[Prognostic significance and immune cell infiltration analysis of differentially expressed genes in malignant pleural mesothelioma].

Objective: To explore and analyze differential expressed genes in malignant pleural mesothelioma (MPM) by bioinformatics method, and to study their prognostic value in MPM and their potential role in immunotherapy. Methods: In January 2022, the dataset GSE51024 was downloaded from the GEO database, and MPM (55 cases) and normal tissue (41 cases) samples were obtained. Using R software and HMDD and miRNet database, MPM-related differential genes were screened and co-expressed genes were identified. Co-expressed genes were enriched and functionally annotated, and protein-protein interaction (PPI) networks were constructed and key genes were identified using the STRING database and Cytoscape software. TRRUST and GEPIA databases were used to predict transcription factors of key genes and to analyze prognosis and survival. The correlation between key genes and the degree of infiltration of immune cells was analyzed using TIMER. Results: A total of 435 co-expressed genes were obtained, which were mainly concentrated in the extracellular matrix tissue and the signaling pathways of cell adhesion molecules. Combined with PPI and TRRUST database, seven key MPM prognostic genes were identified. Among them, cyclin 20 (CDC20) , cell cycle checkpoint kinase 1 (CHEK1) , enhancer of Zeste homolog 2 (EZH2) , ribonucleotide reductase subunit M2 (RRM2) , topoisomerase 2A (TOP2A) , ubiquitin like plant homeodomain and ring finger domain 1 (UHRF1) were significantly up-regulated in MPM, while cyclin A1 (CCNA1) was significantly down-regulated. The expressions of CCNA1, CDC20, CHEK1, EZH2, RRM2, TOP2A and UHRF1 genes were significantly associated with MPM overall survival (P<0.05) . The expressions of CDC20, CHEK1, EZH2, RRM2 and TOP2A genes were positively correlated with B cells and dendritic cells (P<0.05) , and negatively correlated with neutrophils (P<0.05) . Conclusion: CCNA1, CDC20, CHEK1, EZH2, RRM2, TOP2A and UHRF1 may be potential prognostic markers in MPM patients, and their expressions may be related to MPM tumor immunity.

[Plasma differentially expressed genes and bioinformatics analysis of workers occupationally exposed to mercury].

Objective: To screen and identify plasma differentially expressed genes and related signal pathway by human gene expression profile array and fluorescent quantitative PCR. Methods: From September 2018 to October 2019, 291 workers from a Mercury-in-glass thermometer factory in Jiangsu Province were selected for an occupational health examination, a total of 60 persons were divided into two groups: high and low mercury exposure groups (30 persons in each group) . Plasma total RNA samples from the high exposure group and the low exposure group (10 cases each) were detected by gene expression microarray, and differentially expressed genes (DEGs) with fold change >2 were selected. DEGs were submitted to David and Metascape for gene function clustering, pathway and protein interaction network analysis. Finally, fluorescence quantitative PCR was performed to verify the changes in the expression levels of key DEGs in the high exposure group and the low exposure group (another 20 cases in each group) . Results: A total of 269 DEGs, of which 203 up regulated and 66 down regulated were identified in the differential expression analysis of gene expression microarray. Bioinformatics analysis suggested that, DEGs were involved in forebrain development, glial cell fate determinants of GO biological process and PID NF-KB, PTEN signal pathway. NFE2L1, SOX8, SOX6 and RNF2 (P<0.05) were confirmed down regulated in high level group by fluorescent quantitative PCR compared with the low level group (fold changes were 2.10, 11.52, 2.19, and 4.38 respectively) . Conclusion: The plasma NFE2L1, SOX8, SOX6 and RNF2 gene expressions are significantly altered in occupa tional high mercury exposure population. PTEN signaling pathway and fate of glia cells determines the biological process may be closely related to the body injury caused by mercury exposure.

[Prevalence characters of peripheral artery disease and associated factors among Beijing residents aged equal and above 35 years old].

Objective: To investigate the prevalence characters of peripheral artery disease (PAD) and associated factors among people aged 35 and above in Beijing. Methods: This was a cross-sectional study. A total of 5 208 community-based individuals aged equal and above 35 in Beijing were chosen with stratified multistage random sampling method. Structure questionnaire was used to collected the information of demographic factors, habits and chronic disease history. Ankle brachial blood pressure was detected and ankle brachial index (ABI) was calculated. ABI was used to diagnose PAD (ABI≤0.90). Based on the 2010 Beijing Municipal Population Census, the age-and gender-specific weight-adjusted sample was acquired to estimate the prevalence of PAD and corresponding 95% confidence intervals (CI). Multivariate logistic regression analysis was performed to estimate the associated factors of PAD. Results: The age-and sex-standardized prevalence of PAD was 3.84% (200/5 208, 95%CI 3.32%-4.36%). There was no significant difference between male and female (3.83%(102/2 664, 95%CI 3.10%-4.56%) vs. 3.85% (98/2 544, 95%CI 3.10%-4.60%), P=0.965). The prevalence of PAD in urban was higher than that in rural (4.34% (163/3 755, 95%CI 3.69%-4.99%) vs. 2.55% (37/1 453, 95%CI 1.74%-3.36%), P=0.001). Furthermore, the prevalence of PAD increased with age (P(trend)<0.01), and the difference between genders did not change with ageing (all P>0.05). In addition, age (OR=1.03, 95%CI 1.01-1.04), urban (OR=1.52, 95%CI 1.08-2.12), smoking (OR=1.83, 95%CI 1.29-2.59), hypertension (OR=1.61, 95%CI 1.17-2.22) and diabetes (OR=1.44, 95%CI 1.08-1.93) were related with increased risk of PAD in logistic regression analysis models. Conclusions: The prevalence of PAD increases with age in Beijing and there are significant difference between urban and rural on prevalence of PAD. Age, urban, smoking, hypertension and diabetes are related with increased risk of PAD.