A prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer
Abstract Safe and noninvasive methods for breast cancer screening with improved accuracy are urgently needed. Volatile organic compounds (VOCs) in biological samples such as breath and blood have been investigated as noninvasive novel markers of cancer. We investigated volatile organic compounds in...
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Nature Portfolio
2021
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oai:doaj.org-article:5b74586ff87a40af8f9e5238cdd419962021-12-02T18:01:48ZA prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer10.1038/s41598-021-99396-52045-2322https://doaj.org/article/5b74586ff87a40af8f9e5238cdd419962021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99396-5https://doaj.org/toc/2045-2322Abstract Safe and noninvasive methods for breast cancer screening with improved accuracy are urgently needed. Volatile organic compounds (VOCs) in biological samples such as breath and blood have been investigated as noninvasive novel markers of cancer. We investigated volatile organic compounds in urine to assess their potential for the detection of breast cancer. One hundred and ten women with biopsy-proven breast cancer and 177 healthy volunteers were enrolled. The subjects were divided into two groups: a training set and an external validation set. Urine samples were collected and analyzed by gas chromatography and mass spectrometry. A predictive model was constructed by multivariate analysis, and the sensitivity and specificity of the model were confirmed using both a training set and an external set with reproducibility tests. The training set included 60 breast cancer patients (age 34–88 years, mean 60.3) and 60 healthy controls (age 34–81 years, mean 58.7). The external validation set included 50 breast cancer patients (age 35–85 years, mean 58.8) and 117 healthy controls (age 18–84 years, mean 51.2). One hundred and ninety-one compounds detected in at least 80% of the samples from the training set were used for further analysis. The predictive model that best-detected breast cancer at various clinical stages was constructed using a combination of two of the compounds, 2-propanol and 2-butanone. The sensitivity and specificity in the training set were 93.3% and 83.3%, respectively. Triplicated reproducibility tests were performed by randomly choosing ten samples from each group, and the results showed a matching rate of 100% for the breast cancer patient group and 90% for the healthy control group. Our prediction model using two VOCs is a useful complement to the current diagnostic tools. Further studies inclusive of benign tumors and non-breast malignancies are warranted.Shoko KureSera SatoiToshihiko KitayamaYuta NagaseNobuo NakanoMarina YamadaNoboru UchiyamaSatoshi MiyashitaShinya IidaHiroyuki TakeiMasao MiyashitaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) |
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Medicine R Science Q Shoko Kure Sera Satoi Toshihiko Kitayama Yuta Nagase Nobuo Nakano Marina Yamada Noboru Uchiyama Satoshi Miyashita Shinya Iida Hiroyuki Takei Masao Miyashita A prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer |
| description |
Abstract Safe and noninvasive methods for breast cancer screening with improved accuracy are urgently needed. Volatile organic compounds (VOCs) in biological samples such as breath and blood have been investigated as noninvasive novel markers of cancer. We investigated volatile organic compounds in urine to assess their potential for the detection of breast cancer. One hundred and ten women with biopsy-proven breast cancer and 177 healthy volunteers were enrolled. The subjects were divided into two groups: a training set and an external validation set. Urine samples were collected and analyzed by gas chromatography and mass spectrometry. A predictive model was constructed by multivariate analysis, and the sensitivity and specificity of the model were confirmed using both a training set and an external set with reproducibility tests. The training set included 60 breast cancer patients (age 34–88 years, mean 60.3) and 60 healthy controls (age 34–81 years, mean 58.7). The external validation set included 50 breast cancer patients (age 35–85 years, mean 58.8) and 117 healthy controls (age 18–84 years, mean 51.2). One hundred and ninety-one compounds detected in at least 80% of the samples from the training set were used for further analysis. The predictive model that best-detected breast cancer at various clinical stages was constructed using a combination of two of the compounds, 2-propanol and 2-butanone. The sensitivity and specificity in the training set were 93.3% and 83.3%, respectively. Triplicated reproducibility tests were performed by randomly choosing ten samples from each group, and the results showed a matching rate of 100% for the breast cancer patient group and 90% for the healthy control group. Our prediction model using two VOCs is a useful complement to the current diagnostic tools. Further studies inclusive of benign tumors and non-breast malignancies are warranted. |
| format |
article |
| author |
Shoko Kure Sera Satoi Toshihiko Kitayama Yuta Nagase Nobuo Nakano Marina Yamada Noboru Uchiyama Satoshi Miyashita Shinya Iida Hiroyuki Takei Masao Miyashita |
| author_facet |
Shoko Kure Sera Satoi Toshihiko Kitayama Yuta Nagase Nobuo Nakano Marina Yamada Noboru Uchiyama Satoshi Miyashita Shinya Iida Hiroyuki Takei Masao Miyashita |
| author_sort |
Shoko Kure |
| title |
A prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer |
| title_short |
A prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer |
| title_full |
A prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer |
| title_fullStr |
A prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer |
| title_full_unstemmed |
A prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer |
| title_sort |
prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/5b74586ff87a40af8f9e5238cdd41996 |
| work_keys_str_mv |
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