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At least 1,639 records · Page 91
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DOE OSTI2021

Quantitative resilience evaluation on recovery from emergency situations in nuclear power plants

Here, this paper mainly introduces how to develop a resilience evaluation model by quantifying the relationship between resilience and resilience components in regard to recovering from emergency situations in nuclear power plants (NPPs). It is an extension of the author’s previous researches. To develop the model, the first step was to analyze event reports published in Republic of Korea in terms of resilience factors from different safety perspectives. Second, statistical methods such as factor analysis, principal component analysis, and multivariate regression analysis were applied to the dataset to identify relations among resilience factors and develop a quantitative resilience evaluation model for recovery from emergency situations in NPPs. Third, the model was validated using the recent event report data released in Korea. Lastly, we present a discussion on how to apply the quantitative resilience evaluation model to results from stress tests performed in Korea.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Reduced-order modeling for efficient cross section library development in high-temperature gas reactor pebble-bed depletion analysis

Accurate modeling of running-in and equilibrium conditions in pebble-bed reactors (PBRs) requires precise microscopic multigroup neutron cross sections. In Griffin, deterministic neutronics calculations rely on multivariate interpolation over large cross section libraries, resulting in significant memory usage and performance bottlenecks. This work, together with a companion paper on Griffin integration, explores reduced-order models (ROMs) to replace interpolation with lightweight surrogates. Several ROM techniques are benchmarked, with deep neural networks (DNNs) demonstrating superior memory efficiency, scalability, and predictive accuracy. A total of 295 DNNs were trained to build a comprehensive isotope library, integrated into Griffin through a custom LibTorch interface for depletion analysis. Initial results demonstrate that DNN-based ROMs drastically reduce memory demands while preserving accuracy, enabling finer tabulations and additional state variables without overhead. In conclusion, the framework also supports online cross section generation and real-time DNN updates through transfer learning, improving fidelity by capturing self-shielding and evolving nuclide compositions during burnup.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Gaussian ensemble for quantum integrable dynamics

We propose a Gaussian ensemble as a description of the long-time dynamics of isolated quantum integrable systems. Our approach extends the Generalized Gibbs Ensemble (GGE) by incorporating fluctuations of integrals of motion. It is asymptotically exact in the classical limit irrespective of the system size and, under appropriate conditions, in the thermodynamic limit irrespective of the value of Planck’s constant. Moreover, it captures quantum corrections near the classical limit, finite size corrections near the thermodynamic limit, and is valid in the presence of non-local interactions. The Gaussian ensemble bridges the gap between classical integrable systems, where a generalized microcanonical ensemble is exact even for few degrees of freedom, and GGE, which requires thermodynamic limit. We illustrate our results with examples of increasing complexity. - Highlights: • We propose a microcanonical ensemble for the dynamics of quantum integrable systems. • The proposed ensemble is a multivariable Gaussian in the integrals of motion. • This Gaussian ensemble is exact in the classical limit at any system size. • It reproduces leading quantum corrections and finite size corrections to GGE. • The Gaussian ensemble also describes fluctuations of observables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

An efficient optimization of well placement and control for a geothermal prospect under geological uncertainty

This work applies an efficient optimization technique based on a multivariate adaptive regression spline (MARS) technique to determine the optimal design and engineering of a potential geothermal production operation at a prospect near Superstition Mountain in Southern California, USA. The faster MARS-based statistical model is used as a surrogate for higher-fidelity physical models within the intensive optimization process. Its use allows for the exploration of the impacts of specific engineering design parameters in the context of geologic uncertainty as a means to both understand and maximize profitability of the production operation. The MARS model is initially developed from a training dataset generated by a finite set of computationally complex hydrothermal models applied to the prospect. Its application reveals that the optimal engineering design variables can differ considerably assuming different choices of hydrothermal flow properties, which, in turn, indicates the importance of reducing the uncertainty of key geologic properties. The major uncertainty sources in the natural-system are identified and ranked first by an efficient MARS-enabled total order sensitivity quantification, which is then used to assist evaluating the effect of geological uncertainties on optimized results. At the Southern California prospect, this parameter sensitivity analysis suggests that groundwater circulation through high permeable structures, rather than heat conduction through impermeable granite, is the primary heat transfer method during geothermal extraction. Reservoir histories simulated using optimal parameters with different constraints are analyzed and compared to investigate the longevity and maximum profit of the geothermal resources. The comparison shows that the longevity and profit are very likely to be overestimated by optimizations without appropriate constraints on natural conditions. In addition to geothermal energy production, this optimization approach can also be used to manage other geologic resource operations, such as hydrocarbon production or CO 2 sequestration, under uncertain reservoir conditions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

