The ITGA6-AEBP2 Complex Promotes Transcriptional Activation of GSK3β to Augment Wntβ-Catenin Signaling and Stemness in Pancreatic Cancer

The ITGA6-AEBP2 Complex Promotes Transcriptional Activation of GSK3β to Augment Wntβ-Catenin Signaling and Stemness in Pancreatic Cancer | 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 The ITGA6-AEBP2 Complex Promotes Transcriptional Activation of GSK3β to Augment Wntβ-Catenin Signaling and Stemness in Pancreatic Cancer Xiaofeng Yang, yuexin Ren, Wenchao Ma, Zhengquan Li, Sijun Chen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8683090/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Pancreatic cancer (PC) remains one of the most lethal malignancies in the digestive system, posing a significant threat to human health due to the critical lack of therapeutic targets. While integrin α6 (ITGA6) has been implicated as a key regulator in multiple cancer types, its precise functional role and molecular mechanisms in pancreatic tumorigenesis remain poorly understood. Methods We first identified differentially expressed genes (DEGs) with prognostic significance in PC using TCGA database analysis. ITGA6 expression was validated at protein and mRNA levels through immunohistochemistry (IHC), quantitative real-time PCR (qRT-PCR), and western blotting. Lentiviral-based overexpression and knockdown systems were established to modulate ITGA6 expression in PC cells. Cellular phenotypes were assessed using CCK-8 proliferation assays, flow cytometry, Transwell migration/invasion chambers, and wound healing assays. In vivo validation was performed using subcutaneous xenograft mouse models. The underlying molecular mechanism was investigated through co-immunoprecipitation (Co-IP), dual-luciferase reporter assays, and chromatin immunoprecipitation (ChIP) to delineate the ITGA6-AEBP2-GSK3β regulatory axis. Results This study revealed significantly elevated ITGA6 expression in PC tissues, with high ITGA6 levels correlating with poor patient survival outcomes. Genetic knockdown of ITGA6 effectively suppressed malignant phenotypes, including proliferation and migration in PC cells. Mechanistically, we identified that ITGA6 physically interacted with the transcription factor AEBP2 to enhance transcriptional activation of GSK3β. Importantly, the ITGA6-AEBP2-GSK3β axis was found to promote PC progression and chemoresistance through Wnt/β-catenin pathway-mediated augmentation of cancer stem cell properties. Conclusion ITGA6 promoted PC progression and conferred treatment resistance through the AEBP2/GSK3-β/β-catenin signaling axis. These results not only elucidated a novel molecular mechanism underlying PC aggressiveness but also identified ITGA6 as a promising therapeutic target for this lethal malignancy. Pancreatic cancer ITGA6 Transcription Wnt/β-catenin signaling Stemness Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Pancreatic ductal adenocarcinoma (PDAC)—a highly aggressive malignancy arising from pancreatic ductal epithelium—is among the most lethal gastrointestinal cancers due to its insidious onset and invasive propensity [ 1 ]. The dismal prognosis (5–10% 5-year survival rate) and high mortality of PDAC stem from its characteristic early dissemination, nonspecific symptomatology, and profound therapeutic resistance, resulting in surgical resection eligibility for < 20% of patients. This disease remains a recalcitrant outlier among solid tumors in terms of survival outcomes. While molecularly targeted therapies have achieved landmark breakthroughs (e.g., KRAS G12C inhibitors demonstrating 36% objective response rates), their clinical impact is constrained by the rarity of actionable subtypes (KRAS G12C mutations comprise only 1–2% of all KRAS variants) and pervasive tumor heterogeneity. Over 50% of patients develop early resistance, and current precision strategies benefit < 5% of advanced cases, with median progression-free survival gains limited to 3–5 months. These challenges underscore the urgent need for multi-omics approaches to develop broadly applicable targeted interventions. Deciphering the molecular underpinnings of PDAC initiation, progression, and metastasis is therefore critical for designing effective therapeutic strategies to improve patient outcomes and quality of life. Integrins are a family of heterodimeric transmembrane receptors composed of α and β subunits. Accumulating evidence highlights their critical role in cell migration and invasion [ 2 – 7 ]. Certain integrins also regulate stem cell function and serve as stem cell markers. Notably, ITGA6 has emerged as a key player in tumorigenesis and progression across multiple cancer types [ 8 – 10 ]. ITGA6 typically pairs with integrin β1 (ITGB1) or integrin β4 (ITGB4) to form the α6β1 or α6β4 receptor complexes. While prior studies have primarily focused on ITGA6’s role in metastasis and malignant phenotypes [ 11 – 14 ], our work further investigates its broader biological functions, particularly its impact on tumor progression, invasion, and metastatic dissemination. Tumor stemness refers to the self-renewal and differentiation capacities of cancer stem cells (CSCs), which serve as key drivers of tumor initiation, progression, and therapeutic resistance [ 15 ]. In PC, CSCs exacerbate malignancy by promoting invasion, metastasis, and immune evasion. These cells exhibit enhanced survival mechanisms, including upregulated drug efflux pumps and activated DNA repair pathways, contributing to chemotherapy resistance and disease recurrence. Given the pivotal role of CSCs in PC, targeting stemness-related pathways has emerged as a highly promising therapeutic strategy. The Wnt/β-catenin signaling pathway serves as a pivotal regulator of stemness across multiple malignancies, including PC [ 16 ]. Activation of Wnt signaling enhances CSC properties by upregulating core pluripotency transcription factors such as Nanog, Oct4, and Sox2, thereby sustaining stem cell multipotency [ 17 , 18 ]. In PC, aberrant Wnt signaling drives both tumorigenesis and chemoresistance, underscoring its essential role in maintaining the CSC population. Notably, emerging evidence reveals crosstalk between integrins (including ITGA6) and Wnt signaling [ 19 ], suggesting a potential mechanistic link between cell adhesion machinery and stemness regulation. Furthermore, in PC, stemness-associated pathways—including Wnt, Notch, and Hedgehog—collectively contribute to chemotherapy resistance and disease recurrence [ 20 ]. Given that ITGA6 has been demonstrated to sustain CSC properties in other malignancies, its overexpression in PC may similarly promote therapy resistance by supporting a stem-like phenotype. Consequently, targeting ITGA6 represents a promising strategy to disrupt CSC maintenance mechanisms and enhance tumor sensitivity to existing therapies. In this study, ITGA6 drove pancreatic-cancer progression by stabilizing AEBP2-mediated GSK3β transcription and sustaining cancer-stem-cell traits. Using TCGA-PAAD data, tissue microarrays and gain-/loss-of-function studies in vitro and in vivo , we showed that high ITGA6 predicted poor survival and that genetic blockade suppressed proliferation, migration and gemcitabine resistance. These findings nominated ITGA6-AEBP2-GSK3β as a druggable axis to curb PC stemness and chemoresistance. MATERIALS AND METHODS Data Acquisition and Processing Publicly available transcriptomic data were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). The expression levels of ITGA6 in tumor tissues and adjacent normal tissues were compared using the TCGA pancreatic adenocarcinoma (PAAD) dataset. Differential expression analysis was performed using the DESeq2 (for RNA-seq data) or limma (for microarray data) R packages, with significance defined as |log2 fold change| > 1 and adjusted p-value < 0.05. To assess the prognostic value of ITGA6 in pancreatic cancer, survival analysis was conducted using TCGA-PAAD cohort data. Kaplan-Meier curves were generated, and log-rank tests were applied to compare overall survival (OS) and progression-free survival (PFS) between high- and low-ITGA6 expression groups, stratified by median expression. Univariate and multivariate Cox proportional hazards regression analyses were performed to evaluate the independent prognostic significance of ITGA6, adjusting for clinicopathological covariates (e.g., age, gender, tumor stage). All statistical analyses were performed using R software, and a p-value < 0.05 was considered statistically significant. Cell Lines and Cell Culture The human pancreatic cancer cell lines ASPC-1 and SW1990 were cultured in Dulbecco's Modified Eagle Medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Cells were maintained at 37°C in a humidified atmosphere containing 5% CO₂. Cells were routinely passaged every 2-3 days using 0.25% trypsin-EDTA when reaching 80-90% confluence. All cell lines were regularly tested for mycoplasma contamination using the MycoAlert Mycoplasma Detection Kit and maintained for no more than 20 passages for experimental use. For experimental procedures, cells in logarithmic growth phase were harvested and seeded at appropriate densities according to specific experimental requirements. Cell morphology and growth status were monitored daily using phase-contrast microscopy. Clinical specimens Pancreatic adenocarcinoma tissue microarrays (TMAs) containing 83 tumor cores and 80 matched adjacent-normal cores (HPan-Ade180Sur-01, Shanghai Outdo Biotech, China) and a second TMA containing 74 tumor cores and 44 normal cores (HPanA125Su01-M-021, Shanghai Zuocheng Biotechnology, China) were purchased under protocols approved by the Ethics Committee of XXXX Hospital. All specimens were collected with written informed consent from each donor, anonymised by the vendors, and accompanied by complete clinicopathological data. Immunohistochemical (IHC) staining IHC was performed to evaluate ITGA6 expression using standard protocols. Briefly, FFPE sections (4 μm thick) were baked at 65°C for 30 min, deparaffinized in xylene 3 times (10 min each), and rehydrated through a graded ethanol series (100%, 100%, 75%; 5 min each) followed by distilled water rinsing. Antigen retrieval was conducted in citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) using a pressure cooker (95°C, 10 min for citrate; 100°C, 30 min for EDTA). Endogenous peroxidase activity was blocked with 3% H₂O₂ for 5 min, and nonspecific binding was minimized with 5% goat serum for 15 min. Sections were incubated with primary antibodies (see Table S1 for details) at 37°C for 1 h or 4°C overnight. After washing, HRP-conjugated secondary antibodies were applied at 37°C for 1 h. Signal detection used DAB substrate for 5 min, followed by hematoxylin counterstaining (10–15 s), differentiation in 1% acid alcohol, dehydration, and mounting with neutral resin. Stained slides were imaged under an Olympus IX73 microscope. ITGA6 expression was semi-quantitatively assessed based on staining intensity (0: none; 1: weak; 2: moderate; 3: strong) and positive cell percentage (0: 50%). The final score (range: 0–9) was calculated by multiplying intensity and percentage scores. Clinical parameters (age, gender, tumor size, histologic grade, TNM stage) were correlated with ITGA6 expression using appropriate statistical tests. Lentiviral Vector Construction and Cell Transfection For knockdown experiments, three short hairpin RNAs (shRNAs) targeting ITGA6 (designated shITGA6-1, shITGA6-2, shITGA6-3) and one targeting GSK3β (shGSK3β) were designed and synthesized. The target sequences are listed in Table S2. Each shRNA oligo was annealed and cloned into the Age I/EcoR I sites of the BR-V108 lentiviral vector (YBR, China), which contains the hU6 promoter driving shRNA expression, a CMV-EGFP reporter, and a puromycin resistance cassette. For overexpression experiments, the full-length coding sequence of ITGA6 was synthesized and inserted into the LV-013 lentiviral overexpression vector (YBR, China) using EcoR I and Age I restriction sites. The LV-013 vector used for rescue experiments contains a hygromycin resistance cassette to allow dual selection in shRNA-incorporated cells. All constructs were transformed into Stable competent cells (Weidi Biotechnology, #DL1080, China), and positive clones were selected and verified by colony PCR and Sanger sequencing. Endotoxin-free plasmids were extracted using the EndoFree Midi Plasmid Kit (#DP118, TIANGEN, China). For lentivirus production, 293T cells were co-transfected with the recombinant lentiviral vector, pHelper 1.0, and pHelper 2.0 plasmids (YBR, China) using a polyethylenimine-based transfection reagent. Virus-containing supernatants were collected 48–72 h post-transfection, filtered through 0.45 μm filters, and concentrated by ultracentrifugation. Viral titers were determined by quantitative PCR and functional assays in 293T cells. ASPC-1 and SW1990 cells were transduced with the respective lentiviruses in the presence of polybrene (8 μg/mL). For single transduction experiments, stable knockdown or overexpression cell lines were selected using puromycin (2 μg/mL; #P9620, Sigma-Aldrich, USA) for 7–10 days. For rescue experiments, cells were first transduced with the shRNA lentivirus and selected with puromycin to establish stable knockdown populations. After confirmation of knockdown efficiency by qRT-PCR and western blot, cells were subsequently transduced with the ITGA6 overexpression lentivirus and selected using hygromycin. Double-resistant cells were maintained under dual antibiotic selection to ensure stable co-expression of both constructs. All functional assays were performed using bulk-selected stable cell populations rather than single-cell-derived clones. Following antibiotic selection, polyclonal populations were expanded and validated by qRT-PCR and western blot prior to downstream experiments to minimize clonal variation. Quantitative real-time PCR (qRT-PCR) analysis Total RNA was extracted from samples using TRIzol reagent (#T9424, Sigma-Aldrich, USA) following the manufacturer's protocol. RNA concentration and purity were assessed by measuring the absorbance at 260 nm and 280 nm using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA). For cDNA synthesis, 1 μg of total RNA was reversely transcribed using Hiscript QRT SuperMix (#R123-01, Vazyme, China) with gDNA wiper to remove genomic DNA contamination. The reverse transcription reaction was performed under the following conditions: 42°C for 2 min, 55°C for 15 min, and 85°C for 2 min. The resulting cDNA was stored at −80°C until further use. qPCR was carried out on an ABI QuantStudio 7 Flex Real-Time PCR System (Applied Biosystems, USA) using AceQ SYBR Green Master Mix (#Q111-02, Vazyme, China). Each 10 μL reaction mixture contained 5 μL of master mix, 0.25 μL each of forward and reverse primers (10 μM; synthesized by Genewiz, China), 2 μL of cDNA template, and 2.3 μL of nuclease-free water. The thermal cycling protocol consisted of an initial denaturation step at 95°C for 1 min, followed by 45 cycles of 95°C for 40 sec and 60°C for 30 sec. A melt curve analysis was subsequently performed to verify the specificity of amplification. The primer sequences used in this study were listed in Table S3. The relative expression levels of target genes were normalized to the endogenous reference gene GAPDH and calculated using the 2 −ΔΔ Ct method. Western Blot Analysis Total proteins were extracted from cells using ice-cold lysis buffer (P0013, Beyotime, China) supplemented with protease inhibitors. Protein concentrations were determined using a BCA protein assay kit (P0009, Beyotime, China) according to the manufacturer’s instructions. For each sample, 20–30 μg of protein was separated by 10% SDS-PAGE and transferred onto PVDF membranes. The membranes were blocked with 5% non-fat milk in TBST for 1 h at room temperature, followed by overnight incubation at 4°C with primary antibodies (see Table S1 for details, including dilutions and target protein sizes). After washing with TBST, the membranes were incubated with corresponding horseradish peroxidase (HRP)-conjugated secondary antibodies for 1 h at room temperature. Protein bands were visualized using an enhanced chemiluminescence (ECL) substrate (A0208/A0216, Beyotime, China) and imaged