Platelet-Derived Growth Factor C Facilitates Malignant Behavior of Pancreatic Ductal Adenocarcinoma by Regulating SREBP1 Mediated Lipid Metabolism.

Lipid metabolism reprogramming stands as a fundamental hallmark of cancer cells. Unraveling the core regulators of lipid biosynthesis holds the potential to find promising therapeutic targets in pancreatic ductal adenocarcinoma (PDAC). Here, it is demonstrated that platelet-derived growth factor C (PDGFC) orchestrated lipid metabolism, thereby facilitated the malignant progression of PDAC. Expression of PDGFC is upregulated in PDAC cohorts and is corelated with a poor prognosis. Aberrantly high expression of PDGFC promoted proliferation and metastasis of PDAC both in vitro and in vivo. Mechanistically, PDGFC accelerated the malignant progression of PDAC by upregulating fatty acid accumulation through sterol regulatory element-binding protein 1 (SREBP1), a key transcription factor in lipid metabolism. Remarkably, Betulin, an inhibitor of SREBP1, demonstrated the capability to inhibit proliferation and metastasis of PDAC cell lines, along with attenuating the process of liver metastasis in vivo. Overall, the study underscores the pivotal role of PDGFC-mediated lipid metabolism in PDAC progression, suggesting PDGFC as a potential biomarker for PDAC metastasis. Targeting PDGFC-induced lipid metabolism emerges as a promising therapeutic strategy for metastatic PDAC, with the potential to improve clinical outcomes.

Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance.

Increasing evidence indicates that the remodeling of immune microenvironment heterogeneity influences pancreatic cancer development, as well as sensitivity to chemotherapy and immunotherapy. However, a gap remains in the exploration of the immunosenescence microenvironment in pancreatic cancer. In this study, we identified two immunosenescence-associated isoforms (IMSP1 and IMSP2), with consequential differences in prognosis and immune cell infiltration. We constructed the MLIRS score, a hazard score system with robust prognostic performance (area under the curve, AUC = 0.91), based on multiple machine learning algorithms (101 cross-validation methods). Patients in the high MLIRS score group had worse prognosis (P < 0.0001) and lower abundance of immune cell infiltration. Conversely, the low MLIRS score group showed better sensitivity to chemotherapy and immunotherapy. Additionally, our MLIRS system outperformed 68 other published signatures. We identified the immunosenescence microenvironmental windsock GLUT1 with certain co-expression properties with immunosenescence markers. We further demonstrated its positive modulation ability of proliferation, migration, and gemcitabine resistance in pancreatic cancer cells. To conclude, our study focused on training of composite machine learning algorithms in multiple datasets to develop a robust machine learning modeling system based on immunosenescence and to identify an immunosenescence-related microenvironment windsock, providing direction and guidance for clinical prediction and application.

N6-methyladenosine modified TGFB2 triggers lipid metabolism reprogramming to confer pancreatic ductal adenocarcinoma gemcitabine resistance.

Gemcitabine resistance is a major obstacle to the effectiveness of chemotherapy in pancreatic ductal adenocarcinoma (PDAC). Therefore, new strategies are needed to sensitize cancer cells to gemcitabine. Here, we constructed gemcitabine-resistant PDAC cells and analyzed them with RNA-sequence. Employing an integrated approach involving bioinformatic analyses from multiple databases, TGFB2 is identified as a crucial gene in gemcitabine-resistant PDAC and is significantly associated with poor gemcitabine therapeutic response. The patient-derived xenograft (PDX) model further substantiates the gradual upregulation of TGFB2 expression during gemcitabine-induced resistance. Silencing TGFB2 expression can enhance the chemosensitivity of gemcitabine against PDAC. Mechanistically, TGFB2, post-transcriptionally stabilized by METTL14-mediated m6A modification, can promote lipid accumulation and the enhanced triglyceride accumulation drives gemcitabine resistance by lipidomic profiling. TGFB2 upregulates the lipogenesis regulator sterol regulatory element binding factor 1 (SREBF1) and its downstream lipogenic enzymes via PI3K-AKT signaling. Moreover, SREBF1 is responsible for TGFB2-mediated lipogenesis to promote gemcitabine resistance in PDAC. Importantly, TGFB2 inhibitor imperatorin combined with gemcitabine shows synergistic effects in gemcitabine-resistant PDAC PDX model. This study sheds new light on an avenue to mitigate PDAC gemcitabine resistance by targeting TGFB2 and lipid metabolism and develops the potential of imperatorin as a promising chemosensitizer in clinical translation.

