Abstract

   The limited success of current targeted therapies for pancreatic cancer
   underscores an urgent demand for novel treatment modalities. The
   challenge in mitigating this malignancy can be attributed to the
   digestive organ expansion factor (DEF), a pivotal yet underexplored
   factor in pancreatic tumorigenesis. The study uses a blend of in vitro
   and in vivo approaches, complemented by the theoretical analyses, to
   propose DEF as a promising anti‐tumor target. Analysis of clinical
   samples reveals that high expression of DEF is correlated with
   diminished survival in pancreatic cancer patients. Crucially, the
   depletion of DEF significantly impedes tumor growth. The study further
   discovers that DEF binds to p65, shielding it from degradation mediated
   by the ubiquitin‐proteasome pathway in cancer cells. Based on these
   findings and computational approaches, the study formulates a
   DEF‐mimicking peptide, peptide‐031, designed to disrupt the DEF‐p65
   interaction. The effectiveness of peptide‐031 in inhibiting tumor
   proliferation has been demonstrated both in vitro and in vivo. This
   study unveils the oncogenic role of DEF while highlighting its
   prognostic value and therapeutic potential in pancreatic cancer. In
   addition, peptide‐031 is a promising therapeutic agent with potent
   anti‐tumor effects.

   Keywords: computational design, digestive organ expansion factor, p65,
   pancreatic ductal adenocarcinoma, peptide therapeutics
     __________________________________________________________________

   The study identifies the digestive organ expansion factor (DEF) as a
   key player in pancreatic cancer, correlating high DEF expression with
   poor patient survival. DEF interacts with p65, preventing it from
   ubiquitination‐induced degradation and promoting tumor cell survival.
   Peptide‐031, designed to disrupt this interaction, effectively inhibits
   tumor growth in vitro and in vivo, offering a novel therapeutic
   approach with significant anti‐tumor potential.

   graphic file with name ADVS-11-2401845-g003.jpg

1. Introduction

   Pancreatic cancer is a leading cause of cancer‐related mortality
   globally. The incidence of pancreatic cancer has risen drastically over
   the past decades.^[ [47]^1 , [48]^2 , [49]^3 ^] Surgical intervention
   is only feasible for 10–15% of patients with localized disease, most
   incidences initially necessitate neoadjuvant or palliative systemic
   treatment.^[ [50]^4 ^] Targeted therapy holds promise in achieving
   long‐term disease control with reduced systemic toxicity. Despite the
   approval of targeted therapies for pancreatic cancer,^[ [51]^5 , [52]^6
   ^] their overall survival benefits remain limited. For instance, the
   Pancreas Olaparib Ongoing (POLO) trial demonstrated improved
   progression‐free survival through olaparib, a poly (ADP‐ribose)
   polymerase inhibitor (PARPi), in metastatic patients harboring germline
   BRCA1/2 variations.^[ [53]^7 , [54]^8 ^] Although efforts have been
   made to develop treatments targeting low‐frequency variants such as
   Tyrosine Kinase Receptor (TRK) fusion (<1%),^[ [55]^9 ^] Neuregulin 1
   (NRG1)  fusion (<1%),^[ [56]^10 ^] and Kirsten Rat Sarcoma Viral
   oncogene (KRAS^G12C ) mutation (≈2%)^[ [57]^11 , [58]^12 ^]; progress
   in the development of targeted therapies has been hindered by small
   patient cohorts enrolled in these trials. Therefore, enhanced and
   broader therapeutic targets for pancreatic cancer need to be identified
   urgently.

   The digestive organ expansion factor (DEF), also known as U3 small
   nucleolar RNA‐associated protein 25 homolog (UTP25), is an integral
   component of the ribosomal small subunit (SSU) processome. It is
   involved in the initial stages of 40S ribosomal subunit assembly and
   facilitates the modification of 18S ribosomal RNA.^[ [59]^13 , [60]^14
   ^] DEF is indispensable for digestive organogenesis and was first
   identified in zebrafish. Mutations in DEF result in a small liver
   phenotype, along with a reduction in pancreas size and thinning of the
   intestine.^[ [61]^15 ^] The loss of DEF function leads to hypoplasia of
   digestive organs.^[ [62]^16 ^] Research has indicated that DEF
   facilitates the degradation of the p53 protein through a mechanism that
   is independent of the proteasome pathway.^[ [63]^17 ^] As a critical
   tumor suppressor, the mutations in p53 have been observed in a
   significant proportion of pancreatic cancer cases and are considered a
   hallmark of the disease.^[ [64]^18 , [65]^19 ^] Instead of acting as a
   guardian of the genome, mutant p53 can acquire oncogenic properties,
   thereby disrupting key cellular processes and contributing to the
   initiation and progression of pancreatic cancer.^[ [66]^20 , [67]^21 ^]
   However, the precise role of DEF in the progression of pancreatic
   cancer, along with its potential interactions with pathways associated
   with pancreatic oncogenesis in humans, continue to be subjects of
   significant interest. Additionally, the specific vulnerability of the
   DEF‐mediated pathways in pancreatic cancer, compared to healthy
   pancreatic tissues, requires further elucidation.

   The NF‐κB signaling pathway has been implicated in the development and
   progression of pancreatic cancer, and p65, a subunit of the NF‐κB
   transcription factor complex, plays a crucial role in this process.^[
   [68]^22 , [69]^23 ^] The activation of p65 has been associated with
   various detrimental effects in pancreatic cancer, including genetic
   alterations, metabolic changes, acquisition of cancer stem cell
   properties, epithelial‐to‐mesenchymal transition, invasion,
   angiogenesis, metastasis, therapy resistance, and suppression of
   anti‐tumor immunity.^[ [70]^24 , [71]^25 ^] Therefore, inhibition of
   p65 activation in cancer appears to be a promising option for improving
   the efficacy of conventional anti‐cancer therapies with the potential
   for minimal side effects.^[ [72]^26 , [73]^27 ^]

   In this study, we comprehensively investigated the role of DEF in
   pancreatic cancer progression and its potential as a novel target for
   anti‐tumor therapy. Through a series of in vitro and in vivo
   experiments, along with theoretical calculations, we systematically
   explored the significance of DEF in tumor progression and its potential
   as a promising therapeutic target. Our analysis of clinical samples,
   followed by several experimental validations, confirmed that DEF plays
   a crucial role in promoting pancreatic cancers and shows potential as
   an effective therapeutic target. Importantly, we observed that the
   depletion of DEF inhibits the NF‐κB pathway without impacting the p53
   pathway. Mechanistically, we identified specific binding sites where
   DEF forms a complex with the p65 protein. This complex stabilizes p65,
   preventing ubiquitination‐induced degradation and promoting tumor cell
   survival. Based on these findings, we mimicked this binding mode and
   designed a blocking peptide (peptide‐031) that targeted p65 and
   disrupted the critical interaction between DEF and p65. Subsequent in
   vitro and in vivo experiments demonstrated that treating pancreatic
   cancer cells with peptide‐031 effectively downregulated p65 expression
   and inhibited tumor growth. Overall, the study findings highlight the
   oncogenic role of DEF in pancreatic cancer while establishing it as a
   valuable therapeutic target for intervention (Figure [74]1 ).

Figure 1.

   Figure 1
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   Schematic illustration of the design and mechanism of DEF‐mimicking
   peptide for anti‐tumor therapy in pancreatic cancer. DEF exhibits high
   expression levels in pancreatic cancer and is associated with poor
   prognosis. The depletion of DEF leads to profound inhibition of cell
   proliferation and inhibits tumor progression in pancreatic cancer.
   Notably, DEF interacts with p65 and enhances its stability by
   preventing ubiquitination‐mediated degradation in pancreatic cancer
   cells. Consequently, a DEF‐ mimicking peptide was developed with the
   specific aim of targeting p65 and exerting remarkable anti‐tumor
   effects in pancreatic cancer.

