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NCT Number: NCT06843707

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

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.

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Key information

Conditions

Lung Cancer Bronchial Neoplasms Carcinoma, Bronchogenic Carcinoma, Non-Small-Cell Lung Lung Lung Cancer (NSCLC) Lung Diseases Lung Neoplasms Metabolomics Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Observational

Primary location

the First Affiliated of Guangzhou Medical University

Guangzhou, Guangdong, 510120, China

Location status: Recruiting

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.

Exclusion criteria

  • 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.

Treatment and study plan

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.

Primary outcomes

  1. Area Under the Curve

    Time frame: 3 Years

    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.

Secondary outcomes

  1. Differentially Expressed Metabolites

    Time frame: 3 years

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Jianxing He, Professer

CONTACT

[email protected]

86-20-83337792

Sponsors and collaborators

Lead sponsor

The First Affiliated Hospital of Guangzhou Medical University

Other

Registry information

Official study title

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

Important dates

Study start
2024
Primary completion
2026
Study completion
2027
First posted
Feb 25, 2025
Registry last updated
Feb 25, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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