Lung Cancer
Conditions
Keywords
Blood plasma, Exosome, Liquid biopsy, Lung cancer, Biomarker, Deep-learning analysis
Brief summary
Lung cancer is a leading cause of cancer death worldwide. Early diagnosis is linked to a better prognosis. Further, surgical resection at the early stages of non-small cell lung cancer (NSCLC) results in markedly improved survival rates. Computed tomography (CT)- or bronchoscopy-guided needle biopsies are standard definitive diagnostic procedures for lung cancer and are used to obtain tissue for pathological examination. However, these procedures are invasive, difficult to repeat, expensive, and risk exposure to radiation. Conversely, liquid biopsies, such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and extracellular vesicles (EVs), are simple and less invasive procedures that can be repeated more frequently than tissue biopsies. To analyze the exosomes abundantly present in the blood and to conduct clinical studies to determine whether it is possible to diagnose lung cancer. To this end, blood samples from normal people (n = 150) and lung cancer patients (n = 320) are obtained from the Human biobank of five hospitals participating in the study.
Interventions
DIAGNOSTIC_TESTExosome sampling
* Centrifugation of blood plasma * Size exclusion chromatography * ELISA assay, Western blotting * Deep-learning analysis
Sponsors
Korea University Guro Hospital
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
40 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Patients with primary adenocarcinoma of lung with permanent pathology of N0 or N1 2. Patients with T1mi, Tsi, T1a, T1b, T1c, T2a, and T2b stage 3. An adult of Korean nationality 4. Patients without prior chemo/radiation treatment prior to lung cancer surgery 5. Patients who have not been diagnosed with other cancers prior to lung cancer surgery
Exclusion criteria
* Patients who do not meet the inclusion criteria
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Evaluation of the distinction between healthy controls and lung cancer patients through deep-learning analysis of exosomes | 3 years | Comparative evaluation of whether it is possible to distinguish between healthy controls and lung cancer patients through deep-learning analysis of exosomes |
| Evaluating the possibility of distinguishing between normal and lung cancer patients through the analysis of lung cancer-specific exosomal protein | 3 years | Quantitative analysis using lung cancer-specific exosomal protein evaluated the possibility of distinguishing between healthy controls and lung cancer patients. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Evaluation of the possibility of distinguishing the early pathological stages in lung cancer patients through deep-learning analysis of exosomes | 3 years | Evaluating whether the early stages of lung cancer patients can be distinguished using deep-learning analysis of exosomes |
| Evaluation of the possibility of distinguishing the early pathological stages in lung cancer patients through quantitative analysis of lung cancer specific exosomal proteins | 3 years | Evaluating whether the early stages of lung cancer patients can be distinguished using quantitative analysis of lung cancer specific exosomal proteins |
Countries
South Korea
Outcome results
None listed