Identification of prognostic indicators and markers of endometrial carcinoma based on shared metabolism-related genes in gynecological tumors.

e17589 Background: Aberrant metabolism is a major characteristic of cancer and it is important to determine whether there is a common metabolic genetic signature among gynecological cancers. Endometrial cancer, as one of the gynecological tumors, has shown increasing morbidity and mortality rates annually. Investigating shared metabolic molecular markers in gynecological tumors can enhance our understanding of endometrial cancer. Methods: Transcriptome data for gynecological tumors, including ovarian cancer (OV), endometrial cancer (UCEC) and cervical cancer (CESC) were obtained from The Cancer Genome Atlas database. Transcriptome data for normal cervix uteri, uterus and ovary tissues were downloaded from the Genotype-Tissue Expression Project. Metabolism-related gene sets were sourced from the Kyoto Encyclopedia of Genes and Genomes for subsequent analysis (KEGG) for subsequent analysis. Weighted gene co-expression network analysis (WGCNA) was employed to identify gene modules associated with OV, UCEC and CESC. The intersection of three cancer-related modules and metabolism-related gene sets yielded shared SMRGs (Shared Metabolism-Related Genes). UCEC samples were randomly divided into training set and testing set by caret R package in a 5:5 ratio. A risk model based on SMRGs, capable of predicting the overall survival (OS) of UCEC patients, was constructed using LASSO regression and Cox regression analyses. Results: A total of 463 genes were identified as sharing SMRGs through the intersection of modules. And 81 genes associated with OS was further selected by Univariate Cox regression analysis combined with expression and clinical data. Eventually, 8 genes (CYP11B1, SRD5A1, OTC, ACACB, ASS1, ALG3, PLA2G4F and POC1B-GALNT4) were screened out by LASSO regression analysis to construct the prognostic characteristics. The training set was divided into high and low-risk groups based on the computed risk threshold. Kaplan-Meier analysis demonstrated that patients in the low-risk group exhibited better prognosis. Similar differential survival results were observed in the testing and entire sets. Furthermore, analysis of immune and stromal infiltrates indicated increased infiltration of CD8+ T cells in the low-risk group, and PLA2G4F was identified as the core gene. The expression level of PLA2G4F increased with the progression of UCEC and was closely associated with various immune checkpoint factors, including CD48, HAVCR2, CD86, TIGIT, CTLA4 and BTLA. Conclusions: This study established a shared metabolism-related gene prognostic model applicable to gynecological cancers and analyzed its potential role in the treatment and prognosis assessment of endometrial cancer. Additionally, PLA2G4F was identified as a potential immune-related biomarker for UCEC patients.