This study focuses on the discovery of protein biomarkers for the early diagnosis of gastric cancer and the development of corresponding detection methods. By employing advanced mass spectrometry-based proteomic technologies, the investigators conducted an in-depth analysis of a large cohort of clinical samples to identify specific protein biomarkers capable of accurately distinguishing gastric cancer patients from healthy individuals. The findings from this research are expected to facilitate the development of novel non-invasive or minimally invasive diagnostic approaches, thereby improving early detection, enhancing patient prognosis, and increasing survival rates.
Study Type
OBSERVATIONAL
Enrollment
1,200
Diagnostic Performance of the Serum Protein Biomarker-Based Model for Gastric Cancer
Evaluation of the sensitivity and specificity of a diagnostic model constructed using key serum protein biomarkers for the early detection of gastric cancer.
Time frame: 3 years
Predictive performance of the serum protein model for the progression risk from precancerous gastric lesions to gastric cancer
Predictive performance of the serum protein model for the progression risk from precancerous gastric lesions to gastric cancer, evaluated by prediction accuracy and area under the ROC curve (AUC).
Time frame: 3 years
Predictive performance of the serum protein model for response to neoadjuvant therapy in gastric cancer
Predictive performance of the serum protein model for response to neoadjuvant therapy in gastric cancer, assessed by prediction accuracy and AUC.
Time frame: 3 years
Prognostic performance of the serum protein model in gastric cancer patients
Prognostic performance of the serum protein model in gastric cancer patients, measured by prediction accuracy and AUC.
Time frame: 3 years
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