Establishing a Longitudinal Cohort Study of Lung Cancer Using Tissue and Peripheral Blood Metabolomics.

The First Affiliated Hospital of Guangzhou Medical University2,500 enrolled

Overview

This study will utilize tissue and peripheral blood samples for metabolomics analysis and establish a longitudinal metabolomics cohort at multiple critical treatment time points to comprehensively investigate the role of metabolomics in the diagnosis, prognosis, and therapeutic monitoring of lung cancer. By profiling metabolic alterations, this study aims to identify potential biomarkers for distinguishing benign and malignant lung nodules, predicting therapeutic efficacy, and assessing long-term prognosis. Key time points include initial screening for lung nodules, postoperative evaluation to predict treatment outcomes, and therapeutic monitoring to assess efficacy after medication or other interventions. Through these analyses, the study seeks to uncover underlying metabolic mechanisms and provide valuable insights into personalized lung cancer management.

Study Type

OBSERVATIONAL

Enrollment

2,500

Conditions

Lung Cancer Lung Lung Cancer (NSCLC)Metabolomics

Interventions

This study focuses on monitoring serum metabolites in lung cancer patients by utilizing tissue and peripheral blood samples. By analyzing the metabolic profiles of serum, the research aims to identify significant metabolic alterations associated with lung cancer progression, treatment response, and overall prognosis. The study seeks to provide a comprehensive understanding of how metabolic changes in serum reflect disease dynamics and therapeutic outcomes, ultimately contributing to the development of more accurate diagnostic and prognostic biomarkers for lung cancer management.

Eligibility

Sex: ALLMin age: 18 YearsMax age: 75 Years

Medical Language ↔ Plain English

Inclusion Criteria: 1. Signing of the informed consent form; 2. Male or female, aged 18-75 years; 3. Patients with lung nodules confirmed by CT examination; 4. Good preoperative pulmonary function cooperation and complete reporting; 5. Preoperative chest single/dual phase CT scans without significant artefacts and with complete imaging; 6. The interval between preoperative pulmonary function and single/dual phase CT scans does not exceed one month. Exclusion Criteria: 1. Poor preoperative pulmonary function cooperation or missing reports; 2. Preoperative chest single/dual phase CT scans exhibit significant artefacts or image omission; 3. The interval between preoperative pulmonary function and single/dual phase CT scans exceeds one month; 4. Complication with severe respiratory disorders (such as lung transplantation, pneumothorax, giant bullae, etc.); 5. Coexisting with other severe functional impairments; 6. Patients with obstructive lesions such as airway or esophageal stenosis; (8) Medication use before pulmonary function testing that does not meet the cessation guidelines; (9) Pulmonary function report quality graded D-F.

Outcomes

Primary Outcomes

Area Under the Curve

AUC, or Area Under the Curve, is a commonly used metric in statistical and machine learning models, particularly for evaluating the performance of classification models. It refers to the area under the Receiver Operating Characteristic (ROC) curve, which plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. An AUC value ranges from 0 to 1, where: * 1 indicates a perfect model, * 0.5 suggests a model no better than random guessing, * \< 0.5 reflects a model performing worse than random.

Time frame: 3 Years

Secondary Outcomes

Differentially Expressed Metabolites

Differential metabolites, or differentially expressed metabolites (DEMs), refer to metabolites that show significant changes in abundance between different biological or experimental conditions, such as disease vs. healthy states, treated vs. untreated groups, or across time points in longitudinal studies. These metabolites are identified through quantitative metabolomics techniques, including mass spectrometry or nuclear magnetic resonance (NMR), and analyzed using statistical or bioinformatics tools to determine significance.

Time frame: 3 years