PKC-iota drives EGFR-TKI resistance in EGFR-mutated NSCLC by phosphorylating FASN to reprogram lipid metabolism

Introduction

Lung cancer is the most frequently diagnosed malignancy and the leading cause of cancer-related death worldwide (1). Non-small cell lung cancer (NSCLC) accounts for ~85% of lung cancer, with adenocarcinoma (ADC) and squamous cell carcinoma (SQCC) as the predominant subtypes (2). Epidermal growth factor receptor (EGFR)-driven mutations (e.g., 21L858R, 19DEL) happen in nearly 50% of Asian NSCLC (3,4). Although EGFR tyrosine kinase inhibitors (TKIs) have markedly improved outcomes in EGFR-mutant NSCLC over the past two decades, intrinsic resistance occurs in 20–30% of cases, and acquired resistance develops in nearly all patients (5,6), giving rise to a critical unresolved clinical challenge in EGFR-mutant NSCLC. EGFR-TKI-resistant-associated EGFR mutations (e.g., Ex20ins, T790M), activation of bypass signaling pathways (e.g., MET/HER2/HER3), and histologic/phenotypic transformation are the most common mechanisms of resistance, thereby significantly increasing the complexity of subtyping and treatment decision-making after progression. However, the resistance mechanisms remain undefined in approximately 30–40% of cases (7), hampering the clinical application of EGFR-TKIs. Since the multi-mechanism coexistence of resistance suggests that single-gene-level explanations are often insufficient (8,9). Therefore, understanding the molecular basis of EGFR-TKI resistance is essential for developing effective therapeutic strategies.

The protein kinase C (PKC) family comprises at least 12 serine (Ser)/threonine (Thr) kinases classified into classical, novel, and atypical subgroups (10-12). Atypical PKC-iota, the sole bona-fide human oncogene among PKCs, is frequently overexpressed in multiple cancers, including NSCLC, where it promotes tumor growth, invasion, and progression (13,14). Previous work has reported that PRKCI gene amplification may be the mechanism underlying PKC-iota protein overexpression in NSCLC tumors, while certain molecules (e.g., phosphatidylinositol 3,4,5 trisphosphate, NGF) can activate PKC-iota to be involved in tumor progression (14,15). While pharmacological inhibition of PKC-iota has shown potential in restoring EGFR-TKI sensitivity in resistant NSCLC models, the mechanisms underlying PKC-iota-mediated drug resistance remain unclear (16,17).

Abnormal lipid metabolism is a hallmark of cancer, with de novo fatty acid (FA) synthesis supporting tumor growth and signaling (18,19). Several recurring “lipid features” have been implicated in EGFR-TKI resistance. Sustained de novo lipogenesis, often via the transcription factor sterol-regulatory element binding protein 1 (SREBP1) programs, can become a hallmark of resistance and contributes to drug insensitivity/resistance, as inhibition of SREBP1 has been shown to resensitize EGFR-mutant-resistant models to gefitinib (20). Moreover, perturbation of lipid rafts has been shown to trigger ligand-independent EGFR activation, providing a mechanistic rationale for why lipid remodeling can translate into aberrant receptor activation states (21,22). Fatty acid synthase (FASN), a key enzyme in FA de novo synthesis, is upregulated in many cancer types to promote tumor malignant progression, including inducing EGFR-TKI resistance in EGFR-mutated NSCLC (23-26). Membrane lipid composition directly influences EGFR activity by regulating membrane fluidity, which affects EGFR endocytosis and degradation (27-29). Increased ratios of unsaturated to saturated FAs, or of short- to long-chain FAs, enhance membrane fluidity, thereby stabilizing EGFR signaling and promoting drug resistance (23,30-32). However, whether PKC-iota regulates FASN activity and FA synthesis to drive EGFR-TKI resistance remains unknown.

Based on previous studies, we hypothesized that PKC-iota directly phosphorylates FASN, thereby promoting FA synthesis in EGFR-mutated NSCLC. This metabolic reprogramming alters membrane fluidity, impairs lipid raft–mediated EGFR endocytosis and degradation, and ultimately promotes EGFR-TKI resistance. The PKC-iota/FASN axis may serve as a promising target for overcoming EGFR-TKI resistance. Therefore, we established four distinct cell lines and animal models to investigate the role of PKC-iota in metabolic reprogramming and in driving EGFR-TKI resistance in vitro and in vivo. We present this article in accordance with the ARRIVE and MDAR reporting checklists (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1260/rc).

Methods

Cell culture, antibodies and reagents

EGFR-mutated human NSCLC cell lines H1975 (ATCC, VA, USA, CRL-5908) and PC9 (FuHeng Cell Center, Shanghai, China, CRL-5908) were maintained in RPMI1640 (Gibco, NY, USA). 293T cells (ATCC, CRL-3216) were maintained in DMEM (Gibco). Routinely, media were supplemented with 10% fetal bovine serum (FBS, Gibco). All cell lines were cultured at 37 ℃ in a humidified atmosphere containing 5% CO₂. Cells were passaged every 3–4 days upon reaching approximately 80–90% confluence. During passaging, cells were gently detached using 0.25% trypsin-EDTA (CellorLab, Shanghai, China) and reseeded at appropriate densities to ensure exponential growth. Microbial contamination (e.g., mycoplasma, bacteria, fungi) was routinely checked to ensure culture purity. The cells were seeded in well plate and incubated for 24 hours to allow cell adhesion. The antibodies used in this study are listed in Table S1. The following additional reagents were in the present study: MG132 (HY-13259, MCE, NJ, USA); cycloheximide (HY-12320, MCE); EGFT-TKIs gefitinib (HY-50895, MCE), afatinib (HY-10261, MCE), and osimertinib (HY-15772, MCE); Dynasore (HY-15304, MCE); Pitstop2 (HY-115604, MCE); TMA-DPH (HY-D0986, MCE); EGF (HY-P7109, MCE).

Plasmid, siRNA, and transfection

The open reading frame of PRKCI was constructed into Flag-tagged pcDNA3.1-MCS2 (Invitrogen, CA, USA) and GST-tagged PGEX4-T-1 (Invitrogen). The open reading frame of FASN was constructed into HA-tagged pcDNA3.1-MCS2 (Invitrogen). All plasmids were confirmed by DNA sequencing. SiRNA and negative control were designed and synthesized by GenePharma (Shanghai, China). Lipofectamine 2000 (Invitrogen) and Hieff Trans Liposomal Transfection Reagent (Yeasen, Shanghai, China) were used for transient transfection, following the manufacturer’s protocols. The siRNA sequences used are shown in Table S2.