A regression-based approach to estimating retrofit savings using the Building Performance Database

Retrofitting building systems is known to provide cost-effective energy savings. This article addresses how the Building Performance Database is used to help identify potential savings. Currently, prioritizing retrofits and computing their expected energy savings and cost/benefits can be a complicated, costly, and an uncertain effort. Prioritizing retrofits for a portfolio of buildings can be even more difficult if the owner must determine different investment strategies for each of the buildings. Meanwhile, we are seeing greater availability of data on building energy use, characteristics, and equipment. These data provide opportunities for the development of algorithms that link building characteristics and retrofits empirically. In this paper we explore the potential of using such data for predicting the expected energy savings from equipment retrofits for a large number of buildings. We show that building data with statistical algorithms can provide savings estimates when detailed energy audits and physics-based simulations are not cost- or time-feasible. We develop a multivariate linear regression model with numerical predictors (e.g., operating hours, occupant density) and categorical indicator variables (e.g., climate zone, heating system type) to predict energy use intensity. The model quantifies the contribution of building characteristics and systems to energy use, and we use it to infer the expected savings when modifying particular equipment. We verify the model using residual analysis and cross-validation. We demonstrate the retrofit analysis by providing a probabilistic estimate of energy savings for several hypothetical building retrofits. We discuss the ways understanding the risk associated with retrofit investments can inform decision making. The contributions of this work are the development of a statistical model for estimating energy savings, its application to a large empirical building dataset, and a discussion of its use in informing building retrofit decisions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Geologic CO 2 sequestration monitoring design: A machine learning and uncertainty quantification based approach

Monitoring is a crucial aspect of geologic carbon dioxide (CO 2 ) sequestration risk management. Effective monitoring is critical to ensure CO 2 is safely and permanently stored throughout the life-cycle of a geologic CO 2 sequestration project. Effective monitoring involves deciding: (i) where is the optimal location to place the monitoring well(s), and (ii) what type of data (pressure, temperature, CO 2 saturation, etc.) should be measured taking into consideration the uncertainties at geologic sequestration sites. We have developed a filtering-based data assimilation procedure to design effective monitoring approaches. To reduce the computational cost of the filtering-based data assimilation process, a machine-learning algorithm: Multivariate Adaptive Regression Splines is used to derive computationally effcient reduced order models from results of full-physics numerical simulations of CO 2 injection in saline aquifer and subsequent multi-phase fluid flow. We use example scenarios of CO 2 leakage through legacy wellbore and demonstrate a monitoring strategy can be selected with the aim of reducing uncertainty in metrics related to CO 2 leakage. We demonstrate the proposed framework with two synthetic examples: a simple validation case and a more complicated case including multiple monitoring wells. The examples demonstrate that the proposed approach can be effective in developing monitoring approaches that take into consideration uncertainties.

58 GEOSCIENCES↗
DOE OSTI2021

A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO 2 sequestration accelerated with machine learning

Successful geologic CO 2 storage projects depend on numerical simulations to predict reservoir performance during site selection, injection verification, and post-injection monitoring phases of the project. These numerical simulations solve non-linear sets of coupled partial differential equations, while accounting for multi-phase fluid dynamics on the basis of constitutive equations that are embedded into the solution scheme. As a consequence, individual simulations often require tens to hundreds of hours to complete on high-performance computing clusters. Moreover, laboratory experiments reveal that parametric functions for capillary pressure and relative permeability exhibit substantial variability, even within the same rock type. This combination of computational expense and wide-ranging parametric variability means that there remains substantial uncertainty in the behavior of multi-phase CO 2 -water systems, particularly in the context of feedbacks between relative permeability and capillary pressure. To bridge this knowledge gap, here we develop a novel workflow that utilizes physics-based numerical simulation to train an artificial neural network (ANN) emulator for interrogating the multivariate parameter space that governs both capillary pressure and relative permeability. With this approach, the ANN is trained to emulate both fluid pressure distribution and CO 2 saturation, which are then interrogated quantitatively to generate parametric response surface mappings with high-fidelity resolution. Results from this study initially show that capillary entry pressure is the dominant control on both CO 2 plume geometry and fluid pressure propagation when considering the combined effects of capillary pressure and relative permeability, particularly when phase interference is low and residual CO 2 saturation is high. Moreover, the ANN emulator provides tremendous computational speed-up by computing 2691 individual simulations in several minutes; whereas, the same simulation ensemble would have required ~3 years of simulation time using only physics-based simulation methods (25,000 times speed up).