2. Results

2.1. Expression of DEF is Significantly Upregulated in Pancreatic Cancer and
Correlated with Unfavorable Prognosis

   To assess the clinical significance of DEF in pancreatic cancer, we
   conducted a bioinformatic analysis using data from The Cancer Genome
   Atlas (TCGA) public database. Compared to normal pancreatic tissues,
   tumor samples exhibited significantly elevated expression levels of DEF
   (Figure [76]2a), which was consistent with the results of protein‐level
   analysis of clinical samples (n = 149) obtained from patients with
   pancreatic ductal adenocarcinoma (PDAC) and their paired normal tissues
   (Figure [77]2b,c; Figure [78]S1a, Supporting Information). In addition,
   Kaplan‐Meier analysis showed that higher DEF expression was associated
   with poorer overall and disease‐free survival in patients with PDAC
   from the TCGA database (Figure [79]2d,e). Immunohistochemistry (IHC)
   analysis of a PDAC tissue microarray containing the 149 paired clinical
   samples confirmed the upregulation of DEF in tumor tissues
   (Figure [80]2f), further revealing that patients with higher DEF
   expression levels had reduced survival compared to those with lower
   levels (Figure [81]2g). Furthermore, an examination of clinical
   characteristics indicated a correlation between increased abundance of
   DEF and advanced pathological tumor stage. Notably, patients in
   advanced stages exhibited higher levels of DEF expression
   (Figure [82]2h,i). The results of the tissue microarray (Table [83]S1,
   Supporting Information) indicate that DEF was also associated with the
   tumor‐node‐metastasis (TNM) stage, lymphatic and distant metastases.
   Collectively, these findings establish a significant correlation
   between DEF expression and prognosis in pancreatic cancer.

Figure 2.

   Figure 2
   [84]Open in a new tab

   DEF is highly expressed in pancreatic ductal adenocarcinoma (PDAC) and
   is associated with poor prognosis. a) TCGA data analysis showed the
   differential expression profile of DEF (n = 179 patients for pancreatic
   cancers; n = 171 patients for normal pancreas) in paired tumor and
   normal pancreatic tissues. The relative DEF expression in pancreatic
   cancer was analyzed using large‐scale RNA‐Seq datasets of PDAC from the
   TCGA database. b,c) DEF expression was measured in paired tumor and
   normal pancreatic tissue by IHC staining with representative images
   (scale bars: 100 µm) (b) and statistical results were shown (n = 10)
   (c). d) Kaplan–Meier survival revealed the association of DEF mRNA
   abundance with the survival of pancreatic cancer patients. The log‐rank
   p values are shown. Overall survival (OS) of patients with pancreatic
   cancer with high or low concentrations of DEF (n = 143). e) Disease
   free survival (DFS) of patients with pancreatic cancer with high or low
   concentrations of DEF (n = 143). f) Representative images of DEF
   immunostaining in the pancreatic cancer tissue microarray, with 149
   pancreatic cancer samples. Scale bars: 50 µm. DEF IHC staining grades
   were quantified by multiplying the intensity of the signal with the
   percentage of positive cells (negative, low, moderate, and high
   groups). The IHC staining of the tumor was categorized as low
   (including negative and low groups, n = 58) and high (including
   moderate and high groups, n = 91). g) Tissue microarray analysis of the
   prognostic role of DEF in pancreatic cancer. h) Modified quantitative
   H‐scores of DEF between pancreatic tumor tissues with different
   clinical stages in the pancreatic cancer tissue microarray, with 149
   pancreatic cancer samples. i) The association with DEF levels in
   pancreatic cancers and TNM stages in multiple patient cohorts of
   pancreatic cancer tissue microarray. ns, not significant, ^* p < 0.05,
   ^** p < 0.01 according to a two‐tailed t‐test. All data are
   representative of three independently performed experiments.

2.2. Depletion of DEF Significantly Impedes Cellular Proliferation and
Neoplastic Progression in Pancreatic Cancer

   To evaluate the relevance of DEF in pancreatic cancer, we selected
   distinct cell lines: PANC1, a human PDAC cell line harboring a p53
   mutation; HPAC, a human PDAC cell line expressing wild‐type p53; and
   KPC, a cell line derived from KRAS ^LSL‐G12D;TP53^LSL‐R172H; Pdx1‐Cre
   (KPC) mice with PDAC. After DEF knockdown (KD) (Figure [85]3a; Figure
   [86]S1b, Supporting Information), we observed that the depletion of DEF
   in both mutant and wild‐type p53 cells significantly suppressed the
   proliferation of PDAC cells (Figure [87]3b,c; Figure [88]S1c,d,
   Supporting Information). Consistently, the downregulation of DEF
   expression led to substantial inhibition of cell growth as determined
   by an EdU assay in PDAC cells (Figure [89]S1e, Supporting Information).
   The percentage of DEF KD cells undergoing apoptosis was higher than
   that of control cells as depicted in Figure [90]3d and Figure [91]S1f
   (Supporting Information). Furthermore, pancreatic cancer cell lines
   with different p53 mutation statuses exhibited similar phenotypes
   following DEF KD. To further investigate the oncogenic role of DEF in
   pancreatic cancer progression, control and DEF KD pancreatic cancer
   cells were subcutaneously injected into nude mice (Figure [92]3e). The
   results revealed a significant reduction in xenograft tumor growth in
   mice injected with DEF KD cells compared to those injected with control
   cells for up to 40 days post‐injection. The reduction in tumor volume
   and weight was significantly greater in the DEF KD group
   (Figure [93]3f–h; Figure [94]S1g–i, Supporting Information).
   Additionally, DEF KD tumors exhibited decreased levels of Ki67
   expression, while displaying increased cleaved caspase 3 expression in
   contrast with control tumors (Figure [95]3i). Therefore, our findings
   substantiate the role of DEF in fostering tumor progression in
   pancreatic cancer.

Figure 3.

   Figure 3
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   DEF is required for tumorigenesis. a) Western blot analysis of the
   indicated proteins in control and DEF knockdown cells. b) Cell Counting
   Kit‐8 assay comparing control and DEF knockdown cells are shown. c)
   Colony formation and statistical analysis of PANC1 and HPAC cells. d)
   Flow cytometric analysis was used to evaluate the incidence of
   apoptosis after DEF knockdown in PANC1 and HPAC cells. e) Schematic
   protocols displaying the xenograft tumors after subcutaneous injections
   into immunodeficient mice with control and DEF‐depleted pancreatic
   cancer cells. f–h) Representative images of tumors (n = 5) (f), g)
   tumor volumes, and h) tumor weights were measured in the indicated
   groups. Tumors were measured at specified time points and dissected at
   the endpoints (n = 5). i) Representative images of the results of the
   H&E staining, the IHC staining of Ki 67, and cleaved‐caspase 3 in DEF
   knockdown tumors within immunodeficient nude mice. Scale bars: 100 µm.
   Results are presented as mean ± SD. ^* p < 0.05, ^** p < 0.01, ^*** p <
   0.001 according to a two‐tailed t‐test. All data are representative of
   three independently performed experiments.

2.3. DEF Interacts with p65 to Inhibit its Ubiquitination‐Mediated
Degradation in Pancreatic Cancer Cells, While Exerting no Effect on p53

   Previous studies have demonstrated that the depletion of DEF results in
   the accumulation of p53 protein, suggesting that DEF mediates p53
   degradation through the cysteine protease calpain3, independent of the
   proteasome pathway.^[ [97]^17 ^] Analysis of TCGA database revealed a
   significant correlation between DEF and p53 expression (Pearson's r =
   0.75, p < 0.001), as well as between p53 and calpain3 expression
   (Pearson's r = 0.35, p < 0.001) in normal pancreas tissue (Figure
   [98]S2a, Supporting Information). However, we observed no significant
   alteration in p53 mRNA or protein expression level upon DEF knockdown
   in pancreatic cancer cells (Figure [99]4a,b). Also, p53 expression was
   not considerably affected by DEF overexpression in pancreatic cancer
   cells (Figure [100]S2b, Supporting Information). In addition, no
   correlation was observed between DEF and p53 expression (Pearson's r =
   0.24, p = 0.0012) or between p53 and calpain3 expression (Person's
   r = 0.11, p = 0.12) in pancreatic cancers (Figure [101]S2c, Supporting
   Information). These results suggest that DEF may play distinct
   regulatory roles in pancreatic cancer independent of its interaction
   with p53.

