SGLT2 Inhibition Induces Autophagic Flux Blockade and Sensitizes Pancreatic Cancer to EGFR-Targeted Therapy

SGLT2 Inhibition Induces Autophagic Flux Blockade and Sensitizes Pancreatic Cancer to EGFR-Targeted Therapy | 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 SGLT2 Inhibition Induces Autophagic Flux Blockade and Sensitizes Pancreatic Cancer to EGFR-Targeted Therapy Yuxin Wang, Enkui Zhang, Rui Ma, Weikang Liu, Qi Wang, Jun Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8820380/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with profound metabolic rewiring and resistance to therapy. Sodium-glucose co-transporter 2 (SGLT2) regulates glucose uptake, but its role in PDAC remains unclear. Methods SGLT2 expression was analyzed in clinical samples and public datasets. PDAC cell lines were subjected to genetic knockdown or canagliflozin (CANA) treatment to assess proliferation, migration, apoptosis, and glucose metabolism. Mechanistic studies investigated AMPK-ULK1 signaling, autophagy dynamics, oxidative stress, and EGFR signaling. Xenograft models were used to assess in vivo efficacy. Results SGLT2 was upregulated in PDAC and associated with poor prognosis. SGLT2 inhibition suppressed proliferation and migration while promoting apoptosis. Mechanistically, CANA induced ATP deficiency and initiated autophagy, but concurrently impaired autophagosome-lysosome fusion. This dual effect led to autophagic flux blockade, resulting in excessive ROS accumulation, mitochondrial dysfunction, and apoptosis. Inhibition of AMPK reduced ROS levels, while ROS scavenging partially rescued mitochondrial damage and cell death. Notably, SGLT2 inhibition enhanced sensitivity to EGFR-targeted therapy, producing synergistic anti-tumor effects in vitro and in vivo. Conclusions SGLT2 maintains metabolic and autophagic homeostasis in PDAC. Its inhibition induces metabolic stress, autophagic flux blockade, and ROS-driven mitochondrial apoptosis. In addition, targeting SGLT2 sensitizes tumors to EGFR-targeted therapy, offering a novel combinatorial strategy. SGLT2 pancreatic ductal adenocarcinoma glucose metabolism autophagy EGFR inhibitor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies worldwide, with a 5-year survival rate around 13% [1] . Surgical resection remains the only potentially curative option, but fewer than 20% of patients are eligible at diagnosis [2] . Chemotherapy and targeted therapy provide limited benefits due to both intrinsic and acquired resistance [3] . Its aggressive biology, dense desmoplastic stroma, and profound metabolic rewiring contribute to both intrinsic and acquired resistance to standard treatments [3] . As such, identifying tumor-specific vulnerabilities that integrate metabolic and signaling dependencies is a critical unmet need in PDAC research. One hallmark of PDAC is its reliance on glucose metabolism to sustain rapid proliferation in a nutrient-deprived microenvironment [4] . Although PDAC cells can utilize glutamine, fatty acids, and amino acids as alternative fuels, glucose remains the dominant substrate that drives energy production and biosynthesis [5] . Despite the preference for aerobic glycolysis (the Warburg effect), PDAC cells also retain the capacity to enhance oxidative phosphorylation (OXPHOS) under stress [6] . Metastatic cells may activate the pentose phosphate pathway to produce nucleotides and maintain redox balance, thereby promoting tumor progression [6] . Moreover, the high glycolytic flux contributes to lactate accumulation, acidifying the tumor microenvironment and modulating immune cell function [5] . To meet the elevated metabolic demands, PDAC cells activate autophagy as a survival strategy [7] . Within the nutrient-scarce tumor microenvironment, autophagy recycles cellular components to generate alternative nutrients, thereby sustaining glycolytic flux and supporting continued proliferation under metabolic stress [7] . Glucose uptake and utilization are regulated by specific transporters, including the sodium-glucose co-transporter 2 (SGLT2, gene name SLC5A2) [8] . SGLT2 is a transmembrane protein primarily responsible for active glucose reabsorption in the renal proximal tubule [9] . Emerging evidence indicates that SGLT2 is also expressed in several tumors, potentially supporting glucose uptake under metabolic stress [10, 11] . Pharmacological inhibitors of SGLT2, such as canagliflozin (CANA), are widely used for type 2 diabetes management by inhibiting SGLT2-mediated glucose reabsorption in the renal proximal tubules [8] . Preclinical studies suggest that these agents may have anti-tumor properties in certain cancer types [12–14] . CANA attenuates the proliferation of cancer cells by inhibiting glucose uptake [15] and mitochondrial complex I-supported respiration [16] . In vitro experiments showed that CANA promotes mitochondrial dysfunction and reticulophagy in colorectal cancer cells [17] . Moreover, several studies have reported that CANA can enhance the efficacy of chemotherapy [18, 19] , radiotherapy [20] , or immunotherapy [13] in certain tumors. Clinical evidence further indicates that SGLT2 inhibitor use is associated with a 23% reduced risk of prostate cancer in diabetic men [21] . However, studies on the expression and function of SGLT2 in PDAC remain inconclusive [22, 23] , and the underlying mechanisms remain largely unexplored. Addressing these gaps may reveal novel therapeutic opportunities. In parallel, aberrant epidermal growth factor receptor (EGFR) signaling is frequently observed in PDAC and contributes to tumor progression [24, 25] . EGFR activates downstream pathways such as PI3K/AKT and MAPK, driving tumor growth and survival [26] . Despite widespread EGFR expression in PDAC, clinical responses to EGFR-targeted therapies are limited [27, 28] . Resistance mechanisms include compensatory signaling, metabolic plasticity, and stromal-mediated protection [27–29] . Previous studies have shown that EGFR activity is closely intertwined with glucose metabolism, where it promotes glycolysis and enhances glucose uptake in cancer cells [30, 31] . This metabolic-signaling crosstalk raises the possibility that metabolic disruption may sensitize PDAC cells to EGFR inhibition, providing a rationale for combinatorial strategies. In this study, we investigated SGLT2 expression and its functional role in PDAC. We aimed to examine how SGLT2 influences cellular metabolism and stress responses and to explore potential interactions with EGFR-targeted therapy, with the goal of identifying metabolic vulnerabilities that may inform combinatorial treatment strategies. Materials and Methods Public Database Analysis Pan-cancer expression analysis of SGLT2 was performed using Gene Expression Profiling Interactive Analysis 2 (GEPIA2, http://gepia2.cancer-pku.cn/#index) [32] based on The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets. Single-cell RNA Sequencing Data Analysis Single-cell RNA sequencing (scRNA-seq) data for PDAC were obtained from the Gene Expression Omnibus (GEO) database (GSE205013 [33] ). Untreated PDAC samples were included for downstream analysis. Quality control was performed using Scanpy (v1.10.4) in Python 3.12.2. Cells with fewer than 200 detected genes or with >15% mitochondrial gene content were excluded, and genes expressed in fewer than 3 cells were removed. High-quality cells with ≥500 detected genes and ≤15% mitochondrial proportion were retained, resulting in 80,642 cells for subsequent analyses. Gene expression matrices were normalized and log-transformed, followed by scaling to unit variance. Principal component analysis (PCA) was applied for dimensionality reduction, and batch effects across samples were corrected using the Harmony algorithm. Uniform Manifold Approximation and Projection (UMAP) was then used for visualization. Cell clustering was performed using the Leiden algorithm, and cell types were manually annotated based on canonical marker gene expression and established literature. Immunohistochemistry (IHC) The tissue microarray (TMA) used in this study contained 49 PDAC tissues and paired adjacent normal paraffin-embedded specimens, provided by the Department of Hepatobiliary and Pancreatic Surgery, Peking University First Hospital, in accordance with the guidelines of the Ethics Committee of Peking University First Hospital (approval number: 2024-194). Written informed consent was obtained from all participants prior to inclusion in the study. Paraffin sections were baked at 62 °C for 1 h, dewaxed, and subjected to heat-mediated antigen retrieval. After washing, the sections were incubated with the primary anti-SGLT2 antibody (Invitrogen, PA5-101893; Thermo Fisher Scientific, Waltham, MA, USA; 1:100) overnight at 4 °C. The following day, sections were incubated with a horseradish peroxidase (HRP)-conjugated secondary antibody for 30 min at 37 °C. Finally, slides were mounted with neutral gum and cover slipped. Following immunohistochemical staining, two independent pathologists evaluated the expression levels of the target protein. Quantitative analysis of staining intensity and the percentage of positive tumor cells was performed using ImageJ IHC Profiler. Cell Culture Normal pancreatic ductal epithelial cells (HPNE) and pancreatic cancer cell lines (PANC-1, Capan-1, MIA PaCa-2, AsPC-1, BxPC-3, PA-TU-8988T, and T3M-4) were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA), the German Collection of Microorganisms and Cell Cultures (DSMZ, Braunschweig, Germany), or AcceGen (Fairfield, NJ, USA). Cells were cultured in DMEM or RPMI-1640 medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% fetal bovine serum (FBS; Procell Life Science & Technology, Wuhan, China) at 37 °C in a humidified atmosphere containing 5% CO₂. Cell Proliferation and Colony Formation Cell viability was measured using the Cell Counting Kit-8 (CCK-8; Dojindo Laboratories, Kumamoto, Japan; CK04). For colony formation, 500 PANC-1 cells or 700 Capan-1 cells per well were seeded into six-well plates and cultured for 10-14 days until visible colonies formed. Colonies were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Wound-healing and Transwell Assays Wound-healing and Transwell assays were performed to evaluate cell migration. For wound healing, confluent cells were scratched with a pipette tip, washed, and incubated in serum-free medium. Images were captured at 0, 24, and 48 h. For Transwell assays, 3 × 10⁴ Capan-1 cells or 1 × 10⁴ PANC-1 cells in serum-free medium were seeded into the upper chamber (6.5 mm, 8.0 μm pore; Corning Inc., Corning, NY, USA; Cat. No. 3422) without Matrigel. After 48 h, migrated cells were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and counted. For knockdown experiments, cells were analyzed 48 h after seeding, while for drug treatment experiments, drugs were added 24 h after seeding and cells were analyzed 48 h later. Images were acquired using a DP74 Microscope Digital Camera (Olympus, Tokyo, Japan). Flow Cytometry Apoptosis was assessed using an Annexin V-APC/7-AAD apoptosis detection kit (KeyGEN BioTECH, Jiangsu, China; KGA1106-50) according to the manufacturer’s protocol. Samples were analyzed on a CytoFLEX flow cytometer (Beckman Coulter, USA). Metabolic Assays Glucose concentration in culture supernatants was determined using the Glucose Assay Kit-WST (Dojindo; G264). Lactate, Adenosine triphosphate (ATP), Nicotinamide adenine dinucleotide (oxidized form, NAD⁺), and Nicotinamide adenine dinucleotide (reduced form, NADH) levels were measured using the L-Lactate Assay Kit with WST-8 (Beyotime Biotechnology, Shanghai, China; S0208S), ATP Assay Kit (Beyotime Biotechnology; S0026), and NAD⁺/NADH Assay Kit with WST-8 (Beyotime Biotechnology; S0175), respectively, according to the manufacturers’ protocols and normalized to protein content. Mitochondrial function was assessed using the Seahorse XF Cell Mito Stress Test Kit (Agilent Technologies, Santa Clara, CA, USA) with a Seahorse XFe24 Analyzer. Final drug concentrations were 1.5 μM oligomycin, 0.5 μM rotenone/antimycin A (Rot/AA), and Carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP) at 1.5 μM for Capan-1 cells and 2.5 μM for PANC-1 cells. ROS, Mitochondrial Membrane Potential, and Lysosomal Detection Intracellular ROS was detected using the Reactive Oxygen Species Assay Kit (DHE; Beijing Solarbio Science & Technology Co., Ltd., Beijing, China; CA1420). Mitochondrial membrane potential (ΔΨm) was measured using the Mitochondrial Membrane Potential Assay Kit with TMRE (Beyotime Biotechnology; C2001S). Lysosomes were stained with Lyso-Tracker Red (Beyotime Biotechnology; C1046). Nuclei were counterstained with Hoechst 33342 Staining Solution for Live Cells (100X; Beyotime Biotechnology; C1028). Images were captured using the Olympus DP74 camera. Dual-Fluorescence LC3B Reporter Assay Cells were infected with pLenti-CMV-mCherry-GFP-LC3B-IRES-Puro-WPRE lentivirus (OBiO Technology). Images were acquired using a Confocal Laser Scanning Microscope ( Leica Microsystems, Wetzlar, Germany). In Vivo Tumor Xenograft Assays Male BALB/c-nu mice were obtained at 3 weeks of age (average 13 g; Charles River Laboratories, Viatonglihua, CAnN.Cg-Foxn1nu/Crl, code 401) and acclimated for 1 week. At 4 weeks of age (average 16 g), mice were randomly assigned to different groups. PANC-1 cells were resuspended in a 1:1 mixture of DMEM and Corning® Matrigel® Basement Membrane Matrix High Concentration (HC; Corning, 354248) at 4 × 10⁶ cells per 100 μL and injected subcutaneously into the axilla of each mouse. One week after injection, mice received daily oral gavage of 100 μL vehicle (10% DMSO, 40% PEG300, 10% Tween 80, 40% H₂O), Canagliflozin (50 mg/kg), Erlotinib (25 mg/kg), or the combination. Tumor volume and body weight were measured three times per week. Tumor volume was calculated as 𝑉=length×width 2 /2. Experiments were planned to terminate when tumors reached 1000 mm³, after 4 weeks of treatment, or if body weight decreased by more than 15%. In practice, tumors did not exceed 800 mm³, and experiments were stopped accordingly. All procedures were approved by the Ethics Committee for Animal Research at Peking University First Hospital (approval number J2025049) and followed institutional guidelines. Drug Treatment and Gene Silencing Canagliflozin (S2760; Selleck Chemicals, Houston, TX, USA), Erlotinib (S7786; Selleck Chemicals), and Compound C (HY-13418A; MedChemExpress, MCE, Monmouth Junction, NJ, USA) were dissolved in DMSO and diluted in culture medium. N-acetylcysteine (HY-B0215; MCE) was dissolved in water and diluted in culture medium. SGLT2 knockdown was achieved using a lentiviral shRNA vector (pSLenti-U6-shRNA(SLC5A2)-CMV-EGFP-F2A-Puro-WPRE; OBiO Technology, Shanghai, China). Stable cells were selected with puromycin (HY-K1057; MCE), and knockdown efficiency was confirmed by Western blot. Western Blot and qRT-PCR Proteins were extracted using RIPA buffer supplemented with protease and phosphatase inhibitors (Beijing LABLEAD, Beijing, China). Equal amounts of protein were separated by SDS-PAGE and transferred to 0.22 μm PVDF membranes. Membranes were blocked with 5% nonfat milk for 1 h at room temperature, incubated with primary antibodies overnight at 4 °C, and then washed three times with TBST. After incubation with HRP-conjugated secondary antibodies (Abclonal) for 1 h at room temperature, signals were detected using the enhanced chemiluminescence (ECL) reagent. The following primary antibodies were used: SGLT2 (Invitrogen, PA5-101893, 1:1000), Phospho-AMPKα (Thr172; #2535, Cell Signaling Technology, CST, Danvers, MA, USA; 1:1000), AMPKα1/α2 (A27099, Abclonal, Wuhan, China; 1:2000), Phospho-ULK1 (Ser555, #5869, CST; 1:1000), ULK1 (#8054, CST; 1:1000), SQSTM1/p62 (#5114, CST; 1:1000), LC3B (#3868, CST; 1:1000), Bax (#2772, CST; 1:1000), Bcl-2 (#3498, CST; 1:1000), Cleaved Caspase-3 (#9661, CST; 1:1000), Caspase-3 (#14220, CST; 1:1000), EGFR (A11351, Abclonal; 1:2000), and Phospho-EGFR (Tyr1068, AP0994, Abclonal; 1:1000). β-Actin (AC026, Abclonal; 1:80000) was used as the loading control. HRP-conjugated secondary antibodies were from Abclonal (AS014, goat anti-rabbit IgG (H+L), 1:10000; AS003, goat anti-mouse IgG (H+L), 1:10000). Total RNA was extracted using TRIzol reagent (Invitrogen, Thermo Fisher Scientific). Reverse transcription was performed with Hifair® AdvanceFast 1st Strand cDNA Synthesis SuperMix for qPCR(Yeasen Biotechnology, Shanghai, China). qRT-PCR