[Translocation, Accumulation, and Comprehensive Risk Assessment of Heavy Metals in Soil-Crop Systems in an Old Industrial City, Shizuishan, Ningxia, Northwest China].

In order to explore the environmental geochemistry characteristics of heavy metals (HMs) in soil-crop systems in an old industrial city, the concentration and fraction of HMs in the paddy, wheat, and maize root soil and their seeds were detected and analyzed. Subsequently, statistical methods, risk assessment coding (RAC), the bio-enrichment coefficient factor (BCF), influence index of comprehensive quality (IICQ), and ArcGIS spatial interpolation were used to conduct the translocation, accumulation, and comprehensive risk assessment of HMs in soil-crop systems. The results showed that the average concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in root soil were ranked respectively as follows:12.56, 0.19, 63.48, 23.52, 0.038, 28.86, 21.68, and 69.47 mg·kg-1. HMs in root soil were accumulated to some extent in comparison with the soil background value in Ningxia, especially Cd and Hg, but did not exceed the soil environmental pollution screening value (GB 14618-2018). The average concentrations of the eight aforementioned elements in supporting crop seeds were 0.0149, 0.0112, 0.075, 6.7, 0.0015, 0.67, 0.0427, and 20.48 mg·kg-1 in turn. The over-limit ratio of As, Pb, and Cr in crop seeds was 4%, 3%, and 1%, respectively, relative to the national food safety standards (GB 2762-2017), whereas the other five elements were within the allowable range. In comparison to those in paddy and wheat, HMs hardly tended to translocate to maize seeds from root soil. According to the results of IICQ in soil-crop systems, the cultivated soil was in the state of slight sub-contamination regionally, and only 10% of sampling points showed slight (sub-)contamination-submoderate contamination, where we could replant maize to reduce HMs contamination risk.

[Threshold of Se-rich Soil Based on Available-Se and Influencing Factors of Available-Se].

Available selenium (Se) in soil was the predominant factor affecting the content of Se in crops. In order to reasonably delineate the Se-rich soil range and propose theoretical guidance for the cultivation of natural Se-rich crops in a region where the surface soils had a high level of available-Se and a low level of total-Se, 8814 samples in surface soil and 195 root-crop matching samples were collected in Shizuishan in northern Ningxia. On the basis of the main line of analysis of available-Se, the following research was conducted: by synthetically studying the total-Se and available-Se in surface soil and root soil, the morphology of Se in surface soil, as well as Se in crops, deep and coordinated analyses of content among total-Se, available-Se, and Se in root-crop matching samples were carried out, and the suitable threshold for Shizuishan was confirmed. A multiple regression model of available-Se was established to determine the main physical and chemical indexes affecting available-Se, which were expected to improve the Se enrichment rate of crops through the enhancement of available-Se. The results demonstrated that ω(Se) and ω(Seavailable)in the surface soil in Shizuishan were 0.26 mg·kg-1 and 12.85 µg·kg-1, respectively, and the characteristics of Se and available-Se in root-crop matching samples could represent those in surface soil. Thus, it was recommended to use 0.24 mg·kg-1 as the suitable threshold of Se-rich soil. The multiple regression model of available-Se showed that the increase in total-Se and soil elements affecting soil fertility could promote the enrichment of available-Se.

Initial CT-based radiomics nomogram for predicting in-hospital mortality in patients with traumatic brain injury: a multicenter development and validation study.

OBJECTIVE: To develop and validate a radiomic prediction model using initial noncontrast computed tomography (CT) at admission to predict in-hospital mortality in patients with traumatic brain injury (TBI). METHODS: A total of 379 TBI patients from three cohorts were categorized into training, internal validation, and external validation sets. After filtering the unstable features with the minimum redundancy maximum relevance approach, the CT-based radiomics signature was selected by using the least absolute shrinkage and selection operator (LASSO) approach. A personalized predictive nomogram incorporating the radiomic signature and clinical features was developed using a multivariate logistic model to predict in-hospital mortality in patients with TBI. The calibration, discrimination, and clinical usefulness of the radiomics signature and nomogram were evaluated. RESULTS: The radiomic signature consisting of 12 features had areas under the curve (AUCs) of 0.734, 0.716, and 0.706 in the prediction of in-hospital mortality in the internal and two external validation cohorts. The personalized predictive nomogram integrating the radiomic and clinical features demonstrated significant calibration and discrimination with AUCs of 0.843, 0.811, and 0.834 in the internal and two external validation cohorts. Based on decision curve analysis (DCA), both the radiomic features and nomogram were found to be clinically significant and useful. CONCLUSION: This predictive nomogram incorporating the CT-based radiomic signature and clinical features had maximum accuracy and played an optimized role in the early prediction of in-hospital mortality. The results of this study provide vital insights for the early warning of death in TBI patients.

GNG5 is an unfavourable independent prognostic indicator of gliomas.

