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Development and validation of SIRT3-related nomogram predictive of overall survival in patients with serous ovarian cancer.

ABSTRACT: OBJECTIVE:Our aim is to analyzed the expression pattern of sirtuin(SIRT) superfamily and evaluated their prognostic values in serous ovarian cancer patients. METHODS:We first analyzed the differential expression of SIRT members among fallopian tube epithelium (FTE), primary serous ovarian cancers/tubal cancers (PSOCs/PSTCs), and omental metastases using GSE10971 and GSE30587 datasets. The prognostic values of SIRT members were evaluated using TCGA and GSE9891 dataset. RESULTS:SIRT3 and SIRT5 expression were significantly decreased and increased in PSOCs/PSTCs compared with that in normal counterparts, respectively. SIRT6 and SIRT7 were overexpressed in ometal metastases compared with corresponding primary counterparts. With respect to recurrence free survival, however, SIRT7 overexpression was correlated with better prognosis. A similar trend was observed by multivariable analysis. Regarding overall survival (OS), increased expression of SIRT3, SIRT5, and SIRT7 were associated with better survival by univariable analysis. Subsequent multivariable analysis showed that SIRT3 remained an independent favorable prognostic factor for OS. The SIRT3-related nomogram illustrated age at initial diagnosis as sharing the largest contribution to OS, followed by SIRT3 expression and FIGO stage. The C-index for OS prediction was 0.65 (95%CI, 0.61-0.69) in training cohort (TCGA dataset) and 0.65 (95%CI, 0.59-0.71) in validation cohort (GSE9891 dataset), respectively. The calibration plots showed optimal agreement between the prediction by SIRT3-related nomogram and actual observation for 1-, 3-, and 5-year OS probability. CONCLUSION:In conclusion, SIRT3 was an independent favorable prognostic factor for OS in serous ovarian cancer, and added prognostic value to the traditional clinicopathological factors used to evaluate patients' prognosis.

SUBMITTER: Li J

PROVIDER: S-EPMC6530022 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Development and validation of SIRT3-related nomogram predictive of overall survival in patients with serous ovarian cancer.

Li Jun J Yue Huiran H Yu Hailin H Lu Xin X Xue Xiaohong X

Journal of ovarian research 20190521 1

<h4>Objective</h4>Our aim is to analyzed the expression pattern of sirtuin(SIRT) superfamily and evaluated their prognostic values in serous ovarian cancer patients.<h4>Methods</h4>We first analyzed the differential expression of SIRT members among fallopian tube epithelium (FTE), primary serous ovarian cancers/tubal cancers (PSOCs/PSTCs), and omental metastases using GSE10971 and GSE30587 datasets. The prognostic values of SIRT members were evaluated using TCGA and GSE9891 dataset.<h4>Results</ ...[more]

PMID: 31113446

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Project description:BackgroundHigh-grade serious ovarian carcinoma (HGSOC) is a subtype of ovarian cancer with a different prognosis attributable to genetic heterogeneity. The prognosis of patients with advanced HGSOC requires prediction by genetic markers. This study systematically analyzed gene expression profile data to establish a genetic marker for predicting HGSOC prognosis.MethodsThe RNA-seq data set and information on clinical follow-up of HGSOC were retrieved from Gene Expression Omnibus (GEO) database, and the data were standardized by DESeq2 as a training set. On the other hand, HGSOC RNA sequence data and information on clinical follow-up were retrieved from The Cancer Genome Atlas (TCGA) as a test set. Additionally, ovarian cancer microarray data set was obtained from GEO as the external validation set. Prognostic genes were screened from the training set, and characteristic selection was performed using the least absolute shrinkage and selection operator (LASSO) with 80% re-sampling for 5000 times. Genes with a frequency of more than 2000 were selected as robust biomarkers. Finally, a gene-related prognostic model was validated in both the test and GEO validation sets.ResultsA total of 148 genes were found to be significantly correlated with HGSOC prognosis. The expression profile of these genes could stratify HGSOC prognosis and they were enriched to multiple tumor-related regulatory pathways such as tyrosine metabolism and AMPK signaling pathway. AKR1B10 and ANGPT4 were obtained after 5000-time re-sampling by LASSO regression. AKR1B10 was associated with the metastasis and progression of several tumors. In this study, Cox regression analysis was performed to create a 2-gene signature as an independent prognostic factor for HGSOC, which has the ability to stratify risk samples in all three data sets (p < 0.05). The Gene Set Enrichment Analysis (GSEA) discovered abnormally active REGULATION_OF_AUTOPHAGY and OLFACTORY_TRANSDUCTION pathways in the high-risk group samples.ConclusionThis study resulted in the creation of a 2-gene molecular prognostic classifier that distinguished clinical features and was a promising novel prognostic tool for assessing the prognosis of HGSOC. RiskScore was a novel prognostic model which might be effective in guiding accurate prognosis of HGSOC.

Project description:BackgroundThis study aimed to investigate the differences in the clinicopathological characteristics of younger and older patients with endometrial cancer (EC) and develop a nomogram to assess the prognosis of early onset EC in terms of overall survival.MethodsPatients diagnosed with EC from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were selected. Clinicopathological characteristics were compared between younger and older patients, and survival analysis was performed for both groups. Prognostic factors affecting overall survival in young patients with EC were identified using Cox regression. A nomogram was created and internal validation was performed using the consistency index, decision curve analysis, receiver operating characteristic curves, and calibration curves. External validation used data from 70 patients with early onset EC. Finally, Kaplan-Meier curves were plotted to compare survival outcomes across the risk subgroups.ResultsA total of 1042 young patients and 12,991 older patients were included in this study. Younger patients were divided into training (732) and validation (310) cohorts in a 7:3 ratio. Cox regression analysis identified age, tumorsize, grade, FIGO stage(International Federation of Gynecology and Obstetrics) and surgery as independent risk factors for overall survival, and a nomogram was constructed based on these factors. Internal and external validations demonstrated the good predictive power of the nomogram. In particular, the C-index for the overall survival nomogram was 0.832 [95% confidence interval (0.797-0.844)] in the training cohort and 0.839 (0.810-0.868) in the internal validation cohort. The differences in the Kaplan-Meier curves between the different risk subgroups were statistically significant.ConclusionsIn this study, a nomogram for predicting overall survival of patients with early onset endometrial cancer based on the SEER database was developed to help assess the prognosis of patients and guide clinical treatment.

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Development and validation of SIRT3-related nomogram predictive of overall survival in patients with serous ovarian cancer.

Publications

Development and validation of SIRT3-related nomogram predictive of overall survival in patients with serous ovarian cancer.

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