Additional file 1: of Physical interaction of STAT1 isoforms with TGF-β receptors leads to functional crosstalk between two signaling pathways in epithelial ovarian cancer

Table S1. PCR primer and siRNA sequence used in experiments. Table S2. Comparison of pSTAT1-Y701, pSTAT1-S727, and STAT1 immunostaining in the ovarian tissues. Table S3. The expression of pSTAT1-Y701, pSTAT1-S727, and STAT1 in human ovarian tissues. (DOCX 50 kb

Additional file 2: of Physical interaction of STAT1 isoforms with TGF-β receptors leads to functional crosstalk between two signaling pathways in epithelial ovarian cancer

Figure S1. STAT1 expression in human epithelial-type ovarian tumors. Tissue microarray shows the immunohistochemical (IHC) staining of pSTAT1-Y701, pSTAT1-S727, and total STAT1 in serous, mucinous, endometrioid, transitional cell, and metastatic tumors. Figure S2. STAT1 expression in ovarian surface epithelial cells. a STAT1 mRNA expression detected by quantitative RT-PCR. b STAT1 protein expression detected by immunoblotting. c Densitometric analysis of the gels. Figure S3. Effect of TGF-β1 on the phosphorylation of STAT1. (DOCX 1304 kb

DataSheet1_Transcriptomic analysis identifies the shared diagnostic biomarkers and immune relationship between Atherosclerosis and abdominal aortic aneurysm based on fatty acid metabolism gene set.docx

Background:Epidemiological research has demonstrated that there is a connection between lipid metabolism disorder and an increased risk of developing arteriosclerosis (AS) and abdominal aortic aneurysm (AAA). However, the precise relationship between lipid metabolism, AS, and AAA is still not fully understood. The objective of this study was to examine the pathways and potential fatty acid metabolism-related genes (FRGs) that are shared between AS and AAA.Methods:AS- and AAA-associated datasets were retrieved from the Gene Expression Omnibus (GEO) database, and the limma package was utilized to identify differentially expressed FRGs (DFRGs) common to both AS and AAA patients. Functional enrichment analysis was conducted on the (DFRGs), and a protein-protein interaction (PPI) network was established. The selection of signature genes was performed through the utilization of least absolute shrinkage and selection operator (LASSO) regression and random forest (RF). Subsequently, a nomogram was developed using the results of the screening process, and the crucial genes were validated in two separate external datasets (GSE28829 and GSE17901) as well as clinical samples. In the end, single-sample gene set enrichment analysis (ssGSEA) was utilized to assess the immune cell patterns in both AS and AAA. Additionally, the correlation between key crosstalk genes and immune cell was evaluated.Results:In comparison to control group, both AS and AAA patients exhibited a decrease in fatty acid metabolism score. We found 40 DFRGs overlapping in AS and AAA, with lipid and amino acid metabolism critical in their pathogenesis. PCBD1, ACADL, MGLL, BCKDHB, and IDH3G were identified as signature genes connecting AS and AAA. Their expression levels were confirmed in validation datasets and clinical samples. The analysis of immune infiltration showed that neutrophils, NK CD56dim cells, and Tem cells are important in AS and AAA development. Correlation analysis suggested that these signature genes may be involved in immune cell infiltration.Conclusion:The fatty acid metabolism pathway appears to be linked to the development of both AS and AAA. Furthermore, PCBD1, ACADL, MGLL, BCKDHB, and IDH3G have the potential to serve as diagnostic markers for patients with AS complicated by AAA.</p