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

Establishing a Longitudinal Cohort Study of Lung Cancer Using Tissue and Peripheral Blood Metabolomics to Explore Biomarkers and Therapeutic Mechanisms.

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 Overview

• Status: Recruiting
• Conditions: Lung Cancer; Lung; Metabolomics; Lung Cancer (NSCLC)
• Intervention / Treatment: Other: Monitoring serum metabolites in lung cancer patients using tissue and peripheral blood samples.

• Study Type: Observational
• Enrollment (Estimated): 2500

Contacts and Locations

• Study Contact: Name: Jianxing He, Professer; Phone Number: 86-20-83337792; Email: drjiaxing.he@gmail.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510120
      • Recruiting
      • The First Affiliated of Guangzhou Medical University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patients with lung nodules confirmed by CT examination.

Description

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.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	3 Years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	3 years

Collaborators and Investigators

• Sponsor: The First Affiliated Hospital of Guangzhou Medical University

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2024
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2027

Study Registration Dates

• First Submitted: January 12, 2025
• First Submitted That Met QC Criteria: February 24, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: February 24, 2025
• Last Verified: January 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: LungMet; 2025-01-12 (Other Identifier: Ethics Committee of the First Affiliated of Guangzhou Medical University)

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No