was carried out using Hieff® qPCR SYBR Green II Master Mix (Yeasen Biotechnology). Primers were synthesized by Generay Biotech (Shanghai, China). The qRT-PCR primer sequences were as follows: SLC5A2, forward 5′-CTGTTTGCACCCGTGTACCT-3′, reverse 5′-CCTGTCACCGTGTAAATCATGG-3′; ACTB, forward 5′-CCTGGACTTCGAGCAAGAGATGG-3′, reverse 5′-CAGGAAGGAAGGCTGGAAGAGTG-3′. Statistical Analysis All experiments were performed at least three times independently. Data are presented as mean ± standard error of the mean (SEM). Statistical analyses were conducted using GraphPad Prism (version 10.1.2; GraphPad Software, San Diego, CA, USA). The Wilcoxon signed-rank test was adopted for the analysis of paired IHC data. Comparisons between two groups were performed using two-tailed Student’s t-test, and multiple group comparisons were performed using one-way ANOVA followed by Tukey’s post hoc test. Kaplan-Meier survival curves were analyzed using the log-rank test. Differences were considered statistically significant at p < 0.05. Results SGLT2 Is Overexpressed in Pancreatic Cancer and Correlates with Poor Prognosis We first evaluated the expression pattern of SGLT2 across various cancer types using transcriptomic data from the GEPIA2 platform. SGLT2 expression was found to be upregulated in multiple cancer types, including PDAC (Fig. 1A) . Subsequently, single-cell RNA sequencing data were obtained from a publicly available pancreatic cancer dataset (GSE205013). The expression pattern of SGLT2 was further examined using single-cell RNA sequencing data. After rigorous quality control and filtering, 11 PDAC samples were integrated, resulting in a total of 80,642 high-quality cells retained for downstream analyses (Fig. 1B) . Unsupervised clustering identified 23 distinct clusters, which were subsequently annotated into 11 major cell types based on canonical marker gene expression profiles. Specifically, NK/T cells (CD3D, CD3E, CD2, CD7, IL7R), ductal cells (EPCAM, KRT19, KRT7, MUC1, MUC5AC, CEACAM5, CEACAM6), acinar cells (PRSS1, CPA1, CELA3A, CELA3B), endocrine cells (INS, IAPP), fibroblasts (DCN, COL1A1, COL1A2, COL3A1, ACTA2), endothelial cells (VWF, PECAM1, PLVAP), macrophages/monocytes (CD14, S100A8, S100A9, C1QA, C1QB, FCGR3A), pericytes (RGS5, PDGFRB), Schwann cells (S100B), mast cells (TPSAB1, TPSB2), and B cells (CD19, MS4A1, CD79A, CD79B) were identified. These results provide a comprehensive cellular landscape of PDAC, forming the basis for subsequent analyses of cell-type-specific SGLT2 expression (Fig. 1C-E) . Although SGLT2 expression was generally low across the PDAC single-cell landscape, it was specifically enriched in subsets of ductal epithelial cells and acinar cells, as illustrated by UMAP feature plots and violin plots (Fig. 1F, G) . This distribution suggests potential cell type-specific metabolic or signaling roles of SGLT2 within the tumor microenvironment. We further verified and analyzed the expression of SGLT2 in pancreatic cancer tissues at the histological level. IHC was performed on a tissue microarray containing paired pancreatic tumor and adjacent normal samples. Representative images demonstrated stronger SGLT2 staining localized predominantly to tumor epithelial regions compared to adjacent normal tissues (Fig. 1H) . Quantitative analysis of percentage of SGLT2 + cells revealed significantly elevated SGLT2 expression in tumor samples ( Fig. 1I ; p = 0.0002). To investigate the prognostic relevance of SGLT2, Kaplan-Meier survival analysis was conducted. Patients with high SGLT2 expression (percentage of SGLT2 + cells ≤ median, 19.7067) exhibited significantly shorter overall survival than those with low expression (percentage of SGLT2+ cells > median, 19.7067) ( Fig. 1J ). SGLT2 Promotes Proliferation, Migration, and Tumorigenicity of Pancreatic Cancer Cells To evaluate the biological significance of SGLT2 in pancreatic cancer, its mRNA expression levels were across a panel of pancreatic cancer cell lines. Compared with the non-tumorigenic pancreatic ductal epithelial cell line HPNE, all tested cancer cell lines showed markedly elevated SGLT2 expression, with Capan-1 and PANC-1 exhibiting the highest levels (Fig. 2A) . Consistently, Western blot analysis confirmed increased protein expression of SGLT2, with the highest expression observed in PA-TU-8988T, followed by PANC-1 and Capan-1 (Fig. 2B) . Given their robust expression and extensive use as in vitro models, PANC-1 and Capan-1 were selected for subsequent functional studies. Stable knockdown of SGLT2 was achieved in both Capan-1 and PANC-1 cells using two independent shRNAs. Knockdown efficiency was confirmed by Western blot (Fig. 2C) . CCK-8 assays revealed that SGLT2 knockdown significantly inhibited cell proliferation in both cell line. Compared with control, both shRNA groups displayed consistently lower viability over time (Fig. 2D, E) . The effect on suppressing long-term growth was further supported by colony formation assays, which showed a significant decrease in colony numbers in knockdown groups (Fig. 2F) . SGLT2 knockdown also attenuated cell motility, as evidenced by reduced cell migration in Transwell assays ( Fig. 2G ; p < 0.0001 for Capan-1, p < 0.01 for PANC-1), and corroborated by wound healing assays (Fig. 2H, I) . To assess the impact of SGLT2 on pancreatic cancer cell survival, Annexin V-APC/7-AAD staining combined with flow cytometric analysis demonstrated a marked increase in apoptosis in SGLT2-deficient cells (Fig. 2J, K) , indicating that SGLT2 may contribute to the survival in pancreatic cancer cells. We next assessed the impact of SGLT2 silencing on tumorigenicity in vivo. PANC-1 cells transduced with shNC or shSGLT2 were injected subcutaneously into nude mice, and tumor formation was monitored. Tumors derived from shSGLT2 group exhibited markedly smaller size and reduced mass compared with control ( Fig. 2L, M ). Consistently, growth curves showed that SGLT2 knockdown significantly suppressed tumor progression over time (Fig. 2N) sh Together, these findings indicate that SGLT2 promotes pancreatic cancer cell survival and growth. Pharmacological Inhibition of SGLT2 Suppresses Proliferation and Induces Apoptosis in Pancreatic Cancer Cells To further evaluate the therapeutic potential of SGLT2 inhibition in pancreatic cancer, we examined the effect of selective SGLT2 inhibitor CANA in vitro. A dose-dependent cytotoxicity was observed via CCK-8 assays following 48-hour treatment, with IC₅₀ values of 132.4 μM for Capan-1 and 99.11 μM for PANC-1 cells (Fig. 3A, B) . In addition, CANA suppressed cell proliferation in a time-dependent manner (Fig. 3C, D) . Colony formation assays revealed a significant reduction in long-term proliferative potential following CANA treatment (Fig. 3E) . CANA treatment also impaired migration capacity. Transwell assays showed a marked decrease in the number of migrated cells upon treatment (Fig. 3F) . Consistently, wound healing assays revealed the effect on suppressing the migration of both Capan-1 and PANC-1 cells (Fig. 3G, H) . After 48 hours of exposure to 50 μM CANA, the migration area rate was reduced significantly. Furthermore, flow cytometric analysis of Annexin V-APC/7-AAD staining demonstrated that CANA induced an increased proportion of apoptotic cells compared to control (Fig. 3I, J) . Collectively, these findings indicate that inhibition of SGLT2 by CANA inhibits proliferation and induces the apoptosis in pancreatic cancer cells. SGLT2 Inhibition Disrupts Glucose Metabolism and Mitochondrial Function, Triggering Energy Crisis and Oxidative Stress in Pancreatic Cancer Cells To elucidate the mechanisms underlying the cytotoxic effects induced by SGLT2 inhibition, we next examined its impact on cellular energy metabolism and mitochondrial function. Following 48-hour treatment with CANA, the glucose uptake, lactate generation, and ATP production were significantly decreased (Fig. 4A-C) . To further characterize oxidative metabolism, we performed Seahorse XF Cell Mito Stress Test. CANA exposure resulted in a pronounced reduction in ATP production and maximal respiration (Fig. 4D-F) . These observations suggest that SGLT2 activity is essential for sustaining energy homeostasis in pancreatic cancer cells, and its inhibition impairs both glycolytic activity and mitochondrial oxidative phosphorylation. In parallel, the NAD⁺/NADH ratio declined after treatment (Fig. 4G) , suggesting NADH accumulation and disruption of redox homeostasis (Fig. 4H) . To assess downstream consequences of energy dysregulation, we evaluated intracellular oxidative stress and mitochondrial integrity. DHE staining showed a marked increase in reactive oxygen species (ROS) levels following CANA treatment (Fig. 4I, J), suggesting increased oxidative stress. In parallel, measurement of mitochondrial membrane potential using TMRE staining revealed a significant hyperpolarization (Fig. 4K, L) , a hallmark of early mitochondrial dysfunction and apoptotic priming. To determine whether these alterations culminate in mitochondria-dependent apoptosis, we assessed apoptosis-related protein expression by Western blot. While BCL-2-associated X protein (Bax) expression remained relatively unchanged, BCL-2 levels were significantly reduced and cleaved cysteine-aspartic acid protease 3 (Caspase-3) was markedly upregulated, indicating activation of mitochondria-dependent apoptosis (Fig. 4M) . Finally, metabolomic profiling and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that CANA treatment led to significant alterations in metabolic pathways, particularly those involved in nucleotide biosynthesis, redox regulation, energy metabolism, and stress signaling (Fig. 4N) . Top enriched pathways included purine metabolism, nicotinate and nicotinamide metabolism, oxidative phosphorylation, and the adenosine monophosphate-activated protein kinase (AMPK) signaling pathway, consistent with an energy crisis and metabolic stress. Similar metabolic and mitochondrial changes were observed in SGLT2-knockdown cells (Supplementary Fig. S1) , further supporting the notion that SGLT2 plays a crucial role in the metabolic reprogramming of pancreatic cancer, particularly in maintaining energy homeostasis within the tumor microenvironment. SGLT2 Inhibition Causes Autophagic Flux Blockade, Leading to Oxidative Stress and Mitochondrial Apoptosis In response to this energy deficit and redox imbalance, we next investigated whether key metabolic signaling pathways were activated. AMPK, a well-established energy sensor, is rapidly activated under conditions of metabolic stress. Western blot showed increased phosphorylation of AMPK and unc-51-like kinase 1 (ULK1), accompanied by accumulation of microtubule-associated protein 1 light chain 3 beta (LC3B)-II and sequestosome 1 (p62) after CANA treatment (Fig. 5A) . Inhibition of AMPK with Compound C (CC) reduced p-AMPK and reversed the increase of LC3B-II/I and p62 (Fig. 5B) , suggesting that CANA-induced autophagy initiation is AMPK dependent. Time-course Western blot analysis showed that, compared with DMSO, CANA treatment led to a progressive increase of LC3B-II/I and p62 from 6 to 48 h (Fig. 5C, D) . P62 accumulation indicates a blockade in autophagosome degradation. Consistent with this, confocal imaging of cells expressing mCherry-GFP-LC3B revealed increased puncta formation and a higher yellow/red ratio after CANA treatment (Fig. 5E, F) , further supporting impaired autophagosome-lysosome fusion. LysoTracker staining showed that lysosomal acidification was not reduced (Fig. 5G, H) , suggesting that the blockade was not due to lysosomal dysfunction. We next examined whether the AMPK-dependent autophagy contributed to ROS accumulation and mitochondrial changes. CC treatment partially reversed CANA-induced ROS increase (Fig. 5I, J) . Similarly, the ROS scavenger N-Acetylcysteine (NAC) partially reduced mitochondrial hyperpolarization and apoptosis caused by CANA (Fig. 5K-N) . These results indicate that CANA activates AMPK-ULK1-dependent autophagy initiation but impairs autophagosome-lysosome fusion. The autophagy blockade results in excessive ROS generation, mitochondrial dysfunction, and apoptotic cell death. SGLT2 Inhibition Enhances the Sensitivity of Pancreatic Cancer to EGFR-Targeted Therapy To further investigate essential pathways involved in the cellular response to SGLT2 inhibition, we performed transcriptomic analysis of PANC-1 cells following CANA treatment. Differential expression analysis revealed a distinct gene expression profile, as shown in the volcano plot (Fig. 6A) . KEGG pathway enrichment analysis of upregulated genes identified 12 significantly enriched pathways (adjusted p < 0.05), including the ErbB signaling pathway (Fig. 6B) . As EGFR is a central effector in the ErbB family and commonly overexpress in pancreatic cancer, we next evaluated EGFR expression. Western blot analysis showed that both total EGFR and phosphorylated EGFR (Tyr1068) levels were upregulated following CANA treatment compared to DMSO controls (Fig. 6C) , while SGLT2 knockdown led to decreased expression of both total and phosphorylated EGFR (Fig. 6D) . These findings suggest that SGLT2 may be involved in maintaining EGFR signaling under baseline conditions, and its inhibition induces compensatory upregulation of EGFR, supporting the rationale for combination therapy. To assess the therapeutic potential of combined SGLT2 and EGFR inhibition, Capan-1 and PANC-1 cells were co-treated with CANA and the EGFR tyrosine kinase inhibitor (EGFR-TKI) erlotinib (Erlo). Combination index (CI) analysis demonstrated synergistic effects (CI < 1) at moderate to high fraction affected in both cell lines (Fig. 6E, F) . Consistently, combination treatment more effectively suppressed short-term proliferation (Fig. 6G, H) and long-term colony formation (Fig. 6I) than either agent alone. Cell migration was also more strongly impaired by the combination, as shown in Transwell (Fig. 6J) and wound healing assays (Fig. 6K, L) . Furthermore, flow cytometry analysis revealed a significant increase in apoptotic cells in the combination group relative to monotherapies (Fig. 6M, N) . To validate these findings in vivo, a subcutaneous xenograft model was established using PANC-1 cells. Tumor growth was significantly suppressed by the combination of CANA and erlotinib compared to either monotherapy or vehicle control (Fig. 6O-Q) . At endpoint, the mean tumor volume in the combination group was 135.8 mm³, markedly lower than that in the control (565.6 mm³), CANA (419.2 mm³), and erlotinib (521.8 mm³) groups. The mean reduction relative to control was 429.8 mm³ (95% CI: 337.3-522.3). No significant body weight loss was observed during treatment, indicating acceptable tolerability (Fig. 6R) . Collectively, these results support a synergistic interaction between SGLT2 and EGFR inhibition in pancreatic cancer, and provide a preclinical rationale for combination therapeutic strategies targeting these pathways. Discussion In this study, we identified that SGLT2 is significantly upregulated in pancreatic cancer tissues and correlates with poor overall survival. Functional experiments demonstrated that both genetic knockdown and pharmacologic inhibition of SGLT2 suppressed tumor cell proliferation, clonogenic capacity, and migration, while promoting apoptosis. Mechanistically, SGLT2 targeting disrupted energy homeostasis, impaired autophagic flux, induced oxidative stress, and triggered mitochondrial apoptosis. Notably, co-treatment with an EGFR inhibitor exerted a synergistic anti-tumor effect. SGLT2 has been reported to be overexpressed in several types of malignancies, including lung [34] , gastric [35] , and breast cancers [36] . However, SGLT2 expression in PDAC has not been fully characterized. We re-evaluated its expression in this study. In our cohort, although the proportion of SGLT2-positive tumor cells was low, its expression was significantly higher in tumor tissues compared with paired adjacent normal tissues (n = 39). More importantly, high SGLT2 expression was associated with significantly shorter overall survival (OS) among PDAC patients (n = 47), suggesting its potential as a negative prognostic marker. Despite limitations in sample volume and follow-up, our data provide clinically relevant evidence linking SGLT2 to PDAC progression. Recent studies indicated that SGLT2 inhibitors may reduce pancreatic cancer risk in patients with type 2 diabetes compared to other