SLC16A3 Drives Lung Adenocarcinoma Progression and Gefitinib Resistance through Coordinated Regulation of Ferroptosis and Lactate Metabolism

SLC16A3 Drives Lung Adenocarcinoma Progression and Gefitinib Resistance through Coordinated Regulation of Ferroptosis and Lactate Metabolism | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SLC16A3 Drives Lung Adenocarcinoma Progression and Gefitinib Resistance through Coordinated Regulation of Ferroptosis and Lactate Metabolism Wenhan Cai, Yiming Liu, Kai Zhao, Zirui Zhu, Jiamei Jin, Jiaxin Wen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6833528/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Ferroptosis is an iron-dependent form of regulated cell death that plays a critical role in tumor suppression and therapy response. However, the metabolic mechanisms that drive ferroptosis resistance in lung adenocarcinoma (LUAD), particularly in the context of EGFR-TKI tolerance, remain unclear. Methods We integrated transcriptomic and clinical data from the TCGA LUAD cohort and performed survival and enrichment analyses. Functional assays including proliferation, invasion, ferroptosis indicators, and in vivo xenograft models were used to evaluate the role of SLC16A3. Lactate rescue, transcription factor prediction (JASPAR), mIHC, and luciferase reporter assays were applied to dissect regulatory mechanisms. Pharmacological inhibition of SLC16A3 was used to assess therapeutic potential. Results SLC16A3 expression was elevated in LUAD and correlated with poor prognosis. Knockdown of SLC16A3 suppressed tumor cell growth and enhanced ferroptosis, as indicated by increased lipid peroxidation, iron accumulation, and mitochondrial depolarization. Lactate supplementation partially reversed ferroptosis induction. Mechanistically, SLC16A3 was transcriptionally activated by HIF1A, and the HIF1A-SLC16A3 axis conferred ferroptosis resistance and gefitinib tolerance. In vivo, SLC16A3 inhibition restored ferroptotic sensitivity and enhanced EGFR-TKI efficacy in xenograft models. Conclusion Our findings reveal that the HIF1A-SLC16A3-lactate axis orchestrates ferroptosis suppression and therapeutic resistance in LUAD. Targeting SLC16A3 represents a promising metabolic strategy to overcome EGFR-TKI resistance by reactivating ferroptosis. Lung adenocarcinoma (LUAD) SLC16A3 ferroptosis lactate metabolism gefitinib resistance hypoxia-inducible factor 1-alpha (HIF1A) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer (NSCLC) and remains a leading cause of cancer-related mortality worldwide [ 1 ] . Although targeted therapies such as epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have significantly improved clinical outcomes for patients with activating EGFR mutations, acquired resistance inevitably occurs, typically within a year of treatment initiation [ 2 , 3 ] . This resistance is driven not only by secondary EGFR mutations or bypass signaling, but also by changes in cellular metabolism and stress adaptation [ 4 , 5 ] , underscoring the need to clarify resistance mechanisms and subsequent treatment options. Lactate metabolism has emerged as a critical factor in tumor progression and therapeutic resistance. Lactate, once considered a mere metabolic byproduct, is now recognized as a signaling molecule that promotes angiogenesis, immune evasion, and cell survival [ 6 , 7 ] . Tumor cells rely on monocarboxylate transporters such as SLC16A3 (also known as MCT4) to export excess lactate and maintain intracellular pH balance [ 8 , 9 ] . Elevated SLC16A3 expression is frequently observed in solid tumors and correlates with poor prognosis [ 10 , 11 ] . Ferroptosis, a form of iron-dependent non-apoptotic cell death driven by lipid peroxidation, has emerged as a critical regulator of tumor biology [ 12 , 13 ] . Unlike apoptosis, ferroptosis is closely linked to metabolic and redox homeostasis, and mounting evidence suggests that its evasion contributes to tumor growth, metastasis, and resistance to targeted therapies [ 13 ] . In LUAD, ferroptosis is suppressed by multiple oncogenic pathways [ 14 , 15 ] , but the metabolic mechanisms that govern ferroptotic vulnerability remain incompletely defined. Notably, recent studies have proposed that ferroptosis suppression may underlie resistance to EGFR-TKIs in non-small cell lung cancer, although the upstream regulators of this process are not fully understood [ 16 ] . In parallel, lactate has been implicated as a metabolic modulator that buffers oxidative stress and protects cancer cells from ferroptosis [ 17 , 18 ] . Building on these observations, we investigated the role of the lactate transporter SLC16A3 in linking lactate metabolism to ferroptosis resistance in LUAD. In this study, we identify SLC16A3 as a metabolic checkpoint that integrates lactate export and ferroptosis suppression to promote LUAD progression and gefitinib tolerance. Using TCGA data, clinical samples, functional assays, and in vivo models, we show that SLC16A3 overexpression is associated with poor prognosis and resistance phenotypes. Mechanistically, we demonstrate that SLC16A3-driven lactate export protects LUAD cells from ferroptosis and is transcriptionally regulated by hypoxia-inducible factor 1-alpha (HIF1A). These findings highlight the HIF1A-SLC16A3-lactate axis as a critical mediator of therapeutic escape and nominate SLC16A3 as a targetable metabolic vulnerability in LUAD. Materials and Methods Clinical tissue samples A total of 23 pairs of lung adenocarcinoma (LUAD) tumor tissues and matched adjacent normal lung tissues were collected from patients who underwent surgical resection at the Department of Thoracic Surgery, First Medical Center of the Chinese PLA General Hospital (Beijing, China). The histological and pathological diagnosis of all specimens was independently confirmed by at least two board-certified pathologists according to the 2015 WHO classification of lung tumors. All patients provided written informed consent prior to enrollment, and the study was approved by the Institutional Review Board of the Chinese PLA General Hospital (Approval No. S2024-377-01). Immediately after resection, samples were either snap-frozen in liquid nitrogen and stored at − 80°C or fixed in 10% neutral buffered formalin and paraffin-embedded. These tissues were used for Western blotting, immunohistochemistry (IHC), and multiplex immunohistochemistry (mIHC). Clinicopathological characteristics of the LUAD cohort are summarized in Table 1 . Table 1 Baseline clinicopathological characteristics of LUAD patients stratified by SLC16A3 expression levels Clinicopathological feature SLC16A3_low (n = 258) SLC16A3_high (n = 258) P_value Survival status 0.006 Alive 180 149 Dead 78 109 Age Mean (SD) 65.7 (10.2) 64.9 (9.9) 0.393 Gender 0.331 Female 133 145 Male 125 113 Smoking history Non-smoking 40 35 0.54 Smoking 208 219 T stage 0.017 T1 102 67 T2 129 149 T3 18 29 T4 7 12 TX 2 1 N stage 0.008 N0 181 151 N1 38 58 N2 30 44 N3 1 2 NX 8 3 M stage 0.405 M0 170 177 M1 13 12 MX 75 69 TNM stage 0.019 I 157 122 II 54 71 III 33 52 IV 14 13 Table 2 Baseline clinicopathological characteristics of the 23 LUAD patients included for gene expression correlation analysis Clinicopathological feature n = 23 Age (years, mean ± SD) 64.1 ± 8.3 Gender(Female/Male) 10/13 smoking history(Yes/No) 12/10 T stage T1 9 T2 9 T3 5 T4 1 N stage N0 12 N1 7 N2 4 M stage M0 23 TNM stage I 14 II 8 III 1 Data Acquisition and Bioinformatics Analysis Transcriptomic RNA-seq data and corresponding clinical annotations for lung adenocarcinoma (LUAD) patients were downloaded from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ). Gene expression comparisons and Kaplan–Meier survival analyses were performed using R software (version 4.2.1). KEGG pathway enrichment analysis was conducted using the DAVID Bioinformatics Resources ( https://david.ncifcrf.gov/ ). Transcription factor binding motif predictions for the SLC16A3 promoter were retrieved from the JASPAR database ( https://jaspar.genereg.net/ ). Protein expression profiles of SLC16A3 in normal and LUAD tissues were assessed using the Human Protein Atlas ( https://www.proteinatlas.org/ ). Cell culture and drug exposure Human lung adenocarcinoma (LUAD) cell lines PC9 and gefitinib-resistant PC9GR, as well as the normal bronchial epithelial cell line BEAS-2B, were obtained from the MeisenChinese Tissue Culture Collections (Hangzhou, China). PC9 and PC9GR cells were cultured in RPMI-1640 medium (Gibco), and BEAS-2B cells were maintained in DMEM (Gibco), both supplemented with 10% fetal bovine serum (FBS; Gibco) and 1% penicillin-streptomycin (Gibco). All cells were incubated in a humidified chamber with 5% CO₂ at 37°C and were confirmed negative for Mycoplasma contamination. Gefitinib, MSC-4381, ferrostatin-1 (Fer-1), Z-VAD-FMK, and chloroquine (CQ) were purchased from Abmole (Shanghai, China). All drugs were dissolved in dimethyl sulfoxide (DMSO) to prepare stock solutions and stored at − 20°C. Working dilutions were freshly prepared in culture medium prior to use, and control cells were treated with an equal concentration of DMSO. RNA extraction and quantitative real-time PCR Total RNA was extracted using the RNA-easy™ Isolation Kit (Vazyme, China) following the manufacturer’s instructions. Complementary DNA was synthesized using the HiScript® III RT SuperMix for qPCR (Vazyme, China). Quantitative real-time PCR was performed using ChamQ Universal SYBR qPCR Master Mix (Vazyme, China) on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems, USA). GAPDH was used as the internal control, and relative mRNA expression was calculated using the 2^–ΔΔCt method. Primer sequences are listed in Supplementary Table S1 . Each condition was tested in triplicate (n = 3). Gene silencing and overexpression Stable knockdown or overexpression of SLC16A3 was achieved via lentiviral transduction using shRNA or full-length cDNA constructs. Lentiviral vectors carrying SLC16A3-targeting shRNAs (shSLC16A3#1/2/3) or control sequences (scramble) were purchased from Genechem (Shanghai, China) and cloned into the pLKO.1 vector. Packaging was performed in 293TN cells using psPAX2 and pMD2.G helper plasmids (Addgene) and jetPRIME transfection reagent (Polyplus, France). Viral supernatants were harvested, filtered, and used to infect PC9 and PC9GR cells in the presence of polybrene (8 µg/mL). Stable clones were selected with puromycin (0.5 µg/mL) for 7 days. The efficiency of knockdown or overexpression was confirmed by qRT-PCR and Western blotting. For transient gene knockdown, small interfering RNAs (siRNAs) targeting SLC16A3 or candidate transcription factors (including HIF1A, KLF4, MXI1, RORA, ZNF460, and ZNF682) were synthesized by RiboBio (Guangzhou, China). Transfections were performed using Lipofectamine 3000 (Invitrogen, Cat No. L3000015) in Opti-MEM medium (Invitrogen), following the manufacturer’s protocol. Cells were harvested 48 hours post-transfection for downstream assays. Sequences of all shRNAs and siRNAs are listed in Supplementary Table S2. Western blotting Cells or tissues were lysed using RIPA buffer (Beyotime, Shanghai, China) supplemented with protease and phosphatase inhibitor cocktails (Roche, Switzerland). Total protein concentration was determined using the BCA Protein Assay Kit (Thermo Fisher Scientific, USA). Equal amounts of protein (25 µg) were separated by SDS-PAGE and transferred to 0.45 µm PVDF membranes (Millipore, USA). Membranes were blocked with 5% non-fat milk for 2 hours at room temperature, then incubated overnight at 4°C with primary antibodies against SLC16A3 (Proteintech), HIF1A (Abcam), SLC7A11 (Abcam), GPX4 (Abcam), FSP1 (Abcam), TFRC (Abcam), DHODH (Proteintech), and β-actin (Proteintech). After three washes with TBST, membranes were incubated with HRP-conjugated secondary antibodies (Cell Signaling Technology) at room temperature for 1 hour. Protein bands were visualized using ECL substrate (Thermo Fisher Scientific) and imaged with a chemiluminescence detection system. Band intensities were quantified using ImageJ software and normalized to β-actin. All experiments were repeated independently in triplicate (n = 3). Antibody details are listed in Supplementary Table S3. Cell proliferation and colony formation assays For cell viability analysis, 5 × 10³ cells per well were seeded into 96-well plates and incubated overnight at 37°C with 5% CO₂. The following day, cells were treated with gradient concentrations of the indicated compounds for 24 h. Cell viability