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Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer

New breast cancer biomarkers have been sought for better tumor characterization and treatment. Among these putative markers, there is Biglycan (BGN). BGN is a class I small leucine-rich proteoglycan family of proteins characterized by a protein core with leucine-rich repeats. The objective of this s...

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Autores principales: Thiesen, Ana Paula, Mielczarski, Bruna, Savaris, Ricardo Francalacci
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042358/
https://www.ncbi.nlm.nih.gov/pubmed/36972253
http://dx.doi.org/10.1371/journal.pone.0282176
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author Thiesen, Ana Paula
Mielczarski, Bruna
Savaris, Ricardo Francalacci
author_facet Thiesen, Ana Paula
Mielczarski, Bruna
Savaris, Ricardo Francalacci
author_sort Thiesen, Ana Paula
collection PubMed
description New breast cancer biomarkers have been sought for better tumor characterization and treatment. Among these putative markers, there is Biglycan (BGN). BGN is a class I small leucine-rich proteoglycan family of proteins characterized by a protein core with leucine-rich repeats. The objective of this study is to compare the protein expression of BGN in breast tissue with and without cancer, using immunohistochemical technique associated with digital histological score (D-HScore) and supervised deep learning neural networks (SDLNN). In this case-control study, 24 formalin–fixed, paraffin-embedded tissues were obtained for analysis. Normal (n = 9) and cancerous (n = 15) tissue sections were analyzed by immunohistochemistry using BGN monoclonal antibody (M01-Abnova) and 3,3’-Diaminobenzidine (DAB) as the chromogen. Photomicrographs of the slides were analysed with D-HScore, using arbitrary DAB units. Another set (n = 129) with higher magnification without ROI selection, was submitted to the inceptionV3 deep neural network image embedding recognition model. Next, supervised neural network analysis, using stratified 20 fold cross validation, with 200 hidden layers, ReLu activation, and regularization at α = 0.0001 were applied for SDLNN. The sample size was calculated for a minimum of 7 cases and 7 controls, having a power = 90%, an α error = 5%, and a standard deviation of 20, to identify a decrease from the average of 40 DAB units (control) to 4 DAB units in cancer. BGN expression in DAB units [median (range)] was 6.2 (0.8 to 12.4) and 27.31 (5.3 to 81.7) in cancer and normal breast tissue, respectively, using D-HScore (p = 0.0017, Mann-Whitney test). SDLNN classification accuracy was 85.3% (110 out of 129; 95%CI = 78.1% to 90.3%). BGN protein expression is reduced in breast cancer tissue, compared to normal tissue.
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spelling pubmed-100423582023-03-28 Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer Thiesen, Ana Paula Mielczarski, Bruna Savaris, Ricardo Francalacci PLoS One Research Article New breast cancer biomarkers have been sought for better tumor characterization and treatment. Among these putative markers, there is Biglycan (BGN). BGN is a class I small leucine-rich proteoglycan family of proteins characterized by a protein core with leucine-rich repeats. The objective of this study is to compare the protein expression of BGN in breast tissue with and without cancer, using immunohistochemical technique associated with digital histological score (D-HScore) and supervised deep learning neural networks (SDLNN). In this case-control study, 24 formalin–fixed, paraffin-embedded tissues were obtained for analysis. Normal (n = 9) and cancerous (n = 15) tissue sections were analyzed by immunohistochemistry using BGN monoclonal antibody (M01-Abnova) and 3,3’-Diaminobenzidine (DAB) as the chromogen. Photomicrographs of the slides were analysed with D-HScore, using arbitrary DAB units. Another set (n = 129) with higher magnification without ROI selection, was submitted to the inceptionV3 deep neural network image embedding recognition model. Next, supervised neural network analysis, using stratified 20 fold cross validation, with 200 hidden layers, ReLu activation, and regularization at α = 0.0001 were applied for SDLNN. The sample size was calculated for a minimum of 7 cases and 7 controls, having a power = 90%, an α error = 5%, and a standard deviation of 20, to identify a decrease from the average of 40 DAB units (control) to 4 DAB units in cancer. BGN expression in DAB units [median (range)] was 6.2 (0.8 to 12.4) and 27.31 (5.3 to 81.7) in cancer and normal breast tissue, respectively, using D-HScore (p = 0.0017, Mann-Whitney test). SDLNN classification accuracy was 85.3% (110 out of 129; 95%CI = 78.1% to 90.3%). BGN protein expression is reduced in breast cancer tissue, compared to normal tissue. Public Library of Science 2023-03-27 /pmc/articles/PMC10042358/ /pubmed/36972253 http://dx.doi.org/10.1371/journal.pone.0282176 Text en © 2023 Thiesen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Thiesen, Ana Paula
Mielczarski, Bruna
Savaris, Ricardo Francalacci
Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer
title Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer
title_full Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer
title_fullStr Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer
title_full_unstemmed Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer
title_short Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer
title_sort deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042358/
https://www.ncbi.nlm.nih.gov/pubmed/36972253
http://dx.doi.org/10.1371/journal.pone.0282176
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