Development of a lipid metabolism-related gene model to predict prognosis in patients with pancreatic cancer

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.

Multimodal Imaging of Pancreatic Cancer Microenvironment in Response to an Antiglycolytic Drug

“…One study found that LysoDGTS and DGTS levels were significantly higher in pancreatic cancer patients than in healthy controls. [70] Mutation of p53 proteins leads to loss of tumor suppressive activities and may lead to additional oncogenic functions that allow cells to grow and survive. [71] 3-BP has been shown to degrade p53 to reduce the mutated p53.…”

Hepatic transcript profiling in beef cattle: Effects of rumen-protected niacin supplementation

“…The glycosylation process starts by the addition of O-linked monosaccharide β-N-acetylglucosamine (GlcNAc) onto serine or threonine hydroxyl groups, which are included in the KEGG pathway “Other types of O-glycan biosynthesis” [ 34 ]. In our study, there was a downregulation of polypeptide N-acetylgalactosaminyltransferase 16 ( GALNT16 , logFC = -2.24, FDR = 0.04) and protein O-fucosyltransferase 2 ( POFUT2 , logFC = -2.26, FDR = 0.03), which are responsible for transferring N-acetyl-D-galactosamine and fucose to a serine or threonine residue, respectively [ 35 , 36 ]. A possible explanation for the inhibition of these genes could be associated with the effect of rumen-protected niacin on hepatic apolipoproteins (apo) B belonging to VLDL and LDL, however, our study did not find significant changes in ApoB expression.…”

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

“…Significantly higher levels of phosphatidyl ethanolamine, phosphatidylcholine (PC), phosphatidylserine, phosphatidylmethanol, and diacylglyceryl trimethylhomoserine and phosphatidylethanol that related to lipid metabolism could assist clinicians in the prognosis of pancreatic cancer patients [77]. Untargeted metabolomics revealed that lipids including PC (30:2) and PC (30:1) as biomarkers of prognosis play a key role in discriminating early hepatocellular carcinoma and compensated cirrhosis [78].…”