Identification of an unfolded protein response-related signature for predicting the prognosis of pancreatic ductal adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive lethal malignancy. An effective prognosis prediction model is urgently needed for treatment optimization.MethodsThe differentially expressed unfolded protein response (UPR)‒related genes between pancreatic tumor and normal tissue were analyzed using the TCGA-PDAC dataset, and these genes that overlapped with UPR‒related prognostic genes from the E-MTAB-6134 dataset were further analyzed. Univariate, LASSO and multivariate Cox regression analyses were applied to establish a prognostic gene signature, which was evaluated by Kaplan‒Meier curve and receiver operating characteristic (ROC) analyses. E‒MTAB‒6134 was set as the training dataset, while TCGA-PDAC, GSE21501 and ICGC-PACA-AU were used for external validation. Subsequently, a nomogram integrating risk scores and clinical parameters was established, and gene set enrichment analysis (GSEA), tumor immunity analysis and drug sensitivity analysis were conducted.ResultsA UPR-related signature comprising twelve genes was constructed and divided PDAC patients into high- and low-risk groups based on the median risk score. The UPR-related signature accurately predicted the prognosis and acted as an independent prognostic factor of PDAC patients, and the AUCs of the UPR-related signature in predicting PDAC prognosis at 1, 2 and 3 years were all more than 0.7 in the training and validation datasets. The UPR-related signature showed excellent performance in outcome prediction even in different clinicopathological subgroups, including the female (p<0.0001), male (p<0.0001), grade 1/2 (p<0.0001), grade 3 (p=0.028), N0 (p=0.043), N1 (p<0.001), and R0 (p<0.0001) groups. Furthermore, multiple immune-related pathways were enriched in the low-risk group, and risk scores in the low-risk group were also associated with significantly higher levels of tumor-infiltrating lymphocytes (TILs). In addition, DepMap drug sensitivity analysis and our validation experiment showed that PDAC cell lines with high UPR-related risk scores or UPR activation are more sensitive to floxuridine, which is used as an antineoplastic agent.ConclusionHerein, we identified a novel UPR-related prognostic signature that showed high value in predicting survival in patients with PDAC. Targeting these UPR-related genes might be an alternative for PDAC therapy. Further experimental studies are required to reveal how these genes mediate ER stress and PDAC progression

A Fourier-Based Image Formation Algorithm for Geo-Stationary GNSS-Based Bistatic Forward-Looking Synthetic Aperture Radar

A Geo-Stationary GNSS-based Bistatic Forward-Looking Synthetic Aperture Radar (GeoSta-GNSS-BFLSAR) system is a particular kind of passive bistatic SAR system. In this system, a geo-stationary GNSS is used as the transmitter, while the receiver is deployed on a moving aircraft, which travels towards a target in a straight line. It is expected that such a radar system has potential for self-landing, self-navigation and battlefield information acquisition applications, etc. Up to now, little information from a research perspective can be found about GeoSta-GNSS-BFLSAR systems. To address this information gap, this paper proposes a preliminary image formation algorithm for GeoSta-GNSS-BFLSAR. The full details of the mathematical derivation are given. It is highlighted that, to overcome the long dwell time and spatial variance of GeoSta-GNSS-BFLSAR, a modified migration correction factor must be designed. In addition, the system performances and technical limitations of GeoSta-GNSS-BFLSAR such as focusing depth and spatial resolution are analytically discussed. In the end, a set of simulations including the image formation algorithm, focusing depth and spatial resolution were conducted for verification. It is demonstrated that the focusing performances of the proposed algorithm have a high level of similarity with the theoretical counterparts. This article thus proves the feasibility of GeoSta-GNSS-BFLSAR systems from a simulation level and establishes a foundation for the real applications of such a radar scheme in the future

Interplay of Breast Cancer Resistance Protein (BCRP) and Metabolizing Enzymes.

The recent identification of the interplay between metabolizing enzymes and BCRP has drawn more and more attention from people. BCRP, a transporter belonging to ATP-binding cassette (ABC) family, has been hypothesized to play roles in many aspects including protecting the human body against therapeutics because it is expressed in the tissues that function as barriers in vivo. Efficient coupling of BCRP and metabolizing enzymes enables rapid elimination of foreign compounds from the body because BCRP could facilitate the excretion of metabolites catalyzed by phase I and II enzymes into bile, urine and feces. Without BCRP coupling, pass through the cell membrane may be difficult for them by passive diffusion because of the increment of the molecular weight and water solubility. Thus the metabolism-efflux alliance has extraordinary importance to drug metabolism, distribution, pharmacological effect, toxicity and elimination. In this manuscript, a brief discussion about the interplays of BCRP and metabolizing enzymes in liver, intestine, kidney, lung and other organs were presented and summarized. Many endogenous and exogenous compounds belong to different chemical groups, for instance, the dietary flavonoids and the steroidal hormones were involved. Clarifying the cooperation mechanisms of BCRP and enzymes could lead to a better prediction of drug clearance in vitro