γ1-adaptin expression is associated with cancer progression and poor prognosis in pancreatic ductal adenocarcinoma: Cellular and histopathological analyses

γ1-adaptin expression is associated with cancer progression and poor prognosis in pancreatic ductal adenocarcinoma: Cellular and histopathological analyses | 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 γ1-adaptin expression is associated with cancer progression and poor prognosis in pancreatic ductal adenocarcinoma: Cellular and histopathological analyses Shigeyuki Tsukida, Takefumi Uemura, Naoya Sato, Yasuhide Kofunato, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9301878/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Background The adaptor protein complex-1 (AP-1) is a clathrin adaptor involved in intracellular trafficking at the trans-Golgi network and endosomes. Despite its fundamental role in membrane trafficking, the clinical and pathological significance of the γ1-adaptin-containing AP-1 in pancreatic ductal adenocarcinoma (PDAC) remains unclear. Methods γ1-adaptin knockout PANC-1 cell lines were generated to assess malignant phenotypes. γ1-adaptin expression was examined using immunohistochemistry in surgical specimens from 123 patients with PDAC and correlated with clinicopathological factors and overall survival. Prognostic significance was evaluated using Cox proportional hazards models and the Kaplan–Meier analysis with the log-rank test. Results γ1-adaptin knockout PANC-1 cells showed significantly reduced proliferation, migration, and invasion, accompanied by decreased expression of epidermal growth factor receptor and MET receptor tyrosine kinase. γ1-adaptin expression was significantly higher in tumor tissues than in normal pancreatic tissues. High γ1-adaptin expression was associated with shorter overall survival (median overall survival, 23.8 vs. 44.8 months; p = 0.002), and multivariable analysis identified high γ1-adaptin expression as an independent poor prognostic factor in PDAC (hazard ratio, 1.974; p = 0.007). Subgroup analyses indicated that the high γ1-adaptin group exhibited worse overall survival among patients with low CA19-9 levels, other histological types (non–well-differentiated), and the presence of lymph node metastasis. Conclusions γ1-adaptin promotes malignant behavior and serves as an independent prognostic factor in PDAC, highlighting its potential as a prognostic biomarker and therapeutic target. adaptor protein complex 1 EGFR immunohistochemistry MET receptor tyrosine kinase pancreatic cancer Figures Figure 1 Figure 2 Figure 3 Figure 5 Figure 6 Background Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies and is associated with an extremely poor prognosis, largely due to its high invasiveness, metastatic potential, and resistance to existing therapies [ 1 , 2 ]. Several prognostic factors for PDAC have been identified, including tumor size, local invasion, and lymph node metastasis, which are used in the staging classification of pancreatic cancer. Additionally, the widely used adenocarcinoma marker carbohydrate antigen 19 − 9 (CA19-9) and histological subtype have been reported to correlate with prognosis [ 3 – 5 ]. Although these factors aid in patient stratification and guide treatment decisions, their predictive accuracy remains limited [ 6 , 7 ]. Therefore, additional molecular markers for effective diagnostic or prognostic assessment, as well as therapeutic targets, are needed. Adaptor protein complex-1 (AP-1) is a clathrin adaptor that regulates the intracellular trafficking of specific cargo proteins, including the transport of mannose 6-phosphate receptors from the trans-Golgi network (TGN) to endosomes [ 8 , 9 ]. AP-1 is a heterotetramer complex composed of β1-, γ-, µ1-, and σ1-adaptins with multiple isoforms of γ-, µ1-, and σ1-adaptins [ 8 ]. γ1-adaptin, an isoform of γ-adaptin, interacts with ADP-ribosylation factor 1 to facilitate AP-1 recruitment to the TGN/endosomal membrane. In the context of cancer biology, we and others have previously reported that AP-1 subunits are highly expressed in breast cancer, glioma, non-small-cell lung carcinoma (NSCLC), and PDAC [ 10 – 14 ]. Previously, we utilized immunohistochemistry (IHC) to demonstrate that endosomal expression of γ1-adaptin was positively correlated with breast cancer malignancy and could be a novel prognostic marker [ 14 ]. As a mechanistic basis for the tumor-promoting effects of AP-1 subunits in cancer cells, we have demonstrated that γ1-adaptin sustains the expression of receptor tyrosine kinases, including epidermal growth factor receptor (EGFR) and/or MET receptor tyrosine kinase (MET), in the NSCLC-derived H1975 cell line [ 13 ]and HER2-positive breast cancer cell line SK-BR-3 [ 14 ]. However, the protein expression and clinical significance of γ1-adaptin-containing AP-1 in PDAC remain unclear. Therefore, in this study, we investigated the relationship between γ1-adaptin expression, assessed using IHC in patient specimens, and PDAC malignancy. Methods Cell Lines and Human Tissue Samples PANC-1 cells were obtained from the Cell Bank of RIKEN BioResource Research Center. They were cultured in DMEM with high glucose and 10% FBS (Nichirei Bioscience, Tokyo, Japan) in an incubator containing 5% CO 2 at 37°C. Patients with PDAC who underwent pancreatectomy at Fukushima Medical University Hospital (Fukushima, Japan) between 2003 and 2020 were included in this study. Cases pathologically diagnosed as non-PDAC, such as acinar cell carcinoma, neuroendocrine neoplasm, or intraductal papillary mucinous neoplasm, were excluded. Patients who received neoadjuvant therapy were excluded. Among the remaining patients, those with pathological stage IV disease or a short follow-up period (< 90 days) were further excluded. In addition, cases with insufficient pathological information for accurate evaluation or unevaluable histological slides due to technical artifacts were excluded. For clinicopathological and statistical analyses, histological types were classified into well-differentiated and other histological types (including moderately/poorly differentiated adenocarcinoma and other histological subtypes). Informed consent was obtained from all participants included in the study. A tissue array of PDAC (OD-CT-DgPan03-002) containing 30 cases of PDAC with each adjacent non-tumor region was purchased from Shanghai Outdo Biotech Co. (Shanghai, China). The detailed information is provided in Additional file 1. Studies involving human samples were approved by the Ethics Committee of Fukushima Medical University (Numbers: 29390 and 2943). Antibodies An antibody against γ1-adaptin (BD Bioscience, San Jose, NJ, USA [610385]) was used for IHC (1:1000 dilution), immunofluorescence (IF; 1:1000 dilution), and western blotting (WB; 1:2000 dilution). An antibody against EEA1 (Abcam, Cambridge, UK [ab109110]) was used for IF (1:200 dilution). An antibody against TGN46 (Bio-Rad, Hercules, CA, USA [AHP500GT]) was used for IF (1:400 dilution). An antibody against GAPDH (Cell Signaling Technology, Danvers, MA, USA [2118]) was used as a loading control for WB (1:2000 dilution). Donkey anti-mouse or donkey anti-rabbit antibodies conjugated with Alexa Fluor fluorescent dye (Jackson ImmunoResearch, West Grove, PA, USA [anti-mouse: 715-545-150, anti-rabbit: 711-585-152]) were used for IF (1:800 dilution). For WB, sheep anti-mouse IgG, HRP-linked F(ab’)2 fragment (Cytiva, Tokyo, Japan [NA9310-1ML]) and donkey anti-rabbit IgG, HRP-linked F(ab’)2 fragment (Cytiva, Tokyo, Japan [NA9340-1ML]) were used (1:2000 dilution). For IHC, a peroxidase-labeled anti-mouse antibody (Histofine Simple stain MAX-PO (M), Nichirei Biosciences Inc., Tokyo, Japan) was used. shRNA experiment For knockdown of γ1-adaptin, shRNA was introduced as previously described [ 13 , 14 ]. Briefly, lentiviruses carrying the pLKO.1 puro vector (Sigma) encoding γ1-adaptin shRNA (5ʹ-AGGAAGUUAUGUUCGUGAU-3ʹ) or pLKO.1 empty vector was obtained by collecting the culture supernatant of HEK293T cells transfected with the respective lentiviral vectors. PANC-1 cells were infected with the lentivirus in DMEM containing 10% FBS and 8 µg/ml polybrene for 48 h. The medium was then replaced with fresh DMEM containing 5 µg/ml puromycin. The resulting bulk population of puromycin-resistant cells was used for WB. Gene Knockout Transfection was performed using Lipofectamine™ CRISPRMAX™ Cas9 Transfection Reagent (Thermo Fisher Scientific, Waltham, MA, USA), according to the manufacturer’s instructions. Briefly, for the standard concentration, 25 µL of Opti-MEM I Reduced Serum Medium (Thermo Fisher Scientific, Waltham, MA, USA) was mixed with Cas9 protein (final concentration of 23 ng/µL), sgRNA (CRISPR699909_SGM, 5ʹ- CAAACGCATTGGCTATTTAG-3ʹ, Cat# A32042; Thermo Fisher Scientific, Waltham, MA, USA; final concentration of 4.4 ng/µL), and 2.5 µL of Cas9 Plus™ Reagent (Thermo Fisher Scientific, Waltham, MA, USA). Another 25 µL of Opti-MEM I Reduced Serum Medium was mixed with 1.5 µL CRISPRMAX™ transfection reagent and incubated at room temperature for 1 min. After incubation, both solutions were thoroughly mixed and incubated at room temperature for 10–15 min. The resulting solution was added to each well containing 500 µL of the culture medium. On the 4th day after transfection, IF staining was performed to evaluate the knockout (KO) efficiency of the target gene in the transfected cell lines. Single Cell Cloning Monoclonal preparations were performed using the limiting dilution method with conditioned medium. Mixed clone cells were seeded in a 10 cm dish containing 12 mL DMEM with 10% FBS. On the 2nd day after seeding, the supernatant was collected from the conditioned medium. The cells were centrifuged at 770 ×g for 10 min to remove any cellular components. The cell suspension was diluted to a concentration of 0.85 cells/100 µL with the conditioned medium using the limiting dilution method. After mixing, the cells were divided into 96‑well plates, and 100 µL of conditioned medium was added to each well for monoclonal culture. Single cells in each well were tracked, and the formation of single-cell clones was observed. When sufficiently large monoclonal colonies were formed, they were successively expanded by further culturing them. IF staining confirmed the monoclonal KO nature of these cells, and the loss of expression of γ1-adaptin was verified using WB. These validated KO cells were established as KO cell lines and compared with wild-type (WT) controls. WB experiments WT and KO cells were washed with cold PBS and lysed in cold 1% Triton X-100/PBS supplemented with cOmplete™ EDTA-free, protease inhibitor cocktail (Roche, Mannheim, Germany) and PhosSTOP™ (Roche, Mannheim, Germany). Equal amounts of protein were separated using SDS-PAGE and transferred onto a PVDF membrane. After blocking with 5% skim milk, the membranes were incubated with primary antibodies for 60 min, followed by incubation with HRP-conjugated secondary antibodies for 30 min. Chemiluminescent signals were detected using Amersham™ ECL™ Select (Cytiva, Tokyo, Japan) and LAS4000 mini (GE Healthcare, Tokyo, Japan). Densitometric analysis of the blots was performed using the Band/Peak Quantification macro tool for ImageJ, developed by Ohgane et al [ 15 ]. IF Microscopy for Culture Cells WT and KO cells were seeded onto circular glass coverslips placed in 24-well plates and used for IF staining. The coverslips with cells were recovered and fixed with 4% paraformaldehyde/0.1 M phosphate buffer for 15 min at room temperature. After washing with PBS, the cells were permeabilized and blocked with 0.1% Triton X-100 and 0.4% BSA in PBS for 30 min at room temperature. After permeabilization and blocking, the cells were incubated with primary antibodies for 60 min at room temperature. Following another PBS wash, the cells were incubated with a mixture of secondary antibodies and Hoechst nuclear stain for 25 min at room temperature. The reacted coverslips were then mounted onto glass slides using Aqua/Poly Mount (Polysciences Inc. Warrington, PA, USA) mounting medium. The cells were observed using a confocal laser microscope (FLUOVIEW FV1000, OLYMPUS, Tokyo, Japan). Cell Proliferation Assay The CCK-8 (Dojindo Laboratories, Kumamoto, Japan) assay was used to measure the proliferation of cell lines. Cell suspensions (100 µL, 2000 cells/well) were seeded in a 96-well plate and incubated under standard