Unbiasedly decoding the tumor microenvironment with single-cell multiomics analysis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a poor prognosis and limited therapeutic options. Research on the tumor microenvironment (TME) of PDAC has propelled the development of immunotherapeutic and targeted therapeutic strategies with a promising future. The emergence of single-cell sequencing and mass spectrometry technologies, coupled with spatial omics, has collectively revealed the heterogeneity of the TME from a multiomics perspective, outlined the development trajectories of cell lineages, and revealed important functions of previously underrated myeloid cells and tumor stroma cells. Concurrently, these findings necessitated more refined annotations of biological functions at the cell cluster or single-cell level. Precise identification of all cell clusters is urgently needed to determine whether they have been investigated adequately and to identify target cell clusters with antitumor potential, design compatible treatment strategies, and determine treatment resistance. Here, we summarize recent research on the PDAC TME at the single-cell multiomics level, with an unbiased focus on the functions and potential classification bases of every cellular component within the TME, and look forward to the prospects of integrating single-cell multiomics data and retrospectively reusing bulk sequencing data, hoping to provide new insights into the PDAC TME.

Human Equilibrative Nucleoside Transporter 1: Novel Biomarker and Prognostic Indicator for Patients with Gemcitabine-Treated Pancreatic Cancer.

Aim: This article aimed to find appropriate pancreatic cancer (PC) patients to treat with Gemcitabine with better survival outcomes by detecting hENT1 levels. Methods: We collected surgical pathological tissues from PC patients who received radical surgery in our hospital from September 2004 to December 2014. A total of 375 PC tissues and paired adjacent nontumor tissues were employed for the construction of 4 tissue microarrays (TMAs). The quality of the 4 TMAs was examined by HE staining. We performed immunohistochemistry analysis to evaluate hENT1 expression in the TMAs. Moreover, we detected hENT1 expression level and proved the role of hENT1 in cell proliferation, drug resistance, migration and invasion in vivo and vitro. Results: The results indicated that low hENT1 expression indicated a significantly poor outcome in PC patients, including shortened DFS (21.6±2.8 months versus 36.9±4.0 months, p<0.001) and OS (33.6±3.9 versus 39.6±3.9, p=0.004). Meanwhile, patients in stage I/II of TNM stage had a longer OS (40.2±3.4 versus 15.4±1.7, p=0.002) and DFS (31.0±3.1 versus 12.4±1.9, p=0.016) than patients in stage III/IV. Patients in M0 stage had a longer OS (39.7±3.4 versus 16.2±1.9, p=0.026) and DFS(30.7±3.0 versus 11.8±2.2, p=0.031) than patients in M1 stage, and patients with tumors not invading the capsule had a better DFS than those with tumor invasion into the capsule (30.8±3.0 versus 12.6±2.3, p=0.053). Patients with preoperative CA19-9 values ≤467 U/mL have longer DFS than that of patients who had preoperative CA19-9 values >467 U/mL (37.9±4.1 versus 22.9±4.0, p=0.04). In the subgroup analysis, a high hENT1 expression level was related to a longer OS(39.4±4.0 versus 31.5±3.9, p=0.001) and DFS(35.7±4.0 versus 20.6±2.7; p<0.0001) in the Gemcitabine subgroup. Conclusion: PC patients with high hENT1 expression have a better survival outcomes when receiving Gemcitabine. hENT1 expression can be a great prognostic indicator for PC patients to receive Gemcitabine treatment.

FBXO32 Stimulates Protein Synthesis to Drive Pancreatic Cancer Progression and Metastasis.

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death worldwide, primarily due to its rapid progression. The current treatment options for PDAC are limited, and a better understanding of the underlying mechanisms responsible for PDAC progression is required to identify improved therapeutic strategies. Here, we identified FBXO32 as an oncogenic driver in PDAC. FBXO32 was aberrantly upregulated in PDAC, and high FBXO32 expression was significantly associated with an unfavorable prognosis in PDAC patients. FRG1 deficiency promoted FBXO32 upregulation in PDAC. FBXO32 promoted cell migration and invasion in vitro and tumor growth and metastasis in vivo. Mechanistically, FBXO32 directly interacted with eEF1A1 and promoted its polyubiquitination at the K273 site, leading to enhanced activity of eEF1A1 and increased protein synthesis in PDAC cells. Moreover, FBXO32-catalyzed eEF1A1 ubiquitination boosted the translation of ITGB5 mRNA and activated FAK signaling, thereby facilitating focal adhesion assembly and driving PDAC progression. Importantly, interfering with the FBXO32-eEF1A1 axis or pharmaceutical inhibition of FAK by defactinib, an FDA-approved FAK inhibitor, substantially inhibited PDAC growth and metastasis driven by aberrantly activated FBXO32-eEF1A1 signaling. Overall, this study uncovers a mechanism by which PDAC cells rely on FBXO32-mediated eEF1A1 activation to drive progression and metastasis. FBXO32 may serve as a promising biomarker for selecting eligible PDAC patients for treatment with defactinib.

