Dataset Information

Optimal cutoff age for predicting prognosis associated with serous epithelial ovarian cancer: what is the best age cutoff?

ABSTRACT:

Objective

Elderly age is one of the poor prognostic factors in epithelial ovarian cancer (EOC), but the optimal age cut-off is not known. The present study sought to identify the ideal age cutoff that represents a negative prognostic factor in EOC, considering the geriatric assessment.

Methods

Hazard ratios (HRs) with p-values were calculated using all possible age cutoffs with stage, histology, grade, optimality and comorbidities as covariates in multivariate Cox regression model. The trends of p-value and HR by age cutoff were further evaluated in a subgroup of histology and in The Cancer Genome Atlas (TCGA) dataset. In addition, propensity score-matching analysis using the identified age cutoff was performed.

Results

An age of 66 years was shown to be the most significant cutoff for defining old age with independent prognostic power (HR=1.45; 95% confidence interval=1.04-2.03; p=0.027). This result was also observed with the analyses of serous histology subgroup and with the analysis of a TCGA dataset with serous EOC. In survival analysis, patients aged ?66 years had significantly worse overall survival compared with younger individuals (56 months vs. 87 months; p=0.006), even following propensity score matching (57 vs. 78 months; p=0.038).

Conclusion

An age of 66 years is the best cutoff to define elderly age in serous EOC patients considering the geriatric assessment, and this information can be used in the administration of individualized therapies in elderly EOC patients.

SUBMITTER: Kim J

PROVIDER: S-EPMC6304408 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Optimal cutoff age for predicting prognosis associated with serous epithelial ovarian cancer: what is the best age cutoff?

Kim Jihye J Chang Youjean Y Kim Tae Joong TJ Lee Jeong Won JW Kim Byoung Gie BG Bae Duk Soo DS Choi Chel Hun CH

Journal of gynecologic oncology 20181022 1

<h4>Objective</h4>Elderly age is one of the poor prognostic factors in epithelial ovarian cancer (EOC), but the optimal age cut-off is not known. The present study sought to identify the ideal age cutoff that represents a negative prognostic factor in EOC, considering the geriatric assessment.<h4>Methods</h4>Hazard ratios (HRs) with p-values were calculated using all possible age cutoffs with stage, histology, grade, optimality and comorbidities as covariates in multivariate Cox regression model ...[more]

PMID: 30479095

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Project description:PurposeThe survival duration of elderly patients with epithelial ovarian carcinoma is shorter than that of their younger counterparts. This variation in survival duration is likely attributed to differences in the distribution of histological type or grade, International Federation of Gynecology and Obstetrics (FIGO) staging, and undertreatment, but this observation remains controversial. This study aimed to investigate the biological factors other than selection bias associated with the decreased survival of elderly patients with ovarian carcinoma.MethodsA total of 314 serous ovarian cancer (SOC) patients from Jiangsu Institute of Cancer Research (JICR, PRC) between 2002 and 2012 were retrospectively analyzed, and 774 cases from MD Anderson Cancer Center (MDACC, USA) between 1992 and 2012 were used for validation. The 8-hydroxy-2'-deoxyguanine (8-OHdG) concentration in leukocyte DNA was evaluated by using commercially available enzyme-linked immunosorbent assay kits, and tissue expression was assayed through immunohistochemistry. The associations between survival durations and covariates were assessed by using a Cox proportional hazards model and by conducting a log-rank test.ResultsAdvanced age ≥ 65 years was correlated with high histological grade (p = 0.02), performance status (p = 0.03), primary treatment (p = 0.00), and suboptimal surgery outcome (p = 0.04) in SOC patients from JICR. Age, FIGO stage, histological grade, and optimal surgery were independently associated with the progression-free survival (PFS; p = 0.03, p = 0.03, p = 0.02, and p = 0.04, respectively) and overall survival (OS; p = 0.02, p = 0.04, p = 0.02, and p = 0.02, respectively) of the SOC patients from JICR. The 8-OHdG concentration in the leukocyte DNA was higher in the elderly patients than in the younger cases. The high 8-OHdG concentration in the leukocyte DNA indicated poorer median OS (30.0 months, confidence interval [CI]: 23.5-36.5 vs. 42.8 months, [CI] 38.3-47.2) and PFS (14.6 months, [CI] 11.9-17.2 vs. 18.9 months, [CI] 14.4-23.4) than those of their corresponding counterparts in the SOC patients who achieved a clinical complete response from primary treatment.ConclusionsCompared with younger cases, elderly patients with SOC were commonly characterized by high tumor grade, poor performance status, and undertreatment. High 8-OHdG concentration in leukocyte DNA was associated with advanced age and poor prognosis in SOC patients.

