UNASSIGNED: pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a very poor prognosis and a complex tumor microenvironment, which plays a key role in tumor progression and treatment resistance. Glycosylation plays an important role in processes such as cell signaling, immune response and protein stability.
UNASSIGNED: single-cell RNA sequencing data and spatial transcriptome data were obtained from GSE197177 and GSE224411, respectively, and RNA-seq data and survival information were obtained from UCSC Xena and TCGA. Multiple transcriptomic data were comprehensively analyzed to explore the role of glycosylation processes in tumor progression, and functional experiments were performed to assess the effects of MGAT1 overexpression on PDAC cell proliferation and migration.
UNASSIGNED: In PDAC tumor samples, the glycosylation level of macrophages was significantly higher than that of normal samples. MGAT1 was identified as a key glycosylation-related gene, and its high expression was associated with better patient prognosis. Overexpression of MGAT1 significantly inhibited the proliferation and migration of PDAC cells and affected intercellular interactions in the tumor microenvironment.
UNASSIGNED: MGAT1 plays an important role in PDAC by regulating glycosylation levels in macrophages, influencing tumor progression and improving prognosis.MGAT1 is a potential therapeutic target for PDAC and further studies are needed to develop targeted therapeutic strategies against MGAT1 to improve clinical outcomes.

The intratumor microbiome imbalance in pancreatic cancer promotes a tolerogenic immune response and triggers immunotherapy resistance. Here we show that Lactobacillus rhamnosus GG probiotics, outfitted with a gallium-polyphenol network (LGG@Ga-poly), bolster immunotherapy in pancreatic cancer by modulating microbiota-immune interactions. Upon oral administration, LGG@Ga-poly targets pancreatic tumors specifically, and selectively eradicates tumor-promoting Proteobacteria and microbiota-derived lipopolysaccharides through a gallium-facilitated disruption of bacterial iron respiration. This elimination of intratumor microbiota impedes the activation of tumoral Toll-like receptors, thus reducing immunosuppressive PD-L1 and interleukin-1β expression by tumor cells, diminishing immunotolerant myeloid populations, and improving the infiltration of cytotoxic T lymphocytes in tumors. Moreover, LGG@Ga-poly hampers pancreatic tumor growth in both preventive and therapeutic contexts, and amplifies the antitumor efficacy of immune checkpoint blockade in preclinical cancer models in female mice. Overall, we offer evidence that thoughtfully designed biomaterials targeting intratumor microbiota can efficaciously augment immunotherapy for the challenging pancreatic cancer.

BACKGROUND: Pancreatic adenocarcinomas (PAADs) often exhibit a \"cold\" or immunosuppressive tumor milieu, which is associated with resistance to immune checkpoint blockade therapy; however, the underlying mechanisms are incompletely understood. Here, we aimed to improve our understanding of the molecular mechanisms occurring in the tumor microenvironment and to identify biomarkers, therapeutic targets, and potential drugs to improve PAAD treatment.
METHODS: Patients were categorized according to immunologically hot or cold PAAD subtypes with distinct disease outcomes. Cox regression and weighted correlation network analysis were performed to construct a novel gene signature, referred to as \'Downregulated in hot tumors, Prognostic, and Immune-Related Genes\' (DPIRGs), which was used to develop prognostic models for PAAD via machine learning (ML). The role of DPIRGs in PAAD was comprehensively analyzed, and biomarker genes able to distinguish PAAD immune subtypes and predict prognosis were identified by ML. The expression of biomarkers was verified using public single-cell transcriptomic and proteomic resources. Drug candidates for turning cold tumors hot and corresponding target proteins were identified via molecular docking studies.
RESULTS: Using the DPIRG signature as input data, a combination of survival random forest and partial least squares regression Cox was selected from 137 ML combinations to construct an optimized PAAD prognostic model. The effects and molecular mechanisms of DPIRGs were investigated by analysis of genetic/epigenetic alterations, immune infiltration, pathway enrichment, and miRNA regulation. Biomarkers and potential therapeutic targets, including PLEC, TRPV1, and ITGB4, among others, were identified, and the cell type-specific expression of the biomarkers was validated. Drug candidates, including thalidomide, SB-431542, and bleomycin A2, were identified based on their ability to modulate DPIRG expression favorably.
CONCLUSIONS: By combining multiple ML algorithms, we developed a novel prognostic model with excellent performance in PAAD cohorts. ML also proved to be powerful for identifying biomarkers and potential targets for improved PAAD patient stratification and immunotherapy.

