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

Stage IV ovarian cancer prognosis nomogram and analysis of racial differences: A study based on the SEER database

Purpose

Stage IV ovarian cancer is a tumor with a poor prognosis and lacks prognostic models. This study constructed and validated a model to predict overall survival (OS) in patients with newly diagnosed stage IV ovarian cancer.

Methods

The data of this study were extracted from SEER database. Cox regression analysis was used to construct the nomogram model and implemented it in an online web application. Concordance index (C-index), calibration curve, area under receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to verify the performance of the model.

Results

A total of 6062 patients were collected in this study. The analysis showed that age, race, histological grade, histological differentiation, T stage, CA125, liver metastasis, primary site surgery, and chemotherapy were independent prognostic parameters, and were used to construct the nomogram model. The C-index of the training group and the verification group was 0.704 and 0.711, respectively. Based on the score of the nomogram responding risk classification system is constructed. The online interface of Alfalfa-IVOC-OS is free to use. In addition, the racial analysis found that Asian or Pacific Islander people had higher survival rates than white and black people.

Conclusion

This study established a new survival prediction model and risk classification system designed to predict OS time in patients with stage IV ovarian cancer to help clinicians evaluate the prognosis of patients with stage IV ovarian cancer.

Pan-immune-inflammation value: a new prognostic index in epithelial ovarian cancer

BACKGROUND

Epithelial ovarian cancer (EOC) is one of the deadliest gynaecological malignancies worldwide. The aim of this retrospective study was to create a predictive scoring model based on simple immunological and inflammatory parameters to predict overall survival (OS) and progression-free survival (PFS) in patients with EOC.

METHODS

We obtained 576 EOC patients and randomly assigned them to the training set (n = 405) and the validation set (n = 171) in a ratio of 7:3. We retrospectively evaluated the association between PIV and OS and PFS using a novel immunoinflammatory marker, according to the optihmal treshold of PIV, we divided the patients into two different subgroups, high PIV (PIV > 254.9) and low PIV (PIV ≤ 254.9). Pan-immune Inflammatory Value (PIV) was computed as follows: neutrophil count (109/L) × platelet count (109/L) × monocyte count (109/L)/lymphocyte count (109/L). Then developed a simple score prediction model based on several independent prognostic parameters using Cox regression analysis. We used receiver operator characteristic (ROC) curves, calibration plots, and decision analysis (DCA) curves to evaluate the performance of the model. Finally, we used Kaplan-Meier curves to ensure that the model could distinguish well between low- and high-risk groups.

RESULTS

There was a significant difference in survival outcomes between high PIV (PIV > 310.2) and low PIV (PIV ≤ PIV310.2) (3-year survival rates of 61.34% and 76.71%, respectively); 5-year OS, 25.21% and 51.14%, respectively; 3-year PFS, 40.90% and 65.30%; 5-year PFS, 19.33% and 39.73%, respectively). Column plots of OS and PFS were constructed using independent prognostic factors. In the training module, the 3-, 5-, and 10-year AUCs for OS and PFS column charts were 0.713, 0.796, 0.839, and 0.730, 0.799, 0.826, respectively.In the validation cohort, the 3-, 5-, and 10-year AUCs for OS and PFS column charts were 0.676, 0.803, 0.685, and 0.700, respectively, 0.754, 0.727. The calibration curves showed good agreement between predicted survival and actual observations. The decision analysis curves also showed that the current model has good accuracy and clinical applicability. 3-year OS was 61.34% and 76.71%, respectively; 5-year OS was 25.21% and 51.14%, respectively; 3-year PFS was 40.90% and 65.30%, respectively; 5-year PFS was 19.33% and 39.73%, respectively.

CONCLUSIONS

We constructed and validated a PIV-based nomogram to predict OS and PFS in EOC patients, with a view to helping gynaecologists converge on oncologists in their treatment and follow-up expertise in epithelial ovarian cancer.

Comprehensive analyses of mitophagy-related genes and mitophagy-related lncRNAs for patients with ovarian cancer

BACKGROUND

Both mitophagy and long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC). We sought to explore the characteristics of mitophagy-related gene (MRG) and mitophagy-related lncRNAs (MRL) to facilitate treatment and prognosis of OC.

METHODS

The processed data were extracted from public databases (TCGA, GTEx, GEO and GeneCards). The highly synergistic lncRNA modules and MRLs were identified using weighted gene co-expression network analysis. Using LASSO Cox regression analysis, the MRL-model was first established based on TCGA and then validated with four external GEO datasets. The independent prognostic value of the MRL-model was evaluated by Multivariate Cox regression analysis. Characteristics of functional pathways, somatic mutations, immunity features, and anti-tumor therapy related to the MRL-model were evaluated using abundant algorithms, such as GSEA, ssGSEA, GSVA, maftools, CIBERSORT, xCELL, MCPcounter, ESTIMATE, TIDE, pRRophetic and so on.

