Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC)
(1) Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Although various serum enzymes have been utilized for the diagnosis and prognosis of HCC, the currently available biomarkers lack the sensitivity needed to detect HCC at early stages and ac...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/14/9/2061 |
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| author | Taehee Lee Piper A. Rawding Jiyoon Bu Sunghee Hyun Woosun Rou Hongjae Jeon Seokhyun Kim Byungseok Lee Luke J. Kubiatowicz Dawon Kim Seungpyo Hong Hyuksoo Eun |
| author_facet | Taehee Lee Piper A. Rawding Jiyoon Bu Sunghee Hyun Woosun Rou Hongjae Jeon Seokhyun Kim Byungseok Lee Luke J. Kubiatowicz Dawon Kim Seungpyo Hong Hyuksoo Eun |
| author_sort | Taehee Lee |
| collection | DOAJ |
| description | (1) Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Although various serum enzymes have been utilized for the diagnosis and prognosis of HCC, the currently available biomarkers lack the sensitivity needed to detect HCC at early stages and accurately predict treatment responses. (2) Methods: We utilized our highly sensitive cell-free DNA (cfDNA) detection system, in combination with a machine learning algorithm, to provide a platform for improved diagnosis and prognosis of HCC. (3) Results: cfDNA, specifically alpha-fetoprotein (AFP) expression in captured cfDNA, demonstrated the highest accuracy for diagnosing malignancies among the serum/plasma biomarkers used in this study, including AFP, aspartate aminotransferase, alanine aminotransferase, albumin, alkaline phosphatase, and bilirubin. The diagnostic/prognostic capability of cfDNA was further improved by establishing a cfDNA score (cfD<sub>HCC</sub>), which integrated the total plasma cfDNA levels and cfAFP-DNA expression into a single score using machine learning algorithms. (4) Conclusion: The cfD<sub>HCC</sub> score demonstrated significantly improved accuracy in determining the pathological features of HCC and predicting patients’ survival outcomes compared to the other biomarkers. The results presented herein reveal that our cfDNA capture/analysis platform is a promising approach to effectively utilize cfDNA as a biomarker for the diagnosis and prognosis of HCC. |
| first_indexed | 2024-03-10T04:19:07Z |
| format | Article |
| id | doaj.art-94e9c802e55f480c8b8d02968456b43d |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T04:19:07Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-94e9c802e55f480c8b8d02968456b43d2023-11-23T07:54:19ZengMDPI AGCancers2072-66942022-04-01149206110.3390/cancers14092061Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC)Taehee Lee0Piper A. Rawding1Jiyoon Bu2Sunghee Hyun3Woosun Rou4Hongjae Jeon5Seokhyun Kim6Byungseok Lee7Luke J. Kubiatowicz8Dawon Kim9Seungpyo Hong10Hyuksoo Eun11Department of Biomedical Laboratory Science, Daegu Health College, Daegu 41453, KoreaPharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USAPharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USADepartment of Senior Healthcare, Graduate School, Eulji University, Uijeongbu-si 11759, KoreaDepartment of Internal Medicine, Chungnam National University Sejong Hospital (CNUSH), Sejong 30099, KoreaDepartment of Internal Medicine, Chungnam National University Sejong Hospital (CNUSH), Sejong 30099, KoreaDepartment of Internal Medicine, Chungnam National University Hospital, Daejeon 35015, KoreaDepartment of Internal Medicine, Chungnam National University Hospital, Daejeon 35015, KoreaPharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USAPharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USAPharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USADepartment of Internal Medicine, Chungnam National University Hospital, Daejeon 35015, Korea(1) Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Although various serum enzymes have been utilized for the diagnosis and prognosis of HCC, the currently available biomarkers lack the sensitivity needed to detect HCC at early stages and accurately predict treatment responses. (2) Methods: We utilized our highly sensitive cell-free DNA (cfDNA) detection system, in combination with a machine learning algorithm, to provide a platform for improved diagnosis and prognosis of HCC. (3) Results: cfDNA, specifically alpha-fetoprotein (AFP) expression in captured cfDNA, demonstrated the highest accuracy for diagnosing malignancies among the serum/plasma biomarkers used in this study, including AFP, aspartate aminotransferase, alanine aminotransferase, albumin, alkaline phosphatase, and bilirubin. The diagnostic/prognostic capability of cfDNA was further improved by establishing a cfDNA score (cfD<sub>HCC</sub>), which integrated the total plasma cfDNA levels and cfAFP-DNA expression into a single score using machine learning algorithms. (4) Conclusion: The cfD<sub>HCC</sub> score demonstrated significantly improved accuracy in determining the pathological features of HCC and predicting patients’ survival outcomes compared to the other biomarkers. The results presented herein reveal that our cfDNA capture/analysis platform is a promising approach to effectively utilize cfDNA as a biomarker for the diagnosis and prognosis of HCC.https://www.mdpi.com/2072-6694/14/9/2061cell-free DNA (cfDNA)circulating tumor DNA (ctDNA)hepatocellular carcinoma (HCC)liquid biopsyprincipal component analysis (PCA) |
| spellingShingle | Taehee Lee Piper A. Rawding Jiyoon Bu Sunghee Hyun Woosun Rou Hongjae Jeon Seokhyun Kim Byungseok Lee Luke J. Kubiatowicz Dawon Kim Seungpyo Hong Hyuksoo Eun Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC) Cancers cell-free DNA (cfDNA) circulating tumor DNA (ctDNA) hepatocellular carcinoma (HCC) liquid biopsy principal component analysis (PCA) |
| title | Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC) |
| title_full | Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC) |
| title_fullStr | Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC) |
| title_full_unstemmed | Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC) |
| title_short | Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC) |
| title_sort | machine learning based clinical biomarker using cell free dna for hepatocellular carcinoma hcc |
| topic | cell-free DNA (cfDNA) circulating tumor DNA (ctDNA) hepatocellular carcinoma (HCC) liquid biopsy principal component analysis (PCA) |
| url | https://www.mdpi.com/2072-6694/14/9/2061 |
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