culture conditions. At days 0 (2 h), 1 (24 h), 3 (72 h), 5 (120 h), 7 (168 h), and 9 (216 h), 10 µL of CCK-8 reagent was added to each well, and the cells were incubated for 2 h at 37°C. The absorbance reflecting cell proliferation was measured at 450 nm using Multiskan™ GO (Thermo Fisher Scientific, Waltham, MA, USA). The growth rate at each time point was calculated relative to that on day 0. Migration and Invasion Assays The CytoSelect Cell Migration and Invasion Kit (Cell Biolabs, Inc., San Diego, CA, USA) was used according to the manufacturer’s protocol. For the migration assay, cells were suspended in a serum-free medium at a density of 1.0 × 10 6 /mL. Suspended cells (300 µL) were seeded in the upper chambers, and the lower chambers contained 10% FBS as a chemoattractant. Following incubation at 37°C for 6 h, the cells that migrated through the membrane were stained with a cell staining solution at room temperature for 10 min. After staining, the membranes were washed with PBS and observed under a microscope. Then, the migrated cells were dissolved in an extraction solution. The dissolved solutions were quantified by measuring the absorbance at 560 nm using Multiskan™ GO (Thermo Fisher Scientific, Waltham, MA, USA). The invasion assay was conducted using the same procedure as the migration assay, except that a chamber coated with basement membrane matrix proteins was used, the cell density was adjusted to 0.5 × 10 6 /mL, and the incubation time was extended to 24 h. IF Microscopy and IHC for Human Tissues Tissue array specimens were processed for antigen retrieval in 1% ImmunoSaver (Nissin EM, Tokyo, Japan) for 20 min at 98°C using a microwave processor (MI-77, AZUMAYA, Japan), followed by blocking of nonspecific binding with 5% donkey serum (Jackson ImmunoResearch Inc., West Grove, PA, USA) for 20 min at room temperature. They were further incubated with an anti-γ1-adaptin antibody or combinations of anti-γ1-adaptin and anti-EEA1 or anti-TGN46 antibodies for 2 days at 4°C, followed by incubation with secondary antibodies conjugated with Alexa Fluor fluorescent dye for 30 min at room temperature. IF microscopy was performed using a confocal laser scanning microscope (FLUOVIEW FV1000, OLYMPUS, Tokyo, Japan). For IHC, 3-µm-thick sections were prepared from paraffin-embedded tissue samples of patients with pancreatic cancer. After deparaffinization, the sections were processed for antigen retrieval in a 1% ImmunoSaver (Nissin EM, Tokyo, Japan) for 20 min at 98°C using a microwave processor (MI-77, AZUMAYA, Japan) followed by inactivation of endogenous peroxidase with 0.3% H 2 O 2 in methanol for 20 min at room temperature and blocking of nonspecific binding with 10% goat serum (Jackson ImmunoResearch Inc., West Grove, PA, USA) for 20 min at room temperature. They were further incubated with an anti-γ1-adaptin antibody for 2 days at 4°C, followed by incubation with peroxidase-labeled anti-mouse antibody for 30 min at room temperature. Peroxidase activity was detected using 0.0125% 3,3ʹ-DAB and 0.002% H 2 O 2 in 0.05 M Tris–HCl buffer (pH 7.6). Image Analysis for Quantification To quantify the staining intensity, images of γ1-adaptin-stained IHC specimens were captured using a virtual slide scanner (Nanozoomer-SQ, Hamamatsu Photonics, Japan) equipped with a ×40 lens, which was observed using NDP.view2 (Hamamatsu Photonics, Japan). Quantification was performed using the Fiji software (National Institute of Health, MD, USA) [ 16 ], in accordance with previous studies [ 14 , 17 ]. All images were converted to 256‑level grayscale images and inverted. Five rectangular regions of interest (ROIs), each measuring 425 × 266 µm and containing relatively strong immunoreactivity, were selected per specimen. In each ROI, a common thresholding process based on staining intensity was performed, and the average staining intensity value of the threshold pixels was measured, which was considered the γ1-adaptin intensity of the ROI. The average of the five ROIs was used as the value for each case. Differences in γ1-adaptin intensity across experiments were normalized using five identical reference cases included in all experimental runs. The image analysis was performed without knowledge of the clinical outcomes, and in a standardized and semi-automated manner to minimize observer-related bias. Statistical Analysis Student’s t-test and one-way analysis of variance followed by Dunnett’s post hoc test were used for densitometric analysis of WB data and to analyze the results of the proliferation, migration, and invasion assays. Student’s t-test was used for quantitative comparison of staining intensity between normal and tumorous tissue regions. Pearson’s chi-squared test was used to evaluate the correlations between γ1-adaptin expression and clinicopathological factors. Overall survival (OS) was defined as the time from surgery to death from any cause. Survival analyses were performed using the Kaplan–Meier method with the log-rank test. Cox proportional hazards models were used for univariable and multivariable analyses. IBM SPSS Statistics 29 (IBM Inc., Armonk, NY, USA) and GraphPad Prism 10 (GraphPad Software, CA, USA) were used for all statistical analyses. Statistical significance was set at p < 0.05. Cases with missing clinicopathological or follow-up data were excluded from the analysis. The cutoff values for γ1-adaptin staining intensity and selected clinical variables (age, body mass index, and tumor size) were defined as their median values, whereas the cutoff value for serum CA19-9 level was defined as the upper limit of normal (37 U/mL). Results Establishment of γ1-adaptin KO Cell Lines Based on our previous findings demonstrating a close association between γ1-adaptin and EGFR/MET expression in multiple cancer cell lines, we extended this analysis to a PDAC-derived cell line. The PANC-1 cell line harboring the KRAS G12D mutation was selected, as this mutation depends on EGFR signaling for the initiation of PDAC [ 18 ]. WB showed that transduction of shRNA targeting γ1-adaptin significantly reduced EGFR and MET expression (Fig. 1 a and b). To examine the role of γ1-adaptin in PANC-1 cells, we generated two γ1-adaptin KO cell lines (KO1 and 2) using CRISPR/Cas9-mediated gene editing. WB revealed the absence of γ1-adaptin in the KO cell lines (Fig. 1 c). IF microscopy showed typical perinuclear staining of γ1-adaptin in parental WT cells, which was absent in the KO cells (Fig. 1 d). These findings confirmed the successful establishment of γ1-adaptin KO cell lines. WB revealed that the band intensity of EGFR and MET were significantly reduced in the KO cell lines (Fig. 1 c and e), suggesting that γ1-adaptin regulates receptor tyrosine kinase expression. [insert Fig. 1 here] γ1-adaptin Deficiency Reduced Cell Proliferation, Migration, and Invasion To assess the impact of γ1-adaptin deficiency on tumor cell behavior, proliferation, migration, and invasion assays were performed. In the proliferation assay, the KO cell lines showed a significantly reduced growth rate from day 5 onward. By day 9, the growth rate of the KO cell lines reached approximately 40% of that of WT cells (Fig. 2 a). In the migration and invasion assays, the number of migrated and invading cells was significantly reduced in each KO cell line compared with WT cells (Fig. 2 b-e). These findings suggest that γ1-adaptin promotes tumor cell activity at the cell culture level. Therefore, we investigated the relationship between γ1-adaptin expression and clinicopathological factors in PDAC cases. [insert Fig. 2 here] γ1-adaptin Expression in PDAC by Immunohistological Analyses We first examined the protein expression of γ1-adaptin using IF microscopy in a commercially available PDAC tissue array comprising 30 cases. In this experiment, we used an antibody whose specificity was validated in Fig. 1 a, c, d, and in previous studies [ 13 , 14 ]. Cytoplasmic granular structures positive for γ1-adaptin were more evident in tumor regions than in non-tumor regions in 63.3% of cases (19/30) (Fig. 3 a), and the mean signal intensity was significantly higher in tumor regions than in non-tumor regions (Fig. 3 b). We then investigated the γ1-adaptin expression by IHC in PDAC cases from our institute. γ1-adaptin was detected as a weak, diffuse signal in non-tumor regions, whereas multiple cytoplasmic granules were observed in tumor regions, with varying intensities among the cases (Fig. 3 c). Double IF microscopy using markers for endosomes (EEA1) and the TGN (TGN46) revealed that the majority of γ1-adaptin localized to endosomes, but not to the TGN (Fig. 3 d). [insert Fig. 3 here] Relationships between γ1-adaptin Expression and Clinicopathological Factors Quantification of IHC images from 30 randomly selected cases showed that γ1-adaptin staining intensity was significantly higher in tumor regions than in non-tumor regions (Fig. 5 a), consistent with the results of the tissue array analysis (Fig. 3 b). When quantification was performed in tumor regions from 123 cases (Fig. 4 ), the intensity values exhibited clear inter-case variability (Fig. 5 b), with a median value of 97.32. This value was used as a cutoff to classify the cases into high- and low- expression groups for subsequent analyses. [insert Fig. 4 and Fig. 5 here] Association analysis revealed no significant correlations between γ1-adaptin intensities and preoperative CA19-9 levels, lymph node metastasis, histological type, or other clinicopathological features (Table 1 ). Table 1 Association between γ1-adaptin staining intensity and clinicopathological factors Clinicopathological factors γ1-adaptin staining intensity p -value a Low (n = 62) High (n = 61) Age 71 [67.0-77.5] 70 [62.0–76.0] 0.654 21.9 30 (48.4) 32 (52.5) ASA-PS 0.162 1 7 (11.3) 7 (11.5) 2 44 (71.0) 50 (82.0) 3 11 (17.7) 4 (6.5) CA19-9 (U/mL) 94.0 [15.8-321.4] 127 [26.2-900.9] 0.379 ≤ 37 24 (55.8) 19 (44.2) > 37 38 (47.5) 42 (52.5) Tumor location 0.113 Head 34 (54.8) 44 (72.1) Body 16 (25.8) 8 (13.1) Tail 12 (19.4) 9 (14.8) Operation procedure 0.300 PD 36 (58.1) 44 (72.1) DP 24 (38.7) 16 (26.2) TP 2 (3.2) 1 (1.7) Tumor size (mm) 25 [21.3–39.5] 30 [22.0–36.0] 0.176 ≤ 28 35 (56.5) 27 (44.3) > 28 27 (43.5) 34 (55.7) T factor 0.805 1 7 (11.3) 6 (9.8) 2 4 (6.5) 2 (3.3) 3 51 (82.2) 53 (86.9) Lymph node metastasis 0.177 Positive 36 (58.1) 28 (45.9) Negative 26 (41.9) 33 (54.1) Histological type 0.522 Well-differentiated 22 (35.5) 23 (37.8) Moderately differentiated 31 (50.0) 26 (42.6) Poorly differentiated 1 (1.6) 4 (6.6) Other histological types 8 (12.9) 6 (9.8) Resection status 0.363 R0 50 (80.6) 45 (73.8) R1 12 (19.4) 16 (26.2) Postoperative complication 0.833 CD grade < 3a 52 (83.9) 52 (85.2) CD grade ≥ 3a 10 (16.1) 9 (14.8) Adjuvant chemotherapy 0.181 Yes 34 (54.8) 40 (65.6) No 28 (45.2) 20 (32.8) Missing 0 1 (1.6) Recurrence b Yes 38 (61.3) 47 (77.0) No 24 (38.7) 12 (19.7) Missing 0 2 (3.3) Continuous variables are presented as median [IQR] and categorical variables as n (%) a: p -values were calculated using the chi-square test after dichotomization of variables according to predefined cutoff values. b: Recurrence was analyzed using survival analysis (Fig. 4 ) and is presented descriptively. IQR: Interquartile range, BMI: Body mass index, ASA-PS: American Society of Anesthesiologists Physical Status, CA19-9: Carbohydrate antigen 19 − 9, PD: Pancreaticoduodenectomy, DP: Distal pancreatectomy, TP: Total pancreatectomy, CD: Clavien–Dindo [insert Table 1 here] However, survival analysis showed that patients with high γ1-adaptin intensity had significantly shorter recurrence-free survival than those with low γ1-adaptin intensity (median recurrence-free survival, 11.3 months [95% confidence interval (CI), 6.0–16.6] vs. 17.0 months [95% CI, 8.2–25.8]; p = 0.012; hazard ratio (HR) [95% CI] = 1.717 [1.116–2.642]; Fig. 5 c). Similarly, the high-intensity group showed significantly shorter OS than the low-intensity group (median OS, 23.8 months [95% CI, 15.6–32.0] vs. 44.8 months [95% CI, not estimable]; p = 0.002; HR [95% CI] = 2.035 [1.290–3.211]; Fig. 5 d). Regarding factors other than γ1-intensity, high CA19-9 levels, T factor (≥ 3), lymph node metastasis, and resection status (R1 resection) were significantly associated with 5-year OS. A low body mass index, histologic type (other histological types), and tumor location (pancreatic head) were also significantly associated with 5-year OS (Additional file 2). The results of the univariable analysis using Cox proportional hazards model were also similar (Table 2 ). Table 2 Cox proportional hazards analysis of prognostic variables Variables Univariable analysis Multivariable analysis Unfavorable/Favorable Hazard ratio 95% CI p -value Hazard ratio 95% CI p -value γ1-adaptin staining intensity High/Low 2.044 1.292–3.234 ** 0.002 1.828 1.111–3.006 * 0.018 Sex Male/Female 1.178 0.743–1.866 0.485 Age ≥ 71/< 71 1.064 0.678–1.668 0.788 BMI (kg/m 2 ) < 21.9/≥ 21.9 1.542 0.976–2.434 # 0.063 1.389 0.858–2.248 0.181 CA19-9 (U/mL) ≥ 37/ 28/≤ 28 1.395 0.888–2.191 0.148 0.987 0.610–1.597 0.956 Histological type Other histological types/Well-differentiated 1.532 0.949–2.474 # 0.081 1.865 1.129–3.079 * 0.015 T factor T3/T1, T2 2.343 1.075–5.107 * 0.032 1.707 0.713–4.087 0.230 Lymph node metastasis Positive/Negative 2.263 1.430–3.582 ** < 0.001 2.007 1.237–3.256 ** 0.005 Resection status R1/R0 1.855 1.109–3.101 * 0.019 1.577 0.917–2.711 0.099 Adjuvant therapy Yes/No 1.129 0.704–1.811 0.615 BMI: Body mass index, CA19-9: Carbohydrate antigen 19 − 9, CI: Confidence interval #: p < 0.1, *: p < 0.05, **: p < 0.01 [insert Table 2 here] Multivariable analysis further revealed that γ1-adaptin intensity was significantly associated with 5-year OS ( p = 0.018; HR [95% CI] = 1.828 [1.111–3.006]); additionally, it was identified as an independent poor prognostic factor, along with CA19-9 levels, histologic type, and lymph node metastasis (Table 2 ). Subgroup analyses stratified by CA19-9 levels, histological type, and lymph node status showed poorer OS in the high γ1-adaptin group among patients with low CA19-9, other histological types, and lymph node metastasis (Fig. 6 ). [insert Fig. 6 here] Discussion In this study, we analyzed immunohistochemical expression of an AP-1 subunit, γ1-adaptin, in two independent cohorts of PDAC, a commercially available tissue array and our institutional cases, and found that its expression was significantly higher in tumor regions compared with adjacent non-tumor regions. Furthermore, correlation analysis with clinicopathological factors revealed that γ1-adaptin expression, as assessed by IHC, could serve as a novel independent prognostic factor in PDAC. Notably, our subgroup survival analyses suggest that γ1-adaptin expression may serve as an additional stratification marker within high-malignancy subgroups defined by known prognostic factors, including histological subtypes and lymph node metastasis. In addition, as γ1-adaptin expression can predict prognosis even in patients with normal levels of CA19-9, it may be particularly useful in Lewis-negative patients who are unable to produce CA19-9[ 19 ]. To establish the clinical significance of γ1-adaptin expression, future studies with larger patient cohorts and multicenter validation will be required. Previous analyses using the Cancer Genome Atlas database have demonstrated that elevated expression of AP1S3 , which encodes σ1C (an isoform of σ1), is associated with poor outcomes in breast cancer [ 10 ], PDAC [ 11 ], and glioma [ 12 ]. Overexpression of AP1S1 , which encodes σ1A (an isoform of σ1) is also associated with poor prognosis in lung cancer [ 20 , 21 ]. At the protein level, targeted proteomics revealed that µ1-adaptin expression was upregulated in central nervous system metastasis of triple-negative breast cancer (TNBC), suggesting that it could serve as a prognostic marker [ 22 ]. Consistently, we have recently demonstrated that immunohistochemical expression of γ1-adaptin is an independent prognostic marker in breast cancer [ 14 ]. Therefore, this study underscores the prognostic significance of AP-1 subunits in cancer by examining γ1-adaptin expression using IHC in PDAC. AP-1 subunits, µ1A-, σ1A-, σ1C-, or γ1-adaptin, support cancer cell activities in several cell lines derived from hepatocellular carcinoma (HepG2) [ 23 ], glioma (SW1783 and U373) [ 12 ], NSCLC (H1975) [ 13 , 20 ], TNBC (MDA-MB-231) [ 10 ], and Her2-positive breast cancer (SK-BR-3) [ 14 ]. In this study, we utilized the PDAC-derived cell line PANC-1 and demonstrated that γ1-adaptin KO suppressed cell proliferation, migration, and invasion. These findings are consistent with those of previous reports and suggest that γ1-adaptin supports the malignant phenotype of PANC-1 cells. Since knock-down of γ1-adaptin caused a depletion of the other subunits in ARPE-19 cells [ 13 ] and MDCK cells [ 24 ], the expression level of γ1-adaptin would reflect the number of AP-1 complexes in the cells. Given that AP-1 regulates the trafficking of not only mannose 6-phosphate receptors but also transferrin receptor, low-density lipoprotein receptor, EGFR, megalin, syntaxin3, sodium/iodide symporter, ATP7A, and STING [ 8 , 9 , 13 , 24 – 30 ], depletion of the AP-1 complex is likely to impair the proper localization and function of these proteins. We have previously demonstrated that depletion of γ1-adaptin reduces the expression of EGFR and MET in the NSCLC-derived H1975 cell line [ 13 ], as well as EGFR in the HER2-positive breast cancer cell line SK-BR-3 [ 14 ]. Consistently, both receptors were decreased in γ1-adaptin-knockdown or -KO PANC-1 cells, suggesting that this downregulation may underlie the reduced cancer cell activities. As a mechanism for the reduced EGFR, we previously proposed that AP-1 promotes EGFR recycling at RAB11-positive endosomes, thereby maintaining high levels of EGFR on the cell surface and supporting cancer growth [ 13 ]. Moreover, our previous studies have shown preferential localization of AP-1 in endosomes in hepatocellular carcinoma, NSCLC, colorectal carcinoma [ 13 ], and breast cancer [ 14 ]. As this study extends these findings to PDAC (Fig. 3 d), endosomal localization of AP-1 may be a common and critical feature that supports cancer cell function in multiple tumor types. However, a recent surface proteome analysis demonstrated that γ1-adaptin KO in HeLa cells led to the downregulation of expression of approximately 1,000 proteins [ 31 ]. Therefore, it is conceivable that the loss of γ1-adaptin disrupts additional cellular mechanisms, which warrant further investigation. This study has several limitations. First, it was conducted at a single institution with a retrospective design, which may introduce selection bias. Second, the sample size was relatively limited. Third, although our findings suggest a functional role of γ1-adaptin in PDAC progression, the precise molecular mechanisms underlying these effects remain to be fully elucidated. Finally, external validation using larger, multicenter cohorts is warranted to confirm the generalizability of our findings. Abbreviations AP-1, adaptor protein complex-1 CA19-9, carbohydrate antigen 19-9 CI, confidence interval EGFR, epidermal growth factor receptor HR, hazard ratio IHC, immunohistochemistry MET, MET receptor tyrosine kinase NSCLC, non-small-cell lung carcinoma OS, overall survival PDAC, pancreatic ductal adenocarcinoma ROI, region of interest TGN, trans-Golgi network TNBC, triple-negative breast cancer WT, wild-type Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and its later amendments. This study was approved by the Ethics Committee of Fukushima Medical University (No. 29390 and 2943). Written informed consent was obtained from all participants. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding authors upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Japan Society for the Promotion of Science KAKENHI (Grant Nos. 24K10361 [to TU], 21K07104 [to TU], and 25K12033 [to ST]), Takeda Science Foundation (to SW), and the Strategic Research Promotion Program at Fukushima Medical University (to SW). Authors’ contributions Conceptualization, S.W., T.U., and S.T.; data curation, S.T.; formal analysis, T.U. and S.T.; funding acquisition, S.W., T.U., and S.T.; investigation, T.U., and S.T.; methodology, S.W., T.U., and S.T.; project administration, S.W.; resources, S.W., T.U., S.M., and S.T.; supervision, S.W.; visualization, T.U., and S.T.; writing-original draft preparation, S.W., T.U., and S.T.; writing-review and editing, S.W., T.U., S.T., S.M., A.K., T.K., T.I., Y.K., and N.S. All authors have read and approved the final manuscript. Acknowledgements The authors would like to thank Takayuki Yabe, Yumiko Kurosu, Mutsuko Honda, Kasumi Takayama, Hidemi I, and Yasuko Sato for their technical support in the histological analyses. We would like to thank Editage (www.editage.jp) for English language editing. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work, the authors used ChatGPT (OpenAI) to improve the clarity and readability of the English language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. References Wang S, Zheng Y, Yang F, Zhu L, Zhu XQ, Wang ZF et al. The molecular biology of pancreatic adenocarcinoma: translational challenges and clinical perspectives. Signal Transduct Target Ther. 2021;6:249. Hu ZY, Ding D, Song Y, Deng YF, Zhang CM, Yu T. Molecular mechanism of pancreatic ductal adenocarcinoma: The heterogeneity of cancer-associated fibroblasts and key signaling pathways. World J Clin Oncol. 2025;16:97007. Humphris JL, Chang DK, Johns AL, Scarlett CJ, Pajic M, Jones MD et al. The prognostic and predictive value of serum CA19.9 in pancreatic cancer. Ann Oncol. 2012;23:1713-1722. Sumiyoshi T, Uemura K, Shintakuya R, Okada K, Baba K, Harada T et al. Clinical Utility of the Combined Use of CA19-9 and DUPAN-2 in Pancreatic Adenocarcinoma. Ann Surg Oncol. 2024;31:4665-4672. Liang Y, Sheng G, Guo Y, Zou Y, Guo H, Li Z et al. Prognostic significance of grade of malignancy based on histopathological differentiation and Ki-67 in pancreatic ductal adenocarcinoma. Cancer Biol Med. 2024;21:416-432. Hu K, Bian C, Yu J, Jiang D, Chen Z, Zhao F et al. Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images. BMC Gastroenterol. 2024;24:387. Ahmed HS. Beyond Traditional Tools: Exploring Convolutional Neural Networks as Innovative Prognostic Models in Pancreatic Ductal Adenocarcinoma. Arq Gastroenterol. 2024;61:e23107. Sanger A, Hirst J, Davies AK, Robinson MS. Adaptor protein complexes and disease at a glance. J Cell Sci. 2019;132:jcs222992 Ghosh P, Griffith J, Geuze HJ, Kornfeld S. Mammalian GGAs act together to sort mannose 6-phosphate receptors. J Cell Biol. 2003;163:755-766. Toda H, Kurozumi S, Kijima Y, Idichi T, Shinden Y, Yamada Y et al. Molecular pathogenesis of triple-negative breast cancer based on microRNA expression signatures: antitumor miR-204-5p targets AP1S3. J Hum Genet. 2018;63:1197-1210. Khalid M, Idichi T, Seki N, Wada M, Yamada Y, Fukuhisa H et al. Gene Regulation by Antitumor miR-204-5p in Pancreatic Ductal Adenocarcinoma: The Clinical Significance of Direct RACGAP1 Regulation. Cancers (Basel). 2019;11. Ye T, Cheng Y, Li C. Adaptor Protein Complex 1 Sigma 3 Is Highly Expressed in Glioma and Could Enhance Its Progression. Comput Math Methods Med. 2021;2021:5086236. Uemura T, Suzuki T, Dohmae N, Waguri S. Clathrin adapters AP-1 and GGA2 support expression of epidermal growth factor receptor for cell growth. Oncogenesis. 2021;10:80. Hoshi N, Uemura T, Tachibana K, Abe S, Murakami-Nishimagi Y, Okano M et al. Endosomal protein expression of gamma1-adaptin is associated with tumor growth activity and relapse-free survival in breast cancer. Breast Cancer. 2024;31:305-316. Ohgane K, Yoshioka H. Quantification of Gel Bands by an Image J Macro, Band/Peak Quantification Tool. Protocols.io. 2019; doi:10.17504/protocols.io.7vghn3w Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9:676-682. Okabe N, Ezaki J, Yamaura T, Muto S, Osugi J, Tamura H et al. FAM83B is a novel biomarker for diagnosis and prognosis of lung squamous cell carcinoma. Int J Oncol. 2015;46:999-1006. Meyers N, Gerard C, Lemaigre FP, Jacquemin P. Differential impact of the ERBB receptors EGFR and ERBB2 on the initiation of precursor lesions of pancreatic ductal adenocarcinoma. Sci Rep. 2020;10:5241. Luo G, Jin K, Deng S, Cheng H, Fan Z, Gong Y et al. Roles of CA19-9 in pancreatic cancer: Biomarker, predictor and promoter. Biochim Biophys Acta Rev Cancer. 2021;1875:188409. Jeong J, Hwang YE, Lee M, Keum S, Song S, Kim JW et al. Downregulation of AP1S1 causes the lysosomal degradation of EGFR in non-small cell lung cancer. J Cell Physiol. 2023;238:2335-2347. Liu Y, Li F, Wu B, Huang L, Qi Y. The clathrin adaptor AP1-S1 is associated with immune infiltration and HLA loss, as a potential therapeutic target in lung adenocarcinoma. Int Immunopharmacol. 