The development of the pancreatic head originates from the fusion of the ventral and dorsal pancreatic primordia during embryonic development. Theoretically, the origin of pancreatic head cancer also exists from the ventral pancreas and the dorsal pancreas. Among 49 patients with pancreatic head cancer, pancreatic head cancer was divided into pancreatic head cancer originating from the ventral (PHCv) or dorsal pancreas (PHCd) through imaging and pathological classification. The clinical data was collected and compared between the PHCv group and the PHCd group. The results showed that the patients from the PHCd group had worse long-term survival than those from the PHCv group (10 months vs 14.5 months). Similarly, the progression-free survival (PFS) results also indicate that patients from the PHCd group had a shorter time than those from the PHCv group (5 months vs 9.5 months). Further stratified analysis of potentially related factors showed that microvascular invasion is related to poor prognosis, and patients with pancreatic head cancer derived from the dorsal pancreas are more likely to develop microvascular invasion.

The aim of this study was to evaluate the cutoff value for identifying malignance in main pancreatic duct (MPD)-involved intraductal papillary mucinous neoplasm (IPMN) with an MPD diameter ranging from 5 to 10 mm. Clinical-radiological characteristics of 142 patients, including MPD-involved IPMNs (n = 53) and branch-duct (BD)-IPMNs (n = 89) were analyzed. Logistic regression analysis was used to determine the risk factors of malignant IPMNs and invasive carcinoma. ROC curves were used to identify different cutoffs in terms of preoperative MPD values to predict the presence of invasive carcinoma as well as malignant IPMNs, and the prediction performance was evaluated. For MPD-involved IPMNs (5 mm < MPD < 10 mm), MPD diameter of 7.5 mm for discriminating malignant IPMNs (area under curve [AUC] = 0.67) and 7.7 mm for discriminating invasive IPMNs (AUC = 0.56) were found to be the optimal cutoff values at receiver operating characteristic curve (ROC) analysis. MPD > 7.5 mm and carbohydrate antigen19-9 (Ca19-9) > 37 U/ml were found to be predictors of malignant IPMNs at univariate, and MPD > 7.5 mm was a predictor in multivariate analysis in MPD-involved IPMNs. The AUC of the ROC curve of MPD (7.5 mm) combined with Ca19-9 in identifying malignant IPMNs was 0.73 in MPD-involved IPMNs. MPD (7.5 mm) combined with Ca19-9 performed well in identifying malignant IPMNs in MPD-involved IPMNs.

OBJECTIVE: Hepatic metastasis frequently occurs in patients who have undergone radical pancreatic resection for pancreatic cancer. Besides chemotherapy, various local treatment approaches targeting hepatic lesions have been explored. However, research on radiofrequency ablation (RFA) as a localized therapy for hepatic metastasis is limited. Therefore, we conducted this retrospective study to provide clinical evidence.
METHODS: This is a single-center, retrospective, cohort study. After radical pancreaticoduodenectomy, 32 patients developed metachronous hepatic metastasis with fewer than 3 lesions, the largest of which was less than 3 cm in diameter. These patients underwent combined treatment with chemotherapy and RFA. After 8 weeks of chemotherapy, patients received RFA for hepatic lesions. Additional chemotherapy was administered, and the patients\' tumor status and survival were monitored. The primary endpoint of this study was overall survival (OS). Factors affecting OS were analyzed using the Cox risk model.
RESULTS: Among the 32 patients, the mean OS was 28.4 months. Univariate and multivariate Cox regression analysis revealed that the time (in months) of liver metastasis (HR = 0.04, 95% CI: 0.01 to 0.19; P < 0.001), the number of liver metastases (HR = 7.08, 95% CI: 1.85 to 27.08, P = 0.004), and PD (progressive disease) response to the second round of chemotherapy (HR = 29.50, 95% CI: 1.46 to 597.27; P = 0.027) were independent predictors of poorer survival.
CONCLUSIONS: Combined therapy with RFA and chemotherapy is safe in patients with hepatic metastasis after radical pancreaticoduodenectomy. Early recurrence (≤12 months), three liver metastatic lesions, and a poor response to the second round of chemotherapy were associated with poor survival.

