Furmonertinib inhibits non-small cell lung cancer progression through ANGPT1-mediated regulation of cell migration and apoptosis

Introduction

Global cancer statistics reveal that lung cancer has become the leading cause of cancer-related morbidity and mortality worldwide, surpassing all other types of cancer (1). Lung cancer poses a significant threat to human health, with the 5-year relative survival rate improving from 11.5% in 1975 to approximately 20.5% in 2010–2016, reflecting advances in early detection and treatment (2). However, survival rates remain low compared to other malignancies, with age-standardized 5-year relative survival rates typically ranging from 10% to 20% across most regions globally (3). Furthermore, small cell lung cancer (SCLC), which accounts for approximately 15% of all lung cancers, carries a particularly poor prognosis, with overall 5-year survival rates remaining below 7% (4), although recent breakthroughs including consolidative durvalumab immunotherapy for limited-stage disease have demonstrated meaningful survival benefits (5).

Recent advancements in lung cancer treatment have been revolutionary, driven by targeted therapies, immunotherapies, and improved surgical and chemoradiotherapy strategies. VATS offers better functional outcomes and lower adverse events than open resection (6). Immunotherapy has become widely utilized, showing efficacy in advanced, perioperative, and neoadjuvant settings (7-9) . For instance, the CheckMate 816 trial demonstrated that neoadjuvant nivolumab plus chemotherapy significantly improved event-free survival (5-year rate: 45% vs. 26%) and overall survival (OS) (5-year rate: 65% vs. 55%) compared to chemotherapy alone in patients with resectable NSCLC, establishing perioperative immunotherapy as a new standard of care (9). In targeted therapy, lorlatinib demonstrated superior efficacy over crizotinib in advanced ALK-positive NSCLC (10). Adagrasib, a KRASG12C inhibitor, has shown promising clinical results in NSCLC patients with KRASG12C mutations without significant safety concerns (11). Additionally, a phase 2 clinical trial of Sotorasib in patients with advanced NSCLC harboring KRAS p.G12C mutations demonstrated robust anticancer activity (12). For EGFR-mutated advanced NSCLC, the combination of amivantamab and lazertinib has demonstrated superior efficacy compared to osimertinib alone, with significantly prolonged progression-free survival and has been approved by the FDA as a first-line chemotherapy-free treatment option (13). Furthermore, tyrosine kinase inhibitors (TKIs) targeting EGFR and HER2 are increasingly recognized for their potential in lung cancer treatment.

Furmonertinib (FUR) is a third-generation TKI used for the first-line treatment of NSCLC. Multiple clinical studies have demonstrated its unique advantages in EGFR-mutated NSCLC. For instance, a phase 2 clinical trial involving EGFR T790M-mutated NSCLC patients from multiple Chinese hospitals showed that FUR achieved an objective response rate of 74%, with all safety events being manageable (14). Compared to the first-generation TKI gefitinib, FUR has shown superior efficacy in EGFR-mutated advanced NSCLC patients, with significantly longer median progression-free survival and a notably lower incidence of adverse events (15). Notably, FUR also exhibits potential clinical benefits for NSCLC patients with brain metastasis. A clinical study reported that FUR treatment achieved a 50% objective response rate for leptomeningeal metastasis in NSCLC patients, with abnormal methylated fragment changes in cerebrospinal fluid correlating significantly with treatment response (16). Overall, FUR plays a significant role in lung cancer therapy. While numerous studies have confirmed its promising efficacy in NSCLC, the specific mechanism of action of FUR remains unclear. As a third-generation TKI, it overcomes the resistance limitations of earlier-generation TKIs, effectively targeting EGFR T790M-mutated NSCLC, which highlights its selective targeting of the EGFR T790M variant (17,18). However, further investigation into its unique target and mechanism of action is warranted.

While FUR is established as a third-generation EGFR-TKI, its complete mechanism of action remains incompletely understood. Through comprehensive analysis, we aim to identify EGFR-independent therapeutic targets of FUR in lung cancer using publicly available databases, thereby exploring potential non-canonical mechanisms that may contribute to its anti-tumor efficacy beyond EGFR inhibition. We conducted Kaplan-Meier survival analysis, correlation analysis, and gene set intersection analysis to pinpoint candidate targets. Subsequently, we will perform Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses to elucidate the functional roles and associated signaling pathways of these candidate genes. To validate the functional roles of the target genes in FUR -mediated inhibition of lung cancer cells, we will employ Western blotting, wound healings, and flow cytometry. Furthermore, to investigate the downstream regulatory mechanisms of the target genes, we will identify related genes and perform enrichment analysis to preliminarily characterize the downstream signaling pathways regulated by these target genes. To dissect potential EGFR-independent mechanisms of FUR, we initially employed A549 cells (EGFR wild-type) as a model system that excludes confounding effects of EGFR mutation-driven signaling. To validate the clinical relevance of identified targets in FUR’s approved indication, key findings were subsequently replicated in PC-9 cells harboring EGFR exon 19 deletion, a genotype representative of FUR’s primary clinical population. We present this article in accordance with the MDAR reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1335/rc).