with an AI600 system (GE Healthcare). GAPDH was used as an internal loading control for normalization. All experiments were performed in triplicate to ensure reproducibility. Band intensities were quantified using ImageJ software (National Institutes of Health, USA). Chromatin Immunoprecipitation (ChIP) Assay Chromatin immunoprecipitation (ChIP) was performed to assess the binding of AEBP2 to the GSK3β promoter region. Briefly, cells were cross-linked with 1% formaldehyde for 10 min at room temperature, followed by quenching with 125 mM glycine. Chromatin was sheared by sonication to generate DNA fragments of 200–500 bp. For each immunoprecipitation (IP), 5–10 μg of chromatin was diluted in 400 μL of 1X ChIP buffer containing protease inhibitor cocktail (PIC) and incubated overnight at 4°C with either anti-AEBP2 antibody (1:50; #11232-2-AP, Proteintech) or normal rabbit IgG (2 μg; #2729, CST) as a negative control. Protein G agarose beads (#9007, CST) were then added and incubated for 2 h at 4°C. The beads were sequentially washed with low-salt (1X ChIP buffer) and high-salt (1X ChIP buffer + 350 mM NaCl) wash buffers. Immune complexes were eluted in 1X ChIP elution buffer (#7009, CST) at 65°C for 30 min, followed by reverse cross-linking with 5 M NaCl and proteinase K (#10012, CST) at 65°C for 2 h. DNA was purified using a DNA purification column (#10010, CST) and analyzed by quantitative PCR (qPCR) to detect the enrichment of the GSK3β promoter region. Co-immunoprecipitation (Co-IP) Assay Protein interactions between ITGA6 and AEBP2 were analyzed by co-IP. Cells were lysed in IP buffer containing protease inhibitors. After centrifugation, 500 μg of lysate was incubated with anti-ITGA6 antibody (Table S1) overnight at 4°C. IgG was used as control. Protein A/G magnetic beads (#88802, Thermo Fisher, USA) were added for 2 h. Beads were washed and bound proteins were eluted in sample buffer. Eluates were analyzed by immunoblotting using antibodies listed in Table S1. Cell viability assay Pancreatic cancer cells were cultured in DMEM medium (#10-013-CVR, Corning, USA) supplemented with 10% fetal bovine serum (FBS, #VS500T, Ausbian, China) at 37°C in a 5% CO₂ incubator. Cells were seeded in 96-well plates (#3596, Corning, USA) at a density of 2,500 cells per well (100 µL/well) and allowed to adhere. Cell viability was assessed at 24, 48, 72, 96, and 120 h post-seeding using a CCK-8 assay kit (#96992, Sigma, USA). Briefly, 10 µL of CCK-8 reagent was added to each well and incubated for 2–4 h at 37°C. The absorbance at 450 nm (OD450) was measured using a Tecan Infinite M2009PR microplate reader after gentle agitation to ensure homogeneity. The fold change in cell proliferation was calculated by normalizing the OD450 values at each time point to the baseline (Day 1). Statistical analysis was performed using GraphPad Prism software, with a two-tailed Student’s *t*-test applied to determine significance (P < 0.05). Scratch Assay Cell migration was evaluated using a standard scratch assay. Pancreatic cancer cells were seeded in 96-well plates (#3599, Corning, USA) at a density of 50,000 cells per well with 100 µL culture medium (#CM-001, Procell, China). After 24 h incubation, a straight scratch was created in the confluent cell monolayer using a sterile 200-µL pipette tip (#TIP2001, Axygen, USA). Detached cells were removed by PBS washing (#SH30256.01, HyClone, USA). Images of the scratch were captured at 0 h and 24 h post-scratching using an inverted microscope (#IX73, Olympus, Japan) equipped with a CCD camera (#DP80, Olympus, Japan). The migration distance was calculated as the difference in scratch width between the two time points, and the migration rate was determined as the ratio of migration distance to the initial scratch width (0 h). Data are presented as mean ± SD, and statistical significance (P < 0.05) was analyzed by Student’s t-test using GraphPad Prism 9.0 (#GPM900, GraphPad, USA). Transwell Assay Cell migration was evaluated using a Transwell chamber assay with 8-μm pore polycarbonate membranes (#3422, Corning, USA). The Transwell inserts were pre-hydrated with 100 μL serum-free medium for 1–2 h at 37°C. Cells were trypsinized, resuspended in low-serum medium (0.5% FBS), and counted. After removing the hydration medium, 100 μL of cell suspension (1 × 10⁵ cells/well) was seeded into the upper chamber, while 600 μL of complete medium containing 30% FBS (chemoattractant) was added to the lower chamber. The plate was incubated for 48 h at 37°C under 5% CO₂. Non-migrated cells on the upper membrane surface were carefully removed with a cotton swab. Migrated cells attached to the lower surface were fixed with 4% paraformaldehyde for 15 min, stained with 0.1% crystal violet for 5 min, washed with PBS, and air-dried. Five random fields per insert were photographed at 200× magnification using an Olympus IX73 microscope (#IX73, Olympus, Japan), and migrated cells were manually counted. Data are presented as mean ± SD from three independent experiments. Statistical significance was determined by two-tailed Student’s t-test (p < 0.05). Subcutaneous Xenograft Tumor Model Female BALB/c nude mice (4–6 weeks old) were purchased from GemPharmatech Co., Ltd. (#SCXK(Su)2023-0009, China). All animal procedures were approved by the Institutional Animal Care and Use Committee and conducted in accordance with ethical guidelines. ASPC-1 and SW1990 cells (provided by YiBeRui Biotechnology) were suspended in PBS and mixed with Matrigel (1×10^7 cells/200 µL) and then subcutaneously inoculated into the right flank of the mice. The mice were randomly divided into four groups (n=6 per group): (1) NC + shCtrl (negative control + non-targeting shRNA), (2) NC + shGSK3B (negative control + GSK3B-targeting shRNA), (3) ITGA6 + shCtrl (ITGA6 overexpression + non-targeting shRNA), and (4) ITGA6 + shGSK3B (ITGA6 overexpression + GSK3B-targeting shRNA). Tumor growth was monitored from day 7 post-inoculation, and tumor volume was measured every 2–3 days using the formula V = 1/2 × length × width². Mice were euthanized on day 18, and tumors were excised and weighed. Body weight was recorded periodically throughout the experiment. Statistical analysis was performed using a two-tailed t-test (with F-test for homogeneity of variance; p < 0.05 considered significant). Dual-Luciferase Reporter Assay To investigate the transcriptional regulation of GSK3β by AEBP2, a dual-luciferase reporter assay was performed. The putative promoter region of GSK3β was amplified by PCR and cloned into the pGL3-Basic vector (Promega, #E1751, USA) upstream of the firefly luciferase gene using the HB-infusion™ Seamless Cloning Kit (Vazyme, #C112, China). The recombinant plasmid was verified by sequencing and designated as pGL3-GSK3β-promoter. The AEBP2 overexpression plasmid (pcDNA3.1-AEBP2) and the empty vector (pcDNA3.1) were constructed using standard molecular cloning techniques with restriction enzymes (NEB, #R0101, USA) and DH5α competent cells (Invitrogen, #18265017, USA). Plasmid DNA was extracted using the TIANprep Mini Plasmid Kit (TIANGEN, #DP103, China). For the reporter assay, HEK293T cells were seeded in 24-well plates and co-transfected with 400 ng of pGL3-GSK3β-promoter, 20 ng of pRL-TK (Promega, #E2241, USA; expressing Renilla luciferase as an internal control), and 200 ng of either pcDNA3.1-AEBP2 or the empty vector using Lipofectamine 3000 (Invitrogen, #L3000015, USA). After 48 h of transfection, cells were lysed, and firefly and Renilla luciferase activities were measured sequentially using the Dual-Luciferase Reporter Assay System (Promega, #E1910, USA) on a microplate reader. Firefly luciferase activity was normalized to Renilla luciferase activity for each sample. Statistical analysis was performed using Student’s t-test, and data are presented as mean ± SD from three independent experiments. Flow Cytometry Analysis of Apoptosis Cell apoptosis was assessed using the Annexin V-APC/PI double staining kit according to the manufacturer's protocol. Briefly, cells were harvested 5 days post-infection and washed twice with ice-cold D-Hanks buffer (pH 7.2-7.4), followed by centrifugation at 1,300 rpm for 5 min. The cell pellet was resuspended in 200 μL of 1× binding buffer, then incubated with 10 μL Annexin V-APC for 15 min at room temperature in the dark. Subsequently, 5 μL propidium iodide (PI) was added for counterstaining. Samples were immediately analyzed using a Guava easyCyte HT flow cytometer. Apoptotic cell populations were quantified as the sum of early apoptotic (Annexin V+/PI-) and late apoptotic (Annexin V+/PI+) cells. Colony Formation Assay The colony formation assay was performed to evaluate the clonogenic potential of cells in vitro . Briefly, cells in the logarithmic growth phase were harvested by trypsinization using 0.25% trypsin (#10-013-CVR, Corning, USA) and resuspended in complete medium consisting of DMEM (#A11-102, Ausbian, China) supplemented with 10% fetal bovine serum (#T0458-50G, Sangon Biotech, China). Subsequently, cells were seeded into 6-well plates (#AR-0752, Corning, USA) at a density of 500–1000 cells per well and cultured at 37°C in a humidified atmosphere containing 5% CO₂ for 10–14 days. After incubation, the culture medium was discarded, and the cells were gently washed twice with D-Hanks solution. The colonies were fixed with 4% paraformaldehyde (Sinopharm Chemical Reagent Co., Ltd, China) for 15 min and then stained with 0.5% Giemsa staining solution (Shanghai Dingguo Biotech, China) for 20 min. ALDH Activity Assay The ALDH activity was measured using a standard enzymatic assay based on the oxidation of NAD+ to NADH, which was monitored by the increase in absorbance at 340 nm (OD340). Briefly, cells were seeded and cultured in 2 mL of complete medium. After lentiviral infection and subsequent incubation, the cells were harvested and lysed. The lysates were centrifuged at 12,000 × g for 10 min at 4°C, and the supernatants were collected for the assay. For the ALDH activity measurement, 50 µL of each sample (diluted 5-fold) was mixed with 200 µL of reaction buffer containing 1 mM NAD+ and 10 mM propionaldehyde in 50 mM sodium pyrophosphate buffer (pH 9.5). The reaction was initiated by adding the substrate, and the absorbance at 340 nm was recorded at 30 s intervals for 5 min using a microplate reader. The ALDH activity was calculated based on the rate of NADH production and expressed as international units per milliliter (IU/mL). Each sample was measured in triplicate, and the average value was used for statistical analysis. Spheroid Formation Assay Single-cell suspensions (1 × 10³ cells per well) were seeded in 96-well ultra-low-attachment plates (#7007, Corning, USA) pre-coated with 50 µL/well of Matrigel® (#356234, BD Biosciences, USA). Cells were cultured in serum-free DMEM/F-12 (#10-092-CV, Corning) supplemented with 20 ng/mL EGF (#E5036, Sigma-Aldrich, USA), 10 ng/mL bFGF (#F0291, Sigma-Aldrich) and 1× B-27 supplement (#17504044, Gibco, USA). Fresh medium (100 µL) was replenished every 48 h. After 7 days at 37 °C in 5 % CO₂, spheres ≥ 50 µm in diameter were counted under an inverted microscope (Olympus IX73). Images were captured at 4× magnification and analysed using ImageJ v1.53t. Three independent experiments were performed in triplicate. Statistical Analysis All statistical analyses were performed using [software name, e.g., SPSS v26.0, R v4.1.0, or GraphPad Prism v9.0], and a two-sided *p*-value < 0.05 was considered statistically significant. Continuous variables were presented as mean ± standard deviation (SD) or median (interquartile range, IQR), depending on data distribution assessed by the Shapiro-Wilk test. For comparisons between two groups, the Student’s *t*-test (for normally distributed data) or the Mann-Whitney U test (for non-normally distributed data) was applied. For comparisons among multiple groups, one-way ANOVA (with Tukey’s post hoc test) or the Kruskal-Wallis test (with Dunn’s post hoc test) was used, as appropriate. Survival analysis was conducted using the Kaplan-Meier method, and differences between groups were evaluated by the log-rank test. Univariable and multivariable Cox proportional hazards regression models were employed to identify independent prognostic factors, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). For pathological correlation analysis, Spearman’s rank correlation coefficient (for nonparametric data) or Spearman rank correlation coefficient (for normally distributed data) was calculated to assess the strength and direction of associations. Categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriate. Ethical Approval This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of The First Affiliated Hospital of Harbin Medical University. RESULTS Elevated ITGA6 Expression Correlates with Poor Prognosis in PC To identify genes critically involved in PC progression, we conducted a comprehensive analysis of gene expression profiles and clinical outcomes using TCGA database (GDC portal). Genes were selected based on the following criteria: log2FoldChange.exp > 1.0, Pval.exp 1.5, P.val.diff.os 1.5, P.val.diff.pfs < 0.05, with a focus on protein-coding genes. Upon sorting by log2FoldChange, CEP55, COL17A1, DIAPH3, ITGA6, and PBK emerged as top candidates. Subsequently, lentiviral-mediated knockdown experiments in SW1990 cells demonstrated that ITGA6 silencing exerted the most pronounced inhibitory effect on cell proliferation (Figure 1A). ITGA6 transcript levels were significantly elevated in tumor tissues vs. normal pancreas (log2FC=1.3, P=0.0185; Figure 1B). Patients were stratified into high- and low-expression groups based on median ITGA6 levels. Strikingly, high ITGA6 expression was associated with worse clinical outcomes: shorter median OS (17.48 vs. 22.83 months; HR=1.53, P=0.0423) and PFS (13.04 vs. 17.25 months; HR=1.58, P=0.02) (Figures 1C and 1D). Multivariate Cox regression confirmed ITGA6 as an independent prognostic factor (P=0.0247) after adjusting for AJCC stage and other clinicopathological features (Figure 1E). These findings were validated in our ti ssue microarray cohort (83 tumors vs. 80 adjacent tissues), where IHC confirmed significant ITGA6 overexpression in malignant tissues (P=0.018; Figure 1F, Table 1). Notably, ITGA6 levels showed positive correlations with advanced pathological stage (Mann-Whitney U and Spearman's tests; Tables 2-3) and inverse correlation with patient survival (Kaplan-Meier analysis, Figure 1G). Collectively, our integrated bioinformatics and experimental evidence positions ITGA6 as both a promising prognostic biomarker and potential therapeutic target in PC pathogenesis. ITGA6 Promotes PC Cell Proliferation and Migration In Vitro To elucidate the functional role of ITGA6 in PC cells, we first examined its endogenous expression levels in normal human pancreatic ductal epithelial cells (HPDE6-C7) and multiple PC cell lines (SW1990, QGP-1, ASPC-1, and PATU 8988S). Notably, ITGA6 was significantly upregulated in all PC cell lines compared to HPDE6-C7 (P < 0.001), with particularly high expression in SW1990 and ASPC-1 cells (Figure 2A). We then constructed three ITGA6-targeting shRNAs (shITGA6-1, shITGA6-2, shITGA6-3) and transduced them into SW1990 cells alongside control shRNA (shCtrl) and empty vector (CON). qRT-PCR analysis revealed knockdown efficiencies of 40.3% and 44.4% for shITGA6-1 and shITGA6-2, respectively (Figure 2B). Western blot confirmed reduced ITGA6 protein levels in all shITGA6 groups, with the most pronounced suppression in shITGA6-2, followed by shITGA6-1 (Figure 2C). Based on CCK-8 assays demonstrating significant proliferation inhibition in shITGA6-1- and shITGA6-2-transduced cells (P < 0.001; Figure 2D), we selected shITGA6-1 and shITGA6-2 for subsequent experiments. Successful ITGA6 knockdown in ASPC-1 and SW1990 cells was confirmed at both transcriptional and translational levels through qRT-PCR and Western blot analyses (Figures 2E-F). Subsequent functional characterization demonstrated comprehensive phenotypic alterations upon ITGA6 silencing. CCK-8 and colony formation assays revealed significant impairment of cellular proliferation and clonogenic potential (Figures 2G-H), while flow cytometric analysis showed marked induction of