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Risk-based stratification of carcinomas concurrently involving the endometrium and ovary.

ABSTRACT: OBJECTIVE:Determining whether carcinomas concurrently involving endometrium and ovary are independent primary tumors (IPTs) or endometrial carcinomas with ovarian metastases (at least stage IIIA endometrial cancers, IIIA-EC) using clinicopathologic criteria is often challenging. Recent genomic studies showed that most such tumors are clonally related. We sought to identify clinicopathologic features associated with clinical outcomes, and to separate women with these tumors into clinically low-risk and high-risk groups. METHODS:We reviewed clinical and pathologic data from 74 women who, between 1993 and 2014, underwent primary surgery for endometrial cancer and had concurrent ovarian involvement. RESULTS:The endometrial carcinomas were endometrioid (EECs, n?=?41) or non-endometrioid (ENECs, n?=?33). Nineteen (26%) cases were originally classified as IPTs using clinicopathologic criteria. Multivariate analysis revealed that lymph node involvement (hazard ratio (HR)?=?2.38, 95% CI 1.13-5.02, p?=?0.023) and non-endometrioid endometrial tumor histology (HR?=?6.27, 95% CI 2.6-15.13, p?

SUBMITTER: Turashvili G

PROVIDER: S-EPMC6321787 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Risk-based stratification of carcinomas concurrently involving the endometrium and ovary.

Turashvili Gulisa G Gómez-Hidalgo Natalia R NR Flynn Jessica J Gonen Mithat M Leitao Mario M MM Soslow Robert A RA Murali Rajmohan R

Gynecologic oncology 20181106 1

<h4>Objective</h4>Determining whether carcinomas concurrently involving endometrium and ovary are independent primary tumors (IPTs) or endometrial carcinomas with ovarian metastases (at least stage IIIA endometrial cancers, IIIA-EC) using clinicopathologic criteria is often challenging. Recent genomic studies showed that most such tumors are clonally related. We sought to identify clinicopathologic features associated with clinical outcomes, and to separate women with these tumors into clinicall ...[more]

PMID: 30413340

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Project description:Predicting the clinical course of breast cancer is often difficult because it is a diverse disease comprised of many biological subtypes. Gene expression profiling by microarray analysis has identified breast cancer signatures that are important for prognosis and treatment. In the current article, we use microarray analysis and a real-time quantitative reverse-transcription (qRT)-PCR assay to risk-stratify breast cancers based on biological 'intrinsic' subtypes and proliferation.Gene sets were selected from microarray data to assess proliferation and to classify breast cancers into four different molecular subtypes, designated Luminal, Normal-like, HER2+/ER-, and Basal-like. One-hundred and twenty-three breast samples (117 invasive carcinomas, one fibroadenoma and five normal tissues) and three breast cancer cell lines were prospectively analyzed using a microarray (Agilent) and a qRT-PCR assay comprised of 53 genes. Biological subtypes were assigned from the microarray and qRT-PCR data by hierarchical clustering. A proliferation signature was used as a single meta-gene (log2 average of 14 genes) to predict outcome within the context of estrogen receptor status and biological 'intrinsic' subtype.We found that the qRT-PCR assay could determine the intrinsic subtype (93% concordance with microarray-based assignments) and that the intrinsic subtypes were predictive of outcome. The proliferation meta-gene provided additional prognostic information for patients with the Luminal subtype (P = 0.0012), and for patients with estrogen receptor-positive tumors (P = 3.4 x 10-6). High proliferation in the Luminal subtype conferred a 19-fold relative risk of relapse (confidence interval = 95%) compared with Luminal tumors with low proliferation.A real-time qRT-PCR assay can recapitulate microarray classifications of breast cancer and can risk-stratify patients using the intrinsic subtype and proliferation. The proliferation meta-gene offers an objective and quantitative measurement for grade and adds significant prognostic information to the biological subtypes.

Project description:ObjectiveWe aimed to study the relationship between ferroptosis proteins and reproductive outcomes of infertile patients with PCOS and construct the related prognostic model.MethodsThese endometrium samples of the study were collected from 33 women with PCOS and 7 control women with successful pregnancies at the Reproductive Center of Lanzhou University Second Hospital, September 2019 to September 2020. The 40 patients' endometrium was identified the differentially expressed proteins (DEPs) using liquid chromatography tandem mass spectrometry. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) showed that the DEPs related pathways and functions between PCOS and controls. Subsequently, univariate Cox regression analysis and Lasso regression were used to identifying independent prognostic ferroptosis proteins, which were utilized to establish a prognostic model. Then the performance of the prognostic model was evaluated by receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Then clinical data and prognostic model were used to predict the reproductive outcomes of PCOS patients by constructing the nomograms. Finally, we performed the single sample gene set enrichment analysis (ssGSEA) to explore the correlation between risk scores and immune status.ResultsA total of 5331 proteins were identified, 391 proteins were differentially expressed in the PCOS and controls. The KEGG analysis revealed that the ferroptosis pathway was significantly different between PCOS and controls. 5 ferroptosis proteins (GPX4, DPP4, G6PD, PCBP1, and PCBP2) prognostic model (FerSig) was constructed via Cox regression and Lasso regression. Patients were separated into high and low-risk groups according to the FerSig. Kaplan-Meier curve showed that patients in the low-risk group had much better reproductive outcomes than those in the high-risk group. The DCA showed that the risk score was an independent predictive factor for reproductive outcomes. Compared with clinical data, ROC curve analysis indicated the FerSig proteins as a potential diagnostic and prognostic factor in PCOS patients. Functional analysis revealed that the FerSig proteins and immune microenvironment were correlated to the prognosis of PCOS.ConclusionThe prognostic model focused on the FerSig proteins could predict the reproductive outcomes of PCOS patients with decreased endometrial receptivity, and provided theoretical basis for individualized treatment.

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Risk-based stratification of carcinomas concurrently involving the endometrium and ovary.

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Risk-based stratification of carcinomas concurrently involving the endometrium and ovary.

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