GST pull-down assay

The GST-tagged PKC-iota protein was expressed in E. coli BL21(DE3) cells (WEIDI, Shanghai, China) through induction with 0.4 mmol/L IPTG (Ameresco, MA, USA). Bacterial cells were lysed by sonication, and the supernatant was collected. The supernatant was incubated with GST magnetic beads (Shanghai Epizyme Biomedical Technology Co., Ltd., Shanghai, China) overnight at 4 ℃. The following day, the bound GST magnetic beads were incubated overnight at 4 ℃ with appropriate cell lysates before immunoblotting.

Mass spectrometry

GST-PKC-iota-bound proteins were resolved by SDS–PAGE and stained with Coomassie blue R250 (Yeasen). We extracted approximately 273 kDa protein bands from the gels and subjected them to liquid chromatography-mass spectrometry (LC-MS; EASY-nLC 1200; Thermo Fisher Scientific).

For intracellular lipids assays, lung cancer cells were cotransfected with siRNA and plasmid for 48 hours, then cells were lysed in cell lysis buffer P0013 (Beyotime) at 4 ℃. Lipid molecules in lysates were then tested by gas chromatography-mass spectrometry (GC-MS; Agilent Technologies). The lipid quantity was normalized to total protein levels.

Molecular docking

Human PKC-iota (PDB ID: 1ZRZ) and FASN (PDB ID: 8VLE) were downloaded from PDB (http://www.rcsb.org/). All crystallographic water and other small molecules were removed. The Zdock score and the visualized docking structures were analyzed by Discovery Studio software (BIOVIA, CA, USA). A high Zdock score means a high affinity between proteins.

Co-immunoprecipitation (Co-IP)

Lung cancer cells were lysed in IP lysis buffer containing phosphatase and protease inhibitors. Immunoprecipitation was carried out by appropriate cell lysates overnight at 4 ℃ with Protein A/G Magnetic Beads (YJ003) or Anti-HA Magnetic Beads (YJ005) or Anti-DYKDDDDK Magnetic Beads (YJ007) (Epizyme). The co-precipitated proteins bound to the beads were then collected for immunoblotting.

Proximity ligation assay (PLA)

Seed lung cancer cells into 24-well plates containing pre-inserted glass slides. Once the cells reach approximately 70% confluence, wash them twice with PBS, fix with 4% paraformaldehyde (PFA) for 30 minutes at room temperature, and then wash twice with precooled PBS. PLA was performed using the Duolink^®In situ Red Starter Kit Mouse/Rabbit (Sigma-Aldrich, MO, USA) according to the manufacturer’s instructions. Slides were stored at −20 ℃ before analyzed by confocal micros +copy (Leica TCS SP8) (Carl Zeiss, Jena, GER) using a 63× objective.

Immunofluorescence (IF)

Lung cancer cells (5×10³ cells/well) were seeded into a 24-well plate with glass coverslips for 24 hours before staining. The cells were then fixed with 4% paraformaldehyde, permeabilized with 0.1% Triton X-100, and blocked with 1% BSA for 2 hours at room temperature. The primary antibodies were then added and incubated overnight at 4 ℃. The secondary antibody conjugated to Alexa Fluor 488 (green) and Alexa Fluor 594 (red) (Invitrogen) were added and incubated at 37 ℃ for 2 hours. Nuclei were stained using 4’,6-diamidino-2-phenylindole (DAPI, Invitrogen). IF was visualized under a confocal microscope (Leica TCS SP8) (Carl Zeiss, Jena, Germany) using a 63× objective.

Lipid staining

For cell lipid staining, cells seeded in 6-well plates were washed with PBS, and then Lipid staining was performed after 48 hours of transfection according to the manufacturer’s protocol of the Oil Red O staining kit (Solarbio, Beijing, China) or the Nile Red staining kid (Solarbio). The stained-lipid droplets were observed via a general (Oil Red O) or fluorescent (Nile Red) microscope.

For Oil Red O staining of tissues, tumor samples excised from mice were immediately snap-frozen in liquid nitrogen and then processed into cryosections according to established protocols (33). Fixed sections (6–10 µm thick) were immersed in 60% isopropanol before staining with Oil Red O working solution for 5–10 minutes. Nuclei were counterstained with Mayer’s hematoxylin. Stained slides were observed under a microscope.

Gene expression analysis

Total RNA of cells was extracted using E.Z.N.A total RNA Kit I (Omega Bio-Tek, GA, USA). cDNA was synthesized using Primer Script RT reagent Kit (Takara, OSTU, Japan). The real-time PCR reaction was performed according to the protocol of the SYBR Premix Ex Taq kit (Takara) using a Step-One-Plus Real-Time PCR System (Applied Biosystems, USA). The fluorescence data of the detected genes were normalized to cyclophilin B (CB) expression using the 2^-ΔΔCT method. Primers used are listed in Table S3.

Cell membrane fluidity assay

After 24 hours of transfection, cells were evenly plated onto 3.5 cm confocal dishes and cultured overnight to reach approximately 50–60% confluency. The fluorescent probe TMA-DPH (MCE) was then added under light-protected conditions and incubated at 37 ℃ for 30 minutes. Cell observation was performed using a microscope. Subsequently, the cells were detached using 0.25% trypsin-EDTA and centrifuged at 1,000 rpm for 5 minutes, and the supernatant was discarded. The cell pellet was washed three times with 1× PBS buffer, then the cell suspension was diluted to 4 mL and immediately subjected to fluorescence polarization measurement using fluorescence spectrophotometer, excitation wavelength of 355 nm, emission wavelength of 430 nm, slit 10 nm, temperature 25 ℃. The degree of polarization (P), and membrane lipid fluidity (LFU), respectively, were calculated according to the following formula:

P=(Iv−Ih)/(Iv+Ih),LFU=(0.5−P)/P2[1]

I_v: the polarizer and the detector optical axis of the light intensity in the vertical direction. I_h: axis of the polarizer in a vertical direction, the detector of the optical axis of the light intensity in the horizontal direction.