was assessed using the CCK-8 kit (Dojindo, Japan) according to the manufacturer’s instructions, and absorbance at 450 nm was measured to calculate relative viability. For colony formation assays, 500 cells per well were seeded into 6-well plates and cultured for 10–14 days. Colonies were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet, followed by quantification using ImageJ software. Each assay was performed in triplicate (n = 3). Wound healing, migration and invasion assays For the wound healing assay, PC9 and PC9GR cells were seeded into six-well plates and grown to confluence. A straight scratch was made using a sterile 200 µL pipette tip (0 h), and the wells were gently washed with PBS to remove detached cells. Cells were then cultured in serum-free medium and imaged at 0 h and 24 h under a light microscope. Wound closure was quantified as the percentage of wound area reduction relative to 0 h using ImageJ software. Cell migration and invasion assays were performed using 24-well transwell chambers with 8 µm pore polycarbonate membrane inserts (Corning, USA). For the invasion assay, the upper chambers were pre-coated with Matrigel (BD Biosciences, USA). Cells (5 × 10⁴ for migration and 1 × 10⁵ for invasion) were resuspended in 200 µL serum-free medium and seeded into the upper chambers. The lower chambers were filled with 500 µL complete medium containing 15% FBS. After incubation for 24 h at 37°C with 5% CO₂, non-migrated or non-invaded cells were removed from the upper surface using cotton swabs. The cells on the lower membrane surface were fixed with 4% paraformaldehyde and stained with 0.5% crystal violet (Solarbio, China). Migrated or invaded cells were imaged and counted in three randomly selected fields per insert. Ferroptosis and cell death assays Lipid peroxidation was assessed using BODIPY 581/591 C11 (Thermo Fisher Scientific), and mitochondrial membrane potential was measured using JC-1 dye (Sigma-Aldrich). Intracellular Fe²⁺ levels were detected using FerroOrange (MedChemExpress, MCE) according to the manufacturer’s protocol. To assess the role of different cell death pathways, cells were treated with ferrostatin-1 (2 µM), Z-VAD-FMK (10 µM), or chloroquine (10 µM) for 24 hours, and viability was determined using the CCK-8 assay (Sigma-Aldrich). All conditions were tested in triplicate (n = 3). Transmission electron microscopy (TEM) To assess mitochondrial ultrastructure, cells were collected from 10 cm dishes after confirming interference efficiency and centrifuged at 4°C. Cell pellets were fixed overnight in 2.5% glutaraldehyde, followed by post-fixation with 1% osmium tetroxide and 2% uranyl acetate. After dehydration in graded ethanol and acetone, samples were embedded in 812 epoxy resin (1:1 mixture with embedding agent for 2–4 h, then pure resin infiltration for 5–8 h at 37°C). Polymerization was performed at 60°C for 48 h. Ultrathin sections (60–80 nm) were prepared using an ultramicrotome, stained with uranyl acetate and lead citrate, and air-dried. Ultrastructural images were acquired using a Hitachi HT7800 transmission electron microscope at 120 kV. Lactate, glucose uptake and ATP quantification Intracellular lactate levels were measured using the Lactate Assay Kit (Beyotime, China) following the manufacturer’s instructions. Intracellular glucose levels were detected using the Glucose Assay Kit based on the O-toluidine method (Beyotime, China). Cellular ATP content was quantified using the ATP Determination Kit (Beyotime, China). All measurements were conducted in triplicate (n = 3). Animal experiments All animal procedures were conducted in accordance with institutional guidelines and approved by the Institutional Animal Care and Use Committee of the Chinese PLA General Hospital. Female BALB/c nude mice (4–6 weeks old) were purchased from HFKBio (Beijing, China) and maintained under specific pathogen-free (SPF) conditions. PC9 cells (5 × 10⁶ in 100 µL PBS mixed 1:1 with Matrigel) were subcutaneously injected into the axillary region of each mouse. For genetic perturbation experiments, cells stably expressing shSLC16A3, OE-SLC16A3, shHIF1A, or corresponding control vectors were used. For in vivo drug treatment, tumor-bearing mice were randomly divided into groups and treated with vehicle (DMSO), gefitinib (30 mg/kg), MSC-4381 (30 mg/kg), or a combination of both via daily oral gavage. In the ferroptosis rescue group, ferrostatin-1 (5 mg/kg) was co-administered by intraperitoneal injection along with MSC-4381. Tumor volumes were measured every 4 days using calipers and calculated using the formula: (length × width²)/2. On day 24 after injection, all mice were euthanized, and tumors were collected for imaging, weighing, and further analyses. Each group consisted of four mice (n = 4). Immunohistochemistry Formalin-fixed, paraffin-embedded (FFPE) tumor tissues from xenograft models or clinical LUAD samples were sectioned at 4 µm thickness. Sections were deparaffinized, rehydrated, and subjected to antigen retrieval using citrate buffer (pH 6.0). Endogenous peroxidase activity was blocked using 3% hydrogen peroxide, followed by nonspecific blocking with 5% BSA. After overnight incubation at 4°C with primary antibodies including anti-SLC16A3 and anti-Ki-67, sections were incubated with HRP-conjugated secondary antibodies. Immunoreactive signals were visualized using diaminobenzidine as the chromogen. Slides were counterstained with hematoxylin and imaged using a Leica DM6 microscope. Two independent pathologists blinded to group information scored the immunostaining based on both intensity and percentage of positive tumor cells. Staining intensity was graded as 0 (no staining), 1 (light yellow), 2 (yellow brown), and 3 (deep brown); the percentage of positive cells was scored as 0 (none), 1 (0–25%), 2 (26–50%), 3 (51–75%), and 4 (76–100%). The final immunoreactivity score was calculated as (Score1 × 1) + (Score2 × 2) + (Score3 × 3), with a maximum score of 300. Antibody details are provided in Supplementary Table S4, and all experiments were performed in triplicate (n = 3). Multiplex immunohistochemistry (mIHC) Formalin-fixed paraffin-embedded (FFPE) tumor tissues derived from xenograft models (shHIF1A and control groups) were sectioned at 4 µm thickness. Slides were deparaffinized in xylene (2 × 10 min), rehydrated through graded ethanol (100%, 95%, and 70%; 5 min each), and subjected to antigen retrieval by microwave heating in citrate buffer (pH 6.0) for 15 min at 20% maximum power. After cooling and washing in Tris-buffered saline (TBS, pH 7.6; 3 × 5 min), sequential rounds of staining were performed using primary antibodies against HIF1A and SLC16A3. Each round included incubation with primary antibody (30 min, room temperature), HRP-conjugated secondary antibody, and signal development with the TSA Fluorescence Penta Staining Kit (RK05905, ReduX Biosciences, China). SLC16A3 and HIF1A signals were detected using red and green fluorophores, respectively. Antigen retrieval was repeated between rounds to strip previous antibodies. Nuclei were counterstained with DAPI (Thermo Fisher), and slides were coverslipped. Fluorescent images were captured using a Nikon A1R + laser confocal microscope (Nikon, Japan), and quantitative analysis was performed using inForm software (PerkinElmer). Antibody details are listed in Supplementary Table S5. All experiments were performed in duplicate using tumors from each group. Statistical analysis Statistical analysis was performed using GraphPad Prism 10 and R software (version 4.2.1). Data are presented as mean ± s.e.m. from at least three independent experiments. Differences between two groups were analyzed using unpaired Student’s t-test. One-way ANOVA was used for multiple group comparisons. Survival curves were analyzed using the Kaplan–Meier method and compared by log-rank test. Univariate and multivariate Cox regression analyses were used to evaluate prognostic factors. Significant differences were represented as *P < 0.05, **P < 0.01, ***P < 0.001, ****p < 0.0001 unless otherwise indicated. Results SLC16A3 is upregulated in lung adenocarcinoma and predicts poor prognosis Analysis of transcriptomic data from the TCGA LUAD cohort revealed that SLC16A3 expression was significantly upregulated in tumor tissues compared to adjacent normal tissues (Fig. 1 A). Stratification by clinical stage showed that SLC16A3 expression increased with both advanced clinical stage and pathological T stage (Fig. 1 B). Kaplan-Meier survival analysis demonstrated that high SLC16A3 expression was associated with worse overall survival in LUAD patients (Fig. 1 C). Multivariate Cox regression analysis further confirmed SLC16A3 as an independent prognostic factor for LUAD (Fig. 1 D). Consistently, Western blot analysis of six paired LUAD and adjacent normal tissues confirmed elevated SLC16A3 protein expression in tumors (Fig. 1 E). In parallel, qRT-PCR analysis validated upregulated SLC16A3 mRNA levels in tumor tissues (Fig. 1 F). Representative IHC images from the HPA database showed elevated SLC16A3 expression in LUAD tissues compared to normal lung (Fig. 1 G), with IHC validation in our clinical LUAD cohort further confirming enhanced SLC16A3 expression in tumor tissues compared to matched normal samples (Fig. 1 H). SLC16A3 promotes LUAD cell proliferation, migration, invasion, and tumor growth To investigate the functional role of SLC16A3, we performed loss-of-function studies in PC9 cells using three independent shRNAs. Efficient knockdown of SLC16A3 was confirmed by Western blot and qRT-PCR (Fig. 2 A,B). SLC16A3 silencing significantly impaired cell proliferation, as shown by colony formation assays (Fig. 2 C), and suppressed migration and invasion, as assessed by wound healing and transwell assays (Fig. 2 D,E). In vivo xenograft assays using BALB/c nude mice demonstrated that SLC16A3 knockdown markedly reduced tumor growth, as evidenced by reduced tumor weight and volume over time (Fig. 2 F–H). IHC staining of xenograft tissues confirmed decreased SLC16A3 expression in shRNA-treated tumors (Fig. 2 I, Supplementary Fig. A). Moreover, metabolic profiling revealed that SLC16A3 silencing significantly reduced intracellular ATP levels, lactate production, and glucose concentration(Fig. 2 J), supporting its role in promoting glycolytic metabolism. SLC16A3 overexpression enhances migration and invasion in LUAD cells Gain-of-function studies in PC9 cells further validated the pro-invasive function of SLC16A3. Western blot and qRT–PCR confirmed successful overexpression of SLC16A3 (Fig. 3 A,B). Compared to vector controls, SLC16A3-overexpressing cells exhibited significantly enhanced migration (Fig. 3 C) and invasion (Fig. 3 D), supporting a functional role of SLC16A3 in promoting malignant phenotypes. SLC16A3 knockdown promotes ferroptosis via dysregulation of iron and redox homeostasis, and is partially modulated by lactate To explore the mechanism by which SLC16A3 contributes to tumor progression, transcriptomic analysis was performed in PC9 cells stably expressing shSLC16A3 or scramble control. KEGG enrichment analysis of differentially expressed genes revealed ferroptosis as the top enriched pathway (Fig. 4 A). Western blot analyses confirmed that SLC16A3 knockdown reduced the expression of key ferroptosis defense proteins SLC7A11 and GPX4, whereas FSP1, TFRC, and DHODH levels remained largely unchanged (Fig. 4 B). Fluorescent staining with FerroOrange showed marked iron accumulation in shSLC16A3 cells (Fig. 4 C), and C11-BODIPY assays indicated enhanced lipid peroxidation (Fig. 4 D). Transmission electron microscopy revealed characteristic ferroptotic ultrastructural changes, including swollen mitochondria, disrupted outer membranes, and reduced or absent cristae (Fig. 4 E). JC-1 staining further demonstrated mitochondrial depolarization (Fig. 4 F). Given the known role of lactate in modulating redox balance, we examined whether exogenous lactate could rescue ferroptosis induced by SLC16A3 silencing. Western blot analysis showed that lactate treatment reduced the expression of SLC7A11 and GPX4 (Supplementary Fig. B). Lactate administration induced mitochondrial damage (Fig. 4 G) and increased lipid peroxidation levels (Fig. 4 H, Supplementary Fig. C), indicating activation of ferroptosis. Finally, cell viability assays showed that apoptosis (Z-VAD-FMK), autophagy (CQ), and ferroptosis (ferrostatin-1) inhibitors all reversed SLC16A3 knockdown-induced cell death to varying degrees, with