glucose-lowering medications [37, 38] . Together, these findings support a functional and prognostic role for SGLT2 in PDAC. Given the metabolic function of SGLT2 and the dependence of PDAC cells on glucose-derived energy, we investigated how SGLT2 supports tumor survival. SGLT2 inhibition induces acute ATP depletion, activating the AMPK/ULK1 axis, which promotes autophagy initiation under metabolic stress [39] . However, autophagic flux is blocked due to impaired autophagosome-lysosome fusion. Autophagic flux blockade impairs mitochondrial clearance and leads to the accumulation of dysfunctional mitochondria [40] . Moreover, under downstream flux blockade, the enhanced formation of autophagosomes promotes the accumulation of membranous structures [41] , which may further increase metabolic stress. As a result, the compensatory autophagy process can paradoxically induce oxidative stress [42] . Our experimental results further support this mechanism, as AMPK inhibition with CC partially mitigated the ROS accumulation induced by SGLT2 inhibition. Excessive ROS can damage mitochondrial membranes and compromise mitochondrial quality control [43] . ROS disrupts mitochondrial homeostasis, as indicated by hyperpolarized mitochondrial membrane potential, and promotes mitochondria-dependent apoptosis, which can be partially rescued by ROS scavenger NAC. While previous studies reported that SGLT2 inhibition by CANA induces autophagy in benign and malignant diseases [17, 44, 45] , our findings reveal a distinct context in PDAC, where SGLT2 inhibition initiates but fails to complete autophagy. Together, these results suggest that SGLT2 serves as a metabolic stabilizer in PDAC cells by maintaining autophagic homeostasis under energy stress. Targeting this vulnerability provides a novel rationale for therapeutic intervention. Another key finding of our study is that SGLT2 inhibition modulates EGFR signaling and enhances the efficacy of EGFR-targeted therapy in PDAC. Although EGFR is widely expressed in pancreatic cancer [24] , the clinical benefit of EGFR inhibitors has been limited [46, 47] . In our experiments, pharmacologic inhibition of SGLT2 led to increased EGFR phosphorylation, while genetic knockdown reduced EGFR signaling. These effects imply that metabolic stress induced by SGLT2 inhibition may alter EGFR-related signaling. Previous studies have shown that EGFR signaling is closely integrated with glucose metabolism. EGFR has been shown to promote aerobic glycolysis in triple-negative breast cancer and lung adenocarcinoma cells [30, 31] , and ligand binding to EGFR and other ErbB family receptors can enhance glucose uptake [48] . This crosstalk appears to have therapeutic implications. In both Capan-1 and the relatively EGFR-insensitive PANC-1 cell lines, SGLT2 inhibition significantly enhanced sensitivity to erlotinib, showing synergistic effects in vitro (CI < 1). In xenograft models using PANC-1 cells, the combination treatment produced greater tumor suppression than either monotherapy. These observations suggest that SGLT2 inhibition may expose a metabolic vulnerability that enhances EGFR dependency, thereby opening a therapeutic window for combinatorial intervention. Consistently, prior studies in NSCLC have demonstrated that glucose deprivation synergizes with autophagy or AKT inhibitors to overcome acquired resistance to EGFR-targeted therapy [49] , and blocking glucose metabolism increases EGFR-TKI sensitivity in resistant tumors [50] . The underlying mechanism remains unclear, but may involve metabolic stress-induced receptor activation, altered trafficking, or adaptive responses to disrupted glucose homeostasis. Despite the insights provided by this study, several limitations should be acknowledged. The mechanisms underlying autophagic disruption and EGFR signaling modulation by SGLT2 inhibition remain to be fully elucidated. Although CANA is clinically approved, potential off-target effects at experimental doses cannot be excluded and should be addressed in future studies. Additionally, while EGFR inhibitors have shown modest efficacy in K-Ras wild-type PDAC [47] , both cell lines used in this study (Capan-1 and PANC-1) harbor K-Ras mutations, and in vivo validation was limited to PANC-1 xenografts. Therefore, whether SGLT2 inhibition broadly enhances EGFR-targeted therapy requires further validation across metabolically heterogeneous PDAC models. In conclusion, Our study identifies SGLT2 as a key metabolic regulator in PDAC, with high expression linked to poor prognosis. SGLT2 inhibition disrupts energy and redox homeostasis. Mechanistically, SGLT2 inhibition blocks autophagic flux, leading to ROS accumulation and apoptosis. Importantly, SGLT2 inhibition sensitizes PDAC cells to EGFR-targeted therapy, revealing a metabolic vulnerability with translational potential. Further studies in genetically diverse and clinically relevant models are warranted to support clinical translation. Abbreviations PDAC: Pancreatic Ductal Adenocarcinoma SGLT2: Sodium-glucose Co-transporter 2 CANA: Canagliflozin OXPHOS: Oxidative Phosphorylation EGFR: Epidermal Growth Factor Receptor GEPIA2: Gene Expression Profiling Interactive Analysis 2 TCGA: The Cancer Genome Atlas GTEx: Genotype-Tissue Expression GEO: Gene Expression Omnibus PCA: Principal Component Analysis UMAP: Uniform Manifold Approximation and Projection IHC: Immunohistochemistry CCK-8: Cell Counting Kit-8 OCR: oxygen consumption rate ATP: Adenosine Triphosphate NAD + : Nicotinamide Adenine Dinucleotide (Oxidized Form) NADH: Nicotinamide Adenine Dinucleotide (Reduced Form) Rot/AA: Rotenone/antimycin A FCCP: Carbonyl Cyanide-p-trifluoromethoxyphenylhydrazone ROS: Reactive Oxygen Species Bax: Bcl-2-associated X Protein Bcl-2: B-cell Lymphoma 2 Caspase-3: Cysteine-aspartic Acid Protease 3 KEGG: Kyoto Encyclopedia of Genes and Genomes AMPK: Adenosine Monophosphate-activated Protein Kinase ULK1: Unc-51-like Kinase 1 p62: Sequestosome 1 LC3B: Microtubule-associated Protein 1 light Chain 3 Beta CC: Compound C NAC: N-Acetylcysteine EGFR-TKI: EGFR Tyrosine Kinase Inhibitor Erlo: Erlotinib CI: Combination Index Declarations Ethics Approval and Consent to Participate This study was approved by the Ethics Committee of Peking University First Hospital (approval no. 2024-194-002). Written informed consent was obtained from the patient. Animal experiments were approved by the Institutional Animal Care and Use Committee of Peking University First Hospital (approval no. J2025049) and conducted in accordance with institutional guidelines for animal welfare. Consent for Publication Not applicable. Competing Interests The authors declare no competing interests. Funding This study was supported by National Natural Science Foundation of China (NO. 82171722, 82271764, 82471772 and 82571996), Beijing Natural Science Foundation (L246015). Author Contribution Y.W. and E.Z. contributed equally to this work. Y.W. performed most of the experiments, analyzed the data, and drafted the manuscript. E.Z. supervised the experimental design and data interpretation. R.M. and W.L. assisted with cell culture and molecular assays. Q.W. and J.Z. contributed to data analysis and figure preparation. Y.Y. provided technical support and critical discussion. Y.M. and X.T. conceived and supervised the study, revised the manuscript, and approved the final version. All authors read and approved the final manuscript. Acknowledgements Not available. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. References R.L. Siegel, A.N. Giaquinto, A. Jemal, Cancer statistics, 2024. CA Cancer J Clin. 74(1) , 12-49. (2024). doi:10.3322/caac.21820 W. Niesen, T. Hank, M. Büchler, O. Strobel, Local radicality and survival outcome of pancreatic cancer surgery. Ann Gastroenterol Surg. 3(5) , 464-475. (2019). doi:10.1002/ags3.12273 T. du Toit-Thompson, L. Leck, J. Gillson, N. Pavlakis, A.J. Gill, J.S. Samra, A. Mittal, S. Sahni, Overcoming therapy resistance in pancreatic cancer: challenges and emerging strategies. Adv Drug Deliv Rev. 224 , 115647. (2025). doi:10.1016/j.addr.2025.115647 C. Qin, G. Yang, J. Yang, B. Ren, H. Wang, G. Chen, F. Zhao, L. You, W. Wang, Y. Zhao, Metabolism of pancreatic cancer: paving the way to better anticancer strategies. Mol Cancer. 19(1) , 50. (2020). doi:10.1186/s12943-020-01169-7 L. Yan, P. Raj, W. Yao, H. Ying, Glucose Metabolism in Pancreatic Cancer. Cancers (Basel). 11(10) , (2019). doi:10.3390/cancers11101460 M.C. De Santis, B. Bockorny, E. Hirsch, P. Cappello, M. Martini, Exploiting pancreatic cancer metabolism: challenges and opportunities. Trends Mol Med. 30(6) , 592-604. (2024). doi:10.1016/j.molmed.2024.03.008 J. Li, X. Chen, R. Kang, H. Zeh, D.J. Klionsky, D. Tang, Regulation and function of autophagy in pancreatic cancer. Autophagy. 17(11) , 3275-3296. (2020). doi:10.1080/15548627.2020.1847462 H. Koepsell, The Na+-D-glucose cotransporters SGLT1 and SGLT2 are targets for the treatment of diabetes and cancer. Pharmacol Ther. 170 , 148-165. (2016). doi:10.1016/j.pharmthera.2016.10.017 G. Gyimesi, J. Pujol-Giménez, Y. Kanai, M.A. Hediger, Sodium-coupled glucose transport, the SLC5 family, and therapeutically relevant inhibitors: from molecular discovery to clinical application. Pflugers Arch. 472(9) , 1177-1206. (2020). doi:10.1007/s00424-020-02433-x C. Scafoglio, B.A. Hirayama, V. Kepe, J. Liu, C. Ghezzi, N. Satyamurthy, N.A. Moatamed, J. Huang, H. Koepsell, J.R. Barrio, E.M. Wright, Functional expression of sodium-glucose transporters in cancer. Proc Natl Acad Sci U S A. 112(30) , E4111-E4119. (2015). doi:10.1073/pnas.1511698112 C.R. Scafoglio, B. Villegas, G. Abdelhady, S.T. Bailey, J. Liu, A.S. Shirali, W.D. Wallace, C.E. Magyar, T.R. Grogan, D. Elashoff, T. Walser, J. Yanagawa, D.R. Aberle, J.R. Barrio, S.M. Dubinett, D.B. Shackelford, Sodium-glucose transporter 2 is a diagnostic and therapeutic target for early-stage lung adenocarcinoma. Sci Transl Med. 10(467) , (2018). doi:10.1126/scitranslmed.aat5933 D. Papadopoli, O. Uchenunu, R. Palia, N. Chekkal, L. Hulea, I. Topisirovic, M. Pollak, J. St-Pierre, Perturbations of cancer cell metabolism by the antidiabetic drug canagliflozin. Neoplasia. 23(4) , 391-399. (2021). doi:10.1016/j.neo.2021.02.003 L. Ding, X. Chen, W. Zhang, X. Dai, H. Guo, X. Pan, Y. Xu, J. Feng, M. Yuan, X. Gao, J. Wang, X. Xu, S. Li, H. Wu, J. Cao, Q. He, B. Yang, Canagliflozin primes antitumor immunity by triggering PD-L1 degradation in endocytic recycling. J Clin Invest. 133(1) , (2023). doi:10.1172/JCI154754 W. Wu, Z. Zhang, D. Jing, X. Huang, D. Ren, Z. Shao, Z. Zhang, SGLT2 inhibitor activates the STING/IRF3/IFN-β pathway and induces immune infiltration in osteosarcoma. Cell Death Dis. 13(6) , 523. (2022). doi:10.1038/s41419-022-04980-w K. Kaji, N. Nishimura, K. Seki, S. Sato, S. Saikawa, K. Nakanishi, M. Furukawa, H. Kawaratani, M. Kitade, K. Moriya, T. Namisaki, H. Yoshiji, Sodium glucose cotransporter 2 inhibitor canagliflozin attenuates liver cancer cell growth and angiogenic activity by inhibiting glucose uptake. Int J Cancer. 142(8) , 1712-1722. (2017). doi:10.1002/ijc.31193 L.A. Villani, B.K. Smith, K. Marcinko, R.J. Ford, L.A. Broadfield, A.E. Green, V.P. Houde, P. Muti, T. Tsakiridis, G.R. Steinberg, The diabetes medication Canagliflozin reduces cancer cell proliferation by inhibiting mitochondrial complex-I supported respiration. Mol Metab. 5(10) , 1048-1056. (2016). doi:10.1016/j.molmet.2016.08.014 C. Anastasio, I. Donisi, V. Del Vecchio, A. Colloca, L. Mele, C. Sardu, R. Marfella, M.L. Balestrieri, N. D'Onofrio, SGLT2 inhibitor promotes mitochondrial dysfunction and ER-phagy in colorectal cancer cells. Cell Mol Biol Lett. 29(1) , 80. (2024). doi:10.1186/s11658-024-00599-1 Y. Zeng, H. Jiang, X. Zhang, J. Xu, X. Wu, Q. Xu, W. Cai, H. Ying, R. Zhou, Y. Ding, K. Ying, X. Song, Z. Chen, L. Zeng, L. Zhao, F. Yu, Canagliflozin reduces chemoresistance in hepatocellular carcinoma through PKM2-c-Myc complex-mediated glutamine starvation. Free Radic Biol Med. 208 , 571-586. (2023). doi:10.1016/j.freeradbiomed.2023.09.006 H. Huang, F.-L. Kung, Y.-W. Huang, C.-C. Hsu, J.-H. Guh, L.-C. Hsu, Sensitization of cancer cells to paclitaxel-induced apoptosis by canagliflozin. Biochem Pharmacol. 223 , 116140. (2024). doi:10.1016/j.bcp.2024.116140 A. Ali, B. Mekhaeil, O.-D. Biziotis, E.E. Tsakiridis, E. Ahmadi, J. Wu, S. Wang, K. Singh, G. Menjolian, T. Farrell, A. Mesci, S. Liu, T. Berg, J.L. Bramson, G.R. Steinberg, T. Tsakiridis, The SGLT2 inhibitor canagliflozin suppresses growth and enhances prostate cancer response to radiotherapy. Commun Biol. 6(1) , 919. (2023). doi:10.1038/s42003-023-05289-w J. Zheng, J. Lu, J. Qi, Q. Yang, H. Zhao, H. Liu, Z. Chen, L. Huang, Y. Ye, M. Xu, Y. Xu, T. Wang, M. Li, Z. Zhao, R. Zheng, S. Wang, H. Lin, C. Hu, C.S. Ling Chui, S.L. Au Yeung, S. Luo, O. Dimopoulou, P. Dixon, S. Harrison, Y. Liu, J. Robinson, J. Yarmolinsky, P. Haycock, J. Yuan, S. Lewis, Z. Yuan, T.R. Gaunt, G.D. Smith, G. Ning, R.M. Martin, B. Cui, W. Wang, Y. Bi, The effect of SGLT2 inhibition on prostate cancer: Mendelian randomization and observational analysis using electronic healthcare and cohort data. Cell Rep Med. 5(8) , 101688. (2024). doi:10.1016/j.xcrm.2024.101688 J. Du, J. Gu, J. Deng, L. Kong, Y. Guo, C. Jin, Y. Bao, D. Fu, J. Li, The expression and survival significance of sodium glucose transporters in pancreatic cancer. BMC Cancer. 22(1) , 116. (2022). doi:10.1186/s12885-021-09060-4 D. Ren, Y. Sun, D. Zhang, D. Li, Z. Liu, X. Jin, H. Wu, SGLT2 promotes pancreatic cancer progression by activating the Hippo signaling pathway via the hnRNPK-YAP1 axis. Cancer Lett. 519 , 277-288. (2021). doi:10.1016/j.canlet.2021.07.035 K. Tobita, H. Kijima, S. Dowaki, H. Kashiwagi, Y. Ohtani, Y. Oida, H. Yamazaki, M. Nakamura, Y. Ueyama, M. Tanaka, S. Inokuchi, H. Makuuchi, Epidermal growth factor receptor expression in human pancreatic cancer: Significance for liver metastasis. Int J Mol Med. 11(3) , 305-309. (2003). doi: G. Mucciolo, J. Araos Henríquez, M. Jihad, S. Pinto Teles, J.S. Manansala, W. Li, S. Ashworth, E.G. Lloyd, P.S.W. Cheng, W. Luo, A. Anand, A. Sawle, A. Piskorz, G. Biffi, EGFR-activated myofibroblasts promote metastasis of pancreatic cancer. Cancer Cell. 42(1) , (2024). doi:10.1016/j.ccell.2023.12.002 P. Wee, Z. Wang, Epidermal Growth Factor Receptor Cell Proliferation Signaling Pathways. Cancers (Basel). 9(5) , (2017). doi:10.3390/cancers9050052 N. Ioannou, A.M. Seddon, A. Dalgleish, D. Mackintosh, F. Solca, H. Modjtahedi, Acquired resistance of pancreatic cancer cells to treatment with gemcitabine and HER-inhibitors is accompanied by increased sensitivity to STAT3 inhibition. Int J Oncol. 48(3) , 908-918. (2016). doi:10.3892/ijo.2016.3320 N. Sharma, A. Bhushan, J. He, G. Kaushal, V. Bhardwaj, Metabolic plasticity imparts erlotinib-resistance in pancreatic cancer by upregulating glucose-6-phosphate dehydrogenase. Cancer Metab. 8 , 19. (2020). doi:10.1186/s40170-020-00226-5 X. Shi, M. Wang, Y. Zhang, X. Guo, M. Liu, Z. Zhou, Y. Zhao, R. He, Y. Gao, Y. Liu, S. Pan, M. Zhou, C. Zhao, T. Yin, X. Li, H. Wang, J. Yang, F. Zhu, M. Li, R. Qin, Hypoxia activated HGF expression in pancreatic stellate cells confers resistance of pancreatic cancer cells to EGFR inhibition. EBioMedicine. 86 , 104352. (2022). doi:10.1016/j.ebiom.2022.104352 S.-O. Lim, C.-W. Li, W. Xia, H.-H. Lee, S.-S. Chang, J. Shen, J.L. Hsu, D. Raftery, D. Djukovic, H. Gu, W.-C. Chang, H.-L. Wang, M.-L. Chen, L. Huo, C.-H. Chen, Y. Wu, A. Sahin, S.M. Hanash, G.N. Hortobagyi, M.-C. Hung, EGFR Signaling Enhances Aerobic Glycolysis in Triple-Negative Breast Cancer Cells to Promote Tumor Growth and Immune Escape. Cancer Res. 76(5) , 1284-1296. (2016). doi:10.1158/0008-5472.CAN-15-2478 H. Makinoshima, M. Takita, S. Matsumoto, A. Yagishita, S. Owada, H. Esumi, K. Tsuchihara, Epidermal growth factor receptor (EGFR) signaling regulates global metabolic pathways in EGFR-mutated lung adenocarcinoma. J Biol Chem. 289(30) , 20813-20823. (2014). doi: Z. Tang, B. Kang, C. Li, T. Chen, Z. Zhang, GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 47(W1) , W556-W560. (2019). doi:10.1093/nar/gkz430 G. Werba, D. Weissinger, E.A. Kawaler, E. Zhao, D. Kalfakakou, S. Dhara, L. Wang, H.B. Lim, G. Oh, X. Jing, N. Beri, L. Khanna, T. Gonda, P. Oberstein, C. Hajdu, C. Loomis, A. Heguy, M.H. Sherman, A.W. Lund, T.H. Welling, I. Dolgalev, A. Tsirigos, D.M. Simeone, Single-cell RNA sequencing reveals the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment. Nat Commun. 