Figure 4.

   Figure 4
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   DEF positively correlates and interacts with p65 in pancreatic cancer.
   a) RT‐qPCR analysis of p53 in control and DEF knockdown PANC1 and HPAC
   cells is shown. b) Western blot analysis of p53 expression is shown in
   control and DEF knockdown PANC1 and HPAC cells. c) Schematic diagram of
   RNA‐sequence in control (n = 3) relative to DEF knockdown (n = 3) PANC1
   cells. d) Expression heatmaps depict the genes that were downregulated
   and upregulated in the comparisons between the control and DEF
   knockdown groups (Fold change >1.5, p < 0.05). e) Kyoto Encyclopedia of
   Genes and Genomes analysis shows the top 20 downregulated hallmark
   pathways. f) Pathway diagram of the classical NF‐κB signaling. g)
   Western blot analysis of p65 and p‐p65 with control and DEF knockdown
   in PANC1 and HPAC cells is shown. h) Representative images of IHC
   staining of DEF and p65 in a tissue microarray. Scale bars: 100 µm. i)
   Immunofluorescence analysis showing co‐localization of DEF with p65 in
   KPC cells, scale bars: 10 µm. The intensity trace (offset white line)
   is plotted at the bottom. j) Co‐ip and western blot analysis of the
   endogenous DEF/p65 proteins interaction in the PANC1 and HPAC. k) Co‐ip
   and western blot analysis of the exogenous DEF/p65 proteins interaction
   in the 293T cells co‐transfected with Flag‐tagged DEF and His‐tagged
   p65. l) Stability analysis of p65 in control and DEF knockdown PANC1
   cells treated with CHX (300 µm) for the indicated time. The
   representative image is shown. m) Ubiquitination assay of p65 in
   control and DEF KD PANC1 cells, subjected to anti‐p65 IP and
   anti‐ubiquitin western blot analysis after treatment with MG132(10 µM,
   24 h). All the data were calculated by unpaired two‐tailed
   Students’t‐tests. ns, not significant, ^** p < 0.01, ^*** p < 0.001.
   RT‐qPCR, real‐time quantitative polymerase chain reaction.
   Representative data from triplicate experiments are shown, and error
   bars represent SEM. All data are representative of three independently
   performed experiments.

   To investigate the underlying molecular mechanisms by which DEF
   modulates pancreatic cancer progression, we performed RNA sequencing on
   PANC1 cells to identify transcriptomic changes following DEF KD
   (Figure [103]4c). Heatmaps depicting differentially expressed genes
   between the control and DEF KD groups are presented in Figure [104]4d.
   According to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
   analysis, a significant downregulation of the NF‐κB signaling pathway
   was observed (Figure [105]4e).^[ [106]^28 ^] The NF‐κB signaling
   pathway is activated in response to stimuli such as cytokines and
   pathogens. Upon activation, the IKK complex phosphorylates IκBα,
   leading to its degradation and subsequent release of NF‐κB(p65/p50).
   NF‐κB then translocates to the nucleus where it binds to specific DNA
   sequences and initiates the transcription of target genes
   (Figure [107]4f).^[ [108]^22 ^] To elucidate the potential involvement
   of DEF in regulating the NF‐κB signaling pathway, we assessed the
   protein expression levels of key factors (Figure [109]S2d, Supporting
   Information). The knockdown of DEF resulted in the suppression of the
   mRNA expression levels of the cytokines IL‐1β, IL‐6, and IL‐8 (Figure
   [110]S2e, Supporting Information). Similarly silencing DEF in PDAC
   cells significantly reduced the mRNA levels of TNFAIP3, TRAF2, and
   C‐IAP2 (Figure [111]S2f,g, Supporting Information), as well as the
   protein expression levels of BCL2 and C‐IAP1(Figure [112]S2h,
   Supporting Information). Therefore, our findings imply that the role of
   DEF in PDAC is likely intricately associated with the NF‐κB signaling
   pathway.

   Considering that p65 plays a crucial role as a transcription factor in
   the NF‐κB signaling pathway, we assessed the clinical significance of
   p65 in pancreatic cancer. Similar to DEF, higher expression of p65 was
   observed in tumor samples compared to paired normal tissues (Figure
   [113]S3a,b, Supporting Information). Depletion of p65 induced apoptosis
   and inhibited cancer cell growth, thereby highlighting a strong
   correlation between p65 and the prognosis of pancreatic cancer patients
   (Figure [114]S3c–n, Supporting Information). Consequently, we
   hypothesized that DEF and p65 were potentially associated. To explore
   this hypothesis, we evaluated the mRNA levels of p65 after DEF
   knockdown in pancreatic cancer cells and observed that the
   transcription level of p65 remained unchanged (Figure [115]S4a,
   Supporting Information). We then examined protein expression levels of
   p65 after DEF KD. The results demonstrated that DEF KD cells exhibited
   reduced abundance of both p65(Figure [116]4g; Figure [117]S4b,
   Supporting Information) and phosphorylated p65 (p‐p65)
   (Figure [118]4g). Given that p65 is an important mediator in tumor
   progression, we further explored the regulatory effects of DEF on p65
   expression. Positive correlations were consistently observed across
   multiple experimental assays including protein analysis in paired
   clinical tissue samples (Figure [119]S4c, Supporting Information) and
   tumor tissue microarrays (Figure [120]4h; Figure [121]S4d, Supporting
   Information). These discoveries provide additional support for the
   significance of DEF in this context. Moreover, immunofluorescence
   analysis revealed a diffuse cytoplasmic distribution of DEF in
   pancreatic cancer cells, which co‐localized with p65 (Figure [122]4i;
   Figure [123]S3f, Supporting Information). To further investigate the
   relationship within the cytoplasm, we conducted co‐immunoprecipitation
   (Co‐ip) experiments. The results of these experiments suggested an
   endogenous interaction between DEF and p65 in pancreatic cancer cells
   (Figure [124]4j; Figure [125]S4e, Supporting Information) as well as an
   exogenous interaction in 293T cells (Figure [126]4k). We treated tumor
   cells with cycloheximide to investigate the degradation of p65 protein
   following DEF KD, revealing that DEF KD induces accelerated degradation
   of p65 (Figure [127]4l). Previous studies have demonstrated that the
   degradation of p65 is mediated by ubiquitination^[ [128]^29 , [129]^30
   ^]; therefore, we examined this process in pancreatic cancer cells with
   DEF KD. Our results indicate that DEF is involved in the inhibition of
   ubiquitination‐mediated p65 degradation (Figure [130]4m). Collectively,
   these findings suggest that DEF impedes ubiquitination‐mediated
   proteasomal degradation of p65, thereby facilitating tumor progression
   in pancreatic cancer.

