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Background and aimsVarious studies have been previously conducted on the diagnosis of lymphadenopathy as benign or malignant, but the results vary. These studies did not describe the inter-rater agreement on the EUS features of lymphadenopathy. In this study, we evaluate the inter-rater agreement on EUS features and propose EUS diagnostic norms for lymphadenopathy based on inter-rater agreement.MethodA total of 68 lymph nodes subjected to EUS-fine needle aspiration (FNA) were reviewed by five endoscopic experts. The EUS features evaluated lymph node size, shape, border, margin, echogenicity, homogeneity, and the hilum of the lymph node. Inter-rater agreement (multi-rater kappa statics) was performed. We established new criteria using results with a high degree of inter-rater agreement from EUS features and compared them with the former criteria.ResultThere was a moderate agreement on shape, kappa (K) = 0.44 (95% confidence interval [CI]: 0.34-0.54), and fair agreement on echogenicity, homogeneity, border, and hilum of the lymph node, K (95% CI) = 0.33 (0.17-0.38), 0.34 (0.26-0.35), 0.22 (0.21-0.31), and 0.22 (0.11-0.26), respectively. This resulted in the establishment of new EUS diagnostic criteria using shape, long axis > 20 mm and short axis > 10 mm. New criteria were superior to old criteria (area under the curve 0.82 vs 0.52, P < 0.001).ConclusionEUS diagnostic criteria for lymphadenopathy based on inter-rater agreement were more accurate than old criteria. This result will be useful for the diagnosis of lymphadenopathy.

Ovarian clear cell carcinoma (CCC) is generally associated with chemoresistance and poor clinical outcome, even with early diagnosis; whereas high-grade serous carcinomas (SCs) and endometrioid carcinomas (ECs) are commonly chemosensitive at advanced stages. Although an integrated genomic analysis of SC has been performed, conclusive views on copy number and expression profiles for CCC are still limited. In this study, we performed single nucleotide polymorphism analysis with 57 epithelial ovarian cancers (31 CCCs, 14 SCs, and 12 ECs) and microarray expression analysis with 55 cancers (25 CCCs, 16 SCs, and 14 ECs). We then evaluated PIK3CA mutations and ARID1A expression in CCCs. SNP array analysis classified 13% of CCCs into a cluster with high frequency and focal range of copy number alterations (CNAs), significantly lower than for SCs (93%, P < 0.01) and ECs (50%, P = 0.017). The ratio of whole-arm to all CNAs was higher in CCCs (46.9%) than SCs (21.7%; P < 0.0001). SCs with loss of heterozygosity (LOH) of BRCA1 (85%) also had LOH of NF1 and TP53, and LOH of BRCA2 (62%) coexisted with LOH of RB1 and TP53. Microarray analysis classified CCCs into three clusters. One cluster (CCC-2, n = 10) showed more favorable prognosis than the CCC-1 and CCC-3 clusters (P = 0.041). Coexistent alterations of PIK3CA and ARID1A were more common in CCC-1 and CCC-3 (7/11, 64%) than in CCC-2 (0/10, 0%; P < 0.01). Being in cluster CCC-2 was an independent favorable prognostic factor in CCC. In conclusion, CCC was characterized by a high ratio of whole-arm CNAs; whereas CNAs in SC were mainly focal, but preferentially caused LOH of well-known tumor suppressor genes. As such, expression profiles might be useful for sub-classification of CCC, and might provide useful information on prognosis.

Statins exert atheroprotective effects through the induction of specific transcriptional factors in multiple organs. In endothelial cells, statin-dependent atheroprotective gene up-regulation is mediated by Kruppel-like factor (KLF) family transcription factors. To dissect the mechanism of gene regulation, we sought to determine molecular targets by performing microarray analyses of human umbilical vein endothelial cells (HUVECs) treated with pitavastatin, and KLF4 was determined to be the most highly induced gene. In addition, it was revealed that the atheroprotective genes induced with pitavastatin, such as nitric oxide synthase 3 (NOS3) and thrombomodulin (THBD), were suppressed by KLF4 knockdown. Myocyte enhancer factor-2 (MEF2) family activation is reported to be involved in pitavastatin-dependent KLF4 induction. We focused on MEF2C among the MEF2 family members and identified a novel functional MEF2C binding site 148 kb upstream of the KLF4 gene by chromatin immunoprecipitation along with deep sequencing (ChIP-seq) followed by luciferase assay. By applying whole genome and quantitative chromatin conformation analysis {chromatin interaction analysis with paired end tag sequencing (ChIA-PET), and real time chromosome conformation capture (3C) assay}, we observed that the MEF2C-bound enhancer and transcription start site (TSS) of KLF4 came into closer spatial proximity by pitavastatin treatment. 3D-Fluorescence in situ hybridization (FISH) imaging supported the conformational change in individual cells. Taken together, dynamic chromatin conformation change was shown to mediate pitavastatin-responsive gene induction in endothelial cells.

It is suggested that histone modifications and/or histone variants influence the nucleosomal DNA length. We sequenced both ends of mononucleosomal and dinucleosomal DNA fragments of the filamentous fungus Aspergillus fumigatus, after treatment with the histone deacetylase inhibitor trichostatin A (TSA). After mapping the DNA fragments to the genome, we identified >7 million mononucleosome positions and >7 million dinucleosome positions. We showed that the distributions of the lengths of the mononucleosomal DNA fragments after 15-min and 30-min treatments with micrococcal nuclease (MNase) showed a single peak at 168 nt and 160 nt, respectively. The distributions of the lengths of the dinucleosomal DNA fragments after 15-min- and 30-min-treatment with MNase showed a single peak at 321 nt and 306 nt, respectively. The nucleosomal DNA fragments obtained from the TSA-treated cells were significantly longer than those obtained from the untreated cells. On the other hand, most of the genes did not undergo any change after treatment. Between the TSA-treated and untreated cells, only 77 genes had >or=2-fold change in expression levels. In addition, our results showed that the locations where mononucleosomes were frequently detected were conserved between the TSA-treated cells and untreated cells in the gene promoters (lower density of the nucleosomes). However, these locations were less conserved in the bodies (higher density of the nucleosomes) of genes with >or=2-fold changes. Our findings indicate that TSA influences the nucleosome positions, especially of the regions with high density of the nucleosomes by elongation of the nucleosomal DNA. However, most of the nucleosome positions are conserved in the gene promoters, even after treatment with TSA, because of the low density of nucleosomes in the gene promoters.