Background: Cuproptosis is a new type of cell death that induces protein toxic stress and eventually leads to cell death. Hence, regulating cuproptosis in tumor cells is a new therapeutic approach. However, studies on cuproptosis-related long noncoding RNA (lncRNA) in head and neck squamous cell carcinoma (HNSC) have not been found. This study aimed to explore the cuproptosis-related lncRNAs prognostic marker and their relationship to immune microenvironment in HNSC by using bioinformatics methods.Methods: RNA sequencing, genomic mutations, and clinical data of TCGA_HNSC were downloaded from The Cancer Genome Atlas. HNSC patients were randomly assigned to either a training group or a validation cohort. The least absolute shrinkage and selection operator Cox regression and multivariate Cox regression models were used to determine the prognostic model in the training cohort, and its independent prognostic effect was further confirmed in the validation and entire cohorts.Results: Based on previous literature, we collected 19 genes associated with cuproptosis. Afterward, 783 cuproptosis-related lncRNAs were obtained through coexpression. Cox model revealed and constructed eight cuproptosis-related lncRNAs prognostic marker (AL132800.1, AC090587.1, AC079160.1, AC011462.4, AL157888.1, GRHL3-AS1, SNHG16, and AC021148.2). Patients were divided into high- and low-risk groups based on the median risk score. The Kaplan–Meier survival curve revealed that the overall survival between the high- and low-risk groups was statistically significant. The receiver operating characteristic curve and principal component analysis demonstrated the accurate prognostic ability of the model. Univariate and multivariate Cox regression analysis showed that risk score was an independent prognostic factor. In addition, we used multivariate Cox regression to establish a nomogram of the predictive power of prognostic markers. The tumor mutation burden showed significant differences between the high- and low-risk groups. HNSC patients in the high-risk group responded better to immunotherapy than those in the low-risk group. We also found that risk scores were significantly associated with drug sensitivity in HNSC.Conclusion: In summary, our study identified eight cuprotosis-related lncRNAs signature of HNSC as the prognostic predictor, which may be promising biomarkers for predicting the benefit of HNSC immunotherapy as well as drug sensitivity.
AimEvidence linking trace minerals and periodontitis is limited. To investigate the relationship between trace minerals (selenium, manganese, lead, cadmium, and mercury) and periodontitis, data from the National Health and Nutrition Examination Survey (NHANES) were analyzed after accounting for potential confounding factors. No known studies have explored the relationship between these five trace minerals and periodontitis.Materials and methodsA total of 4,964 participants who had undergone a full-mouth periodontal examination and laboratory tests for five trace minerals were studied in a cross-sectional study. Clinical attachment loss (CAL) and periodontitis grading were used to measure periodontitis severity. Linear and logistic regression models were used to evaluate the association between trace minerals and periodontitis. Further subgroup analyses were performed.ResultsBlood lead and cadmium levels were positively associated with mean CAL, and blood selenium was negatively associated with mean CAL; however, blood mercury, blood manganese, and mean CAL were not significantly associated. The association between trace minerals and mean CAL was more significant in males, the elderly, and patients with diabetes. There was a threshold effect between blood cadmium levels and mean CAL. Among the Black population, the relationship between blood cadmium levels and mean CAL followed an inverted U-shaped curve. There was a saturation effect in the study of blood lead in people aged 45–59 years old.ConclusionOur study highlighted that blood selenium, lead, and cadmium levels were significantly associated with periodontitis in a nationally representative sample of United States adults.
