ArticlePathology oncology research : POR2019
Unsupervised Clustering of Immunohistochemical Markers to Define High-Risk Endometrial Cancer.
Article in Pathology oncology research : POR, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- In-depth comparative analysis of IlluminaMolecular ecology resources · 2019Article
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Authors and funding
5 authors.
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Abstract
Considerable heterogeneity exists in outcomes of early endometrial cancer (EC) according to the type but also the histological grading. Our goal was to describe the immunohistochemical profiles of type I EC according to grades and type II EC, to identify groups of interacting proteins using principal component analysis (PCA) and unsupervised clustering. We studied 13 immunohistochemical markers (steroid receptors, pro/anti-apoptotic proteins, metalloproteinases (MMP) and tissue inhibitor of metalloproteinase (TIMP), and CD44 isoforms known for their role in endometrial pathology. Co-expressed proteins associated with the type, grade and outcome of EC were determined by PCA and unsupervised clustering. PCA identified three functional groups of proteins from 43 tissue samples (38 type I and 5 type II EC): the first was characterized by p53 expression; the second by MMPs, bcl-2, PR B and CD44v6; and the third by ER alpha, PR A, TIMP-2 and CD44v3. Unsupervised clustering found two main clusters of proteins, with both type I grade 3 and type II EC exhibiting the same cluster profile. PCA and unsupervised clustering of immunohistochemical markers in EC contribute to a better comprehension and classification of the disease.
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