Project description:BackgroundEpithelial ovarian cancer (EOC) is a spectrum of different diseases, which makes their treatment a challenge. Forkhead box M1 (FOXM1) is an oncogene aberrantly expressed in many solid cancers including serous EOC, but its role in non-serous EOCs remains undefined. We examined FOXM1 expression and its correlation to prognosis across the three major EOC subtypes, and its role in tumorigenesis and chemo-resistance in vitro.MethodsGene signatures were generated by microarray for 14 clear-cell and 26 endometrioid EOCs, and 15 normal endometrium snap-frozen biopsies. Validation of FOXM1 expression was performed by RT-qPCR and immunohistochemistry in the same samples and additionally in 50 high-grade serous EOCs and in their most adequate normal controls (10 luminal fallopian tube and 20 ovarian surface epithelial brushings). Correlations of FOXM1 expression to clinic-pathological parameters and patients' prognosis were evaluated by Kaplan-Meier and Cox proportional-hazards analyses. OVCAR-3 and two novel deeply characterized EOC cell lines (EOC-CC1 and OSPC2, with clear-cell and serous subtype, respectively) were employed for in vitro studies. Effects of FOXM1 inhibition by transient siRNA transfection were evaluated on cell-proliferation, cell-cycle, colony formation, invasion, and response to conventional first- and second-line anticancer agents, and to the PARP-inhibitor olaparib. Gene signatures of FOXM1-silenced cell lines were generated by microarray and confirmed by RT-qPCR.ResultsA significant FOXM1 mRNA up-regulation was found in EOCs compared to normal controls. FOXM1 protein overexpression significantly correlated to serous histology (p = 0.001) and advanced FIGO stage (p = 0.004). Multivariate analyses confirmed FOXM1 protein overexpression as an independent indicator of worse disease specific survival in non-serous EOCs, and of shorter time to progression in platinum-resistant cases. FOXM1 downregulation in EOC cell lines inhibited cell growth and clonogenicity, and promoted the cytotoxic effects of platinum compounds, doxorubicin hydrochloride and olaparib. Upon FOXM1 knock-down in EOC-CC1 and OSPC2 cells, microarray and RT-qPCR analyses revealed the deregulation of several common and other unique subtype-specific FOXM1 putative targets involved in cell cycle, metastasis, DNA repair and drug response.ConclusionsFOXM1 is up-regulated in all three major EOCs subtypes, and is a prognostic biomarker and a potential combinatorial therapeutic target in platinum resistant disease, irrespective of tumor histology.

Project description:Serous ovarian cancer is one of the most fatal gynecological tumors with an extremely low 5-year survival rate. Most patients are diagnosed at an advanced stage with wide metastasis. The dysregulation of genes serves an important role in the metastasis progression of ovarian cancer. Differentially expressed genes (DEGs) between primary tumors and metastases of serous ovarian cancer were screened out in the gene expression profile of GSE73168 from Gene Expression Omnibus (GEO). Cytoscape plugin cytoHubba and weighted gene co-expression network analysis (WGCNA) were utilized to select hub genes. Univariate and multivariate Cox regression analyses were used to screen out prognosis-associated genes. Furthermore, the Oncomine validation, prognostic analysis, methylation mechanism, gene set enrichment analysis (GSEA), TIMER database analysis and administration of candidate molecular drugs were conducted for hub genes. Nine hundred and fifty-seven DEGs were identified in the gene expression profile of GSE73168. After using Cytoscape plugin cytoHubba, 83 genes were verified. In co-expression network, the blue module was most closely related to tumor metastasis. Furthermore, the genes in Cytoscape were analyzed, showing that the blue module and screened 17 genes were closely associated with tumor metastasis. Univariate and multivariate Cox regression revealed that the age, stage and STMN2 were independent prognostic factors. The Cancer Genome Atlas (TCGA) suggested that the up-regulated expression of STMN2 was related to poor prognosis of ovarian cancer. Thus, STMN2 was considered as a new key gene after expression validation, survival analysis and TIMER database validation. GSEA confirmed that STMN2 was probably involved in ECM receptor interaction, focal adhesion, TGF beta signaling pathway and MAPK signaling pathway. Furthermore, three candidate small molecule drugs for tumor metastasis (diprophylline, valinomycin and anisomycin) were screened out. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and western blot showed that STMN2 was highly expressed in ovarian cancer tissue and ovarian cancer cell lines. Further studies are needed to investigate these prognosis-associated genes for new therapy target.

Project description:Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry-based proteomics methods. We conducted label-free liquid chromatography-tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073-2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.

Dataset Information

Optimal cutoff age for predicting prognosis associated with serous epithelial ovarian cancer: what is the best age cutoff?

Objective

Methods

Results

Conclusion

Publications

Optimal cutoff age for predicting prognosis associated with serous epithelial ovarian cancer: what is the best age cutoff?

Similar Datasets

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