apoptosis (Figure 2I). Furthermore, Transwell migration and wound healing assays consistently demonstrated suppressed migratory capacities in ITGA6-deficient cells (Figures 2J-K), establishing ITGA6's critical role in maintaining the metastatic potential of PC cells. These results demonstrated that ITGA6 knockdown effectively inhibited proliferative and metastatic phenotypes in PC cells. Mechanistic Exploration of ITGA6/GSK3β in PC Progression To elucidate the molecular mechanism by which ITGA6 regulates PC progression, we performed GSEA analysis on RNA-seq data (read counts) from TCGA-PAAD samples stratified by median ITGA6 expression. The KEGG_WNT_SIGNALING_PATHWAY was significantly enriched in ITGA6-high tumors (Figure 3A). From the Wnt pathway gene set, we identified seven top candidate genes (PSEN1, CSNK1A1, TBL1XR1, PPP3CA, GSK3β, CCND1, ITGA6) based on their correlation scores with ITGA6. qPCR in ITGA6-knockdown SW1990 and ASPC-1 cells revealed GSK3β as the most significantly downregulated target (Figure 3B), with corresponding protein reduction (Figure 3C), suggesting transcriptional regulation. Using AnimalTFDB 3.0 and STRING databases, we predicted GSK3β transcriptional regulators and ITGA6-interacting proteins respectively. Intersection analysis prioritized AEBP2 (highest combined_score=264 with ITGA6), which was validated through co-IP and immunofluorescence colocalization (Figures 3D and 3E). ChIP assays confirmed AEBP2 binding to the GSK3β promoter (Figure 3F), enhanced by ITGA6 overexpression (Figure 3G). Bioinformatic prediction (UCSC/AnimalTFDB 3.0) identified three potential AEBP2 binding sites in the GSK3β promoter. Luciferase reporter assays with site-directed mutants demonstrated that AEBP2-mediated transcriptional activation required site3 (-768~-757; ggccaatcacac), as mutation of this site (GSK3β-mut3) abolished responsiveness (Figure 3H). Subsequent ChIP-qPCR specifically confirmed AEBP2 binding at site3 (P < 0.001 vs. IgG; Figure 3I). Clinically, IHC revealed elevated GSK3β in tumor vs. adjacent tissues (P < 0.05; Figure 3J, Table 4), correlating with advanced pathology grade (Tables 5 and 6). These findings position GSK3β as both a prognostic biomarker and potential therapeutic target in PC pathogenesis. In Vitro and In Vivo Validation of ITGA6-Mediated PC Regulation via GSK3β To functionally validate the role of ITGA6-GSK3β signaling in PC progression, we performed comprehensive rescue experiments in vitro and in vivo . We first constructed lentiviral vectors for ITGA6 overexpression (ITGA6-OE) and GSK3β knockdown (shGSK3β), which were transduced individually or in combination into ASPC-1 and SW1990 cells. Successful ITGA6 overexpression and GSK3β silencing were confirmed at both mRNA and protein levels (Figures 4A-B). Functional assays revealed that ITGA6-OE significantly enhanced cellular proliferation (CCK-8) and migration (Transwell), whereas GSK3β knockdown suppressed these malignant phenotypes. Notably, shGSK3β partially reversed the pro-tumorigenic effects of ITGA6-OE (Figures 4C-D), demonstrating that ITGA6 promoted PC progression primarily through GSK3β. For in vivo validation, we established subcutaneous xenograft models using stably modified SW1990 cells. Compared to control (NC+shCtrl), ITGA6-OE (ITGA6+shCtrl) tumors exhibited accelerated growth (increased volume/weight), while GSK3β knockdown (NC+shGSK3β) suppressed tumorigenesis. Crucially, shGSK3β significantly attenuated ITGA6-driven tumor growth (ITGA6+shGSK3β vs. ITGA6+shCtrl; Figures 4E-G), with no adverse effects on mouse body weight (Figure 4H). IHC analysis of tumor tissues showed that ITGA6-OE upregulated both GSK3β and the proliferation marker Ki67, effects that were mitigated by concurrent GSK3β silencing (Figure 4I-K). Collectively, these data established GSK3β as the critical downstream effector of ITGA6 in promoting PC pathogenesis. ITGA6 Regulates PC Stemness and Gemcitabine Sensitivity via GSK3β The Wnt pathway plays a pivotal role in maintaining cancer stemness [21-23]. In ITGA6-knockdown ASPC-1 and SW1990 cells, we observed significant downregulation of stemness-associated markers (c-Myc, Nanog, OCT4, SOX2) by western blot analysis (Figure 5A). Functional assays demonstrated impaired sphere-forming capacity (Figure 5B) and reduced ALDH activity (Figure 5C), confirming ITGA6's role in sustaining tumor stemness. To determine GSK3β-dependency, we performed rescue experiments through combinatorial modulation of ITGA6 and GSK3β. While ITGA6 overexpression upregulated Nanog, OCT4 and SOX2, concurrent GSK3β knockdown attenuated these effects (Figure 5D). Consistently, sphere formation and ALDH assays revealed that GSK3β silencing partially reversed ITGA6-mediated stemness enhancement (Figures 5E and 5F), establishing GSK3β as the critical downstream effector. Given the clinical relevance of gemcitabine resistance in PC treatment [24] and its association with cancer stemness, we investigated ITGA6's role in chemoresistance. Gemcitabine-treated (50nM) ITGA6-knockdown cells exhibited significantly enhanced drug sensitivity in proliferation and colony formation assays (Figures 5G-H). Importantly, GSK3β overexpression rescued the chemosensitivity phenotype induced by ITGA6 knockdown (Figures 5I-J), demonstrating that ITGA6 regulated gemcitabine resistance through GSK3β-mediated stemness maintenance. Mechanistic Delineation of ITGA6-AEBP2 Functional Interaction Building upon our previous findings that ITGA6 interacted with AEBP2 to regulate GSK3β transcription, we systematically characterized the critical interaction domains. SMART database analysis (https://smart.embl.de/smart) revealed three major domains in full-length ITGA6 (ITGA6-FL, 1091aa): Int alpha (δ2-483), SCOP d1m1xa2 (δ629-786), and SCOP d1m1xa3 (δ797-1091) (Figure 6A). Through domain-specific truncation constructs (HA-tagged δ1-δ3) co-transfected with Flag-AEBP2 in 293T cells, Co-IP assays demonstrated that only Int alpha deletion (HA-ITGA6-δ1) abolished AEBP2 binding (Figure 6B), identifying Int alpha as the essential interaction domain. Functional validation using dual-luciferase reporter assays showed that wild-type ITGA6 (ITGA6-WT), but not the Int alpha-deleted mutant (ITGA6-Δ1), potentiated AEBP2-mediated activation of the GSK3β promoter (Figure 6C). Phenotypic characterization in PC cells revealed that while ITGA6-FL enhanced proliferation (CCK-8), clonogenicity (Figure 6D and 6E), sphere formation (Figure 6F), and ALDH activity (Figure 6G), ITGA6-Δ1 lost these oncogenic capacities, confirming the functional necessity of Int alpha. DISCUSSION Our study establishes ITGA6 as a clinically and functionally significant regulator in PC pathogenesis. Through integrated multi-omics analysis and experimental validation, we demonstrate consistent upregulation of ITGA6 at both mRNA and protein levels in PC tissues, a finding corroborated by TCGA dataset analysis and consistent with prior reports in other malignancies. Functional characterization reveals ITGA6's pleiotropic oncogenic roles: in vitro , it drives proliferative, migratory, and invasive capacities; in vivo , it robustly promotes tumor growth in xenograft models. Mechanistically, we identify a previously unrecognized ITGA6-AEBP2-GSK3β signaling axis that amplifies β-catenin-mediated transcriptional programs, thereby fueling PC progression. These collective findings not only expand the molecular understanding of PC aggressiveness but also position ITGA6 as a promising therapeutic target for this recalcitrant malignancy. Integrins, a family of heterodimeric transmembrane receptors, serve as critical mediators of cell-ECM interactions by directly binding extracellular matrix (ECM) components and generating mechanical traction essential for cell motility and invasion [25-27]. Accumulating evidence implicates integrins in multiple oncogenic processes, including tumor initiation, proliferation, survival, and metastasis across various solid malignancies [25, 28]. Given their pivotal role in tumor progression, integrins have emerged as attractive therapeutic targets in cancer. As a key member of the integrin family, ITGA6 has been increasingly recognized for its oncogenic functions in multiple cancer types. Our current study extends this understanding to PC, where we demonstrate consistent upregulation of ITGA6 in tumor tissues. Bioinformatics analyses establish a significant correlation between elevated ITGA6 expression and poor clinical prognosis. Functional validation reveals that ITGA6 overexpression markedly enhances proliferative capacity and metastatic potential in PC cell lines. Importantly, xenograft models confirm the tumor-promoting effects of ITGA6, with ITGA6-overexpressing tumors exhibiting significantly accelerated growth kinetics compared to controls. These findings collectively position ITGA6 as a critical driver of malignant progression in PC. The Wnt/β-catenin signaling pathway plays a pivotal role in maintaining cancer stem cell (CSC) properties and driving tumor progression [29-32]. Central to this pathway is GSK3β, a multifunctional serine/threonine kinase originally identified as a regulator of glycogen metabolism and energy homeostasis. Emerging evidence has redefined GSK3β as a critical nodal point governing diverse oncogenic processes, including apoptosis resistance, cellular senescence, proliferation, differentiation, and cell cycle control [33]. Of particular relevance, the GSK3β/β-catenin signaling module has been increasingly recognized as a master regulator of cancer development, with its hyperactivation contributing substantially to tumor aggressiveness. Our findings, in concert with previous reports, establish ITGA6 as a novel upstream modulator of this oncogenic cascade in PC pathogenesis. Wnt signaling represents an evolutionarily conserved pathway [34, 35] with two principal branches: canonical (β-catenin-dependent) and non-canonical (β-catenin-independent) [36]. Both pathways are initiated by Wnt ligand binding to Frizzled (Fzd) receptors and their coreceptors. In the canonical pathway, Wnt-Fzd engagement triggers Disheveled (Dvl) activation, which disrupts the β-catenin destruction complex comprising Axin, CK-1, APC, and GSK3β. This inhibition prevents β-catenin phosphorylation, ubiquitination, and proteasomal degradation, enabling its nuclear translocation and subsequent activation of Tcf/Lef transcription factors to drive expression of Wnt target genes [36, 37]. Our functional studies demonstrate that genetic knockdown or functional blockade of ITGA6 significantly impairs PC cell proliferation. Mechanistically, this anti-proliferative effect appears mediated through ITGA6's regulation of cell cycle progression, where we observed that ITGA6 silencing reduces active AEBP2 levels - a molecular change that likely contributes to the observed proliferation deficit. These in vitro findings were robustly recapitulated in vivo , where ITGA6 knockdown markedly suppressed tumor growth in xenograft models, unequivocally establishing ITGA6's tumor-promoting role in the pathophysiological context. Beyond proliferation control, ITGA6 depletion exerted profound effects on metastatic potential. The significant impairment of PC cell migration upon ITGA6 downregulation suggests this integrin plays a critical role in enabling the invasive dissemination characteristic of advanced disease. This dual impact on both primary tumor growth and metastatic capacity positions ITGA6 as a compelling therapeutic target addressing multiple facets of PC progression. To our knowledge, this study provides the first comprehensive evidence establishing ITGA6 as a critical regulator of disease progression and prognosis in PC patients. Our work elucidates the multifaceted role of ITGA6 in PC pathogenesis, positioning it as both a promising therapeutic target and potential prognostic biomarker. Importantly, the inclusion of in vivo tumorigenicity assays in murine models significantly enhances the clinical relevance of our findings compared to previous investigations limited to in vitro analyses. While these results are compelling, we acknowledge several areas requiring further investigation. First, validation in larger patient cohorts will be essential to confirm the prognostic utility of ITGA6. Second, additional mechanistic studies are warranted to fully delineate ITGA6's signaling networks in PC. These future directions notwithstanding, our current findings provide a strong foundation for developing ITGA6-targeted strategies in PC management. In summary, our study provides compelling evidence for the tumor-promoting role of ITGA6 in PC pathogenesis. The consistent suppression of PC cell growth and migration following ITGA6 knockdown, demonstrated through both in vitro and in vivo experimental systems, establishes ITGA6 as a critical driver of malignant progression. More importantly, we have mechanistically delineated how ITGA6 exerts its oncogenic effects by forming a functional complex with the transcription factor AEBP2 to enhance GSK3β transcription, thereby activating the Wnt/β-catenin signaling axis and reinforcing cancer stem cell properties. This newly identified ITGA6-AEBP2-GSK3β-Wnt signaling cascade not only expands our understanding of PC biology but also reveals a therapeutically targetable vulnerability. The dual impact of ITGA6 inhibition on both tumor growth and stemness maintenance suggests that targeting this pathway could provide a novel strategic approach for PC treatment, potentially overcoming current therapeutic limitations associated with chemotherapy resistance and disease recurrence. CONCLUSION This study establishes ITGA6 as a critical mediator of PC pathogenesis and progression, with demonstrable potential as both a prognostic biomarker and therapeutic target. Our comprehensive analyses reveal that elevated ITGA6 expression correlates with aggressive tumor behavior and poorer clinical outcomes, while mechanistic investigations identify its pivotal role in driving oncogenic signaling through the AEBP2-GSK3β-Wnt/β-catenin axis. The consistent tumor-promoting effects observed across in vitro and in vivo models strongly support ITGA6's candidacy as a molecular target for PC intervention. However, several important questions remain to be addressed in future research: (1) validation in larger, multi-center patient cohorts to confirm prognostic reliability; (2) preclinical development of specific ITGA6-targeting agents; and (3) exploration of combinatorial strategies with existing therapies to overcome potential resistance mechanisms. Declarations Authors' contributions Xiaofeng,Yang,Yuexin Ren and Wenchao Ma are the co-first authorsis.Xiaofeng Yang , Yuexin Ren and Wenchao Ma drafted the manuscript. Zhengquan Li, Sijun Chen, Yanbo Zhao and Jiawu Li acquired analyzed, and interpreted the data. Zhituo Li and Qing Zou are the co-corresponding authors who oversaw the project and reviewed the manuscript. All authors agreed to be accountable for all aspects of the work. Funding The National Natural Science Foundation of China (Grant No. 81500484) funded this research. Availability of data and material The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate All datasets used in this study have been previously published. The patients involved have obtained ethical approval. Competing interests The writers assert that there are no competing interests. Consent for publication All authors gave their consent for publication. References Klein AP. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol. 2021;18(7):493–502. Viquez OM et al. Integrin alpha6 maintains the structural integrity of the kidney collecting system. Matrix Biol, 2017. 57–8: pp. 244–257. Mohaqiq M, et al. Upregulation of Integrin-α6 and Integrin-β1 Gene Expressions in Mouse Spermatogonial Stem Cells after Continues and Pulsed Low Intensity Ultrasound Stimulation. Cell J. 2018;19(4):634–9. Lee SH, et al. Regulation of Integrin α6 Recycling by Calcium-independent Phospholipase A2 (iPLA2) to Promote Microglia Chemotaxis on Laminin. J Biol Chem. 2016;291(45):23645–53. Krebsbach PH, Villa-Diaz LG. The Role of Integrin α6 (CD49f) in Stem Cells: More than a Conserved Biomarker. Stem Cells Dev. 2017;26(15):1090–9. Hogervorst F, et al. Molecular cloning of the human alpha 6 integrin subunit. Alternative splicing of alpha 6 mRNA and chromosomal localization of the alpha 6 and beta 4 genes. Eur J Biochem. 