluation

A total of 482 LUAD patient records were collected from the TCGA database, including 164 cases with LNM and 318 without LNM (see table available online https://cdn.amegroups.cn/static/public/tlcr-2025-1-1476-1.docx). With RNA-seq data for analyzing differentially expressed LMRGs for the LNM and non-LNM groups, 21 DEGs were obtained, including 12 up-regulated and 9 down-regulated genes (Table S1). A gene set of 189 LMRGs was extracted from the KEGG database. Through univariate Cox analysis (HR-unicox), six lipid metabolism genes related to prognosis were selected for further LASSO regression analysis. Finally, three prognostic LMRGs (GPD1L, SPHK1, and ST3GAL4) were identified as associated with LNM in LUAD patients. Among them, GPD1L was down-regulated, and SPHK1 and ST3GAL4 were up-regulated in the LUAD-LNM group (Figure 1A). Subsequently, the patients were further divided into low-risk and high-risk LMRG groups using risk score [risk score = GPD1L × (−0.259) + SPHK1 × 0.087 + ST3GAL4 × 0.168] as the cut-off value, and their pathological N-stage and survival difference were evaluated in the TCGA-LUAD cohort. As shown in Figure 1B, the ROC curve based on the high-risk LMRGs exhibited a certain degree of prediction concordance of the pathological N-stage [pN1 area under the curve (AUC) =0.621, pN2 AUC =0.639, pN3 AUC =0.716].

Figure 1 Identification of LMRGs in nodal staging-specific LUAD and construction of the prognostic LMRG signature. (A) Volcano plot of the up- and down-regulated lipid metabolism genes related to lymph node metastasis of lung adenocarcinoma in TCGA database. (B) ROC curve analysis for predicting the pathological N-stage for high-risk and low-risk LMRG subgroups. (C) ROC curve analysis of survival difference between LMRG subgroups in TCGA cohort; (D) ROC curve analysis of survival difference between LMRG subgroups in GSE50081 dataset. (E) Validation of LMRG signature in protein expression level using the HPA database (SPHK1/ST3GAL4) and IHC staining on a commercial tissue microarray (GPD1L) (https://www.proteinatlas.org/ENSG00000110080-ST3GAL4/cancer/lung+cancer#img). (F) Using the median of risk score as the cut-off value for the prediction (risk score =0.98) to divide LUAD patients into high- and low-risk LMRG subgroups. (G) Kaplan-Meier survival curve of LMRG signature in LUAD. AUC, area under the curve; CI, confidence interval; HR, hazard ratio; LMRGs, lipid metabolism-related genes; LUAD, lung adenocarcinoma; N, node; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas.

Construction and validation of a prognostic model based on LMRG signature

We then tested the prognostic model comprising the LMRG signature in the TCGA-LUAD cohort. As shown in Figure 1C,1D, patients in the LMRG high-risk group had a significantly worse prognosis (P≤0.001). Moreover, the LMRG signature exhibited good prognostic efficiency in predicting the OS of LUAD patients (12-month AUC =0.762, 24-month AUC =0.755, 36-month AUC =0.716, n=482). We then used the GSE50081 dataset derived from the GEO database to validate he TCGA-based results. As the GEO-based results were consistent with those of the TCGA training set, we found that the high-risk group had a significantly worse prognosis, and the LMRG signature model had a good predictive value (12-month AUC =0.712, 24-month AUC =0.706, 36-month AUC =0.69, n=127).

Protein levels confirmed the identified LMRG signature

We then evaluated the protein expression of LMRGs by checking the HPA database. The results indicated that the SPHK1 and ST3GAL4 (data existing in the public domain) were overexpressed in LUAD tumors (Figure 1E, Figure S1). Due to the lack of public data for GPD1L protein expression, we conducted IHC staining using a commercial tissue assay (TMA) of LUAD patients. The result was consistent with the TCGA sequencing data, in which the GPD1L showed low expression in LUAD tumors compared to the adjacent normal tissue (Figure S1E). Furthermore, low expression was closely associated with N-staging and poor survival in this LUAD cohort of 75 cases (Table S2). Pathological N-stage and survival differences were evaluated in the TCGA-LUAD cohort (Figure 1F). The survival curves for the LMRG signatures are provided in Figure 1G.