2025;152:114385. Rojas LK, Trilla-Fuertes L, Gámez-Pozo A, Chiva C, Sepúlveda J, Manso L et al. Proteomics characterisation of central nervous system metastasis biomarkers in triple negative breast cancer. Ecancermedicalscience. 2019;13:891. Kou Y, Yan X, Liu Q, Wei X, Zhang B, Li X et al. HBV upregulates AP-1 complex subunit mu-1 expression via the JNK pathway to promote proliferation of liver cancer cells. Oncol Lett. 2019;18:456-464. Gravotta D, Perez Bay A, Jonker CTH, Zager PJ, Benedicto I, Schreiner R et al. Clathrin and clathrin adaptor AP-1 control apical trafficking of megalin in the biosynthetic and recycling routes. Mol Biol Cell. 2019;30:1716-1728. Bonifacino JS. Adaptor proteins involved in polarized sorting. J Cell Biol. 2014;204:7-17. Sorkina T, Bild A, Tebar F, Sorkin A. Clathrin, adaptors and eps15 in endosomes containing activated epidermal growth factor receptors. J Cell Sci. 1999;112 ( Pt 3):317-327. Yi L, Kaler SG. Direct interactions of adaptor protein complexes 1 and 2 with the copper transporter ATP7A mediate its anterograde and retrograde trafficking. Hum Mol Genet. 2015;24:2411-2425. Koumarianou P, Fernández-Méndez C, Fajardo-Delgado D, Mielu LM, Santisteban P, De la Vieja A. Basolateral Sorting of the Sodium/Iodide Symporter Is Mediated by Adaptor Protein 1 Clathrin Adaptor Complexes. Thyroid. 2022;32:1259-1270. Holloway ZG, Velayos-Baeza A, Howell GJ, Levecque C, Ponnambalam S, Sztul E et al. Trafficking of the Menkes copper transporter ATP7A is regulated by clathrin-, AP-2-, AP-1-, and Rab22-dependent steps. Mol Biol Cell. 2013;24:1735-1748, S1731-1738. Liu Y, Xu P, Rivara S, Liu C, Ricci J, Ren X et al. Clathrin-associated AP-1 controls termination of STING signalling. Nature. 2022;610:761-767. Wan C, Crisman L, Wang B, Tian Y, Wang S, Yang R et al. AAGAB is an assembly chaperone regulating AP1 and AP2 clathrin adaptors. J Cell Sci. 2021;134:jcs258587. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9301878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":640399403,"identity":"c6e4d1fb-9300-4486-beb2-b8d7a6727d55","order_by":0,"name":"Shigeyuki Tsukida","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACA3Yog5/5AITB2EBICzOUIdmWQKoWg2MJRDrMnJnH8NGNCps842PcaRIMNXYMzLMJWGPZzGNsnHMmrdjsGO82CYZjyQyMcw4QcNhhHjPp3LbDidvu9wK1sB1gYJxBwIVALea/QVo2t4Fs+UecFjNmkJYNbEAtjG1EaLFsZiuWBvolccYx3s0WiX3JPAT9Ys7evPFzToVNYn8b78YbH77ZyRkSCjFUAHQSj+EMUnSAgbwEyVpGwSgYBaNgmAMAMrE/REk1pdYAAAAASUVORK5CYII=","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shigeyuki","middleName":"","lastName":"Tsukida","suffix":""},{"id":640399404,"identity":"163248c1-3457-4742-bcfa-dc84882dc3c9","order_by":1,"name":"Takefumi Uemura","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Takefumi","middleName":"","lastName":"Uemura","suffix":""},{"id":640399405,"identity":"bdc95657-2541-4200-9421-ac84930bb77f","order_by":2,"name":"Naoya Sato","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Naoya","middleName":"","lastName":"Sato","suffix":""},{"id":640399406,"identity":"3798ba52-244e-46c2-9350-d43fd637a2a0","order_by":3,"name":"Yasuhide Kofunato","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yasuhide","middleName":"","lastName":"Kofunato","suffix":""},{"id":640399407,"identity":"5ed2572f-9617-4b24-a471-3b4742f0014e","order_by":4,"name":"Teruhide Ishigame","email":"","orcid":"","institution":"Fukushima Medical University Aizu Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Teruhide","middleName":"","lastName":"Ishigame","suffix":""},{"id":640399408,"identity":"d3610715-3168-4457-bf98-ed2db2317697","order_by":5,"name":"Takashi Kimura","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Takashi","middleName":"","lastName":"Kimura","suffix":""},{"id":640399409,"identity":"a350cc47-0018-4e7f-ac3f-93be40ac62df","order_by":6,"name":"Akira Kenjo","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Akira","middleName":"","lastName":"Kenjo","suffix":""},{"id":640399410,"identity":"4ec0eaa6-beda-48c0-b310-0bc7584ec13b","order_by":7,"name":"Shigeru Marubashi","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shigeru","middleName":"","lastName":"Marubashi","suffix":""},{"id":640399411,"identity":"7153b338-ee23-4d85-98c0-c2b4d48d6eaf","order_by":8,"name":"Satoshi Waguri","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Satoshi","middleName":"","lastName":"Waguri","suffix":""}],"badges":[],"createdAt":"2026-04-02 10:25:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9301878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9301878/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109330621,"identity":"1803e082-7ce2-4e84-abef-f23630070a3c","added_by":"auto","created_at":"2026-05-15 16:07:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":343863,"visible":true,"origin":"","legend":"\u003cp\u003eGeneration of γ1-adaptin knockdown and KO cells from PANC-1 cells, and their effects on EGFR and MET expression\u003c/p\u003e\n\u003cp\u003e(a) Representative Western blot analysis of γ1-adaptin, EGFR, MET, and GAPDH in γ1-adaptin knockdown cells using γ1-adaptin shRNA (sh-γ1) and control (Ctrl). kDa indicates molecular weight. Full-length blots are presented in Additional file 3.\u003c/p\u003e\n\u003cp\u003e(b) Quantification of EGFR and MET expression in (a). Band intensities were normalized to GAPDH and presented as ratios relative to the Ctrl. Statistical differences were analyzed using Student’s t-test, with three independent experiments. *: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ***: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n\u003cp\u003e(c) Representative Western blot analysis of γ1-adaptin, EGFR, MET, and GAPDH in γ1-adaptin KO cells (KO1 and KO2) and parental PANC-1 cells (WT). kDa indicates molecular weight. Full-length blots are presented in Additional file 4.\u003c/p\u003e\n\u003cp\u003e(d) Immunofluorescence microscopy of WT, KO1, and KO2 cells using anti-γ1-adaptin antibody (green). Nuclei were stained with Hoechst (blue). Scale bars, 30 μm.\u003c/p\u003e\n\u003cp\u003e(e) Quantification of EGFR and MET expression in (c). Band intensities were normalized to GAPDH and presented as ratios relative to WT. Statistical differences were analyzed using one-way analysis of variance followed by Dunnett’s post hoc test, with five independent experiments. *: \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, **: \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ***: \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001, ****: \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.0001\u003c/p\u003e\n\u003cp\u003eKO: knockout, WT: Wild-type, EGFR: epidermal growth factor receptor, MET: MET receptor tyrosine kinase\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9301878/v1/1a75925ef17d635191931fc4.png"},{"id":109405880,"identity":"64671eec-f01b-408f-be65-034549356bfb","added_by":"auto","created_at":"2026-05-17 13:20:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":367863,"visible":true,"origin":"","legend":"\u003cp\u003eCancer cell activities of γ1-adaptin KO cells\u003c/p\u003e\n\u003cp\u003e(a) Cell proliferation assay in WT, KO1, and KO2 cells. Cells were cultured for 1, 3, 5, 7, and 9 days, and the cell proliferation was assessed. Values were calculated relative to day 0 and are presented as the mean ± standard deviation of six replicates per cell line. Statistical differences between WT and each KO cell line were analyzed using one-way analysis of variance followed by Dunnett’s post hoc test. *:\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.05, ****: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, compared with WT.\u003c/p\u003e\n\u003cp\u003e(b-e) Cell migration assay (b, d) and invasion assay (c, e) in WT, KO1, and KO2 cells. Representative microscopic images of cells that migrated through the bottom membrane of the chamber (b) or invaded through the bottom membrane coated with basement membrane matrix proteins (c) and quantified data (d, e) are shown. The absorbance (OD 560 nm) for each cell line is plotted as the mean ± standard deviation of three replicates per cell line. Statistical differences were analyzed using one-way analysis of variance followed by Dunnett’s post hoc test. **: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ****: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, compared with WT.\u003c/p\u003e\n\u003cp\u003eWT: wild type, KO: knockout\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9301878/v1/d121561026e0c5a7ace53dc9.png"},{"id":109330624,"identity":"202094ad-c436-4f41-a754-99d0f6cd21c8","added_by":"auto","created_at":"2026-05-15 16:07:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":520587,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and subcellular localization of γ1-adaptin in PDAC tissues\u003c/p\u003e\n\u003cp\u003e(a) Immunofluorescence images of γ1-adaptin in the normal pancreatic region (N) and tumor region (T) from the same case in the PDAC tissue array. Scale bars, 10 μm.\u003c/p\u003e\n\u003cp\u003e(b) γ1-adaptin intensity was quantitatively compared between paired normal (N) and tumor (T) regions in PDAC tissue arrays (n = 30 for each). Statistical differences were analyzed using paired t-test. ***: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003cbr\u003e\nPDAC: pancreatic ductal adenocarcinoma\u003c/p\u003e\n\u003cp\u003e(c) Representative immunohistochemical images of γ1-adaptin in normal pancreatic region (N) and tumor regions with high-intensity (T-high) and low intensity (T-low). Boxed regions are magnified and shown below. Scale bars, 30 μm.\u003c/p\u003e\n\u003cp\u003e(d) Double immunofluorescence images stained for γ1-adaptin (green) and EEA1 (upper panel, red) or TGN46 (lower panel, red). Nuclei were stained with Hoechst (blue). Scale bars, 10 μm.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9301878/v1/bece58a498b0e54cf33aebf4.png"},{"id":109405563,"identity":"9abf900f-a0ee-4aab-9cd1-80138683757e","added_by":"auto","created_at":"2026-05-17 13:19:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":123638,"visible":true,"origin":"","legend":"\u003cp\u003eQuantification of γ1-adaptin staining intensity in PDAC tissues and survival analysis\u003c/p\u003e\n\u003cp\u003e(a) γ1-adaptin intensity was quantitatively compared between normal and tumor tissues from our institution (30 cases). Statistical differences were analyzed using paired t-test. ***: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n\u003cp\u003e(b) Histogram of case numbers according to γ1-adaptin staining intensity across 123 PDAC cases. The cases were divided into two groups (Low and High) at the median value of 97.32.\u003c/p\u003e\n\u003cp\u003e(c, d) Kaplan–Meier curves of RFS (c) and OS (d) according to γ1-adaptin staining intensity (High vs. Low). Statistical differences were assessed using the log-rank test. The number of patients and \u003cem\u003ep\u003c/em\u003e-values are indicated. Numbers at risk are shown below the graph. Median RFS or OS, and their 95% CIs are provided beneath the risk tables.\u003c/p\u003e\n\u003cp\u003ePDAC: pancreatic ductal adenocarcinoma, RFS: recurrence-free survival, OS: overall survival, CI: confidence interval\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9301878/v1/0a7225530aa3a38a0086f9fd.png"},{"id":109330630,"identity":"07ffb75a-4dbb-4371-9372-39ce20e2bb13","added_by":"auto","created_at":"2026-05-15 16:07:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":206057,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup survival analysis according to γ1-adaptin staining intensity.\u003c/p\u003e\n\u003cp\u003eThe cases were divided into two groups (Low vs. High γ1-adaptin staining intensity) based on the median γ1-adaptin staining intensity. OS was analyzed using the Kaplan–Meier method within each predefined subgroup, and statistical differences were assessed using the log-rank test. The case number and \u003cem\u003ep\u003c/em\u003e-values are indicated. The numbers at risk are shown below the graph.