Background
S100A2, a member of the S100 protein family, is abnormally expressed and plays a vital role in multiple cancers. However, little is known about the clinical significance of S100A2 in endometrial carcinoma.
Methods
Clinicopathological data were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Expression Omnibus (GEO), and Clinical Proteomic Tumor Analysis Consortium (CPTAC). First, the expression and prognostic value of different S100 family members in endometrial carcinoma were evaluated. Subsequently, the Kaplan–Meier plotter and Cox regression analysis were used to assess the prognostic significance of S100A2, while the association between S100A2 expression and clinical characteristics in endometrial carcinoma was also analyzed using logistic regression. A receiver operating characteristic (ROC) curve and a nomogram were constructed. The putative underlying cellular mechanisms were explored using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and gene set enrichment analysis (GSEA).
Results
Our results revealed that S100A2 expression was significantly higher in endometrial carcinoma tissue than in non-cancerous tissue at both the mRNA and protein levels. Analysis of Kaplan–Meier plotter data revealed that patients with high S100A2 expression had shorter overall survival (OS) and disease specific survival (DSS) compared with those of patients with low S100A2 expression. Multivariate Cox analysis further confirmed that high S100A2 expression was an independent risk factor for OS in patients with endometrial carcinoma. Other clinicopathologic features found to be related to worse prognosis in endometrial carcinoma included age, clinical stage, histologic grade, and tumor invasion. Importantly, ROC analysis also confirmed that S100A2 has a high diagnostic value in endometrial carcinoma. KEGG enrichment analysis and GSEA revealed that the estrogen and IL-17 signaling pathways were significantly upregulated in the high S100A2 expression group, in which estrogen response, JAK-STAT3, K-Ras, and TNFα/NF-κB were differentially enriched.
Conclusions
S100A2 plays an important role in endometrial carcinoma progression and may represent an independent diagnostic and prognostic biomarker for endometrial carcinoma.
The vision is the only way that people receives image information. Retinex is the theory and method of image enhancement that is based on experiments and analysis of vision. Retinex enhances the image by processing reflection R and incident light L from the image S to obtain better vision quality. It is described that the image appears to reflect or transmit more or less of the light, varying from black to white for image and from black to colorless for transparent volume in this paper. The calculation shows the improvement of global and local image enhancement by Retinex algorithm for X-Ray medical images. Finally the analysis and comparison by experiments are discussed.
In recent years, research on fault diagnosis of grids is becoming increasingly important, because it ensures the stable operation of power systems, and meets high demands on the power quality by power customers. In this paper, an intelligent approach for fault diagnosis of distributed power generation systems is proposed based on maximum overlap discrete wavelet transform and artificial neural network. In the proposed scheme, the fault data are first collected. Then, maximum overlap discrete wavelet transform is applied to detect faults and extract features. Finally, artificial neural network is constructed to classify the fault types. Results show that the method can identify faults precisely, classify fault types accurately, and is not affected by the change of electrical parameters. In addition, compared with several existing intelligent diagnosis techniques, the proposed approach can provide better fault classification accuracy. To evaluate the performance, the algorithm is verified by the case of the modified simulation model of IEEE-13 bus standard system.
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