Novel Early Diagnosis Method for Lung Adenocarcinoma Based on DNA Methylation in Peripheral Blood Mononuclear Cells

Background

Lung adenocarcinoma (LUAD) is one of the leading causes of cancer-related deaths worldwide, accounting for approximately 40% of lung cancer cases. Despite significant advancements in treatment, the prognosis of LUAD remains poor, largely due to the challenges of early diagnosis. Most patients are diagnosed at an advanced stage, leading to suboptimal treatment outcomes. Currently, low-dose computed tomography (LDCT) is a common tool for early LUAD screening, but its false-positive rate is as high as 96.4%. Additionally, existing serum biomarkers, such as CEA, exhibit low sensitivity and specificity in early LUAD diagnosis. Therefore, there is an urgent need to develop a non-invasive and efficient method for early LUAD detection.

DNA methylation, a common epigenetic modification, plays a pivotal role in regulating gene expression. Studies have shown that global hypomethylation and localized hypermethylation are characteristic features of cancer development, particularly in tumor suppressor genes and oncogenes. Peripheral blood mononuclear cells (PBMCs), which include monocytes and lymphocytes, are crucial components of the immune system. Changes in PBMCs may reflect immune responses during tumorigenesis, providing a theoretical basis for early cancer diagnosis based on immune cell profiling.

Source of the Study

This study was conducted by a collaborative research team from multiple institutions in China, including the Second Hospital of Shandong University, Qilu Hospital of Shandong University, and Guangdong Provincial People’s Hospital. The primary authors include Peilong Li, Shibiao Liu, and Tiantian Wang. The paper was published online on September 30, 2024, in the International Journal of Surgery, titled “Multisite DNA Methylation Alterations of Peripheral Blood Mononuclear Cells Serve as Novel Biomarkers for the Diagnosis of AIS/Stage I Lung Adenocarcinoma: A Multicenter Cohort Study.”

Research Process and Results

1. Study Design and Sample Collection

This multicenter cohort study enrolled 1,115 participants, divided into four cohorts: the Discovery Cohort, the Two-Step DMP Screening Cohort, the LDP Score Development Cohort, and the Prospective Cohort. The primary objective was to develop a novel biomarker for early LUAD diagnosis by analyzing DNA methylation patterns in PBMCs.

• Discovery Cohort: Included 35 LUAD patients and 50 healthy controls. Genome-wide DNA methylation analysis was performed using the Illumina 850K microarray, identifying 1,415 differentially methylated positions (DMPs).
• Two-Step DMP Screening Cohort: Candidate DMPs were validated using pyrosequencing and multiple target region methylation enrichment sequencing (MTRMES), ultimately confirming three effective DMPs.
• LDP Score Development Cohort: Based on the three DMPs, combined with age and sex, an early LUAD diagnostic model (LDP Score) was constructed and systematically evaluated in training and validation sets.
• Prospective Cohort: Included 46 high-risk individuals for lung cancer to assess the early warning potential of the LDP Score.

2. Experimental Methods and Data Analysis

• PBMC Isolation and DNA Extraction: PBMCs were isolated using Histopaque-1077, and DNA was extracted using the Tiangen Gel Extraction Kit, followed by bisulfite conversion.
• Illumina 850K Microarray Analysis: Genome-wide DNA methylation analysis was performed on the Discovery Cohort samples, with data normalization and batch effect correction using R software.
• Pyrosequencing and MTRMES Validation: Candidate DMPs were validated for methylation levels using pyrosequencing and MTRMES.
• Chip-Based Digital PCR Detection: A chip-based digital PCR method was developed to simultaneously detect the methylation status of the three DMPs and construct the LDP Score model.

3. Key Findings

• Genome-Wide DNA Methylation Analysis: In the Discovery Cohort, 1,415 significant DMPs were identified between LUAD patients and healthy controls, with 1,178 hypermethylated and 237 hypomethylated sites.
• DMP Screening and Validation: Through stringent screening criteria, three DMPs (cg08194293, cg19197556, and cg21634628) were validated, showing significant methylation changes in the early stages of LUAD (AIS and Stage I).
• LDP Score Model Construction and Validation: The LDP Score model achieved an AUC of 0.921 in the training set and 0.914 in the validation set, significantly outperforming traditional CEA and CT methods. Additionally, the LDP Score demonstrated excellent diagnostic performance for 6-20 mm pulmonary nodules and ground-glass nodules (GGNs).
• Prospective Cohort Results: The LDP Score could predict LUAD occurrence approximately two years before clinical diagnosis, highlighting its potential for early warning.

4. Conclusions and Significance

This study developed a novel early LUAD diagnostic method (LDP Score) based on DNA methylation in PBMCs, demonstrating high sensitivity and specificity, and outperforming existing diagnostic tools. The LDP Score not only effectively distinguishes early LUAD patients from healthy controls but also provides early warning in high-risk populations. This research offers new insights and methods for early LUAD diagnosis, with significant clinical application value.

Highlights of the Study

• Innovation: This is the first study to develop biomarkers for early LUAD diagnosis based on DNA methylation in PBMCs.
• Efficiency: The LDP Score outperformed traditional CEA and CT methods in early LUAD diagnosis, particularly for 6-20 mm pulmonary nodules and GGNs.
• Prospective Value: The LDP Score can predict LUAD approximately two years before clinical diagnosis, demonstrating its potential for early warning.

Additional Valuable Information

The genome-wide DNA methylation data from this study are publicly available in the Gene Expression Omnibus (GEO) database under accession number GSE275371. Additionally, the research team plans to further optimize the detection time and cost of the LDP Score to better meet clinical needs.

Through this study, the research team has provided a new tool and method for early LUAD diagnosis, which holds promise for widespread clinical application in the future, thereby improving early detection rates and treatment outcomes for LUAD.