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

• Published in: Cancers Vol. 15; no. 18; p. 4556
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023; MDPI
• Subjects: Algorithms; Big data; Biological analysis; Biomarkers; Biopsy; Cancer; Carcinoembryonic antigen; CEA (Oncology); cell-free DNA; Chromosome rearrangements; Chromosomes; Classification; Clinical outcomes; Copy number; copy number variation; Deoxyribonucleic acid; Diagnosis; Disease; DNA; DNA methylation; DNA sequencing; Epigenetics; Genomes; Hospitals; Information management; Learning algorithms; liquid biopsy; Lung cancer; Machine learning; Medical diagnosis; Methods; multi-omics; Neural networks; Next-generation sequencing; Oncology, Experimental; Patients; Regression analysis; Research ethics; Taiwan; United States > US
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15184556

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.