Methods

Data collection

Gene expression datasets with matched clinicopathological information for the TCGA-LUAD cohort were retrieved through two bioinformatics resources: the UCSC Xena platform (available at https://xena.ucsc.edu) and the Genomic Data Commons portal (accessible via https://portal.gdc.cancer.gov). Gene expression datasets from GEO database were incorporated for differential analysis, including GSE19188 with transcriptome profiles of 65 matched non-cancerous tissues and 91 tumor specimens, along with GSE40791 containing bulk mRNA data from 94 malignant lesions and 100 normal pulmonary samples. Prognostic evaluation was performed using two independent GEO cohorts: GSE13213 (n=117 NSCLC cases with documented survival outcomes) and GSE31210 (n=246 LUAD patients with OS records). Structural information of FUR was extracted from PubChem, followed by target prediction through computational approaches employing PharmMapper and SwissTargetPrediction platforms. Consensus targets were defined as overlapping predictions generated by both algorithms. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Analysis of differential expression and prognostic

Gene expression disparities were computationally analyzed through the DESeq2 algorithm (v1.38.3) to detect transcriptional variations across tissue types. Statistically significant differential genes were defined by stringent thresholds: Benjamini-Hochberg adjusted P value <0.05 accompanied by |log2-transformed fold change (FC)| ≥1.0 when comparing pathological specimens to non-neoplastic controls. Prognostic evaluation leveraged R-based survival analytics tools, implementing the maximally selected rank statistic via surv_cutpoint() with minprop =0.3 to establish optimal expression-based stratification. Kaplan-Meier estimators visualized temporal survival patterns between molecularly defined subgroups, while inter-group divergence in mortality risk was statistically validated through Mantel-Cox testing (significance criterion: α=0.05).

GSEA functional enrichment analysis

Functional annotation exploration of pivotal genes was conducted by stratifying TCGA-LUAD specimens into dual expression cohorts using median expression thresholds for individual pivotal genes. Log₂FC metrics for non-key genes were computed and ordered descendingly. Enrichment profiling was executed via the clusterProfiler toolkit (v4.10.0) in R environment, implementing Gene Set Enrichment Analysis (GSEA) framework. The Hallmark gene collection (h.all.v2024.1.Hs.symbols.gmt) served as the reference dataset, obtained from the Molecular Signatures Database (MSigDB) repository (access portal: http://www.gsea-msigdb.org/gsea/msigdb).

Functional enrichment analysis

The GO resource serves as a framework for systematic functional annotation of gene products through three principal domains: cellular localization (CC), molecular activities (MF), and biological mechanisms (BP). As a complementary pathway database, the KEGG provided an integrated knowledge base encompassing genomic data, biochemical pathways, disease mechanisms, and pharmaceutical compounds. Enrichment evaluation of target genes across GO categories and KEGG pathways was implemented through computational analysis. Multiple testing correction was performed through the Benjamini-Hochberg procedure to control false discovery rates, with statistical significance established at a corrected P value cutoff of 0.05.

Construction of PPI network

Interconnectivity patterns among cellular proteins were analyzed through interaction network modeling. For this investigation, molecular interaction data were extracted from the STRING platform (https://string-db.org) using a confidence threshold of 0.4 (medium reliability filter), with remaining parameters maintained as predefined configurations. The topological architecture of protein associations was subsequently rendered through Cytoscape software (v3.9.1, National Resource for Network Biology) for graphical interpretation.

Materials

Furmonertinib (HY-112870) and LY294002 (HY-10108) were purchased from Med Chem Express (New Jersey, USA). Transfection reagents including Lipo8000™ (Cat# C0533-7.5ml) were procured from Beyotime Biotech Inc. (Shanghai, China). Gene-specific siRNA sequences were custom-synthesized by Sangon Biotech (Shanghai headquarters). Cell culture medium supplements (Opti-MEM) were obtained from Yuanye Bio-Technology (Shanghai, China). Immunoblotting analyses employed the following reagents: polyclonal antibody targeting human ANGPT1 (CSB-PA05739A0Rb-50ug; CUSABIO, Wuhan, China) and GAPDH-specific monoclonal antibody (bsm-33033M; Bioss Antibodies, Beijing, China).