58 GEOSCIENCES↗
DOE OSTI2015

Long-term measurements of submicrometer aerosol chemistry at the Southern Great Plains (SGP) using an Aerosol Chemical Speciation Monitor (ACSM)

In this study the long-term trends of non-refractory submicrometer aerosol (NR-PM1) composition and mass concentration measured by an Aerosol Chemical Speciation Monitor (ACSM) at the Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site are discussed. NR-PM1 data was recorded at ~30 min intervals over a period of 19 months between November 2010 and June 2012. Positive Matrix Factorization (PMF) was performed on the measured organic mass spectral matrix using a rolling window technique to derive factors associated with distinct sources, evolution processes, and physiochemical properties. The rolling window approach also allows us to capture the dynamic variations of the chemical properties in the organic aerosol (OA) factors over time. Three OA factors were obtained including two oxygenated OA (OOA) factors, differing in degrees of oxidation, and a biomass burning OA (BBOA) factor. Back trajectory analyses were performed to investigate possible sources of major NR-PM1 species at the SGP site. Organics dominated NR-PM1 mass concentration for the majority of the study with the exception of winter, when ammonium nitrate increases due to transport of precursor species from surrounding urban and agricultural areas and also due to cooler temperatures. Sulfate mass concentrations have little seasonal variation with mixed regional and local sources. In the spring BBOA emissions increase and are mainly associated with local fires. Isoprene and carbon monoxide emission rates were obtained by the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the 2011 U.S. National Emissions Inventory to represent the spatial distribution of biogenic and anthropogenic sources, respectively. The combined spatial distribution of isoprene emissions and air mass trajectories suggest that biogenic emissions from the southeast contribute to SOA formation at the SGP site during the summer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon

Modeling tools can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations, but subject to uncertainties associated with parameters in model physical parameterizations. Different sensitivity analysis (SA) approaches may lead to different conclusions about the underlying sensitivities. In this study, we quantify simulation uncertainties related to parameter perturbations, and use different approaches to conduct parameter SA on the WRF model pertaining to L-A coupling strength for simulations over the Amazon region. A total of twenty parameters from the Yonsei University (YSU) planetary boundary layer (PBL) and the revised MM5 surface layer (SL) schemes were selected in this analysis. Three different SA methods, the Morris One-at-A-Time (MOAT) method, the Multivariate Adaptive Regression Splines (MARS) method, and the Sobol’ method, were employed to analyze seven WRF-simulated variables and five L-A coupling metrics. Results show that 1) parameter perturbations cause large simulation uncertainties which are comparable to those in the observations; 2) three different SA methods give consistent L-A coupling strength outcomes; 3) six out of the twenty parameters contribute 80%–95% of the total variance in the metrics analyzed, and first-order effects dominate over interaction effects; 4) the twelve variables/metrics of interest show similar sensitivity patterns to the selected parameters, which is consistent across all the methods used. Physical mechanisms for how the sensitive parameters act in determining the L-A coupling strength and associated variables also are illustrated. Our results will help quantifying L-A coupling strength and establishing a basis for parameter calibration over the Amazon region.

Wang, Chen↗
DOE OSTI2025

Local practically safe extremum seeking with assignable rate of attractivity to the safe set

We present Assignably Safe Extremum Seeking (ASfES), an algorithm designed to minimize a measured, static objective function while maintaining a measured, static metric of safety (a control barrier function or CBF) to be positive in a practical sense. We ensure that for trajectories with safe initial conditions, the violation of safety can be made arbitrarily small through appropriately chosen design constants. We also guarantee an assignable “attractivity” rate: from unsafe initial conditions, the trajectories approach the safe set, in the sense of the measured CBF, at a rate no slower than a user-assigned rate. Similarly, from safe initial conditions, the trajectories approach the unsafe set, in the sense of the CBF, no faster than the assigned attractivity rate. The feature of assignable attractivity is not present in the semiglobal version of safe extremum seeking, where the semiglobality of convergence is achieved by slowing the adaptation. We also demonstrate local convergence of the parameter to a neighborhood of the minimum of a quadratic objective function constrained to the safe set with a linear CBF. The ASfES algorithm and analysis are multivariable, but we also extend the algorithm to a Newton-Based ASfES scheme which we show is only useful in the scalar case. The proven properties of the designs are illustrated through simulation examples.

