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Research
Xin Yu Wang
Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8488-7910
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background and objectives: Most patients with pancreatic cancer were diagnosed at a late stage because identifying pancreatic cancer early was difficult. Therefore, it was of great significance to screen specific biomarkers in early stage of pancreatic cancer. The aim of this study was to demonstrate the profile and characteristics of lipid metabolites in patients with pancreatic cancer and to correlate the expression level of these metabolites with the tumor.
Methods: A total of 9 tissue samples from patients with pancreatic cancer were collected and divided into cancer group and para-cancer group according to different sites. All patients’ samples were performed a metabolomics analysis based on Liquid Tandem Chromatography Quadrupole Time of Flight Mass Spectrometry.
Results: PCA based on lipidomics analysis could clearly distribute in different regions. OPLS-DA analysis could filter out the irrelevant variables in metabolites and obtain more reliable information about the intergroup differences of metabolites. The volcano plot was used to visualize all variables with VIP>1 and presented the important variables with P<0.05 and |FC|>2. Euclidean distance matrix was calculated for the quantitative values of the differential metabolites, and the differential metabolites were clustered by using the full linkage method and heat map was used to demonstrate. The different metabolites of each group were compared and analyzed by radar map, the corresponding ratio of the quantitative value of differential metabolites were calculated. Heatmap of correlation analysis showed the correlation coefficient between different metabolites. Bar plot visualized the variation degree and classification information of metabolites. Bubble plot showed the variation degree, difference significance degree and classification information of metabolites.
Conclusion: The tissue samples of pancreatic cancer had the characteristics of lipidomics, and the difference of lipid metabolites could be used as potential tumor markers of pancreatic cancer.
Keywords
Lipidomics, Pancreatic cancer, Laparoscopic pancreaticoduodenectomy, Liomarkers
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This is a preprint. It has not completed peer review.
Research
Xin Yu Wang
Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8488-7910
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 16 Dec, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background and objectives: Most patients with pancreatic cancer were diagnosed at a late stage because identifying pancreatic cancer early was difficult. Therefore, it was of great significance to screen specific biomarkers in early stage of pancreatic cancer. The aim of this study was to demonstrate the profile and characteristics of lipid metabolites in patients with pancreatic cancer and to correlate the expression level of these metabolites with the tumor.
Methods: A total of 9 tissue samples from patients with pancreatic cancer were collected and divided into cancer group and para-cancer group according to different sites. All patients’ samples were performed a metabolomics analysis based on Liquid Tandem Chromatography Quadrupole Time of Flight Mass Spectrometry.
Results: PCA based on lipidomics analysis could clearly distribute in different regions. OPLS-DA analysis could filter out the irrelevant variables in metabolites and obtain more reliable information about the intergroup differences of metabolites. The volcano plot was used to visualize all variables with VIP>1 and presented the important variables with P<0.05 and |FC|>2. Euclidean distance matrix was calculated for the quantitative values of the differential metabolites, and the differential metabolites were clustered by using the full linkage method and heat map was used to demonstrate. The different metabolites of each group were compared and analyzed by radar map, the corresponding ratio of the quantitative value of differential metabolites were calculated. Heatmap of correlation analysis showed the correlation coefficient between different metabolites. Bar plot visualized the variation degree and classification information of metabolites. Bubble plot showed the variation degree, difference significance degree and classification information of metabolites.
Conclusion: The tissue samples of pancreatic cancer had the characteristics of lipidomics, and the difference of lipid metabolites could be used as potential tumor markers of pancreatic cancer.
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