Membrane protein extraction

Membrane protein fractions were isolated using the Membrane and Cytosol Protein Extraction Kit (EpiZyme, Shanghai, China) according to the manufacturer’s instructions, followed by immunoblotting.

Half maximal inhibitory concentration (IC50) analysis

The IC₅₀ of EGFR-TKIs was measured using the Cell Counting Kit-8 (CCK-8) (CellorLab, Shanghai, China). Cells were seeded in a 96-well plate (5,000 cells per well, three parallel wells) and incubated in medium containing varying concentrations of EGFR-TKIs for 72 hours. 10 µL of CCK-8 reagent was then added to each well, and the plate was incubated at 37 ℃ for 2 hours. Absorbance was measured at 450 nm using a microplate reader and normalized by the total protein level. The IC₅₀ values were calculated using GraphPad Prism 9.5.0 (GraphPad Software, San Diego, CA, USA).

Cell growth assay

For the cell count assay, different cells were plated on 96-well plates (5,000 cells per well, three parallel wells) and then harvested and counted at different time points. Trypan blue staining was used to identify dead cells when counting.

CCK-8 (Yeasen) was also used to assess cell proliferation. In brief, cells were seeded in 96-well plates (5,000 cells per well, three parallel wells) before being incubated with different reagents.

For the cell colony formation assay, different cells were seeded in 6-well plates (1,000 cells per well, three parallel wells) for 2 weeks at 37 ℃ with 5% CO₂. Colonies were fixed with methanol and stained with 0.1% crystal violet (Sigma-Aldrich) for 15 minutes. The number of colonies was quantified using ImageJ software (NIH, MD, USA).

Cell apoptosis assay

Following 72-hour treatment with osimertinib (1 µmol/L), both the cells in the cell culture medium and the adherent cells were collected and washed with 1× PBS. Apoptosis assay was done using a Annexin V-FITC/PI Apoptosis Detection Kit (EpiZyme), following the manufacturer’s protocol. The samples were finally analyzed using a BD FACSCanto II flow cytometer (BD Biosciences, CA, USA), with 10,000 cells were collected and counted per sample. The experiment was performed in triplicate, and the results were analyzed using FlowJo V10.8.1 (BD Biosciences).

Lentivirus production

The PRKCI sequence was inserted into EGFP-tagged 3×Flag-PGK-Puro (Obio Technology, Shanghai, China). Annealing and connection of the shRNAs were performed before insertion into pLV-U6-EGFP-Puro (Obio Technology). All the above products were transformed into 293T cells using lentivirus packaging reagents (GenePharma) according to the manufacturer’s protocol to generate the virus. The shRNA sequences used in our research are shown in Table S4.

Xenograft study

Different viruses were infected into H1975 cells to form stable H1975 with or without ectopic PKC-iota overexpression, and with or without FASN knockdown. Different cells (5×10⁶ cells in 100 µL PBS) were subcutaneously inoculated into the right scapular region of BALB/c nude mice (48, female, 5-week-old, Beijing Biotechnology Co., Ltd., Beijing, China). Mice were maintained in a specific pathogen-free (SPF) chamber under conditions of 25 ℃, 50% humidity, and a 12-h light/12-h dark cycle. Starting on the 5th day, tumor size was measured every 3 days, and tumor volume was calculated using the formula V = (length × width × height)/3. On day 10, mice were randomly divided into control group (90% corn oil, oral gavage; 6 mice each cell type) and osimertinib-treatment group (2 mg/kg in 100 uL 90% corn oil, oral gavage; 6 mice each cell type) to minimize potential confounders, the grouping was carried out by an investigator blinded to the experimental conditions. Mice were euthanized after 21 days of treatment, and subcutaneous tumors were removed and weighed. No mice were excluded from this study. The O-sensitivity of tumors was analyzed by comparing the volume or weight difference in O-treated and untreated tumors, to obtain the O-induced volume or weight decrease rate of tumors, which means the drug sensitivity of tumors. All animal experiments were-approved by the Animal Ethics Committee of Shanghai-Chest Hospital, Shanghai Jiao Tong University School of Medicine (No. KS24021), in compliance with institutional guidelines for the care and use of animals. A protocol was prepared before the study without registration.

Patient selection

Forty-five unresectable NSCLC patients with EGFR-TKI-sensitive mutation who underwent EGFR-TKIs (including the first-, second-, and third-generation EGFR-TKIs, shown in Table S5) as first-line treatment and with detailed follow-up data in Shanghai Chest Hospital during Jun 2019 to Aug 2022 were enrolled in this study. Patient features enrolled in this study are shown in Table S5. Drug responses were assessed on the basis of blinded independent central review, according to the Response Evaluation Criteria in Solid Tumors, version 1.1 (34). The study was conducted in accordance with the Declaration of Helsinki and-its subsequent amendments. This study was approved by the Human Assurance Committee of Shanghai Chest Hospital (approval No. IS24179). Informed consent was obtained from all patients for tissue sample collection.

Immunohistochemistry (IHC)

Formalin-fixed paraffin sections (thickness: 5 µm) were used for IHC staining as previously reported (33,35-38).

PKC-iota and FASN expression in NSCLC tissues was scored blindly and independently by two experienced pathologists. The score of staining frequency (0=0%, 1=1–9%, 2=10–49%, 3=50–100%) and intensity (0= negative, 1= weak, 2= moderate, and 3= strong staining) were multiplied to obtain an overall staining score (OSS). An OSS score of 0–4 was deemed low, and 6–9 was deemed high.

Statistical analyses

Statistical analyses were performed using GraphPad Prism 9.5.0 (GraphPad Software) or SPSS 19.0 (SPSS, IL, USA). Quantitative data were expressed as the mean ± standard deviation (SD) values of at least three independent experiments. The paired or unpaired two-tailed t-test assessed statistical differences between two groups. The chi-squared test was used to compare rates. Spearman’s rank correlation was used in correlation analysis. Differences identified with * (denoting a P value <0.05) were considered statistically significant.

Results

PKC-iota physically interacts with FASN

Given the established role of PKC-iota in tumor progression (13), GST pull-down followed by mass spectrometry was performed to identify its interacting partners. As a result, the key lipid metabolic enzyme FASN (~273KD) was detected as a PKC-iota-binding protein (Figure 1A), in accordance with the result of molecular docking analysis (ZDOCK score =29.9) (Figure 1B). Co-IP validated this interaction at both endogenous and exogenous levels (Figure 1C-1F), while PLA and IF demonstrated predominant cytoplasmic co-localization of protein FASN and PKC-iota in H1975 cells (Figure 1G,1H). Collectively, these findings confirm the physical association between PKC-iota and FASN.