42 ENGINEERING↗
DOE OSTI2018

Potential influence of interleukin-6 on the therapeutic effect of gefitinib in patients with advanced non-small cell lung cancer harbouring EGFR mutations

Highlights: • Overexpression of IL-6 in tumour cells is postulated to be a potential mechanism for the resistance or low sensitivity to EGFR-TKIs in preclinical models. • We found that patients with IL-6-postitive, EGFR-mutant tumours clearly exhibited worse progression-free survival than those with IL-6-negative, EGFR-mutant, non-small-cell lung cancer (NSCLC). • Tumour IL-6 levels might indicate a subpopulation of EGFR-mutant NSCLC that benefits less from gefitinib monotherapy. Although epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are a key therapy used for patients with EGFR-mutant non-small cell lung cancer (NSCLC), some of whom do not respond well to its therapy. Cytokine including IL-6 secreted by tumour cells is postulated as a potential mechanism for the primary resistance or low sensitivity to EGFR-TKIs. Fifty-two patients with advanced EGFR-mutant NSCLC who had received gefitinib were assessed retrospectively. The protein expression of IL-6 in the tumour cells was assessed by immunostaining and judged as positive if ≥ 50 of 100 tumour cells stained positively. Of the 52 patients, 24 (46%) and 28 (54%) were defined as IL-6-postitive (group P) and IL-6-negative (group N), respectively. Group P had worse progression-free survival (PFS) than that of group N, which was retained in the multivariate analysis (hazard ratio: 2.39; 95 %CI: 1.00–5.68; p < 0.05). By contrast, the PFS after platinum-based chemotherapy did not differ between groups P and N (p = 0.47). In cell line-based model, the impact of IL-6 on the effect of EGFR-TKIs was assessed. The combination of EGFR-TKI and anti-IL-6 antibody moderately improved the sensitivity of EGFR-TKI in lung cancer cell with EGFR mutation. Interestingly, suppression of EGFR with EGFR-TKI accelerated the activation of STAT3 induced by IL-6. Taken together, tumour IL-6 levels might indicate a subpopulation of EGFR-mutant NSCLC that benefits less from gefitinib monotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

miR-455-3p serves as prognostic factor and regulates the proliferation and migration of non-small cell lung cancer through targeting HOXB5

MicroRNAs (miRs) have been reported to play significantly roles in the initiation and progression of human cancers. miR-455-3p has been recently found could function as tumor suppressor in various human cancers. However, its expression and biological role in non-small cell lung cancer (NSCLC) remains elusive. In this study, we found miR-455-3p was markedly downregulated in NSCLC tissues and cell lines. Chi-square test to analyze the correlations between miR-455-3p expression and clinicopathological features revealed that miR-455-3p expression was correlated with poorly differentiated cancer and advanced tumor stage (P P = 0.029). Univariate and multivariate analyses identified low miR-455-3p expression was an unfavorable prognostic factor for overall survival. Gain-of-function and loss-of-function studies revealed that miR-455-3p inhibits cell proliferation and migration in vitro. Computer algorithm and dual-luciferase reporter assay revealed that miR-455-3p directly targets and suppresses HOXB5 in NSCLC. Further studies demonstrated that knockdown of HOXB5 attenuated the effect of miR-455-3p downregulation on cell proliferation and migration. Taken together, our results for the first time suggested that miR-455-3p was downregulated in NSCLC and was correlated with the poor prognosis of NSCLC patients. Also, miR-455-3p functions as tumor suppressor by directly targeting HOXB5 in NSCLC progression and may be used as a potential target for NSCLC treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Lipidomic profiling of plasma samples from patients with mitochondrial disease

