Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation

Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

Background. An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Methods. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n=300; GSE15459, n=191; and GSE26901, n=109). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort (n=600) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n=432; GSE84437, n=431; and TCGA, n=336). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. Results. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort (AUC>0.7). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC (p<0.001). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group (p<0.001), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor (p=0.011). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score (p=0.00085). The patients’ risk score increased with the progression of the clinicopathological stage. Conclusion. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC

Improving the Overall Efficiency of Marine Power Systems through Co-Optimization of Top-Bottom Combined Cycle by Means of Exhaust-Gas Bypass: A Semi Empirical Function Analysis Method

The mandatory implementation of the standards laid out in the Energy Efficiency Existing Ship Index (EEXI) and the Carbon Intensity Indicator (CII) requires ships to improve their efficiency and thereby reduce their carbon emissions. To date, the steam Rankine cycle (RC) has been widely used to recover wasted heat from marine main engines to improve the energy-conversion efficiency of ships. However, current marine low-speed diesel engines are usually highly efficient, leading to the low exhaust gas temperature. Additionally, the temperature of waste heat from exhaust gas is too low to be recovered economically by RC. Consequently, a solution has been proposed to improve the overall efficiency by means of waste heat recovery. The exhaust gas is bypassed before the turbocharger, which can decrease the air excess ratio of main engine to increase the exhaust gas temperature, and to achieve high overall efficiency of combined cycle. For quantitative assessments, a semi-empirical formula related to the bypass ratio, the excess air ratio, and the turbocharging efficiency was developed. Furthermore, the semi-empirical formula was verified by testing and engine model. The results showed that the semi-empirical formula accurately represented the relationships of these parameters. Assessment results showed that at the turbocharging efficiency of 68.8%, the exhaust temperature could increase by at least 75 °C, with a bypass ratio of 15%. Moreover, at the optimal bypass ratio of 11.1%, the maximum overall efficiency rose to 54.84% from 50.34%. Finally, EEXI (CII) decreased from 6.1 (4.56) to 5.64 (4.12), with the NOx emissions up to Tier II standard

Improved multivariate modeling for soil organic matter content estimation using hyperspectral indexes and characteristic bands.

Soil organic matter (SOM) is a key index of soil fertility. Calculating spectral index and screening characteristic band reduce redundancy information of hyperspectral data, and improve the accuracy of SOM prediction. This study aimed to compare the improvement of model accuracy by spectral index and characteristic band. This study collected 178 samples of topsoil (0-20 cm) in the central plain of Jiangsu, East China. Firstly, visible and near-infrared (VNIR, 350-2500 nm) reflectance spectra were measured using ASD FieldSpec 4 Std-Res spectral radiometer in the laboratory, and inverse-log reflectance (LR), continuum removal (CR), first-order derivative reflectance (FDR) were applied to transform the original reflectance (R). Secondly, optimal spectral indexes (including deviation of arch, difference index, ratio index, and normalized difference index) were calculated from each type of VNIR spectra. Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. Meanwhile, SOM prediction models were established using characteristic wavelengths, denoted here as CARS-based models. Finally, this research compared and assessed accuracy of SI-based models and CARS-based models, and selected optimal model. Results showed: (1) The correlation between optimal spectral indexes and SOM was enhanced, with absolute value of correlation coefficient between 0.66 and 0.83. The SI-based models predicted SOM content accurately, with the coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.80 to 0.87, 2.40 g/kg to 2.88 g/kg in validation sets, and relative percent deviation (RPD) value between 2.14 and 2.52. (2) The accuracy of CARS-based models differed with models and spectral transformations. For all spectral transformations, PLSR and SVR combined with CARS displayed the best prediction (R2 and RMSE values ranged from 0.87 to 0.92, 1.91 g/kg to 2.56 g/kg in validation sets, and RPD value ranged from 2.41 to 3.23). For FDR and CR spectra, DNN and RF models achieved more accuracy (R2 and RMSE values ranged from 0.69 to 0.91, 1.90 g/kg to 3.57 g/kg in validation sets, and RPD value ranged from 1.73 to 3.25) than LR and R spectra (R2 and RMSE values from 0.20 to 0.35, 5.08 g/kg to 6.44 g/kg in validation sets, and RPD value ranged from 0.96 to 1.21). (3) Overall, the accuracy of SI-based models was slightly lower than that of CARS-based models. But spectral index had a good adaptability to the models, and each SI-based model displayed the similar accuracy. For different spectra, the accuracy of CARS-based model differed from modeling methods. (4) The optimal CARS-based model was model CARS-CR-SVR (R2 and RMSE: 0.92 and 1.91 g/kg in validation set, RPD: 3.23). The optimal SI-based model was model SI3-SVR (R2 and RMSE: 0.87 and 2.40 g/kg in validation set, RPD: 2.57) and model SI-SVR (R2 and RMSE: 0.84 and 2.63 g/kg in validation set, RPD: 2.35)

