ABSTRACT
(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) for Fédération Internationale de Gynécologie et d'Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer.
ABSTRACT
(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.
Subject(s)
Biomarkers, Tumor , Cystadenocarcinoma, Serous/etiology , Cystadenocarcinoma, Serous/metabolism , Lipid Metabolism , Ovarian Neoplasms/etiology , Ovarian Neoplasms/metabolism , Computational Biology , Cystadenocarcinoma, Serous/mortality , Cystadenocarcinoma, Serous/pathology , Databases, Genetic , Disease Susceptibility , Female , Gene Expression Profiling , Gene Expression Regulation, Neoplastic , Gene Regulatory Networks , Humans , Kaplan-Meier Estimate , Ovarian Neoplasms/mortality , Ovarian Neoplasms/pathology , Prognosis , ROC Curve , TranscriptomeABSTRACT
BACKGROUD: Prostaglandin E2 (PGE2), an inflammatory mediator, modulates cytokines, regulates immune responses in reproductive processes and stimulates inflammatory reactions via the prostaglandin E2 receptor 2 (EP2). However, the regulatory effects of EP2 signaling on trophoblasts and its role in unexplained recurrent miscarriage (uRM) remains unclear. PATIENTS AND METHODS: A total of 19 placentas from patients with a history of more than two consecutive pregnancy losses of unknown cause (uRM group) and placentas of 19 healthy patients following a legal termination of their pregnancy were used for PGE2 receptor (EP1, EP2 and EP4) expression analyses via immunohistochemistry. Double immunofluorescence was also used to identify EP2 expressing cells in the decidua. Finally, HTR-8/SVneo cells were used to clarify the role of EP2 in in vitro experiments. RESULTS: The expression of EP2 and EP4 was found to be reduced in the syncytiotrophoblast and decidua of uRM patients. A selective EP2 receptor antagonist (PF-04,418,948) reduced the proliferation and secretion of ß-hCG, inhibited interleukin -6 (IL-6) and interleukin-8 (IL-8) and up-regulated the production of the tumor necrosis factor-α (TNF-α) and plasminogen activator inhibitor type 1 (PAI-1) in HTR-8/SVneo cells in vitro. CONCLUSION: PGE2-EP2 signaling pathway may represent a novel therapy option for uRM. The involvement of EP2 in uRM acts perhaps via inflammatory cytokines and indicates that the PGE2-EP2 signaling pathway might represent an unexplored etiology for uRM.
