LcProt: Proteomics Longitudinal Cohort Study on Lung Cancer

LcProt: Proteomics Longitudinal Cohort Study on Lung CancerProspective Longitudinal Cohort Study of Lung Cancer Based on Peripheral Blood and Tissue Proteomics

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 Overview

• Status: Recruiting
• Conditions: Cancer; NSCLC; Lung Cancer; Lung
• Intervention / Treatment: Other: 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

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

Contacts and Locations

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

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• 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. 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).	3 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	3 years

Collaborators and Investigators

• Sponsor: The First Affiliated Hospital of Guangzhou Medical University

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2019
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026

Study Registration Dates

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

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: January 12, 2025
• Last Verified: January 1, 2025

More Information

Terms related to this study

• Keywords: NSCLC; Lung cancer; Proteomics; Protein
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: LcProt; ES-2924-051-02 (Other Identifier: First Affiliated Hostipal 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