ferrostatin-1 exhibiting the most pronounced protective effect (Fig. 4 I), indicating that ferroptosis is the predominant death mechanism downstream of SLC16A3 loss. HIF1A transcriptionally regulates SLC16A3 and modulates ferroptosis sensitivity Transcription factor binding motif analysis based on the JASPAR database predicted HIF1A as a potential regulator of SLC16A3 promoter activity (Fig. 5 A). siRNA-mediated knockdown of HIF1A and several other predicted transcription factors reduced SLC16A3 mRNA expression, with HIF1A exhibiting the strongest inhibitory effect (Fig. 5 B). Expression correlation analyses in TCGA (Fig. 5 C) and clinical LUAD samples (Fig. 5 D) showed strong positive correlation between HIF1A and SLC16A3. Western blot confirmed that HIF1A knockdown reduced SLC16A3 protein levels (Fig. 5 E). mIHC further confirmed the co-localization of HIF1A and SLC16A3 in xenograft tumors, with SLC16A3 expression markedly reduced in shHIF1A tissues (Fig. 5 J), supporting the in vivo relevance of HIF1A-mediated transcriptional regulation. Functionally, HIF1A knockdown suppressed invasion (Fig. 5 F), migration (Fig. 5 G), and colony formation (Fig. 5 H). In vivo, SLC16A3 overexpression partially rescued the tumor-suppressive effect of HIF1A knockdown (Fig. 5 I). Mechanistically, HIF1A silencing downregulated ferroptosis-protective proteins SLC7A11 and GPX4 (Fig. 5 K), and these were restored by re-expression of SLC16A3. Moreover, SLC16A3 overexpression conferred resistance to erastin-induced ferroptosis (Fig. 5 L), as confirmed by cell viability assay (Fig. 5 M). Targeting the HIF1A-SLC16A3 axis sensitizes gefitinib-resistant LUAD cells to ferroptosis We next explored whether the HIF1A-SLC16A3 axis mediates resistance to gefitinib. Both HIF1A and SLC16A3 were significantly upregulated in gefitinib-resistant PC9GR cells compared with parental PC9 cells (Fig. 6 A), and were further induced by gefitinib treatment (Fig. 6 B). Silencing either SLC16A3 (Fig. 6 C) or HIF1A (Fig. 6 D) downregulated GPX4 and SLC7A11 in PC9GR cells. Restoration of SLC16A3 rescued this effect (Fig. 6 E), confirming that HIF1A regulates ferroptosis via SLC16A3. Functional assays showed that co-treatment with MSC-4381 (SLC16A3 inhibitor) and gefitinib significantly reduced colony formation, migration, and invasion in PC9GR cells compared to either agent alone (Fig. 6 F, Supplementary Fig. D-F). Western blot showed that MSC-4381 decreased GPX4 and SLC7A11 expression (Fig. 6 G), and BODIPY staining confirmed enhanced lipid peroxidation (Fig. 6 H, Supplementary Fig. G). JC-1 assays indicated mitochondrial damage consistent with ferroptosis induction (Fig. 6 I, Supplementary Fig. H). Pharmacological inhibition of SLC16A3 enhances gefitinib efficacy in vivo through ferroptosis induction To validate the therapeutic potential of SLC16A3 inhibition in vivo, we treated PC9 xenograft-bearing mice with MSC-4381, gefitinib, or both. Co-treatment significantly suppressed tumor growth compared to either agent alone (Fig. 7 A). Ferrostatin-1 partially attenuated the anti-tumor effect of MSC-4381, confirming ferroptosis as a key mechanism (Fig. 7 B). IHC staining showed that co-treatment reduced Ki-67 expression, indicative of impaired proliferation (Fig. 7 C), whereas ferroptosis inhibition restored Ki-67 positivity (Fig. 7 D). Discussion Ferroptosis, a regulated form of non-apoptotic cell death driven by iron-dependent lipid peroxidation, has emerged as a critical vulnerability in cancer therapy [ 19 , 20 ] . In lung adenocarcinoma (LUAD), ferroptosis is often suppressed to promote tumor survival and therapy resistance [ 21 , 22 ] . Although key regulators such as GPX4, SLC7A11, and FSP1 have been widely studied [ 23 – 25 ] , the metabolic cues that govern ferroptotic evasion remain incompletely defined. Our study identifies SLC16A3, a lactate exporter, as a central metabolic node that suppresses ferroptosis and contributes to EGFR-TKI resistance in LUAD. SLC16A3 (also known as MCT4) is a hypoxia-inducible transporter that facilitates lactate efflux during aerobic glycolysis [ 26 ] . Its overexpression has been correlated with tumor aggressiveness, metastasis, and poor prognosis across multiple cancers, including breast, prostate, and lung cancer [ 27 , 28 ] . In line with previous reports, we show that SLC16A3 is upregulated in LUAD tumors and correlates with advanced clinical stage and poor outcome. While most studies focus on its role in pH regulation and metabolic adaptation, we now demonstrate its capacity to regulate ferroptosis by maintaining redox balance through lactate export. Recent evidence has linked lactate accumulation to ferroptosis resistance via multiple mechanisms, including NADPH generation, glutathione maintenance, and lipid remodeling [ 29 – 31 ] . Lactate can serve as an alternative carbon source for NADH/NADPH production under stress, thereby countering lipid ROS accumulation [ 32 ] . Our findings support this metabolic buffering model, showing that SLC16A3 inhibition enhances lipid peroxidation, depletes ferroptosis-protective proteins (GPX4, SLC7A11), and disrupts mitochondrial integrity. Ferroptosis ultrastructural features, including condensed mitochondria and cristae loss, were observed upon SLC16A3 knockdown, reinforcing its role in ferroptotic regulation. Among several death pathway inhibitors, only ferrostatin-1 significantly rescued cell viability, excluding contributions from apoptosis, autophagy, or necroptosis. These results highlight SLC16A3-mediated lactate export as a previously underappreciated metabolic brake on ferroptosis in LUAD. Pharmacologic inhibition of SLC16A3 using MSC-4381 phenocopied genetic knockdown, impaired redox homeostasis, and sensitized LUAD cells to ferroptosis. Moreover, MSC-4381 synergized with gefitinib in vitro and in vivo, and this effect was abrogated by ferrostatin-1, confirming ferroptosis as the mechanistic basis of the combination therapy. These findings echo recent studies showing that ferroptosis induction enhances the efficacy of EGFR inhibitors and overcomes acquired resistance [ 33 ] . Our results support a growing paradigm where targeting metabolic regulators restores ferroptosis sensitivity in therapy-resistant tumors. In particular, the dual role of SLC16A3 in lactate clearance and ferroptosis suppression positions it as a promising target in LUAD. However, further studies are warranted. First, although MSC-4381 shows efficacy in subcutaneous xenografts, its pharmacokinetics, toxicity, and delivery efficiency in orthotopic and patient-derived models remain unclear. Second, comprehensive metabolic flux analyses and redox profiling are needed to quantify NADPH/NADH and glutathione dynamics upon SLC16A3 loss. Third, whether this mechanism applies to KRAS- or ALK-driven LUAD or other solid tumors requires broader validation. In conclusion, this study uncovers a metabolic axis linking HIF1A-induced SLC16A3 expression to ferroptosis suppression and EGFR-TKI resistance via lactate export. By showing that SLC16A3 inhibition restores ferroptotic sensitivity and synergizes with EGFR-targeted therapy, we highlight its potential as a metabolic target to overcome therapeutic resistance in LUAD. Conclusion This study reveals the HIF1A-SLC16A3 axis as a key mediator of ferroptosis resistance and EGFR-TKI tolerance in lung adenocarcinoma. By linking lactate metabolism to ferroptosis suppression, we uncover a metabolic mechanism of therapeutic escape. Targeting SLC16A3 restores ferroptosis sensitivity and enhances gefitinib efficacy in resistant models, nominating it as a clinically actionable metabolic vulnerability. These findings provide a rationale for developing ferroptosis-based combination strategies in LUAD. Declarations Acknowledgements This study was supported by the Beijing Natural Science Foundation (No. 7222164). Author contributions ZQX and WHC conceived the project, designed the experiments, and wrote the manuscript. WHC YML and KZ performed the experiments. WHC and JMJ analyzed and interpretated the data. JMJ, ZRZ and JXW contributed to unpublished essential data. WHC and ZQX revised the manuscript. All authors approved the final version of the manuscript. Data availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Publicly available data used in this study can be accessed from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). Competing interests This manuscript is original and has not been published or submitted elsewhere, in whole or in part. All authors have read and approved the final version of the manuscript and affirm that it complies with the policies of your journal. Each author has made a substantial contribution to the work, and there are no conflicts of interest to declare. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all patients for the use of their clinical specimens in scientific research. The protocol was approved by the Ethics Committee of the Chinese PLA General Hospital. References SIEGEL R L, MILLER K D, WAGLE N S, et al. Cancer statistics, 2023 [J]. CA Cancer J Clin, 2023, 73(1): 17-48. MOK T S, WU Y L, THONGPRASERT S, et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma [J]. N Engl J Med, 2009, 361(10): 947-57. ZHOU C, WU Y L, CHEN G, et al. 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LIU X, QIN H, ZHANG L, et al. Hyperoxia induces glucose metabolism reprogramming and intracellular acidification by suppressing MYC/MCT1 axis in lung cancer [J]. Redox Biol, 2023, 61: 102647. CHATTERJEE P, BHOWMIK D, ROY S S. A systemic analysis of monocarboxylate transporters in ovarian cancer and possible therapeutic interventions [J]. Channels (Austin), 2023, 17(1): 2273008. ZHU T, GE X, GONG S, et al. Prognostic value of lactate transporter SLC16A1 and SLC16A3 as oncoimmunological biomarkers associating tumor metabolism and immune evasion in glioma [J]. Cancer Innov, 2022, 1(3): 229-39. SHEN J, WU Z, ZHOU Y, et al. Knockdown of SLC16A3 decreases extracellular lactate concentration in hepatocellular carcinoma, alleviates hypoxia and induces ferroptosis [J]. Biochem Biophys Res Commun, 2024, 733: 150709. FRIEDMANN ANGELI J P, KRYSKO D V, CONRAD M. Ferroptosis at the crossroads of cancer-acquired drug resistance and immune evasion [J]. Nat Rev Cancer, 2019, 19(7): 405-14. MA C, HU H, LIU H, et al. Lipotoxicity, lipid peroxidation and ferroptosis: a dilemma in cancer therapy [J]. Cell Biol Toxicol, 2025, 41(1): 75. HUANG Z, CHEN X, WANG Y, et al. SLC7A11 inhibits ferroptosis and downregulates PD-L1 levels in lung adenocarcinoma [J]. Front Immunol, 2024, 15: 1372215. SUN S, GUO W, LV F, et al. Comprehensive Analysis of Ferroptosis Regulators in Lung Adenocarcinomas Identifies Prognostic and Immunotherapy-Related Biomarkers [J]. Front Mol Biosci, 2021, 8: 587436. WEI X, LI X, HU S, et al. Regulation of Ferroptosis in Lung Adenocarcinoma [J]. Int J Mol Sci, 2023, 24(19). HUANG J, XIE H, LI J, et al. Histone lactylation drives liver cancer metastasis by facilitating NSF1-mediated ferroptosis resistance after microwave ablation [J]. Redox Biol, 2025, 81: 103553. YANG P, LI H, SUN M, et al. Zinc deficiency drives ferroptosis resistance by lactate production in esophageal squamous cell carcinoma [J]. Free Radic Biol Med, 2024, 213: 512-22. MOU Y, WANG J, WU J, et al. Ferroptosis, a new form of cell death: opportunities and challenges in cancer [J]. J Hematol Oncol, 2019, 12(1): 34. ZHAO L, ZHOU X, XIE F, et al. Ferroptosis in cancer and cancer immunotherapy [J]. Cancer Commun (Lond), 2022, 42(2): 88-116. ZHANG X, YU K, MA L, et al. Endogenous glutamate determines ferroptosis sensitivity via ADCY10-dependent YAP suppression in lung adenocarcinoma [J]. Theranostics, 2021, 11(12): 5650-74. DING Y, GAO J, CHEN J, et al. BUB1b impairs chemotherapy sensitivity via resistance to ferroptosis in lung adenocarcinoma [J]. Cell Death Dis, 2024, 15(7): 525. ZHAO G, LIANG J, SHAN G, et al. KLF11 regulates lung adenocarcinoma ferroptosis and chemosensitivity by suppressing GPX4 [J]. Commun Biol, 2023, 6(1): 570. SEN U, COLEMAN C, GANDHI N, et al. SCD1 Inhibition Blocks the AKT-NRF2-SLC7A11 Pathway to Induce Lipid Metabolism Remodeling and Ferroptosis Priming in Lung Adenocarcinoma [J]. Cancer Res, 2025. MENG X, PENG F, YU S, et al. Knockdown of NADK promotes LUAD ferroptosis via NADPH/FSP1 axis [J]. J Cancer Res Clin Oncol, 2024, 150(5): 228. CONTRERAS-BAEZA Y, SANDOVAL P Y, ALARCON R, et al. Monocarboxylate transporter 4 (MCT4) is a high affinity transporter capable of exporting lactate in high-lactate microenvironments [J]. J Biol Chem, 2019, 294(52): 20135-47. WANG C, XUE L, ZHU W, et al. Lactate from glycolysis regulates inflammatory macrophage polarization in breast cancer [J]. Cancer Immunol Immunother, 2023, 72(6): 1917-32. YANG P, YIN J, ZHANG G, et al. Comprehensive pan-cancer analysis identified SLC16A3 as a potential prognostic and diagnostic biomarker [J]. Cancer Cell Int, 2025, 25(1): 168. YANG J, LEE Y, HWANG C S. The ubiquitin-proteasome system links NADPH metabolism to ferroptosis [J]. Trends Cell Biol, 2023, 33(12): 1088-103. ALVAREZ S W, SVIDERSKIY V O, TERZI E M, et al. NFS1 undergoes positive selection in lung tumours and protects cells from ferroptosis [J]. Nature, 2017, 551(7682): 639-43. KRAFT V A N, BEZJIAN C T, PFEIFFER S, et al. GTP Cyclohydrolase 1/Tetrahydrobiopterin Counteract Ferroptosis through Lipid Remodeling [J]. ACS Cent Sci, 2020, 6(1): 41-53. WISE A D, TENBARGE E G, MENDONCA J D C, et al. Mitochondria sense bacterial lactate and drive release of neutrophil extracellular traps [J]. Cell Host Microbe, 2025, 33(3): 341-57 e9. ZHANG Y, QIAN J, FU Y, et al. Inhibition of DDR1 promotes ferroptosis and overcomes gefitinib resistance in non-small cell lung cancer [J]. Biochim Biophys Acta Mol Basis Dis, 2024, 1870(7): 167447. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6833528","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471223518,"identity":"dbf88bfc-e5f8-45fd-835c-fd1bb45be876","order_by":0,"name":"Wenhan Cai","email":"","orcid":"","institution":"Graduate School of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenhan","middleName":"","lastName":"Cai","suffix":""},{"id":471223519,"identity":"9466f9dd-5686-4c55-8c27-aad090f12ef9","order_by":1,"name":"Yiming Liu","email":"","orcid":"","institution":"Graduate School of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Liu","suffix":""},{"id":471223520,"identity":"730721e7-a5eb-4057-ae2c-2b22b31133c5","order_by":2,"name":"Kai Zhao","email":"","orcid":"","institution":"Hainan Hospital of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhao","suffix":""},{"id":471223521,"identity":"dd28f1fe-5932-4e7b-982c-3ed6c10b7d16","order_by":3,"name":"Zirui Zhu","email":"","orcid":"","institution":"Hainan Hospital of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zirui","middleName":"","lastName":"Zhu","suffix":""},{"id":471223522,"identity":"dded6509-17c7-446e-9727-7972cab47a45","order_by":4,"name":"Jiamei Jin","email":"","orcid":"","institution":"Nankai University","correspondingAuthor":false,"prefix":"","firstName":"Jiamei","middleName":"","lastName":"Jin","suffix":""},{"id":471223523,"identity":"729e1cb0-5900-4669-9db6-38a0b8716460","order_by":5,"name":"Jiaxin Wen","email":"","orcid":"","institution":"First Medical Center of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Wen","suffix":""},{"id":471223524,"identity":"c45c495a-d249-40af-8340-7a43538ffdee","order_by":6,"name":"Zhiqiang Xue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIie3RsYrCMBjA8ZRAXb6ja4Ll7hU+6ahcfZRAwS4F+wiFDi4+QMX3uLnSweU4HQM6FAo3R1wdTKCrsaNg/ksofL82aQhxuV40SpCAWVuBMwhGxXDio8oXIV/XQ0gfr1QzQzm3j6NMmmueT8NguzpGgAcgknjqkj0mvFqIqMIU2Pk37wBP4G0Lyjc/j0nAMkwAG/3yTESG0LD26YeF+GypGkO+NBkD/oHPhJ3or3ilISjTmldYAzwjfP0fUdBnmchM/2S9SQa70noW3CfdFW7T+FOmXStu33G8L3fqYiF95moA+weveDrfk1E7ZNLlcrnesDvCC00AQ0Uc6wAAAABJRU5ErkJggg==","orcid":"","institution":"First Medical Center of Chinese PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Xue","suffix":""}],"badges":[],"createdAt":"2025-06-06 04:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6833528/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6833528/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84690057,"identity":"3dbf111f-0793-498a-9554-68ecdc4a1e37","added_by":"auto","created_at":"2025-06-16 09:37:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":810975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSLC16A3 is upregulated in lung adenocarcinoma and predicts poor prognosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, SLC16A3 mRNA expression is significantly elevated in LUAD tumors (n = 516) compared with adjacent normal tissues (n = 59) from the TCGA cohort.\u003c/p\u003e\n\u003cp\u003eB, Expression of SLC16A3 increases with advanced clinical stage (left) and pathological T stage (right).\u003c/p\u003e\n\u003cp\u003eC, Kaplan-Meier survival analysis of overall survival in LUAD patients with high \u003cem\u003evs.\u003c/em\u003e low SLC16A3 expression.\u003c/p\u003e\n\u003cp\u003eD, Forest plots showing multivariate and univariate Cox regression analyses of clinical variables including SLC16A3 expression, age, smoking status, and TNM staging.\u003c/p\u003e\n\u003cp\u003eE, Representative Western blot analysis of SLC16A3 protein levels in six paired LUAD tumor (T1–T6) and adjacent normal tissues (N1–N6).\u003c/p\u003e\n\u003cp\u003eF, Quantification of SLC16A3 protein levels from Western blot data, normalized to β-actin (n = 6 pairs).\u003c/p\u003e\n\u003cp\u003eG, Representative immunohistochemistry (IHC) staining images of LUAD and normal lung tissues obtained from the Human Protein Atlas (https://www.proteinatlas.org/). Scale bar, 500 µm.\u003c/p\u003e\n\u003cp\u003eH, IHC staining of clinical LUAD samples and paired adjacent normal tissues confirming elevated SLC16A3 expression in tumors. Scale bars: 200 μm (10×), 50 μm (40×).\u003c/p\u003e\n\u003cp\u003eData are presented as mean ± s.e.m. P-values were calculated using unpaired two-tailed t-tests or log-rank test where appropriate. P \u0026lt; 0.05, P \u0026lt; 0.01, ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6833528/v1/89c7cef59a4420cc814a11e4.jpg"},{"id":84691163,"identity":"2ec48f38-1a64-4896-8c61-ff72daf8047c","added_by":"auto","created_at":"2025-06-16 09:45:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1125591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKnockdown of SLC16A3 suppresses lung adenocarcinoma cell proliferation, invasion, and tumorigenesis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B, Western blot and qRT-PCR analysis confirming knockdown efficiency of three independent SLC16A3 shRNAs in PC9 cells.\u003c/p\u003e\n\u003cp\u003eC, Colony formation assay showing reduced proliferative capacity upon SLC16A3 knockdown.\u003c/p\u003e\n\u003cp\u003eD, Wound healing assay evaluating cell migration after SLC16A3 silencing.\u003c/p\u003e\n\u003cp\u003eE, Transwell invasion assay showing significantly reduced invasiveness in shSLC16A3 cells.\u003c/p\u003e\n\u003cp\u003eF-H, In vivo tumorigenesis assay using BALB/c nude mice injected subcutaneously with PC9 cells expressing scramble or SLC16A3 shRNA (n = 4 per group). Scale bar, 100 µm.\u003c/p\u003e\n\u003cp\u003eI, IHC staining of SLC16A3 in xenograft tissues. Scale bar, 50 µm.\u003c/p\u003e\n\u003cp\u003eJ, Metabolic alterations upon SLC16A3 knockdown: intracellular ATP levels, intracellular and extracellular lactate production, and intracellular glucose levels in LUAD cells.\u003c/p\u003e\n\u003cp\u003eData are presented as mean ± s.e.m. of three independent experiments. Statistical significance was assessed by one-way ANOVA with Tukey’s post hoc test or two-tailed unpaired t-test, as appropriate. ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6833528/v1/759388ce1efef6902c162207.jpg"},{"id":84690063,"identity":"6b24cdb5-c325-4d0e-a9be-77cc41118a19","added_by":"auto","created_at":"2025-06-16 09:37:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":385480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverexpression of SLC16A3 enhances LUAD cell migration and invasion in vitro.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B, Validation of SLC16A3 overexpression in PC9 cells. Western blot (A) and qRT-PCR(B) analyses confirm significantly elevated SLC16A3 protein and mRNA levels following transduction with OE-SLC16A3 vector compared to empty vector control.\u003c/p\u003e\n\u003cp\u003eC, Wound healing assay demonstrating increased migratory capacity in OE-SLC16A3 cells compared to vector control.\u003c/p\u003e\n\u003cp\u003eD, Transwell invasion assay showing elevated invasive potential upon SLC16A3 overexpression. Scale bar, 100 µm.\u003c/p\u003e\n\u003cp\u003eData are presented as mean ± s.e.m. of three independent experiments. Statistical analysis was performed using two-tailed unpaired t-test. ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6833528/v1/d94e60ef31a493860db5a40b.jpg"},{"id":84690062,"identity":"ad9a69dd-afa8-4563-b878-e6dc4dac30aa","added_by":"auto","created_at":"2025-06-16 09:37:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":851946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSLC16A3 knockdown promotes ferroptosis by disrupting iron and redox homeostasis, partially regulated by lactate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, KEGG enrichment analysis of differentially expressed genes upon SLC16A3 knockdown identified ferroptosis as the top enriched pathway.\u003c/p\u003e\n\u003cp\u003eB, Western blot showing decreased expression of ferroptosis-protective proteins (SLC7A11, GPX4) and unchanged levels of FSP1, TFRC, and DHODH following SLC16A3 knockdown.\u003c/p\u003e\n\u003cp\u003eC, FerroOrange staining showing increased intracellular Fe²⁺ accumulation in shSLC16A3 cells; right panel shows quantification of fluorescence intensity. Scale bars: 20 μm (left), 5 μm (right).\u003c/p\u003e\n\u003cp\u003eD, C11-BODIPY staining showing increased lipid peroxidation in shSLC16A3 cells (green: oxidized, red: reduced); right panel quantifies oxidized/reduced signal. Scale bars: 20 μm (left), 5 μm (right).\u003c/p\u003e\n\u003cp\u003eE, Transmission electron microscopy reveals mitochondrial shrinkage, membrane density increase, and cristae loss in shSLC16A3 cells, consistent with ferroptotic morphology. Scale bars: 1 μm (top), 500 nm (bottom).\u003c/p\u003e\n\u003cp\u003eF, JC-1 staining showing mitochondrial depolarization after SLC16A3 knockdown; bar graph shows decreased aggregate/monomer ratio. Scale bars: 20 μm (left), 5 μm (right).\u003c/p\u003e\n\u003cp\u003eG, TEM images of PC9 cells treated with PBS or lactate (10 mM) showing mitochondrial swelling, membrane disruption, and cristae reduction after lactate exposure. Scale bars: 1 μm (top), 500 nm (bottom).\u003c/p\u003e\n\u003cp\u003eH, C11-BODIPY staining shows increased lipid peroxidation following lactate treatment in PC9 cells. Scale bars: 20 μm (left), 5 μm (right).\u003c/p\u003e\n\u003cp\u003eI, Cell viability assay following treatment with ferrostatin-1 (Fer-1), Z-VAD-FMK, or chloroquine (CQ) in SLC16A3-silenced cells; only Fer-1 shows substantial rescue.\u003c/p\u003e\n\u003cp\u003eData represent mean ± s.e.m. from at least three independent experiments. Statistical analysis was performed using unpaired two-tailed t-test (C–E) or one-way ANOVA (G). ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6833528/v1/06c4b0dfdca110f0be229ccb.jpg"},{"id":84691164,"identity":"0a7f7645-441a-4380-bb5a-580ff17a3537","added_by":"auto","created_at":"2025-06-16 09:45:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":688526,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHIF1A regulates SLC16A3 transcription and ferroptosis sensitivity in lung adenocarcinoma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, Predicted transcription factor binding motifs for the SLC16A3 promoter region.\u003c/p\u003e\n\u003cp\u003eB, Knockdown of candidate transcription factors followed by qRT-PCR confirms HIF1A as a major positive regulator of SLC16A3 expression.\u003c/p\u003e\n\u003cp\u003eC-D, Correlation analysis between HIF1A and SLC16A3 mRNA expression in LUAD based on TCGA dataset (C, n=516) and LUAD clinical samples (D, n=24).\u003c/p\u003e\n\u003cp\u003eE, Western blot and qRT-PCR validation showing that silencing HIF1A reduces SLC16A3 expression at both protein and mRNA levels.\u003c/p\u003e\n\u003cp\u003eF-H, Functional assays following HIF1A knockdown demonstrate decreased cell invasion (F), migration (G), and colony formation (H) in LUAD cells. Scale bar, 100 µm.\u003c/p\u003e\n\u003cp\u003eI, Rescue experiment in BALB/c nude mice: tumor growth inhibition induced by shHIF1A is partially reversed by SLC16A3 overexpression.\u003c/p\u003e\n\u003cp\u003eJ, Multiplex immunohistochemistry (mIHC) images of LUAD tissues showing co-expression of HIF1A and SLC16A3 in scramble vs. shHIF1A tumors. Scale bar, 50 µm.\u003c/p\u003e\n\u003cp\u003eK, Western blot analysis showing that knockdown of HIF1A downregulates ferroptosis-protective proteins (SLC16A3, SLC7A11, GPX4).