2.4. DEF Specifically Interacts with p65 through Two Motifs

   Subsequently, we employed computational modeling and in vitro
   experiments to investigate the intricate binding mechanism of this
   pivotal interaction. Initially, a series of DEF and p65 truncations
   were generated to evaluate their respective binding capabilities. Based
   on the structure model predicted by Alphafold,^[ [131]^31 , [132]^32 ^]
   the full‐length DEF was rationally divided into three domains: DEF
   domain 1 (D1, residue 1–159), DEF domain 2 (D2, residue 160–561), and
   DEF domain 3 (D3, residue 562–756) (Figure [133]5a). Similarly, p65 was
   partitioned into two domains: p65 domain 1 (P1, residue 1–306) and p65
   domain 2 (P2, residue 307–551) (Figure [134]5a). Co‐immunoprecipitation
   analysis revealed that the crucial protein‐protein interaction
   primarily involved individual D2 and D3 of DEF along with P1 of p65
   (Figure [135]5b). Subsequently, the structure models for the DEF/p65
   complex were constructed using AlphaFold artificial intelligence
   platform (Figure [136]S5a, Supporting Information, for predicted
   complexes of D2/P1, D3/P1 and D2‐D3/P1), which were further refined
   through molecular dynamics (MD) simulations following established
   protocols from our previous studies on protein dynamics^[ [137]^31 ,
   [138]^32 , [139]^33 , [140]^34 , [141]^35 ^] (results shown in Figure
   [142]S5b,c, Supporting Information). A representative snapshot from MD
   simulations of the selected D2‐D3/P1 complex is depicted in
   Figure [143]5c. Dynamic structural analysis (Figure [144]S5d,
   Supporting Information) and in silico mutagenesis (free energy
   perturbation calculations for alanine substitutions) revealed that two
   key binding motifs on DEF, K465‐K466‐R467 (KKR) and L504‐P505‐L506
   (LPL), contributed significantly to this interaction (Figure [145]5d;
   refer to Table [146]S2, Supporting Information, for detailed values).
   Finally, the alanine mutants were generated for both motifs (KKR→AAA
   and LPL→AAA), and their essential roles in mediating DEF/p65
   interaction were validated using Co‐ip analysis (Figure [147]5e). These
   findings strongly support that DEF indeed interacts with p65 via KKR
   (K465‐K466‐R467) and LPL (L504‐P505‐L506) motifs to fulfill its
   critical functional role.

Figure 5.

   Figure 5
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   DEF residues K465‐K466‐K467 and L504‐P505‐L506 are crucial for the
   specific binding of the p65 protein. a) Schematic illustration of the
   structure of DEF and p65. b) Co‐ip and western blot analysis of the
   interaction between Flag‐tagged DEF mutants and His‐tagged p65 in 293T
   cells (at the top). Co‐ip and western blot analysis of the interaction
   between His‐tagged p65 mutants and Flag‐tagged DEF in 293T cells (at
   the bottom). c) Predicted model of the complex of DEF (cyan) and p65
   (deep salmon). The negatively charged (sky blue) and hydrophobic (light
   purple) residues from two loops are mainly involved in the composition
   of the complex interface, represented as spheres and shown in the
   panels below with enlarged views. d) Relative binding free energy
   difference (ΔΔG) of mutations of interacting residues on DEF. Both
   single mutations (light blue) and triple mutations (dark blue) results
   were calculated using the free energy perturbation (FEP) (see Table
   [149]S2, Supporting Information, for detailed values). e) Co‐ip and
   western blot analysis of the interaction between His‐tagged p65 and
   Flag‐tagged DEF mutants (KKR, K465A, K466A, and R467A; LPL, L504A,
   P505A, and L506A; K‐L, K465A, K466A, and R467A, L504A, P505A, and
   L506A) in the 293T cells. Co‐ip analyses were performed with the
   indicated antibodies. Statistical analysis of three independent
   experiments is performed.

2.5. Peptide Designed to Mimic DEF Down‐Regulates p65 Expression and Exerts
Anti‐Tumor Effects

   Given the absence of available DEF inhibitors, the de novo design of a
   DEF‐mimicking peptide that disrupts the DEF‐p65 interaction presents a
   promising approach toward further validating the function of DEF. Based
   on the aforementioned binding interactions between DEF and p65, we
   propose a computational workflow for designing these peptides, which
   encompasses the following steps (Figure [150]6a): i) determination of
   two fragments of DEF containing key interacting motifs (GGEGE KKR DFDF
   and HMNL LPL DSHG); ii) utilization of amino‐PEGn‐acid linkers (where
   PEG represents polyethylene glycol) to connect these fragments with PEG
   linker length tailored to suit their end‐to‐end distances; iii)
   subsequent MD simulations performed to validate the binding
   capabilities of PEGn‐linked peptides with p65 (Figure [151]S5e,
   Supporting Information); and finally, iv) construction and synthesis of
   the desired peptide, namely peptide‐031
   (HMNLLPLDSHG‐PEG6‐GGEGEKKRDFDF), as a potential therapeutic candidate
   for interfering with DEF‐p65 interactions and exerting anti‐tumor
   effects.

Figure 6.

   Figure 6
   [152]Open in a new tab

   DEF‐mimicking peptide that specifically reduces p65 expression was
   designed. a) Computational virtual design flow of DEF‐mimic peptides
   targeting p65. First, the two DEF loops with key interacting residues
   are selected, and the end‐to‐end distances are measured. Subsequently,
   amino‐PEGn‐acid linkers are selected to fit the end‐to‐end distances.
   Finally, molecular dynamics simulations validate the binding between
   the PEGn‐linked peptide and p65. b) Representative images showing
   co‐localization of peptide‐031 (Red) and p65 (Green) in KPC cells with
   p65 overexpression treated with indicates peptides at 0.8 mg mL^−1 for
   24 h. Scale bars: 10 µm. c) Co‐ip assays of DEF‐p65 interaction in
   peptide‐031 treated PANC1 (0.9 mg mL^−1, 24 h) and KPC cells
   (0.8 mg mL^−1, 24 h). d) Western blot analysis of p65 of PANC1 and KPC
   cells treated with the indicated peptides at various concentrations for
   24 h. e) Immunofluorescence staining and statistical analysis of DEF
   and p65 in KPC cells treated with the indicated peptides, scale bars:
   10 µm. Quantification of DEF and p65 fluorescence intensity are plotted
   at right. Unpaired two‐tailed Student's t‐tests were used on all the
   data. ns, not significant, ^**** p < 0.0001. Representative data from
   triplicate experiments are shown, and error bars represent SEM. All
   data are representative of three independently performed experiments.

   To assess the specificity of peptide‐031 toward p65 expression in
   pancreatic cancer cells, we conducted live imaging experiments, which
   revealed that peptide‐031 exhibited remarkable intracellular
   infiltration and demonstrated prominent co‐localization with p65
   (Figure [153]6b). Moreover, our findings indicated that peptide‐031
   effectively attenuated the interaction between DEF and p65
   (Figure [154]6c). Increasing concentrations of peptide‐031 during cell
   incubation resulted in a notable reduction in p65 expression,
   illustrating the dose‐dependent effect of peptide‐031 on the
   suppression of p65 (Figure [155]6d) and phosphorylated p65 (Figure
   [156]S6a, Supporting Information). Additionally, immunofluorescence
   staining revealed a remarkable reduction in the fluorescence intensity
   of p65 after incubation with peptide‐031 while maintaining unchanged
   fluorescence intensity for DEF (Figure [157]6e). These results
   underscore the high specificity of peptide‐031 in targeting p65.

   Subsequently, we evaluated the effects of peptide‐031 on pancreatic
   cancer cells. The IC[50] value of peptide‐031 was determined to be
   740.3 µg mL^−1 for PANC1 and 880.9 µg ml^−1 for KPC (Figure [158]7a).
   Treatment with peptide‐031 resulted in a significant inhibition of
   tumor growth (Figure [159]7b,c), and an increase in the number of dead
   cells (Figure [160]7d). In addition, treatment with peptide‐031 led to
   a significant reduction in the expression of IL‐1β and TNFα (Figure
   [161]S6b, Supporting Information). To assess the impact of peptide‐031
   on tumor growth in vivo, we administered it via intratumoral injection
   to KPC‐bearing mice (Figure [162]7e). Our results showed a substantial
   reduction in both tumor volume (Figure [163]7f,g) and weight
   (Figure [164]7h). However, no difference was observed in the body
   weights of mice between the two groups (Figure [165]7i). We evaluated
   the safety of the peptide‐031 in mice by examining the liver, lung,
   heart, and kidney tissues with H&E staining post‐treatment, the
   findings indicate no adverse morphological changes, implying that the
   peptide‐031 exhibits a favorable safety profile (Figure [166]S6c,
   Supporting Information). These results suggest that developing
   inhibitors to block the binding between DEF and p65 may present a
   potential therapeutic alternative for treating pancreatic cancer.