Figure 1 PKC-iota physically interacts with FASN. (A) Purified GST-PKC-iota protein was incubated with H1975 cell lysates. Bound proteins were eluted and analyzed by SDS-PAGE. Silver staining showed the location of GST-PKC-iota and its associated protein FASN (~273 KD). LC-MS was used to identify the peptides of FASN. (B) Protein-protein docking between human PKC-iota (blue) and FASN (yellow) was performed through the ZDOCK module in Discovery Studio software. (C) H1975 cell extracts were immunoprecipitated with anti-PKC-iota or control rabbit IgG. Western blot analysis was performed using anti-FASN and anti-PKC-iota antibodies. Ten percent input of extracts is shown. (D) H1975 cell extracts were immunoprecipitated with anti-FASN or control rabbit IgG. Western blot analysis was performed using anti-PKC-iota and anti-FASN antibodies. Ten percent input of extracts is shown. (E) Lysates of H1975 cells transfected with Flag-PKC-iota for 48 hours were immunoprecipitated with anti-Flag or control mouse IgG. Western blot analysis was performed using anti-FASN and anti-Flag antibodies. Ten percent input of extracts is shown. (F) Lysates of H1975 cells cotransfected with Flag-PKC-iota and HA-FASN for 48 hours were immunoprecipitated with anti-Flag or control mouse IgG. Western blot analysis was performed using anti-Flag and anti-HA antibodies. Ten percent input of extracts is shown. (G) H1975 cells seeded on glass slides in 24-well plates were subjected to PLA using antibodies against PKC-iota and FASN, showing colocalization (red) of PKC-iota and FASN (scale bar, 20 μm). (H) H1975 cells (5×10³ cells/well) were seeded into a 24-well plate with glass coverslips for 24 hours, and IF staining was performed. The colocalization (yellow) of protein PKC-iota (red) and protein FASN (green) in H1975 was analyzed using confocal microscopy (scale bar, 20 µm). FASN, fatty acid synthase; IF, immunofluorescence; IgG, immunoglobulin G; PKC-iota, protein kinase C-iota; PLA, proximity ligation assay.

PKC-iota stabilizes FASN by inhibiting its ubiquitin–proteasome degradation at the post-translational level

To understand the possible regulation effect of PKC-iota on FASN, we then analyzed both the gene and protein expression of FASN in H1975 and PC9 cells with or without PKC-iota knockdown (Figure S1) or overexpression, while another three key lipid synthesis enzymes acetyl-CoA carboxylase 1 (ACC1, gene as ACACA), stearoyl-CoA desaturase (SCD1), and the transcription factor sterol-regulatory element binding protein 1 (SREBP1, gene as SREBF1) (32) were also tested. As a result, PKC-iota silencing downregulated both the endogenous and the exogenous FASN protein expression in H1975 and PC9 cells, while without affecting SREBP1, ACC1, or SCD1 protein expression (Figure 2A,2B); PKC-iota overexpression upregulated the expression of endogenous and exogenous FASN protein, while not affecting other enzymes’ protein expression (Figure 2C,2D). However, none of the above enzymes were regulated by PKC-iota at the mRNA level (Figure S2A,S2B).

Figure 2 PKC-iota stabilizes FASN by inhibiting ubiquitin-proteasome degradation at the post-translational level. (A) Lysates of H1975 or PC9 cells transfected with siRNA-PKC-iota or siRNA-NC for 48 hours were immunoblotted with anti-FASN, ACC1, SCD1, SREBP1, and PKC-iota. GAPDH provided the loading control. (B) Lysates of H1975 or PC9 cells cotransfected with HA-FASN and siRNA-PKC-iota (or siRNA-NC) for 48 hours were immunoblotted with anti-HA, ACC1, SCD1, SREBP1, and PKC-iota. GAPDH provided the loading control. (C) Lysates of H1975 or PC9 cells transfected with Flag-PKC-iota or empty vector for 48 hours were immunoblotted with anti-FASN, ACC1, SCD1, SREBP1, and PKC-iota. GAPDH provided the loading control. (D) Lysates of H1975 or PC9 cells cotransfected with HA-FASN and Flag-PKC-iota (or empty vector) for 48 hours were immunoblotted with anti-HA, ACC1, SCD1, SREBP1, and PKC-iota. GAPDH provided the loading control. (E,F) H1975 cells were transfected with siRNA-NC or siRNA-PKC-iota for 48 hours (E), and H1975 cells were transfected with empty vector or Flag-PKC-iota for 48 hours (F), followed by 6 hours of MG132 (100 mmol/L) treatment. Lysates were immunoprecipitated with FASN antibody-conjugated Protein A/G beads and normalized before immunoblotting with anti-ubiquitin or phospho-serine/threonine. (G) H1975 cells were transfected with siRNA-NC or siRNA-PKC-iota for 48 hours. MG132 (100 mmol/L) or DMSO (worked as the control) was then added for 6 hours. Lysates were immunoblotted with anti-FASN or PKC-iota. GAPDH provided the loading control. (H) H1975 cells were transfected with siRNA-NC or siRNA-PKC-iota for 48 hours, followed by 0, 1, 2, and 2.5 hours of cycloheximide (CHX; 100 mg/mL) treatment. Lysates were immunoblotted with anti-FASN or PKC-iota. FASN levels were normalized to GAPDH, then normalized to the time =0 controls. (I) H1975 cells were transfected with empty vector or Flag-PKC-iota for 48 hours, followed by 0, 1, 2, and 2.5 hours of CHX (100 mg/mL) treatment. Lysates were immunoblotted with anti-FASN or PKC-iota. FASN levels were normalized to GAPDH, then normalized to the time =0 controls. DMSO, dimethyl sulfoxide; FASN, fatty acid synthase; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; NC, negative control; PKC-iota, protein kinase C-iota.