\u003c/p\u003e\n\u003cp\u003eL, Overexpression of SLC16A3 partially restores ferroptosis resistance in LUAD cells treated with the ferroptosis inducer erastin.\u003c/p\u003e\n\u003cp\u003eM, Cell viability assay showing that SLC16A3 overexpression rescues erastin-induced ferroptosis in LUAD cells.\u003c/p\u003e\n\u003cp\u003eData are shown as mean ± s.e.m. of at least three independent experiments. Statistical comparisons were performed using unpaired two-tailed t-test or one-way ANOVA with post hoc Tukey test. ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001, ns: not significant.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6833528/v1/2baa26c40189e9ba26c59b46.jpg"},{"id":84690075,"identity":"ae8f8bb8-6cd3-4139-9dbb-b792477634c7","added_by":"auto","created_at":"2025-06-16 09:37:42","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":651016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTargeting the HIF1A–SLC16A3 axis sensitizes gefitinib-resistant LUAD cells to ferroptosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, HIF1A and SLC16A3 expression is significantly upregulated in gefitinib-resistant PC9GR cells compared with parental PC9 cells, as shown by Western blot and qRT-PCR.\u003c/p\u003e\n\u003cp\u003eB, HIF1A and SLC16A3 are both upregulated following gefitinib treatment in PC9 cells.\u003c/p\u003e\n\u003cp\u003eC, Knockdown of SLC16A3 in PC9GR cells leads to decreased expression of GPX4 and SLC7A11.\u003c/p\u003e\n\u003cp\u003eD, Knockdown of HIF1A similarly downregulates SLC16A3 and ferroptosis-related proteins.\u003c/p\u003e\n\u003cp\u003eE, Re-expression of SLC16A3 in shHIF1A cells restores GPX4 and SLC7A11 expression, confirming the regulatory role of the HIF1A-SLC16A3 axis.\u003c/p\u003e\n\u003cp\u003eF, Colony formation, wound healing, and transwell invasion assays show that MSC-4381 sensitizes PC9GR cells to gefitinib, suppressing cell proliferation and migration.\u003c/p\u003e\n\u003cp\u003eG, MSC-4381 treatment reduces SLC7A11 and GPX4 levels in PC9GR cells.\u003c/p\u003e\n\u003cp\u003eH, BODIPY-C11 staining indicates elevated lipid peroxidation upon gefitinib and MSC-4381 co-treatment.\u003c/p\u003e\n\u003cp\u003eI, JC-1 staining reveals mitochondrial depolarization in PC9GR cells treated with MSC-4381 and gefitinib, indicating ferroptosis-related mitochondrial damage. Scale bars: 20 μm (left), 5 μm (right).\u003c/p\u003e\n\u003cp\u003eData represent mean ± s.e.m. from at least three independent replicates. P-values were determined using unpaired t-test or one-way ANOVA with Tukey’s post hoc test. **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6833528/v1/c6daa00cfd234d3f75e21c80.jpg"},{"id":84690065,"identity":"ad974ec3-3516-419e-896a-6e8dc07a57d0","added_by":"auto","created_at":"2025-06-16 09:37:42","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":653171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePharmacological inhibition of SLC16A3 enhances gefitinib efficacy in vivo through ferroptosis induction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, In vivo xenograft tumor model using PC9 cells in BALB/c nude mice. Mice were treated with DMSO, gefitinib, MSC-4381 (SLC16A3 inhibitor), or their combination (G+M). Representative tumor images, final tumor weights, and tumor volume curves are shown (n = 4 per group). Co-treatment with gefitinib and MSC-4381 significantly suppressed tumor growth compared to monotherapies.\u003c/p\u003e\n\u003cp\u003eB, MSC-4381-mediated tumor inhibition is reversed by co-administration of ferroptosis inhibitor ferrostatin-1, confirming ferroptosis dependence.\u003c/p\u003e\n\u003cp\u003eC, Immunohistochemical staining of Ki-67 in tumor tissues from the treatment groups in (A) showing reduced proliferative index upon combination therapy. Scale bars, 50 μm.\u003c/p\u003e\n\u003cp\u003eD, Ki-67 IHC in tissues from the ferrostatin-1 rescue experiment in (B) showing that ferroptosis inhibition restores proliferative capacity suppressed by MSC-4381. Scale bars, 50 μm.\u003c/p\u003e\n\u003cp\u003eData are presented as mean ± s.e.m. P values were calculated using one-way ANOVA with post hoc Tukey test. **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001, ns: not significant.\u003c/p\u003e","description":"","filename":"fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6833528/v1/fa7a432c8adb8727a60fe8c1.jpg"},{"id":84863396,"identity":"3e94ffb2-ea68-4cab-93d5-4fd591c7857c","added_by":"auto","created_at":"2025-06-18 07:32:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6681257,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6833528/v1/99f651bb-1cf6-4ad7-98ff-53b9091a47aa.pdf"},{"id":84690068,"identity":"ee954190-06b6-441e-858a-0aa5c1361626","added_by":"auto","created_at":"2025-06-16 09:37:42","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3676953,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-6833528/v1/d7be078d0ab103d53b0ccc4f.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"SLC16A3 Drives Lung Adenocarcinoma Progression and Gefitinib Resistance through Coordinated Regulation of Ferroptosis and Lactate Metabolism","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer (NSCLC) and remains a leading cause of cancer-related mortality worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Although targeted therapies such as epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have significantly improved clinical outcomes for patients with activating EGFR mutations, acquired resistance inevitably occurs, typically within a year of treatment initiation\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. This resistance is driven not only by secondary EGFR mutations or bypass signaling, but also by changes in cellular metabolism and stress adaptation\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, underscoring the need to clarify resistance mechanisms and subsequent treatment options.\u003c/p\u003e \u003cp\u003eLactate metabolism has emerged as a critical factor in tumor progression and therapeutic resistance. Lactate, once considered a mere metabolic byproduct, is now recognized as a signaling molecule that promotes angiogenesis, immune evasion, and cell survival\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Tumor cells rely on monocarboxylate transporters such as SLC16A3 (also known as MCT4) to export excess lactate and maintain intracellular pH balance\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Elevated SLC16A3 expression is frequently observed in solid tumors and correlates with poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFerroptosis, a form of iron-dependent non-apoptotic cell death driven by lipid peroxidation, has emerged as a critical regulator of tumor biology\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Unlike apoptosis, ferroptosis is closely linked to metabolic and redox homeostasis, and mounting evidence suggests that its evasion contributes to tumor growth, metastasis, and resistance to targeted therapies\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In LUAD, ferroptosis is suppressed by multiple oncogenic pathways\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, but the metabolic mechanisms that govern ferroptotic vulnerability remain incompletely defined. Notably, recent studies have proposed that ferroptosis suppression may underlie resistance to EGFR-TKIs in non-small cell lung cancer, although the upstream regulators of this process are not fully understood\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. In parallel, lactate has been implicated as a metabolic modulator that buffers oxidative stress and protects cancer cells from ferroptosis\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBuilding on these observations, we investigated the role of the lactate transporter SLC16A3 in linking lactate metabolism to ferroptosis resistance in LUAD. In this study, we identify SLC16A3 as a metabolic checkpoint that integrates lactate export and ferroptosis suppression to promote LUAD progression and gefitinib tolerance. Using TCGA data, clinical samples, functional assays, and in vivo models, we show that SLC16A3 overexpression is associated with poor prognosis and resistance phenotypes. Mechanistically, we demonstrate that SLC16A3-driven lactate export protects LUAD cells from ferroptosis and is transcriptionally regulated by hypoxia-inducible factor 1-alpha (HIF1A). These findings highlight the HIF1A-SLC16A3-lactate axis as a critical mediator of therapeutic escape and nominate SLC16A3 as a targetable metabolic vulnerability in LUAD.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical tissue samples\u003c/h2\u003e \u003cp\u003eA total of 23 pairs of lung adenocarcinoma (LUAD) tumor tissues and matched adjacent normal lung tissues were collected from patients who underwent surgical resection at the Department of Thoracic Surgery, First Medical Center of the Chinese PLA General Hospital (Beijing, China). The histological and pathological diagnosis of all specimens was independently confirmed by at least two board-certified pathologists according to the 2015 WHO classification of lung tumors. All patients provided written informed consent prior to enrollment, and the study was approved by the Institutional Review Board of the Chinese PLA General Hospital (Approval No. S2024-377-01). Immediately after resection, samples were either snap-frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C or fixed in 10% neutral buffered formalin and paraffin-embedded. These tissues were used for Western blotting, immunohistochemistry (IHC), and multiplex immunohistochemistry (mIHC). Clinicopathological characteristics of the LUAD cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline clinicopathological characteristics of LUAD patients stratified by SLC16A3 expression levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinicopathological feature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSLC16A3_low (n\u0026thinsp;=\u0026thinsp;258)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC16A3_high (n\u0026thinsp;=\u0026thinsp;258)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP_value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.7 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.9 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline clinicopathological characteristics of the 23 LUAD patients included for gene expression correlation analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinicopathological feature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;23\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(Female/Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10/13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esmoking history(Yes/No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Acquisition and Bioinformatics Analysis\u003c/h3\u003e\n\u003cp\u003eTranscriptomic RNA-seq data and corresponding clinical annotations for lung adenocarcinoma (LUAD) patients were downloaded from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gene expression comparisons and Kaplan\u0026ndash;Meier survival analyses were performed using R software (version 4.2.1). KEGG pathway enrichment analysis was conducted using the DAVID Bioinformatics Resources (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Transcription factor binding motif predictions for the SLC16A3 promoter were retrieved from the JASPAR database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jaspar.genereg.net/\u003c/span\u003e\u003cspan address=\"https://jaspar.genereg.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Protein expression profiles of SLC16A3 in normal and LUAD tissues were assessed using the Human Protein Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCell culture and drug exposure\u003c/h3\u003e\n\u003cp\u003eHuman lung adenocarcinoma (LUAD) cell lines PC9 and gefitinib-resistant PC9GR, as well as the normal bronchial epithelial cell line BEAS-2B, were obtained from the MeisenChinese Tissue Culture Collections (Hangzhou, China). PC9 and PC9GR cells were cultured in RPMI-1640 medium (Gibco), and BEAS-2B cells were maintained in DMEM (Gibco), both supplemented with 10% fetal bovine serum (FBS; Gibco) and 1% penicillin-streptomycin (Gibco). All cells were incubated in a humidified chamber with 5% CO₂ at 37\u0026deg;C and were confirmed negative for Mycoplasma contamination. Gefitinib, MSC-4381, ferrostatin-1 (Fer-1), Z-VAD-FMK, and chloroquine (CQ) were purchased from Abmole (Shanghai, China). All drugs were dissolved in dimethyl sulfoxide (DMSO) to prepare stock solutions and stored at \u0026minus;\u0026thinsp;20\u0026deg;C. Working dilutions were freshly prepared in culture medium prior to use, and control cells were treated with an equal concentration of DMSO.