The goal of this observational study is to test in patients with cystic tumor of pancreas. The main questions it aims to answer are: Screening of molecular diagnostic markers for pancreatic cystic tumors by cfDNA methylation sequencing of pancreatic cystic fluid and proteomic assays to distinguish benign from malignant and mucinous/non-mucinous, correlate with pathological features, and find molecular features associated with the degree of malignancy. Participants will Provide post-operative cyst fluid specimens.
Patients with pancreatic cystic tumors were divided into control, mucinous tumor and non-mucinous tumor groups, and cfDNA molecules and protein molecules that were significantly altered between different groups were found by cfDNA methylation sequencing of pancreatic cystic fluid and proteomic detection techniques. Multi-omics analysis was performed to construct a more efficient molecular diagnostic marker model. Correlating models with pathological features to find molecular features associated with the degree of malignancy.
Study Type
OBSERVATIONAL
Enrollment
100
Sequencing of cfDNA methylation and proteomic analysis of two sets of capsular fluid samples
Changhai Hospital
Shanghai, Shanghai Municipality, China
RECRUITINGTypes and concentrations of cfDNA and proteins contained in the vesicular fluid
Identification of protein and circulating gene methylation species in cyst fluid and quantification of their concentrations using mass spectrometry. Characterize cfDNA methylation and proteomics of cyst fluid in patients with pancreatic cystic neoplasm (PCN).
Time frame: 2023-11-31
Establishment of a diagnostic model for cystic tumors of the pancreas
A machine learning approach was used to further screen for characteristically methylated cfDNA and proteins in PCN patients.Establishment of a diagnostic model for pancreatic cystic tumors.
Time frame: 2023-11-31
Establishing a model for the identification of mucus and non-mucus
A machine learning approach was used to further screen for characteristically methylated cfDNA and proteins in PCN patients.To establish a model for the identification of mucinous and non-mucinous.
Time frame: 2023-11-31
Development of a progressive benign malignancy prediction model
A machine learning approach was used to further screen for characteristically methylated cfDNA and proteins in PCN patients.Development of a progression benign malignancy prediction model.
Time frame: 2023-11-31
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