Gliomas are the most common primary brain tumours, and glioblastomas (GBMs) are subgrouped into four distinct molecular subtypes. This study aimed to identify the potential gene related to glioma progression. Weighted gene co-expression network analysis (WGCNA) was used to explore the related gene. Correlation, ROC, survival and Cox regression analyses were performed. Blue module was strongly associated with WHO grade (r = .65, P = 1e-19). GNG5 in gliomas was overexpressed compared with normal samples and associated with clinicopathologic characteristics. GNG5 was frequent in Mesenchymal subtype and lowly expressed in Proneural subtype of GBMs. Survival and Cox regression analyses showed that glioma patients with GNG5 overexpression had shorter survival time, and GNG5 was an independent prognostic indicator of overall survival. Overall, GNG5 expression is closely associated with clinicopathologic characteristics and is an independent prognostic indicator for glioma patients, as well as a promising subtype-associated biomarker in molecular classification of gliomas.

Integrated Transcriptomic Analysis Reveals the Molecular Mechanism of Meningiomas by Weighted Gene Coexpression Network Analysis.

Meningiomas are the most common primary intracranial tumor in adults. However, to date, systemic coexpression analyses for meningiomas fail to explain its pathogenesis. The aim of the present study was to construct coexpression modules and identify potential biomarkers associated with meningioma progression. Weighted gene coexpression network analysis (WGCNA) was performed based on GSE43290, and module preservation was tested by GSE74385. Functional annotations were performed to analyze biological significance. Hub genes were selected for efficacy evaluations and correlation analyses using two independent cohorts. A total of 14 coexpression modules were identified, and module lightcyan was significantly associated with WHO grades. Functional enrichment analyses of module lightcyan were associated with tumor pathogenesis. The top 10 hub genes were extracted. Ten biomarkers, particularly AHCYL2, FGL2, and KCNMA1, were significantly related to grades and prognosis of meningioma. These findings not only construct coexpression modules leading to the better understanding of its pathogenesis but also provide potential biomarkers that represent specific on tumor grades and identify recurrence, predicting prognosis and progression of meningiomas.

Integrated Transcriptome Analyses and Experimental Verifications of Mesenchymal-Associated TNFRSF1A as a Diagnostic and Prognostic Biomarker in Gliomas.

Gliomas are the most prevalent malignant primary brain tumors with poor outcome, and four different molecular subtypes (Mesenchymal, Proneural, Neural, and Classical) are popularly applied in scientific researches and clinics of gliomas. Public databases contain an abundant genome-wide resource to explore the potential biomarker and molecular mechanisms using the informatics analysis. The aim of this study was to discover the potential biomarker and investigate its effect in gliomas. Weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression modules and explore the biomarker among the dataset CGGA mRNAseq_693 carrying 693 glioma samples. Functional annotations, ROC, correlation, survival, univariate, and multivariate Cox regression analyses were implemented to investigate the functional effect in gliomas, and molecular experiments in vitro were performed to study the biological effect on glioma pathogenesis. The brown module was found to be strongly related to WHO grade of gliomas, and KEGG pathway analysis demonstrated that TNFRSF1A was enriched in MAPK signaling pathway and TNF signaling pathway. Overexpressed TNFRSF1A was strongly related to clinical features such as WHO grade, and functioned as an independent poor prognostic predictor of glioma patients. Notably, TNFRSF1A was preferentially upregulated in the Mesenchymal subtype gliomas (Mesenchymal-associated). Knockdown of TNFRSF1A inhibited proliferation and migration of glioma cell lines in vitro. Our findings provide a further understanding of the progression of gliomas, and Mesenchymal-associated TNFRSF1A might be a promising target of diagnosis, therapy, and prognosis of gliomas.

BMX activates Wnt/ß-catenin signaling pathway to promote cell proliferation and migration in breast cancer.

BACKGROUND: Breast cancer has become a dangerous killer for the female, which seriously threatened women's life, leading to huge pressures to society. The present study assessed the mechanism underlying the involvement of bone marrow tyrosine kinase on chromosome X (BMX) in breast cancer development. METHODS: The expression of BMX was examined by qPCR and immunohistochemistry. The effect of BMX on cell proliferation and migration was detected by Clone formation assay and Transwell assay. In vitro study, the correlation of BMX with Wnt/ß-catenin pathway was explored by western blot and TOP/FOP flash assay. RESULTS: In the present study, we found that BMX was up-regulated in breast cancer, which was associated with the tumor differentiation and TNM stage. Oncogenic BMX enhanced the ability of breast cancer cell proliferation and migration. Furthermore, BMX could up-regulate the protein expression levels of p-ß-catenin (Y142), p-ß-catenin(Y654) and inhibit the expression level of p-ß-catenin (S33/37), thus activating Wnt/ß-catenin pathway in MCF-7 and MDA-MB-231 cells. In addition, we revealed that BMX promoted GSK3ß phosphorylation, which suppressed the degradation of ß-catenin. CONCLUSIONS: In this study, we identified that BMX-activated Wnt/ß-catenin signaling pathway, playing an oncogenic role in breast cancer, suggesting that BMX could become a potential treatment target of breast cancer.

