Transcriptome analysis of an AKT inhibitor-resistant endometrial cancer cell line.

BACKGROUND: Drug resistance in endometrial cancer (EC) is a serious problem and a barrier to improving prognosis. The PI3K/AKT/mTOR pathway is highly activated in EC and can serve as a potential therapeutic target. Inhibitors against AKT have been developed, but resistance to these inhibitors is a concern. This study aimed to establish AKT inhibitor resistant cell lines and identify differentially expressed genes (DEGs) between parental and AKT inhibitor resistant cell lines to understand the mechanism of drug resistance to AKT inhibitors in EC. METHODS: The sensitivity of eight EC cell lines to AKT inhibitor was analyzed. One of them was used to establish a drug-resistant cell line. DEGs were examined using RNA sequencing (RNA-seq). Furthermore, DEGs were comprehensively analyzed to identify hub genes. Hub genes were evaluated using quantitative real-time polymerase chain reaction. RESULTS: RNA-seq identified 617 DEGs. Hub genes were selected using bioinformatics analysis. The top 10 hub genes were TNF, CDH1, CCND1, COL1A1, CDH2, ICAM1, CAV1, THBS1, NCAM1, and CDKN2A. Relative mRNA expression was significantly upregulated for TNF, CDH1, CCND1, THBS1, p16INK4a, and p14ARF and significantly downregulated for CDH2, ICAM1, and NCAM1 in borussertib-resistant EC cell line. CONCLUSIONS: Drug resistance to AKT inhibitors may depend on genes related to cell adhesion-mediated resistance and transforming growth factor ß signaling.

Morphometrical Differences among Endometrial Endometrioid Carcinoma Grade 1, Grade 3, and Serous Carcinoma in Endometrial Liquid-Based Cytology Preparations.

INTRODUCTION: In Japan, the direct smearing preparation (conventional preparation) has been widely used for cytological examination of the endometrium. Problems with the conventional preparation can be dissolved by liquid-based cytology (LBC) preparation. The Yokohama System is a method for reporting endometrial cytology, but the system lumps cancers together and does not distinguish between histological types. The objective of this study was to clarify morphometrical differences among endometrial endometrioid carcinoma grade 1 (G1), grade 3 (G3), and serous carcinoma (Serous) by image analysis of endometrial LBC. METHODS: Using Papanicolaou smears prepared by LBC after sampling with a brush from 32 G1, 16 G3, and 16 Serous patients, image analysis was performed concerning the following 11 items: (1) number of layers of cluster, (2) area of cluster, (3) perimeter of cluster, (4) roundness of cluster, (5) complexity of cluster, (6) area of nucleus, (7) perimeter of nucleus, (8) roundness of nucleus, (9) complexity of nucleus, (10) area of nucleolus, and (11) nucleolus/nucleus (N/N) ratio. The data were statistically compared among G1, G3, and Serous. RESULTS: Significant differences were observed in the number of layers of cluster (G1G3G3, G1>Serous), complexity of cluster (G1Serous), and N/N ratio (G1>G3, G3

Morphological Differences between Liquid-Based Cytology and Conventional Preparation in Endometrial Endometrioid Carcinoma Grade 1 and Grade 3, and the Differentiation of Grades in Each Method.

INTRODUCTION: Direct smearing preparation (conventional preparation [CP]) has been widely used for endometrial cytology in Japan. In CP, sampling and screening errors are problematic. In liquid-based cytology preparation (LBC), the problems of CP can be solved. But there is a problem that cytological findings of LBC are different from those of CP. The purpose of this study was to evaluate the differences of morphological findings of endometrial cytology between LBC and CP, and the usefulness of the endometrial LBC to differentiate endometrioid carcinoma grade 1 (G1) from grade 3 (G3). METHODS: Thirteen cases of endometrioid carcinoma G1, and 5 cases of G3 collected by the Softcyte device and prepared by LBC and CP (split specimen) were used. We focused on the following items: (1) the number of clusters per cm2, (2) the number of layers of clusters, (3) area of clusters, (4) perimeter of clusters, (5) roundness of clusters, (6) complexity of clusters, (7) area of nucleus, (8) perimeter of nucleus, (9) roundness of nucleus, (10) complexity of nucleus, (11) area of nucleolus, and (12) nucleolus-nucleus ratio (N/N). RESULTS: Compared with CP, the number of clusters and layers of the clusters in LBC were significantly larger in G1. The area and perimeters of the clusters and the nucleus were significant smaller, and the N/N ratio was greater in LBC than that in CP in both G1 and G3. Regarding morphological differences between G1 and G3 in LBC and CP, the number of layers was significantly larger in G1 than in G3 in LBC and CP. The area of the clusters in LBC was significantly larger in G1 than in G3. The area and perimeters of the nucleus in CP and the area of the nucleolus and N/N ratio in LBC and CP were significantly smaller in G1 than in G3. CONCLUSION: In the endometrial cytology, it became clear that the cell image was different between LBC and CP and between G1 and G3. By microscopic examination understanding the characteristics of the cell image in LBC, endometrial LBC could be useful to diagnose endometrial carcinoma.

Outer Cutoff Value for the Box-Counting Method for Fractal Analysis of the Nucleus Using Kirsch Edge Detection.

OBJECTIVE: The complexity of chromatin (i.e., irregular geometry and distribution) is one of the important factors considered in the cytological diagnosis of cancer. Fractal analysis with Kirsch edge detection is a known technique to detect irregular geometry and distribution in an image. We examined the outer cutoff value for the box-counting (BC) method for fractal analysis of the complexity of chromatin using Kirsch edge detection. MATERIALS: The following images were used for the analysis: (1) image of the nucleus for Kirsch edge detection measuring 97 × 122 pix (10.7 × 13.4 µm) with a Feret diameter of chromatin mesh (n = 50) measuring 17.3 ± 1.8 pix (1.9 ± 0.5 µm) and chromatin network distance (n = 50) measuring 4.4 ± 1.6 pix (0.49 ± 0.18 µm), and (2) sample images for Kirsch edge detection with varying diameters (10.4, 15.9, and 18.1 µm) and network width of 0.4 µm. METHODS: Three types of bias that can affect the outcomes of fractal analysis in cytological diagnosis were defined. (1) Nuclear position bias: images of 9 different positions generated by shifting the original position of the nucleus in the middle of a 256 × 256 pix (28.1 µm) square frame in 8 compass directions. (2) Nuclear rotation bias: images of 8 different rotations obtained by rotating the original position of the nucleus in 45° increments (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°). (3) Nuclear size bias: images of varying size (diameter: 190 pix [10.4 µm], 290 pix [15.9 µm], and 330 pix [18.1 µm]) with the same mesh pattern (network width: 8 pix [0.4 µm]) within a 512 × 512 pix square. Different outer cutoff values for the BC method (256, 128, 64, 32, 16, and 8 pix) were applied for each bias to assess the fractal dimension and to compare the coefficient of variation (CV). RESULTS: The BC method with the outer cutoff value of 32 pix resulted in the least variation of fractal dimension. Specifically, with the cutoff value of 32 pix, the CV of nuclear position bias, nuclear rotation bias, and nuclear size bias were <1% (0.1, 0.4, and 0.3%, respectively), with no significant difference between the position and rotation bias (p = 0.19). Our study suggests that the BC method with the outer cutoff value of 32 pix is suitable for the analysis of the complexity of chromatin with chromatin mesh.