Geochemical Characteristics of Typical Karst Soil Profiles in Anhui Province, Southeastern China

The geographical distributions of Cd and several other heavy metals (HMs) (Hg, Cu, Ni, Pb, Zn, Cr, As, Co, and V) were characterized in 90 (p > 0.05) terra rossa samples across the Anhui karst area. Significant enrichment of HM was observed in this soil, mainly associated with the weathering of Cd-enriched carbonate rocks. Then, this enrichment was developed in 31 profiles. Our investigations revealed pedogenic processes as the dominant factors accounting for the enrichment of Hg, Cu, Ni, As, Co, and V. We also observed that all soil samples had a silty clay texture, with a pH scope of 4.08–8.04 and a median value of 6.50. In addition, the soil samples had relatively high saturation, with basic cations over 6.68%. The enrichment of the HMs based on their distinct factors were as follows: Cd (3.92) > As (2.55) > Zn (1.62) > Ni (1.50) > Cu (1.47) > Pb (1.47) > V (1.43) > Cr (1.23) > Co (1.19) > Hg (1.12). Finally, terra rossa samples derived from carbonate rocks were categorized as Cambisols, Luvisols, and Regosols. The soil profiles of Cambisols and Luvisolsis were less developed, so the HM concentrations were relatively low. The Regosols profile contained the highest total Cd concentration and exhibited a higher capacity to immobilize Cd compared with other soil profiles. Regosols are also characterized by high pH values (scope of 7.05 to 8.22, with an average value of 7.56). The contents of HM also exhibited minor changes across the Regosol, Cambisol, and Luvisol profiles, implying that the karst development degrees of weathering in Anhui were relatively low

Obesity, metabolic dysfunction, and risk of kidney stone disease: a national cross-sectional study

AbstractBackground This study aimed to investigate the association between different metabolic syndrome-body mass index (MetS-BMI) phenotypes and the risk of kidney stones.Materials and Methods Participants aged 20–80 years from six consecutive cycles of the NHANES 2007–2018 were included in this study. According to their MetS status and BMI, the included participants were allocated into six mutually exclusive groups: metabolically healthy normal weight (MHN)/overweight (MHOW)/obesity (MHO) and metabolically unhealthy normal weight (MUN)/overweight (MUOW)/obesity (MUO). To explore the association between MetS-BMI phenotypes and the risk of kidney stones, binary logistic regression was used to determine the odds ratios (ORs).Results A total of 13,589 participants were included. It was revealed that all the phenotypes with obesity displayed higher risks of kidney stones (OR = 1.38, p < 0.01 for MHO & OR = 1.80, p < 0.001 for MUO, in the fully adjusted model). The risk increased significantly when metabolic dysfunction coexisted with overweight and obesity (OR = 1.39, p < 0.05 for MUOW & OR = 1.80, p < 0.001 for MUO, in the fully adjusted model). Of note, the ORs for the MUO and MUOW groups were higher than those for the MHO and MHOW groups, respectively.Conclusions Obesity and unhealthy metabolic status can jointly increase the risk of kidney stones. Assessing the metabolic status of all individuals may be beneficial for preventing kidney stones

Enhanced colonic tumorigenesis in alkaline sphingomyelinase (NPP7) knockout mice.

Intestinal alkaline sphingomyelinase (alk-SMase) generates ceramide and inactivates platelet-activating factor (PAF) and is previously suggested to have anticancer properties. The direct evidence is still lacking. We studied colonic tumorigenesis in alk-SMase knockout (KO) mice. Formation of aberrant crypt foci (ACF) was examined after azoxymethane (AOM) injection. Tumor was induced by AOM alone, a conventional AOM/dextran sulfate sodium (DSS) treatment, and an enhanced AOM/DSS method. beta-catenin was determined by immunohistochemistry, PAF levels by ELISA and sphingomyelin metabolites by mass spectrometry. Without treatment, spontaneous tumorigenesis was not identified but the intestinal mucosa appeared thicker in KO than in wild type (WT) littermates. AOM alone induced more ACF in KO mice but no tumors 28 weeks after injection. However, combination of AOM/DSS treatments induced colonic tumors and the incidence was significantly higher in KO than in WT mice. By the enhanced AOM/DSS method tumor number per mouse increased 4.5 times and tumor size 1.8 times in KO compared to WT mice. While all tumors were adenomas in WT mice, 32% were adenocarcinomas in KO mice. Compared to WT mice, cytosol expression of beta-catenin was significantly decreased and nuclear translocation in tumors was more pronounced in KO mice. Lipid analysis showed decreased ceramide in small intestine and increased sphingosine-1-phosphate in both small intestine and colon in nontreated KO mice. PAF levels in feces were significantly higher in the KO mice after AOM/DSS treatment. In conclusion lack of alk-SMase markedly increases AOM/DSS induced colonic tumorigenesis associated with decreased ceramide and increased sphingosine-1-phosphate and PAF levels