BACKGROUND: Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection. Combining computational pathology and deep learning radiomics can enhance the detection of postoperative liver metastasis in panNET patients.
METHODS: Clinical data, pathology slides, and radiographic images were collected from 163 panNET patients post-R0 resection at Fudan University Shanghai Cancer Center (FUSCC) and FUSCC Pathology Consultation Center. Digital image analysis and deep learning identified liver metastasis-related features in Ki67-stained whole slide images (WSIs) and enhanced CT scans to create a nomogram. The model\'s performance was validated in both internal and external test cohorts.
RESULTS: Multivariate logistic regression identified nerve infiltration as an independent risk factor for liver metastasis (p < 0.05). The Pathomics score, which was based on a hotspot and the heterogeneous distribution of Ki67 staining, showed improved predictive accuracy for liver metastasis (AUC = 0.799). The deep learning-radiomics (DLR) score achieved an AUC of 0.875. The integrated nomogram, which combines clinical, pathological, and imaging features, demonstrated outstanding performance, with an AUC of 0.985 in the training cohort and 0.961 in the validation cohort. High-risk group had a median recurrence-free survival of 28.5 months compared to 34.7 months for the low-risk group, showing significant correlation with prognosis (p < 0.05).
CONCLUSIONS: A new predictive model that integrates computational pathologic scores and deep learning-radiomics can better predict postoperative liver metastasis in panNET patients, aiding clinicians in developing personalized treatments.

The application of multidisciplinary techniques in the management of endocrine-related cancers is crucial for harnessing the advantages of multiple disciplines and their coordinated efforts in eliminating tumors. Due to the malignant characteristics of cancer cells, they possess the capacity to develop resistance to traditional treatments such as chemotherapy and radiotherapy. Nevertheless, despite diligent endeavors to enhance the prediction of outcomes, the overall survival rate for individuals afflicted with endocrine-related malignancy remains quite miserable. Hence, it is imperative to investigate innovative therapy strategies. The latest advancements in therapeutic tactics have offered novel approaches for the therapy of various endocrine tumors. This paper examines the advancements in nano-drug delivery techniques and the utilization of nanomaterials for precise cancer cures through targeted therapy. This review provides a thorough analysis of the potential of combined drug delivery strategies in the treatment of thyroid cancer, adrenal gland tumors, and pancreatic cancer. The objective of this study is to gain a deeper understanding of current therapeutic approaches, stimulate the development of new drug DDS, and improve the effectiveness of treatment for patients with these diseases. The intracellular uptake of pharmaceuticals into cancer cells can be significantly improved through the implantation of synthetic or natural substances into nanoparticles, resulting in a substantial reduction in the development of endocrine malignancies.

BACKGROUND: The relevant survey has shown a high incidence of psychiatric complications in patients with pancreatic cancer. While some studies have explored the factors influencing psychological complications in pancreatic cancer patients, some factors validated in other populations have not been confirmed in the pancreatic cancer population. This study aims to explore the predictors of psychiatric complications in patients with pancreatic cancer.
METHODS: Patients with pancreatic cancer admitted to Yueqing People\'s Hospital Affiliated to Wenzhou Medical University, from January 2021 to January 2022 were retrospectively analyzed. The structured clinical interview (SCID-I) based on Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) was used by nurses to assess the incidence of psychiatric complications during hospitalization (baseline) and 3 months after the start of treatment. Binary logistic regression was used to identify predictors of psychiatric complications.
RESULTS: 80 patients were enrolled in this study and 8 patients were diagnosed with psychiatric complications at base line. Among the rest 72 patients, 8 patients (11.11%) had new-onset psychiatric complications at 3-month follow-up. Gender (Odds Ratio (OR) = 1.674, p = 0.019), monthly income (OR = 1.735, p = 0.023) and sadness (M.D. Anderson Symptom Inventory (MDASI)) (OR = 1.804, p = 0.001) were all predictors for psychiatric complications in patients with pancreatic cancer.
CONCLUSIONS: Gender, monthly income and MDASI score are predictors of psychiatric complications in patients with pancreatic cancer.

BACKGROUND: AGTPBP1 is a cytosolic carboxypeptidase that cleaves poly-glutamic acids from the C terminus or side chains of α/β tubulins. Although its dysregulated expression has been linked to the development of non-small cell lung cancer, the specific roles and mechanisms of AGTPBP1 in pancreatic cancer (PC) have yet to be fully understood. In this study, we examined the role of AGTPBP1 on PC in vitro and in vivo.
METHODS: Immunohistochemistry was used to examine the expression of AGTPBP1 in PC and non-cancerous tissues. Additionally, we assessed the malignant behaviors of PC cells following siRNA-mediated AGTPBP1 knockdown both in vitro and in vivo. RNA sequencing and bioinformatics analysis were performed to identify the differentially expressed genes regulated by AGTPBP1.
RESULTS: We determined that AGTPBP1 was overexpressed in PC tissues and the higher expression of AGTPBP1 was closely related to the location of tumors. AGTPBP1 inhibition can significantly decrease cell progression in vivo and in vitro. Moreover, the knockdown of AGTPBP1 inhibited the expression of ERK1/2, P-ERK1/2, MYLK, and TUBB4B proteins via the ERK signaling pathway.
CONCLUSIONS: Our research indicates that AGTPBP1 may be a putative therapeutic target for PC.