As a protein kinase, PKC-iota can directly phosphorylate specific Ser and/or Thr residues of target proteins to join in physiological or pathological effects, including tumorigenesis (39). The post-transcription regulation of PKC-iota on FASN led us to investigate its role in post-translational phosphorylation (P) modification of PKC-iota on FASN. As expected, PKC-iota knockdown reduced, whereas its overexpression increased, Ser/Thr phosphorylation of FASN in H1975 cells; meanwhile, FASN ubiquitination (UB) was elevated upon PKC-iota knockdown and suppressed by PKC-iota overexpression (Figure 2E,2F), indicating that PKC-iota may negatively regulate the ubiquitin-mediated degradation of FASN (40). Consistently, the proteasome inhibitor MG132 abolished PKC-iota’s effect on FASN levels (Figure 2G), while cycloheximide (CHX) chase assays further confirmed that PKC-iota knockdown accelerated, while overexpression slowed, FASN degradation (Figure 2H,2I).

The above findings demonstrate that PKC-iota stabilizes FASN by preventing its ubiquitin–proteasome degradation at the post-translational level, and likely through phosphorylation-dependent protection.

PKC-iota induces fatty acid metabolism reprogramming to increase EGFR membrane-localization via FASN

To evaluate the functional consequence of the PKC-iota/FASN interaction, we assessed fatty acid (FA) synthesis in EGFR-mutant H1975 cells. MS revealed that PKC-iota overexpression significantly upregulated the production of multiple short- and medium-chain (s/m) FAs (C6-0, C8-0, C9-0, C10-0, C11-0) as well as unsaturated (U) FAs (C16-1n7c, C18-1n9c, C18-1n9t, C18-2n6t, C18-3n3c, C20-3n6c, C22-1n9c, and C22-6n3c), whereas FASN knockdown (Figure S3) attenuated these effects (Figure 3A,3B). Consistently, the triglyceride (TG) assay, Oil Red O staining, and Nile red staining showed that PKC-iota enhanced TG accumulation and lipid droplet formation in H1975 cells, both of which could be suppressed by FASN depletion (Figure 3C-3E).

Figure 3 PKC-iota induces fatty acid metabolism reprogramming to increase EGFR membrane-localization via FASN. (A-E) H1975 cells were cotransfected with siRNA-FASN or siRNA-NC and Flag-PKC-iota or empty vector (worked as a control) for 48 hours: (A,B) FAs metabolites (A. SFAs; B. UFAs) of H1975 cells were analyzed by GC-MS, and normalized by total protein levels; (C) TG levels of H1975 cells were quantitated by a colorimetric method and normalized by total protein levels and normalized to 1 by the control group. ns, not significant; *, P<0.05. (D) Representative Oil Red O staining images in H1975 cells (scale bar, 100 µm); (E) representative Nile Red staining images in H1975 cells (scale bar, 75 µm). (F) After 24 hours of transfection (siRNA-FASN or siRNA-NC and Flag-PKC-iota or empty vector), H1975 cells were plated onto 3.5 cm confocal dishes for 24 hours, followed by 30 min TMA-DPH (2 µmol/L) treatment, measured fluorescence polarization and calculated membrane LFU. ns, nonspecific; *, P<0.05; ***, P<0.001. (G) IF staining was done after 48 hours of transfection (siRNA-FASN or siRNA-NC and Flag-PKC-iota or empty vector) and followed by 24 hours of EGF (5 µg/mL) treatment. Staining for EGFR (red), ATP1A1 (green), DAPI (blue), and the overlays of three channels (the membrane localization of EGFR is shown in yellow) are shown in the first, second, third, and fourth rows, respectively (scale bar, 10 µm). (H) Membrane protein fractions of H1975 cotransfected with siRNA-FASN or siRNA-NC and Flag-PKC-iota or empty vector for 48 hours were immunoblotted with anti-p-EGFR-1092 or anti-EGFR. ATP1A1 provided the membrane loading control. (I) H1975 cells were cotransfected with siRNA-FASN or siRNA-NC and Flag-PKC-iota or empty vector for 24 hours. EGF (5 µg/mL) was then added for 24 hours, followed by 30 min of dynasore (80 µM) treatment. Lysates were immunoblotted with anti-FASN, anti-p-EGFR (Tyr 1092), anti-EGFR, or anti-Flag. β-Tubulin provided the loading control. DAPI, 4’,6-diamidino-2-phenylindole; EGF, epidermal growth factor; EGFR, epidermal growth factor receptor; FAs, fatty acids; FASN, fatty acid synthase; GC-MS, gas chromatography-mass spectrometry; IF, immunofluorescence; LFU, lipid fluidity; NC, negative control; PKC-iota, protein kinase C-iota; SFAs, saturated FAs; TG, triglyceride; TMA-DPH, 1-(4-trimethylammoniumphenyl)-6-phenyl-1,3,5-hexatriene p-toluene sulfonate; UFAs, unsaturated FAs.

Since short-chain and unsaturated FAs increase membrane fluidity (41,42)—thereby stabilizing membrane-localized EGFR (30-32)—we thus then evaluated membrane fluidity and EGFR localization in H1975 cells. As a result, 1-(4-Trimethylammoniumphenyl)-6-phenyl-1,3,5-hexatriene p-toluene sulfonate (TMA-DPH) assay showed that PKC-iota increased membrane fluidity of H1975 cells, while FASN knockdown abolished this effect (Figure 3F). IF assay then demonstrated that PKC-iota enhanced EGFR membrane localization, which could be suppressed by FASN depletion (Figure 3G). WB further showed that PKC-iota increased EGFR membrane localization and its Tyr1092 phosphorylation (Tyr1092-P), which means the activation of EGFR signaling (43), both of which could be suppressed by FASN knockdown (Figure 3H). Additionally, PKC-iota can also increase the Tyr1092-P modification and the protein expression of total cellular EGFR, which can also be restrained by FASN knockdown (Figure 3I). Importantly, inhibition of lipid raft–mediated endocytosis with dynasore (44) equalized EGFR and its Tyr1092-P modification levels (Figure 3I), indicating that PKC-iota/FASN promotes EGFR overactivation through increased membrane fluidity and stabilization of lipid raft-associated EGFR.

The above data indicate that PKC-iota induces fatty acid metabolism reprogramming to increase EGFR membrane-localization via FASN.