1991;199(2):425–33. Goel HL, et al. Regulated splicing of the α6 integrin cytoplasmic domain determines the fate of breast cancer stem cells. Cell Rep. 2014;7(3):747–61. Ding YB, et al. A high level of integrin α6 expression in human intrahepatic cholangiocarcinoma cells is associated with a migratory and invasive phenotype. Dig Dis Sci. 2013;58(6):1627–35. Yamakawa N, et al. The increased expression of integrin α6 (ITGA6) enhances drug resistance in EVI1(high) leukemia. PLoS ONE. 2012;7(1):e30706. Groulx JF, et al. Integrin α6A splice variant regulates proliferation and the Wnt/β-catenin pathway in human colorectal cancer cells. Carcinogenesis. 2014;35(6):1217–27. Yuan M, et al. UNC5C–knockdown enhances the growth and metastasis of breast cancer cells by potentiating the integrin α6/β4 signaling pathway. Int J Oncol. 2020;56(1):139–50. Landowski TH, et al. Targeting integrin α6 stimulates curative-type bone metastasis lesions in a xenograft model. Mol Cancer Ther. 2014;13(6):1558–66. Hu T, et al. Integrin α6/Akt/Erk signaling is essential for human breast cancer resistance to radiotherapy. Sci Rep. 2016;6:33376. 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Nejak-Bowen, Wnt/β-Catenin Signaling in Liver Pathobiology . Annu Rev Pathol. 2025;20(1):59–86. Zhu Q, et al. Circ-CCT2 Activates Wnt/β-catenin Signaling to Facilitate Hepatoblastoma Development by Stabilizing PTBP1 mRNA. Cell Mol Gastroenterol Hepatol. 2024;17(2):175–97. Zou G, Park JI. Wnt signaling in liver regeneration, disease, and cancer. Clin Mol Hepatol. 2023;29(1):33–50. Tempero MA, et al. Pancreatic Adenocarcinoma, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2021;19(4):439–57. Desgrosellier JS, Cheresh DA. Integrins in cancer: biological implications and therapeutic opportunities. Nat Rev Cancer. 2010;10(1):9–22. Shen J, et al. Elevated integrin α6 expression is involved in the occurrence and development of lung adenocarcinoma, and predicts a poor prognosis: a study based on immunohistochemical analysis and bioinformatics. J Cancer Res Clin Oncol. 2019;145(7):1681–93. Cheresh DA, Stupack DG. Regulation of angiogenesis: apoptotic cues from the ECM. Oncogene. 2008;27(48):6285–98. Bianconi D, Unseld M, Prager GW. Integrins in the Spotlight of Cancer. Int J Mol Sci, 2016. 17(12). Takebe N, et al. Targeting cancer stem cells by inhibiting Wnt, Notch, and Hedgehog pathways. Nat Rev Clin Oncol. 2011;8(2):97–106. Lee SH, et al. Wnt/β-catenin signalling maintains self-renewal and tumourigenicity of head and neck squamous cell carcinoma stem-like cells by activating Oct4. J Pathol. 2014;234(1):99–107. Lamb R, et al. Wnt pathway activity in breast cancer sub-types and stem-like cells. PLoS ONE. 2013;8(7):e67811. Cordero JB, Sansom OJ. Wnt signalling and its role in stem cell-driven intestinal regeneration and hyperplasia. Acta Physiol (Oxf). 2012;204(1):137–43. Aberle H et al. beta-catenin is a target for the ubiquitin-proteasome pathway. Embo j, 1997. 16(13): pp. 3797 – 804. Esen E, et al. WNT-LRP5 signaling induces Warburg effect through mTORC2 activation during osteoblast differentiation. Cell Metab. 2013;17(5):745–55. Sherwood V. WNT signaling: an emerging mediator of cancer cell metabolism? Mol Cell Biol. 2015;35(1):2–10. Wang J, Sinha T, Wynshaw-Boris A. Wnt signaling in mammalian development: lessons from mouse genetics. Cold Spring Harb Perspect Biol, 2012. 4(5). Hecht A, Kemler R. Curbing the nuclear activities of beta-catenin. Control over Wnt target gene expression. EMBO Rep. 2000;1(1):24–8. Tables Table 1 Expression patterns of ITGA6 in pancreatic cancer tissues and para-carcinoma tissues revealed in immunohistochemistry analysis. ITGA6 expression Tumor tissue Para-carcinoma tissue p value Cases Percentage Cases Percentage 0.018 Low 43 51.8% 55 68.7% High 40 48.2% 25 31.3% Table 2 Relationship between ITGA6 expression and tumor characteristics in patients with pancreatic cancer Features No. of patients ITGA6 expression p value low high All patients 83 43 40 Age 0.598 ≤ 62years 44 24 20 > 62years 39 19 20 Gender 0.247 Male 51 29 22 Female 32 14 18 Tumor size 0.891 ≤ 4cm 45 23 22 > 4cm 38 20 18 Grade 0.193 I 1 0 1 II 59 34 25 III 21 9 12 IV 2 0 2 Stage 0.007 I 37 25 12 II 44 18 26 IV 2 0 2 Tumor infiltrate 0.732 T1 5 1 4 T2 65 36 29 T3 13 6 7 Table 3 Relationship between ITGA6 expression and tumor characteristics in patients with pancreatic cancer ITGA6 Stage Spearman 0.300 Significance (two-tailed) 0.006 N 83 Table 4 Expression patterns of GSK-3β in pancreatic cancer tissues and para-carcinoma tissues revealed in immunohistochemistry analysis GSK-3β expression Tumor tissue Para-carcinoma tissue p value Cases Percentage Cases Percentage 0.040 Low 37 50% 37 84.1% High 37 50% 7 15.9% Table 5 Relationship between GSK-3β expression and tumor characteristics in patients with pancreatic cancer Features No. of patients GSK-3β expression p value low high All patients 74 37 37 Age (years) 0.488 ≤ 62 35 19 16 > 62 39 18 21 Gender 0.351 Male 42 19 23 Female 32 18 14 Grade 0.005 II 38 25 13 III 35 12 23 IV 1 0 1 Tumor size (cm) 0.162 ≤ 4 42 18 24 > 4 32 19 13 Stage 0.745 I 32 17 15 II 41 19 22 IV 1 1 0 Tumor infiltrate 0.594 T1 1 0 1 T2 60 30 30 T3 13 7 6 lymphatic metastasis (N) 0.245 N0 41 23 18 N1 33 14 19 Metastasis 0.317 M0 73 36 37 M1 1 1 0 Table 6 Relationship between GSK-3β expression and tumor characteristics in patients with pancreatic cancer GSK-3β Grade Spearman 0.331 Significance (two-tailed） 0.004 N 74 Additional Declarations No competing interests reported. Supplementary Files uncroppedGelsandBoltsinages.pdf TableS1S3.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 04 Mar, 2026 Submission checks completed at journal 26 Feb, 2026 First submitted to journal 24 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-8683090","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601833774,"identity":"f0943953-5c9b-4979-a9b4-cc39ebf66517","order_by":0,"name":"Xiaofeng Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Yang","suffix":""},{"id":601833775,"identity":"5d066c5f-e5a6-47e4-aaca-ba4c0420808f","order_by":1,"name":"yuexin Ren","email":"","orcid":"","institution":"Guangdong Provincial Key Laboratory of Gastroenterology, Institute of Gastroenterology of Guangdong Province","correspondingAuthor":false,"prefix":"","firstName":"yuexin","middleName":"","lastName":"Ren","suffix":""},{"id":601833776,"identity":"1ee11c62-9819-4e9d-b91e-b0e5aa4de36a","order_by":2,"name":"Wenchao Ma","email":"","orcid":"","institution":"The forth Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenchao","middleName":"","lastName":"Ma","suffix":""},{"id":601833777,"identity":"08037541-ca49-4efa-8c32-57bc03cc079d","order_by":3,"name":"Zhengquan Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhengquan","middleName":"","lastName":"Li","suffix":""},{"id":601833782,"identity":"06336a88-23f6-4d73-818f-9ab493398713","order_by":4,"name":"Sijun Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sijun","middleName":"","lastName":"Chen","suffix":""},{"id":601833784,"identity":"0941f7d1-b38c-420e-ae6d-9077cef1f76f","order_by":5,"name":"Yanbo Zhao","email":"","orcid":"","institution":"The First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanbo","middleName":"","lastName":"Zhao","suffix":""},{"id":601833786,"identity":"caad927b-d9fc-4101-8e4b-55cd6dc1cb82","order_by":6,"name":"jiawu Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"jiawu","middleName":"","lastName":"Li","suffix":""},{"id":601833791,"identity":"82af3f7f-efe1-4c50-9b7a-1c264b9d3bd1","order_by":7,"name":"zhituo Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIie3RPQrCMBiA4a8UkiXYNVLoGT4JFKEFr5I6xCWI4AWUQl08gMfwDsG6eICCiy6dO3YQNHQWo5tDXjJkyEP+AHy+P2wUAgFpJ1EYbG8d5omTkIFYM96VZnJYKeEmw7AEL7WKWWcKN6G0vd17k0Cj0zjHUAI1p6PjYAuU0ojgoEWmkSyBKdU4SM0tKUquJ1eNbA2cpQ4SVAOpuMZ4irzYuElIBrJn9TwGxG8IsUQtBKf2kfcoBXHdJYrO7bjPs2Rm7Ff2j2cSUVN/JG/2/W25z+fz+d71AotcQgJSH/DcAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"zhituo","middleName":"","lastName":"Li","suffix":""},{"id":601833796,"identity":"61549281-1002-4047-b1f7-5dc6f0dfb747","order_by":8,"name":"Qing Zou","email":"","orcid":"","institution":"The First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zou","suffix":""}],"badges":[],"createdAt":"2026-01-24 01:39:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8683090/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8683090/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104321133,"identity":"7dc5ae8f-d44e-4946-8b08-c1f6db470ffe","added_by":"auto","created_at":"2026-03-10 13:17:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62824979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElevated ITGA6 Expression Correlates with Poor Prognosis in PC. \u003c/strong\u003e(A) Lentiviral shRNA screen in SW1990 cells revealed that ITGA6 knockdown exerted the strongest growth-inhibitory effect among the five TCGA-prioritised candidates. (B) The expression level of ITGA6 mRNA in the TCGA dataset was analyzed. (C, D) Overall survival (OS, C) and progression-free survival (PFS, D) curves stratified by ITGA6 expression level. (E) Multivariable Cox regression analysis of ITGA6 expression in pancreatic cancer. (F) IHC staining for ITGA6 in pancreatic cancer tissue microarrays containing adjacent tissues and normal tissues. (G) Kaplan-Meier analysis of the association between ITGA6 expression and patient survival.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8683090/v1/f592615283524b8a1de73b20.png"},{"id":104779406,"identity":"d4ac9968-dcb0-4f30-a59f-19ad7f1b2036","added_by":"auto","created_at":"2026-03-17 07:39:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3688327,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eITGA6 Promotes PC Cell Proliferation, Migration and Invasion In Vitro. \u003c/strong\u003e(A) The expression of ITGA6 in cells was detected by RT-qPCR.（B）Three distinct shRNAs targeting ITGA6 (shITGA6-1, shITGA6-2, shITGA6-3) were delivered via lentiviral vectors, along with control shCtrl and empty vector (CON). RT-qPCR detected ITGA6 expression levels in different shRNA-treated cancer cell lines. (C) ITGA6 protein expression in shITGA6-1, shITGA6-2, and shITGA6-3 groups. (D) CCK-8 assay showed marked growth inhibition in SW1990 cells transduced with shITGA6-1 or shITGA6-2. (E, F) The knockdown efficiency was validated by qRT-PCR (E) and Western blot (F) analyses. (G, H) Cell proliferation viability was assessed by CCK-8 assay (G) and colony formation assay (H). (I) Apoptosis was analyzed by flow cytometry. (J, K) Cell migration abilities were assessed by Transwell (J) and wound healing assays (K).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8683090/v1/39bbc8981d0f8a66642192c6.png"},{"id":105903767,"identity":"87034316-e6aa-4738-b9a2-8ba76ddf2390","added_by":"auto","created_at":"2026-04-01 09:52:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3563940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMechanistic Exploration of ITGA6/GSK3β in PC Progression.\u003c/strong\u003e (A) The WNT signaling pathway gene set was significantly enriched in the ITGA6-high expression group by GSEA. (B) The mRNA expression levels of PSEN1, CSNK1A1, TBL1XR1, PPP3CA, GSK3β, CCND1, and ITGA6 were analyzed by quantitative real-time PCR (qRT-PCR). (C) Western blot analysis of GSK3β expression in ITGA6-depleted cell lines. (D, E) Co-immunoprecipitation (Co-IP, D) and immunofluorescence co-localization assays (E) were performed to verify the protein interaction and co-localization between AEBP2 and ITGA6. (F) ChIP-qPCR with anti-AEBP2 antibody showed significant enrichment at the GSK3β promoter \u003cem\u003evs.\u003c/em\u003e IgG control. (G) ChIP-qPCR revealed increased AEBP2 binding to the GSK3β promoter upon ITGA6 overexpression. (H) Luciferase reporters carrying serial deletions and point mutations of the GSK3β promoter demonstrated that AEBP2 responsiveness required the -768 to -757 bp motif (site 3); mutation of this sequence (GSK3β-mut3) abolished reporter activity. (I) ChIP-qPCR with site-specific primers confirmed selective enrichment of AEBP2 at site 3. (J) Representative IHC images of a pancreatic-cancer tissue microarray showed elevated GSK3β protein in tumor tissues.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8683090/v1/902381cc06bf6f4ae5fd85e6.png"},{"id":104321128,"identity":"f6ef0e4d-02a2-457a-9cec-ad63f8e02754","added_by":"auto","created_at":"2026-03-10 13:17:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1628805,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn Vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIn Vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eValidation of ITGA6-Mediated PC Regulation via GSK3β.\u003c/strong\u003e (A, B) qRT-PCR analysis (A) and western blot analysis (B) verified ITGA6 overexpression and GSK3β knockdown in ASPC-1 and SW1990 cells. (C) CCK-8 assays were performed to assess the proliferative ability of ASPC-1 and SW1990 cells under the indicated conditions. (D) Transwell migration assays were conducted to evaluate cell migratory capacity. (E) Representative images of xenograft tumors harvested from each experimental group. (F) Tumor growth curves of mice in each group. (G) Quantitative analysis of tumor weight in each group at the endpoint of the experiment. (H) Body weight changes of mice in each group during the experimental period. (I-K) Immunohistochemical staining and analysis of ITGA6, GSK3β, and the proliferation marker Ki67 in tumor tissues from each group.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8683090/v1/7e9aa593871481d9724dfa54.png"},{"id":104321132,"identity":"fa86b07a-a602-4de1-8f93-94ca23a9a874","added_by":"auto","created_at":"2026-03-10 13:17:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6428410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eITGA6 Regulates PC Stemness and Gemcitabine Sensitivity via GSK3β. \u003c/strong\u003e(A) Western blot analysis of stemness markers (c-Myc, Nanog, OCT4, SOX2) in control (shCtrl) and ITGA6-knockdown (shITGA6) ASPC-1 and SW1990 cells. GAPDH served as a loading control. (B) Sphere formation assays of shCtrl and shITGA6 ASPC-1 and SW1990 cells. Quantification of the number of spheres per field is shown on the right. (C) ALDH activity measured by flow cytometry in shCtrl and shITGA6 cells. The percentage of ALDH+ cells is quantified on the right. (D) Western blot analysis of Nanog, OCT4, and SOX2 in cells with ITGA6 overexpression (OE-ITGA6), alone or in combination with GSK3β knockdown (shGSK3β). (E, F) Sphere formation assays (E) and ALDH activity assays (F) in the indicated rescue groups including NC+shCtrl, ITGA6+shCtrl, NC+shGSK3β and ITGA6+shGSK3β groups. (G, H) Cell proliferation (G) and colony formation assays (H) evaluating chemosensitivity in shCtrl and shITGA6 cells treated with 50 nM gemcitabine. Viability and surviving fractions were quantified. (I, J) Cell proliferation (I) and colony formation assays (J) evaluating chemosensitivity in shITGA6 cells with or without concurrent GSK3β overexpression (OE-GSK3β) upon 50 nM gemcitabine treatment. Data are presented as mean ± SD (n=3). * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8683090/v1/50f6ccc57dd104fa8c1b4c44.png"},{"id":104405989,"identity":"168db0fb-a33d-4c25-8fea-3720e05a8a79","added_by":"auto","created_at":"2026-03-11 12:24:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":421028,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMechanistic Delineation of ITGA6-AEBP2 Functional Interaction \u003c/strong\u003e(A) Schematic representation of the domain architecture of full-length ITGA6 (ITGA6-FL) and truncation constructs (δ1–δ3) based on SMART database analysis. (B) Co-immunoprecipitation (Co-IP) assays in 293T cells co-transfected with Flag-AEBP2 and HA-tagged ITGA6 truncations. Immunoblots showed AEBP2 binding requires the Int alpha domain. (C) Dual-luciferase reporter assays measuring GSK3β promoter activity in cells expressing AEBP2 together with either ITGA6-WT or the Int alpha-deleted mutant (ITGA6-Δ1). (D, E) CCK-8 proliferation assays (D) and colony formation assays (E) in PC cells expressing empty vector, ITGA6-FL, or ITGA6-Δ1. (F, G) Sphere formation (F) and ALDH activity assays (G) in PC cells expressing the indicated constructs. ** p \u0026lt; 0.01, *** p \u0026lt; 0.001, ns, not significant\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8683090/v1/b4e39ed922e43bcfd81c9baa.png"},{"id":104405361,"identity":"bfac69ff-39fe-4176-85d7-162f055c4980","added_by":"auto","created_at":"2026-03-11 12:22:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":660714,"visible":true,"origin":"","legend":"","description":"","filename":"uncroppedGelsandBoltsinages.