Biomarker expression in cervical intraepithelial neoplasia: potential progression predictive factors for low-grade lesions

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金沢大学附属病院病理部

The aim of this study was to reveal whether 3 biomarkers (p16INK4a, ProEx C, and human papilloma virus DNA) are useful in the diagnosis of cervical intraepithelial neoplasia and whether they could predict disease progression of cervical intraepithelial neoplasia-1. We analyzed 252 cervical specimens: nondysplastic mucosa (n = 9), cervical intraepithelial neoplasia (n = 229), and squamous cell carcinoma (n = 14). Immunostaining for p16INK4a and ProEx C, and the hybridcapture II assay for human papilloma virus DNA were performed. Expression of p16INK4a and staining for ProEx C were significantly higher in intraepithelial neoplasia 2/3 (96%-100%) than in nondysplastic mucosa (11%) or intraepithelial neoplasia 1 (40%-53%). Human papilloma virus DNA was detected in 69% of intraepithelial neoplasia-1, 95% of intraepithelial neoplasia-2, and 100% of intraepithelial neoplasia 3. Of 99 patients with intraepithelial neoplasia 1 for whom follow-up data was available, 62 (73%) showed spontaneous regression, 17 (20%) demonstrated persistent low-grade lesion, and 7 (7%) progressed to intraepithelial neoplasia 2/3. Expressions of p16INK4a and staining with ProEx C were significantly higher in the progression group than in the regression group. Testing for p16INK4a and ProEx C was sensitive (86%) and moderately specific (60% and 61%, respectively) in predicting the progression of cervical intraepithelial neoplasia 1. Human papilloma virus DNA testing was highly sensitive (100%) but less specific (37%). In conclusion, this study revealed that p16INK4a and ProEx C are useful biomarkers for the diagnosis of cervical intraepithelial neoplasia, and have potential as predictors of progression of low-grade lesions. © 2010 Elsevier Inc. All rights reserved.

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詳細情報 詳細情報について

  • CRID
    1050845760914131200
  • NII論文ID
    120003141445
  • NII書誌ID
    AA00666191
  • ISSN
    00468177
  • Web Site
    http://hdl.handle.net/2297/26567
  • 本文言語コード
    en
  • 資料種別
    journal article
  • データソース種別
    • IRDB
    • CiNii Articles

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