Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods

Abstract Metastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the quantification of the key biomolecular compo...

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Autores principales:	Jianpeng Xue, Yang Pu, Jason Smith, Xin Gao, Chun Wang, Binlin Wu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e6b08328caa9475aad9ba3ea3b88c6e0
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spelling	oai:doaj.org-article:e6b08328caa9475aad9ba3ea3b88c6e02021-12-02T13:23:58ZIdentifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods10.1038/s41598-021-81945-72045-2322https://doaj.org/article/e6b08328caa9475aad9ba3ea3b88c6e02021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81945-7https://doaj.org/toc/2045-2322Abstract Metastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the quantification of the key biomolecular components within tissues and cells using recent optical spectroscopic techniques. The aim of this study was to develop a noninvasive label-free optical biopsy technique to retrieve the characteristic molecular information for detecting different metastatic potentials of prostate cancer cells. Herein we report using native fluorescence (NFL) spectroscopy along with machine learning (ML) to differentiate prostate cancer cells with different metastatic abilities. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key biomolecules that are correlated with metastatic potentials. The relative concentrations of the molecular spectral components were retrieved and used to classify the cancer cells with different metastatic potentials. A multi-class classification was performed using support vector machines (SVMs). The NFL spectral data were collected from three prostate cancer cell lines with different levels of metastatic potentials. The key biomolecules in the prostate cancer cells were identified to be tryptophan, reduced nicotinamide adenine dinucleotide (NADH) and hypothetically lactate as well. The cancer cells with different metastatic potentials were classified with high accuracy using the relative concentrations of the key molecular components. The results suggest that the changes in the relative concentrations of these key fluorophores retrieved from NFL spectra may present potential criteria for detecting prostate cancer cells of different metastatic abilities.Jianpeng XueYang PuJason SmithXin GaoChun WangBinlin WuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jianpeng Xue Yang Pu Jason Smith Xin Gao Chun Wang Binlin Wu Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods
description	Abstract Metastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the quantification of the key biomolecular components within tissues and cells using recent optical spectroscopic techniques. The aim of this study was to develop a noninvasive label-free optical biopsy technique to retrieve the characteristic molecular information for detecting different metastatic potentials of prostate cancer cells. Herein we report using native fluorescence (NFL) spectroscopy along with machine learning (ML) to differentiate prostate cancer cells with different metastatic abilities. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key biomolecules that are correlated with metastatic potentials. The relative concentrations of the molecular spectral components were retrieved and used to classify the cancer cells with different metastatic potentials. A multi-class classification was performed using support vector machines (SVMs). The NFL spectral data were collected from three prostate cancer cell lines with different levels of metastatic potentials. The key biomolecules in the prostate cancer cells were identified to be tryptophan, reduced nicotinamide adenine dinucleotide (NADH) and hypothetically lactate as well. The cancer cells with different metastatic potentials were classified with high accuracy using the relative concentrations of the key molecular components. The results suggest that the changes in the relative concentrations of these key fluorophores retrieved from NFL spectra may present potential criteria for detecting prostate cancer cells of different metastatic abilities.
format	article
author	Jianpeng Xue Yang Pu Jason Smith Xin Gao Chun Wang Binlin Wu
author_facet	Jianpeng Xue Yang Pu Jason Smith Xin Gao Chun Wang Binlin Wu
author_sort	Jianpeng Xue
title	Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods
title_short	Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods
title_full	Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods
title_fullStr	Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods
title_full_unstemmed	Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods
title_sort	identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e6b08328caa9475aad9ba3ea3b88c6e0
work_keys_str_mv	AT jianpengxue identifyingmetastaticabilityofprostatecancercelllinesusingnativefluorescencespectroscopyandmachinelearningmethods AT yangpu identifyingmetastaticabilityofprostatecancercelllinesusingnativefluorescencespectroscopyandmachinelearningmethods AT jasonsmith identifyingmetastaticabilityofprostatecancercelllinesusingnativefluorescencespectroscopyandmachinelearningmethods AT xingao identifyingmetastaticabilityofprostatecancercelllinesusingnativefluorescencespectroscopyandmachinelearningmethods AT chunwang identifyingmetastaticabilityofprostatecancercelllinesusingnativefluorescencespectroscopyandmachinelearningmethods AT binlinwu identifyingmetastaticabilityofprostatecancercelllinesusingnativefluorescencespectroscopyandmachinelearningmethods
_version_	1718393185631207424

Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods

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