LMRG signature is a prognostic factor and is associated with unique patterns of genomic alterations in LUAD

We used Cox proportional hazards regression to assess the association between the risk score derived from the three-gene signature (GPD1L, SPHK1, ST3GAL4) and clinical characteristics in LUAD patients (Figure 2A). Furthermore, a nomogram plot was constructed, including age, sex, pathological T-, N-, M-stage, and LMRGs to predict the patient survival rate. Moreover, the results showed that the LMRG risk score contributed greatly to predicting independent prognostic factors in LUAD (Figure 2B).

Figure 2 The LMRG signature can be used as a prognostic factor for LUAD and its differential genomic profiles. (A) Forest plot showing multivariate Cox regression analysis of the LMRG signature risk score associated with age, gender, and pathological staging of TCGA-LUAD cohort. (B) Nomogram developed using multivariate Cox regression analysis to evaluate LUAD prognosis (3-/5-year rates). (C) Differential somatic mutations in LUAD with low and high risk LMRG signature. (D) Kaplan-Meier survival curve of OS rates associated with LMRGs in LUAD patients with several frequently mutated genes (e.g., TP53 mutation, KRAS mutation, EGFR mutation, P<0.05). CI, confidence interval; LMRG, lipid metabolism-related gene; LUAD, lung adenocarcinoma; M, metastasis; N, node; OS, overall survival; T, tumor; TCGA, The Cancer Genome Atlas.

Additionally, we explored the association between LMRGs and the LUAD genomic profile. The somatic mutation analysis presented a high frequency of mutations in TP53 (61%), TTN (52%), and RYR2 (45%) in the high-risk LMRG subgroup (Figure 2C). Moreover, we were interested in investigating the survival difference between LMRG subgroups of LUAD patients with several common driver genes, including TP53 and KRAS and EGFR. Therefore, we stratified the survival analyses, and the results showed that patients in the high-risk LMRG group had a significantly worse prognosis among the patients with TP53 and KRAS mutations, but not EGFR mutation (Figure 2D).

Analysis of immune infiltration and relationship between LMRG signature and tumor microenvironment (TME)

The immunosuppressive TME is a major barrier to cancer treatment (33,34). In this context, we calculated the immune, stromal, and estimate scores to understand the relationship between metabolic status and TME in LUAD. We also analyzed the relationship between LMRGs and immune infiltrates (types of immune cells) in the TME using the CIBERSORT method. A strong correlation was found between the high and low risk of LNM and immune cell content, especially the M0 resting macrophages contributing most to the high-risk group (Figure 3A). Interestingly, we also noted marked enrichment of mast cells activated in the high-risk signature, while the relatively high abundance of resting mast cells was shown in the low-risk signature. The role of mast cells in contributing to tumor progression or LNM is discussed further below.As shown in Figure 3B, the LUAD tissue for the high-risk LMRG signature showed significantly lower immune and estimated scores, while no significant differences were found for stromal scores. Here, the LMRG signature was significantly negatively correlated with the immune scores, indicating fewer immune cell infiltrates existed in the TME with the high-risk LMRG signature.

Figure 3 The correlation between LMRG signature and immune cells in the TME. (A) Association analysis between immune cell content and patients with low- and high-risk LMRG signature scores. (B) The relationship between LMRG signature and immune, stromal, and estimate scores. ns, not significant; *, P<0.05; **, P<0.01. LMRG, lipid metabolism-related gene; TME, tumor microenvironment.

Collectively, the results suggested that the LMRGs may impact immunotherapy in LUAD patients.

LMRG signature is associated with the TMB and immunotherapy response in LUAD

Since the TMB, MSI, tumor neoantigen score, and PD-L1 expression are currently the most important biomarkers for immunotherapy in clinical practice (35), we first evaluated the relationship between the patients with low- and high-risk signature scores. As shown in Figure 4A, we noted that the TMB significantly increased in high-risk patients with no significant difference in the MSI and tumor neoantigen peptide levels between the two groups. Additionally, predicting the response to immunotherapy based on the TIDE database further showed that the proportion of patients with no response to immunotherapy in the high-risk group was significantly higher than that in low-risk group (P<0.001; Figure 4B). Meanwhile, a significant increase was observed in the expression of CD274 (PD-L1; P<0.001) and LAG3 (P=1.426e−05) in the high-risk signature subgroup (Figure 4C).