Multiomics integration reveals NETosis heterogeneity and TLR2 as a prognostic biomarker in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant neoplasm characterized by a poor prognosis and limited therapeutic strategy. The PDAC tumor microenvironment presents a complex heterogeneity, where neutrophils emerge as the predominant constituents of the innate immune cell population. Leveraging the power of single-cell RNA-seq, spatial RNA-seq, and multi-omics approaches, we included both published datasets and our in-house patient cohorts, elucidating the inherent heterogeneity in the formation of neutrophil extracellular traps (NETs) and revealed the correlation between NETs and immune suppression. Meanwhile, we constructed a multi-omics prognostic model that suggested the patients exhibiting downregulated expression of NETs may have an unfavorable outcome. We also confirmed TLR2 as a potent prognosis factor and patients with low TLR2 expression had more effective T cells and an overall survival extension for 6 months. Targeting TLR2 might be a promising strategy to reverse immunosuppression and control tumor progression for an improved prognosis.

E3 ubiquitin ligase RBCK1 confers ferroptosis resistance in pancreatic cancer by facilitating MFN2 degradation.

Ferroptosis, a novel form of iron-dependent non-apoptotic cell death, plays an active role in the pathogenesis of diverse diseases, including cancer. However, the mechanism through which ferroptosis is regulated in pancreatic ductal adenocarcinoma (PDAC) remains unclear. Here, our study, via combining bioinformatic analysis with experimental validation, showed that ferroptosis is inhibited in PDAC. Genome-wide sequencing further revealed that the ferroptosis activator imidazole ketone erastin (IKE) induced upregulation of the E3 ubiquitin ligase RBCK1 in PDAC cells at the transcriptional or translational level. RBCK1 depletion or knockdown rendered PDAC cells more vulnerable to IKE-induced ferroptotic death in vitro. In a mouse xenograft model, genetic depletion of RBCK1 increased the killing effects of ferroptosis inducer on PDAC cells. Mechanistically, RBCK1 interacts with and polyubiquitylates mitofusin 2 (MFN2), a key regulator of mitochondrial dynamics, to facilitate its proteasomal degradation under ferroptotic stress, leading to decreased mitochondrial reactive oxygen species (ROS) production and lipid peroxidation. These findings not only provide new insights into the defense mechanisms of PDAC cells against ferroptotic death but also indicate that targeting the RBCK1-MFN2 axis may be a promising option for treating patients with PDAC.

The NF-κB/NUAK2 signaling axis regulates pancreatic cancer progression by targeting SMAD2/3.

Nuclear factor kappa B (NF-κB) plays a pivotal role in the development of pancreatic cancer, and its phosphorylation has previously been linked to the regulation of NUAK2. However, the regulatory connection between NF-κB and NUAK2, as well as NUAK2's role in pancreatic cancer, remains unclear. In this study, we observed that inhibiting NUAK2 impeded the proliferation, migration, and invasion of pancreatic cancer cells while triggering apoptosis. NUAK2 overexpression partially resisted apoptosis and reversed the inhibitory effects of the NF-κB inhibitor. NF-κB transcriptionally regulated NUAK2 transcription by binding to the promoter region of NUAK2. Mechanistically, NUAK2 knockdown remarkably reduced the expression levels of p-SMAD2/3 and SMAD2/3, resulting in decreased nuclear translocation of SMAD4. In SMAD4-negative cells, NUAK2 knockdown impacted FAK signaling by downregulating SMAD2/3. Moreover, NUAK2 knockdown heightened the sensitivity of pancreatic cancer cells to gemcitabine, suggesting that NUAK2 inhibitors could be a promising strategy for pancreatic cancer treatment.

Ribosome profiling: a powerful tool in oncological research.