\u003c/p\u003e\n\u003cp\u003e(a, b) Serum CA19-9 level (cutoff: 37 U/mL)\u003c/p\u003e\n\u003cp\u003e(c, d) Histological type (well-differentiated or other histological types)\u003c/p\u003e\n\u003cp\u003e(e, f) Lymph node metastasis (N- or N+)\u003c/p\u003e\n\u003cp\u003eOS: overall survival, CA19-9: carbohydrate antigen 19-9\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9301878/v1/81b2bd5ba8d34bebd52da144.png"},{"id":109405319,"identity":"3b287d83-c4fa-4d8f-988e-ed7f3c5691eb","added_by":"auto","created_at":"2026-05-17 13:16:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26076,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1ver2.0.docx","url":"https://assets-eu.researchsquare.com/files/rs-9301878/v1/e20f958098f72f24e1d78f93.docx"},{"id":109330626,"identity":"688400a1-88bd-485c-bec9-6ab370408c6f","added_by":"auto","created_at":"2026-05-15 16:07:28","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5724505,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3v2.tif","url":"https://assets-eu.researchsquare.com/files/rs-9301878/v1/72008cceac0350e1d3e02d20.tif"},{"id":109405407,"identity":"99e9f237-6158-414f-aad6-362617b880fc","added_by":"auto","created_at":"2026-05-17 13:17:52","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14984,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-9301878/v1/8b35d8e4fcfe9a39441ec5fb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"γ1-adaptin expression is associated with cancer progression and poor prognosis in pancreatic ductal adenocarcinoma: Cellular and histopathological analyses","fulltext":[{"header":"Background","content":"\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies and is associated with an extremely poor prognosis, largely due to its high invasiveness, metastatic potential, and resistance to existing therapies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Several prognostic factors for PDAC have been identified, including tumor size, local invasion, and lymph node metastasis, which are used in the staging classification of pancreatic cancer. Additionally, the widely used adenocarcinoma marker carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA19-9) and histological subtype have been reported to correlate with prognosis [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although these factors aid in patient stratification and guide treatment decisions, their predictive accuracy remains limited [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, additional molecular markers for effective diagnostic or prognostic assessment, as well as therapeutic targets, are needed.\u003c/p\u003e \u003cp\u003eAdaptor protein complex-1 (AP-1) is a clathrin adaptor that regulates the intracellular trafficking of specific cargo proteins, including the transport of mannose 6-phosphate receptors from the trans-Golgi network (TGN) to endosomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. AP-1 is a heterotetramer complex composed of β1-, γ-, \u0026micro;1-, and σ1-adaptins with multiple isoforms of γ-, \u0026micro;1-, and σ1-adaptins [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. γ1-adaptin, an isoform of γ-adaptin, interacts with ADP-ribosylation factor 1 to facilitate AP-1 recruitment to the TGN/endosomal membrane. In the context of cancer biology, we and others have previously reported that AP-1 subunits are highly expressed in breast cancer, glioma, non-small-cell lung carcinoma (NSCLC), and PDAC [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Previously, we utilized immunohistochemistry (IHC) to demonstrate that endosomal expression of γ1-adaptin was positively correlated with breast cancer malignancy and could be a novel prognostic marker [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As a mechanistic basis for the tumor-promoting effects of AP-1 subunits in cancer cells, we have demonstrated that γ1-adaptin sustains the expression of receptor tyrosine kinases, including epidermal growth factor receptor (EGFR) and/or MET receptor tyrosine kinase (MET), in the NSCLC-derived H1975 cell line [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]and HER2-positive breast cancer cell line SK-BR-3 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the protein expression and clinical significance of γ1-adaptin-containing AP-1 in PDAC remain unclear. Therefore, in this study, we investigated the relationship between γ1-adaptin expression, assessed using IHC in patient specimens, and PDAC malignancy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell Lines and Human Tissue Samples\u003c/h2\u003e \u003cp\u003ePANC-1 cells were obtained from the Cell Bank of RIKEN BioResource Research Center. They were cultured in DMEM with high glucose and 10% FBS (Nichirei Bioscience, Tokyo, Japan) in an incubator containing 5% CO\u003csub\u003e2\u003c/sub\u003e at 37\u0026deg;C.\u003c/p\u003e \u003cp\u003ePatients with PDAC who underwent pancreatectomy at Fukushima Medical University Hospital (Fukushima, Japan) between 2003 and 2020 were included in this study. Cases pathologically diagnosed as non-PDAC, such as acinar cell carcinoma, neuroendocrine neoplasm, or intraductal papillary mucinous neoplasm, were excluded. Patients who received neoadjuvant therapy were excluded. Among the remaining patients, those with pathological stage IV disease or a short follow-up period (\u0026lt;\u0026thinsp;90 days) were further excluded. In addition, cases with insufficient pathological information for accurate evaluation or unevaluable histological slides due to technical artifacts were excluded. For clinicopathological and statistical analyses, histological types were classified into well-differentiated and other histological types (including moderately/poorly differentiated adenocarcinoma and other histological subtypes). Informed consent was obtained from all participants included in the study. A tissue array of PDAC (OD-CT-DgPan03-002) containing 30 cases of PDAC with each adjacent non-tumor region was purchased from Shanghai Outdo Biotech Co. (Shanghai, China). The detailed information is provided in Additional file 1. Studies involving human samples were approved by the Ethics Committee of Fukushima Medical University (Numbers: 29390 and 2943).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAntibodies\u003c/h3\u003e\n\u003cp\u003eAn antibody against γ1-adaptin (BD Bioscience, San Jose, NJ, USA [610385]) was used for IHC (1:1000 dilution), immunofluorescence (IF; 1:1000 dilution), and western blotting (WB; 1:2000 dilution). An antibody against EEA1 (Abcam, Cambridge, UK [ab109110]) was used for IF (1:200 dilution). An antibody against TGN46 (Bio-Rad, Hercules, CA, USA [AHP500GT]) was used for IF (1:400 dilution). An antibody against GAPDH (Cell Signaling Technology, Danvers, MA, USA [2118]) was used as a loading control for WB (1:2000 dilution). Donkey anti-mouse or donkey anti-rabbit antibodies conjugated with Alexa Fluor fluorescent dye (Jackson ImmunoResearch, West Grove, PA, USA [anti-mouse: 715-545-150, anti-rabbit: 711-585-152]) were used for IF (1:800 dilution). For WB, sheep anti-mouse IgG, HRP-linked F(ab\u0026rsquo;)2 fragment (Cytiva, Tokyo, Japan [NA9310-1ML]) and donkey anti-rabbit IgG, HRP-linked F(ab\u0026rsquo;)2 fragment (Cytiva, Tokyo, Japan [NA9340-1ML]) were used (1:2000 dilution). For IHC, a peroxidase-labeled anti-mouse antibody (Histofine Simple stain MAX-PO (M), Nichirei Biosciences Inc., Tokyo, Japan) was used.\u003c/p\u003e\n\u003ch3\u003eshRNA experiment\u003c/h3\u003e\n\u003cp\u003eFor knockdown of γ1-adaptin, shRNA was introduced as previously described [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Briefly, lentiviruses carrying the pLKO.1 puro vector (Sigma) encoding γ1-adaptin shRNA (5ʹ-AGGAAGUUAUGUUCGUGAU-3ʹ) or pLKO.1 empty vector was obtained by collecting the culture supernatant of HEK293T cells transfected with the respective lentiviral vectors. PANC-1 cells were infected with the lentivirus in DMEM containing 10% FBS and 8 \u0026micro;g/ml polybrene for 48 h. The medium was then replaced with fresh DMEM containing 5 \u0026micro;g/ml puromycin. The resulting bulk population of puromycin-resistant cells was used for WB.\u003c/p\u003e\n\u003ch3\u003eGene Knockout\u003c/h3\u003e\n\u003cp\u003eTransfection was performed using Lipofectamine\u0026trade; CRISPRMAX\u0026trade; Cas9 Transfection Reagent (Thermo Fisher Scientific, Waltham, MA, USA), according to the manufacturer\u0026rsquo;s instructions. Briefly, for the standard concentration, 25 \u0026micro;L of Opti-MEM I Reduced Serum Medium (Thermo Fisher Scientific, Waltham, MA, USA) was mixed with Cas9 protein (final concentration of 23 ng/\u0026micro;L), sgRNA (CRISPR699909_SGM, 5ʹ- CAAACGCATTGGCTATTTAG-3ʹ, Cat# A32042; Thermo Fisher Scientific, Waltham, MA, USA; final concentration of 4.4 ng/\u0026micro;L), and 2.5 \u0026micro;L of Cas9 Plus\u0026trade; Reagent (Thermo Fisher Scientific, Waltham, MA, USA). Another 25 \u0026micro;L of Opti-MEM I Reduced Serum Medium was mixed with 1.5 \u0026micro;L CRISPRMAX\u0026trade; transfection reagent and incubated at room temperature for 1 min. After incubation, both solutions were thoroughly mixed and incubated at room temperature for 10\u0026ndash;15 min. The resulting solution was added to each well containing 500 \u0026micro;L of the culture medium. On the 4th day after transfection, IF staining was performed to evaluate the knockout (KO) efficiency of the target gene in the transfected cell lines.\u003c/p\u003e\n\u003ch3\u003eSingle Cell Cloning\u003c/h3\u003e\n\u003cp\u003eMonoclonal preparations were performed using the limiting dilution method with conditioned medium. Mixed clone cells were seeded in a 10 cm dish containing 12 mL DMEM with 10% FBS. On the 2nd day after seeding, the supernatant was collected from the conditioned medium. The cells were centrifuged at 770 \u003cem\u003e\u0026times;g\u003c/em\u003e for 10 min to remove any cellular components. The cell suspension was diluted to a concentration of 0.85 cells/100 \u0026micro;L with the conditioned medium using the limiting dilution method. After mixing, the cells were divided into 96‑well plates, and 100 \u0026micro;L of conditioned medium was added to each well for monoclonal culture. Single cells in each well were tracked, and the formation of single-cell clones was observed. When sufficiently large monoclonal colonies were formed, they were successively expanded by further culturing them. IF staining confirmed the monoclonal KO nature of these cells, and the loss of expression of γ1-adaptin was verified using WB. These validated KO cells were established as KO cell lines and compared with wild-type (WT) controls.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWB experiments\u003c/h2\u003e \u003cp\u003eWT and KO cells were washed with cold PBS and lysed in cold 1% Triton X-100/PBS supplemented with cOmplete\u0026trade; EDTA-free, protease inhibitor cocktail (Roche, Mannheim, Germany) and PhosSTOP\u0026trade; (Roche, Mannheim, Germany). Equal amounts of protein were separated using SDS-PAGE and transferred onto a PVDF membrane. After blocking with 5% skim milk, the membranes were incubated with primary antibodies for 60 min, followed by incubation with HRP-conjugated secondary antibodies for 30 min. Chemiluminescent signals were detected using Amersham\u0026trade; ECL\u0026trade; Select (Cytiva, Tokyo, Japan) and LAS4000 mini (GE Healthcare, Tokyo, Japan). Densitometric analysis of the blots was performed using the Band/Peak Quantification macro tool for ImageJ, developed by Ohgane et al [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIF Microscopy for Culture Cells\u003c/h3\u003e\n\u003cp\u003eWT and KO cells were seeded onto circular glass coverslips placed in 24-well plates and used for IF staining. The coverslips with cells were recovered and fixed with 4% paraformaldehyde/0.1 M phosphate buffer for 15 min at room temperature. After washing with PBS, the cells were permeabilized and blocked with 0.1% Triton X-100 and 0.4% BSA in PBS for 30 min at room temperature. After permeabilization and blocking, the cells were incubated with primary antibodies for 60 min at room temperature. Following another PBS wash, the cells were incubated with a mixture of secondary antibodies and Hoechst nuclear stain for 25 min at room temperature. The reacted coverslips were then mounted onto glass slides using Aqua/Poly Mount (Polysciences Inc. Warrington, PA, USA) mounting medium. The cells were observed using a confocal laser microscope (FLUOVIEW FV1000, OLYMPUS, Tokyo, Japan).