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

NCT06843707 | Recruiting DMC

• 2 other identifiers: LungMet2025-01-12
• Study Type: observational
• Target: 2,500
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Lung Cancer; Lung; Lung Cancer (NSCLC); Metabolomics

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• Differentially Expressed Metabolites

  3 years

Interventions

Monitoring serum metabolites in lung cancer patients using tissue and peripheral blood samples. OTHER

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 Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with lung nodules confirmed by CT examination.

You may qualify if:

• Signing of the informed consent form;
• Male or female, aged 18-75 years;
• Patients with lung nodules confirmed by CT examination;
• Good preoperative pulmonary function cooperation and complete reporting;
• Preoperative chest single/dual phase CT scans without significant artefacts and with complete imaging;
• The interval between preoperative pulmonary function and single/dual phase CT scans does not exceed one month.

You may not qualify if:

• Poor preoperative pulmonary function cooperation or missing reports;
• Preoperative chest single/dual phase CT scans exhibit significant artefacts or image omission;
• The interval between preoperative pulmonary function and single/dual phase CT scans exceeds one month;
• Complication with severe respiratory disorders (such as lung transplantation, pneumothorax, giant bullae, etc.);
• Coexisting with other severe functional impairments;
• 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.

Sponsors & Collaborators

• The First Affiliated Hospital of Guangzhou Medical University: lead

Study Sites (1)

the First Affiliated of Guangzhou Medical University

Guangzhou, Guangdong, 510120, China

RECRUITING

MeSH Terms

Conditions

Lung Neoplasms; Carcinoma, Non-Small-Cell Lung

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Carcinoma, Bronchogenic; Bronchial Neoplasms

Central Study Contacts

Jianxing He, Professer

CONTACT

86-20-83337792; drjiaxing.he@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: January 12, 2025
• First Posted: February 25, 2025
• Study Start: January 1, 2024
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2027
• Last Updated: February 25, 2025

Record last verified: 2025-01

Locations

CN (1)