Figure 4 The LMRG signature has potential immunotherapeutic implications. (A) The distribution of TMB, MSI, and neoantigens in the low- and high-LMRG signature. (B) TIDE databases predict the responses of patients in different signature subgroups to immunotherapy. ns, not significant; ***, P<0.001. (C) Evaluation of the correlation between immune checkpoint gene expression (PD-L1 and LAG3) and LMRG signature. (D) Immunotherapy responses, survival analysis, and prognostic value differ significantly among signature subgroups in melanoma, NSCLC, and urothelial carcinoma cohorts treated with immunotherapy. AUC, area under the curve; HExp, high expression; LExp, low expression; LMRG, lipid metabolism-related gene; MSI, microsatellite instability; NSCLC, non-small cell lung cancer; TIDE, Tumor Immune Dysfunction and Exclusion; TMB, tumor mutation burden.

To interrogate the broader applicability of this observation, we expanded our investigation to immunotherapy-treated cohorts spanning distinct malignancies. Strikingly, in melanoma and NSCLC cohorts, patients stratified into the LMRG-high subgroup demonstrated profoundly attenuated antitumor immunity (18% vs. 22%) and inferior OS (P=0.03), with consistent prognostic accuracy across 1- to 3-year horizons (AUC 0.70–0.73). Intriguingly, while the LMRG signature similarly predicted a reduced immune response in urothelial carcinoma (20% vs. 25%), it lacked discriminative power for survival outcomes (P=0.46) and exhibited no prognostic value (AUC 0.49–0.52) (Figure 4D). This pronounced divergence underscores the organ-specific predictive capacity of the LMRG signature, potentially driven by NSCLC-unique crosstalk between lipid metabolism reprogramming and PD-1/PD-L1 axis regulation.

Relationship between LMRG signature and anticancer drug susceptibility

Since drug resistance is a major issue in cancer therapy, we subsequently evaluated the drug sensitivity to commonly used chemotherapeutic or immunotherapeutic drugs for LUAD. The relationship between risk score and drug sensitivity was explored through the GDSC database. The results demonstrated that compared with low-risk patients, high-risk patients were associated with greater IC₅₀s for classical TKI drugs (erlotinib, afatinib, and selumetinib), suggesting that high-risk patients were resistant to some classical TKIs. Conversely, the IC₅₀ of the following drugs in the high-risk patients was markedly lower than in the low-risk group, including multi-targeted anti-angiogenic TKI drugs pazopanib, sunitinib and motesanib, chemotherapy drugs cisplatin/docetaxel/paclitaxel, cell cycle inhibitor, and PPAR (an important pathway of lipid metabolism) inhibitor olaparib, indicating that high-risk patients were sensitive to such drugs (Figure 5A,5B). Together, these results showed that LMRGs were related to drug susceptibility and may be helpful for drug resistance surveillance, or to guide drug combination treatment strategy.

Figure 5 The relationship between LMRG signature and drug sensitivity. (A) The upper panels show representative IC₅₀-resistant drugs between LMRG signatures. (B) The lower panels show representative IC₅₀-sensitive drugs between LMRG signatures; the IC₅₀ value of each drug was displayed in each graph for drug sensitivity measurement. HExp, high expression; LExp, low expression; IC₅₀, half-maximal inhibitory concentration; LMRG, lipid metabolism-related gene.

