Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques
Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cance...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/18/4556 |
| _version_ | 1797580874477207552 |
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| author | Hyuk-Jung Kwon Ui-Hyun Park Chul Jun Goh Dabin Park Yu Gyeong Lim Isaac Kise Lee Woo-Jung Do Kyoung Joo Lee Hyojung Kim Seon-Young Yun Joungsu Joo Na Young Min Sunghoon Lee Sang-Won Um Min-Seob Lee |
| author_facet | Hyuk-Jung Kwon Ui-Hyun Park Chul Jun Goh Dabin Park Yu Gyeong Lim Isaac Kise Lee Woo-Jung Do Kyoung Joo Lee Hyojung Kim Seon-Young Yun Joungsu Joo Na Young Min Sunghoon Lee Sang-Won Um Min-Seob Lee |
| author_sort | Hyuk-Jung Kwon |
| collection | DOAJ |
| description | Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer. |
| first_indexed | 2024-03-10T22:57:15Z |
| format | Article |
| id | doaj.art-5d25e128c37b4c30b6aac1935921d265 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T22:57:15Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-5d25e128c37b4c30b6aac1935921d2652023-11-19T09:55:27ZengMDPI AGCancers2072-66942023-09-011518455610.3390/cancers15184556Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning TechniquesHyuk-Jung Kwon0Ui-Hyun Park1Chul Jun Goh2Dabin Park3Yu Gyeong Lim4Isaac Kise Lee5Woo-Jung Do6Kyoung Joo Lee7Hyojung Kim8Seon-Young Yun9Joungsu Joo10Na Young Min11Sunghoon Lee12Sang-Won Um13Min-Seob Lee14Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEarly detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.https://www.mdpi.com/2072-6694/15/18/4556liquid biopsymulti-omicsmachine learninglung cancercell-free DNAcopy number variation |
| spellingShingle | Hyuk-Jung Kwon Ui-Hyun Park Chul Jun Goh Dabin Park Yu Gyeong Lim Isaac Kise Lee Woo-Jung Do Kyoung Joo Lee Hyojung Kim Seon-Young Yun Joungsu Joo Na Young Min Sunghoon Lee Sang-Won Um Min-Seob Lee Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques Cancers liquid biopsy multi-omics machine learning lung cancer cell-free DNA copy number variation |
| title | Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques |
| title_full | Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques |
| title_fullStr | Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques |
| title_full_unstemmed | Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques |
| title_short | Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques |
| title_sort | enhancing lung cancer classification through integration of liquid biopsy multi omics data with machine learning techniques |
| topic | liquid biopsy multi-omics machine learning lung cancer cell-free DNA copy number variation |
| url | https://www.mdpi.com/2072-6694/15/18/4556 |
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