14(1) , 797. (2023). doi:10.1038/s41467-023-36296-4 X. Zhang, X. Zhang, X. Liu, P. Qi, H. Wang, Z. Ma, Y. Chai, MicroRNA-296, a suppressor non-coding RNA, downregulates SGLT2 expression in lung cancer. Int J Oncol. 54(1) , 199-208. (2019). doi:10.3892/ijo.2018.4599 K. Ren, X. Wang, R. Ma, H. Chen, T. Min, Y. Ma, X. Xie, W. Wang, X. Deng, Z. Zhou, K. Li, K. Zhu, N. Hao, C. Dang, T. Sun, H. Zhang, Dapagliflozin suppressed gastric cancer growth via regulating OTUD5 mediated YAP1 deubiquitination. Eur J Pharmacol. 983 , 177002. (2024). doi:10.1016/j.ejphar.2024.177002 J. Zhou, J. Zhu, S.-J. Yu, H.-L. Ma, J. Chen, X.-F. Ding, G. Chen, Y. Liang, Q. Zhang, Sodium-glucose co-transporter-2 (SGLT-2) inhibition reduces glucose uptake to induce breast cancer cell growth arrest through AMPK/mTOR pathway. Biomed Pharmacother. 132 , 110821. (2020). doi:10.1016/j.biopha.2020.110821 Y.K. Cho, S. Kim, M.J. Kim, W.J. Lee, Y.-J. Kim, C.H. Jung, New users of sodium-glucose cotransporter 2 inhibitors are at low risk of incident pancreatic cancer: A nationwide population-based cohort study. Diabetes Metab. 51(2) , 101605. (2025). doi:10.1016/j.diabet.2025.101605 Y. Tanaka, S. Yokoyama, C. Nakagawa, T. Uno, K. Hosomi, Association between sodium-glucose cotransporter 2 inhibitors and pancreatic cancer in the Japanese working-age population. Int J Clin Pharmacol Ther. 61(11) , 492-502. (2023). doi:10.5414/CP204465 J. Kim, M. Kundu, B. Viollet, K.-L. Guan, AMPK and mTOR regulate autophagy through direct phosphorylation of Ulk1. Nat Cell Biol. 13(2) , 132-141. (2011). doi:10.1038/ncb2152 H. Xia, D.R. Green, W. Zou, Autophagy in tumour immunity and therapy. Nat Rev Cancer. 21(5) , 281-297. (2021). doi:10.1038/s41568-021-00344-2 D.C. Rubinsztein, P. Codogno, B. Levine, Autophagy modulation as a potential therapeutic target for diverse diseases. Nat Rev Drug Discov. 11(9) , 709-730. (2012). doi:10.1038/nrd3802 W. Ornatowski, Q. Lu, M. Yegambaram, A.E. Garcia, E.A. Zemskov, E. Maltepe, J.R. Fineman, T. Wang, S.M. Black, Complex interplay between autophagy and oxidative stress in the development of pulmonary disease. Redox Biol. 36 , 101679. (2020). doi:10.1016/j.redox.2020.101679 B. Wang, Y. Wang, J. Zhang, C. Hu, J. Jiang, Y. Li, Z. Peng, ROS-induced lipid peroxidation modulates cell death outcome: mechanisms behind apoptosis, autophagy, and ferroptosis. Arch Toxicol. 97(6) , 1439-1451. (2023). doi:10.1007/s00204-023-03476-6 C.H. Park, B. Lee, M. Han, W.J. Rhee, M.S. Kwak, T.-H. Yoo, J.-S. Shin, Canagliflozin protects against cisplatin-induced acute kidney injury by AMPK-mediated autophagy in renal proximal tubular cells. Cell Death Discov. 8(1) , 12. (2022). doi:10.1038/s41420-021-00801-9 J. Luo, M. He, C. Liang, X. Huang, Y. Zhu, D. Hu, J. Yan, M. Li, H. Lin, W. Liao, J. Bin, Z. Guan, C. Zheng, Y. Liao, Canagliflozin reverses doxorubicin-induced cardiotoxicity via restoration of autophagic homeostasis. Toxicol Appl Pharmacol. 495 , 117183. (2025). doi:10.1016/j.taap.2024.117183 M.J. Moore, D. Goldstein, J. Hamm, A. Figer, J.R. Hecht, S. Gallinger, H.J. Au, P. Murawa, D. Walde, R.A. Wolff, D. Campos, R. Lim, K. Ding, G. Clark, T. Voskoglou-Nomikos, M. Ptasynski, W. Parulekar, Erlotinib Plus Gemcitabine Compared With Gemcitabine Alone in Patients With Advanced Pancreatic Cancer: A Phase III Trial of the National Cancer Institute of Canada Clinical Trials Group. J Clin Oncol. 41(30) , 4714-4720. (2023). doi:10.1200/JCO.22.02770 S. Qin, J. Li, Y. Bai, Z. Wang, Z. Chen, R. Xu, J. Xu, H. Zhang, J. Chen, Y. Yuan, T. Liu, L. Yang, H. Zhong, D. Chen, L. Shen, C. Hao, D. Fu, Y. Cheng, J. Yang, Q. Wang, B. Qin, H. Pan, J. Zhang, X. Bai, Q. Zheng, Nimotuzumab Plus Gemcitabine for K-Ras Wild-Type Locally Advanced or Metastatic Pancreatic Cancer. J Clin Oncol. 41(33) , 5163-5173. (2023). doi:10.1200/JCO.22.02630 L.A. Orofiamma, D. Vural, C.N. Antonescu, Control of cell metabolism by the epidermal growth factor receptor. Biochim Biophys Acta Mol Cell Res. 1869(12) , 119359. (2022). doi:10.1016/j.bbamcr.2022.119359 M. Ye, S. Wang, T. Wan, R. Jiang, Y. Qiu, L. Pei, N. Pang, Y. Huang, Y. Huang, Z. Zhang, L. Yang, Combined Inhibitions of Glycolysis and AKT/autophagy Can Overcome Resistance to EGFR-targeted Therapy of Lung Cancer. J Cancer. 8(18) , 3774-3784. (2017). doi:10.7150/jca.21035 S.M. Kim, M.R. Yun, Y.K. Hong, F. Solca, J.-H. Kim, H.-J. Kim, B.C. Cho, Glycolysis inhibition sensitizes non-small cell lung cancer with T790M mutation to irreversible EGFR inhibitors via translational suppression of Mcl-1 by AMPK activation. Mol Cancer Ther. 12(10) , 2145-2156. (2013). doi:10.1158/1535-7163.MCT-12-1188 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFig.1.tif SupplementaryFig.1Legends.docx Westernblot.tif GraphicalAbstract.tif Graphical Abstract Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Mar, 2026 Reviews received at journal 08 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 23 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 08 Feb, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8820380","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597019870,"identity":"c42b1890-f43c-4075-b57d-f5fa21e419c9","order_by":0,"name":"Yuxin Wang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Wang","suffix":""},{"id":597019871,"identity":"27294f6f-4a28-4d23-a76d-0614b3448166","order_by":1,"name":"Enkui Zhang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Enkui","middleName":"","lastName":"Zhang","suffix":""},{"id":597019874,"identity":"da00d3c6-06e1-41be-8bda-22f4b6078d91","order_by":2,"name":"Rui Ma","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Ma","suffix":""},{"id":597019877,"identity":"2d8690a0-a1a1-4a55-a5c5-ca349a2fe549","order_by":3,"name":"Weikang Liu","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weikang","middleName":"","lastName":"Liu","suffix":""},{"id":597019879,"identity":"b3e305ff-4ca4-497b-adf3-9c5bca66bee9","order_by":4,"name":"Qi Wang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Wang","suffix":""},{"id":597019880,"identity":"b5f4213f-4d6b-474e-b179-022b89cf3a44","order_by":5,"name":"Jun Zhang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhang","suffix":""},{"id":597019881,"identity":"848b4b61-1501-402f-8a71-79725f09c5de","order_by":6,"name":"Yinmo Yang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yinmo","middleName":"","lastName":"Yang","suffix":""},{"id":597019887,"identity":"3d529192-1859-49ed-bc0a-f75bc172d3a4","order_by":7,"name":"Yongsu Ma","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yongsu","middleName":"","lastName":"Ma","suffix":""},{"id":597019889,"identity":"2414f207-d540-4d43-b590-dc48497ce15f","order_by":8,"name":"Xiaodong Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACZhBhAMQHgPgDQwID+wwCOniQtTDOAGrhuUFIC5wF1MLMQ4wWe3bmZ495Cuzk+I73Hn5tuyMtsUe6gfFzAV6HsZkbzjBINpY8cy7NOvdMTmKPzAFmaXz+AfrFTOKDAXPihhs5Zsa5bRWJ+yUS2Jh58Gph/yaRYFAP0WIJ1NJDWAsPyJbDIC3GjxnbcojQcpinTHKGwXGgX86YMfaeSTPukUhslsanhb3/+DZpnj/VwBDrMf7wc0eybI9E8sHP+LQgAzYJxgYQDSGJAswfSFA8CkbBKBgFIwgAAE88SCJPrfueAAAAAElFTkSuQmCC","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiaodong","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2026-02-08 09:09:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8820380/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8820380/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103526045,"identity":"8bffd450-86db-43be-86d3-5bce1779524f","added_by":"auto","created_at":"2026-02-26 16:03:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7614691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003ePan-cancer SGLT2 mRNA expression based on TCGA and GTEx datasets, showing upregulation in pancreatic adenocarcinoma (PAAD).\u003cstrong\u003e (B-E) \u003c/strong\u003eUMAP visualization of 11 cell clusters from single-cell RNA sequencing of untreated pancreatic cancer samples.\u003cstrong\u003e (F, G) \u003c/strong\u003eFeature and violin plots showing SGLT2 expression in ductal epithelial and acinar cell subsets. \u003cstrong\u003e(H, I) \u003c/strong\u003eRepresentative immunohistochemical images and quantification of SGLT2 expression in paired pancreatic tumor and adjacent normal tissues.\u003cstrong\u003e (J) \u003c/strong\u003eKaplan-Meier curves for overall survival of pancreatic cancer patients stratified by SGLT2 expression. Hazard ratio (HR) = 4.522, 95% confidence interval (CI): 1.098-18.62; p = 0.0316 (*p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001; ****p \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8820380/v1/f4c416ddf1ed9da20679f1bb.png"},{"id":103526041,"identity":"098bd09f-3e7c-4278-b889-e047ae5f6742","added_by":"auto","created_at":"2026-02-26 16:03:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7919794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eRelative SGLT2 mRNA expression and \u003cstrong\u003e(B)\u003c/strong\u003e SGLT2 protein expression in HPNE and pancreatic cancer cell lines. \u003cstrong\u003e(C) \u003c/strong\u003eWestern blot confirming SGLT2 knockdown in Capan-1 and PANC-1. \u003cstrong\u003e(D, E)\u003c/strong\u003e Cell proliferation curves post-knockdown.\u003cstrong\u003e(F)\u003c/strong\u003e Colony formation assays. \u003cstrong\u003e(G) \u003c/strong\u003eTranswell assays. \u003cstrong\u003e(H, I)\u003c/strong\u003eWound healing assays. \u003cstrong\u003e(J, K)\u003c/strong\u003e Apoptotic cell analysis. \u003cstrong\u003e(L)\u003c/strong\u003e Representative tumor images from nude mice injected with shNC or shSGLT2 PANC-1 cells.\u003cstrong\u003e (M)\u003c/strong\u003e Tumor weights at endpoint. shNC vs shSGLT2: 0.3251g vs 0.1841g, p=0.0012 \u003cstrong\u003e(N)\u003c/strong\u003e Tumor growth curves. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8820380/v1/ba74cbf23e127554bead9d54.png"},{"id":103526042,"identity":"1939559d-f2c7-4ec7-9fdf-302bc3adf0af","added_by":"auto","created_at":"2026-02-26 16:03:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6108929,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A, B)\u003c/strong\u003e CCK-8 assays in Capan-1 and PANC-1 cells after 48-hour CANA treatment; IC₅₀ values were calculated. \u003cstrong\u003e(C, D)\u003c/strong\u003eEffect of CANA on cell proliferation. \u003cstrong\u003e(E)\u003c/strong\u003e Colony formation assays. \u003cstrong\u003e(F)\u003c/strong\u003eTranswell assays. \u003cstrong\u003e(G, H)\u003c/strong\u003e Wound healing assays. \u003cstrong\u003e(I, J)\u003c/strong\u003e Apoptotic cell analysis. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8820380/v1/d0caa107e23b89dfc8d6b62f.png"},{"id":104397928,"identity":"9d4ab47f-34db-4808-9508-3cf0f7f3a7e6","added_by":"auto","created_at":"2026-03-11 11:58:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7511639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A, B)\u003c/strong\u003e Glucose and lactate concentration in the culture medium.\u003cstrong\u003e (C) \u003c/strong\u003eATP generation normalized to total protein content.\u003cstrong\u003e (D-F)\u003c/strong\u003e Seahorse XF Cell Mito Stress Test: oxygen consumption rate (OCR) curves, ATP production, maximal respiration. \u003cstrong\u003e(G, H)\u003c/strong\u003e NAD⁺/NADH ratio and NADH levels. \u003cstrong\u003e(I, J)\u003c/strong\u003e Intracellular ROS by DHE staining. \u003cstrong\u003e(K, L)\u003c/strong\u003eMitochondrial membrane potential by TMRE staining. \u003cstrong\u003e(M)\u003c/strong\u003e Western blot of Bax, Bcl-2, cleaved caspase-3, and total caspase-3. \u003cstrong\u003e(N)\u003c/strong\u003e KEGG pathway enrichment of significantly altered metabolites following CANA treatment (top 15 pathways shown). *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8820380/v1/85eeb015c1f005f6f308aa71.png"},{"id":103526044,"identity":"7458a162-2152-421f-a552-a08c97790b0c","added_by":"auto","created_at":"2026-02-26 16:03:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":10941043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Western blot analysis of p-AMPK, p-ULK1, LC3B-II/I and p62 after CANA treatment. \u003cstrong\u003e(B)\u003c/strong\u003e Western blot showing the effects of CC on p-AMPK, LC3B-II/I and p62 in the presence of CANA. \u003cstrong\u003e(C, D)\u003c/strong\u003eTime-course Western blots of LC3B-II/I and p62 at 6, 12, 24 and 48 h in DMSO- and CANA-treated cells. \u003cstrong\u003e(E, F)\u003c/strong\u003e Representative confocal images and quantification of mCherry-GFP-LC3B expressed in cells; puncta counts and yellow/red ratio are shown. \u003cstrong\u003e(G, H)\u003c/strong\u003e Representative images and quantification of lysosomal acidification (LysoTracker). \u003cstrong\u003e(I, J)\u003c/strong\u003eIntracellular ROS levels in cells treated with CANA ± CC. \u003cstrong\u003e(K, L)\u003c/strong\u003eMitochondrial membrane potential in cells treated with CANA ± NAC.\u003cstrong\u003e (M, N)\u003c/strong\u003eApoptosis analysis in cells treated with CANA ± NAC. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ***p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8820380/v1/61d251177c0362aff105afc7.png"},{"id":103526048,"identity":"d05b97d2-5018-484f-ad02-6a9d81962876","added_by":"auto","created_at":"2026-02-26 16:03:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7711215,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Volcano plot showing differentially expressed genes in PANC-1 cells following CANA treatment.\u003cstrong\u003e (B)\u003c/strong\u003e KEGG pathway enrichment of upregulated genes (adjusted p \u0026lt; 0.05), highlighting 12 enriched pathways. \u003cstrong\u003e(C, D) \u003c/strong\u003eWestern blots of total and p-EGFR in CANA-treated or SGLT2-knockdown cells. \u003cstrong\u003e(E, F)\u003c/strong\u003e CI plots for CANA and Erlo co-treatment. \u003cstrong\u003e(G, H)\u003c/strong\u003e CCK-8 assays. \u003cstrong\u003e(I)\u003c/strong\u003e Colony formation assays. \u003cstrong\u003e(J)\u003c/strong\u003eTranswell assays. \u003cstrong\u003e(K, L)\u003c/strong\u003e Wound healing assays. \u003cstrong\u003e(M, N)\u003c/strong\u003e Flow cytometry analysis of apoptosis.\u003cstrong\u003e (O-R)\u003c/strong\u003e Xenograft tumor images, growth curves, and endpoint weights; body weight monitored during treatment. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8820380/v1/ca5b59b2898356da4afadee3.png"},{"id":104780077,"identity":"4fd6bb7d-8f24-4fba-a65c-48fbd10bcae1","added_by":"auto","created_at":"2026-03-17 07:50:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":44335834,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8820380/v1/b4da6576-3357-4f62-8a13-2cf67ea7fc5a.pdf"},{"id":103526046,"identity":"f29cd5fb-a703-44cd-9ab6-f148f5b44e39","added_by":"auto","created_at":"2026-02-26 16:03:54","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7950176,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8820380/v1/06429be65853d5b1559588f2.tif"},{"id":104397931,"identity":"db1da64f-70d4-4300-82a1-8e8cc5a0492a","added_by":"auto","created_at":"2026-03-11 11:58:54","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22770,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.1Legends.docx","url":"https://assets-eu.researchsquare.com/files/rs-8820380/v1/5adb536f91374b6ffe81bb39.docx"},{"id":103526049,"identity":"d394d15d-80bc-4386-a32e-f6f87ea96f5d","added_by":"auto","created_at":"2026-02-26 16:03:54","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7052664,"visible":true,"origin":"","legend":"","description":"","filename":"Westernblot.tif","url":"https://assets-eu.researchsquare.com/files/rs-8820380/v1/4160723b33c142020638228e.tif"},{"id":103526050,"identity":"16fc988c-a4cd-4327-a90e-fb715d5f90b0","added_by":"auto","created_at":"2026-02-26 16:03:54","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1020793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GraphicalAbstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-8820380/v1/616d070821bb5e001a293de4.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"SGLT2 Inhibition Induces Autophagic Flux Blockade and Sensitizes Pancreatic Cancer to EGFR-Targeted Therapy","fulltext":[{"header":"Background","content":"\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies worldwide, with a 5-year survival rate around 13%\u003csup\u003e[1]\u003c/sup\u003e. Surgical resection remains the only potentially curative option, but fewer than 20% of patients are eligible at diagnosis\u003csup\u003e[2]\u003c/sup\u003e. Chemotherapy and targeted therapy provide limited benefits due to both intrinsic and acquired resistance\u003csup\u003e[3]\u003c/sup\u003e. Its aggressive biology, dense desmoplastic stroma, and profound metabolic rewiring contribute to both intrinsic and acquired resistance to standard treatments\u003csup\u003e[3]\u003c/sup\u003e. As such, identifying tumor-specific vulnerabilities that integrate metabolic and signaling dependencies is a critical unmet need in PDAC research.