ABSTRACT
Glioblastoma multiforme (GBM) is a prevalent intracranial brain tumor associated with a high rate of recurrence and treatment difficulty. The prediction of novel molecular biomarkers through bioinformatics analysis may provide new clues into early detection and eventual treatment of GBM. Here, we used data from the GTEx and TCGA databases to identify 1923 differentially expressed genes (DEGs). GO and KEGG analyses indicated that DEGs were significantly enriched in immune response and coronavirus disease-COVID-19 pathways. Survival analyses revealed a significant correlation between high expression of C1R, CCL2, and TNFRSF1A in the coronavirus disease-COVID-19 pathway and the poor survival in GBM patients. Cell experiments indicated that the mRNA expression levels of C1R, CCL2, and TNFRSF1A in GBM cells were very high. Immune infiltration analysis revealed a significant difference in the proportion of immune cells in tumor and normal tissue, and the expression levels of C1R, CCL2, and TNFRSF1A were associated with immune cell infiltration of GBM. Additionally, the protein-protein interaction networks of C1R, CCL2, and TNFRSF1A involved a total of 65 nodes and 615 edges. These results suggest that C1R, CCL2, and TNFRSF1A may be used as molecular biomarkers of prognosis and immune infiltration in GBM patients in the future.
Subject(s)
Brain Neoplasms , COVID-19 , Chemokine CCL2 , Complement C1r , Glioblastoma , Receptors, Tumor Necrosis Factor, Type I , Biomarkers, Tumor/genetics , Brain Neoplasms/pathology , COVID-19/genetics , Chemokine CCL2/genetics , Complement C1r/genetics , Gene Expression Regulation, Neoplastic , Gene Regulatory Networks , Glioblastoma/diagnosis , Glioblastoma/pathology , Humans , Prognosis , Receptors, Tumor Necrosis Factor, Type I/geneticsABSTRACT
The rapid response to the COVID-19 pandemic led to unprecedented decreases in economic activities, thereby reducing the pollutant emissions. A random forest (RF) model was applied to determine the respective contributions of meteorology and anthropogenic emissions to the changes in air quality. The result suggested that the strict lockdown measures significantly decreased primary components such as Cr (-67 %) and Fe (-61 %) inPM2.5 (p<0.01), whereas the higher relative humidity (RH) andNH3 level and the lower air temperature (T) remarkably enhanced the production of secondary aerosol, including SO42- (29 %), NO3- (29 %), and NH4+ (21 %) (p<0.05). The positive matrix factorization (PMF) result suggested that the contribution ratios of secondary formation (SF), industrial process (IP), biomass burning (BB), coal combustion (CC), and road dust (RD) changed from 36 %, 27 %, 21 %, 12 %, and 4 % before the COVID-19 outbreak to 44 %, 20 %, 20 %, 9 %, and 7 %, respectively. The rapid increase in the contribution ratio derived from SF to PM2.5 implied that the intermittent haze events during the COVID-19 period were characterized by secondary aerosol pollution, which was mainly contributed by the unfavorable meteorological conditions and highNH3 level.