\u003c/p\u003e\n\u003ch3\u003eRNA extraction and quantitative real-time PCR\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted using the RNA-easy\u0026trade; Isolation Kit (Vazyme, China) following the manufacturer\u0026rsquo;s instructions. Complementary DNA was synthesized using the HiScript\u0026reg; III RT SuperMix for qPCR (Vazyme, China). Quantitative real-time PCR was performed using ChamQ Universal SYBR qPCR Master Mix (Vazyme, China) on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems, USA). GAPDH was used as the internal control, and relative mRNA expression was calculated using the 2^\u0026ndash;ΔΔCt method. Primer sequences are listed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Each condition was tested in triplicate (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e\n\u003ch3\u003eGene silencing and overexpression\u003c/h3\u003e\n\u003cp\u003eStable knockdown or overexpression of SLC16A3 was achieved via lentiviral transduction using shRNA or full-length cDNA constructs. Lentiviral vectors carrying SLC16A3-targeting shRNAs (shSLC16A3#1/2/3) or control sequences (scramble) were purchased from Genechem (Shanghai, China) and cloned into the pLKO.1 vector. Packaging was performed in 293TN cells using psPAX2 and pMD2.G helper plasmids (Addgene) and jetPRIME transfection reagent (Polyplus, France). Viral supernatants were harvested, filtered, and used to infect PC9 and PC9GR cells in the presence of polybrene (8 \u0026micro;g/mL). Stable clones were selected with puromycin (0.5 \u0026micro;g/mL) for 7 days. The efficiency of knockdown or overexpression was confirmed by qRT-PCR and Western blotting.\u003c/p\u003e \u003cp\u003eFor transient gene knockdown, small interfering RNAs (siRNAs) targeting SLC16A3 or candidate transcription factors (including HIF1A, KLF4, MXI1, RORA, ZNF460, and ZNF682) were synthesized by RiboBio (Guangzhou, China). Transfections were performed using Lipofectamine 3000 (Invitrogen, Cat No. L3000015) in Opti-MEM medium (Invitrogen), following the manufacturer\u0026rsquo;s protocol. Cells were harvested 48 hours post-transfection for downstream assays. Sequences of all shRNAs and siRNAs are listed in Supplementary Table S2.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eCells or tissues were lysed using RIPA buffer (Beyotime, Shanghai, China) supplemented with protease and phosphatase inhibitor cocktails (Roche, Switzerland). Total protein concentration was determined using the BCA Protein Assay Kit (Thermo Fisher Scientific, USA). Equal amounts of protein (25 \u0026micro;g) were separated by SDS-PAGE and transferred to 0.45 \u0026micro;m PVDF membranes (Millipore, USA). Membranes were blocked with 5% non-fat milk for 2 hours at room temperature, then incubated overnight at 4\u0026deg;C with primary antibodies against SLC16A3 (Proteintech), HIF1A (Abcam), SLC7A11 (Abcam), GPX4 (Abcam), FSP1 (Abcam), TFRC (Abcam), DHODH (Proteintech), and β-actin (Proteintech). After three washes with TBST, membranes were incubated with HRP-conjugated secondary antibodies (Cell Signaling Technology) at room temperature for 1 hour. Protein bands were visualized using ECL substrate (Thermo Fisher Scientific) and imaged with a chemiluminescence detection system. Band intensities were quantified using ImageJ software and normalized to β-actin. All experiments were repeated independently in triplicate (n\u0026thinsp;=\u0026thinsp;3). Antibody details are listed in Supplementary Table S3.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell proliferation and colony formation assays\u003c/h3\u003e\n\u003cp\u003eFor cell viability analysis, 5 \u0026times; 10\u0026sup3; cells per well were seeded into 96-well plates and incubated overnight at 37\u0026deg;C with 5% CO₂. The following day, cells were treated with gradient concentrations of the indicated compounds for 24 h. Cell viability was assessed using the CCK-8 kit (Dojindo, Japan) according to the manufacturer\u0026rsquo;s instructions, and absorbance at 450 nm was measured to calculate relative viability. For colony formation assays, 500 cells per well were seeded into 6-well plates and cultured for 10\u0026ndash;14 days. Colonies were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet, followed by quantification using ImageJ software. Each assay was performed in triplicate (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e\n\u003ch3\u003eWound healing, migration and invasion assays\u003c/h3\u003e\n\u003cp\u003eFor the wound healing assay, PC9 and PC9GR cells were seeded into six-well plates and grown to confluence. A straight scratch was made using a sterile 200 \u0026micro;L pipette tip (0 h), and the wells were gently washed with PBS to remove detached cells. Cells were then cultured in serum-free medium and imaged at 0 h and 24 h under a light microscope. Wound closure was quantified as the percentage of wound area reduction relative to 0 h using ImageJ software.\u003c/p\u003e \u003cp\u003eCell migration and invasion assays were performed using 24-well transwell chambers with 8 \u0026micro;m pore polycarbonate membrane inserts (Corning, USA). For the invasion assay, the upper chambers were pre-coated with Matrigel (BD Biosciences, USA). Cells (5 \u0026times; 10⁴ for migration and 1 \u0026times; 10⁵ for invasion) were resuspended in 200 \u0026micro;L serum-free medium and seeded into the upper chambers. The lower chambers were filled with 500 \u0026micro;L complete medium containing 15% FBS. After incubation for 24 h at 37\u0026deg;C with 5% CO₂, non-migrated or non-invaded cells were removed from the upper surface using cotton swabs. The cells on the lower membrane surface were fixed with 4% paraformaldehyde and stained with 0.5% crystal violet (Solarbio, China). Migrated or invaded cells were imaged and counted in three randomly selected fields per insert.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFerroptosis and cell death assays\u003c/h2\u003e \u003cp\u003eLipid peroxidation was assessed using BODIPY 581/591 C11 (Thermo Fisher Scientific), and mitochondrial membrane potential was measured using JC-1 dye (Sigma-Aldrich). Intracellular Fe\u0026sup2;⁺ levels were detected using FerroOrange (MedChemExpress, MCE) according to the manufacturer\u0026rsquo;s protocol. To assess the role of different cell death pathways, cells were treated with ferrostatin-1 (2 \u0026micro;M), Z-VAD-FMK (10 \u0026micro;M), or chloroquine (10 \u0026micro;M) for 24 hours, and viability was determined using the CCK-8 assay (Sigma-Aldrich). All conditions were tested in triplicate (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTransmission electron microscopy (TEM)\u003c/h2\u003e \u003cp\u003eTo assess mitochondrial ultrastructure, cells were collected from 10 cm dishes after confirming interference efficiency and centrifuged at 4\u0026deg;C. Cell pellets were fixed overnight in 2.5% glutaraldehyde, followed by post-fixation with 1% osmium tetroxide and 2% uranyl acetate. After dehydration in graded ethanol and acetone, samples were embedded in 812 epoxy resin (1:1 mixture with embedding agent for 2\u0026ndash;4 h, then pure resin infiltration for 5\u0026ndash;8 h at 37\u0026deg;C). Polymerization was performed at 60\u0026deg;C for 48 h. Ultrathin sections (60\u0026ndash;80 nm) were prepared using an ultramicrotome, stained with uranyl acetate and lead citrate, and air-dried. Ultrastructural images were acquired using a Hitachi HT7800 transmission electron microscope at 120 kV.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLactate, glucose uptake and ATP quantification\u003c/h2\u003e \u003cp\u003eIntracellular lactate levels were measured using the Lactate Assay Kit (Beyotime, China) following the manufacturer\u0026rsquo;s instructions. Intracellular glucose levels were detected using the Glucose Assay Kit based on the O-toluidine method (Beyotime, China). Cellular ATP content was quantified using the ATP Determination Kit (Beyotime, China). All measurements were conducted in triplicate (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnimal experiments\u003c/h2\u003e \u003cp\u003eAll animal procedures were conducted in accordance with institutional guidelines and approved by the Institutional Animal Care and Use Committee of the Chinese PLA General Hospital. Female BALB/c nude mice (4\u0026ndash;6 weeks old) were purchased from HFKBio (Beijing, China) and maintained under specific pathogen-free (SPF) conditions. PC9 cells (5 \u0026times; 10⁶ in 100 \u0026micro;L PBS mixed 1:1 with Matrigel) were subcutaneously injected into the axillary region of each mouse. For genetic perturbation experiments, cells stably expressing shSLC16A3, OE-SLC16A3, shHIF1A, or corresponding control vectors were used. For in vivo drug treatment, tumor-bearing mice were randomly divided into groups and treated with vehicle (DMSO), gefitinib (30 mg/kg), MSC-4381 (30 mg/kg), or a combination of both via daily oral gavage. In the ferroptosis rescue group, ferrostatin-1 (5 mg/kg) was co-administered by intraperitoneal injection along with MSC-4381. Tumor volumes were measured every 4 days using calipers and calculated using the formula: (length \u0026times; width\u0026sup2;)/2. On day 24 after injection, all mice were euthanized, and tumors were collected for imaging, weighing, and further analyses. Each group consisted of four mice (n\u0026thinsp;=\u0026thinsp;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry\u003c/h2\u003e \u003cp\u003eFormalin-fixed, paraffin-embedded (FFPE) tumor tissues from xenograft models or clinical LUAD samples were sectioned at 4 \u0026micro;m thickness. Sections were deparaffinized, rehydrated, and subjected to antigen retrieval using citrate buffer (pH 6.0). Endogenous peroxidase activity was blocked using 3% hydrogen peroxide, followed by nonspecific blocking with 5% BSA. After overnight incubation at 4\u0026deg;C with primary antibodies including anti-SLC16A3 and anti-Ki-67, sections were incubated with HRP-conjugated secondary antibodies. Immunoreactive signals were visualized using diaminobenzidine as the chromogen. Slides were counterstained with hematoxylin and imaged using a Leica DM6 microscope. Two independent pathologists blinded to group information scored the immunostaining based on both intensity and percentage of positive tumor cells. Staining intensity was graded as 0 (no staining), 1 (light yellow), 2 (yellow brown), and 3 (deep brown); the percentage of positive cells was scored as 0 (none), 1 (0\u0026ndash;25%), 2 (26\u0026ndash;50%), 3 (51\u0026ndash;75%), and 4 (76\u0026ndash;100%). The final immunoreactivity score was calculated as (Score1 \u0026times; 1) + (Score2 \u0026times; 2) + (Score3 \u0026times; 3), with a maximum score of 300. Antibody details are provided in Supplementary Table S4, and all experiments were performed in triplicate (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMultiplex immunohistochemistry (mIHC)\u003c/h2\u003e \u003cp\u003eFormalin-fixed paraffin-embedded (FFPE) tumor tissues derived from xenograft models (shHIF1A and control groups) were sectioned at 4 \u0026micro;m thickness. Slides were deparaffinized in xylene (2 \u0026times; 10 min), rehydrated through graded ethanol (100%, 95%, and 70%; 5 min each), and subjected to antigen retrieval by microwave heating in citrate buffer (pH 6.0) for 15 min at 20% maximum power. After cooling and washing in Tris-buffered saline (TBS, pH 7.6; 3 \u0026times; 5 min), sequential rounds of staining were performed using primary antibodies against HIF1A and SLC16A3. Each round included incubation with primary antibody (30 min, room temperature), HRP-conjugated secondary antibody, and signal development with the TSA Fluorescence Penta Staining Kit (RK05905, ReduX Biosciences, China). SLC16A3 and HIF1A signals were detected using red and green fluorophores, respectively. Antigen retrieval was repeated between rounds to strip previous antibodies. Nuclei were counterstained with DAPI (Thermo Fisher), and slides were coverslipped. Fluorescent images were captured using a Nikon A1R\u0026thinsp;+\u0026thinsp;laser confocal microscope (Nikon, Japan), and quantitative analysis was performed using inForm software (PerkinElmer). Antibody details are listed in Supplementary Table S5. All experiments were performed in duplicate using tumors from each group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using GraphPad Prism 10 and R software (version 4.2.1). Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;s.e.m. from at least three independent experiments. Differences between two groups were analyzed using unpaired Student\u0026rsquo;s t-test. One-way ANOVA was used for multiple group comparisons. Survival curves were analyzed using the Kaplan\u0026ndash;Meier method and compared by log-rank test. Univariate and multivariate Cox regression analyses were used to evaluate prognostic factors. Significant differences were represented as *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 unless otherwise indicated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSLC16A3 is upregulated in lung adenocarcinoma and predicts poor prognosis\u003c/h2\u003e \u003cp\u003eAnalysis of transcriptomic data from the TCGA LUAD cohort revealed that SLC16A3 expression was significantly upregulated in tumor tissues compared to adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Stratification by clinical stage showed that SLC16A3 expression increased with both advanced clinical stage and pathological T stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Kaplan-Meier survival analysis demonstrated that high SLC16A3 expression was associated with worse overall survival in LUAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Multivariate Cox regression analysis further confirmed SLC16A3 as an independent prognostic factor for LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsistently, Western blot analysis of six paired LUAD and adjacent normal tissues confirmed elevated SLC16A3 protein expression in tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). In parallel, qRT-PCR analysis validated upregulated SLC16A3 mRNA levels in tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Representative IHC images from the HPA database showed elevated SLC16A3 expression in LUAD tissues compared to normal lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG), with IHC validation in our clinical LUAD cohort further confirming enhanced SLC16A3 expression in tumor tissues compared to matched normal samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSLC16A3 promotes LUAD cell proliferation, migration, invasion, and tumor growth\u003c/h2\u003e \u003cp\u003eTo investigate the functional role of SLC16A3, we performed loss-of-function studies in PC9 cells using three independent shRNAs. Efficient knockdown of SLC16A3 was confirmed by Western blot and qRT-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA,B). SLC16A3 silencing significantly impaired cell proliferation, as shown by colony formation assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), and suppressed migration and invasion, as assessed by wound healing and transwell assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD,E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn vivo xenograft assays using BALB/c nude mice demonstrated that SLC16A3 knockdown markedly reduced tumor growth, as evidenced by reduced tumor weight and volume over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF\u0026ndash;H). IHC staining of xenograft tissues confirmed decreased SLC16A3 expression in shRNA-treated tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI, Supplementary Fig. A). Moreover, metabolic profiling revealed that SLC16A3 silencing significantly reduced intracellular ATP levels, lactate production, and glucose concentration(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ), supporting its role in promoting glycolytic metabolism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSLC16A3 overexpression enhances migration and invasion in LUAD cells\u003c/h2\u003e \u003cp\u003eGain-of-function studies in PC9 cells further validated the pro-invasive function of SLC16A3. Western blot and qRT\u0026ndash;PCR confirmed successful overexpression of SLC16A3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA,B). Compared to vector controls, SLC16A3-overexpressing cells exhibited significantly enhanced migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) and invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), supporting a functional role of SLC16A3 in promoting malignant phenotypes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSLC16A3 knockdown promotes ferroptosis via dysregulation of iron and redox homeostasis, and is partially modulated by lactate\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo explore the mechanism by which SLC16A3 contributes to tumor progression, transcriptomic analysis was performed in PC9 cells stably expressing shSLC16A3 or scramble control. KEGG enrichment analysis of differentially expressed genes revealed ferroptosis as the top enriched pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Western blot analyses confirmed that SLC16A3 knockdown reduced the expression of key ferroptosis defense proteins SLC7A11 and GPX4, whereas FSP1, TFRC, and DHODH levels remained largely unchanged (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Fluorescent staining with FerroOrange showed marked iron accumulation in shSLC16A3 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), and C11-BODIPY assays indicated enhanced lipid peroxidation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Transmission electron microscopy revealed characteristic ferroptotic ultrastructural changes, including swollen mitochondria, disrupted outer membranes, and reduced or absent cristae (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). JC-1 staining further demonstrated mitochondrial depolarization (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven the known role of lactate in modulating redox balance, we examined whether exogenous lactate could rescue ferroptosis induced by SLC16A3 silencing. Western blot analysis showed that lactate treatment reduced the expression of SLC7A11 and GPX4 (Supplementary Fig. B). Lactate administration induced mitochondrial damage (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG) and increased lipid peroxidation levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH, Supplementary Fig. C), indicating activation of ferroptosis. Finally, cell viability assays showed that apoptosis (Z-VAD-FMK), autophagy (CQ), and ferroptosis (ferrostatin-1) inhibitors all reversed SLC16A3 knockdown-induced cell death to varying degrees, with ferrostatin-1 exhibiting the most pronounced protective effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI), indicating that ferroptosis is the predominant death mechanism downstream of SLC16A3 loss.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eHIF1A transcriptionally regulates SLC16A3 and modulates ferroptosis sensitivity\u003c/h2\u003e \u003cp\u003eTranscription factor binding motif analysis based on the JASPAR database predicted HIF1A as a potential regulator of SLC16A3 promoter activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). siRNA-mediated knockdown of HIF1A and several other predicted transcription factors reduced SLC16A3 mRNA expression, with HIF1A exhibiting the strongest inhibitory effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Expression correlation analyses in TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) and clinical LUAD samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) showed strong positive correlation between HIF1A and SLC16A3. Western blot confirmed that HIF1A knockdown reduced SLC16A3 protein levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). mIHC further confirmed the co-localization of HIF1A and SLC16A3 in xenograft tumors, with SLC16A3 expression markedly reduced in shHIF1A tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ), supporting the in vivo relevance of HIF1A-mediated transcriptional regulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunctionally, HIF1A knockdown suppressed invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF), migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG), and colony formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). In vivo, SLC16A3 overexpression partially rescued the tumor-suppressive effect of HIF1A knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). Mechanistically, HIF1A silencing downregulated ferroptosis-protective proteins SLC7A11 and GPX4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK), and these were restored by re-expression of SLC16A3. Moreover, SLC16A3 overexpression conferred resistance to erastin-induced ferroptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eL), as confirmed by cell viability assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eM).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eTargeting the HIF1A-SLC16A3 axis sensitizes gefitinib-resistant LUAD cells to ferroptosis\u003c/h2\u003e \u003cp\u003eWe next explored whether the HIF1A-SLC16A3 axis mediates resistance to gefitinib. Both HIF1A and SLC16A3 were significantly upregulated in gefitinib-resistant PC9GR cells compared with parental PC9 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), and were further induced by gefitinib treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Silencing either SLC16A3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) or HIF1A (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD) downregulated GPX4 and SLC7A11 in PC9GR cells. Restoration of SLC16A3 rescued this effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE), confirming that HIF1A regulates ferroptosis via SLC16A3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunctional assays showed that co-treatment with MSC-4381 (SLC16A3 inhibitor) and gefitinib significantly reduced colony formation, migration, and invasion in PC9GR cells compared to either agent alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, Supplementary Fig. D-F). Western blot showed that MSC-4381 decreased GPX4 and SLC7A11 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), and BODIPY staining confirmed enhanced lipid peroxidation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH, Supplementary Fig. G). JC-1 assays indicated mitochondrial damage consistent with ferroptosis induction (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI, Supplementary Fig. H).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePharmacological inhibition of SLC16A3 enhances gefitinib efficacy in vivo through ferroptosis induction\u003c/h2\u003e \u003cp\u003eTo validate the therapeutic potential of SLC16A3 inhibition in vivo, we treated PC9 xenograft-bearing mice with MSC-4381, gefitinib, or both. Co-treatment significantly suppressed tumor growth compared to either agent alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Ferrostatin-1 partially attenuated the anti-tumor effect of MSC-4381, confirming ferroptosis as a key mechanism (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). IHC staining showed that co-treatment reduced Ki-67 expression, indicative of impaired proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), whereas ferroptosis inhibition restored Ki-67 positivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFerroptosis, a regulated form of non-apoptotic cell death driven by iron-dependent lipid peroxidation, has emerged as a critical vulnerability in cancer therapy\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In lung adenocarcinoma (LUAD), ferroptosis is often suppressed to promote tumor survival and therapy resistance\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Although key regulators such as GPX4, SLC7A11, and FSP1 have been widely studied\u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, the metabolic cues that govern ferroptotic evasion remain incompletely defined. Our study identifies SLC16A3, a lactate exporter, as a central metabolic node that suppresses ferroptosis and contributes to EGFR-TKI resistance in LUAD.\u003c/p\u003e \u003cp\u003eSLC16A3 (also known as MCT4) is a hypoxia-inducible transporter that facilitates lactate efflux during aerobic glycolysis\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Its overexpression has been correlated with tumor aggressiveness, metastasis, and poor prognosis across multiple cancers, including breast, prostate, and lung cancer\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In line with previous reports, we show that SLC16A3 is upregulated in LUAD tumors and correlates with advanced clinical stage and poor outcome. While most studies focus on its role in pH regulation and metabolic adaptation, we now demonstrate its capacity to regulate ferroptosis by maintaining redox balance through lactate export.