\u003c/p\u003e \u003cp\u003eOne hallmark of PDAC is its reliance on glucose metabolism to sustain rapid proliferation in a nutrient-deprived microenvironment\u003csup\u003e[4]\u003c/sup\u003e. Although PDAC cells can utilize glutamine, fatty acids, and amino acids as alternative fuels, glucose remains the dominant substrate that drives energy production and biosynthesis\u003csup\u003e[5]\u003c/sup\u003e. Despite the preference for aerobic glycolysis (the Warburg effect), PDAC cells also retain the capacity to enhance oxidative phosphorylation (OXPHOS) under stress\u003csup\u003e[6]\u003c/sup\u003e. Metastatic cells may activate the pentose phosphate pathway to produce nucleotides and maintain redox balance, thereby promoting tumor progression\u003csup\u003e[6]\u003c/sup\u003e. Moreover, the high glycolytic flux contributes to lactate accumulation, acidifying the tumor microenvironment and modulating immune cell function\u003csup\u003e[5]\u003c/sup\u003e. To meet the elevated metabolic demands, PDAC cells activate autophagy as a survival strategy\u003csup\u003e[7]\u003c/sup\u003e. Within the nutrient-scarce tumor microenvironment, autophagy recycles cellular components to generate alternative nutrients, thereby sustaining glycolytic flux and supporting continued proliferation under metabolic stress\u003csup\u003e[7]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlucose uptake and utilization are regulated by specific transporters, including the sodium-glucose co-transporter 2 (SGLT2, gene name SLC5A2)\u003csup\u003e[8]\u003c/sup\u003e. SGLT2 is a transmembrane protein primarily responsible for active glucose reabsorption in the renal proximal tubule\u003csup\u003e[9]\u003c/sup\u003e. Emerging evidence indicates that SGLT2 is also expressed in several tumors, potentially supporting glucose uptake under metabolic stress\u003csup\u003e[10, 11]\u003c/sup\u003e. Pharmacological inhibitors of SGLT2, such as canagliflozin (CANA), are widely used for type 2 diabetes management by inhibiting SGLT2-mediated glucose reabsorption in the renal proximal tubules\u003csup\u003e[8]\u003c/sup\u003e. Preclinical studies suggest that these agents may have anti-tumor properties in certain cancer types\u003csup\u003e[12\u0026ndash;14]\u003c/sup\u003e. CANA attenuates the proliferation of cancer cells by inhibiting glucose uptake\u003csup\u003e[15]\u003c/sup\u003e and mitochondrial complex I-supported respiration\u003csup\u003e[16]\u003c/sup\u003e. In vitro experiments showed that CANA promotes mitochondrial dysfunction and reticulophagy in colorectal cancer cells\u003csup\u003e[17]\u003c/sup\u003e. Moreover, several studies have reported that CANA can enhance the efficacy of chemotherapy\u003csup\u003e[18, 19]\u003c/sup\u003e, radiotherapy\u003csup\u003e[20]\u003c/sup\u003e, or immunotherapy\u003csup\u003e[13]\u003c/sup\u003e in certain tumors. Clinical evidence further indicates that SGLT2 inhibitor use is associated with a 23% reduced risk of prostate cancer in diabetic men\u003csup\u003e[21]\u003c/sup\u003e. However, studies on the expression and function of SGLT2 in PDAC remain inconclusive\u003csup\u003e[22, 23]\u003c/sup\u003e, and the underlying mechanisms remain largely unexplored. Addressing these gaps may reveal novel therapeutic opportunities.\u003c/p\u003e \u003cp\u003eIn parallel, aberrant epidermal growth factor receptor (EGFR) signaling is frequently observed in PDAC and contributes to tumor progression\u003csup\u003e[24, 25]\u003c/sup\u003e. EGFR activates downstream pathways such as PI3K/AKT and MAPK, driving tumor growth and survival\u003csup\u003e[26]\u003c/sup\u003e. Despite widespread EGFR expression in PDAC, clinical responses to EGFR-targeted therapies are limited\u003csup\u003e[27, 28]\u003c/sup\u003e. Resistance mechanisms include compensatory signaling, metabolic plasticity, and stromal-mediated protection\u003csup\u003e[27\u0026ndash;29]\u003c/sup\u003e. Previous studies have shown that EGFR activity is closely intertwined with glucose metabolism, where it promotes glycolysis and enhances glucose uptake in cancer cells\u003csup\u003e[30, 31]\u003c/sup\u003e. This metabolic-signaling crosstalk raises the possibility that metabolic disruption may sensitize PDAC cells to EGFR inhibition, providing a rationale for combinatorial strategies.\u003c/p\u003e \u003cp\u003eIn this study, we investigated SGLT2 expression and its functional role in PDAC. We aimed to examine how SGLT2 influences cellular metabolism and stress responses and to explore potential interactions with EGFR-targeted therapy, with the goal of identifying metabolic vulnerabilities that may inform combinatorial treatment strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003ePublic Database Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePan-cancer expression analysis of SGLT2 was performed using Gene Expression Profiling Interactive Analysis 2 (GEPIA2, http://gepia2.cancer-pku.cn/#index)\u003csup\u003e[32]\u003c/sup\u003e based on The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell RNA Sequencing Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) data for PDAC were obtained from the Gene Expression Omnibus (GEO) database (GSE205013\u003csup\u003e[33]\u003c/sup\u003e). Untreated PDAC samples were included for downstream analysis. Quality control was performed using Scanpy (v1.10.4) in Python 3.12.2. Cells with fewer than 200 detected genes or with \u0026gt;15% mitochondrial gene content were excluded, and genes expressed in fewer than 3 cells were removed. High-quality cells with\u0026nbsp;\u0026ge;500 detected genes and\u0026nbsp;\u0026le;15% mitochondrial proportion were retained, resulting in 80,642 cells for subsequent analyses. Gene expression matrices were normalized and log-transformed, followed by scaling to unit variance.\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA) was applied for dimensionality reduction, and batch effects across samples were corrected using the Harmony algorithm. Uniform Manifold Approximation and Projection (UMAP) was then used for visualization. Cell clustering was performed using the Leiden algorithm, and cell types were manually annotated based on canonical marker gene expression and established literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry (IHC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe tissue microarray (TMA) used in this study contained 49 PDAC tissues and paired adjacent normal paraffin-embedded specimens, provided by the Department of Hepatobiliary and Pancreatic Surgery, Peking University First Hospital, in accordance with the guidelines of the Ethics Committee of Peking University First Hospital (approval number: 2024-194). Written informed consent was obtained from all participants prior to inclusion in the study. Paraffin sections were baked at 62 \u0026deg;C for 1 h, dewaxed, and subjected to heat-mediated antigen retrieval. After washing, the sections were incubated with the primary anti-SGLT2 antibody (Invitrogen, PA5-101893; Thermo Fisher Scientific, Waltham, MA, USA; 1:100) overnight at 4 \u0026deg;C. The following day, sections were incubated with a horseradish peroxidase (HRP)-conjugated secondary antibody for 30 min at 37 \u0026deg;C. Finally, slides were mounted with neutral gum and cover slipped. Following immunohistochemical staining, two independent pathologists evaluated the expression levels of the target protein. Quantitative analysis of staining intensity and the percentage of positive tumor cells was performed using ImageJ IHC Profiler.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNormal pancreatic ductal epithelial cells (HPNE) and pancreatic cancer cell lines (PANC-1, Capan-1, MIA PaCa-2, AsPC-1, BxPC-3, PA-TU-8988T, and T3M-4) were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA), the German Collection of Microorganisms and Cell Cultures (DSMZ, Braunschweig, Germany), or AcceGen (Fairfield, NJ, USA). Cells were cultured in DMEM or RPMI-1640 medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% fetal bovine serum (FBS; Procell Life Science \u0026amp; Technology, Wuhan, China) at 37 \u0026deg;C in a humidified atmosphere containing 5% CO₂.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Proliferation and Colony Formation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell viability was measured using the Cell Counting Kit-8 (CCK-8; Dojindo Laboratories, Kumamoto, Japan; CK04). For colony formation, 500 PANC-1 cells or 700 Capan-1 cells per well were seeded into six-well plates and cultured for 10-14 days until visible colonies formed. Colonies were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWound-healing and Transwell Assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWound-healing and Transwell assays were performed to evaluate cell migration. For wound healing, confluent cells were scratched with a pipette tip, washed, and incubated in serum-free medium. Images were captured at 0, 24, and 48 h. For Transwell assays, 3 \u0026times; 10⁴ Capan-1 cells or 1 \u0026times; 10⁴ PANC-1 cells in serum-free medium were seeded into the upper chamber (6.5 mm, 8.0 \u0026mu;m pore; Corning Inc., Corning, NY, USA; Cat. No. 3422) without Matrigel. After 48 h, migrated cells were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and counted. For knockdown experiments, cells were analyzed 48 h after seeding, while for drug treatment experiments, drugs were added 24 h after seeding and cells were analyzed 48 h later. Images were acquired using a DP74 Microscope Digital Camera (Olympus, Tokyo, Japan).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow Cytometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApoptosis was assessed using an Annexin V-APC/7-AAD apoptosis detection kit (KeyGEN BioTECH, Jiangsu, China; KGA1106-50) according to the manufacturer\u0026rsquo;s protocol. Samples were analyzed on a CytoFLEX flow cytometer (Beckman Coulter, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolic Assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlucose concentration in culture supernatants was determined using the Glucose Assay Kit-WST (Dojindo; G264). Lactate, Adenosine triphosphate (ATP), Nicotinamide adenine dinucleotide (oxidized form, NAD⁺), and Nicotinamide adenine dinucleotide (reduced form, NADH) levels were measured using the L-Lactate Assay Kit with WST-8 (Beyotime Biotechnology, Shanghai, China; S0208S), ATP Assay Kit (Beyotime Biotechnology; S0026), and NAD⁺/NADH Assay Kit with WST-8 (Beyotime Biotechnology; S0175), respectively, according to the manufacturers\u0026rsquo; protocols and normalized to protein content. Mitochondrial function was assessed using the Seahorse XF Cell Mito Stress Test Kit (Agilent Technologies, Santa Clara, CA, USA) with a Seahorse XFe24 Analyzer. Final drug concentrations were 1.5 \u0026mu;M oligomycin, 0.5 \u0026mu;M rotenone/antimycin A (Rot/AA), and Carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP) at 1.5 \u0026mu;M for Capan-1 cells and 2.5 \u0026mu;M for PANC-1 cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROS, Mitochondrial Membrane Potential, and Lysosomal Detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntracellular ROS was detected using the Reactive Oxygen Species Assay Kit (DHE; Beijing Solarbio Science \u0026amp; Technology Co., Ltd., Beijing, China; CA1420). Mitochondrial membrane potential (\u0026Delta;\u0026Psi;m) was measured using the Mitochondrial Membrane Potential Assay Kit with TMRE (Beyotime Biotechnology; C2001S). Lysosomes were stained with Lyso-Tracker Red (Beyotime Biotechnology; C1046). Nuclei were counterstained with Hoechst 33342 Staining Solution for Live Cells (100X; Beyotime Biotechnology; C1028). Images were captured using the Olympus DP74 camera.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDual-Fluorescence LC3B Reporter Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were infected with pLenti-CMV-mCherry-GFP-LC3B-IRES-Puro-WPRE lentivirus (OBiO Technology). Images were acquired using a Confocal Laser Scanning Microscope ( Leica Microsystems, Wetzlar, Germany).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn Vivo Tumor Xenograft Assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale BALB/c-nu mice were obtained at 3 weeks of age (average 13 g; Charles River Laboratories, Viatonglihua, CAnN.Cg-Foxn1nu/Crl, code 401) and acclimated for 1 week. At 4 weeks of age (average 16 g), mice were randomly assigned to different groups. PANC-1 cells were resuspended in a 1:1 mixture of DMEM and Corning\u0026reg; Matrigel\u0026reg; Basement Membrane Matrix High Concentration (HC; Corning, 354248) at 4 \u0026times; 10⁶ cells per 100 \u0026mu;L and injected subcutaneously into the axilla of each mouse. One week after injection, mice received daily oral gavage of 100 \u0026mu;L vehicle (10% DMSO, 40% PEG300, 10% Tween 80, 40% H₂O), Canagliflozin (50 mg/kg), Erlotinib (25 mg/kg), or the combination. Tumor volume and body weight were measured three times per week. Tumor volume was calculated as\u0026nbsp;𝑉=length\u0026times;width\u003csup\u003e2\u003c/sup\u003e/2. Experiments were planned to terminate when tumors reached 1000 mm\u0026sup3;, after 4 weeks of treatment, or if body weight decreased by more than 15%. In practice, tumors did not exceed 800 mm\u0026sup3;, and experiments were stopped accordingly. All procedures were approved by the Ethics Committee for Animal Research at Peking University First Hospital (approval number J2025049) and followed institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug Treatment and Gene Silencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCanagliflozin (S2760; Selleck Chemicals, Houston, TX, USA), Erlotinib (S7786; Selleck Chemicals), and Compound C (HY-13418A; MedChemExpress, MCE, Monmouth Junction, NJ, USA) were dissolved in DMSO and diluted in culture medium. N-acetylcysteine (HY-B0215; MCE) was dissolved in water and diluted in culture medium. SGLT2 knockdown was achieved using a lentiviral shRNA vector (pSLenti-U6-shRNA(SLC5A2)-CMV-EGFP-F2A-Puro-WPRE; OBiO Technology, Shanghai, China). Stable cells were selected with puromycin (HY-K1057; MCE), and knockdown efficiency was confirmed by Western blot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern Blot and qRT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProteins were extracted using RIPA buffer supplemented with protease and phosphatase inhibitors (Beijing LABLEAD, Beijing, China). Equal amounts of protein were separated by SDS-PAGE and transferred to 0.22 \u0026mu;m PVDF membranes. Membranes were blocked with 5% nonfat milk for 1 h at room temperature, incubated with primary antibodies overnight at 4 \u0026deg;C, and then washed three times with TBST. After incubation with HRP-conjugated secondary antibodies (Abclonal) for 1 h at room temperature, signals were detected using the enhanced chemiluminescence (ECL) reagent. The following primary antibodies were used: SGLT2 (Invitrogen, PA5-101893, 1:1000), Phospho-AMPK\u0026alpha; (Thr172; #2535, Cell Signaling Technology, CST, Danvers, MA, USA; 1:1000), AMPK\u0026alpha;1/\u0026alpha;2 (A27099, Abclonal, Wuhan, China; 1:2000), Phospho-ULK1 (Ser555, #5869, CST; 1:1000), ULK1 (#8054, CST; 1:1000), SQSTM1/p62 (#5114, CST; 1:1000), LC3B (#3868, CST; 1:1000), Bax (#2772, CST; 1:1000), Bcl-2 (#3498, CST; 1:1000), Cleaved Caspase-3 (#9661, CST; 1:1000), Caspase-3 (#14220, CST; 1:1000), EGFR (A11351, Abclonal; 1:2000), and Phospho-EGFR (Tyr1068, AP0994, Abclonal; 1:1000). \u0026beta;-Actin (AC026, Abclonal; 1:80000) was used as the loading control. HRP-conjugated secondary antibodies were from Abclonal (AS014, goat anti-rabbit IgG (H+L), 1:10000; AS003, goat anti-mouse IgG (H+L), 1:10000).