Highlights: • All 14 lysoPCs were dramatically decreased in MD patients. • Acylcarnitines, TGs, PIs, PEs, unsaturated lysoPEs and PCs were significantly increased in MD patients. • PI 38:6, lysoPCs (20:0, 19:0, 18:0, 17:0) may be potentially sensitive and specific biomarkers for MD. Mitochondrial disease (MD) is a rare mitochondrial respiratory chain disorder with a high mortality and extremely challenging to treat. Although genomic, transcriptomic, and proteomic analyses have been performed to investigate the pathogenesis of MD, the role of metabolomics in MD, particularly of lipidomics remains unclear. This study was undertaken to identify potential lipid biomarkers of MD. An untargeted lipidomic approach was used to compare the plasma lipid metabolites in 20 MD patients and 20 controls through Liquid Chromatography coupled to Mass Spectrometry. Volcano plot analysis was performed to identify the different metabolites. Receiver operating characteristic (ROC) curves were constructed and the area under the ROC curves (AUC) was calculated to determine the potentially sensitive and specific biomarkers. A total of 41 lipids were significantly different in MD patients and controls. ROC curve analysis showed the top 5 AUC values of lipids (phosphatidylinositols 38:6, lysoPC 20:0, 19:0, 18:0, 17:0) are more than 0.99. Multivariate ROC curve based exploratory analysis showed the AUC of combination of top 5 lipids is 1, indicating they may be potentially sensitive and specific biomarkers for MD. We propose combination of these lipid species may be more valuable in predicting the development and progression of MD, and this will have important implications for the diagnosis and treatment of MD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Over-expression of lysyl oxidase is associated with poor prognosis and response to therapy of patients with lower grade gliomas

Lower grade gliomas (LGGs) have highly diverse clinical phenotypes. The histological grade and type are insufficient to accurately predict the clinical outcomes of patients with LGGs. Therefore, identification of biomarkers that can facilitate the prediction of clinical outcomes in LGGs is urgently needed. Gene expression of LOX has been identified as a biomarker for various cancers. However, the clinical significance of LOX expression in LGGs has not been investigated. In this study, we analyzed the glioma RNA-seq dataset from TCGA (The Cancer Genome atlas) and identified lysyl oxidase (LOX) as a potential biomarker for LGGs. Kaplan-Meier survival analysis revealed that high LOX expression is associated with worse overall survival and recurrence free survival in LGG patients. Besides, high LOX expression is associated with poor response to primary therapy, follow-up treatment, targeted molecular therapy, and radiation therapy. Univariate and multivariate Cox regression analyses further confirmed LOX expression as an independent prognostic factor for LGG patients. Finally, we observed that LOX expression is significantly correlated with EMT (epithelial to mesenchymal transition) and IDH1 status in LGGs. In conclusion, our analyses suggest that LOX expression is a potential biomarker for prognosis and therapeutic response in LGGs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

UBE2S enhances the ubiquitination of p53 and exerts oncogenic activities in hepatocellular carcinoma

Highlights: • High expression of UBE2S is significantly correlated with poor outcomes in two independent cohorts containing 845 patients. • UBE2S promotes cell proliferation and migration in HCC. • UBE2S exerts oncogenic activities via enhancing the ubiquitination of p53. Ubiquitin-conjugating enzyme E2S (UBE2S) plays pivotal roles in the progression of human cancers. However, its clinical significance and role in hepatocellular carcinoma (HCC) remain unknown. Here, we show that UBE2S is upregulated in HCC and exhibits oncogenic activities via enhancing the ubiquitination of p53. Increased expression of UBE2S was significantly correlated with higher serum AFP level, higher pathological grade, advanced TNM stage, larger tumor size, vascular invasion and unfavorable patient survivals in two independent cohorts containing a total of 845 patients with HCC. Multivariate analyses by cox regression model suggested UBE2S as an independent factor for overall survival. In vitro experiments demonstrated that UBE2S overexpression promoted, whereas UBE2S knockdown suppressed cell proliferation and migration via modulation of p53 signaling pathway. Ectopic expression of UBE2S upregulated the expression of p53 and its downstream effectors, such as p21 and Cyclin D1. Mechanistically, UBE2S enhanced the ubiquitination of p53 protein to facilitate its degradation in HCC cells. Re-expression of p53 partially attenuated the UBE2S-promoted malignant phenotypes. Collectively, our study provides compelling evidence that UBE2S is a potential prognostic factor and functions as an oncogene in HCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Correlation between high expression levels of jumonji domain-containing 4 and short survival in cases of colon adenocarcinoma