Neoplastic cells need to adapt their gene expression pattern to survive in an ever-changing or unfavorable tumor microenvironment. Protein synthesis (or mRNA translation), an essential part of gene expression, is dysregulated in cancer. The emergence of distinct translatomic technologies has revolutionized oncological studies to elucidate translational regulatory mechanisms. Ribosome profiling can provide adequate information on diverse aspects of translation by aiding in quantitatively analyzing the intensity of translating ribosome-protected fragments. Here, we review the primary currently used translatomics techniques and highlight their advantages and disadvantages as tools for translatomics studies. Subsequently, we clarified the areas in which ribosome profiling could be applied to better understand translational control. Finally, we summarized the latest advances in cancer studies using ribosome profiling to highlight the extensive application of this powerful and promising translatomic tool.

Epidemiology of pancreatic cancer: New version, new vision.

Pancreatic cancer (PC) is a devastating malignancy with an extremely high mortality rate and poses significant challenges to healthcare systems worldwide. The prevalence of PC risk factors spiked over the years, leading to a global increase in PC incidence rates. The contribution of different risk factors, however, varied from region to region due to genetic predisposition, environmental, social, and political factors underlying disease prevalence in addition to public health strategies. This comprehensive review aims to provide a thorough analysis of the epidemiology of PC, discussing its incidence, risk factors, screening strategies and socioeconomic burden. We compiled a wide range of seminal studies as well as epidemiological investigations to serve this review as a comprehensive guide for researchers, healthcare professionals, and policymakers keen for a more profound understanding of PC epidemiology. This review highlights the essentiality of persistent research efforts, interdisciplinary collaboration, and public health initiatives to address the expanding burden of this malignancy.

Novel strategies optimize immunotherapy by improving the cytotoxic function of T cells for pancreatic cancer treatment.

Pancreatic cancer (PC) is considered highly malignant due to its unsatisfying prognosis and limited response to therapies. Immunotherapy has therefore been developed to harness the antigen-specific properties and cytotoxicity of the immune system, aiming to induce a robust anti-tumor immune response that specifically demolishes PC cells while minimizing lethality in healthy tissue. The activation and augmentation of cytotoxic T cells play a critical role in the initiation and final success of immunotherapy. PC, however, is often immunotherapy resistant due to its intrinsic immunosuppressive tumor microenvironment that consequently hampers effective T cell priming. Emerging therapeutic approaches are orientated to modulate the tumor microenvironment in PC to enhance immune system involvement and heighten T cell efficacy. These novel strategies have shown promising therapeutic effects in the treatment of PC either as standalone approaches or combinatorial with other therapeutic schemes. The objective of this article is to explore innovative approaches to optimize immunotherapy for PC patients through T cell cytotoxic function augmentation.

Organoid: Bridging the gap between basic research and clinical practice.

Nowadays, the diagnosis and treatment system of malignant tumors has increasingly tended to be more precise and personalized while the existing tumor models are still unable to fully meet the needs of clinical practice. Notably, the emerging organoid platform has been proven to have huge potential in the field of basic-translational medicine, which is expected to promote a paradigm shift in personalized medicine. Here, given the unique advantages of organoid platform, we mainly explore the prominent role of organoid models in basic research and clinical practice from perspectives of tumor biology, tumorigenic microbes-host interaction, clinical decision-making, and regenerative strategy. In addition, we also put forward some practical suggestions on how to construct a new generation of organoid platform, which is destined to vigorously promote the reform of basic-translational medicine.

PRKRA promotes pancreatic cancer progression by upregulating MMP1 transcription via the NF-κB pathway.

Objective: Pancreatic cancer (PC) is highly malignant, but the underlying mechanisms of cancer progression remain unclear. PRKRA is involved in cellular stress response, but its role in PC was unknown. Methods: The expression of PRKRA between normal and tumor tissues were compared, and the prognostic value of PRKRA was evaluated. SiRNA and plasmids were applied to investigate the effects of PRKRA on PC cells. Organoids and cell lines with knockout and overexpression of PRKRA were established by CRISPR/Cas9 and lentivirus. The effects of PRKRA on PC were evaluated in vivo by cell-derived xenografts. The downstream genes of PRKRA were screened by transcriptome sequencing. The regulation of the target gene was validated by RT-qPCR, western blot, ChIP and dual luciferase reporter assay. Besides, the correlation between PRKRA and gemcitabine sensitivity was investigated by PC organoids. Results: PRKRA was significantly overexpressed in PC tissues and independently associated with poor prognosis. PRKRA promoted the proliferation, migration, and chemoresistance of PC cells. The proliferation of PC organoids was decreased by PRKRA knockout. The growth and chemoresistance of xenografts were increased by PRKRA overexpression. Mechanistically, PRKRA upregulated the transcription of MMP1 via NF-κB pathway. ChIP and dual luciferase reporter assay showed that NF-κB subunit P65 could bind to the promoter of MMP1. The sensitivity of PC organoids to gemcitabine was negatively correlated with the expression of PRKRA and MMP1. Conclusions: Our study indicated that the PRKRA/NF-κB/MMP1 axis promoted the progression of PC and may serve as a potential therapeutic target and prognosis marker.

Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer.

PURPOSE: Patients with pancreatic cancer (PC) can be classified into various molecular subtypes and benefit from some precise therapy. Nevertheless, the interaction between metabolic and immune subtypes in the tumor microenvironment (TME) remains unknown. We hope to identify molecular subtypes related to metabolism and immunity in pancreatic cancer METHODS: Unsupervised consensus clustering and ssGSEA analysis were utilized to construct molecular subtypes related to metabolism and immunity. Diverse metabolic and immune subtypes were characterized by distinct prognoses and TME. Afterward, we filtrated the overlapped genes based on the differentially expressed genes (DEGs) between the metabolic and immune subtypes by lasso regression and Cox regression, and used them to build risk score signature which led to PC patients was categorized into high- and low-risk groups. Nomogram were built to predict the survival rates of each PC patient. RT-PCR, in vitro cell proliferation assay, PC organoid, immunohistochemistry staining were used to identify key oncogenes related to PC RESULTS: High-risk patients have a better response for various chemotherapeutic drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. We built a nomogram with the risk group, age, and the number of positive lymph nodes to predict the survival rates of each PC patient with average 1-year, 2-year, and 3-year areas under the curve (AUCs) equal to 0.792, 0.752, and 0.751. FAM83A, KLF5, LIPH, MYEOV were up-regulated in the PC cell line and PC tissues. Knockdown of FAM83A, KLF5, LIPH, MYEOV could reduce the proliferation in the PC cell line and PC organoids CONCLUSION: The risk score signature based on the metabolism and immune molecular subtypes can accurately predict the prognosis and guide treatments of PC, meanwhile, the metabolism-immune biomarkers may provide novel target therapy for PC.

Sensitization of Guinea Pig Skin to Imported Fire Ant Alkaloids and Establishment of an Inflammatory Model.

Imported fire ants (IFAs), Solenopsis invicta, release their venom through multiple stings that induce inflammation, allergies, shock, and even death. Although IFA venom protein sensitization and related subcutaneous immunotherapy have been studied, few studies have examined the potential toxicity or pathogenicity of alkaloids, the main substances in IFA venom. Here, IFA alkaloids were identified and analyzed by gas chromatography-mass spectrometry; we further determined an appropriate extraction method and its effectiveness for extracting high-purity alkaloids through comparative analysis and guinea pig skin sensitivity tests. The alkaloids released from the IFA abdomen included those present in the head and thorax, and the alkaloids in the abdomen accounted for the highest proportion of the total extract. The abdominal extirpation method yielded alkaloids with a purity above 97%, and the skin irritation response score and histopathological diagnosis suggest that intradermal injection of the extracted alkaloids produced symptoms effectively simulating those of IFA stings. The successful establishment of an inflammatory model in guinea pigs stung by IFAs provides a basis for further research on the mechanism of inflammatory diseases caused by IFAs.

mTOR inhibitor, gemcitabine and PD-L1 antibody blockade combination therapy suppresses pancreatic cancer progression via metabolic reprogramming and immune microenvironment remodeling in Trp53flox/+LSL-KrasG12D/+Pdx-1-Cre murine models.