Two trapezoidal plane mirrors of 240 mm in length were fabricated by ion beam figuring (IBF) technology for application in a bendable KB focusing system. The correction of surface height and slope errors in different spatial frequency ranges of the mirrors was studied systematically. After one to two iterations of IBF, the figure height errors of the vertical focusing mirror (VFM) and horizontal focusing mirror (HFM) were improved from 32.4 and 65.4 nm to 2.7 and 7.2 nm (RMS), respectively. If the best-fit sphere of the surface profile was subtracted, the residual two-dimensional height errors were only 1.1 and 1.2 nm (RMS). The slope errors in the low spatial frequency range were corrected much faster than the middle frequency ones (f = ∼1 mm−1), which make the low-frequency slope error much smaller. After IBF, the two-dimensional slope errors of the two mirrors calculated with a spatial interval of 1 and 10 mm were reduced to approximately 0.29 and 0.08 μrad, respectively. Full spatial frequency characterization of the VFM before and after IBF showed that the low-frequency figure errors (f < 1 mm−1) were significantly reduced while the middle- and high-frequency morphologies (f > 1–2 mm−1) remain almost the same as before figuring. The fabricated plane mirrors were applied in the hard X-ray micro-focusing beamline in the Shanghai Synchrotron Radiation Facility (SSRF), which realized a focal spot of 2.4 μm × 2.8 μm at 10 keV.
Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has a serious threat to human health. Oral candidiasis (OC) may be one of the causes of morbidity in severe COVID-19 patients. However, there is currently no treatment for oral candidiasis and COVID-19 (OC/COVID-19). The purpose of this study was to use text mining and data analysis to investigate the target genes for treatment and explore potential therapeutic drugs for OC/COVID-19. Methods: We used the text mining tool pubmed2ensembl to detect genes associated with OC, and the dataset GSE164805 was used for the data analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the two intersection genes using the Database of Annotation, Visualization and Integrated Discovery (DAVID) platform. The protein-protein interaction (PPI) networks were constructed by STRING software, and gene module analysis was performed using Molecular Complex Detection (MCODE), a plug-in in Cytoscape. The most significant genes were selected as hub genes and their functions and pathways were analyzed using Metascape. We revealed the upstream pathway activity of the hub genes. The drug-gene interaction database (DGIdb) and the traditional Chinese medicines integrated database (TCMID) were used to discover potential drugs for the treatment of OC/COVID-19. Results: The analysis indicated that there were 2869 differentially expressed genes (DEGs) in GSE164805. We identified 161 unique genes associated with oral candidiasis through text mining. A total of 20 intersection genes were identified as the therapeutic targets for OC/COVID-19. Based on the bioinformatics analysis, nine genes (TNF, IL1B, IFNG, CSF2, ELANE, CCL2, MMP9, CXCR4, and IL1A) were identified as hub genes that were mainly enriched in the IL-17 signaling pathway, TNF signaling pathway, AGE-RAGE signaling pathway in diabetic complications and NOD-like receptor signaling pathway. We identified four of the nine genes that target five existing drugs, including BKT140, mavorixafor, sivelestat, canakinumab, and rilonacept. Furthermore, twenty herb ingredients were also screened as potential drugs. Conclusion: In this study, TNF, IL1B, IFNG, CSF2, ELANE, CCL2, MMP9, CXCR4, and IL1A were potentially key genes involved in the treatment of OC/COVID-19. Taken together five drugs and twenty herb ingredients were identified as potential therapeutic agents for OC/COVID-19 treatment and management.
Osteoporosis is a systemic skeletal disease that can easily lead to bone fractures. Berberine has been shown to be effective in treating osteoporosis. This study was conducted to identify the potential mechanism of berberine in treating this complaint. We screened potential targets of berberine and identified the osteoporosis-related differentially expressed genes (DEGs) in the microarray dataset GSE56815. Protein–protein interaction (PPI) network construction, hub targets identification, and pathway enrichment were carried out to find the potential targets. Molecular docking and molecular dynamics studies were performed to verify the combination of berberine with its treatment-related central targets. In addition, SwissADME preliminarily evaluated the physicochemical properties of berberine. Through data mining, 23 osteoporosis-related targets of berberine were selected. PPI and module analyses suggested that AKT1, MAPK1, ESR1, AR, TP53, and PTGS2 are the core targets of berberine. Docking and molecular dynamics studies showed that berberine could stably bind to core proteins to form a protein–ligand complex. The enrichment analysis showed that the estrogen signaling pathway and thyroid hormone signaling pathway play important roles in curing osteoporosis. To sum up, berberine primarily acts on AKT1, MAPK1, ESR1, AR, TP53, and PTGS2, mainly regulating the estrogen and thyroid hormone signaling pathways to treat osteoporosis in a multi-target, multi-pathway, and multi-system manner.
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