Cell culture

The A549 human pulmonary carcinoma cell line was commercially sourced from Procell Life Science & Technology Co., Ltd (Wuhan, China). Cellular propagation was maintained in Ham’s F-12K medium (Kaighn’s modification) supplemented with 10% fetal bovine serum and 1% antibiotic-antimycotic cocktail, under standardized culture conditions of 37 ℃ with 5% CO₂ in a humidified incubator.

siRNA transfection

To knock down ANGPT1 expression, cells were transfected with a specific siRNA duplex targeting ANGPT1. The sense and antisense sequences of siRNA are shown in Table 1 (19). Cells were seeded in 6-well plates and grown to approximately 60–70% confluency prior to transfection. siRNA duplexes were diluted in Opti-MEM, and transfection was performed using Lipofectamine according to the manufacturer’s protocol. After 48–72 hours, cells were harvested for subsequent RNA or protein analysis to evaluate knockdown efficiency.

Wound healing assay

A549 cells were seeded in 6-well plates and treated with different concentrations of FUR or siRNA, then cultured in complete medium. After the cells adhered to the plates, they were scratched with a pipette tip of appropriate size and the initial wound width was photographed. Subsequently, the changes in wound width were photographed at two time points, 24 and 48 hours. Images were taken with the Nikon microscope (Tokyo, Japan). Each experiment was performed in triplicate wells per group, with n=3 independent biological replicates.

Western blotting

Following specified interventions, cellular samples were harvested and subjected to lysis using RIPA buffer containing protease inhibitor cocktail. Protein quantification was performed through bicinchoninic acid assay, with subsequent electrophoretic separation of 20-30 µg protein aliquots on SDS-polyacrylamide gels. Electrophoretically resolved proteins were transferred to PVDF membranes via semi-dry immunoblotting. Membrane blocking was conducted with 5% skimmed milk dissolved in Tris-buffered saline containing 0.1% Tween-20 (TBST) for 60 minutes at ambient temperature, followed by overnight incubation at 4 ℃ with primary antibodies: anti-ANGPT1 (1:1,000) and anti-GAPDH (1:5,000) for normalization. After washing with TBST, membranes received horseradish peroxidase-linked secondary antibodies for 1 h at 25 ℃. Chemiluminescent detection was achieved using ECL substrate, with image acquisition performed on a Tanon Science & Technology imaging system (Shanghai). Quantitative densitometry analysis utilized ImageJ software (Version 1.8.0.112; National Institutes of Health, Bethesda, MD). Each Western blot experiment was repeated independently three times using different cell preparations (n=3 biological replicates).

Apoptosis detection

Cell apoptosis was assessed using an Annexin V-FITC/PI Apoptosis Detection Kit following the manufacturer’s instructions. After treatment, cells were harvested, washed twice with cold PBS, and resuspended in 1× binding buffer (1×10⁶ cells/mL). Then, 5 µL Annexin V-FITC and 5 µL PI were added to 100 µL cell suspension, incubated in the dark at room temperature for 15 min, and diluted with 400 µL binding buffer. Samples were immediately analyzed by flow cytometry (BD FACSCalibur), with at least 10,000 events recorded per sample. Data were analyzed using FlowJo software (Version 10.8.1, Tree Star).

Cellular thermal shift assay (CETSA)

Cell thermal shift assay is a widely employed method for detecting drug–target binding (20). In this approach, cells are treated with FUR for 24 hours, after which the harvested cells are incubated at various temperatures for 10 minutes each. Subsequently, the expression levels of the target protein at different temperatures are assessed by western blotting.

Drug affinity response target stability (DARTS) assay

The interaction between FUR and ANGPT1 was assessed using the DARTS assay (21). A549 cells were lysed and centrifuged (4 ℃, 20,000 ×g, 10 min), and the supernatant was extracted and diluted with buffer. After incubating the diluted supernatant with DMSO or FUR for 2 hours, protease was added to a ratio of 2,000:1 or 4,000:1 to total protein, and incubation was continued for 20 min. SDS loading buffer was added, and the protein was boiled (100 ℃, 5 min). The prepared samples were then analyzed by Western blotting.