\u003c/p\u003e\n\u003ch3\u003eCell Proliferation Assay\u003c/h3\u003e\n\u003cp\u003eThe CCK-8 (Dojindo Laboratories, Kumamoto, Japan) assay was used to measure the proliferation of cell lines. Cell suspensions (100 \u0026micro;L, 2000 cells/well) were seeded in a 96-well plate and incubated under standard culture conditions. At days 0 (2 h), 1 (24 h), 3 (72 h), 5 (120 h), 7 (168 h), and 9 (216 h), 10 \u0026micro;L of CCK-8 reagent was added to each well, and the cells were incubated for 2 h at 37\u0026deg;C. The absorbance reflecting cell proliferation was measured at 450 nm using Multiskan\u0026trade; GO (Thermo Fisher Scientific, Waltham, MA, USA). The growth rate at each time point was calculated relative to that on day 0.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMigration and Invasion Assays\u003c/h2\u003e \u003cp\u003eThe CytoSelect Cell Migration and Invasion Kit (Cell Biolabs, Inc., San Diego, CA, USA) was used according to the manufacturer\u0026rsquo;s protocol. For the migration assay, cells were suspended in a serum-free medium at a density of 1.0 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e/mL. Suspended cells (300 \u0026micro;L) were seeded in the upper chambers, and the lower chambers contained 10% FBS as a chemoattractant. Following incubation at 37\u0026deg;C for 6 h, the cells that migrated through the membrane were stained with a cell staining solution at room temperature for 10 min. After staining, the membranes were washed with PBS and observed under a microscope. Then, the migrated cells were dissolved in an extraction solution. The dissolved solutions were quantified by measuring the absorbance at 560 nm using Multiskan\u0026trade; GO (Thermo Fisher Scientific, Waltham, MA, USA).\u003c/p\u003e \u003cp\u003eThe invasion assay was conducted using the same procedure as the migration assay, except that a chamber coated with basement membrane matrix proteins was used, the cell density was adjusted to 0.5 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e/mL, and the incubation time was extended to 24 h.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIF Microscopy and IHC for Human Tissues\u003c/h2\u003e \u003cp\u003eTissue array specimens were processed for antigen retrieval in 1% ImmunoSaver (Nissin EM, Tokyo, Japan) for 20 min at 98\u0026deg;C using a microwave processor (MI-77, AZUMAYA, Japan), followed by blocking of nonspecific binding with 5% donkey serum (Jackson ImmunoResearch Inc., West Grove, PA, USA) for 20 min at room temperature. They were further incubated with an anti-γ1-adaptin antibody or combinations of anti-γ1-adaptin and anti-EEA1 or anti-TGN46 antibodies for 2 days at 4\u0026deg;C, followed by incubation with secondary antibodies conjugated with Alexa Fluor fluorescent dye for 30 min at room temperature. IF microscopy was performed using a confocal laser scanning microscope (FLUOVIEW FV1000, OLYMPUS, Tokyo, Japan).\u003c/p\u003e \u003cp\u003eFor IHC, 3-\u0026micro;m-thick sections were prepared from paraffin-embedded tissue samples of patients with pancreatic cancer. After deparaffinization, the sections were processed for antigen retrieval in a 1% ImmunoSaver (Nissin EM, Tokyo, Japan) for 20 min at 98\u0026deg;C using a microwave processor (MI-77, AZUMAYA, Japan) followed by inactivation of endogenous peroxidase with 0.3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in methanol for 20 min at room temperature and blocking of nonspecific binding with 10% goat serum (Jackson ImmunoResearch Inc., West Grove, PA, USA) for 20 min at room temperature. They were further incubated with an anti-γ1-adaptin antibody for 2 days at 4\u0026deg;C, followed by incubation with peroxidase-labeled anti-mouse antibody for 30 min at room temperature. Peroxidase activity was detected using 0.0125% 3,3ʹ-DAB and 0.002% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in 0.05 M Tris\u0026ndash;HCl buffer (pH 7.6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImage Analysis for Quantification\u003c/h2\u003e \u003cp\u003eTo quantify the staining intensity, images of γ1-adaptin-stained IHC specimens were captured using a virtual slide scanner (Nanozoomer-SQ, Hamamatsu Photonics, Japan) equipped with a \u0026times;40 lens, which was observed using NDP.view2 (Hamamatsu Photonics, Japan). Quantification was performed using the Fiji software (National Institute of Health, MD, USA) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], in accordance with previous studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. All images were converted to 256‑level grayscale images and inverted. Five rectangular regions of interest (ROIs), each measuring 425 \u0026times; 266 \u0026micro;m and containing relatively strong immunoreactivity, were selected per specimen. In each ROI, a common thresholding process based on staining intensity was performed, and the average staining intensity value of the threshold pixels was measured, which was considered the γ1-adaptin intensity of the ROI. The average of the five ROIs was used as the value for each case. Differences in γ1-adaptin intensity across experiments were normalized using five identical reference cases included in all experimental runs. The image analysis was performed without knowledge of the clinical outcomes, and in a standardized and semi-automated manner to minimize observer-related bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStudent\u0026rsquo;s t-test and one-way analysis of variance followed by Dunnett\u0026rsquo;s post hoc test were used for densitometric analysis of WB data and to analyze the results of the proliferation, migration, and invasion assays. Student\u0026rsquo;s t-test was used for quantitative comparison of staining intensity between normal and tumorous tissue regions. Pearson\u0026rsquo;s chi-squared test was used to evaluate the correlations between γ1-adaptin expression and clinicopathological factors. Overall survival (OS) was defined as the time from surgery to death from any cause. Survival analyses were performed using the Kaplan\u0026ndash;Meier method with the log-rank test. Cox proportional hazards models were used for univariable and multivariable analyses. IBM SPSS Statistics 29 (IBM Inc., Armonk, NY, USA) and GraphPad Prism 10 (GraphPad Software, CA, USA) were used for all statistical analyses. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Cases with missing clinicopathological or follow-up data were excluded from the analysis. The cutoff values for γ1-adaptin staining intensity and selected clinical variables (age, body mass index, and tumor size) were defined as their median values, whereas the cutoff value for serum CA19-9 level was defined as the upper limit of normal (37 U/mL).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of γ1-adaptin KO Cell Lines\u003c/h2\u003e \u003cp\u003eBased on our previous findings demonstrating a close association between γ1-adaptin and EGFR/MET expression in multiple cancer cell lines, we extended this analysis to a PDAC-derived cell line. The PANC-1 cell line harboring the KRAS G12D mutation was selected, as this mutation depends on EGFR signaling for the initiation of PDAC [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. WB showed that transduction of shRNA targeting γ1-adaptin significantly reduced EGFR and MET expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and b). To examine the role of γ1-adaptin in PANC-1 cells, we generated two γ1-adaptin KO cell lines (KO1 and 2) using CRISPR/Cas9-mediated gene editing. WB revealed the absence of γ1-adaptin in the KO cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). IF microscopy showed typical perinuclear staining of γ1-adaptin in parental WT cells, which was absent in the KO cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). These findings confirmed the successful establishment of γ1-adaptin KO cell lines. WB revealed that the band intensity of EGFR and MET were significantly reduced in the KO cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and e), suggesting that γ1-adaptin regulates receptor tyrosine kinase expression.\u003c/p\u003e \u003cp\u003e[insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eγ1-adaptin Deficiency Reduced Cell Proliferation, Migration, and Invasion\u003c/h2\u003e \u003cp\u003eTo assess the impact of γ1-adaptin deficiency on tumor cell behavior, proliferation, migration, and invasion assays were performed. In the proliferation assay, the KO cell lines showed a significantly reduced growth rate from day 5 onward. By day 9, the growth rate of the KO cell lines reached approximately 40% of that of WT cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). In the migration and invasion assays, the number of migrated and invading cells was significantly reduced in each KO cell line compared with WT cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-e). These findings suggest that γ1-adaptin promotes tumor cell activity at the cell culture level. Therefore, we investigated the relationship between γ1-adaptin expression and clinicopathological factors in PDAC cases.\u003c/p\u003e \u003cp\u003e[insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eγ1-adaptin Expression in PDAC by Immunohistological Analyses\u003c/h2\u003e \u003cp\u003eWe first examined the protein expression of γ1-adaptin using IF microscopy in a commercially available PDAC tissue array comprising 30 cases. In this experiment, we used an antibody whose specificity was validated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, c, d, and in previous studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Cytoplasmic granular structures positive for γ1-adaptin were more evident in tumor regions than in non-tumor regions in 63.3% of cases (19/30) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), and the mean signal intensity was significantly higher in tumor regions than in non-tumor regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). We then investigated the γ1-adaptin expression by IHC in PDAC cases from our institute. γ1-adaptin was detected as a weak, diffuse signal in non-tumor regions, whereas multiple cytoplasmic granules were observed in tumor regions, with varying intensities among the cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Double IF microscopy using markers for endosomes (EEA1) and the TGN (TGN46) revealed that the majority of γ1-adaptin localized to endosomes, but not to the TGN (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e[insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRelationships between γ1-adaptin Expression and Clinicopathological Factors\u003c/h2\u003e \u003cp\u003eQuantification of IHC images from 30 randomly selected cases showed that γ1-adaptin staining intensity was significantly higher in tumor regions than in non-tumor regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), consistent with the results of the tissue array analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). When quantification was performed in tumor regions from 123 cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the intensity values exhibited clear inter-case variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), with a median value of 97.32. This value was used as a cutoff to classify the cases into high- and low- expression groups for subsequent analyses.\u003c/p\u003e \u003cp\u003e[insert Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eAssociation analysis revealed no significant correlations between γ1-adaptin intensities and preoperative CA19-9 levels, lymph node metastasis, histological type, or other clinicopathological features (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between γ1-adaptin staining intensity and clinicopathological factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinicopathological factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eγ1-adaptin staining intensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 [67.0-77.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 [62.0\u0026ndash;76.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 [19.7\u0026ndash;24.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 [20.6\u0026ndash;23.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA-PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (82.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9 (U/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.0 [15.8-321.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127 [26.2-900.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation procedure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 [21.3\u0026ndash;39.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 [22.0\u0026ndash;36.