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