Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis.

BACKGROUND: Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis. METHOD: We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan-Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT. RESULTS: Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates. CONCLUSIONS: Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.

Enhancing precision medicine: a nomogram for predicting platinum resistance in epithelial ovarian cancer.

BACKGROUND: This study aimed to develop a novel nomogram that can accurately estimate platinum resistance to enhance precision medicine in epithelial ovarian cancer(EOC). METHODS: EOC patients who received primary therapy at the General Hospital of Ningxia Medical University between January 31, 2019, and June 30, 2021 were included. The LASSO analysis was utilized to screen the variables which contained clinical features and platinum-resistance gene immunohistochemistry scores. A nomogram was created after the logistic regression analysis to develop the prediction model. The consistency index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the nomogram's performance. RESULTS: The logistic regression analysis created a prediction model based on 11 factors filtered down by LASSO regression. As predictors, the immunohistochemical scores of CXLC1, CXCL2, IL6, ABCC1, LRP, BCL2, vascular tumor thrombus, ascites cancer cells, maximum tumor diameter, neoadjuvant chemotherapy, and HE4 were employed. The C-index of the nomogram was found to be 0.975. The nomogram's specificity is 95.35% and its sensitivity, with a cut-off value of 165.6, is 92.59%, as seen by the ROC curve. After the nomogram was externally validated in the test cohort, the coincidence rate was determined to be 84%, and the ROC curve indicated that the nomogram's AUC was 0.949. CONCLUSION: A nomogram containing clinical characteristics and platinum gene IHC scores was developed and validated to predict the risk of EOC platinum resistance.

The role of Connexin26 regulated by MiR-2114-3p in the pathogenesis of ovarian cancer.

OBJECTIVES: The purpose of our research was to determine the expression of Cx26 and miR-2114-3p, and their effects on proliferation, migration, and invasion in ovarian cancer and their mechanisms. MATERIALS AND METHODS: Transcriptome sequencing was performed and differentially expressed Cx26 was screened. The mRNA and protein levels of Cx26 in EOC and normal ovarian tissues were verified. The relationship between Cx26 levels and prognostics was analyzed. Cx26 Lentiviral vectors were constructed to detect its effect on ovarian cancer. WB verified that PI3K/AKT pathway was the possible signal pathway regulated by Cx26. The interaction between miR-2114-3p and Cx26 was detected by double luciferase reporter assay and qrt-PCR. CCK8, clone formation, transwell, and flow cytometry assays were conducted in cells transfected miR-2114-3p plasmids. The vivo experiment investigated the effects of Cx26 on subcutaneous tumor growth, PI3K expression, proliferation proteins Ki67 and PCNA. RESULTS: Cx26 was up-regulated in EOC tissue and cell lines, and was associated with poor prognosis of ovarian cancer, while miR-2114-3p was down-regulated in EOC cell lines. Cx26 was a direct target of miR-2114-3p. Cx26 overexpression and miR-2114-3p inhibition promoted the growth, motility, invasiveness, and S phase arrest of EOC cells. Additionally, Cx26 could activated PI3K pathway whatever in vivo and in vitro. CONCLUSIONS: Dysregulation of Cx26 is critical in EOC patients. Manipulation of this mechanism may influence the survival of EOC patients. MiR-2114-3p regulates the tumor-promoting activity of Cx26 in EOC. By inhibiting the PI3K pathway or knocking down Cx26 effectively inhibits tumor growth in EOC cells and Nude mouse model.

Value of the risk of ovarian malignancy algorithm index in predicting the recurrence of epithelial ovarian cancer.

Aim: This study aimed to assess the predictive and diagnostic value of the risk of ovarian malignancy algorithm (ROMA) index for epithelial ovarian cancer (EOC) recurrence. Materials & methods: The clinical features and follow-up data of 159 EOC cases were studied. The ROMA index was calculated by serum CA125 and HE4 levels with menopausal status. Recurrence-free survival was evaluated for an end point. Results: The ROMA was strongly associated with clinical characteristics. The ROMA index above the cutoff value (34.71%) was significantly associated with recurrence-free survival. The ROMA index had a significantly higher sensitivity (90.59%) than CA125 (84.71%) and HE4 (80.80%) for recurrence diagnosis, and its optimal cutoff value was 17.07%. Conclusion: The primary ROMA index is a predictive factor in EOC recurrence and has better performance in the diagnosis of EOC recurrence.