Survival prognosis model for elderly women with epithelial ovarian cancer based on the SEER database

Demographic trends in mortality due to ovarian cancer in the United States, 1999-2020

BACKGROUND

Ovarian carcinoma has the highest mortality rate among all gynecological cancers. Several reproductive and hormonal risk factors, including early menarche, late menopause, limited use of oral contraceptives, and a low pregnancy rate, have been identified as contributors to the increased susceptibility to ovarian cancer. Advancements in cancer therapy over the past century, including the emergence of precision oncology, underscore the importance of early detection and tailored interventions, factors particularly critical in ovarian cancer, where late-stage diagnosis remains a persistent barrier to survival. This challenge is compounded by the lack of a universally endorsed screening program, resulting in late-stage identification and widespread metastasis.

AIM

To evaluate demographic differences in ovarian cancer-related mortality from 1999 to 2020 among adult females aged ≥ 25 years within the United States.

METHODS

Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database was used to collect de-identified death certificate data for malignant neoplasm of the ovaries related deaths in female adults aged 25 years and older from the year 1999 to 2020. Crude mortality rates and age-adjusted mortality rates (AAMRs) per 100000 people were calculated. Join point regression program was used to assess annual percent changes in mortality trends, with statistical significance set at P value < 0.05.

RESULTS

Between 1999 and 2020, 337619 deaths due to ovarian cancer occurred among United States females aged 25 to > 85. The AAMR decreased from 14.62 in 1999 to 10.15 in 2020, with significant declines across various demographics. The AAMRs were highest among non-Hispanic White women, i.e., 13.53. Based on region, they were the highest in the Northeast (13.06) and Midwest (12.94). The steepest decline was observed in metropolitan areas as compared to nonmetropolitan ones. The study highlights significant progress in reducing ovarian cancer mortality across age, race/ethnicity, and geographic regions during this period.

CONCLUSION

The mortality trends for ovarian carcinoma patients showed an overall decrease, with the highest mortality rates observed among older individuals (65 to > 85 years) and non-Hispanic Whites. These disparities underscore the need for equitable healthcare access and targeted policy interventions.

Development of machine learning prognostic models for overall survival of epithelial ovarian cancer patients: a SEER-based study

RESEARCH DESIGN AND METHODS

Data were obtained from the SEER database for women diagnosed with EOC between 2004 and 2020. Clinical features, treatment regimens and overall survival (OS) were analyzed. Cox regression was conducted to identify prognostic factors associated with EOC. We employed 5-fold cross-validation to improve the accuracy of the model. Random Survival Forest (RSF), Gradient Boosting Survival Analysis (GBSA) and Support Vector Machine (SVM) were used to develop ML models, then compared with the Cox model. The predictive performance of these models was assessed using AUC, concordance index (C-index), and Brier scores.

RESULTS

A total of 12,949 EOC patients were selected from the SEER database. We identified 14 independent prognostic factors for OS and constructed predictive models. The GBSA model demonstrated superior AUC, C-index, and Brier scores across different time points, outperforming the Cox model. SHAP analysis showed that FIGO stage, grade, and lymph node dissection were the most important features in the GBSA model.

CONCLUSIONS

The GBSA model outperforms traditional methods in survival prediction, offering a valuable tool for clinicians to make informed decisions about patient prognosis.

Random survival forest model in patients with epithelial ovarian cancer: a study based on SEER database and single center data

Clinical data of 1,780 patients with epithelial ovarian carcinoma (EOC) in the Surveillance, Epidemiology and End Results (SEER) database were retrospectively analyzed. A random survival forest model and a nomogram model were built based on the prognostic factors. The clinical data of 140 patients with EOC treated in Liuzhou Worker's Hospital were collected for the validation of the prognostic model. Age (≥75 years), histology grade (poor differentiation or undifferentiation), histologic types (clear cell carcinoma or carcinosarcoma), T stage (T2 or T3), M stage (M1), surgical conditions, and chemotherapy situation (without chemotherapy) were identified as independent risk factors. Based on these factors, a random forest survival prediction model was established. In the training set, the area under the curve (AUC) for the random forest survival prediction model in predicting 1-, 3- and 5-year survival were 0.848, 0.859 and 0.890, respectively. In the test set, the AUCs for 1-, 3- and 5-year survival were 0.992, 0.795 and 0.883, respectively. A nomogram prediction model was also established. In the training set, the AUCs for the nomogram prediction model for 1-, 3- and 5-year survival were 0.789, 0.803 and 0.838, respectively. In the test set, the AUCs for 1-, 3- and 5-year survival were 0.926, 0.748 and 0.836, respectively. The results indicated that the random forest survival model established in this study holds significant clinical value. Physicians can develop personalized follow-up strategies or treatment regimens for patients based on the predicted survival risk, potentially improving long-term outcomes.

Tanshinone I reprograms glycolysis metabolism to regulate histone H3 lysine 18 lactylation (H3K18la) and inhibits cancer cell growth in ovarian cancer

Salvia miltiorrhiza, the anticancer properties of these components are multifaceted, encompassing the inhibition of tumor growth, prevention of the metastatic spread of cancer cells, enhancement of the sensitivity of cancer cells to chemotherapy and radiation therapy, and the suppression of angiogenesis, which is crucial for tumor growth and survival. In the context of our recent study, we have discovered that tanshinone I, one of the active components of Salvia miltiorrhiza, possesses the ability to inhibit the proliferation of ovarian cancer cells, both in laboratory settings and within living organisms. To further understand the molecular mechanisms behind this effect, we conducted a comprehensive transcriptomic analysis. Our findings indicated that tanshinone I exerts its inhibitory action by downregulating the expression of genes associated with glycolysis. Specifically, tanshinone I decreased the expression of glycolysis-related genes such as HK2 (hexokinase 2), PFK (phosphofructokinase), ENO2 (enolase 2), and LDHA (lactate dehydrogenase A). Inhibiting lactate production by tanshinone I application reduced the level of histone H3 lysine 18 lactylation (H3K18la), which reduced the expression of tumor-associated genes, such as TTK, PDGFRβ, YTHDF2 and RUBCNL. In addition, tanshinone I alleviated the immunosuppressive tumor microenvironment. In summary, tanshinone I blocks glycolysis to regulate histone H3 lysine 18 lactylation (H3K18la), which inhibits ovarian cancer cell growth, revealing the anticancer mechanism of tanshinone I.