Abstract:Background
Greater ovulatory years is associated with increased ovarian cancer risk. Although ovulation leads to an acute pro-inflammatory local environment, how long-term exposure to ovulation impacts ovarian carcinogenesis is not fully understood. Thus, we examined the association between gene expression profiles of ovarian tumors and lifetime ovulatory years to enhance understanding of associated biological pathways.
Methods
RNA sequencing data … Show more
“…Given the connection between ovulation and ovarian cancer 53 – 55 and considering the previously discussed evidence of increased replication stress in the fimbrial ends ( Figures 7A and 7B ), we next focused on exploring the possible correlation between menopausal status and 53BP1 staining levels in the fallopian tubes. To conduct this analysis, we performed 53BP1 staining on sections of the fimbrial ends obtained from premenopausal (pre-M) and post-menopausal (post-M) women, both with and without BRCA1 mutations ( Figure 7E ).…”
“…Given the connection between ovulation and ovarian cancer 53 – 55 and considering the previously discussed evidence of increased replication stress in the fimbrial ends ( Figures 7A and 7B ), we next focused on exploring the possible correlation between menopausal status and 53BP1 staining levels in the fallopian tubes. To conduct this analysis, we performed 53BP1 staining on sections of the fimbrial ends obtained from premenopausal (pre-M) and post-menopausal (post-M) women, both with and without BRCA1 mutations ( Figure 7E ).…”
“…In our study, the MRGs were obtained from the Reactome database, which is an emerging database and has been widely used in several studies (65)(66)(67)(68)(69). However, it did not include some mitophagy receptors such as BNIP3, p62, OPTN and so on.…”
BackgroundAccumulating evidence indicates that mitophagy is crucial for the development of diabetic nephropathy (DN). However, little is known about the key genes involved. The present study is to identify the potential mitophagy-related genes (MRGs) in DN.MethodsFive datasets were obtained from the Gene Expression Omnibus (GEO) database and were split into the training and validation set. Then the differentially expressed MRGs were screened and further analyzed for GO and KEGG enrichment. Next, three algorithms (SVM-RFE, LASSO and RF) were used to identify hub genes. The ROC curves were plotted based on the hub genes. We then used the CIBERSORT algorithm to assess the infiltration of 22 types of immune cells and explore the correlation between hub genes and immune cells. Finally, the Nephroseq V5 tool was used to analyze the correlation between hub genes and GFR in DN patients.ResultsCompared with the tubulointerstitium, the expression of MRGs was more noticeably varied in the glomeruli. Twelve DE-MRGs were identified in glomerular samples, of which 11 genes were down-regulated and only MFN1 was up-regulated. GO and KEGG analysis indicated that several enrichment terms were associated with changes in autophagy. Three genes (MFN1, ULK1 and PARK2) were finally determined as potential hub genes by three algorithms. In the training set, the AUROC of MFN1, ULK1 and PARK2 were 0.839, 0.906 and 0.842. However, the results of the validation set demonstrated that MFN1 and PARK2 had no significant difference in distinguishing DN samples from healthy controls, while the AUROC of ULK1 was 0.894. Immune infiltration analysis using CIBERSORT showed that ULK1 was positively related to neutrophils, whereas negatively related to M1 and M2 macrophages. Finally, ULK1 was positively correlated with GFR in Nephroseq database.ConclusionsULK1 is a potential biomarker for DN and may influence the development of diabetic nephropathy by regulating mitophagy.
“…For each case, treatment naïve primary FFPE ovarian tumor blocks were obtained and a 1.5 mm diameter tumor tissue core was isolated from tumor areas circled by the gynecologic pathologist. DNA and RNA were simultaneously extracted using the Qiagen All‐Prep RNA Isolation Kit as described 14 . In brief, we used the Illumina TruSeq™ RNA Exome Library Preparation Kit (Illumina Inc.) following the manufacturer's protocol to prepare the RNA‐sequencing (RNA‐Seq) libraries and sequenced on the multiple NextSeq 500 High‐output sequencing runs to target about 25 M pairs of 75‐base reads per sample.…”
Section: Methodsmentioning
confidence: 99%
“…All data were then transformed using voom . Using principal component analyses, we did not observed any batch or study effects 14 . Thus, we used pooled data from NHS, NHSII, and NECC.…”
BackgroundAspirin use has been associated with reduced ovarian cancer risk, yet the underlying biological mechanisms are not fully understood. To gain mechanistic insights, we assessed the association between prediagnosis low and regular‐dose aspirin use and gene expression profiles in ovarian tumors.MethodsRNA sequencing was performed on high‐grade serous, poorly differentiated, and high‐grade endometrioid ovarian cancer tumors from the Nurses' Health Study (NHS), NHSII, and New England Case–Control Study (n = 92 cases for low, 153 cases for regular‐dose aspirin). Linear regression identified differentially expressed genes associated with aspirin use, adjusted for birth decade and cohort. False discovery rates (FDR) were used to account for multiple testing and gene set enrichment analysis was used to identify biological pathways.ResultsNo individual genes were significantly differentially expressed in ovarian tumors in low or regular‐dose aspirin users accounting for multiple comparisons. However, current versus never use of low‐dose aspirin was associated with upregulation of immune pathways (e.g., allograft rejection, FDR = 5.8 × 10−10; interferon‐gamma response, FDR = 2.0 × 10−4) and downregulation of estrogen response pathways (e.g., estrogen response late, FDR = 4.9 × 10−8). Ovarian tumors from current regular aspirin users versus never users were also associated with upregulation in interferon pathways (FDR <1.5 × 10−4) and downregulation of multiple extracellular matrix (ECM) architecture pathways (e.g., ECM organization, 4.7 × 10−8).ConclusionOur results suggest low and regular‐dose aspirin may impair ovarian tumorigenesis in part via enhancing adaptive immune response and decreasing metastatic potential supporting the likely differential effects on ovarian carcinogenesis and progression by dose of aspirin.
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