LMRGs enriched in cancer-associated pathways and pathological processes in LUAD

To explore potential biological behavior and molecular pathways, we conducted the relevant enrichment analysis of LNM for low- and high-risk patients. According to the GSVA analysis, patients in the high-risk LMRG group were significantly associated with several important cancer-associated pathways (e.g., REACTIVE OXYGEN SPECIES PATHWAY, HEDGEHOG SIGNALING, and TNFA SIGNALING VIA NFKB pathways), while patients in the low-risk group were strongly related to the SPERMATOGENESIS pathways (Figure 6A). For pathway enrichment analysis, the GSEA results showed that the high-risk signature group was highly related to the classic cancer-associated or metastatic cancer-related pathways, such as DNA replication, mismatch repair, and cell cycle, which further led to a worse prognosis among LUAD patients (Figure 6B,6C).

Figure 6 Functional and pathway enrichment analyses of LMRG signature. (A) GSVA showed the correlations between high-risk LMRGs and cancer-related functions. (B) GSEA showed the associations between high-risk LMRGs and classic cancer-related or metastatic cancer-related pathways. (C) Downstream genes involved in the related pathway, which are potential target molecules for intervention. GSEA, gene set enrichment analysis; GSVA, gene set variation analysis; HExp, high expression; LExp, low expression; LMRG, lipid metabolism-related gene.

Discussion

In LUAD, patients presenting with LNM consistently indicate poor prognoses (36,37). In recent years, growing evidence has suggested that lipid-metabolic reprogramming can play an essential role in regulating the survival of metastatic cancer cells as well as other resident immune or stromal cells in the TME of LNM. Therefore, our study aimed to identify lipid metabolic-related signatures related to clinicopathological features and could be used as a predictive tool in LUAD patients with LNM. In the present study, we identified a three-gene signature of LMRGs associated with distinct clinical characteristics, immune features, drug sensitivities, signaling pathways, and different prognoses, namely, GPD1L, SPHK1, and ST3GAL4.

Here, GPD1L, whose full name is glycerol-3-phosphate dehydrogenase-1-like, has glycerol-3-phosphate dehydrogenase activity. Liu et al. reported that GPD1L expression was low in oral and human papillomavirus (HPV)-oropharyngeal cancer, and patients with early head and neck squamous cell carcinoma with reduced GPD1L levels had a higher risk of LNM (38). SPHK1, full name sphingosine kinase 1, has been confirmed by Nagahashi et al., who demonstrated that SPHK1 was up-regulated in patients with obesity-related breast cancer (39). In breast cancer-bearing mice, targeted inhibition of SPHK1 signaling could reduce obesity-related inflammation, S1P signal transduction, and lung metastasis and prolong survival. Additionally, several studies have shown that SPHK1 catalyzes the formation of S1P and promotes tumor cell proliferation, migration, invasion, and EMT (40-42). ST3GAL4 encodes for β-galactosidase-α 2,3-sialyltransferase 4, showing involvement in the biosynthesis of tumor antigens sulfo-sLe (x) and sLe (x). Moreover, these elevated ST3GAL4 expression levels enhance the invasive ability of gastric adenocarcinoma cells (43).

Accordingly, the above studies resemble our results in terms of gene expression and physiopathological functions. It is suggested that GPD1L, SPHK1, and ST3GAL4 may be used as prognostic biomarkers for LUAD with LNM. Additionally, this signature may participate in the immune response in the TME by affecting various immune cells. Interestingly, we noted many types of antitumor immune cells (e.g., M1 macrophages, activated NK cells, and CD8⁺ T-cells) in the high-risk LMRG group, as well as a higher TMB score and higher expression of PD-L1 and LAG3 (immune checkpoint genes), which were expected to benefit from the immunotherapy and correlated with better clinical outcomes. Based on previous literature, Frossi et al. demonstrated that the mast cells modulate immune responses and the pathogenesis of inflammatory disorders and cancers by turning the “resting state” into the “degranulating state” like a switch (44). Mast cell infiltration has been positively associated with tumor progression and poor prognoses (45), including gastrointestinal cancer (46), colorectal cancer (47,48), pancreatic cancer (49,50), and LUAD (51,52). Specifically, activated mast cells can release excessive histamine, interleukin (IL)-10, and IL-12, which directly and indirectly influence T-cell function, polarizing engaged naive CD4⁺ T-cells toward a Th2 phenotype (53,54) as well as favoring the recruitment of Treg cells (55). Moreover, compelling evidence obtained from mast cells (MCs)-deficient mice showed that MCs could also support myeloid-derived suppressor cell (MDSC) activity in a B16 metastatic melanoma model (56). Rabenhorst et al. revealed that mast cells could enhance neoangiogenesis in T-cell neoplasms by producing vascular endothelial growth factor (VEGF) and tumor necrosis factor-alpha (TNF-α) (57).

