1650: Novel Gene Expression Markers in Renal Tumors Using Rt-PCR on Fixed Tissues Useful in Differentiating Histologic Subtypes

2004 ◽  
Vol 171 (4S) ◽  
pp. 436-436
Author(s):  
John A. Petros ◽  
Audry N. Schuetz ◽  
Andrew N. Young ◽  
Q. Yin Goen ◽  
So Dug Lim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Tumors ◽  
Rt Pcr ◽  
Novel Gene ◽  
Histologic Subtypes

P035 Quantitative RT-PCR gene expression in the special histologic subtypes of invasive breast cancer

The Breast ◽  
2015 ◽  
Vol 24 ◽  
pp. S38-S39 ◽  
Author(s):  
V. Tan ◽  
F. Baehner ◽  
C. Yoshizawa ◽  
J. Anderson ◽  
H. Bailey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Invasive Breast Cancer ◽  
Rt Pcr ◽  
Histologic Subtypes

Quantitative gene expression by RT-PCR in the tubular, cribriform, mucinous, and papillary histologic subtypes of invasive breast cancer.

2013 ◽  
Vol 31 (26_suppl) ◽  
pp. 10-10
Author(s):  
Vivian Tan ◽  
Carl N. Yoshizawa ◽  
Joseph Michael Anderson ◽  
Steven Shak ◽  
Frederick L. Baehner
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Recurrence Score ◽  
Rt Pcr ◽  
Cancer Subtypes ◽  
Gene Group ◽  
Quantitative Expression ◽  
Assay Methods ◽  
Continuous Range ◽  
Histologic Subtypes

10 Background: ER+ special histologic breast cancer subtypes are prognostically significant. Here we report the special histologic subtypes of ER+ breast carcinoma and associated patterns of observed gene expression as measured by the 21 gene OncotypeDX assay. Methods: All tumors from 6/1/04-5/31/12 were included in analyses. Central path used WHO criteria. Ductal NOS (DC), tubular (TC), cribriform (CC), mucinous (MC) and papillary (PC) carcinomas were included. Quantitative expression of 16 cancer related genes was measured on a scale from 2 to 15 (relative to reference genes) where a 1 unit increment is associated with ~2-fold change in expression. Recurrence Score (RS) was calculated as published. Descriptive stats for the RS and individual genes [ER, PR, invasion gene group (IGG) and proliferation gene group (PGG)] were obtained. Comparisons of means were adjusted to control the overall error rate under any complete or partial null hypothesis. Results: DC accounted for 95.1%, TC 0.7% , CC 0.3%, MC 3.1% and PC 0.7% of 246,555 cases. For all types a wide continuous range of RS was noted. DC had the highest mean RS, followed in decreasing order by MC, TC, CC and PC. PC had the highest ER and PC and CC the highest PR. TC had the lowest ER. ER was not different between CC and MC but PR was. The proportion with ER+/PR- phenotype was different among the subtypes: TC (8.1%) and CC (7.1%) had the lowest incidence whereas MC (12.9%) and PC (10.3%) were more similar to DC (14.1%). TC had lowest PGG expression. MC and PC had lower IGG expression compared to other subtypes. Conclusions: The special subtypes had a wide continuous range of RS: DC had the highest mean RS, followed in decreasing order by MC, TC, CC and PC. PC had the highest ER and PC and CC the highest PR. TC had the lowest ER (may reflect submission bias). ER was not different between CC and MC but PR was. The proportion with ER+/PR- phenotype was different among the subtypes: TC and CC had the lowest incidence, MC and PC were more similar to DC. TC had the lowest PGG expression. MC and PC had lower IGG expression. [Table: see text]

The Protective Effect of Metformin against Oxandrolone-Induced Infertility in Male Rats

2020 ◽  
Vol 26 ◽  
Author(s):  
Abdulqader Fadhil Abed ◽  
Yazun Bashir Jarrar ◽  
Hamzeh J Al-Ameer ◽  
Wajdy Al-Awaida ◽  
Su-Jun Lee
Keyword(s):  
Gene Expression ◽  
Male Infertility ◽  
Protective Effect ◽  
Histological Examination ◽  
Sperm Count ◽  
Serum Testosterone ◽  
Male Rats ◽  
P Value ◽  
Protective Effects ◽  
Rt Pcr

Background: Oxandrolone is a synthetic testosterone analogue that is widely used among bodybuilders and athletes. However, oxandrolone causes male infertility. Recently, it was found that metformin reduces the risk of infertility associated with diabetes mellitus. Aim: This study aimed to investigate the protective effects of metformin against oxandrolone-induced infertility in male rats. Methods: Rats continuously received one of four treatments (n=7) over 14 days: control DMSO administration, oxandrolone administration, metformin administration, or co-administration of oxandrolone and metformin. Doses were equivalent to those used for human treatment. Subsequently, testicular and blood samples were collected for morphological, biochemical, and histological examination. In addition, gene expression of the testosterone synthesizing enzyme CYP11A1 was analyzed in the testes using RT-PCR. Results: Oxandrolone administration induced male infertility by significantly reducing relative weights of testes by 48%, sperm count by 82%, and serum testosterone levels by 96% (ANOVA, P value < 0.05). In addition, histological examination determined that oxandrolone caused spermatogenic arrest which was associated with 2-fold downregulation of testicular CYP11A1 gene expression. However, co-administration of metformin with oxandrolone significantly ameliorated toxicological alterations induced by oxandrolone exposure (ANOVA, P value < 0.05). Conclusion: Metformin administration protected against oxandrolone-induced infertility in male rats. Further clinical studies are needed to confirm the protective effect of metformin against oxandrolone-induced infertility among athletes.

scholarly journals Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kwang-Hyun Uhm ◽  
Seung-Won Jung ◽  
Moon Hyung Choi ◽  
Hong-Kyu Shin ◽  
Jae-Ik Yoo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Kidney Cancer ◽  
Renal Tumor ◽  
Renal Tumors ◽  
Abdominal Computed Tomography ◽  
Wide Range ◽  
End To End ◽  
Multi Phase ◽  
Histologic Subtypes

AbstractIn 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors including both benign and malignant tumors on multi-phase CT. Our model is a unified framework to simultaneously identify lesions and classify subtypes for the diagnosis without manual intervention. We trained and tested the model using CT data from 308 patients who underwent nephrectomy for renal tumors. The model achieved an area under the curve (AUC) of 0.889, and outperformed radiologists for most subtypes. We further validated the model on an independent dataset of 184 patients from The Cancer Imaging Archive (TCIA). The AUC for this dataset was 0.855, and the model performed comparably to the radiologists. These results indicate that our model can achieve similar or better diagnostic performance than radiologists in differentiating a wide range of renal tumors on multi-phase CT.

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