Development and validation of a nomogram to predict survival outcome among epithelial ovarian cancer patients with site-distant metastases: a population-based study

Wang, Bo; Wang, Shixuan; Ren, Wu

doi:10.1186/s12885-021-07977-4

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…predictors including age, radiation therapy, surgery and chemotherapy, CA125, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. Currently, several models were established for predicting the cancer-specific survival of stage II-IV epithelial ovarian cancer patients receiving surgery and chemotherapy (17) or for the surgical outcome in ovarian cancer (18) as well as for the survival outcome in epithelial ovarian cancer patients with site-distant metastases (19).…”

Section: Discussionmentioning

confidence: 99%

“…The present study constructed prediction models for 2-year mortality of metastatic ovarian cancer patients based on the predictors including age, radiation therapy, surgery and chemotherapy, CA125, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. Currently, several models were established for predicting the cancer-specific survival of stage II–IV epithelial ovarian cancer patients receiving surgery and chemotherapy ( 17 ) or for the surgical outcome in ovarian cancer ( 18 ) as well as for the survival outcome in epithelial ovarian cancer patients with site-distant metastases ( 19 ). There was still no prediction model for the survival of all ovarian cancer population with different types of metastasis.…”

Section: Discussionmentioning

confidence: 99%

“…Inci et al conducted a prospective study and established a prediction model for the surgical outcome in ovarian cancer based on frailty index ( 18 ). Wang et al built a prediction model for the survival outcome among epithelial ovarian cancer patients with site-distant metastases ( 19 ). Up to date, the prediction model for the survival of all ovarian cancer population such as non-epithelial ovarian cancer patients with metastasis was still lacking ( 20 ).…”

Section: Introductionmentioning

confidence: 99%

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Prediction for 2-year mortality of metastatic ovarian cancer patients based on surveillance, epidemiology, and end results database

Wang

Xue

et al. 2022

Front. Surg.

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AimTo establish prediction models for 2-year overall survival of ovarian cancer patients with metastasis.MethodsIn total, 4,929 participants from Surveillance, Epidemiology, and End Results (SEER) database were randomly divided into the training set (n = 3,451) and the testing set (n = 1,478). Univariate and multivariable regression were conducted in the training set to identify predictors for 2-year overall survival of metastatic ovarian cancer patients. The C-index was calculated for assessing the performance of the models. The nomogram for the model was plotted. The prediction value of the model was validated in the testing set. Subgroup analysis were performed concerning surgery and chemotherapy status of patients and the metastatic site of ovarian cancer in the testing set. The calibration curves were plotted and the decision curve analysis (DCA) were conducted.ResultsAt the end of follow-up, 2,587 patients were survived and 2,342 patients were dead within 2 years. The 2-year survival rate was 52.5%. The prediction models were constructed based on predictors including age, radiation, surgery and chemotherapy, CA125, and bone, liver, and lung metastasis. The prediction model for 2-year overall survival of ovarian cancer patients with metastasis showed good predictive ability with the C-index of the model of 0.719 (95% CI: 0.706–0.731) in the training set and 0.718 (95% CI: 0.698–0.737) in the testing set. In terms of patients with bone metastasis, the C-index was 0.740 (95% CI: 0.652–0.828) for predicting the 2-year overall survival of ovarian cancer patients. The C-index was 0.836 (95% CI: 0.694–0.979) in patients with brain metastasis, 0.755 (95% CI: 0.721–0.788) in patients with liver metastasis and 0.725 (95% CI: 0.686–0.764) in those with lung metastasis for predicting the 2-year overall survival of ovarian cancer patients.ConclusionThe models showed good predictive performance for 2-year overall survival of metastatic ovarian cancer patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction for 2-year mortality of metastatic ovarian cancer patients based on surveillance, epidemiology, and end results database

Wang

Xue

et al. 2022

Front. Surg.

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“…Ovarian cancer is one of the three major malignancies of the reproductive system among women, with an incidence only second to uterine corpus cancer, but the mortality rate ranks first among gynecological malignancies, causing a serious threat to patient life. Surveys suggest that there are approximately 200 000 new cases each year over the world, and the 5‐year survival rate of patients remains less than 30% even after systemic treatment 1,2 . Therefore, further elucidation of the mechanisms involved in the development and progression of ovarian carcinogenesis, as well as the metastatic invasion of distant organ tissues, is meaningful for the clinical diagnosis and treatment of ovarian cancer.…”

Section: Introductionmentioning

confidence: 99%

CRABP2 accelerates epithelial mesenchymal transition in serous ovarian cancer cells by promoting TRIM16 methylation via upregulating EZH2 expression

