Background: Ovarian cancer (OV) is associated with high mortality and poses challenges in diagnosis and prognosis prediction. Ubiquitin-related genes (UbRGs) are involved in the initiation and progression of cancers, but have still not been utilized for diagnosis and prognosis of OV. Methods: K48-linked ubiquitination in ovarian tissues from our OV and control cohort was assessed using immunohistochemistry. UbRGs, including ubiquitin and ubiquitin-like regulators, were screened based on the TCGA-OV and GTEx database. Univariate Cox regression analysis identified survival-associated UbRGs. A risk model was established using the LASSO regression and multivariate Cox regression analysis. The relationship between UbRGs and immune cell infiltration, tumor mutational burden, drug sensitivity, and immune checkpoint was determined using the CIBERSORT, ESTIMATE, and Maftools algorithms, based on the Genomics of Drug Sensitivity in Cancer and TCGA-OV databases. GEPIA2.0 was used to analyze the correlation between FBXO9/UBD and DNA damage repair-related genes. Finally, FBXO9 and UBD were accessed in tissues or cells using immunohistochemistry, qPCR, and Western blot. Results: We confirmed the crucial role for ubiquitination in OV as a significant decrease of K48-linked ubiquitination was observed in primary OV lesions. We identified a prognostic signature utilizing two specific UbRGs, FBXO9 and UBD. The risk score obtained from this signature accurately predicted the overall survival of TCGA-OV training dataset and GSE32062 validation dataset. Furthermore, this risk score also showed association with immunocyte infiltration and drug sensitivity, revealing potential mechanisms for ubiquitination mediated OV risk. In addition, FBXO9, but not UBD, was found to be downregulated in OV and positively correlated with DNA damage repair pathways, suggesting FBXO9 as a potential cancer suppressor, likely via facilitating DNA damage repair. Conclusions: We identified and validated a signature of UbRGs that accurately predicts the prognosis, offers valuable guidance for optimizing chemotherapy and targeted therapies, and suggests a potential role for FBXO9 in OV.

Epithelial ovarian cancer (EOC) is the most lethal gynecological malignant tumor. Endoplasmic reticulum (ER) stress plays an important role in the malignant behaviors of several tumors. In this study, we established a risk classifier based on 10 differentially expressed genes related to ER stress to evaluate the prognosis of patients and help to develop novel medical decision-making for EOC cases. A total of 378 EOC cases with transcriptome data from the TCGA-OV public dataset were included. Cox regression analysis was used to establish a risk classifier based on 10 ER stress-related genes (ERGs). Then, through a variety of statistical methods, including survival analysis and receiver operating characteristic (ROC) methods, the prediction ability of the proposed classifier was tested and verified. Similar results were confirmed in the GEO cohort. In the immunoassay, the different subgroups showed different penetration levels of immune cells. Finally, we conducted loss-of-function experiments to silence TRPM2 in the human EOC cell line. We created a 10-ERG risk classifier that displays a powerful capability of survival evaluation for EOC cases, and TRPM2 could be a potential therapeutic target of ovarian cancer cells.