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Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques

SIMPLE SUMMARY: Early lung cancer detection is vital. Next-generation sequencing (NGS) enables cell-free DNA (cfDNA) liquid biopsy to detect genetic changes, such as copy number variations (CNVs). Recent machine learning (ML) analyses using cancer markers can identify anomalies, and developing metho...

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Autores principales: Kwon, Hyuk-Jung, Park, Ui-Hyun, Goh, Chul Jun, Park, Dabin, Lim, Yu Gyeong, Lee, Isaac Kise, Do, Woo-Jung, Lee, Kyoung Joo, Kim, Hyojung, Yun, Seon-Young, Joo, Joungsu, Min, Na Young, Lee, Sunghoon, Um, Sang-Won, Lee, Min-Seob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10526503/
https://www.ncbi.nlm.nih.gov/pubmed/37760525
http://dx.doi.org/10.3390/cancers15184556
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author Kwon, Hyuk-Jung
Park, Ui-Hyun
Goh, Chul Jun
Park, Dabin
Lim, Yu Gyeong
Lee, Isaac Kise
Do, Woo-Jung
Lee, Kyoung Joo
Kim, Hyojung
Yun, Seon-Young
Joo, Joungsu
Min, Na Young
Lee, Sunghoon
Um, Sang-Won
Lee, Min-Seob
author_facet Kwon, Hyuk-Jung
Park, Ui-Hyun
Goh, Chul Jun
Park, Dabin
Lim, Yu Gyeong
Lee, Isaac Kise
Do, Woo-Jung
Lee, Kyoung Joo
Kim, Hyojung
Yun, Seon-Young
Joo, Joungsu
Min, Na Young
Lee, Sunghoon
Um, Sang-Won
Lee, Min-Seob
author_sort Kwon, Hyuk-Jung
collection PubMed
description SIMPLE SUMMARY: Early lung cancer detection is vital. Next-generation sequencing (NGS) enables cell-free DNA (cfDNA) liquid biopsy to detect genetic changes, such as copy number variations (CNVs). Recent machine learning (ML) analyses using cancer markers can identify anomalies, and developing methods based on ML for patient big data analysis is crucial for predicting cancer. We analyzed blood samples of 92 lung cancer patients and 80 healthy individuals, detecting significant differences in cancer markers, cfDNA concentrations, and CNV screening. Here, we used three algorithms of ML, such as Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR). ML analysis using cancer markers, cell-free DNA, and CNV individually exhibited relatively low discriminative power between cancer patients and healthy individuals. However, integrating multi-omics data into ML significantly improved accuracy, suggesting potential for precise cancer diagnosis. This study suggests the prospect of effectively distinguishing and diagnosing lung cancer from healthy individuals through blood-based ML analysis. ABSTRACT: 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.
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spelling pubmed-105265032023-09-28 Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques Kwon, Hyuk-Jung Park, Ui-Hyun Goh, Chul Jun Park, Dabin Lim, Yu Gyeong Lee, Isaac Kise Do, Woo-Jung Lee, Kyoung Joo Kim, Hyojung Yun, Seon-Young Joo, Joungsu Min, Na Young Lee, Sunghoon Um, Sang-Won Lee, Min-Seob Cancers (Basel) Article SIMPLE SUMMARY: Early lung cancer detection is vital. Next-generation sequencing (NGS) enables cell-free DNA (cfDNA) liquid biopsy to detect genetic changes, such as copy number variations (CNVs). Recent machine learning (ML) analyses using cancer markers can identify anomalies, and developing methods based on ML for patient big data analysis is crucial for predicting cancer. We analyzed blood samples of 92 lung cancer patients and 80 healthy individuals, detecting significant differences in cancer markers, cfDNA concentrations, and CNV screening. Here, we used three algorithms of ML, such as Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR). ML analysis using cancer markers, cell-free DNA, and CNV individually exhibited relatively low discriminative power between cancer patients and healthy individuals. However, integrating multi-omics data into ML significantly improved accuracy, suggesting potential for precise cancer diagnosis. This study suggests the prospect of effectively distinguishing and diagnosing lung cancer from healthy individuals through blood-based ML analysis. ABSTRACT: 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. MDPI 2023-09-14 /pmc/articles/PMC10526503/ /pubmed/37760525 http://dx.doi.org/10.3390/cancers15184556 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kwon, Hyuk-Jung
Park, Ui-Hyun
Goh, Chul Jun
Park, Dabin
Lim, Yu Gyeong
Lee, Isaac Kise
Do, Woo-Jung
Lee, Kyoung Joo
Kim, Hyojung
Yun, Seon-Young
Joo, Joungsu
Min, Na Young
Lee, Sunghoon
Um, Sang-Won
Lee, Min-Seob
Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10526503/
https://www.ncbi.nlm.nih.gov/pubmed/37760525
http://dx.doi.org/10.3390/cancers15184556
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