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 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.