RESULTS

We found 52 differentially expressed MRGs and 22 prognostic MRGs in OC. Enrichment analysis revealed that MRGs were involved in mitophagy. Nine prognostic MRLs were identified and eight optimal MRLs combinations were screened to establish the MRL-model. The MRL-model stratified patients into high- and low-risk groups and remained a prognostic factor (P < 0.05) with independent value (P < 0.05) in TCGA and GEO. We observed that OC patients in the high-risk group also had the unfavorable survival in consideration of clinicopathological parameters. The Nomogram was plotted to make the prediction results more intuitive and readable. The two risk groups were enriched in discrepant functional pathways (such as Wnt signaling pathway) and immunity features. Besides, patients in the low-risk group may be more sensitive to immunotherapy (P = 0.01). Several chemotherapeutic drugs (Paclitaxel, Veliparib, Rucaparib, Axitinib, Linsitinib, Saracatinib, Motesanib, Ponatinib, Imatinib and so on) were found with variant sensitivity between the two risk groups. The established ceRNA network indicated the underlying mechanisms of MRLs.

CONCLUSIONS

Our study revealed the roles of MRLs and MRL-model in expression, prognosis, chemotherapy, immunotherapy, and molecular mechanism of OC. Our findings were able to stratify OC patients with high risk, unfavorable prognosis and variant treatment sensitivity, thus improving clinical outcomes for OC patients.

Differences of survival benefits brought by various treatments in ovarian cancer patients with different tumor stages

PURPOSE

The current study aimed to explore the prognosis of ovarian cancer patients in different subgroup using three prognostic research indexes. The current study aimed to build a prognostic model for ovarian cancer patients.

METHODS

The study dataset was downloaded from Surveillance Epidemiology and End Results database. Accelerated Failure Time algorithm was used to construct a prognostic model for ovary cancer.

RESULTS

The mortality rate in the model group was 51.6% (9,314/18,056), while the mortality rate in the validation group was 52.1% (6,358/12,199). The current study constructed a prognostic model for ovarian cancer patients. The C indexes were 0.741 (95% confidence interval: 0.731-0.751) in model dataset and 0.738 (95% confidence interval: 0.726-0.750) in validation dataset. Brier score was 0.179 for model dataset and validation dataset. The C indexes were 0.741 (95% confidence interval: 0.733-0.749) in bootstrap internal validation dataset. Brier score was 0.178 for bootstrap internal validation dataset.

CONCLUSION

The current research indicated that there were significant differences in the survival benefits of treatments among ovarian cancer patients with different stages. The current research developed an individual mortality risk predictive system that could provide valuable predictive information for ovarian cancer patients.

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

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 2:1. 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

Five thousand five hundred eighty-eight 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. Dynamic nomograms were also developed. The C-index of the OS nomogram and CSS nomogram were 0.713 and 0.729 in the training cohort. In the validation cohort, the C-index of the OS nomogram and CSS nomogram were 0.751 and 0.702. 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, local tumor resection had a higher benefit on the prognosis in all patients. Chemotherapy had a better prognosis in the high-risk groups, but not for the medium- risk and low-risk groups.

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.

Survival Prediction Models for Ovarian Cancer Patients with Lung Metastasis: A Retrospective Cohort Study Based on SEER Database

To develop a random forest prediction model for the and short- and long-term survival of ovarian cancer patients with lung metastasis. This retrospective cohort study enrolled primary ovarian cancer patients with lung metastasis from the surveillance, epidemiology and end results (SEER) database (2010–2015). All eligible women were randomly divided into the training (n = 1357) and testing set (n = 582). The outcomes were 1-, 3- and 5-year survival. Predictive factors were screened by random forest analysis. The prediction models for predicting the 1-, 3- and 5-year survival were conducted using the training set, and the internal validation was carried out by the testing set. The performance of the models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). The subgroups based on the pathological classification further assessed the model’s performance. Totally 1345 patients suffered from death within 5 years. The median follow-up was 7.00 (1.00, 21.00) months. Age at diagnosis, race, marital status, tumor size, tumor grade, TNM stage, brain metastasis, liver metastasis, bone metastasis, etc. were predictors. The AUCs of the prediction model for the 1-, 3-, 5-year survival in the testing set were 0.849 [95% confidence interval (CI): 0.820–0.884], 0.789 (95% CI 0.753–0.826) and 0.763 (95% CI 0.723–0.802), respectively. The results of subgroups on different pathological classifications showed that the AUCs of the model were over 0.7. This random forest model performed well predictive ability for the short- and long-term survival of ovarian cancer patients with lung metastasis, which may be beneficial to identify high-risk individuals for intelligent medical services.