\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted using TRIzol reagent (Invitrogen, Thermo Fisher Scientific). Reverse transcription was performed with Hifair\u0026reg; AdvanceFast 1st Strand cDNA Synthesis SuperMix for qPCR(Yeasen Biotechnology, Shanghai, China). qRT-PCR was carried out using Hieff\u0026reg; qPCR SYBR Green II Master Mix (Yeasen Biotechnology). Primers were synthesized by Generay Biotech (Shanghai, China). The qRT-PCR primer sequences were as follows:\u0026nbsp;\u003cbr\u003e\u0026nbsp;SLC5A2, forward 5\u0026prime;-CTGTTTGCACCCGTGTACCT-3\u0026prime;, reverse 5\u0026prime;-CCTGTCACCGTGTAAATCATGG-3\u0026prime;;\u0026nbsp;\u003cbr\u003e\u0026nbsp;ACTB, forward 5\u0026prime;-CCTGGACTTCGAGCAAGAGATGG-3\u0026prime;, reverse 5\u0026prime;-CAGGAAGGAAGGCTGGAAGAGTG-3\u0026prime;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments were performed at least three times independently. Data are presented as mean \u0026plusmn; standard error of the mean (SEM). Statistical analyses were conducted using GraphPad Prism (version 10.1.2; GraphPad Software, San Diego, CA, USA). The Wilcoxon signed-rank test was adopted for the analysis of paired IHC data. Comparisons between two groups were performed using two-tailed Student\u0026rsquo;s t-test, and multiple group comparisons were performed using one-way ANOVA followed by Tukey\u0026rsquo;s post hoc test. Kaplan-Meier survival curves were analyzed using the log-rank test. Differences were considered statistically significant at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSGLT2 Is Overexpressed in Pancreatic Cancer and Correlates with Poor Prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first evaluated the expression pattern of SGLT2 across various cancer types using transcriptomic data from the GEPIA2 platform. SGLT2 expression was found to be upregulated in multiple cancer types, including PDAC \u003cstrong\u003e(Fig. 1A)\u003c/strong\u003e. Subsequently, single-cell RNA sequencing data were obtained from a publicly available pancreatic cancer dataset (GSE205013). The expression pattern of SGLT2 was further examined using single-cell RNA sequencing data. After rigorous quality control and filtering, 11 PDAC samples were integrated, resulting in a total of 80,642 high-quality cells retained for downstream analyses\u003cstrong\u003e\u0026nbsp;(Fig. 1B)\u003c/strong\u003e. Unsupervised clustering identified 23 distinct clusters, which were subsequently annotated into 11 major cell types based on canonical marker gene expression profiles. Specifically, NK/T cells (CD3D, CD3E, CD2, CD7, IL7R), ductal cells (EPCAM, KRT19, KRT7, MUC1, MUC5AC, CEACAM5, CEACAM6), acinar cells (PRSS1, CPA1, CELA3A, CELA3B), endocrine cells (INS, IAPP), fibroblasts (DCN, COL1A1, COL1A2, COL3A1, ACTA2), endothelial cells (VWF, PECAM1, PLVAP), macrophages/monocytes (CD14, S100A8, S100A9, C1QA, C1QB, FCGR3A), pericytes (RGS5, PDGFRB), Schwann cells (S100B), mast cells (TPSAB1, TPSB2), and B cells (CD19, MS4A1, CD79A, CD79B) were identified. These results provide a comprehensive cellular landscape of PDAC, forming the basis for subsequent analyses of cell-type-specific SGLT2 expression\u003cstrong\u003e\u0026nbsp;(Fig. 1C-E)\u003c/strong\u003e. Although SGLT2 expression was generally low across the PDAC single-cell landscape, it was specifically enriched in subsets of ductal epithelial cells and acinar cells, as illustrated by UMAP feature plots and violin plots \u003cstrong\u003e(Fig. 1F, G)\u003c/strong\u003e. This distribution suggests potential cell type-specific metabolic or signaling roles of SGLT2 within the tumor microenvironment.\u003c/p\u003e\n\u003cp\u003eWe further verified and analyzed the expression of SGLT2 in pancreatic cancer tissues at the histological level. IHC was performed on a tissue microarray containing paired pancreatic tumor and adjacent normal samples. Representative images demonstrated stronger SGLT2 staining localized predominantly to tumor epithelial regions compared to adjacent normal tissues \u003cstrong\u003e(Fig. 1H)\u003c/strong\u003e. Quantitative analysis of percentage of SGLT2\u003csup\u003e+\u003c/sup\u003e cells revealed significantly elevated SGLT2 expression in tumor samples (\u003cstrong\u003eFig. 1I\u003c/strong\u003e; p = 0.0002). To investigate the prognostic relevance of SGLT2, Kaplan-Meier survival analysis was conducted. Patients with high SGLT2 expression (percentage of SGLT2\u003csup\u003e+\u003c/sup\u003e cells \u0026le; median, 19.7067) exhibited significantly shorter overall survival than those with low expression (percentage of SGLT2+ cells \u0026gt; median, 19.7067) (\u003cstrong\u003eFig. 1J\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSGLT2 Promotes Proliferation, Migration, and Tumorigenicity of Pancreatic Cancer Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the biological significance of SGLT2 in pancreatic cancer, its mRNA expression levels were across a panel of pancreatic cancer cell lines. Compared with the non-tumorigenic pancreatic ductal epithelial cell line HPNE, all tested cancer cell lines showed markedly elevated SGLT2 expression, with Capan-1 and PANC-1 exhibiting the highest levels \u003cstrong\u003e(Fig. 2A)\u003c/strong\u003e. Consistently, Western blot analysis confirmed increased protein expression of SGLT2, with the highest expression observed in PA-TU-8988T, followed by PANC-1 and Capan-1 \u003cstrong\u003e(Fig. 2B)\u003c/strong\u003e. Given their robust expression and extensive use as in vitro models, PANC-1 and Capan-1 were selected for subsequent functional studies.\u003c/p\u003e\n\u003cp\u003eStable knockdown of SGLT2 was achieved in both Capan-1 and PANC-1 cells using two independent shRNAs. Knockdown efficiency was confirmed by Western blot \u003cstrong\u003e(Fig. 2C)\u003c/strong\u003e. CCK-8 assays revealed that SGLT2 knockdown significantly inhibited cell proliferation in both cell line. Compared with control, both shRNA groups displayed consistently lower viability over time \u003cstrong\u003e(Fig. 2D, E)\u003c/strong\u003e. The effect on suppressing long-term growth was further supported by colony formation assays, which showed a significant decrease in colony numbers in knockdown groups \u003cstrong\u003e(Fig. 2F)\u003c/strong\u003e. SGLT2 knockdown also attenuated cell motility, as evidenced by reduced cell migration in Transwell assays (\u003cstrong\u003eFig. 2G\u003c/strong\u003e; p \u0026lt; 0.0001 for Capan-1, p \u0026lt; 0.01 for PANC-1), and corroborated by wound healing assays \u003cstrong\u003e(Fig. 2H, I)\u003c/strong\u003e. To assess the impact of SGLT2 on pancreatic cancer cell survival, Annexin V-APC/7-AAD staining combined with flow cytometric analysis demonstrated a marked increase in apoptosis in SGLT2-deficient cells \u003cstrong\u003e(Fig. 2J, K)\u003c/strong\u003e, indicating that SGLT2 may contribute to the survival in pancreatic cancer cells.\u003c/p\u003e\n\u003cp\u003eWe next assessed the impact of SGLT2 silencing on tumorigenicity in vivo. PANC-1 cells transduced with shNC or shSGLT2 were injected subcutaneously into nude mice, and tumor formation was monitored. Tumors derived from shSGLT2 group exhibited markedly smaller size and reduced mass compared with control (\u003cstrong\u003eFig. 2L, M\u003c/strong\u003e). Consistently, growth curves showed that SGLT2 knockdown significantly suppressed tumor progression over time \u003cstrong\u003e(Fig. 2N)\u003c/strong\u003esh Together, these findings indicate that SGLT2 promotes pancreatic cancer cell survival and growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePharmacological Inhibition of SGLT2 Suppresses Proliferation and Induces Apoptosis in Pancreatic Cancer Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further evaluate the therapeutic potential of SGLT2 inhibition in pancreatic cancer, we examined the effect of selective SGLT2 inhibitor CANA in vitro. A dose-dependent cytotoxicity was observed via CCK-8 assays following 48-hour treatment, with IC₅₀ values of 132.4 \u0026mu;M for Capan-1 and 99.11 \u0026mu;M for PANC-1 cells \u003cstrong\u003e(Fig. 3A, B)\u003c/strong\u003e. In addition, CANA suppressed cell proliferation in a time-dependent manner \u003cstrong\u003e(Fig. 3C, D)\u003c/strong\u003e. Colony formation assays revealed a significant reduction in long-term proliferative potential following CANA treatment \u003cstrong\u003e(Fig. 3E)\u003c/strong\u003e. CANA treatment also impaired migration capacity. Transwell assays showed a marked decrease in the number of migrated cells upon treatment \u003cstrong\u003e(Fig. 3F)\u003c/strong\u003e. Consistently, wound healing assays revealed the effect on suppressing the migration of both Capan-1 and PANC-1 cells \u003cstrong\u003e(Fig. 3G, H)\u003c/strong\u003e. After 48 hours of exposure to 50 \u0026mu;M CANA, the migration area rate was reduced significantly. Furthermore, flow cytometric analysis of Annexin V-APC/7-AAD staining demonstrated that CANA induced an increased proportion of apoptotic cells compared to control \u003cstrong\u003e(Fig. 3I, J)\u003c/strong\u003e. Collectively, these findings indicate that inhibition of SGLT2 by CANA inhibits proliferation and induces the apoptosis in pancreatic cancer cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSGLT2 Inhibition Disrupts Glucose Metabolism and Mitochondrial Function, Triggering Energy Crisis and Oxidative Stress in Pancreatic Cancer Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the mechanisms underlying the cytotoxic effects induced by SGLT2 inhibition, we next examined its impact on cellular energy metabolism and mitochondrial function. Following 48-hour treatment with CANA, the glucose uptake, lactate generation, and ATP production were significantly decreased \u003cstrong\u003e(Fig. 4A-C)\u003c/strong\u003e. To further characterize oxidative metabolism, we performed Seahorse XF Cell Mito Stress Test. CANA exposure resulted in a pronounced reduction in ATP production and maximal respiration \u003cstrong\u003e(Fig. 4D-F)\u003c/strong\u003e. These observations suggest that SGLT2 activity is essential for sustaining energy homeostasis in pancreatic cancer cells, and its inhibition impairs both glycolytic activity and mitochondrial oxidative phosphorylation. In parallel, the NAD⁺/NADH ratio declined after treatment \u003cstrong\u003e(Fig. 4G)\u003c/strong\u003e, suggesting NADH accumulation and disruption of redox homeostasis \u003cstrong\u003e(Fig. 4H)\u003c/strong\u003e. To assess downstream consequences of energy dysregulation, we evaluated intracellular oxidative stress and mitochondrial integrity. DHE staining showed a marked increase in reactive oxygen species (ROS) levels following CANA treatment \u003cstrong\u003e(Fig. 4I, J),\u003c/strong\u003e suggesting increased oxidative stress. In parallel, measurement of mitochondrial membrane potential using TMRE staining revealed a significant hyperpolarization \u003cstrong\u003e(Fig. 4K, L)\u003c/strong\u003e, a hallmark of early mitochondrial dysfunction and apoptotic priming. To determine whether these alterations culminate in mitochondria-dependent apoptosis, we assessed apoptosis-related protein expression by Western blot. While BCL-2-associated X protein (Bax) expression remained relatively unchanged, BCL-2 levels were significantly reduced and cleaved cysteine-aspartic acid protease 3 (Caspase-3) was markedly upregulated, indicating activation of mitochondria-dependent apoptosis \u003cstrong\u003e(Fig. 4M)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, metabolomic profiling and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that CANA treatment led to significant alterations in metabolic pathways, particularly those involved in nucleotide biosynthesis, redox regulation, energy metabolism, and stress signaling \u003cstrong\u003e(Fig. 4N)\u003c/strong\u003e. Top enriched pathways included purine metabolism, nicotinate and nicotinamide metabolism, oxidative phosphorylation, and the adenosine monophosphate-activated protein kinase (AMPK) signaling pathway, consistent with an energy crisis and metabolic stress. Similar metabolic and mitochondrial changes were observed in SGLT2-knockdown cells \u003cstrong\u003e(Supplementary Fig. S1)\u003c/strong\u003e, further supporting the notion that SGLT2 plays a crucial role in the metabolic reprogramming of pancreatic cancer, particularly in maintaining energy homeostasis within the tumor microenvironment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSGLT2 Inhibition Causes Autophagic Flux Blockade, Leading to Oxidative Stress and Mitochondrial Apoptosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn response to this energy deficit and redox imbalance, we next investigated whether key metabolic signaling pathways were activated. AMPK, a well-established energy sensor, is rapidly activated under conditions of metabolic stress. Western blot showed increased phosphorylation of AMPK and unc-51-like kinase 1 (ULK1), accompanied by accumulation of microtubule-associated protein 1 light chain 3 beta (LC3B)-II and sequestosome 1 (p62) after CANA treatment \u003cstrong\u003e(Fig. 5A)\u003c/strong\u003e. Inhibition of AMPK with Compound C (CC) reduced p-AMPK and reversed the increase of LC3B-II/I and p62 \u003cstrong\u003e(Fig. 5B)\u003c/strong\u003e, suggesting that CANA-induced autophagy initiation is AMPK dependent. Time-course Western blot analysis showed that, compared with DMSO, CANA treatment led to a progressive increase of LC3B-II/I and p62 from 6 to 48 h \u003cstrong\u003e(Fig. 5C, D)\u003c/strong\u003e. P62 accumulation indicates a blockade in autophagosome degradation. Consistent with this, confocal imaging of cells expressing mCherry-GFP-LC3B revealed increased puncta formation and a higher yellow/red ratio after CANA treatment \u003cstrong\u003e(Fig. 5E, F)\u003c/strong\u003e, further supporting impaired autophagosome-lysosome fusion. LysoTracker staining showed that lysosomal acidification was not reduced \u003cstrong\u003e(Fig. 5G, H)\u003c/strong\u003e, suggesting that the blockade was not due to lysosomal dysfunction.\u003c/p\u003e\n\u003cp\u003eWe next examined whether the AMPK-dependent autophagy contributed to ROS accumulation and mitochondrial changes. CC treatment partially reversed CANA-induced ROS increase \u003cstrong\u003e(Fig. 5I, J)\u003c/strong\u003e. Similarly, the ROS scavenger N-Acetylcysteine (NAC) partially reduced mitochondrial hyperpolarization and apoptosis caused by CANA \u003cstrong\u003e(Fig. 5K-N)\u003c/strong\u003e. These results indicate that CANA activates AMPK-ULK1-dependent autophagy initiation but impairs autophagosome-lysosome fusion. The autophagy blockade results in excessive ROS generation, mitochondrial dysfunction, and apoptotic cell death.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSGLT2 Inhibition Enhances the Sensitivity of Pancreatic Cancer to EGFR-Targeted Therapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate essential pathways involved in the cellular response to SGLT2 inhibition, we performed transcriptomic analysis of PANC-1 cells following CANA treatment. Differential expression analysis revealed a distinct gene expression profile, as shown in the volcano plot \u003cstrong\u003e(Fig. 6A)\u003c/strong\u003e. KEGG pathway enrichment analysis of upregulated genes identified 12 significantly enriched pathways (adjusted p \u0026lt; 0.05), including the ErbB signaling pathway \u003cstrong\u003e(Fig. 6B)\u003c/strong\u003e. As EGFR is a central effector in the ErbB family and commonly overexpress in pancreatic cancer, we next evaluated EGFR expression. Western blot analysis showed that both total EGFR and phosphorylated EGFR (Tyr1068) levels were upregulated following CANA treatment compared to DMSO controls \u003cstrong\u003e(Fig. 6C)\u003c/strong\u003e, while SGLT2 knockdown led to decreased expression of both total and phosphorylated EGFR \u003cstrong\u003e(Fig. 6D)\u003c/strong\u003e. These findings suggest that SGLT2 may be involved in maintaining EGFR signaling under baseline conditions, and its inhibition induces compensatory upregulation of EGFR, supporting the rationale for combination therapy.