Literature DB >> 17220511

Analysis of p53 protein expression levels on ovarian cancer tissue microarray using automated quantitative analysis elucidates prognostic patient subsets.

A Psyrri1, P Kountourakis, Z Yu, C Papadimitriou, S Markakis, R L Camp, T Economopoulos, M A Dimopoulos.   

Abstract

BACKGROUND: p53 protein is regarded as a valuable prognostic marker in cancer with a potential use as a molecular target. Here, we sought to determine the prognostic value of p53 in ovarian cancer using a novel method of compartmentalized in situ protein analysis. PATIENTS AND METHODS: A tissue array composed of 141 advanced stage ovarian cancers uniformly treated was constructed. For evaluation of p53 protein expression, we used an immunofluorescence-based method of automated in situ quantitative measurement of protein analysis (AQUA).
RESULTS: High nuclear p53 expression levels were associated with better outcome for overall survival (OS) (P = 0.0023) and disease-free survival (P = 0.0338) at 5-years. High cytoplasmic p53 expression levels were associated with better outcome for OS (P = 0.0002). In multivariable analysis, high nuclear and high cytoplasmic p53 level with International Federation of Gynecology and Obstetrics (FIGO) stage were the most significant predictor variables for OS and high nuclear p53 level with FIGO stage were the significant predictor variables for disease-free survival.
CONCLUSIONS: Assessment of the prognostic value of p53 protein levels using conventional immunohistochemistry is limited by the nonquantitative nature of the method. AQUA provides precise estimation of p53 protein levels and was able to elucidate the association of p53 protein levels and ovarian cancer prognosis.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17220511     DOI: 10.1093/annonc/mdl479

Source DB:  PubMed          Journal:  Ann Oncol        ISSN: 0923-7534            Impact factor:   32.976


  8 in total

1.  The role of p53 as a surrogate marker for chemotherapeutical responsiveness in ovarian cancer.

Authors:  Dirk O Bauerschlag; Christian Schem; Marion T Weigel; Constantin Von Kaisenberg; Alexander Strauss; Thomas Bauknecht; Nicolai Maass; Ivo Meinhold-Heerlein
Journal:  J Cancer Res Clin Oncol       Date:  2010-01       Impact factor: 4.553

2.  Multianalyte tests for the early detection of cancer: speedbumps and barriers.

Authors:  Michael A Tainsky; Madhumita Chatterjee; Nancy K Levin; Sorin Draghici; Judith Abrams
Journal:  Biomark Insights       Date:  2007-07-10

3.  High frequency of putative ovarian cancer stem cells with CD44/CK19 coexpression is associated with decreased progression-free intervals in patients with recurrent epithelial ovarian cancer.

Authors:  Ming Liu; Gil Mor; Huan Cheng; Xue Xiang; Pei Hui; Thomas Rutherford; Gang Yin; David L Rimm; Jennie Holmberg; Ayesha Alvero; Dan-Arin Silasi
Journal:  Reprod Sci       Date:  2012-11-20       Impact factor: 3.060

4.  SERS-based nanobiosensing for ultrasensitive detection of the p53 tumor suppressor.

Authors:  Fabio Domenici; Anna Rita Bizzarri; Salvatore Cannistraro
Journal:  Int J Nanomedicine       Date:  2011-09-19

5.  A cytohistological study of p53 overexpression in ovarian neoplasms.

Authors:  Monisha Choudhury; Seema Goyal; Mukta Pujani; Meenu Pujani
Journal:  South Asian J Cancer       Date:  2012-10

6.  Beside P53 and PTEN: Identification of molecular alterations of the RAS/MAPK and PI3K/AKT signaling pathways in high-grade serous ovarian carcinomas to determine potential novel therapeutic targets.

Authors:  Shuhui Chen; Elisa Cavazza; Catherine Barlier; Julia Salleron; Pierre Filhine-Tresarrieu; Céline Gavoilles; Jean-Louis Merlin; Alexandre Harlé
Journal:  Oncol Lett       Date:  2016-09-02       Impact factor: 2.967

7.  Modest effect of p53, EGFR and HER-2/neu on prognosis in epithelial ovarian cancer: a meta-analysis.

Authors:  P de Graeff; A P G Crijns; S de Jong; M Boezen; W J Post; E G E de Vries; A G J van der Zee; G H de Bock
Journal:  Br J Cancer       Date:  2009-06-09       Impact factor: 7.640

8.  A chemotherapy response classifier based on support vector machines for high-grade serous ovarian carcinoma.

Authors:  Chao-Yang Sun; Tie-Fen Su; Na Li; Bo Zhou; En-Song Guo; Zong-Yuan Yang; Jing Liao; Dong Ding; Qin Xu; Hao Lu; Li Meng; Shi-Xuan Wang; Jian-Feng Zhou; Hui Xing; Dan-Hui Weng; Ding Ma; Gang Chen
Journal:  Oncotarget       Date:  2016-01-19
  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.