Retrospective Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Dec 16, 2021; 9(35): 10884-10898
Published online Dec 16, 2021. doi: 10.12998/wjcc.v9.i35.10884
Development of a lipid metabolism-related gene model to predict prognosis in patients with pancreatic cancer
Hong Xu, Jian Sun, Ling Zhou, Qian-Cheng Du, Hui-Ying Zhu, Yang Chen, Xin-Yu Wang
Hong Xu, Jian Sun, Ling Zhou, Qian-Cheng Du, Hui-Ying Zhu, Yang Chen, Xin-Yu Wang, General Surgery, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
Author contributions: Xu H and Wang XY performed the surgery; Sun J and Zhou L designed the study; Du QC, Zhu HY and Chen Y wrote the paper; Xu H and Wang XY were responsible for analyzing the data; all authors have read and approved the final manuscript.
Supported by Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine Discipline Boosting Plan, No. SY-XKZT-2019-1006.
Institutional review board statement: The study was reviewed and approved by the Shanghai Fourth People’s Hospital Institutional Review Board (No. 2019057-001).
Conflict-of-interest statement: The authors declare no conflicts of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work noncommercially, and license their derivative works on different terms, provided the original work is properly cited and the use is noncommercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xin-Yu Wang, MD, Attending Doctor, General Surgery, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, No. 1279 Sanmen Road, Hongkou District, Shanghai 200434, China. wang_xinyuvip@163.com
Received: June 25, 2021
Peer-review started: June 25, 2021
First decision: August 19, 2021
Revised: September 1, 2021
Accepted: October 27, 2021
Article in press: October 27, 2021
Published online: December 16, 2021
Processing time: 167 Days and 16.5 Hours
Abstract
BACKGROUND

Pancreatic cancer is a highly heterogeneous disease, making prognosis prediction challenging. Altered energy metabolism to satisfy uncontrolled proliferation and metastasis has become one of the most important markers of tumors. However, the specific regulatory mechanism and its effect on prognosis have not been fully elucidated.

AIM

To construct a prognostic polygene signature of differentially expressed genes (DEGs) related to lipid metabolism.

METHODS

First, 9 tissue samples from patients with pancreatic cancer were collected and divided into a cancer group and a para-cancer group. All patient samples were subjected to metabolomics analysis based on liquid tandem chromatography quadrupole time of flight mass spectrometry. Then, mRNA expression profiles and corresponding clinical data of pancreatic cancer were downloaded from a public database. Least absolute shrinkage and selection operator Cox regression analysis was used to construct a multigene model for The Cancer Genome Atlas.

RESULTS

Principal component analysis and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) based on lipid metabolomics analysis showed a clear distribution in different regions. A Euclidean distance matrix was used to calculate the quantitative value of differential metabolites. The permutation test of the OPLS-DA model for tumor tissue and paracancerous tissue indicated that the established model was consistent with the actual condition based on sample data. A bar plot showed significantly higher levels of the lipid metabolites phosphatidylcholine (PC), phosphatidyl ethanolamine (PE), phosphatidylethanol(PEtOH), phosphatidylmethanol (PMeOH), phosphatidylserine (PS) and diacylglyceryl trimethylhomoserine (DGTS) in tumor tissues than in paracancerous tissues. According to bubble plots, PC, PE, PEtOH, PMeOH, PS and DGTS were significantly higher in tumor tissues than in paracancerous tissues. In total, 12.3% (25/197) of genes related to lipid metabolism were differentially expressed between tumor tissues and adjacent paracancerous tissues. Six DEGs correlated with overall survival in univariate Cox regression analysis (P < 0.05), and a 4-gene signature model was developed to divide patients into two risk groups, with patients in the high-risk group having significantly lower overall survival than those in the low-risk group (P < 0.05). ROC curve analysis confirmed the predictive power of the model.

CONCLUSION

This novel model comprising 4 lipid metabolism-related genes might assist clinicians in the prognostic evaluation of patients with pancreatic cancer.

Keywords: Lipid metabolism; Pancreatic cancer; Gene signature; Overall survival; Prognosis; Bioinformatics

Core Tip: Pancreatic malignant tumors are a highly heterogeneous disease and the seventh leading cause of cancer-related death. Lipid metabolomics analysis suggests differences in lipid metabolites in pancreatic cancer, and the occurrence and development of pancreatic cancer might be linked to lipid metabolism. A cohort from TCGA was used to construct a novel predictive model of a 4-lipid metabolism-related gene signature, which can be used to predict the prognosis of pancreatic cancer.