PTGIS May Be a Predictive Marker for Ovarian Cancer by Regulating Fatty Acid Metabolism

Abstract

Background. Ovarian cancer tends to metastasize to the omentum, which is an organ mainly composed of adipose tissue. Many studies have found that fatty acid metabolism is related to the occurrence and metastasis of cancers. Therefore, it is possible that fatty acid metabolism-related genes (FAMRG) affect the prognosis of ovarian cancer patients. Methods. First, profiles of ovarian cancer and normal ovarian tissue transcriptomes were acquired from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. A LASSO regression predictive model was developed via the “glmnet” R package. The nomogram was created via the “regplot.” Gene Set Variation Analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) analyses were conducted to determine the FAMRGs’ roles. The percentage of immunocyte infiltration was calculated via CIBERSORT. Using “pRRophetic,” the sensitivity of eight regularly used medications and immunotherapy was anticipated. Results. 125 genes were determined as different expression genes (DEGs). Based on RXRA, ECI2, PTGIS, and ACACB, a prognostic model is created and the risk score is calculated. Analyses of univariate and multivariate regressions revealed that the risk score was a distinct prognostic factor (univariate: HR: 2.855, 95% CI: 1.756-4.739, P < 0.001 ; multivariate: HR: 2.943, 95% CI: 1.800-4.812, P < 0.001 ). The nomogram demonstrated that it properly predicted the 1-year survival rate. The expression of memory B molecular units, follicular helper T molecular units, regulatory T molecular units, and M1 macrophages differed remarkably between the groups at high and low risk ( P < 0.05 ). Adipocytokine signaling pathways, cancer pathways, and degradation of valine, leucine, and isoleucine vary between high- and low-risk populations. The findings of the GO enrichment revealed that the extracellular matrix and cellular structure were the two most enriched pathways. PTGIS, which is an important gene in fatty acid metabolism, was identified as the hub gene. This result was verified in ovarian cancer and ovarian tissues. The connection between the gene and survival was statistically remarkable ( P = 0.015 ). The pRRophetic algorithm revealed that the low-risk group was more adaptable to cisplatin, doxorubicin, 5-fluorouracil, and etoposide ( P < 0.001 ). Conclusion. PTGIS may be an indicator of prognosis and a possible therapeutic target for the therapy of ovarian cancer patients. The fatty acid metabolism of immune cells may be controlled, which has an indirect effect on cancer cell growth.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

“…PCSK9 [57], SEMA3A [58] and SFRP4 [59] are associated with pain. PCSK9 [60], CNTN4 [61], SEMA3A [62], PTGIS (prostaglandin I2 synthase) [63], SFRP4 [64], MFAP5 [65], CDH6 [66], GPC6 [67] and PKP2 [68] might serve as genetic markers of ovarian cancer. The expression of PCSK9 [69], SFRP4 [70] and BMP6 [71] were altered in the polycystic ovarian syndrome.…”

Validating the role of PTGIS gene in colorectal cancer by bioinformatics analysis and in vitro experiments

“…The mechanism of the PTGIS gene involved in the progression of colorectal cancer needs further. PGI2 is the main metabolite of AA, and PGI2 can activate PPAR to promote the occurrence of cancer 23 . PTGIS is a key factor in the increase of PCI2 in tumors, and may affect the release of inflammatory factors through the synergistic effect of AA pathway, thus affecting the recruitment of immune cells.…”

Identification of a Novel Circadian Rhythm-Related Signature for Predicting Prognosis and Therapies in Hepatocellular Carcinoma Based on Bulk and Single-Cell RNA Sequencing

“…Te risk score and clinical characteristics (such as age, sex, and clinical grade) were subjected to multivariate Cox regression to obtain the independent risk factor, and the relevant forest plot was drawn using the "ezcox" package in R [36]. A predictive nomogram was constructed using the "regplot" package in R [37] based on the independent risk factors. ROC curves were used to evaluate the performance of the predictive model.…”