PKC-iota/FASN complex drives tumor growth and osimertinib resistance in vitro and in vivo

Given that aberrant EGFR activation drives resistance to EGFR-TKIs (45), we tested the role of the PKC-iota/FASN complex in drug sensitivity of EGFR-mutated NSCLC cell lines H1975 (L858R) or PC9 (19del) to EGFR-TKI. As a result, in PC9 cells, PKC-iota overexpression increased IC₅₀ values for the first-generation EGFR-TKI Gefitinib, the second-generation EGFR-TKI afatinib, and the third-generation EGFR-TKI osimertinib, indicating reduced EGFR-TKI-responsiveness (Figure 4A-4C). Since the third-generation EGFR-TKI, osimertinib, is the current first-line EGFR-TKI for EGFR-mutated NSCLC (46), we next focused on the regulatory effect of the PKC-iota/FASN complex on osimertinib resistance in EGFR-mutated NSCLC. We then observed that in H1975 cells, PKC-iota suppressed osimertinib-induced inhibition of proliferation (Figure 4D) and tumorigenicity (Figure 4E), as well as drug-induced apoptosis (Figure 4F), while FASN knockdown could reverse these effects (Figure 4D-4F, Figure S4A-S4C). Meanwhile, we observed that PKC-iota overexpression promoted proliferation and tumorigenicity while inhibiting apoptosis, effects that were also hampered by FASN knockdown (Figure S4A-S4C).

Figure 4 PKC-iota/FASN complex drives tumor growth and osimertinib resistance in vitro and in vivo. (A-C) PC9 cells were cotransfected with siRNA-FASN or siRNA-NC and Flag-PKC-iota or empty vector (worked as a control) for 16–18 hours, and then plated onto 96-well plates (5,000 cells per well), followed by 72 hours gefitinib (A, 0–100,000 nmol/L), afatinib (B, 0–100 nmol/L), or osimertinib (C, 0–100,000 nmol/L), CCK-8 (10 µL) was then added for 2 hours to quantitate by a colorimetric method. (D) H1975 cells were cotransfected with siRNA-FASN or siRNA-NC and Flag-PKC-iota or empty vector for 16-18 hours, and then plated onto 96-well plates (5,000 cells per well), followed by osimertinib (1 µmol/L) or DMSO treatment, and cell numbers were counted at 96 hours. The osimertinib-induced cell decrease rate = (cell counts in the control group – cell counts in the osimertinib-treated group)/cell counts in the control group × 100%. Data were also shown in Figure S4A. ns, not significant; *, P<0.05. (E) After 24 hours of transfection (siRNA-FASN or siRNA-NC and Flag-PKC-iota or empty vector), osimertinib (10 nmol/L) or DMSO was added into H1975 cells for 7 days, followed by colony formation assay. The osimertinib-induced colony number decrease rate = (colony number in control group – colony number in osimertinib-treated group)/colony number in control group × 100%. Representative images of the colony formation were also shown in Figure S4B. ns, nonspecific; *, P<0.05. (F) After 24 hours of transfection (siRNA-FASN or siRNA-NC and Flag-PKC-iota or empty vector), H1975 cells were treated with osimertinib (1 µmol/L) or DMSO for 72 hours, followed by detection of cell apoptosis by flow cytometry. The osimertinib-induced cell apoptotic rate = apoptotic rate in osimertinib-treated group – apoptotic rate in control group. Representative images of percentage of apoptosis cells were also shown Figure S4C. ns, nonspecific; *, P<0.05; ***, P<0.001. (G-L) Nude mice were injected with 5×10⁶ H1975 cells stable overexpressing PKC-iota or empty vector and shRNA-FASN or shRNA-NC: (G) the volume of tumors was measured at regular intervals, followed by osimertinib treatment at the 10th day. Days means days after injection; (H) image of tumors isolated from nude mice; (I) statistical analysis of tumor weight was performed separately for control groups and osimertinib-treated groups. ns, nonspecific; *, P<0.05. (J) Osimertinib-induced tumor volume decrease rate (tumor volume in control group – tumor volume in osimertinib-treated group)/tumor volume in control group × 100% was analyzed using the volume of 31 day. ns, nonspecific; *, P<0.05. (K) Osimertinib-induced tumor weight decrease rate (tumor weight in control group – tumor weight in osimertinib-treated group)/tumor weight in control group × 100% was analyzed using isolated tumors. ns, nonspecific; *, P<0.05. (L) Representative images of H&E staining, Oil Red O staining and Ki67 IHC staining of tumor samples (scale bar, 100 µm). CCK-8, Cell-Counting Kit-8; DMSO, dimethyl sulfoxide; H&E, hematoxylin and eosin; IHC, immunohistochemistry; FASN, fatty acid synthase; NC, negative control; PKC-iota, protein kinase C-iota.

To further investigate the role of the PKC-iota/FASN complex in EGFR-TKI sensitivity in vivo, we performed xenograft studies. As a result, PKC-iota overexpression markedly increased tumor volume and weight, whereas FASN knockdown abolished this growth advantage, yielding tumors smaller than controls (Figure 4G-4I). Analysis of osimertinib-sensitivity revealed that PKC-iota reduced the tumor shrinkage rate induced by drug treatment, while FASN depletion restored responsiveness (Figure 4J,4K). Histological assays showed that PKC-iota overexpression elevated intra-tumoral lipid droplet accumulation and Ki67 expression (47), both of which were suppressed by FASN knockdown. Osimertinib treatment decreased proliferation [Ki67 (47)] but did not alter lipid droplet levels (Figure 4L).

These results demonstrate that PKC-iota promotes NSCLC growth and reduces osimertinib-sensitivity in vitro and in vivo through FASN-mediated metabolic regulation.

High PKC-iota/FASN co-expression correlates with poor EGFR-TKI response in EGFR-mutated NSCLC patients

To examine the clinical significance of the PKC-iota/FASN axis, we analyzed tumor samples from 45 EGFR-mutant NSCLC patients treated with EGFR-TKIs as first-line therapy. Treatment response was classified as EGFR-TKI sensitive (complete/partial response) or resistant (stable/progressive disease) after three months. IHC assay showed the positive correlation of PKC-iota and FASN protein expression in the 45 tested tumor tissues (Figure 5A,5B; Table S6), and that the high expression of PKC-iota or FASN was correlated with increased T stages and EGFR-TKI resistance in the enrolled 45 NSCLCs (Figure 5C,5D; Tables 1,2). However, we didn’t observe a significant association between PKC-iota or FASN expression with other features (e.g., age, gender, smoking history, biopsy lesions, histologic subtype, EGFR-mutation type, N stage, M stage, TNM stage) (Table 1).