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53 (86.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorly differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther histological types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResection status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative complication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD grade\u0026thinsp;\u0026lt;\u0026thinsp;3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (83.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD grade\u0026thinsp;\u0026ge;\u0026thinsp;3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrence \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eContinuous variables are presented as median [IQR] and categorical variables as n (%)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ea: \u003cem\u003ep\u003c/em\u003e-values were calculated using the chi-square test after dichotomization of variables according to predefined cutoff values.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eb: Recurrence was analyzed using survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and is presented descriptively.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eIQR: Interquartile range, BMI: Body mass index, ASA-PS: American Society of Anesthesiologists Physical Status, CA19-9: Carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9, PD: Pancreaticoduodenectomy, DP: Distal pancreatectomy, TP: Total pancreatectomy, CD: Clavien\u0026ndash;Dindo\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eHowever, survival analysis showed that patients with high γ1-adaptin intensity had significantly shorter recurrence-free survival than those with low γ1-adaptin intensity (median recurrence-free survival, 11.3 months [95% confidence interval (CI), 6.0\u0026ndash;16.6] vs. 17.0 months [95% CI, 8.2\u0026ndash;25.8]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012; hazard ratio (HR) [95% CI]\u0026thinsp;=\u0026thinsp;1.717 [1.116\u0026ndash;2.642]; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Similarly, the high-intensity group showed significantly shorter OS than the low-intensity group (median OS, 23.8 months [95% CI, 15.6\u0026ndash;32.0] vs. 44.8 months [95% CI, not estimable]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; HR [95% CI]\u0026thinsp;=\u0026thinsp;2.035 [1.290\u0026ndash;3.211]; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Regarding factors other than γ1-intensity, high CA19-9 levels, T factor (\u0026ge;\u0026thinsp;3), lymph node metastasis, and resection status (R1 resection) were significantly associated with 5-year OS. A low body mass index, histologic type (other histological types), and tumor location (pancreatic head) were also significantly associated with 5-year OS (Additional file 2). The results of the univariable analysis using Cox proportional hazards model were also similar (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox proportional hazards analysis of prognostic variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eUnivariable analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnfavorable/Favorable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard\u003c/p\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHazard\u003c/p\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eγ1-adaptin staining intensity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.292\u0026ndash;3.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e**\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.111\u0026ndash;3.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e*\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale/Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.743\u0026ndash;1.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;71/\u0026lt; 71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.678\u0026ndash;1.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;21.9/\u0026ge; 21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.976\u0026ndash;2.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e#\u003c/sup\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.858\u0026ndash;2.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9 (U/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;37/\u0026lt; 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.266-3.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e**\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.129\u0026ndash;3.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e*\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHead/Body, Tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.995\u0026ndash;2.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.528\u0026ndash;1.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;28/\u0026le; 28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.888\u0026ndash;2.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.610\u0026ndash;1.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther histological types/Well-differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.949\u0026ndash;2.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e#\u003c/sup\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.129\u0026ndash;3.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e*\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3/T1, T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.075\u0026ndash;5.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e*\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.713\u0026ndash;4.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive/Negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.430\u0026ndash;3.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e**\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.237\u0026ndash;3.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e**\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResection status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR1/R0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.109\u0026ndash;3.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e*\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.917\u0026ndash;2.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjuvant therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.704\u0026ndash;1.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eBMI: Body mass index, CA19-9: Carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9, CI: Confidence interval\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e#: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, *: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eMultivariable analysis further revealed that γ1-adaptin intensity was significantly associated with 5-year OS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018; HR [95% CI]\u0026thinsp;=\u0026thinsp;1.828 [1.111\u0026ndash;3.006]); additionally, it was identified as an independent poor prognostic factor, along with CA19-9 levels, histologic type, and lymph node metastasis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subgroup analyses stratified by CA19-9 levels, histological type, and lymph node status showed poorer OS in the high γ1-adaptin group among patients with low CA19-9, other histological types, and lymph node metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e[insert Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we analyzed immunohistochemical expression of an AP-1 subunit, γ1-adaptin, in two independent cohorts of PDAC, a commercially available tissue array and our institutional cases, and found that its expression was significantly higher in tumor regions compared with adjacent non-tumor regions. Furthermore, correlation analysis with clinicopathological factors revealed that γ1-adaptin expression, as assessed by IHC, could serve as a novel independent prognostic factor in PDAC. Notably, our subgroup survival analyses suggest that γ1-adaptin expression may serve as an additional stratification marker within high-malignancy subgroups defined by known prognostic factors, including histological subtypes and lymph node metastasis. In addition, as γ1-adaptin expression can predict prognosis even in patients with normal levels of CA19-9, it may be particularly useful in Lewis-negative patients who are unable to produce CA19-9[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To establish the clinical significance of γ1-adaptin expression, future studies with larger patient cohorts and multicenter validation will be required.\u003c/p\u003e \u003cp\u003ePrevious analyses using the Cancer Genome Atlas database have demonstrated that elevated expression of \u003cem\u003eAP1S3\u003c/em\u003e, which encodes σ1C (an isoform of σ1), is associated with poor outcomes in breast cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], PDAC [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and glioma [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Overexpression of \u003cem\u003eAP1S1\u003c/em\u003e, which encodes σ1A (an isoform of σ1) is also associated with poor prognosis in lung cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. At the protein level, targeted proteomics revealed that \u0026micro;1-adaptin expression was upregulated in central nervous system metastasis of triple-negative breast cancer (TNBC), suggesting that it could serve as a prognostic marker [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Consistently, we have recently demonstrated that immunohistochemical expression of γ1-adaptin is an independent prognostic marker in breast cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, this study underscores the prognostic significance of AP-1 subunits in cancer by examining γ1-adaptin expression using IHC in PDAC.\u003c/p\u003e \u003cp\u003eAP-1 subunits, \u0026micro;1A-, σ1A-, σ1C-, or γ1-adaptin, support cancer cell activities in several cell lines derived from hepatocellular carcinoma (HepG2) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], glioma (SW1783 and U373) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], NSCLC (H1975) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], TNBC (MDA-MB-231) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and Her2-positive breast cancer (SK-BR-3) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this study, we utilized the PDAC-derived cell line PANC-1 and demonstrated that γ1-adaptin KO suppressed cell proliferation, migration, and invasion. These findings are consistent with those of previous reports and suggest that γ1-adaptin supports the malignant phenotype of PANC-1 cells. Since knock-down of γ1-adaptin caused a depletion of the other subunits in ARPE-19 cells [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and MDCK cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the expression level of γ1-adaptin would reflect the number of AP-1 complexes in the cells. Given that AP-1 regulates the trafficking of not only mannose 6-phosphate receptors but also transferrin receptor, low-density lipoprotein receptor, EGFR, megalin, syntaxin3, sodium/iodide symporter, ATP7A, and STING [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], depletion of the AP-1 complex is likely to impair the proper localization and function of these proteins. We have previously demonstrated that depletion of γ1-adaptin reduces the expression of EGFR and MET in the NSCLC-derived H1975 cell line [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], as well as EGFR in the HER2-positive breast cancer cell line SK-BR-3 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Consistently, both receptors were decreased in γ1-adaptin-knockdown or -KO PANC-1 cells, suggesting that this downregulation may underlie the reduced cancer cell activities. As a mechanism for the reduced EGFR, we previously proposed that AP-1 promotes EGFR recycling at RAB11-positive endosomes, thereby maintaining high levels of EGFR on the cell surface and supporting cancer growth [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, our previous studies have shown preferential localization of AP-1 in endosomes in hepatocellular carcinoma, NSCLC, colorectal carcinoma [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and breast cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As this study extends these findings to PDAC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), endosomal localization of AP-1 may be a common and critical feature that supports cancer cell function in multiple tumor types. However, a recent surface proteome analysis demonstrated that γ1-adaptin KO in HeLa cells led to the downregulation of expression of approximately 1,000 proteins [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, it is conceivable that the loss of γ1-adaptin disrupts additional cellular mechanisms, which warrant further investigation.