Data analysis

For bioinformatics analyses, differential expression was assessed using DESeq2 (Wald test with Benjamini-Hochberg correction), survival outcomes were compared by Mantel-Cox test, and functional enrichment was evaluated by hypergeometric test (GO/KEGG) or weighted Kolmogorov-Smirnov statistic (GSEA), all with multiple testing correction as appropriate. For in vitro experiments, group comparisons were analyzed using one-way ANOVA followed by Tukey’s post-hoc test for multiple comparisons, following confirmation of normality (Shapiro-Wilk test) and homogeneity of variance (Levene’s test). Data are presented as mean ± SD from n=3 biological replicates with individual data points overlaid.

Results

FUR inhibits migration and promotes apoptosis of lung cancer cells

To evaluate the effects of FUR on lung cancer, we treated our laboratory cell lines with various concentrations of FUR (n=3 biological replicates). The results demonstrated that increasing concentrations of FUR led to a reduction in the migratory ability of lung cancer cells (Figure 1A). Flow cytometry further revealed that the percentage of apoptotic cells increased with higher concentrations of FUR (Figure 1B). These findings indicated that FUR effectively inhibits migration and promotes apoptosis in our lung cancer cell lines.

Figure 1 FUR inhibits migration and promotes apoptosis of lung cancer cells. (A) Wound healing was used to detect the changes in the migration ability of A549 cells after treatment with FUR (magnification, ×100). (B) Flow cytometry was used to detect apoptosis of A549 cells after treatment with FUR. Data are presented as mean ± SD from n=3 biological replicates. **, P<0.01, ***, P<0.001, ****, P<0.0001 vs. control group (one-way ANOVA with Tukey’s post-hoc test). ANOVA, analysis of variance; FUR, furmonertinib; SD, standard deviation.

Acquisition and functional enrichment of FUR target genes

To identify the target genes of FUR in lung cancer, we intersected the FUR-related targets from PharmMapper and SwissTargetPrediction databases, resulting in 12 overlapping genes (Figure 2A and Table 2). Following gene intersection analysis, pathway annotation profiling was systematically performed on the shared genomic targets. KEGG analysis demonstrated significant involvement of these signaling pathways, including the Ras, PI3K-Akt, and MAPK signaling pathways (Figure 2B). GO analysis further demonstrated enrichment in peptidyl-tyrosine modification, peptidyl−tyrosine phosphorylation and other functions (Figure 2C). Notably, KEGG and GO analyses both found that the intersection genes have regulatory effects on stem cell function, suggesting that stem cells may be involved in the process of FUR treatment of NSCLC.

Figure 2 Acquisition and functional enrichment of FUR target genes. (A)Venn diagram of target gene intersection. (B) KEGG analysis of intersection genes. (C) GO analysis of intersection genes. FUR, furmonertinib; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

ANGPT1 is the target of FUR in lung cancer

To accurately identify relevant target genes, we performed a PPI expansion analysis of intersecting genes, where the size and color intensity of the circles represented the number of interacting genes. The inner circle depicted the target genes, while the outer circle represented the expanded interacting genes. The results revealed that target genes such as EGFR, IGF1R, and AKT1 exhibited a higher number of associated genes, as did genes like F2, CTSK, and F10. Among the expanded interacting genes, ERBB2, FGFR2, and GRB2 demonstrated the most interactions with the target genes (Figure 3A). Following this approach, functional pathway assessment was performed on the prioritized genomic targets related to FUR’s pharmacological activity. Analytical outcomes demonstrated marked overrepresentation of these molecular targets in numerous signaling cascades and regulatory networks, highlighting their potential mechanistic involvement (Figure 3B,3C). To further narrow down the target genes, we performed differential expression and prognosis analyses of the expanded genes in two GEO differential datasets (GSE19188, GSE40791) and two GEO prognosis datasets (GSE13213, GSE31210). This process identified three candidate target genes with potential effects on lung cancer: ANGPT1, SHC3, and ERBB4 (Figure 3D). Significant analysis of these three genes in the GSE19188 and GSE40791 datasets showed that ANGPT1 exhibited the highest differential significance (Figure 3E). Prognostic risk analysis in the GSE13213 and GSE31210 datasets, as shown in the forest plot, revealed no significant association between SHC3 and ERBB4 and related risks, while ANGPT1 demonstrated a significant association (Figure 3F). Moreover, transcript quantities for each of the three genes exhibited marked elevation in normal tissues relative to neoplastic specimens (Figure S1). Survival analysis indicated no significant association between ERBB4 and patient survival outcomes. In contrast, the expression of ANGPT1 and SHC3 was positively correlated with OS time (Figure 3G-3I). Based on these findings, ANGPT1 emerged as the most promising candidate for functional validation among the intersecting targets.

