Prevalence of human papillomavirus detection in ovarian cancer: a meta-analysis

Abstract

We conducted a meta-analysis of published data to update and estimate the prevalence of HPV in ovarian cancer. A comprehensive literature search was performed according to the PRISMA guidelines. Eligible articles published from 1989 until 2020 by searching Web of Sciences, Pubmed, Embase, and the Cochrane Library Central databases were gathered. A pooled estimation of HPV prevalence with a 95% confidence interval (CI) was calculated based on a random effect model. Quantitative assessment of heterogeneity was explored using Cochrane test and I². Additionally, publication bias, sensitivity, meta-regression, and subgroup analyses were also performed. Twenty-nine studies involving 2280 patients with ovarian cancer were included. The statistical heterogeneity was high (I² = 88%, P<0.0001). The pooled prevalence of HPV in ovarian cancer cases was 15.9% (95% CI, 11-22). In subgroup analyses, the highest prevalence of HPV was reported by studies from Asia (30.9%; 95% CI, 20-44) and Eastern Europe (29.3%; 95% CI, 4.4-78). Furthermore, the most frequently detected HPV genotype was HPV16 (54%; 95% CI, 27.9-55), followed by HPV18 (23.2%; 95% CI, 18.8-28.2). Our meta-analysis suggests a great difference in the prevalence of HPV detected in ovarian cancer by different studies, which is not seen in strongly HPV-associated cancers such as cervical cancer. However, the prevalence varied markedly by geographic region. Considering the substantial heterogeneity found, more studies with control groups and precise assays measuring HPV mRNA expression are needed to further evaluate the link and causative aetiology between HPV and ovarian cancer.

Keywords: Etiologic agent; HPV prevalence; Ovarian malignancy; Subgroup analysis; Worldwide.

Fig. 1

Flow diagram of the studies selected for this meta-analysis

Fig. 2

Forest plot showing HPV rate and 95% confidence intervals in ovarian cancer studies based on random-effect model

Fig. 3

Sensitivity analysis showing the impact of exclusion of any study on the summary effect

Fig. 4

Funnel plot of studies included in the meta-analysis. The distribution of the studies (dots) is asymmetric suggesting an evidence of publication bias that is also found by Egger’s regression test (P=0.006) and Begg and Mazumdar rank correlation (P=0.5)