Pancreatic Ductal Adenocarcinoma (PDAC)
Conditions
Brief summary
Pancreatic ductal adenocarcinoma (PDAC) can be divided into pancreatic head cancer (PHC) and pancreatic body/tail cancer (PBTC) according to the anatomical position of tumors. There is increasing evidence that tumors at different sites exhibit different genetic or molecular features and clinical manifestations, and can affect the survival and outcomes of PDAC patients. Studies have shown that the prognosis of PBTC is worse than that of PHC, which is partly attributed to the relatively late clinical presentation of PBTC patients and the lack of overt symptoms such as obstructive jaundice, which is common in PHC. However, it has also been shown that the worse survival of PBTC compared to PHC is not related to the disease stage. Previous studies have investigated the molecular differences between PHC and PBTC and found that the frequency of SMAD4 mutation in PBTC was significantly higher than that in PHC at early stages (I-II). In the late stage (III-IV), PBTC had higher mutation frequency of Kirsten rat sarcoma viral oncogene homolog (KRAS) and mitogen-activated protein kinase (MAPK) pathway, but lower frequency of genomic alterations which can be targeted by drugs. The above genetic and molecular differences may be related to the clinical differences between PHC and PBTC. However, the differences in microbial composition and metabolism between PHC and PBTC have not been fully studied and discussed, and their relationship with clinical manifestations and prognosis is also unclear. In this study, the investigators aimed to analyze the microbial and metabolic differences between PHC and PBTC through 16S ribosomal ribonucleic acid (rRNA) sequencing and untargeted metabolome analysis to further explore the etiology and pathogenesis of PDAC at different anatomical positions.
Interventions
OTHER16S rRNA amplicon sequencing and untargeted metabolomics
16S rRNA sequencing is a method for large-scale identification of community composition, expression abundance, and phylogenetic analysis by polymerase chain reaction (PCR) amplification of specific variable regions of 16S rRNA, combined with high-throughput sequencing and bioinformatics analysis. It also enables large-scale identification of the entire flora in a given habitat to study microbial diversity. Untargeted metabolomics refers to the use of Liquid Chromatograph Mass Spectrometer (LC-MS), Gas Chromatograph Mass Spectrometer (GC-MS), and nuclear magnetic resonance (NMR) technology. The dynamic changes of small molecular metabolites in cells, tissues, organs or organisms before and after stimulation or disturbance were detected without bias. The differential metabolites were screened by bioinformatics analysis, and the pathway analysis of differential metabolites was performed to reveal the physiological mechanism of their changes.
Sponsors
Qilu Hospital of Shandong University
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Participants aged above 18 years. 2. Patients who signed informed consent. 3. PDAC patients diagnosed via postoperative pathology.
Exclusion criteria
1. Comorbidity with other cancers. 2. Underwent preoperative chemotherapy, radiotherapy, or other biological treatment. 3. Use of antibiotics, probiotics or prebiotics in the previous month.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| the abundance of changed microorganisms of PHC and PBTC | 2023-11-20 to 2023-12-20 | Detect the categories and quantities of microorganisms significantly enriched and decreased in the case group. |
| the abundance of changed metabolites of PHC and PBTC | 2023-11-20 to 2023-12-20 | Detect the categories and quantities of metabolites significantly upregulated or downregulated in the case group. |
Countries
China
Outcome results
None listed