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| ID | Type | Description | Link |
|---|---|---|---|
| 2025-01-12 | Other Identifier | Ethics Committee of the First Affiliated of Guangzhou Medical University |
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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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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| 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:
| 3 Years |
| 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. |
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Inclusion Criteria:
Exclusion Criteria:
(8) Medication use before pulmonary function testing that does not meet the cessation guidelines; (9) Pulmonary function report quality graded D-F.
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Patients with lung nodules confirmed by CT examination.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Jianxing He, Professer | Contact | 86-20-83337792 | drjiaxing.he@gmail.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| the First Affiliated of Guangzhou Medical University | Recruiting | Guangzhou | Guangdong | 510120 | China |
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| 3 years |
| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| D002289 | Carcinoma, Non-Small-Cell Lung |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D002283 | Carcinoma, Bronchogenic |
| D001984 | Bronchial Neoplasms |
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