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, it was conducted at a single institution with a retrospective design, which may introduce selection bias. Second, the sample size was relatively limited. Third, although our findings suggest a functional role of γ1-adaptin in PDAC progression, the precise molecular mechanisms underlying these effects remain to be fully elucidated. Finally, external validation using larger, multicenter cohorts is warranted to confirm the generalizability of our findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAP-1, adaptor protein complex-1\u003c/p\u003e\n\u003cp\u003eCA19-9, carbohydrate antigen 19-9\u003c/p\u003e\n\u003cp\u003eCI, confidence interval\u003c/p\u003e\n\u003cp\u003eEGFR, epidermal growth factor receptor\u003c/p\u003e\n\u003cp\u003eHR, hazard ratio\u003c/p\u003e\n\u003cp\u003eIHC, immunohistochemistry\u003c/p\u003e\n\u003cp\u003eMET, MET receptor tyrosine kinase\u003c/p\u003e\n\u003cp\u003eNSCLC, non-small-cell lung carcinoma\u003c/p\u003e\n\u003cp\u003eOS, overall survival\u003c/p\u003e\n\u003cp\u003ePDAC, pancreatic ductal adenocarcinoma\u003c/p\u003e\n\u003cp\u003eROI, region of interest\u003c/p\u003e\n\u003cp\u003eTGN, trans-Golgi network\u003c/p\u003e\n\u003cp\u003eTNBC, triple-negative breast cancer\u003c/p\u003e\n\u003cp\u003eWT, wild-type\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and its later amendments. This study was approved by the Ethics Committee of Fukushima Medical University (No. 29390 and 2943). Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Japan Society for the Promotion of Science KAKENHI (Grant Nos. 24K10361 [to TU], 21K07104 [to TU], and 25K12033 [to ST]), Takeda Science Foundation (to SW), and the Strategic Research Promotion Program at Fukushima Medical University (to SW).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors’ contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, S.W., T.U., and S.T.; data curation, S.T.; formal analysis, T.U. and S.T.; funding acquisition, S.W., T.U., and S.T.; investigation, T.U., and S.T.; methodology, S.W., T.U., and S.T.; project administration, S.W.; resources, S.W., T.U., S.M., and S.T.; supervision, S.W.; visualization, T.U., and S.T.; writing-original draft preparation, S.W., T.U., and S.T.; writing-review and editing, S.W., T.U., S.T., S.M., A.K., T.K., T.I., Y.K., and N.S. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Takayuki Yabe, Yumiko Kurosu, Mutsuko Honda, Kasumi Takayama, Hidemi I, and Yasuko Sato for their technical support in the histological analyses. We would like to thank Editage (www.editage.jp) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used ChatGPT (OpenAI) to improve the clarity and readability of the English language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWang S, Zheng Y, Yang F, Zhu L, Zhu XQ, Wang ZF et al. The molecular biology of pancreatic adenocarcinoma: translational challenges and clinical perspectives. Signal Transduct Target Ther. 2021;6:249.\u003c/li\u003e\n \u003cli\u003eHu ZY, Ding D, Song Y, Deng YF, Zhang CM, Yu T. Molecular mechanism of pancreatic ductal adenocarcinoma: The heterogeneity of cancer-associated fibroblasts and key signaling pathways. World J Clin Oncol. 2025;16:97007.\u003c/li\u003e\n \u003cli\u003eHumphris JL, Chang DK, Johns AL, Scarlett CJ, Pajic M, Jones MD et al. The prognostic and predictive value of serum CA19.9 in pancreatic cancer. Ann Oncol. 2012;23:1713-1722.\u003c/li\u003e\n \u003cli\u003eSumiyoshi T, Uemura K, Shintakuya R, Okada K, Baba K, Harada T et al. Clinical Utility of the Combined Use of CA19-9 and DUPAN-2 in Pancreatic Adenocarcinoma. Ann Surg Oncol. 2024;31:4665-4672.\u003c/li\u003e\n \u003cli\u003eLiang Y, Sheng G, Guo Y, Zou Y, Guo H, Li Z et al. Prognostic significance of grade of malignancy based on histopathological differentiation and Ki-67 in pancreatic ductal adenocarcinoma. Cancer Biol Med. 2024;21:416-432.\u003c/li\u003e\n \u003cli\u003eHu K, Bian C, Yu J, Jiang D, Chen Z, Zhao F et al. Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images. BMC Gastroenterol. 2024;24:387.\u003c/li\u003e\n \u003cli\u003eAhmed HS. Beyond Traditional Tools: Exploring Convolutional Neural Networks as Innovative Prognostic Models in Pancreatic Ductal Adenocarcinoma. Arq Gastroenterol. 2024;61:e23107.\u003c/li\u003e\n \u003cli\u003eSanger A, Hirst J, Davies AK, Robinson MS. Adaptor protein complexes and disease at a glance. J Cell Sci. 2019;132:jcs222992\u003c/li\u003e\n \u003cli\u003eGhosh P, Griffith J, Geuze HJ, Kornfeld S. Mammalian GGAs act together to sort mannose 6-phosphate receptors. J Cell Biol. 2003;163:755-766.\u003c/li\u003e\n \u003cli\u003eToda H, Kurozumi S, Kijima Y, Idichi T, Shinden Y, Yamada Y et al. Molecular pathogenesis of triple-negative breast cancer based on microRNA expression signatures: antitumor miR-204-5p targets AP1S3. J Hum Genet. 2018;63:1197-1210.\u003c/li\u003e\n \u003cli\u003eKhalid M, Idichi T, Seki N, Wada M, Yamada Y, Fukuhisa H et al. Gene Regulation by Antitumor miR-204-5p in Pancreatic Ductal Adenocarcinoma: The Clinical Significance of Direct RACGAP1 Regulation. Cancers (Basel). 2019;11.\u003c/li\u003e\n \u003cli\u003eYe T, Cheng Y, Li C. Adaptor Protein Complex 1 Sigma 3 Is Highly Expressed in Glioma and Could Enhance Its Progression. Comput Math Methods Med. 2021;2021:5086236.\u003c/li\u003e\n \u003cli\u003eUemura T, Suzuki T, Dohmae N, Waguri S. Clathrin adapters AP-1 and GGA2 support expression of epidermal growth factor receptor for cell growth. Oncogenesis. 2021;10:80.\u003c/li\u003e\n \u003cli\u003eHoshi N, Uemura T, Tachibana K, Abe S, Murakami-Nishimagi Y, Okano M et al. Endosomal protein expression of gamma1-adaptin is associated with tumor growth activity and relapse-free survival in breast cancer. Breast Cancer. 2024;31:305-316.\u003c/li\u003e\n \u003cli\u003eOhgane K, Yoshioka H. Quantification of Gel Bands by an Image J Macro, Band/Peak Quantification Tool. Protocols.io. 2019; doi:10.17504/protocols.io.7vghn3w\u003c/li\u003e\n \u003cli\u003eSchindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9:676-682.\u003c/li\u003e\n \u003cli\u003eOkabe N, Ezaki J, Yamaura T, Muto S, Osugi J, Tamura H et al. FAM83B is a novel biomarker for diagnosis and prognosis of lung squamous cell carcinoma. Int J Oncol. 2015;46:999-1006.\u003c/li\u003e\n \u003cli\u003eMeyers N, Gerard C, Lemaigre FP, Jacquemin P. Differential impact of the ERBB receptors EGFR and ERBB2 on the initiation of precursor lesions of pancreatic ductal adenocarcinoma. Sci Rep. 2020;10:5241.\u003c/li\u003e\n \u003cli\u003eLuo G, Jin K, Deng S, Cheng H, Fan Z, Gong Y et al. Roles of CA19-9 in pancreatic cancer: Biomarker, predictor and promoter. Biochim Biophys Acta Rev Cancer. 2021;1875:188409.\u003c/li\u003e\n \u003cli\u003eJeong J, Hwang YE, Lee M, Keum S, Song S, Kim JW et al. Downregulation of AP1S1 causes the lysosomal degradation of EGFR in non-small cell lung cancer. J Cell Physiol. 2023;238:2335-2347.\u003c/li\u003e\n \u003cli\u003eLiu Y, Li F, Wu B, Huang L, Qi Y. The clathrin adaptor AP1-S1 is associated with immune infiltration and HLA loss, as a potential therapeutic target in lung adenocarcinoma. Int Immunopharmacol. 2025;152:114385.\u003c/li\u003e\n \u003cli\u003eRojas LK, Trilla-Fuertes L, G\u0026aacute;mez-Pozo A, Chiva C, Sep\u0026uacute;lveda J, Manso L et al. Proteomics characterisation of central nervous system metastasis biomarkers in triple negative breast cancer. Ecancermedicalscience. 2019;13:891.\u003c/li\u003e\n \u003cli\u003eKou Y, Yan X, Liu Q, Wei X, Zhang B, Li X et al. HBV upregulates AP-1 complex subunit mu-1 expression via the JNK pathway to promote proliferation of liver cancer cells. Oncol Lett. 2019;18:456-464.\u003c/li\u003e\n \u003cli\u003eGravotta D, Perez Bay A, Jonker CTH, Zager PJ, Benedicto I, Schreiner R et al. Clathrin and clathrin adaptor AP-1 control apical trafficking of megalin in the biosynthetic and recycling routes. Mol Biol Cell. 2019;30:1716-1728.\u003c/li\u003e\n \u003cli\u003eBonifacino JS. Adaptor proteins involved in polarized sorting. J Cell Biol. 2014;204:7-17.\u003c/li\u003e\n \u003cli\u003eSorkina T, Bild A, Tebar F, Sorkin A. Clathrin, adaptors and eps15 in endosomes containing activated epidermal growth factor receptors. J Cell Sci. 1999;112 ( Pt 3):317-327.\u003c/li\u003e\n \u003cli\u003eYi L, Kaler SG. Direct interactions of adaptor protein complexes 1 and 2 with the copper transporter ATP7A mediate its anterograde and retrograde trafficking. Hum Mol Genet. 2015;24:2411-2425.\u003c/li\u003e\n \u003cli\u003eKoumarianou P, Fern\u0026aacute;ndez-M\u0026eacute;ndez C, Fajardo-Delgado D, Mielu LM, Santisteban P, De la Vieja A. Basolateral Sorting of the Sodium/Iodide Symporter Is Mediated by Adaptor Protein 1 Clathrin Adaptor Complexes. Thyroid. 2022;32:1259-1270.\u003c/li\u003e\n \u003cli\u003eHolloway ZG, Velayos-Baeza A, Howell GJ, Levecque C, Ponnambalam S, Sztul E et al. Trafficking of the Menkes copper transporter ATP7A is regulated by clathrin-, AP-2-, AP-1-, and Rab22-dependent steps. Mol Biol Cell. 2013;24:1735-1748, S1731-1738.\u003c/li\u003e\n \u003cli\u003eLiu Y, Xu P, Rivara S, Liu C, Ricci J, Ren X et al. Clathrin-associated AP-1 controls termination of STING signalling. Nature. 2022;610:761-767.\u003c/li\u003e\n \u003cli\u003eWan C, Crisman L, Wang B, Tian Y, Wang S, Yang R et al. AAGAB is an assembly chaperone regulating AP1 and AP2 clathrin adaptors. J Cell Sci. 2021;134:jcs258587.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"adaptor protein complex 1, EGFR, immunohistochemistry, MET receptor tyrosine kinase, pancreatic cancer","lastPublishedDoi":"10.21203/rs.3.rs-9301878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9301878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe adaptor protein complex-1 (AP-1) is a clathrin adaptor involved in intracellular trafficking at the trans-Golgi network and endosomes. Despite its fundamental role in membrane trafficking, the clinical and pathological significance of the γ1-adaptin-containing AP-1 in pancreatic ductal adenocarcinoma (PDAC) remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eγ1-adaptin knockout PANC-1 cell lines were generated to assess malignant phenotypes. γ1-adaptin expression was examined using immunohistochemistry in surgical specimens from 123 patients with PDAC and correlated with clinicopathological factors and overall survival. Prognostic significance was evaluated using Cox proportional hazards models and the Kaplan\u0026ndash;Meier analysis with the log-rank test.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eγ1-adaptin knockout PANC-1 cells showed significantly reduced proliferation, migration, and invasion, accompanied by decreased expression of epidermal growth factor receptor and MET receptor tyrosine kinase. γ1-adaptin expression was significantly higher in tumor tissues than in normal pancreatic tissues. High γ1-adaptin expression was associated with shorter overall survival (median overall survival, 23.8 vs. 44.8 months; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and multivariable analysis identified high γ1-adaptin expression as an independent poor prognostic factor in PDAC (hazard ratio, 1.974; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Subgroup analyses indicated that the high γ1-adaptin group exhibited worse overall survival among patients with low CA19-9 levels, other histological types (non\u0026ndash;well-differentiated), and the presence of lymph node metastasis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eγ1-adaptin promotes malignant behavior and serves as an independent prognostic factor in PDAC, highlighting its potential as a prognostic biomarker and therapeutic target.\u003c/p\u003e","manuscriptTitle":"γ1-adaptin expression is associated with cancer progression and poor prognosis in pancreatic ductal adenocarcinoma: Cellular and histopathological analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 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