Figure 7.

   Figure 7
   [167]Open in a new tab

   DEF‐mimicking peptide effectively exerts anti‐tumor effects. a)
   Determination of IC[50] values of peptide‐031 for PANC1 (top) and KPC
   (bottom) at increasing inhibitor concentrations for 24 h. b) The cell
   viability of peptide‐031 treated in pancreatic cancer cells by CCK‐8
   assays. c) Representative images and quantitation of colony formation
   after peptide‐031 treatment in PANC1 (top) and KPC cells (bottom). d)
   Flow cytometry analysis and quantification of 7‐AAD+ pancreatic cancer
   cells treated with peptide‐031 at various concentrations for 24 h; n =
   3 biologically independent experiments. e) Schematic protocol of the
   combination of peptide‐031 therapy followed for up to 17 days of
   xenograft tumors in nude mice. f) Tumor growth curve of mice treated
   with peptide‐031 (500 µg/mouse) and PBS therapy in xenograft models. g)
   Representative images of tumors (n = 6) and their weights h) were
   measured in the indicated groups. Tumors were measured at specified
   time points and dissected at the endpoints (n = 6). i) The body weight
   of mice was measured in the indicated groups. Unpaired two‐tailed
   Student's t‐tests were used on all the data. ns, not significant, ^** p
   < 0.01, ^*** p < 0.001, ^**** p < 0.0001. Representative data from
   triplicate experiments are shown, and error bars represent SEM.

3. Discussion

   Over the past decade, extensive scientific investigations have
   illuminated the fundamental role of DEF in driving the expansion and
   development of digestive organs.^[ [168]^16 , [169]^17 , [170]^36 ^]
   However, the comprehensive elucidation of DEF's functions beyond its
   established role in digestive organ development remains underexplored.
   Consequently, further investigations are necessary to unravel the
   complex molecular mechanisms by which DEF modulates diverse biological
   processes. In this study, we found that DEF acts as a pro‐tumor factor
   in pancreatic cancer. We observed elevated cytoplasmic expression of
   DEF in pancreatic cancer cells and demonstrated its ability to promote
   tumor proliferation through interaction with p65. Utilizing virtual
   computational screening methods, we successfully developed peptide‐031
   to specifically disrupt the DEF‐p65 interaction, thereby inhibiting
   cancer growth both in vitro and in vivo. Our study not only identifies
   a novel target for pancreatic cancer treatment but also presents
   peptide‐031 as a promising peptide‐based inhibitor worth further
   investigation.

   Previous reports have indicated that DEF is predominantly localized in
   the nucleus and plays a crucial role in ribosome biogenesis and
   facilitating calpain3‐mediated degradation of p53 protein during
   digestive organ development.^[ [171]^17 ^] Intriguingly, our study
   reveals significant cytoplasmic localization of DEF in pancreatic
   cancer cells, which is associated with its ability to promote tumor
   growth by stabilizing p65 independently of p53. This finding highlights
   the diverse biological roles of DEF under different contexts and
   environments,^[ [172]^36 ^] particularly within the realm of pancreatic
   cancer. Therefore, the subcellular distribution of DEF within
   pancreatic cancer cells may significantly influence its functional
   properties: nuclear DEF mediates the degradation of p53,^[ [173]^22 ^]
   however, cytoplasmic DEF exhibits a distinct function that is
   independent of p53 in malignant cells. Similarly, an increased
   cytoplasmic‐to‐nuclear ratio of UTP18 has been observed during
   normal‐adenoma‐carcinoma progression in colorectal tissues. Cytoplasmic
   UTP18 has been shown to regulate cell cycle progression through
   mediating the degradation of tumor suppressor p21.^[ [174]^37 ^] These
   findings indicate that cytoplasmic localization of UTP family members
   might be common in tumor cells. However, the underlying regulatory
   mechanism responsible for this shift remains unclear and could
   potentially involve post‐translational modifications such as
   deSUMOylatio‐mediated processes.^[ [175]^37 ^] Further investigations
   into the dynamic nucleocytoplasmic localization mechanisms governing
   UTP family members are warranted to gain comprehensive insights into
   their distinct roles in tumorigenesis and to reinforce their potential
   as therapeutic targets in cancer treatment.

   To date, no specific anti‐tumor medication exploiting the binding
   mechanism of DEF to p65 has been developed. Peptides, polymers ranging
   from 5 to 40 amino acids in length, have emerged as a crucial area of
   investigation in both biological and medical studiese.^[ [176]^38 ^]
   Their compact size, high specificity and selectivity, coupled with low
   immunogenicity, make peptides attractive as potential cancer
   therapeutics.^[ [177]^39 , [178]^40 ^] In this study, we employed in
   silico methods including AlphaFold artificial intelligence platform and
   protein dynamics simulations to predict the structure of DEF and its
   bound state with p65. Based on the predicted complex model, we
   identified and validated two key binding motifs on DEF that contribute
   predominantly to its interaction with p65. Furthermore, leveraging a
   computational‐assisted design workflow, we develop a DEF‐mimicking
   peptide, named peptide‐031, which specifically target p65. This
   synthetic inhibitor exerts a potent anti‐tumor effect by mimicking the
   key properties of DEF and competitively binding to p65 while preserving
   DEF's other functions intact. These results illustrate that DEF
   promotes cancer progression in pancreatic cancer, primarily by
   interacting with p65. Therefore, targeting the interaction between DEF
   and p65 could be a promising innovative strategy for the development of
   targeted therapies for pancreatic cancer.

   However, the clinical applications of peptide‐031 are limited. This
   DEF‐mimicking inhibitor we constructed has limited binding affinity to
   p65, likely due to the limited binding affinity of the DEF‐p65
   interaction. Therefore, peptide‐031 can be modified to enhance its
   binding capacity to p65, resulting in more effective inhibition of
   DEF‐p65 interactions. Notably, p65 is constitutively expressed in
   various types of normal cells^[ [179]^41 ^]; therefore, targeted
   inhibitors or peptides against p65 must possess sufficient specificity
   and selectivity to ensure their exclusive impact on p65 in tumor cells
   while sparing normal cells.^[ [180]^42 , [181]^43 ^] Consequently,
   further research is warranted to assess the efficacy, safety profile,
   tolerability, and potential side effects of peptide‐031. Furthermore,
   pancreatic cancer cells are enveloped by dense stroma, which impedes
   the penetration of peptides into tumors and hampers their access to
   cancerous cells.^[ [182]^44 ^] Henceforth, it will be important to
   enhance the selectivity of peptides toward pancreatic cancer by
   conjugation with specific peptide sequences to augment binding
   affinity, or by utilizing nanotechnology or liposomes for targeted
   delivery, thus ensuring specificity to the tumor cells. Furthermore,
   comprehensive investigations are necessary for optimizing the in vivo
   performance of peptide‐031.

   In conclusion, our findings demonstrate that DEF serves as a prognostic
   factor in pancreatic cancer. DEF interacts with and stabilizes p65,
   protecting it from ubiquitination‐mediated degradation, thereby
   promoting tumor proliferation in pancreatic cancer. Subsequently, by
   elucidating the underlying binding mechanism, a specific peptide‐031
   was successfully developed to disrupt the formation of the DEF‐p65
   complex. Remarkably, peptide‐031 exerts promising anti‐tumor effects
   both in pancreatic cancer cells and mouse models. Consequently, our
   clinical analyses establish the prognostic significance of DEF in
   pancreatic cancer while highlighting the potential therapeutic value of
   both DEF and peptide‐031 for further preclinical studies of pancreatic
   cancer treatment.