However, although the lipid metabolism-related disorder may condition the poor immune checkpoint inhibitor (ICI) treatment outcome in NSCLC, we hypothesized that multi-targeted anti-angiogenic TKI or chemotherapy combined with ICI can significantly improve OS in high LMRG signature of cancer. Chemotherapy combined with ICI treatment is the standard first-line in advanced cancer, and Multi-targeted anti-angiogenic TKI plus ICI also has great potential in advanced cancer.

Therefore, we further focus on the drug susceptibility and pathological mechanism in the high-risk LMRG subgroup. Conversely, patients in the high-risk group had increased tolerance to some classical TKIs and were sensitive to some chemotherapy drugs (e.g., cisplatin, docetaxel, paclitaxel), multi-targeted anti-angiogenic TKI (pazopanib, sunitinib and motesanib), and PPAR inhibitor olaparib.

Furthermore, the mechanism may be related to the REACTIVE OXYGEN SPECIES PATHWAY and TNFA SIGNALING VIA NFKB signaling, as well as classic cancer-related or metastatic cancer-associated pathways (e.g., DNA replication, mismatch repair, and cell cycle). As also found in a previous study, Canli et al. provided convincing evidence that increased ROS can transform non-invasive tumors into invasive tumors by crosstalk between the ROS molecules, oxidative DNA damage, and TNF-α regulated signaling orchestrate a microenvironment for promoting tumor progression (58). Reactive oxygen species (ROS) can induce cell death (e.g., ferroptosis, apoptosis) and modulate immune responses in tumors (59). This dual role highlights the potential of targeting ROS pathways for cancer therapy (60), especially in metastatic contexts. The results suggest LUAD patients with LNM who fail to respond to immunotherapy could be classified as having the high-risk LMRG signature and further considered for combined treatment with relevant chemotherapeutic drugs, VEGF inhibitors, or PPAR inhibitors.

Inevitably, the present study has several limitations. First, the study is retrospective and relies on publicly available datasets, which may introduce selection bias. Second, we only validated the protein expression of LMRGs between the LUAD tumor and normal tissue due to a lack of knowledge regarding the lymph node metastatic site staining in the HPA database as well as the commercial TMA. Third, conclusions about LMRG-specific immune infiltration, immunotherapy response, and drug sensitivity are constrained by their derivation from RNA-seq-based bioinformatic algorithms without functional validation. Despite these limitations, the LMRG signature enables precision risk stratification (AUC 0.70–0.76), guides immunotherapy combination strategies (anti-angiogenic TKIs/chemotherapy to overcome TME immunosuppression), informs personalized drug selection (avoiding EGFR-TKI resistance in favor of platinum-based/anti-angiogenic agents), and identifies ROS/NF-κB pathway targets, offering a multi-dimensional molecular framework for clinical decision-making in LUAD with LNM. Future work will focus on functional validation of LMRGs in metastatic niches using multi-platform integration of proteomics, spatial transcriptomics, and clinical cohort, and mechanistic studies to dissect how LMRGs regulate immune evasion and drug resistance.

Conclusions

In summary, we identified an LMRG signature based on LUAD data stratified by LNM. Moreover, this finding of the three-gene signature reveals distinct metabolic features and the immune status of the TME for LUAD patients with or without LNM; it provides new insights into the exploration of molecular mechanisms and precision therapies LUAD regional metastasis.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1476/rc

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1476/prf

Funding: This study is supported by China National Natural Science Foundation (Nos. 82103403 and 82022048) and Natural Science Foundation of Guangdong Province, China (No. 2024A1515013161).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1476/coif). W.L. serves as an unpaid Associate Editor-in-Chief of Translational Lung Cancer Research from May 2025 to April 2026. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

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

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