DNA Methylation, Pancreatic Neoplasms, Proteome
Conditions
Keywords
Pancreas cystic neoplasm
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTcfDNA methylation sequencing and proteome analysis
Sequencing of cfDNA methylation and proteomic analysis of two sets of capsular fluid samples
Sponsors
Oriental Hepatobiliary Surgery Hospital Affiliated to Naval Military Medical University
Zhejiang University
The First Affiliated Hospital of Nanchang University
West China Hospital
LanZhou University
First Affiliated Hospital Xi'an Jiaotong University
Peking University Cancer Hospital & Institute
First Hospital of China Medical University
First Affiliated Hospital of Guangxi Medical University
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
The Second Affiliated Hospital of Baotou Medical College
Shandong Province Third hospital
Zhaoshen Li
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Subjects are ≥ 18 years old, regardless of gender; * Preoperative imaging examination (EUS, CT, MRI) diagnosed cystic mass of pancreas and suspected cystic tumor; * Subjects voluntarily accepted radical resection of pancreatic cystic tumors; * Subjects or family members voluntarily sign the informed consent form for clinical research.
Exclusion criteria
* The excised sample does not contain cystic components, or the cystic fluid cannot be collected; * Receive tumor-related treatment (radiotherapy, chemotherapy, immunotherapy, etc.) before operation; * Receive antibiotic treatment within 1 week before operation; * Subjects refused to accept radical resection of pancreatic cystic tumor, or had any situation that they could not accept radical resection of pancreatic cystic tumor; * The subjects or their families refused to sign the informed consent form for clinical research.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Types and concentrations of cfDNA and proteins contained in the vesicular fluid | 2023-11-31 | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Establishment of a diagnostic model for cystic tumors of the pancreas | 2023-11-31 | 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. |
| Establishing a model for the identification of mucus and non-mucus | 2023-11-31 | 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. |
| Development of a progressive benign malignancy prediction model | 2023-11-31 | 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. |
Countries
China
Contacts
Primary ContactKaixuan Wang
Outcome results
None listed