Figure 3 ANGPT1 is the target of FUR in lung cancer. (A) PPI expanded genes (inner circle: target genes; outer circle: expanded interacting genes). (B) GO enrichment analysis of extended genes. (C) KEGG enrichment analysis of extended genes. (D) Intersection Venn diagram of the four data sets (GSE19188, GSE40791, GSE13213 and GSE31210). (E) Differential analysis of target genes. (F) Prognostic results of target genes. (G) Prognostic analysis of ANGPT1, (H) SHC3 and (I) ERBB4. FC, fold change; FUR, furmonertinib; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; OS, overall survival; PPI, protein-protein interaction.

FUR inhibits lung cancer progression by promoting the expression of ANGPT1

To validate our hypothesis, we conducted in vitro experiments using various concentrations of FUR to investigate the expression changes of ANGPT1 protein via Western blot analysis (n=3 biological replicates). The results demonstrated that ANGPT1 protein expression increased with rising concentrations of FUR (Figure 4A), indicating that FUR promotes the expression of ANGPT1. Furthermore, we established ANGPT1-knockdown cell lines and treated them with FUR, followed by wound healing and flow cytometry to assess changes in cell migration and apoptosis, respectively (Figure 4B). The wound healing revealed that ANGPT1 knockdown enhanced cell migration, while FUR treatment attenuated this effect (Figure 4C). Flow cytometry showed that ANGPT1 knockdown reduced the proportion of apoptotic cells, whereas FUR treatment increased apoptosis, especially at 48 h (Figure 4D). These findings suggest that ANGPT1 deficiency promotes cell migration and inhibits apoptosis, an effect that FUR can reverse. CETSA showed that FUR treatment enhanced the stability of ANGPT1 under high-temperature conditions (Figure 4E). Simultaneously, limited proteolysis experiments indicated that FUR reduced the sensitivity of ANGPT1 to pronase (Figure 4F). In summary, FUR stabilizes ANGPT1 protein and upregulates its expression, thereby inhibiting cell migration and promoting apoptosis. These findings are consistent with the hypothesis that ANGPT1 serves as a functional mediator of FUR’s anti-tumor effects, although direct physical interaction remains to be validated by orthogonal biophysical methods.

Figure 4 FUR inhibits lung cancer progression by promoting the expression of ANGPT1. (A) Western blotting was used to detect the changes in ANGPT1 expression levels after cells were treated with different concentrations of FUR. (B) Western blotting to detect the effect of siRNA knockdown of ANGPT1. (C) Wound healing to detect the migration ability of cells after ANGPT1 knockdown or simultaneous treatment with FUR (magnification, ×100). (D) Flow cytometry was used to detect the changes in the proportion of apoptotic cells after ANGPT1 knockdown or simultaneous treatment with FUR. (E) CETSA was used to detect the stability of ANGPT1 under high temperature conditions after FUR treatment. (F) DARTS analysis was used to detect the sensitivity of ANGPT1 to proteases in the presence of FUR. **, P<0.01; ***, P<0.001; ****, P<0.0001 vs. siNC or siANGPT1 group. Data are presented as mean ± SD from n=3 biological replicates. **, P<0.01; ***, P<0.001; ****, P<0.0001 vs. control group (one-way ANOVA with Tukey’s post-hoc test). ANOVA, analysis of variance; CETSA, cell thermal stability analysis; FUR, furmonertinib; NC, negative control; SD, standard deviation.

ANGPT1 may affect the migration ability of lung cancer cells by regulating cell adhesion and ECM processes

To further elucidate the mechanism of ANGPT1 in lung cancer cells, we performed bioinformatics analyses to identify downstream target genes regulated by ANGPT1. GSEA analysis revealed that downstream genes were primarily associated with E2F-TARGETS and G2M-CHECKPOINT pathways (Figure 5A). Additionally, we intersected differentially expressed genes from single-gene differential expression analysis with target genes identified by weighted gene co-expression network analysis (WGCNA) , resulting in 253 overlapping genes (Figure 5B). GO and KEGG analyses of these intersecting genes indicated that they were mainly enriched in functions such as cell-substrate adhesion, cell-matrix adhesion, cytoskeleton organization in muscle cells, cell adhesion molecules, and ECM-receptor interaction, all of which are related to cell migration (Figure 5C,5D). In summary, FUR inhibits the migration and promotes apoptosis of lung cancer cells by upregulating ANGPT1 expression. Additionally, ANGPT1 may regulate cell migration through its involvement in cell adhesion and ECM processes.