\u003c/p\u003e\n\u003cp\u003eTo assess the therapeutic potential of combined SGLT2 and EGFR inhibition, Capan-1 and PANC-1 cells were co-treated with CANA and the EGFR tyrosine kinase inhibitor (EGFR-TKI) erlotinib (Erlo). Combination index (CI) analysis demonstrated synergistic effects (CI \u0026lt; 1) at moderate to high fraction affected in both cell lines \u003cstrong\u003e(Fig. 6E, F)\u003c/strong\u003e. Consistently, combination treatment more effectively suppressed short-term proliferation \u003cstrong\u003e(Fig. 6G, H)\u003c/strong\u003e and long-term colony formation \u003cstrong\u003e(Fig. 6I)\u003c/strong\u003e than either agent alone. Cell migration was also more strongly impaired by the combination, as shown in Transwell \u003cstrong\u003e(Fig. 6J)\u003c/strong\u003e and wound healing assays \u003cstrong\u003e(Fig. 6K, L)\u003c/strong\u003e. Furthermore, flow cytometry analysis revealed a significant increase in apoptotic cells in the combination group relative to monotherapies \u003cstrong\u003e(Fig. 6M, N)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTo validate these findings in vivo, a subcutaneous xenograft model was established using PANC-1 cells. Tumor growth was significantly suppressed by the combination of CANA and erlotinib compared to either monotherapy or vehicle control \u003cstrong\u003e(Fig. 6O-Q)\u003c/strong\u003e. At endpoint, the mean tumor volume in the combination group was 135.8 mm\u0026sup3;, markedly lower than that in the control (565.6 mm\u0026sup3;), CANA (419.2 mm\u0026sup3;), and erlotinib (521.8 mm\u0026sup3;) groups. The mean reduction relative to control was 429.8 mm\u0026sup3; (95% CI: 337.3-522.3). No significant body weight loss was observed during treatment, indicating acceptable tolerability \u003cstrong\u003e(Fig. 6R)\u003c/strong\u003e. Collectively, these results support a synergistic interaction between SGLT2 and EGFR inhibition in pancreatic cancer, and provide a preclinical rationale for combination therapeutic strategies targeting these pathways.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified that SGLT2 is significantly upregulated in pancreatic cancer tissues and correlates with poor overall survival. Functional experiments demonstrated that both genetic knockdown and pharmacologic inhibition of SGLT2 suppressed tumor cell proliferation, clonogenic capacity, and migration, while promoting apoptosis. Mechanistically, SGLT2 targeting disrupted energy homeostasis, impaired autophagic flux, induced oxidative stress, and triggered mitochondrial apoptosis. Notably, co-treatment with an EGFR inhibitor exerted a synergistic anti-tumor effect.\u003c/p\u003e \u003cp\u003eSGLT2 has been reported to be overexpressed in several types of malignancies, including lung\u003csup\u003e[34]\u003c/sup\u003e, gastric\u003csup\u003e[35]\u003c/sup\u003e, and breast cancers\u003csup\u003e[36]\u003c/sup\u003e. However, SGLT2 expression in PDAC has not been fully characterized. We re-evaluated its expression in this study. In our cohort, although the proportion of SGLT2-positive tumor cells was low, its expression was significantly higher in tumor tissues compared with paired adjacent normal tissues (n\u0026thinsp;=\u0026thinsp;39). More importantly, high SGLT2 expression was associated with significantly shorter overall survival (OS) among PDAC patients (n\u0026thinsp;=\u0026thinsp;47), suggesting its potential as a negative prognostic marker. Despite limitations in sample volume and follow-up, our data provide clinically relevant evidence linking SGLT2 to PDAC progression. Recent studies indicated that SGLT2 inhibitors may reduce pancreatic cancer risk in patients with type 2 diabetes compared to other glucose-lowering medications\u003csup\u003e[37, 38]\u003c/sup\u003e. Together, these findings support a functional and prognostic role for SGLT2 in PDAC.\u003c/p\u003e \u003cp\u003eGiven the metabolic function of SGLT2 and the dependence of PDAC cells on glucose-derived energy, we investigated how SGLT2 supports tumor survival. SGLT2 inhibition induces acute ATP depletion, activating the AMPK/ULK1 axis, which promotes autophagy initiation under metabolic stress\u003csup\u003e[39]\u003c/sup\u003e. However, autophagic flux is blocked due to impaired autophagosome-lysosome fusion. Autophagic flux blockade impairs mitochondrial clearance and leads to the accumulation of dysfunctional mitochondria\u003csup\u003e[40]\u003c/sup\u003e. Moreover, under downstream flux blockade, the enhanced formation of autophagosomes promotes the accumulation of membranous structures\u003csup\u003e[41]\u003c/sup\u003e, which may further increase metabolic stress. As a result, the compensatory autophagy process can paradoxically induce oxidative stress\u003csup\u003e[42]\u003c/sup\u003e. Our experimental results further support this mechanism, as AMPK inhibition with CC partially mitigated the ROS accumulation induced by SGLT2 inhibition. Excessive ROS can damage mitochondrial membranes and compromise mitochondrial quality control\u003csup\u003e[43]\u003c/sup\u003e. ROS disrupts mitochondrial homeostasis, as indicated by hyperpolarized mitochondrial membrane potential, and promotes mitochondria-dependent apoptosis, which can be partially rescued by ROS scavenger NAC. While previous studies reported that SGLT2 inhibition by CANA induces autophagy in benign and malignant diseases\u003csup\u003e[17, 44, 45]\u003c/sup\u003e, our findings reveal a distinct context in PDAC, where SGLT2 inhibition initiates but fails to complete autophagy. Together, these results suggest that SGLT2 serves as a metabolic stabilizer in PDAC cells by maintaining autophagic homeostasis under energy stress. Targeting this vulnerability provides a novel rationale for therapeutic intervention.\u003c/p\u003e \u003cp\u003eAnother key finding of our study is that SGLT2 inhibition modulates EGFR signaling and enhances the efficacy of EGFR-targeted therapy in PDAC. Although EGFR is widely expressed in pancreatic cancer\u003csup\u003e[24]\u003c/sup\u003e, the clinical benefit of EGFR inhibitors has been limited\u003csup\u003e[46, 47]\u003c/sup\u003e. In our experiments, pharmacologic inhibition of SGLT2 led to increased EGFR phosphorylation, while genetic knockdown reduced EGFR signaling. These effects imply that metabolic stress induced by SGLT2 inhibition may alter EGFR-related signaling. Previous studies have shown that EGFR signaling is closely integrated with glucose metabolism. EGFR has been shown to promote aerobic glycolysis in triple-negative breast cancer and lung adenocarcinoma cells\u003csup\u003e[30, 31]\u003c/sup\u003e, and ligand binding to EGFR and other ErbB family receptors can enhance glucose uptake\u003csup\u003e[48]\u003c/sup\u003e. This crosstalk appears to have therapeutic implications. In both Capan-1 and the relatively EGFR-insensitive PANC-1 cell lines, SGLT2 inhibition significantly enhanced sensitivity to erlotinib, showing synergistic effects in vitro (CI\u0026thinsp;\u0026lt;\u0026thinsp;1). In xenograft models using PANC-1 cells, the combination treatment produced greater tumor suppression than either monotherapy. These observations suggest that SGLT2 inhibition may expose a metabolic vulnerability that enhances EGFR dependency, thereby opening a therapeutic window for combinatorial intervention. Consistently, prior studies in NSCLC have demonstrated that glucose deprivation synergizes with autophagy or AKT inhibitors to overcome acquired resistance to EGFR-targeted therapy\u003csup\u003e[49]\u003c/sup\u003e, and blocking glucose metabolism increases EGFR-TKI sensitivity in resistant tumors\u003csup\u003e[50]\u003c/sup\u003e. The underlying mechanism remains unclear, but may involve metabolic stress-induced receptor activation, altered trafficking, or adaptive responses to disrupted glucose homeostasis.\u003c/p\u003e \u003cp\u003eDespite the insights provided by this study, several limitations should be acknowledged. The mechanisms underlying autophagic disruption and EGFR signaling modulation by SGLT2 inhibition remain to be fully elucidated. Although CANA is clinically approved, potential off-target effects at experimental doses cannot be excluded and should be addressed in future studies. Additionally, while EGFR inhibitors have shown modest efficacy in K-Ras wild-type PDAC\u003csup\u003e[47]\u003c/sup\u003e, both cell lines used in this study (Capan-1 and PANC-1) harbor K-Ras mutations, and in vivo validation was limited to PANC-1 xenografts. Therefore, whether SGLT2 inhibition broadly enhances EGFR-targeted therapy requires further validation across metabolically heterogeneous PDAC models.\u003c/p\u003e \u003cp\u003eIn conclusion, Our study identifies SGLT2 as a key metabolic regulator in PDAC, with high expression linked to poor prognosis. SGLT2 inhibition disrupts energy and redox homeostasis. Mechanistically, SGLT2 inhibition blocks autophagic flux, leading to ROS accumulation and apoptosis. Importantly, SGLT2 inhibition sensitizes PDAC cells to EGFR-targeted therapy, revealing a metabolic vulnerability with translational potential. Further studies in genetically diverse and clinically relevant models are warranted to support clinical translation.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003ePDAC: Pancreatic Ductal Adenocarcinoma\u003c/p\u003e\n\u003cp\u003eSGLT2: Sodium-glucose Co-transporter 2\u003c/p\u003e\n\u003cp\u003eCANA: Canagliflozin\u003c/p\u003e\n\u003cp\u003eOXPHOS: Oxidative Phosphorylation\u003c/p\u003e\n\u003cp\u003eEGFR: Epidermal Growth Factor Receptor\u003c/p\u003e\n\u003cp\u003eGEPIA2: Gene Expression Profiling Interactive Analysis 2\u003c/p\u003e\n\u003cp\u003eTCGA: The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eGTEx: Genotype-Tissue Expression\u003c/p\u003e\n\u003cp\u003eGEO: Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003ePCA: Principal Component Analysis\u003c/p\u003e\n\u003cp\u003eUMAP: Uniform Manifold Approximation and Projection\u003c/p\u003e\n\u003cp\u003eIHC: Immunohistochemistry\u003c/p\u003e\n\u003cp\u003eCCK-8: Cell Counting Kit-8\u003c/p\u003e\n\u003cp\u003eOCR:\u0026nbsp;oxygen consumption rate\u003c/p\u003e\n\u003cp\u003eATP: Adenosine Triphosphate\u003c/p\u003e\n\u003cp\u003eNAD\u003csup\u003e+\u003c/sup\u003e: Nicotinamide Adenine Dinucleotide (Oxidized Form)\u003c/p\u003e\n\u003cp\u003eNADH: Nicotinamide Adenine Dinucleotide (Reduced Form)\u003c/p\u003e\n\u003cp\u003eRot/AA: Rotenone/antimycin A\u003c/p\u003e\n\u003cp\u003eFCCP: Carbonyl Cyanide-p-trifluoromethoxyphenylhydrazone\u003c/p\u003e\n\u003cp\u003eROS: Reactive Oxygen Species\u003c/p\u003e\n\u003cp\u003eBax: Bcl-2-associated X Protein\u003c/p\u003e\n\u003cp\u003eBcl-2: B-cell Lymphoma 2\u003c/p\u003e\n\u003cp\u003eCaspase-3: Cysteine-aspartic Acid Protease 3\u003c/p\u003e\n\u003cp\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eAMPK: Adenosine Monophosphate-activated Protein Kinase\u003c/p\u003e\n\u003cp\u003eULK1: Unc-51-like Kinase 1\u003c/p\u003e\n\u003cp\u003ep62: Sequestosome 1\u003c/p\u003e\n\u003cp\u003eLC3B: Microtubule-associated Protein 1 light Chain 3 Beta\u003c/p\u003e\n\u003cp\u003eCC: Compound C\u003c/p\u003e\n\u003cp\u003eNAC: N-Acetylcysteine\u003c/p\u003e\n\u003cp\u003eEGFR-TKI: EGFR Tyrosine Kinase Inhibitor\u003c/p\u003e\n\u003cp\u003eErlo: Erlotinib\u003c/p\u003e\n\u003cp\u003eCI: Combination Index\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Peking University First Hospital (approval no. 2024-194-002). Written informed consent was obtained from the patient. Animal experiments were approved by the Institutional Animal Care and Use Committee of Peking University First Hospital (approval no. J2025049) and conducted in accordance with institutional guidelines for animal welfare.\u003c/p\u003e\n\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by National Natural Science Foundation of China (NO. 82171722, 82271764, 82471772 and 82571996), Beijing Natural Science Foundation (L246015).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eY.W. and E.Z. contributed equally to this work. Y.W. performed most of the experiments, analyzed the data, and drafted the manuscript. E.Z. supervised the experimental design and data interpretation. R.M. and W.L. assisted with cell culture and molecular assays. Q.W. and J.Z. contributed to data analysis and figure preparation. Y.Y. provided technical support and critical discussion. Y.M. and X.T. conceived and supervised the study, revised the manuscript, and approved the final version. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot available.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eR.L. Siegel, A.N. Giaquinto, A. Jemal, Cancer statistics, 2024. CA Cancer J Clin. \u003cstrong\u003e74(1)\u003c/strong\u003e, 12-49. (2024). doi:10.3322/caac.21820\u003c/li\u003e\n\u003cli\u003eW. Niesen, T. Hank, M. B\u0026uuml;chler, O. Strobel, Local radicality and survival outcome of pancreatic cancer surgery. Ann Gastroenterol Surg. \u003cstrong\u003e3(5)\u003c/strong\u003e, 464-475. (2019). doi:10.1002/ags3.12273\u003c/li\u003e\n\u003cli\u003eT. du Toit-Thompson, L. Leck, J. Gillson, N. Pavlakis, A.J. Gill, J.S. Samra, A. Mittal, S. Sahni, Overcoming therapy resistance in pancreatic cancer: challenges and emerging strategies. Adv Drug Deliv Rev. \u003cstrong\u003e224\u003c/strong\u003e, 115647. (2025). doi:10.1016/j.addr.2025.115647\u003c/li\u003e\n\u003cli\u003eC. Qin, G. Yang, J. Yang, B. Ren, H. Wang, G. Chen, F. Zhao, L. You, W. Wang, Y. Zhao, Metabolism of pancreatic cancer: paving the way to better anticancer strategies. Mol Cancer. \u003cstrong\u003e19(1)\u003c/strong\u003e, 50. (2020). doi:10.1186/s12943-020-01169-7\u003c/li\u003e\n\u003cli\u003eL. Yan, P. Raj, W. Yao, H. Ying, Glucose Metabolism in Pancreatic Cancer. Cancers (Basel). \u003cstrong\u003e11(10)\u003c/strong\u003e, (2019). doi:10.3390/cancers11101460\u003c/li\u003e\n\u003cli\u003eM.C. De Santis, B. Bockorny, E. Hirsch, P. Cappello, M. Martini, Exploiting pancreatic cancer metabolism: challenges and opportunities. Trends Mol Med. \u003cstrong\u003e30(6)\u003c/strong\u003e, 592-604. (2024). doi:10.1016/j.molmed.2024.03.008\u003c/li\u003e\n\u003cli\u003eJ. Li, X. Chen, R. Kang, H. Zeh, D.J. Klionsky, D. Tang, Regulation and function of autophagy in pancreatic cancer. Autophagy. \u003cstrong\u003e17(11)\u003c/strong\u003e, 3275-3296. (2020). doi:10.1080/15548627.2020.1847462\u003c/li\u003e\n\u003cli\u003eH. Koepsell, The Na+-D-glucose cotransporters SGLT1 and SGLT2 are targets for the treatment of diabetes and cancer. Pharmacol Ther. \u003cstrong\u003e170\u003c/strong\u003e, 148-165. (2016). doi:10.1016/j.pharmthera.2016.10.017\u003c/li\u003e\n\u003cli\u003eG. Gyimesi, J. Pujol-Gim\u0026eacute;nez, Y. Kanai, M.A. Hediger, Sodium-coupled glucose transport, the SLC5 family, and therapeutically relevant inhibitors: from molecular discovery to clinical application. Pflugers Arch. \u003cstrong\u003e472(9)\u003c/strong\u003e, 1177-1206. (2020). doi:10.1007/s00424-020-02433-x\u003c/li\u003e\n\u003cli\u003eC. Scafoglio, B.A. Hirayama, V. Kepe, J. Liu, C. Ghezzi, N. Satyamurthy, N.A. Moatamed, J. Huang, H. Koepsell, J.R. Barrio, E.M. Wright, Functional expression of sodium-glucose transporters in cancer. Proc Natl Acad Sci U S A. \u003cstrong\u003e112(30)\u003c/strong\u003e, E4111-E4119. (2015). doi:10.1073/pnas.1511698112\u003c/li\u003e\n\u003cli\u003eC.R. Scafoglio, B. Villegas, G. Abdelhady, S.T. Bailey, J. Liu, A.S. Shirali, W.D. Wallace, C.E. Magyar, T.R. Grogan, D. Elashoff, T. Walser, J. Yanagawa, D.R. Aberle, J.R. Barrio, S.M. Dubinett, D.B. Shackelford, Sodium-glucose transporter 2 is a diagnostic and therapeutic target for early-stage lung adenocarcinoma. Sci Transl Med. \u003cstrong\u003e10(467)\u003c/strong\u003e, (2018). doi:10.1126/scitranslmed.aat5933\u003c/li\u003e\n\u003cli\u003eD. Papadopoli, O. Uchenunu, R. Palia, N. Chekkal, L. Hulea, I. Topisirovic, M. Pollak, J. St-Pierre, Perturbations of cancer cell metabolism by the antidiabetic drug canagliflozin. Neoplasia. \u003cstrong\u003e23(4)\u003c/strong\u003e, 391-399. (2021). doi:10.1016/j.neo.2021.02.003\u003c/li\u003e\n\u003cli\u003eL. Ding, X. Chen, W. Zhang, X. Dai, H. Guo, X. Pan, Y. Xu, J. Feng, M. Yuan, X. Gao, J. Wang, X. Xu, S. Li, H. Wu, J. Cao, Q. He, B. Yang, Canagliflozin primes antitumor immunity by triggering PD-L1 degradation in endocytic recycling. J Clin Invest. \u003cstrong\u003e133(1)\u003c/strong\u003e, (2023). doi:10.1172/JCI154754\u003c/li\u003e\n\u003cli\u003eW. Wu, Z. Zhang, D. Jing, X. Huang, D. Ren, Z. Shao, Z. Zhang, SGLT2 inhibitor activates the STING/IRF3/IFN-\u0026beta; pathway and induces immune infiltration in osteosarcoma. Cell Death Dis. \u003cstrong\u003e13(6)\u003c/strong\u003e, 523. (2022). doi:10.1038/s41419-022-04980-w\u003c/li\u003e\n\u003cli\u003eK. Kaji, N. Nishimura, K. Seki, S. Sato, S. Saikawa, K. Nakanishi, M. Furukawa, H. Kawaratani, M. Kitade, K. Moriya, T. Namisaki, H. Yoshiji, Sodium glucose cotransporter 2 inhibitor canagliflozin attenuates liver cancer cell growth and angiogenic activity by inhibiting glucose uptake. Int J Cancer. \u003cstrong\u003e142(8)\u003c/strong\u003e, 1712-1722. (2017). doi:10.1002/ijc.31193\u003c/li\u003e\n\u003cli\u003eL.A. Villani, B.K. Smith, K. Marcinko, R.J. Ford, L.A. Broadfield, A.E. Green, V.P. Houde, P. Muti, T. Tsakiridis, G.R. Steinberg, The diabetes medication Canagliflozin reduces cancer cell proliferation by inhibiting mitochondrial complex-I supported respiration. Mol Metab. \u003cstrong\u003e5(10)\u003c/strong\u003e, 1048-1056. (2016). doi:10.1016/j.molmet.2016.08.014\u003c/li\u003e\n\u003cli\u003eC. Anastasio, I. Donisi, V. Del Vecchio, A. Colloca, L. Mele, C. Sardu, R. Marfella, M.L. Balestrieri, N. D\u0026apos;Onofrio, SGLT2 inhibitor promotes mitochondrial dysfunction and ER-phagy in colorectal cancer cells. Cell Mol Biol Lett. \u003cstrong\u003e29(1)\u003c/strong\u003e, 80. (2024). doi:10.1186/s11658-024-00599-1\u003c/li\u003e\n\u003cli\u003eY. Zeng, H. Jiang, X. Zhang, J. Xu, X. Wu, Q. Xu, W. Cai, H. Ying, R. Zhou, Y. Ding, K. Ying, X. Song, Z. Chen, L. Zeng, L. Zhao, F. Yu, Canagliflozin reduces chemoresistance in hepatocellular carcinoma through PKM2-c-Myc complex-mediated glutamine starvation. Free Radic Biol Med. \u003cstrong\u003e208\u003c/strong\u003e, 571-586. (2023). doi:10.1016/j.freeradbiomed.2023.09.006\u003c/li\u003e\n\u003cli\u003eH. Huang, F.