Expression Profiling in Ovarian Cancer Reveals Coordinated Regulation of BRCA1/2 and Homologous Recombination Genes

The Associations and Causal Relationships of Ovarian Cancer - Construction of a Prediction Model

Purpose

To explore the risk and protective factors for developing ovarian cancer and construct a risk prediction model.

Methods

Information related to patients diagnosed with ovarian cancer on the electronic medical record data platform of three tertiary hospitals in Guangdong Province from May 2018 to September 2023 was collected as the case group. Patients with non-ovarian cancer who attended the clinic during the same period were included in the control group. Logistic regression analysis was used to screen the independent variables and explore the factors associated with the development of ovarian cancer. An ovarian cancer risk prediction model was constructed using a decision tree C4.5 algorithm. The ROC and calibration curves were plotted, and the model was validated.

Results

Logistic regression analysis identified independent risk and protective factors for ovarian cancer. The sample size was divided into training and test sets in a ratio of 7:3 for model construction and validation. The AUC of the training and test sets of the decision tree model were 0.961 (95% CI:0.944-0.978) and 0.902 (95% CI:0.840-0.964), respectively, and the optimal cut-off values and their coordinates were 0.532 (0.091, 0.957), and 0.474 (0.159, 0.842) respectively. The accuracies of the training and test sets were 93.3% and 84.2%, respectively, and their sensitivities were 95.7% and 84.2%, respectively.

Conclusion

The constructed ovarian cancer risk prediction model has good predictive ability, which is conducive to improving the efficiency of early warning of ovarian cancer in high-risk groups.

An artificial intelligence prediction model based on extracellular matrix proteins for the prognostic prediction and immunotherapeutic evaluation of ovarian serous adenocarcinoma

Background: Ovarian Serous Adenocarcinoma is a malignant tumor originating from epithelial cells and one of the most common causes of death from gynecological cancers. The objective of this study was to develop a prediction model based on extracellular matrix proteins, using artificial intelligence techniques. The model aimed to aid healthcare professionals to predict the overall survival of patients with ovarian cancer (OC) and determine the efficacy of immunotherapy. Methods: The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) data collection was used as the study dataset, whereas the TCGA-Pancancer dataset was used for validation. The prognostic importance of 1068 known extracellular matrix proteins for OC were determined by the Random Forest algorithm and the Lasso algorithm establishing the ECM risk score. Based on the gene expression data, the differences in mRNA abundance, tumour mutation burden (TMB) and tumour microenvironment (TME) between the high- and low-risk groups were assessed. Results: Combining multiple artificial intelligence algorithms we were able to identify 15 key extracellular matrix genes, namely, AMBN, CXCL11, PI3, CSPG5, TGFBI, TLL1, HMCN2, ESM1, IL12A, MMP17, CLEC5A, FREM2, ANGPTL4, PRSS1, FGF23, and confirm the validity of this ECM risk score for overall survival prediction. Several other parameters were identified as independent prognostic factors for OC by multivariate COX analysis. The analysis showed that thyroglobulin (TG) targeted immunotherapy was more effective in the high ECM risk score group, while the low ECM risk score group was more sensitive to the RYR2 gene-related immunotherapy. Additionally, the patients with low ECM risk scores had higher immune checkpoint gene expression and immunophenoscore levels and responded better to immunotherapy. Conclusion: The ECM risk score is an accurate tool to assess the patient's sensitivity to immunotherapy and forecast OC prognosis.

Possible Association of Hysterectomy Accompanied with Opportunistic Salpingectomy with Early Menopause: A Retrospective Cohort Study

Opportunistic salpingectomies (OSs) are concurrently performed with hysterectomies to prevent epithelial ovarian cancer. This study aimed to investigate the correlation between OS and early menopause in females who have undergone hysterectomies. This was a retrospective cohort study involving 79 females who had undergone a hysterectomy, with or without an OS, between January 2007 and December 2015. Their ages at surgery, at menopause, and the lengths of time from surgery to menopause were compared. An OS had been performed in 54 and not performed in 25 of the enrolled patients, comprising the OS and non-OS groups. Body mass index was significantly higher in the OS group (OS: 25.27 ± 4.17 vs. non-OS: 22.97 ± 3.27, p = 0.01). Additionally, menopausal sleep problems were more prevalent in the OS group than in the non-OS group (41% vs. 12%, p = 0.01). Notably, the time from surgery to menopause was significantly shorter in the OS group than in the non-OS group (OS: 1.84 ± 1.85 vs. non-OS: 2.93 ± 2.43, p = 0.031). After adjusting the covariates, the OS group was associated with a significantly shorter period between surgery and menopause (p = 0.029). In conclusion, these results showed that a hysterectomy plus an OS might cause earlier menopause than a hysterectomy only. An OS should be preoperatively discussed with patients regarding the possibility of early menopause. The findings of this study require further large-scale investigations to reinforce the results.