Figure 5 ANGPT1 may affect the migration ability of lung cancer cells by regulating cell adhesion and ECM processes. (A) GSEA analysis of ANGPT1. (B) Venn diagram of the intersection of single gene differential analysis and WGCNA analysis of ANGPT1. (C) GO enrichment of intersection genes (ANGPT1). (D) KEGG enrichment of intersection genes (ANGPT1). DEG, differentially expressed gene; ECM, extracellular matrix; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; LUAD, lung adenocarcinoma; TCGA, The Cancer Genome Atlas; WGCNA, weighted gene co-expression network analysis.

ANGPT1 modulates FUR sensitivity via the PI3K/AKT signaling pathway in A549 cells

To investigate the role of ANGPT1 in regulating the PI3K/AKT signaling pathway and its impact on FUR sensitivity in A549 cells (n=3 biological replicates), Western blot analysis was performed. As shown in (Figure 6A), knockdown of ANGPT1 significantly increased the phosphorylation levels of PI3K and AKT without affecting total PI3K and AKT protein levels, indicating activation of the PI3K/AKT pathway. Treatment with FUR partially reversed the elevated levels of p-PI3K and p-AKT induced by siANGPT1. Functional assays further demonstrated that ANGPT1 silencing enhanced cell migratory ability, as evidenced by accelerated wound closure in the wound healing assay at 24 and 48 h. FUR treatment attenuated this effect, and the addition of the PI3K inhibitor LY294002 further suppressed cell migration (Figure 6B). Consistently, flow cytometry analysis revealed that inhibition of PI3K signaling markedly increased apoptosis rates compared with both the control and siANGPT1 groups. The combination of siANGPT1, LY294002, and FUR exhibited a synergistic effect in promoting apoptosis (Figure 6C). Collectively, these findings suggest that ANGPT1 regulates the sensitivity of A549 cells to FUR by modulating the PI3K/AKT signaling pathway.

Figure 6 ANGPT1 modulates FUR sensitivity via the PI3K/AKT signaling pathway in A549 cells. (A) Western blot analysis of p-PI3K, PI3K, p-AKT, and AKT in A549 cells transfected with siANGPT1 and treated with or without FUR. (B) Wound healing assay was performed to assess cell migration under different treatment conditions (siNC, siANGPT1 + FUR, siNC + LY294002, and siANGPT1 + LY294002 + FUR). (C) Apoptosis analysis was conducted by flow cytometry using Annexin V/PI staining. Data are presented as mean ± SD from n=3 biological replicates. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001 vs. control group (one-way ANOVA with Tukey’s post-hoc test). ANOVA, analysis of variance; FUR, furmonertinib; SD, standard deviation.

Validation of the FUR-ANGPT1 axis in EGFR-mutant PC-9 cells

To assess whether the FUR-mediated ANGPT1 regulation was conserved in the clinically relevant EGFR-mutant context, we replicated key experiments in PC-9 cells (n=3 biological replicates), an EGFR exon 19 deletion mutant line representative of FUR’s primary clinical population. Consistent with A549 findings, FUR treatment dose-dependently upregulated ANGPT1 protein expression (Figure S2A), inhibited cell migration in wound healing assays (Figure S2B), and promoted apoptosis as detected by flow cytometry (Figure S2C). These results support that FUR-mediated ANGPT1 upregulation represents a non-canonical mechanism parallel to EGFR inhibition, with functional relevance in both EGFR wild-type and mutant backgrounds.

Discussion

Lung cancer remains the leading cause of cancer-related deaths worldwide, posing a significant threat to human health (1). TKIs have increasingly demonstrated their therapeutic potential in lung cancer, with the third-generation TKI, FUR, exhibiting unique advantages (17,22,23). However, the mechanisms underlying the action of FUR in lung cancer remain poorly understood, and its specific molecular targets and associated signal pathways are not well-defined. This study aimed to identify the targets of FUR using publicly available databases and validate the biological functions of these targets through experimental studies, thereby elucidating its mechanisms of action and providing new insights into lung cancer clinical treatment.

