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Clinical and Emergent Biomarkers and Their Relationship to the Prognosis of Ovarian Cancer.

ABSTRACT: OBJECTIVE:Ovarian cancer is the most lethal gynecological malignancy, but information relevant to prognosis and outcomes remain unknown. Here, we used statistical methods to focus specifically on interactions between candidate prognostic variables. METHODS AND RESULTS:Univariate, multivariate, and elastic net modeling of 42 variables were applied to a cohort of 542 ovarian cancer patients with 393 episodes of cancer recurrence/death. In univariate analyses, overexpression of TFF3, MDM2, and p53 were associated with improved recurrence-free survival. In multivariate analyses adjusted for age, histology, stage, grade, ascites, and residual disease, overexpression of PR appeared to provide a protective effect [hazard ratio for >50% of cells positive, 0.64 (95% confidence interval 0.44-0.94) compared to <1%], and TFF3 showed a nonlinear association. Importantly, we observed no interactions among variables. However, patients with tumors with moderate TFF3 expression were at a marginally increased risk of recurrence, and patients with tumors with high expression were at a similar to slightly lower risk, compared to those with tumors with no TFF3 expression. CONCLUSIONS:Although no interactions among variables were observed, this study provides important precedent for seeking interactions between clinical and tumor variables in future studies.

SUBMITTER: Jatoi A

PROVIDER: S-EPMC4764381 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Clinical and Emergent Biomarkers and Their Relationship to the Prognosis of Ovarian Cancer.

Jatoi Aminah A Vierkant Robert A RA Hawthorne Kieran M KM Block Matthew S MS Ramus Susan J SJ Larson Nicholas B NB Fridley Brooke L BL Goode Ellen L EL

Oncology 20160119 2

<h4>Objective</h4>Ovarian cancer is the most lethal gynecological malignancy, but information relevant to prognosis and outcomes remain unknown. Here, we used statistical methods to focus specifically on interactions between candidate prognostic variables.<h4>Methods and results</h4>Univariate, multivariate, and elastic net modeling of 42 variables were applied to a cohort of 542 ovarian cancer patients with 393 episodes of cancer recurrence/death. In univariate analyses, overexpression of TFF3, ...[more]

PMID: 26780780

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Project description:BackgroundOvarian cancer(OC) is the gynecological tumor with the highest mortality rate, effective biomarkers are of great significance in improving its prognosis. In recent years, there have been many studies on alternative splicing (AS) events, and the role of AS events in tumor has become a focus of attention.MethodsData were downloaded from the TCGA database and Univariate Cox regression analysis was performed to determine AS events associated with OC prognosis.Eight prognostic models of OC were constructed in R package, and the accuracy of the models were evaluated by the time-dependent receiver operating characteristic (ROC) curves.Eight types of survival curves were drawn to evaluate the differences between the high and low risk groups.Independent prognostic factors of OC were analyzed by single factor independent analysis and multi-factor independent prognostic analysis.Again, Univariate Cox regression analysis was used to analyze the relationship between splicing factors(SF) and AS events, and Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis were performed on OS-related SFs to understand the pathways.ResultsUnivariate Cox regression analysis showed that among the 15,278 genes, there were 31,286 overall survival (OS) related AS events, among which 1524 AS events were significantly correlated with OS. The area under the time-dependent receiver operating characteristic curve (AUC) of AT and ME were the largest and the RI was the smallest,which were 0.757 and 0.68 respectively. The constructed models have good value for the prognosis assessment of OC patients. Among the eight survival curves, AP was the most significant difference between the high and low risk groups, with a P value of 1.61e - 1.The results of single factor independent analysis and multi-factor independent prognostic analysis showed that risk score calculated by the model and age could be used as independent risk factors.According to univariate COX regression analysis,109 SFs were correlated with AS events and adjusted in two ways: positive and negative.ConclusionsSFs and AS events can directly or indirectly affect the prognosis of OC patients. It is very important to find effective prognostic markers to improve the survival rate of OC.

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Clinical and Emergent Biomarkers and Their Relationship to the Prognosis of Ovarian Cancer.

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Clinical and Emergent Biomarkers and Their Relationship to the Prognosis of Ovarian Cancer.

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