Survival Outcomes of Patients with International Federation of Gynecology and Obstetrics Stage IV Ovarian Cancer: Cytoreduction Still Matters

PURPOSE

There is still no consensus on the therapeutic strategies for patients with International Federation of Gynecology and Obstetrics (FIGO) stage IV ovarian cancer (OC). We aim to outline the clinical characteristics and optimal therapeutic strategies of patients with FIGO stage IV OC.

METHODS

This single center retrospective study analyzed the clinical features and survival of patients with FIGO stage IV OC that underwent cytoreduction or received at least one course of chemotherapy between January 2014 and December 2020.

RESULTS

One hundred and twenty patients were included. Surgery, especially optimal cytoreduction without residual mass improved the overall survival of patients in surgery group (P = .047, HR .432, 95% CI .181-.987). Secondly, the completion of chemotherapy improved median overall survival of patients either with (53.0 months vs 25.0 months, P < .001, HR 7.015, 95% CI 1.372-35.881) or without cytoreduction (43.0 months vs 6.0 months, P = .006, HR 5.969, 95% CI 1.115-31.952). In patients with FIGO stage IVB, those with only extra-abdominal lymph node metastases had better survival.

CONCLUSIONS

In patients with FIGO stage IV, complete resection of intra-abdominal tumor foci and completion of chemotherapy provided considerable survival benefits to patients with FIGO stage IV OC. Among patients with FIGO stage IVB, those with only extra-abdominal lymph node metastases had a better prognosis.

Agent-based modeling predicts RAC1 is critical for ovarian cancer metastasis

Experimental and computational studies pinpoint rate-limiting step(s) in metastasis governed by Rac1. Using ovarian cancer cell and animal models, Rac1 expression was manipulated, and quantitative measurements of cell-cell and cell-substrate adhesion, cell invasion, mesothelial clearance, and peritoneal tumor growth discriminated the tumor behaviors most highly influenced by Rac1. The experimental data were used to parameterize an agent-based computational model simulating peritoneal niche colonization, intravasation, and hematogenous metastasis to distant organs. Increased ovarian cancer cell survival afforded by the more rapid adhesion and intravasation upon Rac1 overexpression is predicted to increase the numbers of and the rates at which tumor cells are disseminated to distant sites. Surprisingly, crowding of cancer cells along the blood vessel was found to decrease the numbers of cells reaching a distant niche irrespective of Rac1 overexpression or knockdown, suggesting that sites for tumor cell intravasation are rate limiting and become accessible if cells intravasate rapidly or are displaced due to diminished viability. Modeling predictions were confirmed through animal studies of Rac1-dependent metastasis to the lung. Collectively, the experimental and modeling approaches identify cell adhesion, rapid intravasation, and survival in the blood as parameters in the ovarian metastatic cascade that are most critically dependent on Rac1.

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

Aim

To establish prediction models for 2-year overall survival of ovarian cancer patients with metastasis.

Methods

In 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.

Results

At 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.

Conclusion

The models showed good predictive performance for 2-year overall survival of metastatic ovarian cancer patients.

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

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 co-immunoprecipitation 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.

A prognostic model of patients with ovarian mucinous adenocarcinoma: a population-based analysis

BACKGROUND

Ovarian mucinous carcinoma is a disease that requires unique treatment. But for a long time, guidelines for ovarian serous carcinoma have been used for the treatment of ovarian mucinous carcinoma. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with ovarian mucinous adenocarcinoma.

METHODS

In this study, patients initially diagnosed with ovarian mucinous adenocarcinoma from 2004 to 2015 were screened from the Surveillance, Epidemiology, and End Results (SEER) database, and divided into the training group and the validation group at a ratio of 7:3. Independent risk factors for OS and CSS were determined by multivariate Cox regression analysis, and nomograms were constructed and validated.

RESULTS

In this study, 1309 patients with ovarian mucinous adenocarcinoma were finally screened and randomly divided into 917 cases in the training group and 392 cases in the validation group according to a 7:3 ratio. Multivariate Cox regression analysis showed that the independent risk factors of OS were age, race, T_stage, N_stage, M_stage, grade, CA125, and chemotherapy. Independent risk factors of CSS were age, race, marital, T_stage, N_stage, M_stage, grade, CA125, and chemotherapy. According to the above results, the nomograms of OS and CSS in ovarian mucinous adenocarcinoma were constructed. In the training group, the C-index of the OS nomogram was 0.845 (95% CI: 0.821-0.869) and the C-index of the CSS nomogram was 0.862 (95%CI: 0.838-0.886). In the validation group, the C-index of the OS nomogram was 0.843 (95% CI: 0.810-0.876) and the C-index of the CSS nomogram was 0.841 (95%CI: 0.806-0.876). The calibration curve showed the consistency between the predicted results and the actual results, indicating the high accuracy of the nomogram.

CONCLUSION

The nomogram provides 3-year and 5-year OS and CSS predictions for patients with ovarian mucinous adenocarcinoma, which helps clinicians predict the prognosis of patients and formulate appropriate treatment plans.