4. Experimental Section

Cell Culture and Cell Transfection

   The KPC cells were generated from spontaneous tumors in the
   Kras^LSL‐G12D;Trp53^LSL‐R172H; Pdx1‐Cre mouse model, which was a kind
   gift from the laboratory of Prof. Raghu Kalluri (MD Anderson Cancer
   Center, Houston, TX, USA). KPC and Capan2 cells were cultured in
   modified McCoy's 5A Medium (Thermo Fisher Scientific, Waltham, MA, USA)
   with 10% fetal bovine serum (FBS, Gibco), 100 U mL^−1 penicillin and
   100 µg mL^−1 streptomycin (Genom, Hangzhou, China). PANC1 and HEK293T
   cells were cultured in high glucose Dulbecco's Modified Eagle Medium
   (DMEM) (Gibco, Grand Island, NY, USA) with 10% fetal bovine serum and
   100 U mL^−1 penicillin‐streptomycin. HPAC cells were cultured in
   Advanced DMEM/F‐12 (Thermo Fisher Scientific, Waltham, MA, USA) with 5%
   FBS, 100 U mL^−1 penicillin‐streptomycin, 0.002 mg mL^−1 insulin
   (Selleck, S6955), 0.005 mg mL^−1 transferrin (MedChemExpress,
   #HY‐[183]P70620),40 ng mL^−1 hydrocortisone (MedChemExpress,
   #HY‐N0583), and 10 ng mL^−1 mouse epidermal growth factor
   (MedChemExpress, #HY‐P1960). The PANC1, HPAC, Capan2, and HEK193T cell
   lines were obtained from the American Type Culture Collection
   (Manassas, VA, USA). All cells were incubated at 37 °C in a humidified
   chamber containing 5% CO[2]. The presence of mycoplasma contamination
   was routinely evaluated in all cells using PCR.

Western Blot and Co‐Immunoprecipitation (Co‐IP)

   Cells were washed with PBS and lysed using RIPA lysis buffer (Beyotime
   Biotechnology, #P0013B) containing phenylmethanesulfonyl fluoride
   (Beyotime Biotechnology, #ST505) and protease inhibitors (Beyotime
   Biotechnology, #P1005) for 30 min on ice. After that, the mixture was
   centrifuged at 12000 r.p.m. for 15 min. For nuclear and cytoplasmic
   extraction, NE‐PER Nuclear and Cytoplasmic Extraction Reagents kit
   (Thermo Fisher Scientific, #78 833) was used according to the
   manufacturer's instructions. The protein concentrations were measured
   by a bicinchoninic acid (BCA) reagent (Beyotime Biotechnology, #P0012).
   Protein samples were boiled to 100 °C in NuPAGE LDS sample buffer (4x)
   (Thermo Fisher Scientific, #NP0007) for 3–5 min. The lysates are
   separated by sodium dodecyl sulfate‐polyacrylamide gel electrophoresis
   and transferred to polyvinylidene fluoride membranes (Millipore, MA,
   USA). The membranes were incubated with the indicated antibody at 4 °C
   overnight after blocking with 5% milk in TBST for 2 h at room
   temperature. After washing with TBST thrice, the membranes were
   incubated with species‐specific primary secondary antibodies for 2 h at
   room temperature. The signals from immunoreactive proteins were
   detected using an Immobilon Western Chemiluminescent HRP Substrate
   (Millipore, #WBKLS0500). For the peptide treatment, PANC1 and KPC cells
   were treated with the indicated concentrations for 24 h, then conducted
   the above procedures. The correspondent bands were imaged using
   ChemiScopeTouch (Clinx Science Instruments, Shanghai, China). Three
   replicates were performed for all western blot analysis.

   For co‐immunoprecipitation (IP) experiments, cells were lysed in
   IP/western lysing solution (50 mm Tris‐HCl pH 7.6, 320 mm NaCl, 0.1 mm
   EDTA, 0.5% NP‐40) including a phosphatase inhibitor cocktail (Bimake,
   #[184]B15001) and a protease inhibitor cocktail (Bimake, #[185]B14001)
   for 60 min on ice, then centrifuged 12000 r.p.m. for 20 min. The
   soluble protein supernatant was collected and incubated with indicated
   primary antibodies overnight at 4 °C. Then antibody‐protein complexes
   were precipitated with protein A/G beads (Bimake, #[186]B23201) for 4 h
   at 4 °C. The protein beads were heated to 100 °C in NuPAGE LDS sample
   buffer(4x) for 3–5 min, then immunoprecipitation and western blot
   assays were described above. For the peptide treatment, PANC1
   (0.9 mg mL^−1, 24 h) and KPC cells (0.8 mg mL^−1, 24 h) were treated
   with the indicated concentrations for 24 h, then conducted the above
   procedures. The intensity of immunoblot bands was evaluated using Image
   J 1.53t software (National Institutes of Health, Bethesda, USA).

Generation of DEF/p65 Knockdown Cells

   Two DEF‐specific shRNAs and two p65‐specific shRNAs were designed
   (sequences listed in Table [187]S4, Supporting Information), and a
   control nonsense sequence was used as a negative control (Oobio,
   Shanghai, China). 7.5 µg lentiviral plasmid, 5.625 µg pdPAX2, and
   1.875 µg pMD2.G were transfected into HEK293T cells in 10 cm plates by
   polyethylenimine (Polysciences, #24765‐1) according to the
   manufacturer's instructions. After 24 h, the fresh medium was replaced.
   Lentivirus‐containing media were harvested and filtered through a
   0.45 µm filter after 48 h. Then target cells were infected with
   lentiviruses and replaced with fresh medium after 10 h. After 72 h, the
   transfected cells were selected by 10 µg mL^−1 puromycin (InvivoGen,
   San Diego, CA, USA) for one week for stable knockdown cells.

RNA Extraction and Quantitative Real‐Time PCR

   Total RNA was exacted from cells or tissues using FastPure Cell/Tissue
   Total RNA Isolation Kit V2 (Vazyme Biotech Co., Ltd). RNA concentration
   was measured by a Thermo Scientific NanoDrop One instrument. RNA was
   reverse transcribed into cDNA using a PrimeScript RT reagent kit
   (Takara, #RR047A) followed by the manufacturer's instructions.
   Quantitative real‐time PCR was conducted using a TB Green Prime Ex Taq
   II Kit (Takara, #RR820A) with an Applied Biosystems 7500 Fast Real‐Time
   PCR System. The relative transcript levels of the target genes were
   normalized to GAPDH and calculated by the standard 2^−△△Ct method.
   Sequence information for primers for quantitative real‐time PCR was
   listed in Table [188]S3 (Supporting Information).

Cell Growth Assay, Edu Assays, and Colony Formation Assay

   Cells were cultured at 80% confluence in 10 cm plates, after washing
   twice with phosphate‐buffered saline (PBS), cells were digested and
   seeded into 24‐well plates for cell growth assay (7000 cells each well)
   and into 6‐well plates for colony formation assay (500 cells each
   well). For cell growth assay, at days 1, 2, 3, and 4, 1:20 dilution of
   Cell Counting Kit‐8 (GLPBIO, #GK10001) was added into each well, and
   the cells were incubated for 2 h while protected from light. Then the
   optical density was detected at OD450 nm with a microplate reader
   (Synergy Neo2, BioTek, USA). For the colony formation assay, after two
   weeks cultured, colonies were fixed with 4% paraformaldehyde (PFA)
   (Thermo Fisher Scientific, #28 908) for 30 min. After washing with PBS
   3 times, the cells were stained with 0.1% crystal violet for 20 min.
   Visible colonies were counted and scanned. Each treatment group was
   measured in triplicate. For EdU assay, BeyoClick EdU Cell Proliferation
   Kit with Alexa Fluor 594 (Beyotime, #C0078S) was used to measure cell
   proliferation, following the manufacturer's instructions. After EdU
   staining, cells were imaged with a Leica Stellaris 8 confocal laser
   scanning microscope, and the percentage of EdU‐positive cells was
   calculated in the total cells.

