The identification of hub-methylated differentially expressed genes in osteoarthritis patients is based on epigenomic and transcriptomic data.

Introduction: Osteoarthritis (OA) refers to a commonly seen degenerative joint disorder and a major global public health burden. According to the existing literature, osteoarthritis is related to epigenetic changes, which are important for diagnosing and treating the disease early. Through early targeted treatment, costly treatments and poor prognosis caused by advanced osteoarthritis can be avoided. Methods: This study combined gene differential expression analysis and weighted gene co-expression network analysis (WGCNA) of the transcriptome with epigenome microarray data to discover the hub gene of OA. We obtained 2 microarray datasets (GSE114007, GSE73626) in Gene Expression Omnibus (GEO). The R software was utilized for identifying differentially expressed genes (DEGs) and differentially methylated genes (DMGs). By using WGCNA to analyze the relationships between modules and phenotypes, it was discovered that the blue module (MEBlue) has the strongest phenotypic connection with OA (cor = 0.92, p = 4e-16). The hub genes for OA, also known as the hub methylated differentially expressed genes, were identified by matching the MEblue module to differentially methylated differentially expressed genes. Furthermore, this study used Gene set variation analysis (GSVA) to identify specific signal pathways associated with hub genes. qRT-PCR and western blotting assays were used to confirm the expression levels of the hub genes in OA patients and healthy controls. Results: Three hub genes were discovered: HTRA1, P2RY6, and RCAN1. GSVA analysis showed that high HTRA1 expression was mainly enriched in epithelial-mesenchymal transition and apical junction; high expression of P2RY6 was mainly enriched in the peroxisome, coagulation, and epithelial-mesenchymal transition; and high expression of RCAN1 was mainly enriched in epithelial-mesenchymal-transition, TGF-ß-signaling, and glycolysis. The results of the RT-qPCR and WB assay were consistent with the findings. Discussion: The three genes tested may cause articular cartilage degeneration by inducing chondrocyte hypertrophy, regulating extracellular matrix accumulation, and improving macrophage pro-inflammatory response, resulting in the onset and progression of osteoarthritis. They can provide new ideas for targeted treatment of osteoarthritis.

[Analysis of influencing factors on infrared spectroscopic determination of the content of free silica in quartz sand].

OBJECTIVE: To find a suitable method for detecting SiO2 in quartz sand and to analyze the influencing factors on infrared spectroscopic determination of the content of free silica in quartz sand. METHODS: The infrared spectroscopy was used to detect the free silica content of quartz sand, the various factors of influencing the results were analyzed and the control scheme was proposed. RESULTS: The number of particles less than 5 um and the proportion of free silica content increased with the grinding time. When the grinding time was 10-20 min, the results of detecting the free silica content tended to be stable. When the ashing temperature was below 550 degrees C, there was no effect on the free silica content. Although the silica content decreased slightly at ashing temperature 600 degrees C as compared to ashing temperature 550 degrees C, the difference of the free silica content between 550 degrees C and 600 degrees C was not significant (P > 0.05). When the ashing temperature was 600 degrees C, the free silica content in quartz sand samples did not change obviously in 1 h (F = 4.231, P > 0.05). The free silica content in quartz sand samples decreased significantly at 2 h of ashing time, as compared with 2 h of ashing time (F = 10.231, P < 0.05). The average content of free silica was 88.56% +/- 5.75% by pyrophosphate determination, which was significantly higher than that (21.23% +/- 11.25%) by infrared determination (P < 0.05). There was no significant correlation of the average content of free silica between pyrophosphate determination and infrared determination (r = 0.411, P > 0.05). CONCLUSION: The free silica contents detected by pyrophosphate determination were significantly higher than those detected by infrared determination for the same quartz sand samples. It is suggested that the method of detecting the free silica contents in quartz sand samples prefers the pyrophosphate determination to infrared determination.

A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer.

Growing evidence has underscored long non-coding RNAs (lncRNAs) serving as potential biomarkers for cancer prognosis. However, systematic tracking of a lncRNA signature for prognosis prediction in non-small cell lung cancer (NSCLC) has not been accomplished yet. Here, comprehensive analysis with differential gene expression analysis, univariate and multivariate Cox regression analysis based on The Cancer Genome Atlas (TCGA) database was performed to identify the lncRNA signature for prediction of the overall survival of NSCLC patients. A risk-score model based on a 14-lncRNA signature was identified, which could classify patients into high-risk and low-risk groups and show poor and improved outcomes, respectively. The receiver operating characteristic (ROC) curve revealed that the risk-score model has good performance with high AUC value. Multivariate Cox's regression model and stratified analysis indicated that the risk-score was independent of other clinicopathological prognostic factors. Furthermore, the risk-score model was competent for the prediction of metastasis-free survival in NSCLC patients. Moreover, the risk-score model was applicable for prediction of the overall survival in the other 30 caner types of TCGA. Our study highlighted the significant implications of lncRNAs as prognostic predictors in NSCLC. We hope the lncRNA signature could contribute to personalized therapy decisions in the future.

Characters related to higher starch accumulation in cassava storage roots.

Cassava (Manihot esculenta) is valued mainly for high content starch in its roots. Our understanding of mechanisms promoting high starch accumulation in the roots is, however, still very limited. Two field-grown cassava cultivars, Huanan 124(H124) with low root starch and Fuxuan 01(F01) with high root starch, were characterised comparatively at four main growth stages. Changes in key sugars in the leaves, stems and roots seemed not to be strongly associated with the final amount of starch accumulated in the roots. However, when compared with H124, F01 exhibited a more compact arrangement of xylem vascular bundles in the leaf axils, much less callose around the phloem sieve plates in the stems, higher starch synthesis-related enzymatic activity but lower amylase activity in the roots, more significantly up-regulated expression of related genes, and a much higher stem flow rate (SFR). In conclusion, higher starch accumulation in the roots results from the concurrent effects of powerful stem transport capacity highlighted by higher SFR, high starch synthesis but low starch degradation in the roots, and high expression of sugar transporter genes in the stems. A model of high starch accumulation in cassava roots was therefore proposed and discussed.