Figure 5 High PKC-iota/FASN co-expression correlates with poor EGFR-TKI response in EGFR-mutated NSCLC. (A) The representative PKC-iota and FASN co-low and co-high IHC images (scale bar, 100 µm); (B) the OSS of PKC-iota and FASN in the 45 EGFR-mutated NSCLC were positively correlated (R =0.483, P=0.001). (C) EGFR-TKI-resistance tumors showed higher PKC-iota OSS than that in EGFR-TKI-sensitive tumors (7.00±2.24 vs. 3.18±1.66, P<0.001). (D) EGFR-TKI-resistance tumors showed higher FASN OSS than that in EGFR-TKI-sensitive tumors (6.46±1.81 vs. 3.38±2.22, P<0.001). (E) Schematic representation of EGFR-TKI resistance driven by PKC-iota/FASN axis in EGFR-mutated NSCLC. PKC-iota hampers ubiquitin-proteasome-degradation of FASN by directly phosphorylating FASN, stabilizing it, and thereby upregulating short- and medium-chain, unsaturated fatty acids and increasing membrane fluidity in EGFR-mutated NSCLC. Increased membrane fluidity inhibits lipid raft-mediated EGFR endocytosis and degradation, prolonging EGFR retention at the cell membrane and leading to uncontrolled EGFR activation, ultimately inducing malignant growth and EGFR-TKI resistance in EGFR-mutated NSCLC. *, P<0.05. EGFR, epidermal growth factor receptor; EGFR-TKI, epidermal growth factor receptor tyrosine kinase inhibitor; FASN, fatty acid synthase; IHC, immunohistochemistry; NSCLC, non-small cell lung cancer; OSS, overall staining score; PKC-iota, protein kinase C-iota.

We also analyzed the associations between EGFR-TKI response and clinicopathological parameters, and tumor PKC-iota and FASN co-expression levels, and found that EGFR-TKI resistance is associated with higher TNM stages (Table 2), which may be explained by the increased heterogeneity accompanying tumor progression, which always results in poor treatment response (48). However, the correlations between other clinicopathologic features (e.g., age, gender, smoking history, biopsy lesions, histologic subtype, EGFR-mutation type, EGFR-TKIs type) and EGFR-TKI response were not significant (Table 2). Notably, we observed that NSCLCs with co-high PKC-iota/FASN expression exhibited the highest EGFR-TKI resistance rate (50%, 9/18), whereas all co-low cases responded (100%, 14/14) (Table 2, P<0.001); while the subsequent multivariate analysis confirmed co-high PKC-iota/FASN expression as an independent predictor of resistance (hazard ratio =3.226), after adjusting for TNM stage and individual protein levels (Table 3).

These findings underscore the clinical relevance of PKC-iota/FASN co-expression in driving tumor progression and intrinsic EGFR-TKI resistance of EGFR-mutated NSCLC.

Discussion

EGFR-TKIs have substantially reshaped the therapeutic paradigm for patients with EGFR-mutant NSCLC, particularly in Asian populations, where EGFR mutations are present in approximately half of all cases (4,48). Despite these advances, both intrinsic and acquired resistance to EGFR-TKIs remain major clinical challenges (5,6). In this study, we identified a novel resistance mechanism that PKC-iota directly phosphorylates and stabilizes FASN, thereby promoting fatty acid synthesis, altering membrane fluidity, and preventing lipid raft-mediated EGFR endocytosis and degradation (Figure 5E). This process sustains EGFR signaling despite EGFR-TKI exposure, ultimately driving drug resistance. The PKC-iota/FASN axis thus emerges as a critical link between metabolic reprogramming and oncogenic signal persistence.

PKC-iota, the sole oncogenic PKC isoform, has previously been implicated in tumor proliferation and invasion (13); however, its role in lipid metabolic regulation has been underexplored. Here, we demonstrate that PKC-iota physically interacts with FASN, a central enzyme in de novo fatty acid synthesis, and enhances its stability via the ubiquitin–proteasome pathway. The phosphorylation of FASN by PKC-iota appears to underlie this stabilization, establishing a novel post-translational regulatory mechanism. Notably, this effect is specific to FASN and does not extend to other lipid metabolic regulators such as SREBP1, ACC1, or SCD1. This contrasts with the canonical metabolic model in which SREBP1 acts as a transcriptional activator of FASN (49), suggesting that PKC-iota operates through a non-canonical pathway to promote lipogenesis.

Our finding that PKC-iota directly phosphorylates FASN expands the mechanistic framework of lipid metabolism-driven drug resistance. While FASN overexpression has previously been linked to alterations in membrane fluidity that sustain EGFR activity (23), the upstream kinase responsible for this regulation remained unknown. The identification of PKC-iota as a FASN-regulator highlights a novel axis of post-translational lipid metabolic control. Furthermore, the specificity of PKC-iota toward FASN, rather than other lipogenic enzymes (e.g., SREBP1, ACC1, SCD1), strengthens its unique capacity to shape fatty acid composition and thereby regulate EGFR membrane dynamics. These insights align with emerging evidence that spatial organization and membrane localization of EGFR, rather than expression levels alone, critically dictate therapeutic sensitivity (50). Together, these findings reveal that PKC-iota/FASN signaling contributes to lipid metabolic heterogeneity and suggest that specific fatty acid species may function as molecular switches controlling cell signaling and therapeutic response.

The regulation of membrane fluidity by the PKC-iota/FASN complex represents another key mechanistic insight. By elevating the ratios of unsaturated to saturated fatty acids and s/m-FAs to long-chain FAs, PKC-iota/FASN signaling enhances membrane fluidity, thereby inhibiting lipid raft–mediated EGFR internalization and lysosomal degradation (29). As a result, EGFR persists at the plasma membrane in an activated state, even in the presence of ligands such as EGF or tumor necrosis factor-alpha (TNF-α), contributing to sustained signaling and drug resistance (27,32). Experimental evidence confirmed this mechanism: PKC-iota overexpression increased EGFR phosphorylation and membrane retention, effects that were reversed by FASN knockdown. Furthermore, pharmacological inhibition of membrane fluidity using the dynamin inhibitor dynasore partially restored EGFR-TKI sensitivity, equalizing EGFR activity across treatment groups. These findings demonstrate that PKC-iota/FASN–driven changes in membrane biophysics constitute an important resistance mechanism, operating independently of or in synergy with secondary EGFR mutations such as T790M or C797S (51). Furthermore, the persistence of activated EGFR in lipid rafts may prime alternative survival pathways, such as upregulation of the mesenchymal-epithelial transition factor (MET) or human epidermal growth factor receptor 3 (HER3), which warrant future investigation (52,53), suggesting broader implications for adaptive resistance.