Immunohistochemical Analysis (IHC)

   For immunohistochemical analysis, mouse tumors harvested at the end of
   the animal experiments were stored in 10% neutral buffered formalin,
   embedded in paraffin. Tissues were sliced into 4 µm thick sections,
   placed onto super frost+ glass slides, baked for 60–90 min at 68 °C,
   and then deparaffinized. Antigen‐retrieval was performed by incubation
   in citrate antigen retrieval solution (Solarbio Life Science, Beijing,
   China), and the sections were boiled for 10 min, followed that they
   were incubated at room temperature for 25–30 min. Samples were blocked
   by 3% BSA for 30 min at room temperature. Sections were incubated with
   indicated primary antibodies at 4 °C overnight, followed by staining
   using an HRP‐conjugated secondary antibody for 50 min at room
   temperature. The target protein was visualized using a diaminobenzidine
   (DAB) chromogen kit (BDB2004, Biocare Medical, Pacheco, USA), in which
   the brown staining represented the targeted molecule. Slides were
   counterstained with diluted hematoxylin for 3–5 min. Representative
   images per tumor were captured using ImageScope software (Leica
   Biosystems, Wetzlar, Germany). Immunohistochemical staining of
   paraffin‐embedded PDAC tissue microarray slides were performed by Wuhan
   Servicebio Technology (Wuhan, China). The staining images were
   quantified by processing the images using 3D HISTECH quant center 2.1
   software (3DHISTECH Kft, Budapest, Hungary).

Immunofluorescence Staining

   KPC cells were seeded on cover slides in 4‐well Chamber slides and
   cultured for 20 h. For peptide treatment assays, PANC1 (0.9 mg mL^−1,
   24 h) and KPC cells (0.8 mg mL^−1, 24 h) were treated with the
   indicated concentrations for 24 h. Afterward, cells were fixed with 4%
   paraformaldehyde (Sigma, #F8775) for 30 min, permeated by 0.2% Triton
   X‐100 (Sigma, #T8787) for 20 min, and blocked in 5% BSA (Sigma,
   #SRE0096) for 120 min at room temperature. Primary antibodies were
   diluted in 5% BSA and incubated overnight at 4 °C, then secondary
   antibodies were added for 2 h at 37 °C, respectively. Hoechst33342
   (Thermo Fisher Scientific, #62 249) was stained at room temperature for
   40 min before mounting. Finally, cells were observed using the Leica
   Stellaris 8 confocal laser scanning microscopy. Data were analyzed with
   ImageJ 1.53t software.

Flow Cytometric Analysis

   For the cell apoptosis assay, cells were evaluated using an APC Annexin
   V Apoptosis Detection Kit with PI (Biolegend, #640 932). Cells were
   digested from 24 wells and washed twice with the cold Biolegend's cell
   staining buffer, and subsequently incubated cells with 5 µL of Annexin
   V‐APC and 10 µL of Propidium Iodide solution for 20 min at room
   temperature in the dark. For the peptide treatment, PANC1 and KPC cells
   were treated with the indicated concentrations for 24 h, then conducted
   the above procedures. Finally, flow cytometry (Beckman CytoFlex, USA)
   evaluated the percentage of apoptosis cells and analyzed them using
   Flowjo Software (Flowjo, LLC).

Human Subject

   Human pancreatic adenocarcinoma cancer tissue and adjacent normal
   tissue specimens were obtained from the Department of Hepatobiliary and
   Pancreatic Surgery, the First Affiliated Hospital, School of Medicine,
   Zhejiang University. Paraffin‐embedded PDAC tissue array slides
   comprising 156 samples from patients were created by the Wuhan
   Servicebio technology using PDAC tissue specimens from the First
   Affiliated Hospital, School of Medicine, Zhejiang University. The tumor
   specimens contained the following clinical information: patient age,
   gender, CA19‐9, the tumor‐node‐metastasis (TNM) staging system,
   vascular invasion, lymphatic metastasis, and distant metastasis.
   Samples from this study were collected from patients with pancreatic
   cancer between 2012 and 2017. All patients were diagnosed with
   pancreatic cancer by histopathological examination respectively.
   Written informed consent was obtained from all patients at the time of
   enrollment. The protocol was approved by the Institutional Review Board
   at the First Affiliated Hospital, School of Medicine, Zhejiang
   University.

Animal Studies

   All animal studies were conducted according to the guidelines for the
   care and use of laboratory animals and were approved by the
   Institutional Biomedical Research Ethics Committee of the First
   Affiliated Hospital, School of Medicine, Zhejiang University. DEF
   knockdown and control human pancreatic cancer cells were re‐suspended
   in a density of 5 × 10^6 cells in 50 µL serum‐free DMEM and separately
   injected subcutaneously into the right flank of 6‐8‐weeks‐old‐male nude
   mice. The incidence of tumors was recorded. Xenograft tumor growth was
   measured every 5–7 days up to 30 or 40 days. Tumor volume was recorded
   by the formula: tumor volume = (length x width^2)/2. Tumor size and
   weights were recorded. Tumors were harvested for histopathological
   examination and further analysis. For the orthotopic models, a small
   incision was made in the left abdomen near the spleen of
   6‐8‐weeks‐old‐male C57BL/6 mice, where the pancreas was located in
   front of the right side of the spleen. DEF knockdown and control KPC
   cells at a density of 5 × 10^5 cells in 20 µL serum‐free McCoy's 5A
   mixed with 10 µL Matrigel were injected into the pancreas using a
   sterile insulin needle. Following the experiments described above. For
   the survival experiments, the time point of death of each mouse was
   tracked and recorded, and a survival curve was plotted. For peptide
   treatment assays, mice were randomly assigned into two treatment
   groups: PBS and peptide‐031 (n = 5 per group). Mice from the
   peptide‐031 groups were intratumorally injected with 500 µg/ peptide in
   PBS every two days. Mice from the PBS groups were intratumorally
   injected with PBS every two days.

RNA‐Sequencing and Enrichment Analysis of Gene Network and Pathway

   Transcriptomic sequencing was performed in DEF knockdown or control
   PANC1 cells. RNA extraction was performed using TRIzol reagent
   (Invitrogen, #15 596 026, USA) and underwent genome‐wide transcriptomic
   analysis using Genergy‐Bio (Shanghai, China). The number and integrity
   of RNA were assessed using FastQC and RseQC software. According to the
   protocol of Illumina (mRNA‐SEQ Sample Preparation Kit), the cut RNA
   fragment was reverse transcribed to generate the cDNA library after PCR
   amplification. Peer sequencing was performed on an Illumina HiSeq 2500
   (Genergy‐Bio, Shanghai, China), according to manufacturers’
   recommendations. The raw data was handled by Skewer and data quality
   was checked by FastQC (v0.11.2). Clean reads were aligned to the
   reference genome using STAR (2.5.3a). Differentially expressed genes
   (DEGs) were determined by DEGseq2 (v1.16.1). DEGs were selected with
   fold change > 1.5 and P‐value < 0.05. Downregulated DEGs were then used
   as input for the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
   enrichment analysis.

Detecting Action of Peptide‐031 in Cells for IC[50] Testing

   To investigate the influence of peptide‐031 on cell viability, the
   PANC1 and KPC cells were cultured at 80% confluence in 10 cm plates,
   after washing twice with phosphate‐buffered saline (PBS), cells were
   digested and seeded into 96‐well plates for cell growth assay (5000
   cells each well). Cells were treated with peptide‐031 at indicated
   concentrations (0.1, 0.2, 0.4, 0.8, 1.0, and 1.2 mg mL^−1) for 24 h,
   and resulted in a dose‐dependent inhibition of cell growth with control
   cells. For cell growth assay, after 24 h 1:20 dilution of Cell Counting
   Kit‐8(GLPBIO, #GK10001) was added into each well, and the cells were
   incubated for 2 h while protected from light. Then the optical density
   was detected at OD450 nm with a microplate reader (Synergy Neo2,
   BioTek, USA). Plot the drug concentration on the x‐axis and the assay
   signal of absorbance on the y‐axis. Fit the data to a dose‐response
   curve using appropriate software or algorithms. Determine the IC[50]
   value as the peptide‐031 concentration that inhibits the assay signal
   by 50%. Perform statistical analysis to assess the significance of the
   IC[50] value.