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8683090/v1/044df0a37a30065c204456f5.pdf"},{"id":104321126,"identity":"245cda09-46c5-4c45-94cb-c337741b2541","added_by":"auto","created_at":"2026-03-10 13:17:30","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19011,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1S3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8683090/v1/3fec7a14fbd86c1b1a11cf58.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The ITGA6-AEBP2 Complex Promotes Transcriptional Activation of GSK3β to Augment Wntβ-Catenin Signaling and Stemness in Pancreatic Cancer","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC)\u0026mdash;a highly aggressive malignancy arising from pancreatic ductal epithelium\u0026mdash;is among the most lethal gastrointestinal cancers due to its insidious onset and invasive propensity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The dismal prognosis (5\u0026ndash;10% 5-year survival rate) and high mortality of PDAC stem from its characteristic early dissemination, nonspecific symptomatology, and profound therapeutic resistance, resulting in surgical resection eligibility for \u0026lt;\u0026thinsp;20% of patients. This disease remains a recalcitrant outlier among solid tumors in terms of survival outcomes. While molecularly targeted therapies have achieved landmark breakthroughs (e.g., KRAS G12C inhibitors demonstrating 36% objective response rates), their clinical impact is constrained by the rarity of actionable subtypes (KRAS G12C mutations comprise only 1\u0026ndash;2% of all KRAS variants) and pervasive tumor heterogeneity. Over 50% of patients develop early resistance, and current precision strategies benefit\u0026thinsp;\u0026lt;\u0026thinsp;5% of advanced cases, with median progression-free survival gains limited to 3\u0026ndash;5 months. These challenges underscore the urgent need for multi-omics approaches to develop broadly applicable targeted interventions. Deciphering the molecular underpinnings of PDAC initiation, progression, and metastasis is therefore critical for designing effective therapeutic strategies to improve patient outcomes and quality of life.\u003c/p\u003e \u003cp\u003eIntegrins are a family of heterodimeric transmembrane receptors composed of α and β subunits. Accumulating evidence highlights their critical role in cell migration and invasion [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Certain integrins also regulate stem cell function and serve as stem cell markers. Notably, ITGA6 has emerged as a key player in tumorigenesis and progression across multiple cancer types [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. ITGA6 typically pairs with integrin β1 (ITGB1) or integrin β4 (ITGB4) to form the α6β1 or α6β4 receptor complexes. While prior studies have primarily focused on ITGA6\u0026rsquo;s role in metastasis and malignant phenotypes [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], our work further investigates its broader biological functions, particularly its impact on tumor progression, invasion, and metastatic dissemination.\u003c/p\u003e \u003cp\u003eTumor stemness refers to the self-renewal and differentiation capacities of cancer stem cells (CSCs), which serve as key drivers of tumor initiation, progression, and therapeutic resistance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In PC, CSCs exacerbate malignancy by promoting invasion, metastasis, and immune evasion. These cells exhibit enhanced survival mechanisms, including upregulated drug efflux pumps and activated DNA repair pathways, contributing to chemotherapy resistance and disease recurrence. Given the pivotal role of CSCs in PC, targeting stemness-related pathways has emerged as a highly promising therapeutic strategy. The Wnt/β-catenin signaling pathway serves as a pivotal regulator of stemness across multiple malignancies, including PC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Activation of Wnt signaling enhances CSC properties by upregulating core pluripotency transcription factors such as Nanog, Oct4, and Sox2, thereby sustaining stem cell multipotency [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In PC, aberrant Wnt signaling drives both tumorigenesis and chemoresistance, underscoring its essential role in maintaining the CSC population. Notably, emerging evidence reveals crosstalk between integrins (including ITGA6) and Wnt signaling [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], suggesting a potential mechanistic link between cell adhesion machinery and stemness regulation.\u003c/p\u003e \u003cp\u003eFurthermore, in PC, stemness-associated pathways\u0026mdash;including Wnt, Notch, and Hedgehog\u0026mdash;collectively contribute to chemotherapy resistance and disease recurrence [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Given that ITGA6 has been demonstrated to sustain CSC properties in other malignancies, its overexpression in PC may similarly promote therapy resistance by supporting a stem-like phenotype. Consequently, targeting ITGA6 represents a promising strategy to disrupt CSC maintenance mechanisms and enhance tumor sensitivity to existing therapies.\u003c/p\u003e \u003cp\u003eIn this study, ITGA6 drove pancreatic-cancer progression by stabilizing AEBP2-mediated GSK3β transcription and sustaining cancer-stem-cell traits. Using TCGA-PAAD data, tissue microarrays and gain-/loss-of-function studies \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e, we showed that high ITGA6 predicted poor survival and that genetic blockade suppressed proliferation, migration and gemcitabine resistance. These findings nominated ITGA6-AEBP2-GSK3β as a druggable axis to curb PC stemness and chemoresistance.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cem\u003eData Acquisition and Processing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available transcriptomic data were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). The expression levels of ITGA6 in tumor tissues and adjacent normal tissues were compared using the TCGA pancreatic adenocarcinoma (PAAD) dataset. Differential expression analysis was performed using the DESeq2 (for RNA-seq data) or limma (for microarray data) R packages, with significance defined as |log2 fold change| \u0026gt; 1 and adjusted p-value \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eTo assess the prognostic value of ITGA6 in pancreatic cancer, survival analysis was conducted using TCGA-PAAD cohort data. Kaplan-Meier curves were generated, and log-rank tests were applied to compare overall survival (OS) and progression-free survival (PFS) between high- and low-ITGA6 expression groups, stratified by median expression. Univariate and multivariate Cox proportional hazards regression analyses were performed to evaluate the independent prognostic significance of ITGA6, adjusting for clinicopathological covariates (e.g., age, gender, tumor stage). All statistical analyses were performed using R software, and a p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCell Lines and Cell Culture\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe human pancreatic cancer cell lines ASPC-1 and SW1990 were cultured in Dulbecco's Modified Eagle Medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Cells were maintained at 37°C in a humidified atmosphere containing 5% CO₂.\u003c/p\u003e\n\u003cp\u003eCells were routinely passaged every 2-3 days using 0.25% trypsin-EDTA when reaching 80-90% confluence. All cell lines were regularly tested for mycoplasma contamination using the MycoAlert Mycoplasma Detection Kit and maintained for no more than 20 passages for experimental use.\u003c/p\u003e\n\u003cp\u003eFor experimental procedures, cells in logarithmic growth phase were harvested and seeded at appropriate densities according to specific experimental requirements. Cell morphology and growth status were monitored daily using phase-contrast microscopy.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical specimens\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePancreatic adenocarcinoma tissue microarrays (TMAs) containing 83 tumor cores and 80 matched adjacent-normal cores (HPan-Ade180Sur-01, Shanghai Outdo Biotech, China) and a second TMA containing 74 tumor cores and 44 normal cores (HPanA125Su01-M-021, Shanghai Zuocheng Biotechnology, China) were purchased under protocols approved by the Ethics Committee of XXXX Hospital. All specimens were collected with written informed consent from each donor, anonymised by the vendors, and accompanied by complete clinicopathological data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImmunohistochemical (IHC) staining\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIHC was performed to evaluate ITGA6 expression using standard protocols. Briefly, FFPE sections (4 μm thick) were baked at 65°C for 30 min, deparaffinized in xylene \u0026nbsp;3 times (10 min each), and rehydrated through a graded ethanol series (100%, 100%, 75%; 5 min each) followed by distilled water rinsing. Antigen retrieval was conducted in citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) using a pressure cooker (95°C, 10 min for citrate; 100°C, 30 min for EDTA). Endogenous peroxidase activity was blocked with 3% H₂O₂ for 5 min, and nonspecific binding was minimized with 5% goat serum for 15 min. Sections were incubated with primary antibodies (see Table S1 for details) at 37°C for 1 h or 4°C overnight. After washing, HRP-conjugated secondary antibodies were applied at 37°C for 1 h. Signal detection used DAB substrate for 5 min, followed by hematoxylin counterstaining (10–15 s), differentiation in 1% acid alcohol, dehydration, and mounting with neutral resin. Stained slides were imaged under an Olympus IX73 microscope. ITGA6 expression was semi-quantitatively assessed based on staining intensity (0: none; 1: weak; 2: moderate; 3: strong) and positive cell percentage (0: \u0026lt;5%; 1: 5–25%; 2: 26–50%; 3: \u0026gt;50%). The final score (range: 0–9) was calculated by multiplying intensity and percentage scores. Clinical parameters (age, gender, tumor size, histologic grade, TNM stage) were correlated with ITGA6 expression using appropriate statistical tests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLentiviral Vector Construction and Cell Transfection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor knockdown experiments, three short hairpin RNAs (shRNAs) targeting ITGA6 (designated shITGA6-1, shITGA6-2, shITGA6-3) and one targeting GSK3β (shGSK3β) were designed and synthesized. The target sequences are listed in Table S2. Each shRNA oligo was annealed and cloned into the Age I/EcoR I sites of the BR-V108 lentiviral vector (YBR, China), which contains the hU6 promoter driving shRNA expression, a CMV-EGFP reporter, and a puromycin resistance cassette. For overexpression experiments, the full-length coding sequence of ITGA6 was synthesized and inserted into the LV-013 lentiviral overexpression vector (YBR, China) using EcoR I and Age I restriction sites. The LV-013 vector used for rescue experiments contains a hygromycin resistance cassette to allow dual selection in shRNA-incorporated cells. All constructs were transformed into Stable competent cells (Weidi Biotechnology, #DL1080, China), and positive clones were selected and verified by colony PCR and Sanger sequencing. Endotoxin-free plasmids were extracted using the EndoFree Midi Plasmid Kit (#DP118, TIANGEN, China). For lentivirus production, 293T cells were co-transfected with the recombinant lentiviral vector, pHelper 1.0, and pHelper 2.0 plasmids (YBR, China) using a polyethylenimine-based transfection reagent. Virus-containing supernatants were collected 48–72 h post-transfection, filtered through 0.45 μm filters, and concentrated by ultracentrifugation. Viral titers were determined by quantitative PCR and functional assays in 293T cells. ASPC-1 and SW1990 cells were transduced with the respective lentiviruses in the presence of polybrene (8 μg/mL). For single transduction experiments, stable knockdown or overexpression cell lines were selected using puromycin (2 μg/mL; #P9620, Sigma-Aldrich, USA) for 7–10 days. For rescue experiments, cells were first transduced with the shRNA lentivirus and selected with puromycin to establish stable knockdown populations. After confirmation of knockdown efficiency by qRT-PCR and western blot, cells were subsequently transduced with the ITGA6 overexpression lentivirus and selected using hygromycin. Double-resistant cells were maintained under dual antibiotic selection to ensure stable co-expression of both constructs. All functional assays were performed using bulk-selected stable cell populations rather than single-cell-derived clones. Following antibiotic selection, polyclonal populations were expanded and validated by qRT-PCR and western blot prior to downstream experiments to minimize clonal variation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQuantitative real-time PCR (qRT-PCR) analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from samples using TRIzol reagent (#T9424, Sigma-Aldrich, USA) following the manufacturer's protocol. RNA concentration and purity were assessed by measuring the absorbance at 260 nm and 280 nm using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA). For cDNA synthesis, 1 μg of total RNA was reversely transcribed using Hiscript QRT SuperMix (#R123-01, Vazyme, China) with gDNA wiper to remove genomic DNA contamination. The reverse transcription reaction was performed under the following conditions: 42°C for 2 min, 55°C for 15 min, and 85°C for 2 min. The resulting cDNA was stored at −80°C until further use.\u003c/p\u003e\n\u003cp\u003eqPCR was carried out on an ABI QuantStudio 7 Flex Real-Time PCR System (Applied Biosystems, USA) using AceQ SYBR Green Master Mix (#Q111-02, Vazyme, China). Each 10 μL reaction mixture contained 5 μL of master mix, 0.25 μL each of forward and reverse primers (10 μM; synthesized by Genewiz, China), 2 μL of cDNA template, and 2.3 μL of nuclease-free water. The thermal cycling protocol consisted of an initial denaturation step at 95°C for 1 min, followed by 45 cycles of 95°C for 40 sec and 60°C for 30 sec. A melt curve analysis was subsequently performed to verify the specificity of amplification. The primer sequences used in this study were listed in Table S3. The relative expression levels of target genes were normalized to the endogenous reference gene GAPDH and calculated using the\u0026nbsp;2\u003csup\u003e−ΔΔ\u003cem\u003eCt\u003c/em\u003e\u003c/sup\u003emethod.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWestern Blot Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTotal proteins were extracted from cells using ice-cold lysis buffer (P0013, Beyotime, China) supplemented with protease inhibitors. Protein concentrations were determined using a BCA protein assay kit (P0009, Beyotime, China) according to the manufacturer’s instructions. For each sample, 20–30 μg of protein was separated by 10% SDS-PAGE and transferred onto PVDF membranes.\u003c/p\u003e\n\u003cp\u003eThe membranes were blocked with 5% non-fat milk in TBST for 1 h at room temperature, followed by overnight incubation at 4°C with primary antibodies (see Table S1 for details, including dilutions and target protein sizes). After washing with TBST, the membranes were incubated with corresponding horseradish peroxidase (HRP)-conjugated secondary antibodies for 1 h at room temperature. Protein bands were visualized using an enhanced chemiluminescence (ECL) substrate (A0208/A0216, Beyotime, China) and imaged with an AI600 system (GE Healthcare). GAPDH was used as an internal loading control for normalization. All experiments were performed in triplicate to ensure reproducibility. Band intensities were quantified using ImageJ software (National Institutes of Health, USA).