We identified several candidate target genes by analyzing the intersection of two public databases. Further analysis, including PPI network expansion, differential expression analysis, prognosis analysis, and correlation analysis, revealed ANGPT1 as the top candidate for functional validation among intersecting targets. ANGPT1 is located on human chromosome 8 at position 8q23.1 and encodes angiopoietin-1, a member of the angiopoietin family closely associated with angiogenesis, repair, and regulation of vascular permeability (24). ANGPT1 not only regulates angiogenesis but also plays a crucial role in tumor growth, immune evasion, and prognosis in various cancers. Interestingly, ANGPT1 exhibits dual roles in cancer with high specificity. In breast cancer, ANGPT1 expression is positively correlated with OS and distant metastasis-free survival (DMFS) (25), and overexpression slows tumor growth in mouse models (26), suggesting a tumor suppressive role. Conversely, in endometrial cancer, high ANGPT1 expression is associated with poor prognosis and immune evasion (27), indicated pro-tumorigenic potential. In lung cancer, ANGPT1 has been identified as a tumor suppressor in some studies (28,29). However, we emphasize that the present study does not establish ANGPT1 as a universal tumor suppressor. The pro-apoptotic and anti-migratory effects observed here are specific to the FUR-treated NSCLC cell model (A549 and PC-9), and ANGPT1’s functional directionality may differ in other tumor types, microenvironments, or therapeutic contexts. Future studies should systematically evaluate ANGPT1 function across diverse NSCLC subtypes and in vivo models to clarify its context-dependent biology.

An important consideration is how the ANGPT1-PI3K/AKT axis identified here relates to the established EGFR-targeting mechanism of FUR. Our bioinformatics analysis indeed ranked EGFR as the top predicted target (Table 2, Figure 3A), consistent with FUR’s pharmacological identity as a third-generation EGFR-TKI. However, we note that target prediction algorithms identify potential binding partners based on structural similarity and do not preclude additional targets. The functional validation in A549 cells, which harbor wild-type EGFR, is designed to isolate EGFR-independent effects and test whether FUR exerts anti-tumor activity through alternative pathways. The subsequent replication of key findings in PC-9 cells, an EGFR exon 19 deletion mutant line, confirms that ANGPT1-mediated suppression of migration and enhancement of apoptosis are conserved in the clinically relevant EGFR-mutant context. These results are consistent with a hypothetical convergent model in which canonical EGFR inhibition operates alongside non-canonical ANGPT1 upregulation, with both pathways potentially converging on PI3K/AKT suppression. Whether ANGPT1 functions downstream of EGFR or as a parallel pathway remains to be determined, and future studies examining the crosstalk between these axes would be valuable.

Through GSEA, we identified potential downstream genes regulated by ANGPT1, primarily associated with E2F-TARGETS and G2M-CHECKPOINT pathways, which are involved in cell cycle regulation. The ANGPT1 complex promotes Tie2-mediated activation of the PI3K/AKT signaling pathway, which in turn facilitates the progression of periodontal ligament fibroblasts into the S and G2/M phases, ultimately promoting their proliferation (30). In leukemia, chemotherapy-induced reduction of ANGPT1 mRNA levels in the bone marrow niche coincided with hematopoietic stem cells entering the cell cycle, leading to failed hematopoietic reconstitution (31). This suggested a potential connection between ANGPT1 expression and the cell cycle dynamics of hematopoietic stem cells. In a pulmonary hypertension mouse model, miR-495 upregulated ANGPT1 expression, and in vitro experiments demonstrated that miR-495 inhibition promoted cell progression into the G2/M and S phases (32). These findings implied a relationship between ANGPT1 expression and cell cycle regulation. E2F transcription factors are critical regulators of the cell cycle, playing significant roles in cell proliferation, apoptosis, DNA repair, and tumorigenesis (33). Comprehensive analysis across multiple databases revealed that E2F was significantly upregulated in lung adenocarcinoma and correlated with poor patient prognosis (34). Additionally, PLOD1 was shown to promote E2F activation, contributing to lung cancer progression (35). However, to date, no studies have directly elucidated the mutual regulatory relationship between E2F and ANGPT1 in lung cancer, highlighting a critical gap in this field that warrants further investigation.

We also identified intersecting genes between differentially expressed genes from single-gene analysis and target genes screened by WGCNA. Functional enrichment analysis of these intersecting genes revealed significant enrichment in pathways such as “cell-substrate adhesion”, “cell-matrix adhesion”, “cytoskeleton in muscle cells”, “cell adhesion molecules”, and “ECM-receptor interaction”, all of which are closely related to cell migration. This suggested that ANGPT1 may inhibit lung cancer progression by regulating genes and pathways associated with lung cancer cell migration. The ECM is a three-dimensional network structure composed of various structural and functional proteins, such as collagen, laminin, elastin, and fibronectin, located between cells to provide structural support and regulate signaling processes (36). Cell adhesion refers to the physical binding of cells to adjacent cells or the ECM through adhesion molecules, such as integrins and cadherins, playing a critical role in regulating cell migration, proliferation, differentiation, and apoptosis (37). Dysregulation of ECM and cell adhesion pathways is implicated in tumor progression, metastasis, and drug resistance in lung cancer (38-42). These findings are consistent with our hypothesis and provide direction for future experimental validation.