Both in vitro and in vivo evidence support the role of the PKC-iota/FASN complex in promoting tumor growth and mediating resistance to EGFR-TKIs. In cell-based assays, PKC-iota overexpression significantly increased the IC₅₀ values for multiple EGFR-TKIs, including osimertinib, while FASN knockdown restored sensitivity. Consistent results were observed in xenograft models: PKC-iota overexpression yielded larger, more drug-resistant tumors, whereas FASN silencing made tumors more sensitive to osimertinib. These findings highlight the clinical relevance of the PKC-iota/FASN axis and suggest that its inhibition may represent a viable therapeutic strategy. Clinical cohort analyses further reinforced these observations. Patients with concurrent high expression of PKC-iota and FASN exhibited poorer responses to EGFR-TKI therapy and were more frequently associated with advanced T-stage disease. These correlations are consistent with our in vitro and in vivo results, collectively supporting the role of PKC-iota/FASN signaling as both a promoter of tumor proliferation and a driver of therapeutic resistance. This dual role positions the PKC-iota/FASN axis as a potential biomarker for treatment stratification in EGFR-mutant NSCLC.

Current strategies to overcome resistance focus largely on directly inhibiting mutant EGFR (e.g., third-generation TKIs) or targeting parallel pathways such as MET or HER2. By contrast, our findings reveal a metabolic vulnerability upstream of EGFR signaling dynamics. Dual inhibition of PKC-iota and FASN may disrupt the feed-forward loop sustaining EGFR activation, particularly in tumors characterized by alterations in membrane fluidity. Importantly, pharmacological inhibitors of PKC-iota (e.g., auranofin) and FASN (e.g., AZ12756122) have already demonstrated synergistic activity with EGFR-TKIs in preliminary studies (17,26), supporting the translational potential of this approach. Future studies should focus on developing agents that specifically disrupt the PKC-iota/FASN interaction and evaluating their efficacy in combination with EGFR-TKIs. Moreover, the role of this signaling axis in other malignancies marked by lipid metabolic dysregulation warrants investigation.

Notably, the tumor-promoting function of the PKC-iota/FASN complex extends beyond drug resistance. In xenografts derived from EGFR-mutated H1975 cells, lipid droplet accumulation correlated positively with Ki-67 expression, suggesting that PKC-iota/FASN signaling supports tumor aggressiveness through a “metabolic-proliferation” coupling mechanism. Enhanced lipid droplet formation in PKC-iota-overexpressing tumors indicates that this pathway fuels both membrane biogenesis and energy storage—metabolic adaptations that may facilitate metastatic progression (54). Consistent with these findings, clinical data demonstrated that high PKC-iota/FASN expression correlates with increased tumor size, supporting its role as both a prognostic biomarker and a therapeutic target across disease stages.

For future clinical translation, our findings may help guide resistance stratification and treatment in patients. In particular, IHC could be used to identify tumors with high co-expression of PKC-iota and FASN; patients in this subgroup are more likely to develop resistance. Targeting lipid metabolism in these cases may further enhance sensitivity to EGFR-TKIs. Indeed, prior studies have suggested that inhibitors of PKC-iota and/or FASN can act synergistically with EGFR-TKIs (17,26), and this axis may also serve as a basis for the development of novel targeted therapeutics in the future. Collectively, our work has translational potential by providing a clinically feasible approach to identify patients most likely to benefit, thereby enabling tangible improvements in patient outcomes.

We propose a new drug resistance paradigm in which reprogramming lipid metabolism drives EGFR aberrant activation by increasing EGFR on the cell membrane, rather than merely supporting survival as a downstream adaptation due to EGFR mutations (55). And we identify a defined signaling-metabolism coupling axis (PKC-iota phosphorylates and upregulates FASN, leading to increased EGFR membrane localization and aberrant EGFR activation) that forms a complete causal chain from post-translational modification to membrane receptor activation. Compared with most currently reported studies which mainly establish an association between altered lipid metabolism and drug resistance, this work may offer a more complete mechanistic narrative (20).

However, there are several limitations in this study: the exact phosphorylation sites on FASN for PKC-iota have not been determined, and the clinical cohort study was limited by a relatively small sample size, which could potentially introduce selection bias and confounding bias. Therefore, future studies should focus on mapping the precise phosphorylation sites on FASN and defining their functional significance, validating these findings in larger-scale clinical cohort studies enrolling sufficient cases, and investigating the upstream mechanisms for upregulation of PKC-iota expression in EGFR-TKI-resistant strains.

Conclusions

In conclusion, this study elucidates a previously unrecognized mechanism of EGFR-TKI resistance in EGFR-mutant NSCLC, mediated by the PKC-iota/FASN axis. By directly phosphorylating and stabilizing FASN, PKC-iota enhances fatty acid synthesis, remodels membrane fluidity, and sustains EGFR signaling at the plasma membrane. The PKC-iota/FASN complex represents both a biomarker of poor EGFR-TKI response and a potential therapeutic target. Development of inhibitors directed against this axis, or combination regimens targeting both EGFR and lipid metabolism, may offer novel strategies to overcome EGFR-TKI resistance and improve outcomes in patients with EGFR-mutant NSCLC.

Acknowledgments

We extend our heartfelt thanks to the faculty members of the Department of Nuclear Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine; Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine; and School of Biomedical Engineering, Shanghai Jiao Tong University for their valuable support for our research work.

Footnote

Reporting Checklist: The authors have completed the ARRIVE and MDAR reporting checklists. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1260/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1260/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1260/prf

Funding: This study was supported by research grants from the National Natural Science Foundation of China (Nos. 82272044 and 82372007) and the Explorer Program of Shanghai (No. 23TS1400900).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1260/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. The study was conducted in accordance with the Declaration of Helsinki and-its subsequent amendments. This study was approved by the Human Assurance Committee of Shanghai Chest Hospital (approval No. IS24179). Informed consent was obtained from all patients for tissue sample collection. All animal experiments involving the use of animals were approved by the Animal Ethics Committee of Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine (No. KS24021), in compliance with institutional guidelines for the care and use of animals.

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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