\u003c/p\u003e \u003cp\u003eRecent evidence has linked lactate accumulation to ferroptosis resistance via multiple mechanisms, including NADPH generation, glutathione maintenance, and lipid remodeling\u003csup\u003e[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Lactate can serve as an alternative carbon source for NADH/NADPH production under stress, thereby countering lipid ROS accumulation\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Our findings support this metabolic buffering model, showing that SLC16A3 inhibition enhances lipid peroxidation, depletes ferroptosis-protective proteins (GPX4, SLC7A11), and disrupts mitochondrial integrity. Ferroptosis ultrastructural features, including condensed mitochondria and cristae loss, were observed upon SLC16A3 knockdown, reinforcing its role in ferroptotic regulation. Among several death pathway inhibitors, only ferrostatin-1 significantly rescued cell viability, excluding contributions from apoptosis, autophagy, or necroptosis. These results highlight SLC16A3-mediated lactate export as a previously underappreciated metabolic brake on ferroptosis in LUAD.\u003c/p\u003e \u003cp\u003ePharmacologic inhibition of SLC16A3 using MSC-4381 phenocopied genetic knockdown, impaired redox homeostasis, and sensitized LUAD cells to ferroptosis. Moreover, MSC-4381 synergized with gefitinib in vitro and in vivo, and this effect was abrogated by ferrostatin-1, confirming ferroptosis as the mechanistic basis of the combination therapy. These findings echo recent studies showing that ferroptosis induction enhances the efficacy of EGFR inhibitors and overcomes acquired resistance\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur results support a growing paradigm where targeting metabolic regulators restores ferroptosis sensitivity in therapy-resistant tumors. In particular, the dual role of SLC16A3 in lactate clearance and ferroptosis suppression positions it as a promising target in LUAD. However, further studies are warranted. First, although MSC-4381 shows efficacy in subcutaneous xenografts, its pharmacokinetics, toxicity, and delivery efficiency in orthotopic and patient-derived models remain unclear. Second, comprehensive metabolic flux analyses and redox profiling are needed to quantify NADPH/NADH and glutathione dynamics upon SLC16A3 loss. Third, whether this mechanism applies to KRAS- or ALK-driven LUAD or other solid tumors requires broader validation.\u003c/p\u003e \u003cp\u003eIn conclusion, this study uncovers a metabolic axis linking HIF1A-induced SLC16A3 expression to ferroptosis suppression and EGFR-TKI resistance via lactate export. By showing that SLC16A3 inhibition restores ferroptotic sensitivity and synergizes with EGFR-targeted therapy, we highlight its potential as a metabolic target to overcome therapeutic resistance in LUAD.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals the HIF1A-SLC16A3 axis as a key mediator of ferroptosis resistance and EGFR-TKI tolerance in lung adenocarcinoma. By linking lactate metabolism to ferroptosis suppression, we uncover a metabolic mechanism of therapeutic escape. Targeting SLC16A3 restores ferroptosis sensitivity and enhances gefitinib efficacy in resistant models, nominating it as a clinically actionable metabolic vulnerability. These findings provide a rationale for developing ferroptosis-based combination strategies in LUAD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Beijing Natural Science Foundation (No. 7222164).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZQX and WHC conceived the project, designed the experiments, and wrote the manuscript. WHC YML and KZ performed the experiments. WHC and JMJ analyzed and interpretated the data. JMJ, ZRZ and JXW contributed to unpublished essential data. WHC and ZQX revised the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Publicly available data used in this study can be accessed from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript is original and has not been published or submitted elsewhere, in whole or in part. All authors have read and approved the final version of the manuscript and affirm that it complies with the policies of your journal. Each author has made a substantial contribution to the work, and there are no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all patients for the use of their clinical specimens in scientific research. The protocol was approved by the Ethics Committee of the Chinese PLA General Hospital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSIEGEL R L, MILLER K D, WAGLE N S, et al. Cancer statistics, 2023 [J]. CA Cancer J Clin, 2023, 73(1): 17-48.\u003c/li\u003e\n\u003cli\u003eMOK T S, WU Y L, THONGPRASERT S, et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma [J]. N Engl J Med, 2009, 361(10): 947-57.\u003c/li\u003e\n\u003cli\u003eZHOU C, WU Y L, CHEN G, et al. 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Knockdown of SLC16A3 decreases extracellular lactate concentration in hepatocellular carcinoma, alleviates hypoxia and induces ferroptosis [J]. Biochem Biophys Res Commun, 2024, 733: 150709.\u003c/li\u003e\n\u003cli\u003eFRIEDMANN ANGELI J P, KRYSKO D V, CONRAD M. Ferroptosis at the crossroads of cancer-acquired drug resistance and immune evasion [J]. Nat Rev Cancer, 2019, 19(7): 405-14.\u003c/li\u003e\n\u003cli\u003eMA C, HU H, LIU H, et al. Lipotoxicity, lipid peroxidation and ferroptosis: a dilemma in cancer therapy [J]. Cell Biol Toxicol, 2025, 41(1): 75.\u003c/li\u003e\n\u003cli\u003eHUANG Z, CHEN X, WANG Y, et al. SLC7A11 inhibits ferroptosis and downregulates PD-L1 levels in lung adenocarcinoma [J]. Front Immunol, 2024, 15: 1372215.\u003c/li\u003e\n\u003cli\u003eSUN S, GUO W, LV F, et al. Comprehensive Analysis of Ferroptosis Regulators in Lung Adenocarcinomas Identifies Prognostic and Immunotherapy-Related Biomarkers [J]. Front Mol Biosci, 2021, 8: 587436.\u003c/li\u003e\n\u003cli\u003eWEI X, LI X, HU S, et al. Regulation of Ferroptosis in Lung Adenocarcinoma [J]. Int J Mol Sci, 2023, 24(19).\u003c/li\u003e\n\u003cli\u003eHUANG J, XIE H, LI J, et al. Histone lactylation drives liver cancer metastasis by facilitating NSF1-mediated ferroptosis resistance after microwave ablation [J]. Redox Biol, 2025, 81: 103553.\u003c/li\u003e\n\u003cli\u003eYANG P, LI H, SUN M, et al. Zinc deficiency drives ferroptosis resistance by lactate production in esophageal squamous cell carcinoma [J]. Free Radic Biol Med, 2024, 213: 512-22.\u003c/li\u003e\n\u003cli\u003eMOU Y, WANG J, WU J, et al. Ferroptosis, a new form of cell death: opportunities and challenges in cancer [J]. J Hematol Oncol, 2019, 12(1): 34.\u003c/li\u003e\n\u003cli\u003eZHAO L, ZHOU X, XIE F, et al. Ferroptosis in cancer and cancer immunotherapy [J]. Cancer Commun (Lond), 2022, 42(2): 88-116.\u003c/li\u003e\n\u003cli\u003eZHANG X, YU K, MA L, et al. Endogenous glutamate determines ferroptosis sensitivity via ADCY10-dependent YAP suppression in lung adenocarcinoma [J]. Theranostics, 2021, 11(12): 5650-74.\u003c/li\u003e\n\u003cli\u003eDING Y, GAO J, CHEN J, et al. BUB1b impairs chemotherapy sensitivity via resistance to ferroptosis in lung adenocarcinoma [J]. Cell Death Dis, 2024, 15(7): 525.\u003c/li\u003e\n\u003cli\u003eZHAO G, LIANG J, SHAN G, et al. KLF11 regulates lung adenocarcinoma ferroptosis and chemosensitivity by suppressing GPX4 [J]. Commun Biol, 2023, 6(1): 570.\u003c/li\u003e\n\u003cli\u003eSEN U, COLEMAN C, GANDHI N, et al. SCD1 Inhibition Blocks the AKT-NRF2-SLC7A11 Pathway to Induce Lipid Metabolism Remodeling and Ferroptosis Priming in Lung Adenocarcinoma [J]. Cancer Res, 2025.\u003c/li\u003e\n\u003cli\u003eMENG X, PENG F, YU S, et al. Knockdown of NADK promotes LUAD ferroptosis via NADPH/FSP1 axis [J]. J Cancer Res Clin Oncol, 2024, 150(5): 228.\u003c/li\u003e\n\u003cli\u003eCONTRERAS-BAEZA Y, SANDOVAL P Y, ALARCON R, et al. Monocarboxylate transporter 4 (MCT4) is a high affinity transporter capable of exporting lactate in high-lactate microenvironments [J]. J Biol Chem, 2019, 294(52): 20135-47.\u003c/li\u003e\n\u003cli\u003eWANG C, XUE L, ZHU W, et al. Lactate from glycolysis regulates inflammatory macrophage polarization in breast cancer [J]. Cancer Immunol Immunother, 2023, 72(6): 1917-32.\u003c/li\u003e\n\u003cli\u003eYANG P, YIN J, ZHANG G, et al. Comprehensive pan-cancer analysis identified SLC16A3 as a potential prognostic and diagnostic biomarker [J]. Cancer Cell Int, 2025, 25(1): 168.\u003c/li\u003e\n\u003cli\u003eYANG J, LEE Y, HWANG C S. The ubiquitin-proteasome system links NADPH metabolism to ferroptosis [J]. Trends Cell Biol, 2023, 33(12): 1088-103.\u003c/li\u003e\n\u003cli\u003eALVAREZ S W, SVIDERSKIY V O, TERZI E M, et al. NFS1 undergoes positive selection in lung tumours and protects cells from ferroptosis [J]. Nature, 2017, 551(7682): 639-43.\u003c/li\u003e\n\u003cli\u003eKRAFT V A N, BEZJIAN C T, PFEIFFER S, et al. GTP Cyclohydrolase 1/Tetrahydrobiopterin Counteract Ferroptosis through Lipid Remodeling [J]. ACS Cent Sci, 2020, 6(1): 41-53.\u003c/li\u003e\n\u003cli\u003eWISE A D, TENBARGE E G, MENDONCA J D C, et al. Mitochondria sense bacterial lactate and drive release of neutrophil extracellular traps [J]. Cell Host Microbe, 2025, 33(3): 341-57 e9.\u003c/li\u003e\n\u003cli\u003eZHANG Y, QIAN J, FU Y, et al. Inhibition of DDR1 promotes ferroptosis and overcomes gefitinib resistance in non-small cell lung cancer [J]. Biochim Biophys Acta Mol Basis Dis, 2024, 1870(7): 167447.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung adenocarcinoma (LUAD), SLC16A3, ferroptosis, lactate metabolism, gefitinib resistance, hypoxia-inducible factor 1-alpha (HIF1A)","lastPublishedDoi":"10.21203/rs.3.rs-6833528/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6833528/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFerroptosis is an iron-dependent form of regulated cell death that plays a critical role in tumor suppression and therapy response. However, the metabolic mechanisms that drive ferroptosis resistance in lung adenocarcinoma (LUAD), particularly in the context of EGFR-TKI tolerance, remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe integrated transcriptomic and clinical data from the TCGA LUAD cohort and performed survival and enrichment analyses. Functional assays including proliferation, invasion, ferroptosis indicators, and in vivo xenograft models were used to evaluate the role of SLC16A3. Lactate rescue, transcription factor prediction (JASPAR), mIHC, and luciferase reporter assays were applied to dissect regulatory mechanisms. Pharmacological inhibition of SLC16A3 was used to assess therapeutic potential.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSLC16A3 expression was elevated in LUAD and correlated with poor prognosis. Knockdown of SLC16A3 suppressed tumor cell growth and enhanced ferroptosis, as indicated by increased lipid peroxidation, iron accumulation, and mitochondrial depolarization. Lactate supplementation partially reversed ferroptosis induction. Mechanistically, SLC16A3 was transcriptionally activated by HIF1A, and the HIF1A-SLC16A3 axis conferred ferroptosis resistance and gefitinib tolerance. In vivo, SLC16A3 inhibition restored ferroptotic sensitivity and enhanced EGFR-TKI efficacy in xenograft models.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings reveal that the HIF1A-SLC16A3-lactate axis orchestrates ferroptosis suppression and therapeutic resistance in LUAD. Targeting SLC16A3 represents a promising metabolic strategy to overcome EGFR-TKI resistance by reactivating ferroptosis.\u003c/p\u003e","manuscriptTitle":"SLC16A3 Drives Lung Adenocarcinoma Progression and Gefitinib Resistance through Coordinated Regulation of Ferroptosis and Lactate Metabolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 09:37:37","doi":"10.21203/rs.3.rs-6833528/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"26aa154d-c3ec-44e6-a145-203094bebd03","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-18T07:24:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 09:37:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6833528","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6833528","identity":"rs-6833528","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}