It is important to note that CETSA and DARTS detect changes in protein stability rather than direct physical binding per se. These assays cannot distinguish direct binding from indirect stabilization effects (e.g., via chaperone proteins or conformational changes induced by upstream signaling). Therefore, we interpret these data cautiously as suggestive of altered ANGPT1 protein stability in the presence of FUR, and explicitly refrain from concluding direct physical interaction. Definitive confirmation of direct binding would require orthogonal biophysical techniques (e.g., SPR, ITC, or MST), which are beyond the scope of the current study but represent an important future direction. CETSA and DARTS experiments were performed to explore whether FUR functionally modulates ANGPT1 at the protein level, providing preliminary biochemical evidence consistent with FUR-mediated ANGPT1 stabilization, although they do not establish direct physical binding.

This study elucidated the mechanism by which FUR inhibits lung cancer cell migration and induces apoptosis by promoting the expression of ANGPT1. However, despite clear results at the cellular level, the study has notable limitations that warrant further investigation and validation. First, while initial mechanistic studies were conducted in A549 cells (EGFR wild-type) to isolate EGFR-independent effects, we validated key findings in PC-9 cells (EGFR exon 19 deletion) to confirm clinical relevance in FUR’s approved indication. Nevertheless, additional EGFR-mutant lines (e.g., H1975 harboring T790M resistance mutation) and in vivo xenograft models are warranted to fully establish the generalizability and therapeutic contribution of the ANGPT1 axis relative to canonical EGFR inhibition. Second, the relationship between EGFR and ANGPT1 signaling remains correlative rather than mechanistically resolved; whether these pathways operate in parallel, converge at PI3K/AKT, or exhibit feedback crosstalk requires further investigation. The potential clinical efficacy of FUR in lung cancer treatment remains unverified, particularly regarding whether ANGPT1 expression correlates with therapeutic responses in patient samples. Therefore, future studies should incorporate additional clinical tissue samples and in vivo animal models to comprehensively assess the efficacy and stability of FUR-mediated ANGPT1 regulation in actual lung cancer progression and treatment. Third, while this study confirms ANGPT1 as a key target of FUR and provides initial insights into its effects on cell migration and apoptosis, the broader biological functions of ANGPT1 in lung cancer development have not been systematically explored. Given that ANGPT1 is a crucial molecule in angiogenesis and cellular homeostasis, its roles in tumor microenvironment remodeling, immune response modulation, and resistance mechanisms deserve further investigation. Additionally, although this study identified downstream regulatory pathways and candidate target genes through bioinformatics methods such as GSEA and WGCNA, the findings have not been experimentally validated at the molecular level. Without direct experimental evidence for the expression, function, and regulatory mechanisms of these downstream molecules in cellular systems, a comprehensive understanding of the ANGPT1 regulatory network remains incomplete. Despite these limitations, this study provides a foundation for understanding EGFR-independent mechanisms of FUR and identifies ANGPT1 as a candidate mediator worthy of further investigation.

Conclusions

In conclusion, our results support the involvement of ANGPT1 in the process by which FUR inhibits lung cancer progression. By upregulating and stabilizing ANGPT1, FUR disrupts cell migration and enhances apoptotic pathways in lung cancer cells. Notably, CETSA and limited proteolysis assays were consistent with the hypothesis that FUR may enhance ANGPT1 protein stability, suggesting a dual mechanism involving both post-translational stabilization and post-translational stabilization. These findings provide novel insights into the therapeutic potential of FUR and suggest that ANGPT1 may serve as both a biomarker and a functional mediator of its anti-cancer effects (Figure 7).

Figure 7 Schematic diagram illustrating the proposed mechanism of FUR in NSCLC. The diagram depicts FUR-mediated upregulation of ANGPT1 and its functional consequences: PI3K/AKT signaling inhibition, suppression of migration via ECM/cell adhesion pathways, and promotion of apoptosis. We note that the ‘Direct binding to ANGPT1’ label in the diagram represents a preliminary hypothesis based on CETSA and DARTS stabilization assays; these data do not establish direct physical interaction, which requires orthogonal biophysical validation. This ANGPT1-mediated axis was functionally validated in both A549 (EGFR wild-type) and PC-9 (EGFR exon 19 deletion) cells. CETSA, cell thermal stability analysis; DARTS, drug affinity response target stability; ECM, extracellular matrix; FUR, furmonertinib; NSCLC, non-small cell lung cancer.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1335/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1335/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1335/prf

Funding: This study was supported by Inner Mongolia Medical University Science and Technology Million Project (Joint)-YKD2022LH065.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1335/coif). The 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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