LcProt: Proteomics Longitudinal Cohort Study on Lung Cancer

The First Affiliated Hospital of Guangzhou Medical University2,500 enrolled

Overview

This study will utilize tissue and peripheral blood samples for proteomics analysis and establish a longitudinal proteomics cohort at multiple critical treatment time points to explore the research value of proteomics in the diagnosis and treatment of lung cancer. The study includes key time points such as screening, postoperative efficacy prediction, and efficacy prediction after medication.

Study Type

OBSERVATIONAL

Enrollment

2,500

Conditions

Lung Lung Cancer Cancer NSCLC

Interventions

Using tissue and peripheral blood proteomics to distinguish the benign and malignant nature of lung cancer in patients, as well as to evaluate therapeutic efficacy and long-term prognosis during the t

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. In clinical studies, AUC is often used to assess diagnostic tests, where a higher AUC indicates better test accuracy in distinguishing between conditions (e.g., disease vs. no disease).

Time frame: 3 years

Secondary Outcomes

Differentially Expressed Proteins

Differential proteins, or differentially expressed proteins (DEPs), refer to proteins that show significant changes in expression levels between different biological or experimental conditions, such as disease vs. healthy states, treated vs. untreated groups, or across time points in longitudinal studies. These proteins are identified through quantitative proteomics techniques, including mass spectrometry or label-free methods, and analyzed using statistical or bioinformatics tools to determine significance.