Background Ovarian mucinous carcinoma is a disease that requires unique treatment. But for a long time, guidelines for ovarian serous carcinoma have been used for the treatment of ovarian mucinous carcinoma. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with ovarian mucinous adenocarcinoma. Methods In this study, patients initially diagnosed with ovarian mucinous adenocarcinoma from 2004 to 2015 were screened from the Surveillance, Epidemiology, and End Results (SEER) database, and divided into the training group and the validation group at a ratio of 7:3. Independent risk factors for OS and CSS were determined by multivariate Cox regression analysis, and nomograms were constructed and validated. Results In this study, 1309 patients with ovarian mucinous adenocarcinoma were finally screened and randomly divided into 917 cases in the training group and 392 cases in the validation group according to a 7:3 ratio. Multivariate Cox regression analysis showed that the independent risk factors of OS were age, race, T_stage, N_stage, M_stage, grade, CA125, and chemotherapy. Independent risk factors of CSS were age, race, marital, T_stage, N_stage, M_stage, grade, CA125, and chemotherapy. According to the above results, the nomograms of OS and CSS in ovarian mucinous adenocarcinoma were constructed. In the training group, the C-index of the OS nomogram was 0.845 (95% CI: 0.821–0.869) and the C-index of the CSS nomogram was 0.862 (95%CI: 0.838–0.886). In the validation group, the C-index of the OS nomogram was 0.843 (95% CI: 0.810–0.876) and the C-index of the CSS nomogram was 0.841 (95%CI: 0.806–0.876). The calibration curve showed the consistency between the predicted results and the actual results, indicating the high accuracy of the nomogram. Conclusion The nomogram provides 3-year and 5-year OS and CSS predictions for patients with ovarian mucinous adenocarcinoma, which helps clinicians predict the prognosis of patients and formulate appropriate treatment plans.
Epilepsy is a common and severe neurological disorder in which impaired glucose metabolism leads to changes in neuronal excitability that slow or promote the development of epilepsy. Leptin and adiponectin are important mediators regulating glucose metabolism in the peripheral and central nervous systems. Many studies have reported a strong association between epilepsy and these two adipokines involved in multiple signaling cascades and glucose metabolism. Due to the complex regulatory mechanisms between them and various signal activation networks, their role in epilepsy involves many aspects, including the release of inflammatory mediators, oxidative damage, and neuronal apoptosis. This paper aims to summarize the signaling pathways involved in leptin and adiponectin and the regulation of glucose metabolism from the perspective of the pathogenesis of epilepsy. In particular, we discuss the dual effects of leptin in epilepsy and the relationship between antiepileptic drugs and changes in the levels of these two adipokines. Clinical practitioners may need to consider these factors in evaluating clinical drugs. Through this review, we can better understand the specific involvement of leptin and adiponectin in the pathogenesis of epilepsy, provide ideas for further exploration, and bring about practical significance for the treatment of epilepsy, especially for the development of personalized treatment according to individual metabolic characteristics.
Objective: This paper aims to identify potentially related genes of coronary artery disease (CAD) and determine the relationship with immune cell infiltration.Materials and methods: Three datasets (GSE42148, GSE98583, GSE12288) containing coronary heart disease and healthy people are downloaded from the Gene Expression Database (GEO). Use the "limma" package in the "R" software to screen the differentially expressed genes (DEGs) in the three sets of data respectively, use the "pheatmap" package to construct a heatmap of the DEGs, and draw the venn maps of the three sets of differential genes. The DAVID database is used for the analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The String platform and Cytoscape software are used to perform protein interaction analysis on differential genes, create Protein-Protein Interaction (PPI) networks on DEGs, and screen hub genes. The CIBERSORTx web version tool performs immune cell infiltration analysis on sample data sets.Results: GSE42148 screened out 227 differential genes, of which 161 were up-regulated and 66 were down-regulated (p<0.05, |logFC|>1.0); GSE98583 screened out 254 differential genes, of which 141 were up-regulated and 113 were down-regulated (p<0.05, |logFC|>1.0). 68 differential genes were screened in GSE12288, of which 33 were up-regulated and 35 were down-regulated (p<0.05, |logFC|>0.38). There are 8 differential genes in the intersection of the three groups of DEGs, namely MAP7, RIPK4, BAALC, CA6, CXCL14, HIST1H2AE, MS4A3, GPR15. With the help of enrichment analysis and the construction of PPI networks, HIST1H2AE and CXCL14 were finally determined as the key biomarkers of CAD. Immune infiltration analysis suggests that B cells naive, macrophages M0 and T cells CD4 naïve are closely related to the pathogenesis of CAD.Conclusion: HIST1H2AE and CXCL14 can be used as key biomarkers of CAD. Inflammation and immune cell infiltration play a key role in the occurrence and development of CAD.
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