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChromatin Immunoprecipitation (ChIP) Assay\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eChromatin immunoprecipitation (ChIP) was performed to assess the binding of AEBP2 to the GSK3β promoter region. Briefly, cells were cross-linked with 1% formaldehyde for 10 min at room temperature, followed by quenching with 125 mM glycine. Chromatin was sheared by sonication to generate DNA fragments of 200–500 bp. For each immunoprecipitation (IP), 5–10 μg of chromatin was diluted in 400 μL of 1X ChIP buffer containing protease inhibitor cocktail (PIC) and incubated overnight at 4°C with either anti-AEBP2 antibody (1:50; #11232-2-AP, Proteintech) or normal rabbit IgG (2 μg; #2729, CST) as a negative control. Protein G agarose beads (#9007, CST) were then added and incubated for 2 h at 4°C. The beads were sequentially washed with low-salt (1X ChIP buffer) and high-salt (1X ChIP buffer + 350 mM NaCl) wash buffers. Immune complexes were eluted in 1X ChIP elution buffer (#7009, CST) at 65°C for 30 min, followed by reverse cross-linking with 5 M NaCl and proteinase K (#10012, CST) at 65°C for 2 h. DNA was purified using a DNA purification column (#10010, CST) and analyzed by quantitative PCR (qPCR) to detect the enrichment of the GSK3β promoter region.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCo-immunoprecipitation (Co-IP) Assay\u003c/em\u003eProtein interactions between ITGA6 and AEBP2 were analyzed by co-IP. Cells were lysed in IP buffer containing protease inhibitors. After centrifugation, 500 μg of lysate was incubated with anti-ITGA6 antibody (Table S1) overnight at 4°C. IgG was used as control. Protein A/G magnetic beads (#88802, Thermo Fisher, USA) were added for 2 h. Beads were washed and bound proteins were eluted in sample buffer. Eluates were analyzed by immunoblotting using antibodies listed in Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCell viability assay\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePancreatic cancer cells were cultured in DMEM medium (#10-013-CVR, Corning, USA) supplemented with 10% fetal bovine serum (FBS, #VS500T, Ausbian, China) at 37°C in a 5% CO₂ incubator. Cells were seeded in 96-well plates (#3596, Corning, USA) at a density of 2,500 cells per well (100 µL/well) and allowed to adhere. Cell viability was assessed at 24, 48, 72, 96, and 120 h post-seeding using a CCK-8 assay kit (#96992, Sigma, USA). Briefly, 10 µL of CCK-8 reagent was added to each well and incubated for 2–4 h at 37°C. The absorbance at 450 nm (OD450) was measured using a Tecan Infinite M2009PR microplate reader after gentle agitation to ensure homogeneity. The fold change in cell proliferation was calculated by normalizing the OD450 values at each time point to the baseline (Day 1). Statistical analysis was performed using GraphPad Prism software, with a two-tailed Student’s *t*-test applied to determine significance (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScratch Assay\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCell migration was evaluated using a standard scratch assay. Pancreatic cancer cells were seeded in 96-well plates (#3599, Corning, USA) at a density of 50,000 cells per well with 100 µL culture medium (#CM-001, Procell, China). After 24 h incubation, a straight scratch was created in the confluent cell monolayer using a sterile 200-µL pipette tip (#TIP2001, Axygen, USA). Detached cells were removed by PBS washing (#SH30256.01, HyClone, USA). Images of the scratch were captured at 0 h and 24 h post-scratching using an inverted microscope (#IX73, Olympus, Japan) equipped with a CCD camera (#DP80, Olympus, Japan). The migration distance was calculated as the difference in scratch width between the two time points, and the migration rate was determined as the ratio of migration distance to the initial scratch width (0 h). Data are presented as mean ± SD, and statistical significance (P \u0026lt; 0.05) was analyzed by Student’s t-test using GraphPad Prism 9.0 (#GPM900, GraphPad, USA).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTranswell Assay\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCell migration was evaluated using a Transwell chamber assay with 8-μm pore polycarbonate membranes (#3422, Corning, USA). The Transwell inserts were pre-hydrated with 100 μL serum-free medium for 1–2 h at 37°C. Cells were trypsinized, resuspended in low-serum medium (0.5% FBS), and counted. After removing the hydration medium, 100 μL of cell suspension (1 × 10⁵ cells/well) was seeded into the upper chamber, while 600 μL of complete medium containing 30% FBS (chemoattractant) was added to the lower chamber. The plate was incubated for 48 h at 37°C under 5% CO₂.\u003c/p\u003e\n\u003cp\u003eNon-migrated cells on the upper membrane surface were carefully removed with a cotton swab. Migrated cells attached to the lower surface were fixed with 4% paraformaldehyde for 15 min, stained with 0.1% crystal violet for 5 min, washed with PBS, and air-dried. Five random fields per insert were photographed at 200× magnification using an Olympus IX73 microscope (#IX73, Olympus, Japan), and migrated cells were manually counted. Data are presented as mean ± SD from three independent experiments. Statistical significance was determined by two-tailed Student’s t-test (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubcutaneous Xenograft Tumor Model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFemale BALB/c nude mice (4–6 weeks old) were purchased from GemPharmatech Co., Ltd. (#SCXK(Su)2023-0009, China). All animal procedures were approved by the Institutional Animal Care and Use Committee and conducted in accordance with ethical guidelines. ASPC-1 and SW1990 cells (provided by YiBeRui Biotechnology) were suspended in PBS and mixed with Matrigel (1×10^7 cells/200 µL) and then subcutaneously inoculated into the right flank of the mice. The mice were randomly divided into four groups (n=6 per group): (1) NC + shCtrl (negative control + non-targeting shRNA), (2) NC + shGSK3B (negative control + GSK3B-targeting shRNA), (3) ITGA6 + shCtrl (ITGA6 overexpression + non-targeting shRNA), and (4) ITGA6 + shGSK3B (ITGA6 overexpression + GSK3B-targeting shRNA). Tumor growth was monitored from day 7 post-inoculation, and tumor volume was measured every 2–3 days using the formula V = 1/2 × length × width². Mice were euthanized on day 18, and tumors were excised and weighed. Body weight was recorded periodically throughout the experiment. Statistical analysis was performed using a two-tailed t-test (with F-test for homogeneity of variance; p \u0026lt; 0.05 considered significant).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDual-Luciferase Reporter Assay\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the transcriptional regulation of GSK3β by AEBP2, a dual-luciferase reporter assay was performed. The putative promoter region of GSK3β was amplified by PCR and cloned into the pGL3-Basic vector (Promega, #E1751, USA) upstream of the firefly luciferase gene using the HB-infusion™ Seamless Cloning Kit (Vazyme, #C112, China). The recombinant plasmid was verified by sequencing and designated as pGL3-GSK3β-promoter. The AEBP2 overexpression plasmid (pcDNA3.1-AEBP2) and the empty vector (pcDNA3.1) were constructed using standard molecular cloning techniques with restriction enzymes (NEB, #R0101, USA) and DH5α competent cells (Invitrogen, #18265017, USA). Plasmid DNA was extracted using the TIANprep Mini Plasmid Kit (TIANGEN, #DP103, China).\u003c/p\u003e\n\u003cp\u003eFor the reporter assay, HEK293T cells were seeded in 24-well plates and co-transfected with 400 ng of pGL3-GSK3β-promoter, 20 ng of pRL-TK (Promega, #E2241, USA; expressing Renilla luciferase as an internal control), and 200 ng of either pcDNA3.1-AEBP2 or the empty vector using Lipofectamine 3000 (Invitrogen, #L3000015, USA). After 48 h of transfection, cells were lysed, and firefly and Renilla luciferase activities were measured sequentially using the Dual-Luciferase Reporter Assay System (Promega, #E1910, USA) on a microplate reader. Firefly luciferase activity was normalized to Renilla luciferase activity for each sample. Statistical analysis was performed using Student’s t-test, and data are presented as mean ± SD from three independent experiments.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFlow Cytometry Analysis of Apoptosis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCell apoptosis was assessed using the Annexin V-APC/PI double staining kit according to the manufacturer's protocol. Briefly, cells were harvested 5 days post-infection and washed twice with ice-cold D-Hanks buffer (pH 7.2-7.4), followed by centrifugation at 1,300 rpm for 5 min. The cell pellet was resuspended in 200 μL of 1× binding buffer, then incubated with 10 μL Annexin V-APC for 15 min at room temperature in the dark. Subsequently, 5 μL propidium iodide (PI) was added for counterstaining. Samples were immediately analyzed using a Guava easyCyte HT flow cytometer. Apoptotic cell populations were quantified as the sum of early apoptotic (Annexin V+/PI-) and late apoptotic (Annexin V+/PI+) cells.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eColony Formation Assay\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe colony formation assay was performed to evaluate the clonogenic potential of cells \u003cem\u003ein vitro\u003c/em\u003e. Briefly, cells in the logarithmic growth phase were harvested by trypsinization using 0.25% trypsin (#10-013-CVR, Corning, USA) and resuspended in complete medium consisting of DMEM (#A11-102, Ausbian, China) supplemented with 10% fetal bovine serum (#T0458-50G, Sangon Biotech, China). Subsequently, cells were seeded into 6-well plates (#AR-0752, Corning, USA) at a density of 500–1000 cells per well and cultured at 37°C in a humidified atmosphere containing 5% CO₂ for 10–14 days. After incubation, the culture medium was discarded, and the cells were gently washed twice with D-Hanks solution. The colonies were fixed with 4% paraformaldehyde (Sinopharm Chemical Reagent Co., Ltd, China) for 15 min and then stained with 0.5% Giemsa staining solution (Shanghai Dingguo Biotech, China) for 20 min.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eALDH Activity Assay\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe ALDH activity was measured using a standard enzymatic assay based on the oxidation of NAD+ to NADH, which was monitored by the increase in absorbance at 340 nm (OD340). Briefly, cells were seeded and cultured in 2 mL of complete medium. After lentiviral infection and subsequent incubation, the cells were harvested and lysed. The lysates were centrifuged at 12,000 × g for 10 min at 4°C, and the supernatants were collected for the assay. For the ALDH activity measurement, 50 µL of each sample (diluted 5-fold) was mixed with 200 µL of reaction buffer containing 1 mM NAD+ and 10 mM propionaldehyde in 50 mM sodium pyrophosphate buffer (pH 9.5). The reaction was initiated by adding the substrate, and the absorbance at 340 nm was recorded at 30 s intervals for 5 min using a microplate reader. The ALDH activity was calculated based on the rate of NADH production and expressed as international units per milliliter (IU/mL). Each sample was measured in triplicate, and the average value was used for statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSpheroid Formation Assay\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell suspensions (1 × 10³ cells per well) were seeded in 96-well ultra-low-attachment plates (#7007, Corning, USA) pre-coated with 50 µL/well of Matrigel® (#356234, BD Biosciences, USA). Cells were cultured in serum-free DMEM/F-12 (#10-092-CV, Corning) supplemented with 20 ng/mL EGF (#E5036, Sigma-Aldrich, USA), 10 ng/mL bFGF (#F0291, Sigma-Aldrich) and 1× B-27 supplement (#17504044, Gibco, USA). Fresh medium (100 µL) was replenished every 48 h. After 7 days at 37 °C in 5 % CO₂, spheres\u0026nbsp;≥\u0026nbsp;50 µm in diameter were counted under an inverted microscope (Olympus IX73). Images were captured at 4× magnification and analysed using ImageJ v1.53t. Three independent experiments were performed in triplicate.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using [software name, e.g., SPSS v26.0, R v4.1.0, or GraphPad Prism v9.0], and a two-sided *p*-value \u0026lt; 0.05 was considered statistically significant. Continuous variables were presented as mean ± standard deviation (SD) or median (interquartile range, IQR), depending on data distribution assessed by the Shapiro-Wilk test. For comparisons between two groups, the Student’s *t*-test (for normally distributed data) or the Mann-Whitney U test (for non-normally distributed data) was applied. For comparisons among multiple groups, one-way ANOVA (with Tukey’s post hoc test) or the Kruskal-Wallis test (with Dunn’s post hoc test) was used, as appropriate.\u003c/p\u003e\n\u003cp\u003eSurvival analysis was conducted using the Kaplan-Meier method, and differences between groups were evaluated by the log-rank test. Univariable and multivariable Cox proportional hazards regression models were employed to identify independent prognostic factors, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs).\u003c/p\u003e\n\u003cp\u003eFor pathological correlation analysis, Spearman’s rank correlation coefficient (for nonparametric data) or Spearman rank correlation coefficient (for normally distributed data) was calculated to assess the strength and direction of associations. Categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriate.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of The First Affiliated Hospital of Harbin Medical University.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cem\u003eElevated ITGA6 Expression Correlates with Poor Prognosis in PC\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo identify genes critically involved in PC progression, we conducted a comprehensive analysis of gene expression profiles and clinical outcomes using TCGA database (GDC portal). Genes were selected based on the following criteria: log2FoldChange.exp \u0026gt; 1.0, Pval.exp \u0026lt; 0.05, HR.val.diff.os \u0026gt; 1.5, P.val.diff.os \u0026lt; 0.05, HR.val.diff.pfs \u0026gt; 1.5, P.val.diff.pfs \u0026lt; 0.05, with a focus on protein-coding genes. Upon sorting by log2FoldChange, CEP55, COL17A1, DIAPH3, ITGA6, and PBK emerged as top candidates. Subsequently, lentiviral-mediated knockdown experiments in SW1990 cells demonstrated that ITGA6 silencing exerted the most pronounced inhibitory effect on cell proliferation (Figure 1A). ITGA6 transcript levels were significantly elevated in tumor tissues \u003cem\u003evs.\u003c/em\u003e normal pancreas (log2FC=1.3, P=0.0185; Figure 1B). Patients were stratified into high- and low-expression groups based on median ITGA6 levels. Strikingly, high ITGA6 expression was associated with worse clinical outcomes: shorter median OS (17.48 \u003cem\u003evs.\u003c/em\u003e 22.83 months; HR=1.53, P=0.0423) and PFS (13.04 \u003cem\u003evs.\u003c/em\u003e 17.25 months; HR=1.58, P=0.02) (Figures 1C and 1D). Multivariate Cox regression confirmed ITGA6 as an independent prognostic factor (P=0.0247) after adjusting for AJCC stage and other clinicopathological features (Figure 1E). These findings were validated in our ti ssue microarray cohort (83 tumors \u003cem\u003evs.\u003c/em\u003e 80 adjacent tissues), where IHC confirmed significant ITGA6 overexpression in malignant tissues (P=0.018; Figure 1F, Table 1). Notably, ITGA6 levels showed positive correlations with advanced pathological stage (Mann-Whitney U and Spearman's tests; Tables 2-3) and inverse correlation with patient survival (Kaplan-Meier analysis, Figure 1G).