Identification of biomarkers and construction of a microRNA-mRNA regulatory network for ependymoma using integrated bioinformatics analysis.

Ependymomas (EPNs) are one of the most common types of malignant neuroepithelial tumors. In an effort to identify potential biomarkers involved in the pathogenesis of EPN, the mRNA expression profiles of the GSE25604, GSE50161, GSE66354, GSE74195 and GSE86574 datasets, in addition to the microRNA (miRNA/miR) expression profiles of GSE42657 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) between EPN and normal brain tissue samples were identified using the Limma package in R and GEO2R, respectively. Functional and pathway enrichment analyses were conducted using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction network was constructed using the Search Tool for Retrieval of Interacting Genes database, which was visualized using Cytoscape. The targeted genes of DEMs were predicted using miRWalk2.0 and a miRNA-mRNA regulatory network was constructed. Following analysis, a total of 948 DEGs and 129 DEMs were identified. Functional enrichment analysis revealed that 609 upregulated DEGs were significantly enriched in 'PI3K-Akt signaling pathway', while 339 downregulated DEGs were primarily involved in 'cell junction' and 'retrograde endocannabinoid signaling'. In addition, 6 hub genes [cyclin dependent kinase 1, CD44 molecule (Indian blood group) (CD44), proliferating cell nuclear antigen (PCNA), MYC, synaptotagmin 1 (SYT1) and kinesin family member 4A] and 6 crucial miRNAs [homo sapiens (hsa)-miR-34a-5p, hsa-miR-449a, hsa-miR-106a-5p, hsa-miR-124-3p, hsa-miR-128-3p and hsa-miR-330-3p] were identified as biomarkers and potential therapeutic targets for EPN. Furthermore, a microRNA-mRNA regulatory network was constructed to highlight the interactions between DEMs and their target DEGs; this included the hsa-miR-449a-SYT1, hsa-miR-34a-5p-SYT1, hsa-miR-330-3p-CD44 and hsa-miR-124-3p-PCNA pairs, whose expression levels were confirmed using reverse transcription-quantitative polymerase chain reaction. In conclusion, the present study may provide important data for the investigation of the molecular mechanisms of EPN pathogenesis.

Silencing lncRNA SNHG6 suppresses proliferation and invasion of breast cancer cells through miR-26a/VASP axis.

The important role of LncRNA in the development of breast cancer is attracting more and more attention. In the previous study, we found that the expression level of LncRNA SNHG6 in breast cancer tissues and cells was significantly increased, but its mechanism in the development of breast cancer was still unclear. Our study found that knockdown of SNHG6 significantly inhibited the proliferation, migration and invasion of breast cancer cells MCF-7 and MDA-MB-231 cells. Further study showed that knockdown of SNHG6 significantly inhibited the expression level of VASP. More importantly, SNHG6 and VASP both can bind directly to miR-26a, suggesting that SNHG6 could act as a ceRNA to sponge miR-26a, thereby promoting the expression of VASP, which leading to activated proliferation, migration and invasion of breast cancer cells. Taken together, this study revealed the important role of the SNHG6/miR-26a/VASP regulatory network in the development of breast cancer, and provided a reference for exploring new pathogenesis and biomarkers of breast cancer.

Examining the biomarkers and molecular mechanisms of medulloblastoma based on bioinformatics analysis.

Medulloblastoma (MB) is the most common malignant brain tumor in children. The aim of the present study was to predict biomarkers and reveal their potential molecular mechanisms in MB. The gene expression profiles of GSE35493, GSE50161, GSE74195 and GSE86574 were downloaded from the Gene Expression Omnibus (GEO) database. Using the Limma package in R, a total of 1,006 overlapped differentially expressed genes (DEGs) with the cut-off criteria of P<0.05 and |log2fold-change (FC)|>1 were identified between MB and normal samples, including 540 upregulated and 466 downregulated genes. Furthermore, the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool to analyze functional and pathway enrichment. The Search Tool for Retrieval of Interacting Genes database was subsequently used to construct a protein-protein interaction (PPI) network and the network was visualized in Cytoscape. The top 11 hub genes, including CDK1, CCNB1, CCNB2, PLK1, CDC20, MAD2L1, AURKB, CENPE, TOP2A, KIF2C and PCNA, were identified from the PPI network. The survival curves for hub genes in the dataset GSE85217 predicted the association between the genes and survival of patients with MB. The top 3 modules were identified by the Molecular Complex Detection plugin. The results indicated that the pathways of DEGs in module 1 were primarily enriched in cell cycle, progesterone-mediated oocyte maturation and oocyte meiosis; and the most significant functional pathways in modules 2 and 3 were primarily enriched in mismatch repair and ubiquitin-mediated proteolysis, respectively. These results may help elucidate the pathogenesis and design novel treatments for MB.