Complex Modeling

   To predict the binding mode of DEF to p65, DEF and p65 were truncated
   based on pull‐down assays to avoid interference with the predictions by
   disordered regions of the two proteins. DEF D2 (residue 160–561), D3
   (residue 562–756), and D2‐D3 (residue 160–756) were thus chosen to
   predict the complex with p65 P1 (residue 1–306). Five complex models
   were generated using Alphafold‐multimer.^[ [189]^30 , [190]^31 ^] The
   top‐ranked models of D2/P1, D3/P1, and D2‐D3/P1 were compared to
   validate the reproducibility and reliability of the prediction (Figure
   [191]S5a, Supporting Information). The protein models were visualized
   using PyMOL.^[ [192]^45 ^]

Molecular dynamics simulations

   Molecular dynamics (MD) simulations were used to verify the stability
   of D2‐D3/P1 model. The size of the solution system for the D2‐D3/P1
   complex was 17 × 11 × 9 nm.^[ [193]^45 ^] All systems were solvated
   with TIP3P water.^[ [194]^46 ^] Na^+ and Cl^− ions were added to the
   bulk water at a salt (NaCl) concentration of 150 mm. The final system
   sizes are nearly 150 000 atoms. All systems were run using GROMACS
   2020.6^[ [195]^47 ^] for 500 ns in triplicate. The CHARMM36 force field
   for proteins was used.^[ [196]^48 ^] The temperature was maintained at
   310 K and the pressure at 1 atm using V‐rescale^[ [197]^49 ^] and
   Parrinello‐Rahman,^[ [198]^50 ^] respectively. The cutoff distance of
   Van der Waals interactions was 12 Å. The long‐range electrostatic
   interactions were treated using the particle mesh Ewald method.^[
   [199]^51 ^] The covalent bonds with hydrogen atoms were constrained by
   the LINCS algorithm,^[ [200]^52 ^] which allows a time step of 2 fs.
   All trajectory analyses (RMSD and distance, see Figure [201]S5c,d,
   Supporting Information) were performed with VMD.^[ [202]^53 ^]

The Estimation of Differences in Binding Affinity Due to Mutations

   The relative binding free energy differences (ΔΔGs) of alanine
   mutations were calculated by Free Energy Perturbation,^[ [203]^54 ,
   [204]^55 , [205]^56 ^] and combined with Hamiltonian Replica‐Exchange
   Molecular Dynamics (FEP/HREX)^[ [206]^57 ^] for efficient convergence.
   The free energy changes for residue mutations were estimated in both
   the bound state (DEF and p65 complex) ΔG[bound] and the free state
   (isolated DEF) ΔG[free] using Gromacs 2020.6. Thus, the binding free
   energy change caused by residue mutation is estimated as ΔΔG =
   ΔG[bound]− ΔG[free]. PMX^[ [207]^58 , [208]^59 ^] was used for
   preparing the dual‐topology files with CHARMM36 force field. For each
   mutation, 24 windows of sequential annihilation of electrostatics and
   van der Waals were set up and started from the same equilibrated
   system. Every mutation calculation was performed for at least 62 ns
   (1.3 ns × 24 windows  ×  2 states). The soft‐core potentials^[ [209]^60
   ^] (α, the power for lambda term, the power of the radial term, and
   sigma were set to 0.5, 1, 6, and 0.3, respectively) were used during
   simulations. The exchange between neighboring windows was attempted
   every 1 ps. Hamiltonians of the systems were saved every 0.2 ps. ΔΔGs
   and their statistical errors were estimated from the last 1 ns
   simulation of each window using the Multistate Bennett Acceptance Ratio
   (MBAR) method^[ [210]^61 ^] in Alchemical Analysis.^[ [211]^62 ^]

Design Flow of DEF‐Mimicking Peptides

   In order to design DEF‐mimicking peptides targeting p65, a computer
   virtual design was performed using the following process. First, two
   peptides were selected, GGEGEKKRDFDF (DEF residues 460–471) and
   HMNLLPLDSHG (DEF residues 500–510), which consist of 4–5 amino acid
   residues before and after the DEF's negatively charged (KKR) and
   hydrophobic (LPL) motif. Then, the distance was measured between the
   N‐terminus of one peptide and the C‐terminus of the other in the
   D2‐D3/P1 model. Next, the amino‐poly (ethylene glycol)‐acid
   (amino‐PEGn‐acid) of appropriate length was chosen as a linker to
   connect the two peptide termini and obtained various PEGn‐linked
   peptides. Finally, the binding of the PEGn‐linked peptides to p65 was
   verified by MD simulations. The structural models and force fields of
   the amino‐PEGn‐acid residues were generated by the CHARMM‐GUI^[
   [212]^63 ^] module Ligand Reader & Modeler.^[ [213]^64 ^] The models of
   the PEGn‐linked peptide were built manually by PyMOL and equilibrated
   by MD simulations using GROMACS with the C‐α positional restraints. The
   complex models of PEGn‐linked peptide/p65 were built by aligning the
   portion of the amino acids of the peptide to the D2‐D3/P1 model. The
   solution system was built referring to D2‐D3/P1 and run for 500 ns to
   analyze binding stability (Figure [214]S5e, Supporting Information).
   All peptides‐031 and cy5.5‐labeled peptides‐031 were chemically
   synthesized by CPC Scientific, with purity above 98%. All peptides were
   diluted with H[2]O at room temperature.

Statistical Analysis

   A Mann–Whitney U test or a two‐tailed t‐test was used to analyze the
   differences between the two groups. For multiple groups, one‐way or
   two‐way analysis of variance (ANOVA) with appropriate posttests was
   used. Two‐tailed tests were performed for all statistical analyses.
   Spearman's rank correlation was performed to analyze the correlation
   between variables. The log‐rank test was used to evaluate Kaplan–Meier
   survival curves. P values < 0.05 were considered significant.
   Adjusted P value metrics are stated at the end of each figure legend
   where applicable. The experiments were repeated independently with at
   least three biological replicates with similar results to demonstrate
   reproducibility. Microsoft Excel 2023, SPSS (Version 25, USA), and
   GraphPad Prism (Version 8.0, USA) software were performed to analyze
   all the data.

Ethics Statement

   The manuscript was written according to established ethical standards.
   The ethical standards of the Ethics Committee of the First Affiliated
   Hospital of Zhejiang University School of Medicine approved the
   experimental protocol. The animal experiments were performed following
   the applicable guidelines of the Animal Ethics Committee of the First
   Affiliated Hospital of Zhejiang University School of Medicine and the
   China Animal Protection Law.

Conflict of Interest

   The authors declare no conflict of interest.

Author contributions

   S.H., J.Y., and T.X. contributed equally to this work. X.B., T.L.,
   R.Z., and J.Y. conceived and designed the research; S.H. performed most
   of the molecular and biochemical experiments, with assistance from
   J.Y., D.B., Y.H., and X.L.. J.Y. collected the clinical samples and
   information of patients with pancreatic cancer. Xie T and Jiang Y
   performed computer simulation. S.H., J.Y., Y.H., and X.L. performed
   xenograft experiments, IHC analysis, and CyTOF analysis. X.H. performed
   immunofluorescence imaging. X.B., T.L., R.Z., and D.Z. contributed to
   the data discussion. X.B., T.L., and R.Z. supervised the study. S.H.,
   J.Y., and T.X. drafted the manuscript. X.B., T.L., and R.Z. revised and
   all authors approved the final version of the manuscript.

Supporting information

   Supporting Information
   [215]ADVS-11-2401845-s001.docx^ (29.2MB, docx)

Acknowledgements