CSNK2A1 confers gemcitabine resistance to pancreatic ductal adenocarcinoma via inducing autophagy.

Gemcitabine, a pivotal chemotherapeutic agent for pancreatic ductal adenocarcinoma (PDAC), frequently encounters drug resistance, posing a significant clinical challenge with implications for PDAC patient prognosis. In this study, employing an integrated approach involving bioinformatic analyses from multiple databases, we unveil CSNK2A1 as a key regulatory factor. The patient-derived xenograft (PDX) model further substantiates the critical role of CSNK2A1 in gemcitabine resistance within the context of PDAC. Additionally, targeted silencing of CSNK2A1 expression significantly enhances sensitivity of PDAC cells to gemcitabine treatment. Mechanistically, CSNK2A1's transcriptional regulation is mediated by H3K27 acetylation in PDAC. Moreover, we identify CSNK2A1 as a pivotal activator of autophagy, and enhanced autophagy drives gemcitabine resistance. Silmitasertib, an established CSNK2A1 inhibitor, can effectively inhibit autophagy. Notably, the combinatorial treatment of Silmitasertib with gemcitabine demonstrates remarkable efficacy in treating PDAC. In summary, our study reveals CSNK2A1 as a potent predictive factor for gemcitabine resistance in PDAC. Moreover, targeted CSNK2A1 inhibition by Silmitasertib represents a promising therapeutic strategy to restore gemcitabine sensitivity in PDAC, offering hope for improved clinical outcomes.

A radiomics-based interpretable model to predict the pathological grade of pancreatic neuroendocrine tumors.

OBJECTIVES: To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to predict the pathological grade of pancreatic neuroendocrine tumors (pNETs) in a non-invasive manner. METHODS: Patients with pNETs who underwent contrast-enhanced abdominal CT between 2010 and 2022 were included in this retrospective study. Radiomics features were extracted, and five radiomics-based ML models, namely logistic regression (LR), random forest (RF), support vector machine (SVM), XGBoost, and GaussianNB, were developed. The performance of these models was evaluated using a time-independent testing set, and metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) were calculated. The accuracy of the radiomics model was compared to that of needle biopsy. The Shapley Additive Explanation (SHAP) tool and the correlation between radiomics and biological features were employed to explore the interpretability of the model. RESULTS: A total of 122 patients (mean age: 50 ± 14 years; 53 male) were included in the training set, whereas 100 patients (mean age: 48 ± 13 years; 50 male) were included in the testing set. The AUCs for LR, SVM, RF, XGBoost, and GaussianNB were 0.758, 0.742, 0.779, 0.744, and 0.745, respectively, with corresponding accuracies of 73.0%, 70.0%, 77.0%, 71.9%, and 72.9%. The SHAP tool identified two features of the venous phase as the most significant, which showed significant differences among the Ki-67 index or mitotic count subgroups (p < 0.001). CONCLUSIONS: An interpretable radiomics-based RF model can effectively differentiate between G1 and G2/3 of pNETs, demonstrating favorable interpretability. CLINICAL RELEVANCE STATEMENT: The radiomics-based interpretable model developed in this study has significant clinical relevance as it offers a non-invasive method for assessing the pathological grade of pancreatic neuroendocrine tumors and holds promise as an important complementary tool to traditional tissue biopsy. KEY POINTS: • A radiomics-based interpretable model was developed to predict the pathological grade of pNETs and compared with preoperative needle biopsy in terms of accuracy. • The model, based on CT radiomics, demonstrated favorable interpretability. • The radiomics model holds potential as a valuable complementary technique to preoperative needle biopsy; however, it should not be considered a replacement for biopsy.

Comprehensive multimodal management of borderline resectable pancreatic cancer: Current status and progress.

Borderline resectable pancreatic cancer (BRPC) is a complex clinical entity with specific biological features. Criteria for resectability need to be assessed in combination with tumor anatomy and oncology. Neoadjuvant therapy (NAT) for BRPC patients is associated with additional survival benefits. Research is currently focused on exploring the optimal NAT regimen and more reliable ways of assessing response to NAT. More attention to management standards during NAT, including biliary drainage and nutritional support, is needed. Surgery remains the cornerstone of BRPC treatment and multidisciplinary teams can help to evaluate whether patients are suitable for surgery and provide individualized management during the perioperative period, including NAT responsiveness and the selection of surgical timing.