Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-24 @ 11:35 PM
Ignite Modification Date:
2025-12-24 @ 11:35 PM
NCT ID:
NCT05269056
Brief Summary:
The purpose of this study is to enable non-invasive early detection of gastric cancer in high-risk populations through the establishment of a multimodal machine learning model using plasma cell-free DNA fragmentomics. Plasma cell-free DNA from early stage gastric cancer patients and healthy individuals will be subjected to whole-genome sequencing. Features, such as cell-free DNA fragmentation, copy number variations and microbiome, will be assessed to generate this model.
Detailed Description:
Improvement in the specificity of early cancer detection reduces financial and mental burdens from unnecessary screenings. Advances in liquid biopsy approaches have expanded the clinical scope of cell-free DNA analysis in cancer early detection, by moving away from cell-free DNA methylome toward an integrative approach that enables the simultaneous assessment of multimodal cell-free DNA features. Integration of liquid biopsy-based cancer early detection into the clinic requires optimization of detection techniques, large-scale studies and prospective clinical validation. In the early detection of gastric cancer, the top research priorities are to identify relevant target features and to improve the sensitivity and specificity of detection. This large-scale early detection study will randomly enroll 200 stage I/II pathologically diagnosed gastric patients and 100 age- and sex-matched healthy individuals upon providing written informed consent. Plasma samples will be collected and extracted cell-free DNA will be subjected to whole genome sequencing. We aimed to incorporate genome-wide copy number variations, cell-free DNA fragmentomics, and microbiome features into the development of a multimodal biomarker-based prediction model.
Study:
NCT05269056
Study Brief:
Protocol Section:
NCT05269056