\u003c/p\u003e\n\u003cp\u003eCollectively, our integrated bioinformatics and experimental evidence positions ITGA6 as both a promising prognostic biomarker and potential therapeutic target in PC pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eITGA6 Promotes PC Cell Proliferation and Migration In Vitro\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the functional role of ITGA6 in PC cells, we first examined its endogenous expression levels in normal human pancreatic ductal epithelial cells (HPDE6-C7) and multiple PC cell lines (SW1990, QGP-1, ASPC-1, and PATU 8988S). Notably, ITGA6 was significantly upregulated in all PC cell lines compared to HPDE6-C7 (P \u0026lt; 0.001), with particularly high expression in SW1990 and ASPC-1 cells (Figure 2A). We then constructed three ITGA6-targeting shRNAs (shITGA6-1, shITGA6-2, shITGA6-3) and transduced them into SW1990 cells alongside control shRNA (shCtrl) and empty vector (CON). qRT-PCR analysis revealed knockdown efficiencies of 40.3% and 44.4% for shITGA6-1 and shITGA6-2, respectively (Figure 2B). Western blot confirmed reduced ITGA6 protein levels in all shITGA6 groups, with the most pronounced suppression in shITGA6-2, followed by shITGA6-1 (Figure 2C). Based on CCK-8 assays demonstrating significant proliferation inhibition in shITGA6-1- and shITGA6-2-transduced cells (P \u0026lt; 0.001; Figure 2D), we selected shITGA6-1 and shITGA6-2 for subsequent experiments. Successful ITGA6 knockdown in ASPC-1 and SW1990 cells was confirmed at both transcriptional and translational levels through qRT-PCR and Western blot analyses (Figures 2E-F). Subsequent functional characterization demonstrated comprehensive phenotypic alterations upon ITGA6 silencing. CCK-8 and colony formation assays revealed significant impairment of cellular proliferation and clonogenic potential (Figures 2G-H), while flow cytometric analysis showed marked induction of apoptosis (Figure 2I). Furthermore, Transwell migration and wound healing assays consistently demonstrated suppressed migratory capacities in ITGA6-deficient cells (Figures 2J-K), establishing ITGA6's critical role in maintaining the metastatic potential of PC cells. These results demonstrated that ITGA6 knockdown effectively inhibited proliferative and metastatic phenotypes in PC cells.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMechanistic Exploration of ITGA6/GSK3β in PC Progression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the molecular mechanism by which ITGA6 regulates PC progression, we performed GSEA analysis on RNA-seq data (read counts) from TCGA-PAAD samples stratified by median ITGA6 expression. The KEGG_WNT_SIGNALING_PATHWAY was significantly enriched in ITGA6-high tumors (Figure 3A). From the Wnt pathway gene set, we identified seven top candidate genes (PSEN1, CSNK1A1, TBL1XR1, PPP3CA, GSK3β, CCND1, ITGA6) based on their correlation scores with ITGA6. qPCR in ITGA6-knockdown SW1990 and ASPC-1 cells revealed GSK3β as the most significantly downregulated target (Figure 3B), with corresponding protein reduction (Figure 3C), suggesting transcriptional regulation. Using AnimalTFDB 3.0 and STRING databases, we predicted GSK3β transcriptional regulators and ITGA6-interacting proteins respectively. Intersection analysis prioritized AEBP2 (highest combined_score=264 with ITGA6), which was validated through co-IP and immunofluorescence colocalization (Figures 3D and 3E). ChIP assays confirmed AEBP2 binding to the GSK3β promoter (Figure 3F), enhanced by ITGA6 overexpression (Figure 3G). Bioinformatic prediction (UCSC/AnimalTFDB 3.0) identified three potential AEBP2 binding sites in the GSK3β promoter. Luciferase reporter assays with site-directed mutants demonstrated that AEBP2-mediated transcriptional activation required site3 (-768~-757; ggccaatcacac), as mutation of this site (GSK3β-mut3) abolished responsiveness (Figure 3H). Subsequent ChIP-qPCR specifically confirmed AEBP2 binding at site3 (P \u0026lt; 0.001 \u003cem\u003evs.\u003c/em\u003e IgG; Figure 3I). Clinically, IHC revealed elevated GSK3β in tumor\u003cem\u003e\u0026nbsp;vs.\u0026nbsp;\u003c/em\u003eadjacent tissues (P \u0026lt; 0.05; Figure 3J, Table 4), correlating with advanced pathology grade (Tables 5 and 6). These findings position GSK3β as both a prognostic biomarker and potential therapeutic target in PC pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn Vitro and In Vivo Validation of ITGA6-Mediated PC Regulation via GSK3β\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo functionally validate the role of ITGA6-GSK3β signaling in PC progression, we performed comprehensive rescue experiments \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. We first constructed lentiviral vectors for ITGA6 overexpression (ITGA6-OE) and GSK3β knockdown (shGSK3β), which were transduced individually or in combination into ASPC-1 and SW1990 cells. Successful ITGA6 overexpression and GSK3β silencing were confirmed at both mRNA and protein levels (Figures 4A-B). Functional assays revealed that ITGA6-OE significantly enhanced cellular proliferation (CCK-8) and migration (Transwell), whereas GSK3β knockdown suppressed these malignant phenotypes. Notably, shGSK3β partially reversed the pro-tumorigenic effects of ITGA6-OE (Figures 4C-D), demonstrating that ITGA6 promoted PC progression primarily through GSK3β. For \u003cem\u003ein vivo\u003c/em\u003e validation, we established subcutaneous xenograft models using stably modified SW1990 cells. Compared to control (NC+shCtrl), ITGA6-OE (ITGA6+shCtrl) tumors exhibited accelerated growth (increased volume/weight), while GSK3β knockdown (NC+shGSK3β) suppressed tumorigenesis. Crucially, shGSK3β significantly attenuated ITGA6-driven tumor growth (ITGA6+shGSK3β vs. ITGA6+shCtrl; Figures 4E-G), with no adverse effects on mouse body weight (Figure 4H). IHC analysis of tumor tissues showed that ITGA6-OE upregulated both GSK3β and the proliferation marker Ki67, effects that were mitigated by concurrent GSK3β silencing (Figure 4I-K). Collectively, these data established GSK3β as the critical downstream effector of ITGA6 in promoting PC pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eITGA6 Regulates PC Stemness and Gemcitabine Sensitivity via GSK3β\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Wnt pathway plays a pivotal role in maintaining cancer stemness [21-23]. In ITGA6-knockdown ASPC-1 and SW1990 cells, we observed significant downregulation of stemness-associated markers (c-Myc, Nanog, OCT4, SOX2) by western blot analysis (Figure 5A). Functional assays demonstrated impaired sphere-forming capacity (Figure 5B) and reduced ALDH activity (Figure 5C), confirming ITGA6's role in sustaining tumor stemness. To determine GSK3β-dependency, we performed rescue experiments through combinatorial modulation of ITGA6 and GSK3β. While ITGA6 overexpression upregulated Nanog, OCT4 and SOX2, concurrent GSK3β knockdown attenuated these effects (Figure 5D). Consistently, sphere formation and ALDH assays revealed that GSK3β silencing partially reversed ITGA6-mediated stemness enhancement (Figures 5E and 5F), establishing GSK3β as the critical downstream effector. Given the clinical relevance of gemcitabine resistance in PC treatment [24] and its association with cancer stemness, we investigated ITGA6's role in chemoresistance. Gemcitabine-treated (50nM) ITGA6-knockdown cells exhibited significantly enhanced drug sensitivity in proliferation and colony formation assays (Figures 5G-H). Importantly, GSK3β overexpression rescued the chemosensitivity phenotype induced by ITGA6 knockdown (Figures 5I-J), demonstrating that ITGA6 regulated gemcitabine resistance through GSK3β-mediated stemness maintenance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMechanistic Delineation of ITGA6-AEBP2 Functional Interaction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBuilding upon our previous findings that ITGA6 interacted with AEBP2 to regulate GSK3β transcription, we systematically characterized the critical interaction domains. SMART database analysis (https://smart.embl.de/smart) revealed three major domains in full-length ITGA6 (ITGA6-FL, 1091aa): Int alpha (δ2-483), SCOP d1m1xa2 (δ629-786), and SCOP d1m1xa3 (δ797-1091) (Figure 6A). Through domain-specific truncation constructs (HA-tagged δ1-δ3) co-transfected with Flag-AEBP2 in 293T cells, Co-IP assays demonstrated that only Int alpha deletion (HA-ITGA6-δ1) abolished AEBP2 binding (Figure 6B), identifying Int alpha as the essential interaction domain. Functional validation using dual-luciferase reporter assays showed that wild-type ITGA6 (ITGA6-WT), but not the Int alpha-deleted mutant (ITGA6-Δ1), potentiated AEBP2-mediated activation of the GSK3β promoter (Figure 6C). Phenotypic characterization in PC cells revealed that while ITGA6-FL enhanced proliferation (CCK-8), clonogenicity (Figure 6D and 6E), sphere formation (Figure 6F), and ALDH activity (Figure 6G), ITGA6-Δ1 lost these oncogenic capacities, confirming the functional necessity of Int alpha.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study establishes ITGA6 as a clinically and functionally significant regulator in PC pathogenesis. Through integrated multi-omics analysis and experimental validation, we demonstrate consistent upregulation of ITGA6 at both mRNA and protein levels in PC tissues, a finding corroborated by TCGA dataset analysis and consistent with prior reports in other malignancies. Functional characterization reveals ITGA6's pleiotropic oncogenic roles: \u003cem\u003ein vitro\u003c/em\u003e, it drives proliferative, migratory, and invasive capacities; \u003cem\u003ein vivo\u003c/em\u003e, it robustly promotes tumor growth in xenograft models. Mechanistically, we identify a previously unrecognized ITGA6-AEBP2-GSK3β signaling axis that amplifies β-catenin-mediated transcriptional programs, thereby fueling PC progression. These collective findings not only expand the molecular understanding of PC aggressiveness but also position ITGA6 as a promising therapeutic target for this recalcitrant malignancy.\u003c/p\u003e\n\u003cp\u003eIntegrins, a family of heterodimeric transmembrane receptors, serve as critical mediators of cell-ECM interactions by directly binding extracellular matrix (ECM) components and generating mechanical traction essential for cell motility and invasion [25-27]. Accumulating evidence implicates integrins in multiple oncogenic processes, including tumor initiation, proliferation, survival, and metastasis across various solid malignancies [25, 28]. Given their pivotal role in tumor progression, integrins have emerged as attractive therapeutic targets in cancer. As a key member of the integrin family, ITGA6 has been increasingly recognized for its oncogenic functions in multiple cancer types. Our current study extends this understanding to PC, where we demonstrate consistent upregulation of ITGA6 in tumor tissues. Bioinformatics analyses establish a significant correlation between elevated ITGA6 expression and poor clinical prognosis. Functional validation reveals that ITGA6 overexpression markedly enhances proliferative capacity and metastatic potential in PC cell lines. Importantly, xenograft models confirm the tumor-promoting effects of ITGA6, with ITGA6-overexpressing tumors exhibiting significantly accelerated growth kinetics compared to controls. These findings collectively position ITGA6 as a critical driver of malignant progression in PC.\u003c/p\u003e\n\u003cp\u003eThe Wnt/β-catenin signaling pathway plays a pivotal role in maintaining cancer stem cell (CSC) properties and driving tumor progression [29-32]. Central to this pathway is GSK3β, a multifunctional serine/threonine kinase originally identified as a regulator of glycogen metabolism and energy homeostasis. Emerging evidence has redefined GSK3β as a critical nodal point governing diverse oncogenic processes, including apoptosis resistance, cellular senescence, proliferation, differentiation, and cell cycle control \u0026nbsp;[33]. Of particular relevance, the GSK3β/β-catenin signaling module has been increasingly recognized as a master regulator of cancer development, with its hyperactivation contributing substantially to tumor aggressiveness. Our findings, in concert with previous reports, establish ITGA6 as a novel upstream modulator of this oncogenic cascade in PC pathogenesis. Wnt signaling represents an evolutionarily conserved pathway [34, 35] with two principal branches: canonical (β-catenin-dependent) and non-canonical (β-catenin-independent) [36]. Both pathways are initiated by Wnt ligand binding to Frizzled (Fzd) receptors and their coreceptors. In the canonical pathway, Wnt-Fzd engagement triggers Disheveled (Dvl) activation, which disrupts the β-catenin destruction complex comprising Axin, CK-1, APC, and GSK3β. This inhibition prevents β-catenin phosphorylation, ubiquitination, and proteasomal degradation, enabling its nuclear translocation and subsequent activation of Tcf/Lef transcription factors to drive expression of Wnt target genes [36, 37].\u003c/p\u003e\n\u003cp\u003eOur functional studies demonstrate that genetic knockdown or functional blockade of ITGA6 significantly impairs PC cell proliferation. Mechanistically, this anti-proliferative effect appears mediated through ITGA6's regulation of cell cycle progression, where we observed that ITGA6 silencing reduces active AEBP2 levels - a molecular change that likely contributes to the observed proliferation deficit. These \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003efindings were robustly recapitulated \u003cem\u003ein vivo\u003c/em\u003e, where ITGA6 knockdown markedly suppressed tumor growth in xenograft models, unequivocally establishing ITGA6's tumor-promoting role in the pathophysiological context. Beyond proliferation control, ITGA6 depletion exerted profound effects on metastatic potential. The significant impairment of PC cell migration upon ITGA6 downregulation suggests this integrin plays a critical role in enabling the invasive dissemination characteristic of advanced disease. This dual impact on both primary tumor growth and metastatic capacity positions ITGA6 as a compelling therapeutic target addressing multiple facets of PC progression.\u003c/p\u003e\n\u003cp\u003eTo our knowledge, this study provides the first comprehensive evidence establishing ITGA6 as a critical regulator of disease progression and prognosis in PC patients. Our work elucidates the multifaceted role of ITGA6 in PC pathogenesis, positioning it as both a promising therapeutic target and potential prognostic biomarker. Importantly, the inclusion of \u003cem\u003ein vivo\u003c/em\u003e tumorigenicity assays in murine models significantly enhances the clinical relevance of our findings compared to previous investigations limited to \u003cem\u003ein vitro\u003c/em\u003e analyses. While these results are compelling, we acknowledge several areas requiring further investigation. First, validation in larger patient cohorts will be essential to confirm the prognostic utility of ITGA6. Second, additional mechanistic studies are warranted to fully delineate ITGA6's signaling networks in PC. These future directions notwithstanding, our current findings provide a strong foundation for developing ITGA6-targeted strategies in PC management.\u003c/p\u003e\n\u003cp\u003eIn summary, our study provides compelling evidence for the tumor-promoting role of ITGA6 in PC pathogenesis. The consistent suppression of PC cell growth and migration following ITGA6 knockdown, demonstrated through both in vitro and in vivo experimental systems, establishes ITGA6 as a critical driver of malignant progression. More importantly, we have mechanistically delineated how ITGA6 exerts its oncogenic effects by forming a functional complex with the transcription factor AEBP2 to enhance GSK3β transcription, thereby activating the Wnt/β-catenin signaling axis and reinforcing cancer stem cell properties. This newly identified ITGA6-AEBP2-GSK3β-Wnt signaling cascade not only expands our understanding of PC biology but also reveals a therapeutically targetable vulnerability. The dual impact of ITGA6 inhibition on both tumor growth and stemness maintenance suggests that targeting this pathway could provide a novel strategic approach for PC treatment, potentially overcoming current therapeutic limitations associated with chemotherapy resistance and disease recurrence.