Analyzing the relationship between the cytokine profile of invasive breast carcinoma, its histopathological characteristics and metastasis to regional lymph nodes

Abstract This study was aimed at analyzing the relations of metastasis to regional lymph nodes (RLNs) with histopathological indicators of invasive breast carcinoma of no special type (IC-NST) and its cytokine profile. Enzyme-linked immunosorbent assays were performed to determine concentrations of...

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Autores principales: Alexander Autenshlyus, Sergey Arkhipov, Elena Mikhaylova, Igor Marinkin, Valentina Arkhipova, Nikolay Varaksin, Valentin Vavilin, Vyacheslav Lyahovich
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Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/5b71126638954a74835f62d44edde586
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spelling oai:doaj.org-article:5b71126638954a74835f62d44edde5862021-12-02T18:24:53ZAnalyzing the relationship between the cytokine profile of invasive breast carcinoma, its histopathological characteristics and metastasis to regional lymph nodes10.1038/s41598-021-90930-z2045-2322https://doaj.org/article/5b71126638954a74835f62d44edde5862021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90930-zhttps://doaj.org/toc/2045-2322Abstract This study was aimed at analyzing the relations of metastasis to regional lymph nodes (RLNs) with histopathological indicators of invasive breast carcinoma of no special type (IC-NST) and its cytokine profile. Enzyme-linked immunosorbent assays were performed to determine concentrations of IL-2, IL-6, IL-8, IL-10, IL-17, IL-18, IL-1β, IL-1Ra, TNF-α, IFN-γ, G-CSF, GM-CSF, VEGF-A, and MCP-1 in the culture supernatant of IC-NST samples from 48 female patients. Histopathological indicators (degree of tumor cell differentiation, mitoses, and others) and ER, PR, Her2/neu, Ki-67, and CD34 expression levels were determined. By means of three types of neural network models, it was shown that for different parameters of the output layer, different groups of parameters are involved that have predictive value regarding metastasis to RLNs. As a result of multi-dimensional cluster analysis, three clusters were formed with different cytokines profiles of IC-NST. Different correlations between indicators of cytokine production by IC-NST and its histopathological parameters were revealed in groups with different cytokine profiles. It was shown that at simultaneous evaluation of the production of even two cytokines, the importance of which relationship with metastasis was revealed by neural network modeling, can increase the probability of determining the presence of metastasis in the RLNs.Alexander AutenshlyusSergey ArkhipovElena MikhaylovaIgor MarinkinValentina ArkhipovaNikolay VaraksinValentin VavilinVyacheslav LyahovichNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Alexander Autenshlyus
Sergey Arkhipov
Elena Mikhaylova
Igor Marinkin
Valentina Arkhipova
Nikolay Varaksin
Valentin Vavilin
Vyacheslav Lyahovich
Analyzing the relationship between the cytokine profile of invasive breast carcinoma, its histopathological characteristics and metastasis to regional lymph nodes
description Abstract This study was aimed at analyzing the relations of metastasis to regional lymph nodes (RLNs) with histopathological indicators of invasive breast carcinoma of no special type (IC-NST) and its cytokine profile. Enzyme-linked immunosorbent assays were performed to determine concentrations of IL-2, IL-6, IL-8, IL-10, IL-17, IL-18, IL-1β, IL-1Ra, TNF-α, IFN-γ, G-CSF, GM-CSF, VEGF-A, and MCP-1 in the culture supernatant of IC-NST samples from 48 female patients. Histopathological indicators (degree of tumor cell differentiation, mitoses, and others) and ER, PR, Her2/neu, Ki-67, and CD34 expression levels were determined. By means of three types of neural network models, it was shown that for different parameters of the output layer, different groups of parameters are involved that have predictive value regarding metastasis to RLNs. As a result of multi-dimensional cluster analysis, three clusters were formed with different cytokines profiles of IC-NST. Different correlations between indicators of cytokine production by IC-NST and its histopathological parameters were revealed in groups with different cytokine profiles. It was shown that at simultaneous evaluation of the production of even two cytokines, the importance of which relationship with metastasis was revealed by neural network modeling, can increase the probability of determining the presence of metastasis in the RLNs.
format article
author Alexander Autenshlyus
Sergey Arkhipov
Elena Mikhaylova
Igor Marinkin
Valentina Arkhipova
Nikolay Varaksin
Valentin Vavilin
Vyacheslav Lyahovich
author_facet Alexander Autenshlyus
Sergey Arkhipov
Elena Mikhaylova
Igor Marinkin
Valentina Arkhipova
Nikolay Varaksin
Valentin Vavilin
Vyacheslav Lyahovich
author_sort Alexander Autenshlyus
title Analyzing the relationship between the cytokine profile of invasive breast carcinoma, its histopathological characteristics and metastasis to regional lymph nodes
title_short Analyzing the relationship between the cytokine profile of invasive breast carcinoma, its histopathological characteristics and metastasis to regional lymph nodes
title_full Analyzing the relationship between the cytokine profile of invasive breast carcinoma, its histopathological characteristics and metastasis to regional lymph nodes
title_fullStr Analyzing the relationship between the cytokine profile of invasive breast carcinoma, its histopathological characteristics and metastasis to regional lymph nodes
title_full_unstemmed Analyzing the relationship between the cytokine profile of invasive breast carcinoma, its histopathological characteristics and metastasis to regional lymph nodes
title_sort analyzing the relationship between the cytokine profile of invasive breast carcinoma, its histopathological characteristics and metastasis to regional lymph nodes
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/5b71126638954a74835f62d44edde586
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