Impact of IGF-I and CYP19 Gene Polymorphisms on the Survival of Patients With Metastatic Prostate Cancer

2006 ◽  
Vol 24 (13) ◽  
pp. 1982-1989 ◽  
Author(s):  
Norihiko Tsuchiya ◽  
Lizhong Wang ◽  
Hiroyoshi Suzuki ◽  
Takehiko Segawa ◽  
Hisami Fukuda ◽  
...  

Purpose The prognosis of metastatic prostate cancer significantly differs among individuals. While various clinical and biochemical prognostic factors for survival have been suggested, the progression and response to treatment of those patients may also be defined by host genetic factors. In this study, we evaluated genetic polymorphisms as prognostic predictors of metastatic prostate cancer. Patients and Methods One hundred eleven prostate cancer patients with bone metastasis at the diagnosis were enrolled in this study. Thirteen genetic polymorphisms were genotyped using polymerase chain reaction-restriction fragment length polymorphism or an automated sequencer with a genotyping software. Results Among the polymorphisms, the long allele (over 18 [CA] repeats) of insulin-like growth factor-I (IGF-I) and the long allele (over seven [TTTA] repeats) of cytochrome P450 (CYP) 19 were significantly associated with a worse cancer-specific survival (P = .016 and .025 by logrank test, respectively). The presence of the long allele of either the IGF-I or CYP19 polymorphisms was an independent risk factor for death (P = .019 or .026, respectively). Furthermore, the presence of the long allele of both the IGF-I and CYP19 polymorphisms was a stronger predictor for survival (P = .001). Conclusion The prognosis of metastatic prostate cancer patients is suggested to be influenced by intrinsic genetic factors. The IGF-I (CA) repeat and CYP19 (TTTA) repeat polymorphisms may be novel predictors in prostate cancer patients with bone metastasis at the diagnosis.

2020 ◽  
Vol 38 (10) ◽  
pp. 993-996
Author(s):  
Toyokazu Hayakawa ◽  
Ken-ichi Tabata ◽  
Hideyasu Tsumura ◽  
Shogo Kawakami ◽  
Takeo Katakura ◽  
...  

ESMO Open ◽  
2021 ◽  
Vol 6 (5) ◽  
pp. 100261
Author(s):  
A.A. Kulkarni ◽  
N. Rubin ◽  
A. Tholkes ◽  
S. Shah ◽  
C.J. Ryan ◽  
...  

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 518
Author(s):  
Da-Chuan Cheng ◽  
Te-Chun Hsieh ◽  
Kuo-Yang Yen ◽  
Chia-Hung Kao

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions.


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