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Published in: Breast Cancer Research 1/2021

Open Access 01-12-2021 | Breast Cancer | Short report

Robust inflammatory breast cancer gene signature using nonparametric random forest analysis

Authors: Alaa Zare, Lynne-Marie Postovit, John Maringa Githaka

Published in: Breast Cancer Research | Issue 1/2021

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Abstract

Inflammatory breast cancer (IBC) is a rare, aggressive cancer found in all the molecular breast cancer subtypes. Despite extensive previous efforts to screen for transcriptional differences between IBC and non-IBC patients, a robust IBC-specific molecular signature has been elusive. We report a novel IBC-specific gene signature (59 genes; G59) that achieves 100% accuracy in discovery and validation samples (45/45 correct classification) and remarkably only misclassified one sample (60/61 correct classification) in an independent dataset. G59 is independent of ER/HER2 status, molecular subtypes and is specific to untreated IBC samples, with most of the genes being enriched for plasma membrane cellular component proteins, interleukin (IL), and chemokine signaling pathways. Our finding suggests the existence of an IBC-specific molecular signature, paving the way for the identification and validation of targetable genomic drivers of IBC.
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Metadata
Title
Robust inflammatory breast cancer gene signature using nonparametric random forest analysis
Authors
Alaa Zare
Lynne-Marie Postovit
John Maringa Githaka
Publication date
01-12-2021
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2021
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-021-01467-y

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