Introduction
Inflammation is one of the key hallmarks of gastric cancer (GC) development, and the arachidonic acid (AA) pathway with more than 90 members has been shown to play a significant role in this process. Recent advances in omics technology have shed light on the molecular changes in cancer, providing a foundation for the identification of candidate biomarkers for use in diagnosis, prognosis, and screening. Thus, the purpose of this study was to revisit the AA pathway in GC using a transcriptomics approach, in order to identify which genes are particularly dysregulated and thus potentially representing targetable biomarkers.
Aims & Methods
RNA-Seq gene expression data (Level 3) and clinical data of the stomach adenocarcinoma TCGA cohort (STAD), containing 414 tumor samples, was obtained using cBioportal (http://cbioportal.org) and UCSC Xena (http://xena.ucsc.edu/). Shapiro–Wilk normality test was used to determine variable normality, and statistically significant differences of gene expression levels between tumor tissue and adjacent normal tissue were evaluated using the nonparametric Wilcoxon test. Hazard ratios for prognosis assessment were calculated using a univariate Cox regression model. Differential gene expression (DGE) analysis was performed using limma and edgeR R-packages. All statistical analyses were performed using R software (version 4.2.1), and p-values < 0.05 were considered statistically significant.
Results
A total of 53 genes were found to be differentially expressed in GC tumors compared with normal adjacent tissues. Overall, 29 genes were down regulated and 24 were up regulated, with the majority being involved in prostaglandin regulation and synthesis. The top down regulated genes include CYP4B1, AKR1C2, HPGD and AKR1C. Such genes encode proteins involved in AA metabolism and prostaglandin regulation and degradation, except for PTGS1, which encodes COX-1, an enzyme associated with prostaglandin synthesis. SOX9, GPX2, PON1, andFAAH2 accounted for the top up regulated genes. In the univariate analysis, four of the top DEGs correlated with worse overall survival: CYP11A1, SOX9, PON1 and FAAH2.
Conclusion
Overall, using a transcriptomic approach, the AA pathway was shown to be dysregulated in GC, with over half of the genes exhibiting altered expression, and going beyond inflammatory biomarkers, highlighting the pivotal role this pathway has in tumor development and progression and the potential for targetable actions.