Dataset Information

Identification of a Novel Prognostic Classification Model in Epithelial Ovarian Cancer by Cluster Analysis.

ABSTRACT: Background:Heterogeneity plays an essential role in ovarian cancer. Patients with different clinical features may manifest diverse patterns in diagnosis, treatment, and prognosis. The aim of the present study was to identify a novel ovarian cancer-classification model through cluster analysis and assess its significance in prognosis. Methods:Among patients diagnosed with ovarian cancer in the Women's Hospital School of Medicine, Zhejiang University between January 2014 and May 2019, 328 patients were included in a K-mean cluster analysis and 176 patients followed up. Major clinical indicators, overall survival, and recurrence-free survival in different subgroups were compared. Results:Two clusters for ovarian cancer were identified and grouped as noninflammatory (n=247) and inflammatory subtypes (n=81). Compared with the noninflammatory subgroup, the inflammatory subgroup presented a statistically significantly higher level of median CRP (median (IQR) 20.4 [7.8-47.3] vs 1.2 [0.4-3.5], p<0.001), neutrophil percentage (median (IQR) 76.9 [72.6-81.3] vs 66.2 [61.0-72.0], p<0.001), leukocyte count (median (IQR) 8.9 [7.0-10.0] vs 6.0 [5.1-7.2], p<0.001), fibrinogen (median (IQR) 5.0 [4.4-6.0] vs 3.4 [2.9-3.9], p<0.001), and platelet count (median (IQR) 324 [270-405] vs 229 [181.5-269], p<0.001). During a median follow-up of 52 months, 21 participants (16.3%) died in the noninflammatory group, while 14 (29.8%) died in the inflammatory group (HR 2.15, 95% CI 1.09-4.23; p=0.024). Death/recurrence was observed in 38 (29.5%) patients from the noninflammatory group and 25 (53.2%) from the inflammatory group (HR 2.32, 95% CI 1.40-3.85; p<0.001). Conclusion:Our study revealed a novel classification model of ovarian cancer that features inflammation. Inflammation predicts shorter survival and poorer prognosis, suggesting the significance of inflammation in the management of ovarian cancer.

SUBMITTER: Chen K

PROVIDER: S-EPMC7386816 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of a Novel Prognostic Classification Model in Epithelial Ovarian Cancer by Cluster Analysis.

Chen Kelie K Niu Yuequn Y Wang Shengchao S Fu Zhiqin Z Lin Hui H Lu Jiaoying J Meng Xinyi X Yang Bowen B Zhang Honghe H Wu Yihua Y Xia Dajing D Lu Weiguo W

Cancer management and research 20200724

<h4>Background</h4>Heterogeneity plays an essential role in ovarian cancer. Patients with different clinical features may manifest diverse patterns in diagnosis, treatment, and prognosis. The aim of the present study was to identify a novel ovarian cancer-classification model through cluster analysis and assess its significance in prognosis.<h4>Methods</h4>Among patients diagnosed with ovarian cancer in the Women's Hospital School of Medicine, Zhejiang University between January 2014 and May 201 ...[more]

PMID: 32801870

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Project description:PurposeTo analyze the role of six human epididymis protein 4 (HE4)-related mitochondrial ribosomal proteins (MRPs) in ovarian cancer and selected MRPL15, which is most closely related to the tumorigenesis and prognosis of ovarian cancer, for further analyses.MethodsUsing STRING database and MCODE plugin in Cytoscape, six MRPs were identified among genes that are upregulated in response to HE4 overexpression in epithelial ovarian cancer cells. The Cancer Genome Atlas (TCGA) ovarian cancer, GTEX, Oncomine, and TISIDB were used to analyze the expression of the six MRPs. The prognostic impact and genetic variation of these six MRPs in ovarian cancer were evaluated using Kaplan-Meier Plotter and cBioPortal, respectively. MRPL15 was selected for immunohistochemistry and GEO verification. TCGA ovarian cancer data, gene set enrichment analysis, and Enrichr were used to explore the mechanism of MRPL15 in ovarian cancer. Finally, the relationship between MRPL15 expression and immune subtype, tumor-infiltrating lymphocytes, and immune regulatory factors was analyzed using TCGA ovarian cancer data and TISIDB.ResultsSix MRPs (MRPL10, MRPL15, MRPL36, MRPL39, MRPS16, and MRPS31) related to HE4 in ovarian cancer were selected. MRPL15 was highly expressed and amplified in ovarian cancer and was related to the poor prognosis of patients. Mechanism analysis indicated that MRPL15 plays a role in ovarian cancer through pathways such as the cell cycle, DNA repair, and mTOR 1 signaling. High expression of MRPL15 in ovarian cancer may be associated with its amplification and hypomethylation. Additionally, MRPL15 showed the lowest expression in C3 ovarian cancer and was correlated with proliferation of CD8+ T cells and dendritic cells as well as TGFβR1 and IDO1 expression.ConclusionMRPL15 may be a prognostic indicator and therapeutic target for ovarian cancer. Because of its close correlation with HE4, this study provides insights into the mechanism of HE4 in ovarian cancer.

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Dataset Information

Identification of a Novel Prognostic Classification Model in Epithelial Ovarian Cancer by Cluster Analysis.

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Identification of a Novel Prognostic Classification Model in Epithelial Ovarian Cancer by Cluster Analysis.

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