Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models

Bone is the most common site of distant metastasis from malignant tumors, with the highest prevalence observed in breast and prostate cancers. Such bone metastases (BM) cause many painful skeletal-related events, such as severe bone pain, pathological fractures, spinal cord compression, and hypercal...

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Autores principales:	Somayah Albaradei, Mahmut Uludag, Maha A. Thafar, Takashi Gojobori, Magbubah Essack, Xin Gao
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	metastasis bone gene experession machine learining hub genes genetic diagnostic tool Genetics QH426-470
Acceso en línea:	https://doaj.org/article/a69388be46b44f9f88ee2a2d50595ec0
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id	oai:doaj.org-article:a69388be46b44f9f88ee2a2d50595ec0
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spelling	oai:doaj.org-article:a69388be46b44f9f88ee2a2d50595ec02021-11-10T07:44:07ZPredicting Bone Metastasis Using Gene Expression-Based Machine Learning Models1664-802110.3389/fgene.2021.771092https://doaj.org/article/a69388be46b44f9f88ee2a2d50595ec02021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.771092/fullhttps://doaj.org/toc/1664-8021Bone is the most common site of distant metastasis from malignant tumors, with the highest prevalence observed in breast and prostate cancers. Such bone metastases (BM) cause many painful skeletal-related events, such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia, with adverse effects on life quality. Many bone-targeting agents developed based on the current understanding of BM onset’s molecular mechanisms dull these adverse effects. However, only a few studies investigated potential predictors of high risk for developing BM, despite such knowledge being critical for early interventions to prevent or delay BM. This work proposes a computational network-based pipeline that incorporates a ML/DL component to predict BM development. Based on the proposed pipeline we constructed several machine learning models. The deep neural network (DNN) model exhibited the highest prediction accuracy (AUC of 92.11%) using the top 34 featured genes ranked by betweenness centrality scores. We further used an entirely separate, “external” TCGA dataset to evaluate the robustness of this DNN model and achieved sensitivity of 85%, specificity of 80%, positive predictive value of 78.10%, negative predictive value of 80%, and AUC of 85.78%. The result shows the models’ way of learning allowed it to zoom in on the featured genes that provide the added benefit of the model displaying generic capabilities, that is, to predict BM for samples from different primary sites. Furthermore, existing experimental evidence provides confidence that about 50% of the 34 hub genes have BM-related functionality, which suggests that these common genetic markers provide vital insight about BM drivers. These findings may prompt the transformation of such a method into an artificial intelligence (AI) diagnostic tool and direct us towards mechanisms that underlie metastasis to bone events.Somayah AlbaradeiSomayah AlbaradeiMahmut UludagMaha A. ThafarMaha A. ThafarTakashi GojoboriMagbubah EssackXin GaoFrontiers Media S.A.articlemetastasisbonegene experessionmachine learininghub genesgenetic diagnostic toolGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	metastasis bone gene experession machine learining hub genes genetic diagnostic tool Genetics QH426-470
spellingShingle	metastasis bone gene experession machine learining hub genes genetic diagnostic tool Genetics QH426-470 Somayah Albaradei Somayah Albaradei Mahmut Uludag Maha A. Thafar Maha A. Thafar Takashi Gojobori Magbubah Essack Xin Gao Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models
description	Bone is the most common site of distant metastasis from malignant tumors, with the highest prevalence observed in breast and prostate cancers. Such bone metastases (BM) cause many painful skeletal-related events, such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia, with adverse effects on life quality. Many bone-targeting agents developed based on the current understanding of BM onset’s molecular mechanisms dull these adverse effects. However, only a few studies investigated potential predictors of high risk for developing BM, despite such knowledge being critical for early interventions to prevent or delay BM. This work proposes a computational network-based pipeline that incorporates a ML/DL component to predict BM development. Based on the proposed pipeline we constructed several machine learning models. The deep neural network (DNN) model exhibited the highest prediction accuracy (AUC of 92.11%) using the top 34 featured genes ranked by betweenness centrality scores. We further used an entirely separate, “external” TCGA dataset to evaluate the robustness of this DNN model and achieved sensitivity of 85%, specificity of 80%, positive predictive value of 78.10%, negative predictive value of 80%, and AUC of 85.78%. The result shows the models’ way of learning allowed it to zoom in on the featured genes that provide the added benefit of the model displaying generic capabilities, that is, to predict BM for samples from different primary sites. Furthermore, existing experimental evidence provides confidence that about 50% of the 34 hub genes have BM-related functionality, which suggests that these common genetic markers provide vital insight about BM drivers. These findings may prompt the transformation of such a method into an artificial intelligence (AI) diagnostic tool and direct us towards mechanisms that underlie metastasis to bone events.
format	article
author	Somayah Albaradei Somayah Albaradei Mahmut Uludag Maha A. Thafar Maha A. Thafar Takashi Gojobori Magbubah Essack Xin Gao
author_facet	Somayah Albaradei Somayah Albaradei Mahmut Uludag Maha A. Thafar Maha A. Thafar Takashi Gojobori Magbubah Essack Xin Gao
author_sort	Somayah Albaradei
title	Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models
title_short	Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models
title_full	Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models
title_fullStr	Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models
title_full_unstemmed	Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models
title_sort	predicting bone metastasis using gene expression-based machine learning models
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/a69388be46b44f9f88ee2a2d50595ec0
work_keys_str_mv	AT somayahalbaradei predictingbonemetastasisusinggeneexpressionbasedmachinelearningmodels AT somayahalbaradei predictingbonemetastasisusinggeneexpressionbasedmachinelearningmodels AT mahmutuludag predictingbonemetastasisusinggeneexpressionbasedmachinelearningmodels AT mahaathafar predictingbonemetastasisusinggeneexpressionbasedmachinelearningmodels AT mahaathafar predictingbonemetastasisusinggeneexpressionbasedmachinelearningmodels AT takashigojobori predictingbonemetastasisusinggeneexpressionbasedmachinelearningmodels AT magbubahessack predictingbonemetastasisusinggeneexpressionbasedmachinelearningmodels AT xingao predictingbonemetastasisusinggeneexpressionbasedmachinelearningmodels
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Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models

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