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study establishes ITGA6 as a critical mediator of PC pathogenesis and progression, with demonstrable potential as both a prognostic biomarker and therapeutic target. Our comprehensive analyses reveal that elevated ITGA6 expression correlates with aggressive tumor behavior and poorer clinical outcomes, while mechanistic investigations identify its pivotal role in driving oncogenic signaling through the AEBP2-GSK3β-Wnt/β-catenin axis. The consistent tumor-promoting effects observed across in vitro and in vivo models strongly support ITGA6's candidacy as a molecular target for PC intervention. However, several important questions remain to be addressed in future research: (1) validation in larger, multi-center patient cohorts to confirm prognostic reliability; (2) preclinical development of specific ITGA6-targeting agents; and (3) exploration of combinatorial strategies with existing therapies to overcome potential resistance mechanisms. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaofeng,Yang,Yuexin Ren and Wenchao Ma are the co-first authorsis.Xiaofeng Yang , Yuexin Ren and Wenchao Ma drafted the manuscript. Zhengquan Li, Sijun Chen, Yanbo Zhao and \u0026nbsp;Jiawu Li acquired analyzed, and interpreted the data. Zhituo Li and Qing Zou are the co-corresponding authors who oversaw the project and reviewed the manuscript. All authors agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Natural Science Foundation of China (Grant No. 81500484) funded this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets used in this study have been previously published. The patients involved have obtained ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe writers assert that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors gave their consent for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKlein AP. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol. 2021;18(7):493\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViquez OM et al. Integrin alpha6 maintains the structural integrity of the kidney collecting system. Matrix Biol, 2017. 57\u0026ndash;8: pp. 244\u0026ndash;257.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohaqiq M, et al. Upregulation of Integrin-α6 and Integrin-β1 Gene Expressions in Mouse Spermatogonial Stem Cells after Continues and Pulsed Low Intensity Ultrasound Stimulation. Cell J. 2018;19(4):634\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee SH, et al. Regulation of Integrin α6 Recycling by Calcium-independent Phospholipase A2 (iPLA2) to Promote Microglia Chemotaxis on Laminin. J Biol Chem. 2016;291(45):23645\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrebsbach PH, Villa-Diaz LG. The Role of Integrin α6 (CD49f) in Stem Cells: More than a Conserved Biomarker. Stem Cells Dev. 2017;26(15):1090\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHogervorst F, et al. Molecular cloning of the human alpha 6 integrin subunit. Alternative splicing of alpha 6 mRNA and chromosomal localization of the alpha 6 and beta 4 genes. Eur J Biochem. 1991;199(2):425\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoel HL, et al. Regulated splicing of the α6 integrin cytoplasmic domain determines the fate of breast cancer stem cells. Cell Rep. 2014;7(3):747\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing YB, et al. A high level of integrin α6 expression in human intrahepatic cholangiocarcinoma cells is associated with a migratory and invasive phenotype. Dig Dis Sci. 2013;58(6):1627\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamakawa N, et al. The increased expression of integrin α6 (ITGA6) enhances drug resistance in EVI1(high) leukemia. PLoS ONE. 2012;7(1):e30706.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroulx JF, et al. Integrin α6A splice variant regulates proliferation and the Wnt/β-catenin pathway in human colorectal cancer cells. Carcinogenesis. 2014;35(6):1217\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan M, et al. UNC5C\u0026ndash;knockdown enhances the growth and metastasis of breast cancer cells by potentiating the integrin α6/β4 signaling pathway. Int J Oncol. 2020;56(1):139\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandowski TH, et al. Targeting integrin α6 stimulates curative-type bone metastasis lesions in a xenograft model. Mol Cancer Ther. 2014;13(6):1558\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu T, et al. Integrin α6/Akt/Erk signaling is essential for human breast cancer resistance to radiotherapy. Sci Rep. 2016;6:33376.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin YP, et al. miR-143-3p targeting of ITGA6 suppresses tumour growth and angiogenesis by downregulating PLGF expression via the PI3K/AKT pathway in gallbladder carcinoma. Cell Death Dis. 2018;9(2):182.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClevers H. The cancer stem cell: premises, promises and challenges. Nat Med. 2011;17(3):313\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRam Makena M et al. Wnt/β-Catenin Signaling: The Culprit in Pancreatic Carcinogenesis and Therapeutic Resistance. Int J Mol Sci, 2019. 20(17).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, et al. KDM1A promotes thyroid cancer progression and maintains stemness through the Wnt/β-catenin signaling pathway. Theranostics. 2022;12(4):1500\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, et al. PAF-Wnt signaling-induced cell plasticity is required for maintenance of breast cancer cell stemness. Nat Commun. 2016;7:10633.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGang EJ, et al. Integrin α6 mediates the drug resistance of acute lymphoblastic B-cell leukemia. Blood. 2020;136(2):210\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarman S et al. Pancreatic Cancer and Therapy: Role and Regulation of Cancer Stem Cells. Int J Mol Sci, 2021. 22(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarson MD. Nejak-Bowen, \u003cem\u003eWnt/β-Catenin Signaling in Liver Pathobiology\u003c/em\u003e. Annu Rev Pathol. 2025;20(1):59\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Q, et al. Circ-CCT2 Activates Wnt/β-catenin Signaling to Facilitate Hepatoblastoma Development by Stabilizing PTBP1 mRNA. Cell Mol Gastroenterol Hepatol. 2024;17(2):175\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou G, Park JI. Wnt signaling in liver regeneration, disease, and cancer. Clin Mol Hepatol. 2023;29(1):33\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTempero MA, et al. Pancreatic Adenocarcinoma, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2021;19(4):439\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesgrosellier JS, Cheresh DA. Integrins in cancer: biological implications and therapeutic opportunities. Nat Rev Cancer. 2010;10(1):9\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen J, et al. Elevated integrin α6 expression is involved in the occurrence and development of lung adenocarcinoma, and predicts a poor prognosis: a study based on immunohistochemical analysis and bioinformatics. J Cancer Res Clin Oncol. 2019;145(7):1681\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheresh DA, Stupack DG. Regulation of angiogenesis: apoptotic cues from the ECM. Oncogene. 2008;27(48):6285\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBianconi D, Unseld M, Prager GW. Integrins in the Spotlight of Cancer. Int J Mol Sci, 2016. 17(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakebe N, et al. Targeting cancer stem cells by inhibiting Wnt, Notch, and Hedgehog pathways. Nat Rev Clin Oncol. 2011;8(2):97\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee SH, et al. Wnt/β-catenin signalling maintains self-renewal and tumourigenicity of head and neck squamous cell carcinoma stem-like cells by activating Oct4. J Pathol. 2014;234(1):99\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamb R, et al. Wnt pathway activity in breast cancer sub-types and stem-like cells. PLoS ONE. 2013;8(7):e67811.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCordero JB, Sansom OJ. Wnt signalling and its role in stem cell-driven intestinal regeneration and hyperplasia. Acta Physiol (Oxf). 2012;204(1):137\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAberle H et al. \u003cem\u003ebeta-catenin is a target for the ubiquitin-proteasome pathway.\u003c/em\u003e Embo j, 1997. 16(13): pp. 3797\u0026thinsp;\u0026ndash;\u0026thinsp;804.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsen E, et al. WNT-LRP5 signaling induces Warburg effect through mTORC2 activation during osteoblast differentiation. Cell Metab. 2013;17(5):745\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSherwood V. WNT signaling: an emerging mediator of cancer cell metabolism? Mol Cell Biol. 2015;35(1):2\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Sinha T, Wynshaw-Boris A. Wnt signaling in mammalian development: lessons from mouse genetics. Cold Spring Harb Perspect Biol, 2012. 4(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHecht A, Kemler R. Curbing the nuclear activities of beta-catenin. Control over Wnt target gene expression. EMBO Rep. 2000;1(1):24\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Expression patterns of ITGA6 in pancreatic cancer tissues and para-carcinoma tissues revealed in immunohistochemistry analysis.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003eITGA6 expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003eTumor tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 31px;\"\u003e\n \u003cp\u003ePara-carcinoma tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e51.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e68.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e48.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e31.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Relationship between ITGA6 expression and tumor characteristics in patients with \u0026nbsp;pancreatic cancer\u003c/a\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eFeatures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003eNo. of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003eITGA6 expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026le; 62years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026gt; 62years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026le; 4cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026gt; 4cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eTumor infiltrate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Relationship between ITGA6 expression and tumor characteristics in patients with pancreatic cancer\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eITGA6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eSpearman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eSignificance (two-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 Expression patterns of GSK-3\u0026beta; in pancreatic cancer tissues and para-carcinoma tissues revealed in immunohistochemistry analysis\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003eGSK-3\u0026beta; expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003eTumor tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 31px;\"\u003e\n \u003cp\u003ePara-carcinoma tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e84.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e15.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 Relationship between GSK-3\u0026beta; expression and tumor characteristics in patients with pancreatic cancer\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 38px;\"\u003e\n \u003cp\u003eFeatures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003eNo. of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003eGSK-3\u0026beta; expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026le; 62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026gt; 62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eTumor size (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026le; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026gt; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eTumor infiltrate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003elymphatic metastasis (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eMetastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6 Relationship between GSK-3\u0026beta; expression and tumor characteristics in patients with pancreatic cancer\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eGSK-3\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eSpearman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eSignificance (two-tailed）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\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":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pancreatic cancer, ITGA6, Transcription, Wnt/β-catenin signaling, Stemness","lastPublishedDoi":"10.21203/rs.3.rs-8683090/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8683090/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePancreatic cancer (PC) remains one of the most lethal malignancies in the digestive system, posing a significant threat to human health due to the critical lack of therapeutic targets. While integrin α6 (ITGA6) has been implicated as a key regulator in multiple cancer types, its precise functional role and molecular mechanisms in pancreatic tumorigenesis remain poorly understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first identified differentially expressed genes (DEGs) with prognostic significance in PC using TCGA database analysis. ITGA6 expression was validated at protein and mRNA levels through immunohistochemistry (IHC), quantitative real-time PCR (qRT-PCR), and western blotting. Lentiviral-based overexpression and knockdown systems were established to modulate ITGA6 expression in PC cells. Cellular phenotypes were assessed using CCK-8 proliferation assays, flow cytometry, Transwell migration/invasion chambers, and wound healing assays. \u003cem\u003eIn vivo\u003c/em\u003e validation was performed using subcutaneous xenograft mouse models. The underlying molecular mechanism was investigated through co-immunoprecipitation (Co-IP), dual-luciferase reporter assays, and chromatin immunoprecipitation (ChIP) to delineate the ITGA6-AEBP2-GSK3β regulatory axis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study revealed significantly elevated ITGA6 expression in PC tissues, with high ITGA6 levels correlating with poor patient survival outcomes. Genetic knockdown of ITGA6 effectively suppressed malignant phenotypes, including proliferation and migration in PC cells. Mechanistically, we identified that ITGA6 physically interacted with the transcription factor AEBP2 to enhance transcriptional activation of GSK3β. Importantly, the ITGA6-AEBP2-GSK3β axis was found to promote PC progression and chemoresistance through Wnt/β-catenin pathway-mediated augmentation of cancer stem cell properties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eITGA6 promoted PC progression and conferred treatment resistance through the AEBP2/GSK3-β/β-catenin signaling axis. These results not only elucidated a novel molecular mechanism underlying PC aggressiveness but also identified ITGA6 as a promising therapeutic target for this lethal malignancy.\u003c/p\u003e","manuscriptTitle":"The ITGA6-AEBP2 Complex Promotes Transcriptional Activation of GSK3β to Augment Wntβ-Catenin Signaling and Stemness in Pancreatic Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 13:17:25","doi":"10.21203/rs.3.rs-8683090/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"67510793468320161395901969224957069261","date":"2026-05-18T02:53:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T10:26:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276071159722790907348963708056931041691","date":"2026-03-06T11:04:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-04T08:54:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-26T05:48:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Cell International","date":"2026-02-25T03:39:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b7f0111-6410-4851-900c-79c41b180e1b","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"67510793468320161395901969224957069261","date":"2026-05-18T02:53:55+00:00","index":37,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T13:17:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 13:17:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8683090","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8683090","identity":"rs-8683090","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}