Xie¹,

Tan²,

Gao³

et al. 2022

Environmental Toxicology

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Recently, it was covered that cellular retinoic acid-binding protein 2 (CRABP2) is upregulated in ovarian cancer and participates in tumor progression, however, the specific mechanism remains to be explored. The pcDNA-CRABP2 or si-CRABP2 was transfected into SKOV3 and OVCAR3 ovarian cancer cells, respectively, and we observed that overexpression of CRABP2 inhibited cell apoptosis, promoted cell invasion and expression of epithelial mesenchymal transition (EMT) marker proteins, and transfection of si-CRABP2 had the opposite effect. Furthermore, we predicted that EZH2 interacted with CRABP2, and overexpression of CRABP2 promoted EZH2 expression, knockdown of CRABP2 inhibited EZH2 expression, and coimmunoprecipitation assay confirmed their binding relationship. The SKOV3 and OVCAR3 cells were then incubated with pcDNA-CRABP2 alone together with si-EZH2, and we found that si-EZH2 reversed the effect of pcDNA-CRABP2 on promotion of EZH2 expression, cell invasion and EMT maker protein levels. Next, we found that EZH2 could bind to DNMT1, and overexpression of EZH2 inhibited TRIM16 expression and knockdown of EZH2 promoted TRIM16 expression. Moreover, the promoter of TRIM16 contains the CpG island, and ChIP assay observed enriched DNMT1 on the promoter of TRIM16, and overexpression of EZH2 increased the promoter methylation level of TRIM16 and knockdown of EZH2 suppressed the methylation. The SKOV3 cells were incubated with si-EZH2 alone or combined with si-TRIM16, and we found that si-TRIM16 reversed the effect of si-EZH2. In vivo studies showed that knockdown of CRABP2 inhibited tumor volume and weight, suppressed the expression of EZH2 and EMT related proteins vimentin and snail, and increased the expression of TRIM16 and E-cadherin.

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“…Compared with the FIGO stage and AJCC stage system, the nomogram model can predict the survival rate for each patient individually. At present, although several nomograms had been constructed for EOC patients in younger [12], under postoperative [13], and with site-distant metastases [14], there are no nomograms for the elderly EOC patient currently. Elderly patients were often attached to comorbidity, immunosenescence, and organ dysfunction, which lead to more treatment-related toxicity and poor prognosis for elderly patients [15].…”

Section: Introductionmentioning

confidence: 99%

Nomograms for predicting overall survival and cancer-specific survival in elderly patients with epithelial ovarian cancer

Cheng

Xu²,

et al. 2022

Preprint

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Background Epithelial ovarian cancer (EOC) is one of the most fatal gynecological malignancies among elderly patients. We aim to construct two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) in elderly EOC patients. Methods Elderly patients with EOC between 2000 and 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Enrolled patients were randomly divided into the training and validation set at a ratio of 7:3. The OS and CSS were recognized as endpoint times. The independent prognostic factors from the multivariate analysis were used to establish nomograms for predicting the 3-, 5- and 10-year OS and CSS of elderly EOC patients. The improvement of predictive ability and clinical benefits were evaluated by consistency index (C-index), receiver operating characteristic (ROC), calibration curve, decision curve (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Finally, the treatment efficacy of surgery and chemotherapy in low-, medium-, and high-risk groups were displayed by Kaplan-Meier curves. Results A total of 5,588 elderly EOC patients were obtained and randomly assigned to the training set (n = 3724) and validation set (n = 1864). The independent prognostic factors were utilized to construct nomograms for OS and CSS. The C-index of the OS nomogram and CSS nomogram were 0.755 and 0.700 in the training cohort. In the validation cohort, the C-index of the OS nomogram and CSS nomogram were 0.746 and 0.696. The calibration curve demonstrated good concordance between the predicted survival rates and actual observations. Moreover, the NRI, IDI, and DCA curves determined the outperformance of the nomogram compared with the AJCC stage system. Besides, surgery had no benefit on the prognosis in the high-risk group. Chemotherapy had a better prognosis in the medium-, and high-risk groups, but not for the low-risk group. Conclusions We developed and validated nomograms for predicting OS and CSS in elderly EOC patients to help gynecologists to develop an appropriate individualized therapeutic schedule.

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Development and validation of a nomogram to predict survival outcome among epithelial ovarian cancer patients with site-distant metastases: a population-based study

Cited by 11 publications

References 25 publications

Prediction for 2-year mortality of metastatic ovarian cancer patients based on surveillance, epidemiology, and end results database

Prediction for 2-year mortality of metastatic ovarian cancer patients based on surveillance, epidemiology, and end results database

CRABP2 accelerates epithelial mesenchymal transition in serous ovarian cancer cells by promoting TRIM16 methylation via upregulating EZH2 expression

Nomograms for predicting overall survival and cancer-specific survival in elderly patients with epithelial ovarian cancer

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