-L. Kung, Y.-W. Huang, C.-C. Hsu, J.-H. Guh, L.-C. Hsu, Sensitization of cancer cells to paclitaxel-induced apoptosis by canagliflozin. Biochem Pharmacol. \u003cstrong\u003e223\u003c/strong\u003e, 116140. (2024). doi:10.1016/j.bcp.2024.116140\u003c/li\u003e\n\u003cli\u003eA. Ali, B. Mekhaeil, O.-D. Biziotis, E.E. Tsakiridis, E. Ahmadi, J. Wu, S. Wang, K. Singh, G. Menjolian, T. Farrell, A. Mesci, S. Liu, T. Berg, J.L. Bramson, G.R. Steinberg, T. Tsakiridis, The SGLT2 inhibitor canagliflozin suppresses growth and enhances prostate cancer response to radiotherapy. Commun Biol. \u003cstrong\u003e6(1)\u003c/strong\u003e, 919. (2023). doi:10.1038/s42003-023-05289-w\u003c/li\u003e\n\u003cli\u003eJ. Zheng, J. Lu, J. Qi, Q. Yang, H. Zhao, H. Liu, Z. Chen, L. Huang, Y. Ye, M. Xu, Y. Xu, T. Wang, M. Li, Z. Zhao, R. Zheng, S. Wang, H. Lin, C. Hu, C.S. Ling Chui, S.L. Au Yeung, S. Luo, O. Dimopoulou, P. Dixon, S. Harrison, Y. Liu, J. Robinson, J. Yarmolinsky, P. Haycock, J. Yuan, S. Lewis, Z. Yuan, T.R. Gaunt, G.D. Smith, G. Ning, R.M. Martin, B. Cui, W. Wang, Y. Bi, The effect of SGLT2 inhibition on prostate cancer: Mendelian randomization and observational analysis using electronic healthcare and cohort data. Cell Rep Med. \u003cstrong\u003e5(8)\u003c/strong\u003e, 101688. (2024). doi:10.1016/j.xcrm.2024.101688\u003c/li\u003e\n\u003cli\u003eJ. Du, J. Gu, J. Deng, L. Kong, Y. Guo, C. Jin, Y. Bao, D. Fu, J. Li, The expression and survival significance of sodium glucose transporters in pancreatic cancer. BMC Cancer. \u003cstrong\u003e22(1)\u003c/strong\u003e, 116. (2022). doi:10.1186/s12885-021-09060-4\u003c/li\u003e\n\u003cli\u003eD. Ren, Y. Sun, D. Zhang, D. Li, Z. Liu, X. Jin, H. Wu, SGLT2 promotes pancreatic cancer progression by activating the Hippo signaling pathway via the hnRNPK-YAP1 axis. Cancer Lett. \u003cstrong\u003e519\u003c/strong\u003e, 277-288. (2021). doi:10.1016/j.canlet.2021.07.035\u003c/li\u003e\n\u003cli\u003eK. Tobita, H. Kijima, S. Dowaki, H. Kashiwagi, Y. Ohtani, Y. Oida, H. Yamazaki, M. Nakamura, Y. Ueyama, M. Tanaka, S. Inokuchi, H. Makuuchi, Epidermal growth factor receptor expression in human pancreatic cancer: Significance for liver metastasis. Int J Mol Med. \u003cstrong\u003e11(3)\u003c/strong\u003e, 305-309. (2003). doi:\u003c/li\u003e\n\u003cli\u003eG. Mucciolo, J. Araos Henr\u0026iacute;quez, M. Jihad, S. Pinto Teles, J.S. Manansala, W. Li, S. Ashworth, E.G. Lloyd, P.S.W. Cheng, W. Luo, A. Anand, A. Sawle, A. Piskorz, G. Biffi, EGFR-activated myofibroblasts promote metastasis of pancreatic cancer. Cancer Cell. \u003cstrong\u003e42(1)\u003c/strong\u003e, (2024). doi:10.1016/j.ccell.2023.12.002\u003c/li\u003e\n\u003cli\u003eP. Wee, Z. Wang, Epidermal Growth Factor Receptor Cell Proliferation Signaling Pathways. Cancers (Basel). \u003cstrong\u003e9(5)\u003c/strong\u003e, (2017). doi:10.3390/cancers9050052\u003c/li\u003e\n\u003cli\u003eN. Ioannou, A.M. Seddon, A. Dalgleish, D. Mackintosh, F. Solca, H. Modjtahedi, Acquired resistance of pancreatic cancer cells to treatment with gemcitabine and HER-inhibitors is accompanied by increased sensitivity to STAT3 inhibition. Int J Oncol. \u003cstrong\u003e48(3)\u003c/strong\u003e, 908-918. (2016). doi:10.3892/ijo.2016.3320\u003c/li\u003e\n\u003cli\u003eN. Sharma, A. Bhushan, J. He, G. Kaushal, V. Bhardwaj, Metabolic plasticity imparts erlotinib-resistance in pancreatic cancer by upregulating glucose-6-phosphate dehydrogenase. Cancer Metab. \u003cstrong\u003e8\u003c/strong\u003e, 19. (2020). doi:10.1186/s40170-020-00226-5\u003c/li\u003e\n\u003cli\u003eX. Shi, M. Wang, Y. Zhang, X. Guo, M. Liu, Z. Zhou, Y. Zhao, R. He, Y. Gao, Y. Liu, S. Pan, M. Zhou, C. Zhao, T. Yin, X. Li, H. Wang, J. Yang, F. Zhu, M. Li, R. Qin, Hypoxia activated HGF expression in pancreatic stellate cells confers resistance of pancreatic cancer cells to EGFR inhibition. EBioMedicine. \u003cstrong\u003e86\u003c/strong\u003e, 104352. (2022). doi:10.1016/j.ebiom.2022.104352\u003c/li\u003e\n\u003cli\u003eS.-O. Lim, C.-W. Li, W. Xia, H.-H. Lee, S.-S. Chang, J. Shen, J.L. Hsu, D. Raftery, D. Djukovic, H. Gu, W.-C. Chang, H.-L. Wang, M.-L. Chen, L. Huo, C.-H. Chen, Y. Wu, A. Sahin, S.M. Hanash, G.N. Hortobagyi, M.-C. Hung, EGFR Signaling Enhances Aerobic Glycolysis in Triple-Negative Breast Cancer Cells to Promote Tumor Growth and Immune Escape. Cancer Res. \u003cstrong\u003e76(5)\u003c/strong\u003e, 1284-1296. (2016). doi:10.1158/0008-5472.CAN-15-2478\u003c/li\u003e\n\u003cli\u003eH. Makinoshima, M. Takita, S. Matsumoto, A. Yagishita, S. Owada, H. Esumi, K. Tsuchihara, Epidermal growth factor receptor (EGFR) signaling regulates global metabolic pathways in EGFR-mutated lung adenocarcinoma. J Biol Chem. \u003cstrong\u003e289(30)\u003c/strong\u003e, 20813-20823. (2014). doi:\u003c/li\u003e\n\u003cli\u003eZ. Tang, B. Kang, C. Li, T. Chen, Z. Zhang, GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. \u003cstrong\u003e47(W1)\u003c/strong\u003e, W556-W560. (2019). doi:10.1093/nar/gkz430\u003c/li\u003e\n\u003cli\u003eG. Werba, D. Weissinger, E.A. Kawaler, E. Zhao, D. Kalfakakou, S. Dhara, L. Wang, H.B. Lim, G. Oh, X. Jing, N. Beri, L. Khanna, T. Gonda, P. Oberstein, C. Hajdu, C. Loomis, A. Heguy, M.H. Sherman, A.W. Lund, T.H. Welling, I. Dolgalev, A. Tsirigos, D.M. Simeone, Single-cell RNA sequencing reveals the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment. Nat Commun. \u003cstrong\u003e14(1)\u003c/strong\u003e, 797. (2023). doi:10.1038/s41467-023-36296-4\u003c/li\u003e\n\u003cli\u003eX. Zhang, X. Zhang, X. Liu, P. Qi, H. Wang, Z. Ma, Y. Chai, MicroRNA-296, a suppressor non-coding RNA, downregulates SGLT2 expression in lung cancer. Int J Oncol. \u003cstrong\u003e54(1)\u003c/strong\u003e, 199-208. (2019). doi:10.3892/ijo.2018.4599\u003c/li\u003e\n\u003cli\u003eK. Ren, X. Wang, R. Ma, H. Chen, T. Min, Y. Ma, X. Xie, W. Wang, X. Deng, Z. Zhou, K. Li, K. Zhu, N. Hao, C. Dang, T. Sun, H. Zhang, Dapagliflozin suppressed gastric cancer growth via regulating OTUD5 mediated YAP1 deubiquitination. Eur J Pharmacol. \u003cstrong\u003e983\u003c/strong\u003e, 177002. (2024). doi:10.1016/j.ejphar.2024.177002\u003c/li\u003e\n\u003cli\u003eJ. Zhou, J. Zhu, S.-J. Yu, H.-L. Ma, J. Chen, X.-F. Ding, G. Chen, Y. Liang, Q. Zhang, Sodium-glucose co-transporter-2 (SGLT-2) inhibition reduces glucose uptake to induce breast cancer cell growth arrest through AMPK/mTOR pathway. Biomed Pharmacother. \u003cstrong\u003e132\u003c/strong\u003e, 110821. (2020). doi:10.1016/j.biopha.2020.110821\u003c/li\u003e\n\u003cli\u003eY.K. Cho, S. Kim, M.J. Kim, W.J. Lee, Y.-J. Kim, C.H. Jung, New users of sodium-glucose cotransporter 2 inhibitors are at low risk of incident pancreatic cancer: A nationwide population-based cohort study. Diabetes Metab. \u003cstrong\u003e51(2)\u003c/strong\u003e, 101605. (2025). doi:10.1016/j.diabet.2025.101605\u003c/li\u003e\n\u003cli\u003eY. Tanaka, S. Yokoyama, C. Nakagawa, T. Uno, K. Hosomi, Association between sodium-glucose cotransporter 2 inhibitors and pancreatic cancer in the Japanese working-age population. Int J Clin Pharmacol Ther. \u003cstrong\u003e61(11)\u003c/strong\u003e, 492-502. (2023). doi:10.5414/CP204465\u003c/li\u003e\n\u003cli\u003eJ. Kim, M. Kundu, B. Viollet, K.-L. Guan, AMPK and mTOR regulate autophagy through direct phosphorylation of Ulk1. Nat Cell Biol. \u003cstrong\u003e13(2)\u003c/strong\u003e, 132-141. (2011). doi:10.1038/ncb2152\u003c/li\u003e\n\u003cli\u003eH. Xia, D.R. Green, W. Zou, Autophagy in tumour immunity and therapy. Nat Rev Cancer. \u003cstrong\u003e21(5)\u003c/strong\u003e, 281-297. (2021). doi:10.1038/s41568-021-00344-2\u003c/li\u003e\n\u003cli\u003eD.C. Rubinsztein, P. Codogno, B. Levine, Autophagy modulation as a potential therapeutic target for diverse diseases. Nat Rev Drug Discov. \u003cstrong\u003e11(9)\u003c/strong\u003e, 709-730. (2012). doi:10.1038/nrd3802\u003c/li\u003e\n\u003cli\u003eW. Ornatowski, Q. Lu, M. Yegambaram, A.E. Garcia, E.A. Zemskov, E. Maltepe, J.R. Fineman, T. Wang, S.M. Black, Complex interplay between autophagy and oxidative stress in the development of pulmonary disease. Redox Biol. \u003cstrong\u003e36\u003c/strong\u003e, 101679. (2020). doi:10.1016/j.redox.2020.101679\u003c/li\u003e\n\u003cli\u003eB. Wang, Y. Wang, J. Zhang, C. Hu, J. Jiang, Y. Li, Z. Peng, ROS-induced lipid peroxidation modulates cell death outcome: mechanisms behind apoptosis, autophagy, and ferroptosis. Arch Toxicol. \u003cstrong\u003e97(6)\u003c/strong\u003e, 1439-1451. (2023). doi:10.1007/s00204-023-03476-6\u003c/li\u003e\n\u003cli\u003eC.H. Park, B. Lee, M. Han, W.J. Rhee, M.S. Kwak, T.-H. Yoo, J.-S. Shin, Canagliflozin protects against cisplatin-induced acute kidney injury by AMPK-mediated autophagy in renal proximal tubular cells. Cell Death Discov. \u003cstrong\u003e8(1)\u003c/strong\u003e, 12. (2022). doi:10.1038/s41420-021-00801-9\u003c/li\u003e\n\u003cli\u003eJ. Luo, M. He, C. Liang, X. Huang, Y. Zhu, D. Hu, J. Yan, M. Li, H. Lin, W. Liao, J. Bin, Z. Guan, C. Zheng, Y. Liao, Canagliflozin reverses doxorubicin-induced cardiotoxicity via restoration of autophagic homeostasis. Toxicol Appl Pharmacol. \u003cstrong\u003e495\u003c/strong\u003e, 117183. (2025). doi:10.1016/j.taap.2024.117183\u003c/li\u003e\n\u003cli\u003eM.J. Moore, D. Goldstein, J. Hamm, A. Figer, J.R. Hecht, S. Gallinger, H.J. Au, P. Murawa, D. Walde, R.A. Wolff, D. Campos, R. Lim, K. Ding, G. Clark, T. Voskoglou-Nomikos, M. Ptasynski, W. Parulekar, Erlotinib Plus Gemcitabine Compared With Gemcitabine Alone in Patients With Advanced Pancreatic Cancer: A Phase III Trial of the National Cancer Institute of Canada Clinical Trials Group. J Clin Oncol. \u003cstrong\u003e41(30)\u003c/strong\u003e, 4714-4720. (2023). doi:10.1200/JCO.22.02770\u003c/li\u003e\n\u003cli\u003eS. Qin, J. Li, Y. Bai, Z. Wang, Z. Chen, R. Xu, J. Xu, H. Zhang, J. Chen, Y. Yuan, T. Liu, L. Yang, H. Zhong, D. Chen, L. Shen, C. Hao, D. Fu, Y. Cheng, J. Yang, Q. Wang, B. Qin, H. Pan, J. Zhang, X. Bai, Q. Zheng, Nimotuzumab Plus Gemcitabine for K-Ras Wild-Type Locally Advanced or Metastatic Pancreatic Cancer. J Clin Oncol. \u003cstrong\u003e41(33)\u003c/strong\u003e, 5163-5173. (2023). doi:10.1200/JCO.22.02630\u003c/li\u003e\n\u003cli\u003eL.A. Orofiamma, D. Vural, C.N. Antonescu, Control of cell metabolism by the epidermal growth factor receptor. Biochim Biophys Acta Mol Cell Res. \u003cstrong\u003e1869(12)\u003c/strong\u003e, 119359. (2022). doi:10.1016/j.bbamcr.2022.119359\u003c/li\u003e\n\u003cli\u003eM. Ye, S. Wang, T. Wan, R. Jiang, Y. Qiu, L. Pei, N. Pang, Y. Huang, Y. Huang, Z. Zhang, L. Yang, Combined Inhibitions of Glycolysis and AKT/autophagy Can Overcome Resistance to EGFR-targeted Therapy of Lung Cancer. J Cancer. \u003cstrong\u003e8(18)\u003c/strong\u003e, 3774-3784. (2017). doi:10.7150/jca.21035\u003c/li\u003e\n\u003cli\u003eS.M. Kim, M.R. Yun, Y.K. Hong, F. Solca, J.-H. Kim, H.-J. Kim, B.C. Cho, Glycolysis inhibition sensitizes non-small cell lung cancer with T790M mutation to irreversible EGFR inhibitors via translational suppression of Mcl-1 by AMPK activation. Mol Cancer Ther. \u003cstrong\u003e12(10)\u003c/strong\u003e, 2145-2156. (2013). doi:10.1158/1535-7163.MCT-12-1188\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cellular-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ceon","sideBox":"Learn more about [Cellular Oncology](http://link.springer.com/journal/13402)","snPcode":"13402","submissionUrl":"https://submission.nature.com/new-submission/13402/3","title":"Cellular Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"SGLT2, pancreatic ductal adenocarcinoma, glucose metabolism, autophagy, EGFR inhibitor","lastPublishedDoi":"10.21203/rs.3.rs-8820380/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8820380/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with profound metabolic rewiring and resistance to therapy. Sodium-glucose co-transporter 2 (SGLT2) regulates glucose uptake, but its role in PDAC remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSGLT2 expression was analyzed in clinical samples and public datasets. PDAC cell lines were subjected to genetic knockdown or canagliflozin (CANA) treatment to assess proliferation, migration, apoptosis, and glucose metabolism. Mechanistic studies investigated AMPK-ULK1 signaling, autophagy dynamics, oxidative stress, and EGFR signaling. Xenograft models were used to assess in vivo efficacy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSGLT2 was upregulated in PDAC and associated with poor prognosis. SGLT2 inhibition suppressed proliferation and migration while promoting apoptosis. Mechanistically, CANA induced ATP deficiency and initiated autophagy, but concurrently impaired autophagosome-lysosome fusion. This dual effect led to autophagic flux blockade, resulting in excessive ROS accumulation, mitochondrial dysfunction, and apoptosis. Inhibition of AMPK reduced ROS levels, while ROS scavenging partially rescued mitochondrial damage and cell death. Notably, SGLT2 inhibition enhanced sensitivity to EGFR-targeted therapy, producing synergistic anti-tumor effects in vitro and in vivo.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSGLT2 maintains metabolic and autophagic homeostasis in PDAC. Its inhibition induces metabolic stress, autophagic flux blockade, and ROS-driven mitochondrial apoptosis. In addition, targeting SGLT2 sensitizes tumors to EGFR-targeted therapy, offering a novel combinatorial strategy.\u003c/p\u003e","manuscriptTitle":"SGLT2 Inhibition Induces Autophagic Flux Blockade and Sensitizes Pancreatic Cancer to EGFR-Targeted Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 16:03:49","doi":"10.21203/rs.3.rs-8820380/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-09T02:20:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T13:56:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T17:01:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280447281035953286786383429739803257775","date":"2026-02-26T14:14:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T15:09:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259468849093063993640225973488465176887","date":"2026-02-25T14:41:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140311947439411509398657468672324603651","date":"2026-02-24T20:32:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T02:08:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T11:54:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T11:46:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cellular Oncology","date":"2026-02-08T08:55:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cellular-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ceon","sideBox":"Learn more about [Cellular Oncology](http://link.springer.com/journal/13402)","snPcode":"13402","submissionUrl":"https://submission.nature.com/new-submission/13402/3","title":"Cellular Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"17d1de06-41a3-42ee-83d5-216911ac637c","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T08:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 16:03:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8820380","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8820380","identity":"rs-8820380","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}