OBJECTIVE: Resistance to immunotherapy and chemotherapy hinders the prognosis of pancreatic cancer(PC). We hypothesized that the combination of mTOR inhibitor sirolimus and gemcitabine would change the metabolic landscape of PC and enhance the anti-PD-L1 therapy. METHODS: In KPC mice, the following regimens were administered and tumor growth inhibition rates(TGI%) were calculated: sirolimus(S), PD-L1 antibody(P), gemcitabine(G), sirolimus + PD-L1 antibody(SP), sirolimus + gemcitabine(SG), PD-L1 + gemcitabine(PG) and sirolimus + PD-L1 antibody + gemcitabine(SPG). The metabolic changes of tumors were identified by LC-MS and subpopulations of immune cells were measured by flow cytometry. Sirolimus treated macrophages were co-cultured with PC cells in vitro, and the metabolic changes of macrophages and tumor cells as well as tumor cells' viability were detected. RESULTS: The monotherapy of S, P and G didn't inhibit tumor growth significantly. The combination of SP, PG and SG didn't improve the TGI% significantly compared with monotherapy. However, the TGI% of SPG combination was higher than other groups. The proportion of CD68+ macrophages increased in the peripheral blood and CD8+ T cells decreased in the tumor tissues after SPG treatment. LC-MS identified 42 differential metabolites caused by sirolimus in SPG group, among which 10 metabolites had potential effects on macrophages. Sirolimus treated M1 and M2 macrophages inhibited the proliferation of tumor cells and decreased tumor cells' glycolysis. The glycolysis of M2 macrophages was increased by sirolimus. CONCLUSIONS: mTOR inhibitor can change the immune microenvironment of PC via metabolic reprogramming, thus promoting the efficacy of PD-L1 blockade when combined with gemcitabine.

Cancer-specific survival and metastasis in pancreatic mucinous cystadenocarcinoma: A SEER-based cohort study.

Aims: This study aimed to investigate the prognostic value of clinical features for cancer-specific survival (CSS) and metastasis in patients with pancreatic mucinous cystadenocarcinoma (MCAC). We further constructed and validated an effective nomogram to predict CSS. Methods: We screened patients diagnosed with pancreatic MCAC from Surveillance Epidemiology and End Results (SEER) database. Kaplan-Meier curves were used to determine the CSS time. Univariate and multivariate Cox and logistic regression analyses were conducted to identify the prognostic factors for CSS and metastasis. The nomogram was constructed to predict the prognosis of pancreatic MCAC based on the results from the multivariate analysis. We used the concordance index (C-index), the area under the curve (AUC), and the calibration plots to determine the predictive accuracy and discriminability of the nomogram. Results: Multivariate Cox analysis revealed that age, primary site, grade, and radiotherapy were independent prognostic factors associated with CSS. Multivariate logistic regression analysis revealed that surgery and grade were independent risk factors associated with metastasis. The independent risk factors were included to construct a prognosis prediction model for predicting CSS in patients with pancreatic MCAC. The concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration plots of the training cohort and the validation cohort showed that the nomogram had an acceptable predictive performance. Conclusion: We established a nomogram that could determine the 3- and 5-year CSS, which could evaluate individual clinical outcomes and provide individualized clinical decisions.

HIF-1α-regulated stanniocalcin-1 mediates gemcitabine resistance in pancreatic ductal adenocarcinoma via PI3K/AKT signaling pathway.

Pancreatic ductal adenocarcinoma (PDAC) has a poor response to the first-line chemotherapy drug gemcitabine. We previously identified stanniocalcin-1 as a gemcitabine-resistant-related gene, but its specific role and function in pancreatic cancer remain unclear. RT-qPCR and Western blot were used to evaluate differential protein and mRNA expressions. The biological functions of genes were determined using proliferation and drug-resistance experiments. Subcutaneous tumorigenesis experiment was performed on nude mice. Prognostic analysis was performed using public databases and our clinical data. We found HIF-1α-regulated STC1 expression mediated chemoresistance in pancreatic cancer. Deeper, we explored the action mechanism of STC1 and identified PI3K/AKT as the downstream signaling pathway of STC1. Furthermore, we analyzed clinical data and found that STC1 expression was related to the prognosis of gemcitabine-treated patients after surgery. In general, we proved the HIF-1α/STC1/PI3K-AKT axis participated in PDAC progression and chemoresistance, and STC1 may serve as a potential prognostic factor and therapeutic target for PDAC treatment.

DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data.

Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. A deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types and the identification of new cell types. First, deep classification learning is constructed to distinguish calibration cell populations from all cells by softmax classification assignment under a probability threshold, and graph embedding clustering is then used to identify new cell populations sequentially. In the middle of two-layer, cell labels are automatically adjusted between new and unknown cell populations via a feedback loop using an iteration calibration system to reduce the rate of error in the identification of cell types, and a 3-dimensional (3D) visualization platform is finally developed to display the cell clusters with all cell-population types annotated. Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), Isometric Feature Mapping (Isomap), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) with k-means clustering and Gaussian mixture clustering. We observed the DGCyTOF represents a robust complete learning system with high accuracy, speed and visualization by eight measurement criteria. The DGCyTOF displayed F-scores of 0.9921 for CyTOF1 and 0.9992 for CyTOF2 datasets, whereas those scores were only 0.507 and 0.529 for the t-SNE+k-means; 0.565 and 0.59, for UMAP+ k-means. Comparison of DGCyTOF with t-SNE and UMAP visualization in accuracy demonstrated its approximately 35% superiority in predicting cell types. In addition, observation of cell-population distribution was more intuitive in the 3D visualization in DGCyTOF than t-SNE and UMAP visualization. The DGCyTOF model can automatically assign known labels to single cells with high accuracy using deep-learning classification assembling with traditional graph-clustering and dimension-reduction strategies. Guided by a calibration system, the model seeks optimal accuracy balance among calibration cell populations and unknown cell types, yielding a complete and robust learning system that is highly accurate in the identification of cell populations compared to results using other methods in the analysis of single-cell CyTOF data. Application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.

XDeathDB: a visualization platform for cell death molecular interactions.

Lots of cell death initiator and effector molecules, signalling pathways and subcellular sites have been identified as key mediators in both cell death processes in cancer. The XDeathDB visualization platform provides a comprehensive cell death and their crosstalk resource for deciphering the signaling network organization of interactions among different cell death modes associated with 1461 cancer types and COVID-19, with an aim to understand the molecular mechanisms of physiological cell death in disease and facilitate systems-oriented novel drug discovery in inducing cell deaths properly. Apoptosis, autosis, efferocytosis, ferroptosis, immunogenic cell death, intrinsic apoptosis, lysosomal cell death, mitotic cell death, mitochondrial permeability transition, necroptosis, parthanatos, and pyroptosis related to 12 cell deaths and their crosstalk can be observed systematically by the platform. Big data for cell death gene-disease associations, gene-cell death pathway associations, pathway-cell death mode associations, and cell death-cell death associations is collected by literature review articles and public database from iRefIndex, STRING, BioGRID, Reactom, Pathway's commons, DisGeNET, DrugBank, and Therapeutic Target Database (TTD). An interactive webtool, XDeathDB, is built by web applications with R-Shiny, JavaScript (JS) and Shiny Server Iso. With this platform, users can search specific interactions from vast interdependent networks that occur in the realm of cell death. A multilayer spectral graph clustering method that performs convex layer aggregation to identify crosstalk function among cell death modes for a specific cancer. 147 hallmark genes of cell death could be observed in detail in these networks. These potential druggable targets are displayed systematically and tailoring networks to visualize specified relations is available to fulfil user-specific needs. Users can access XDeathDB for free at https://pcm2019.shinyapps.io/XDeathDB/ .

An optimized pulse coupled neural network image de-noising method for a field-programmable gate array based polarization camera.

The quality of polarization images is easy to be affected by the noise in the image acquired by a polarization camera. Consequently, a de-noising method optimized with a Pulse Coupled Neural Network (PCNN) for polarization images is proposed for a Field-Programmable Gate Array (FPGA)-based polarization camera in this paper, in which the polarization image de-noising is implemented using an adaptive PCNN improved by Gray Wolf Optimization (GWO) and Bi-Dimensional Empirical Mode Decomposition (BEMD). Unlike other artificial neural networks, PCNN does not need to be trained, but the parameters of PCNN such as the exponential decay time constant, the synaptic junction strength factor, and the inherent voltage constant play a critical influence on its de-noising performance. GWO is able to start optimization by generating a set of random solutions as the first population and saves the optimized solutions of PCNN. In addition, BEMD can decompose a complicated image into different Bi-Dimensional Intrinsic Mode Functions with local stabilized characteristics according to the input source image, and the decomposition result is able to lower the complexity of heavy noise image analysis. Moreover, the circuit in the polarization camera is accomplished by FPGA so as to obtain the polarization image with higher quality synchronously. These two schemes are combined to attenuate different types of noises and improve the quality of the polarization image significantly. Compared with the state-of-the-art image de-noising algorithms, the noise in the polarization image is suppressed effectively by the proposed optimized image de-noising method according to the indices of peak signal-to-noise ratio, standard deviation, mutual information, structural similarity, and root mean square error.