Identification of a Novel Prognostic Classification Model in Epithelial Ovarian Cancer by Cluster Analysis
Authors Chen K, Niu Y, Wang S, Fu Z, Lin H, Lu J, Meng X, Yang B, Zhang H, Wu Y, Xia D, Lu W
Received 29 February 2020
Accepted for publication 24 June 2020
Published 24 July 2020 Volume 2020:12 Pages 6251—6259
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Sanjeev Srivastava
Kelie Chen,1,2,* Yuequn Niu,3,* Shengchao Wang,1,* Zhiqin Fu,4 Hui Lin,1,2 Jiaoying Lu,2 Xinyi Meng,2 Bowen Yang,2 Honghe Zhang,1 Yihua Wu,1,2 Dajing Xia,1,2 Weiguo Lu1
1Department of Gynecologic Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, People’s Republic of China; 2Department of Toxicology, School of Public Health, School of Medicine, Zhejiang University, Hangzhou 310058, People’s Republic of China; 3Department of Thoracic Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, People’s Republic of China; 4Department of Gynecological Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Dajing Xia
Department of Toxicology, School of Public Health, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, People’s Republic of China
Tel/ Fax +86-0571-88208140
Department of Gynecologic Oncology, Women’s Hospital, School of Medicine, Zhejiang University, 1 Xueshi Road, Hangzhou, Zhejiang 310006, People’s Republic of China
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.
Keywords: ovarian cancer, classification, heterogeneity, cluster analysis, inflammation, prognosis
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