Highlights: • The play an important role of JMJD4 high expression in tumorgenesis of colon adenocarcinoma. • JMJD4 protein expression in colon adenocarcinoma was associated with histological grade and M-class. • The high expression of JMJD4 was significantly related with poor survival in patients with colon adenocarcinoma. • Cox proportional-hazards model analysis indicated JMJD4 could be a prognostic indicator for overall survival. In this study, we investigated the expression of jumonji domain-containing 4 (JMJD4) in colon adenocarcinoma (CA) look for evidences for future studies on clinical diagnostic and prognostic value. The immunohistochemical (IHC) reactivity of JMJD4 was assessed in human tissue microarrays using monoclonal antibodies. An initial investigation revealed that the expression of JMJD4 protein was significantly higher in tumor tissue of the colon and liver than in normal tissue. Upon further investigation, we observed significant positivity of JMJD4 between 59 paired samples from CA tissue and adjacent normal tissue. JMJD4 protein expression in CA differed significantly according to the histological grade and M-class (distant metastasis). We also determined that the mRNA or protein expression of JMJD4 was significantly associated with poor survival in patients with CA. Finally, univariate and multivariate analysis revealed that JMJD4 expression could be a prognostic indicator for patients with CA and may provide a new target for the development of novel therapies for the treatment of CA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

As an independent prognostic factor, USP6 promotes the invasion and metastasis of colon cancer

Highlights: • USP6 is highly expressed in colon cancer tissues at both mRNA and protein levels. • High USP6 expression was closely associated with the invasion and metastasis of colon cancer. • USP6 overexpression promotes colon cancer cell invasion and metastasis both in vitro and in vivo. • USP6 is considered as a novel gene in promoting the invasion and metastasis of colon cancer. The lack of efficient tumor invasion and metastatic biomarkers led to high mortality rates in colon cancer patients. Aberrant expression of ubiquitin-specific protease 6 (USP6) was involved in several diseases including cancer, while its role in the progression of colon cancer was still unclear. In this study, USP6 was evaluated at both mRNA and protein levels by using RT-PCR, western blot and immunohistochemistry staining analyses. The results revealed that high USP6 expression predicted poor disease-specific survival and overall survival through Kaplan-Meier analyses with log-rank tests, univariate and multivariate Cox analyses. Furthermore, cell function assay demonstrated that USP6 could promote colon cancer cells’ invasion in vitro and liver metastasis in vivo. These findings indicated that high USP6 expression contributed to the progression of colon cancer and USP6 may be a valuable prognostic factor in patients with colon cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Platelet-derived growth factor receptor alpha (PDGFRα) is overexpressed in NK/T-cell lymphoma and mediates cell survival

Nasal-type natural killer/T-cell lymphoma (NKTCL) is a subtype of non-Hodgkin lymphoma (NHL) that is clinically aggressive and has a poor prognosis. Platelet-derived growth factor receptors (PDGFRs) and their ligands (PDGFs) play important roles in angiogenesis, cancer cell proliferation, survival, migration and poor prognosis in various tumours. However, the significance of PDGFRs in NKTCL remains unknown. Herein, the present study aimed to investigate the important role of PDGFRα in pathogenesis, progression and prognisis of NKTCL. Firstly, we performed immunohistochemical staining, qRT-PCR and western blotting to determine PDGFRα expression in formalin-fixed, paraffin-embedded tissue sections from 78 NKTCL cases and in cell lines. Secondly, correlations between PDGFRα expression and NKTCL clinical parameters and prognosis were analysed. Moreover, a biological assessment of PDGFRα blockade in two NKTCL cell lines was conducted through proliferation assay, cell-cycle evaluation and apoptosis detection by flow cytometry analyses. Furthermore, we detected in vivo activity of imatinib in mouse model of NKTCL. We found that the expression of PDGFRα was significantly higher in NKTCL tissues compared to the reactive lymphoid hyperplasia of the nasopharynx (P = 0.028). High PDGFRα expression was strongly associated with a high LDH level (P = 0.028) and III-IV stage (P = 0.013). NKTCL patients with high PDGFRα expression displayed a reduced median overall survival time and progression-free survival time when compared with those with low PDGFRα expression (P = 0.011, P = 0.005, respectively). Cox multivariate analysis showed that III-IV stage (P = 0.024) and high PDGFRα expression (P = 0.003) were independent prognostic factors in NKTCL patients. Biological assessment assays in two NKTCL cell lines revealed that a specific PDGFR antagonist, imatinib, inhibited cell viability, blocked cell cycle progression at G0/G1 stage and induced apoptosis. Similarly, the in vivo assay showed that imatinib delayed mouse model tumour growth. In conclusion, NKTCL tumour cells have prominent PDGFRα expression, which can serve as a candidate prognostic marker. PDGFR antagonists have significant biological effect on NKTCL and may be useful therapeutic agents for treatment of NKTCL.

60 APPLIED LIFE SCIENCES↗