Characteristics of the Cervicovaginal Microenvironment in Childbearing-Age Women with Different Degrees of Cervical Lesions and HR-HPV Positivity
Artikel-Kategorie: original-paper
Online veröffentlicht: 20. Dez. 2021
Seitenbereich: 489 - 500
Eingereicht: 31. Aug. 2021
Akzeptiert: 10. Nov. 2021
DOI: https://doi.org/10.33073/pjm-2021-046
Schlüsselwörter
© 2021 Qingzhi Zhai et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Cervical cancer is common cancer in women (Thun et al. 2010; Liang et al. 2019), and almost all cervical cancer cases are linked to human papillomavirus (HPV) infection, especially persistent infection with high-risk human papillomavirus (HR-HPV). Approximately 70–80% of women will develop at least one HPV infection in their lifetime (Ojesina et al. 2014; Bober et al. 2019), but most HPV infections can be cleared within 8–12 months. Fewer than 10% of patients develop persistent infection and even cervical cancer (Bober et al. 2019). Previous studies suggested that vaginal microecology can modulate HPV infection and is closely related to the progression of cervical intraepithelial neoplasia (Bober et al. 2019; Wiik et al. 2019). When histological changes occur in the cervical epithelium and vaginal mucosa, the abundance of Lactobacilli in vaginal microbes will decrease, and the rate of bacterial dysregulation will increase. These changes will also promote HPV infection and cervical lesions (Mitra et al. 2015; He et al. 2018). Therefore, vaginal microecology plays a vital role in developing cervical precancerous lesions and invasive cervical cancer and can be used as a biological indicator for cervical cancer tests (Curty et al. 2019). In addition, the risks of virus clearance and malignant transformation after HPV infection were also associated with vaginal flora (Zhang et al. 2018).
A large number of microorganisms gather in the female reproductive tract, including Gram-positive aerobic bacteria, Gram-negative aerobic bacteria, anaerobic bacteria,
An increasing number of studies have focused on the vaginal microbiome in gynecological diseases, especially the correlation among vaginal microbiome, cervical lesions, and HR-HPV infection (Ojesina et al. 2014; Bober et al. 2019; Romero-Morelos et al. 2019; Zheng et al. 2019), and revealed that the diversities of the vaginal microbiome in cervical lesion patients with HPV infection were higher than those in healthy people (Silva et al. 2014; Bober et al. 2019). However, few studies have been conducted on the different degrees of cervical intraepithelial neoplasia before cancer appearance, and the influence of physiological factors on the microbiome is usually ignored.
This study collected cervical microecology samples from patients with HR-HPV infection and different degrees of cervical lesions under strict physiological conditions. 16S rRNA sequencing analysis was performed for all samples to detect the composition of the vaginal microbiome. To investigate the dominant microbiome in each group, a comparison analysis was performed between different groups. Our study may provide new insight into treating HR-HPV infection and blocking cervical lesions by speculating microecological regulation.
Analysis of essential information in each group.
Group | Cases number | Age | BMI |
---|---|---|---|
Group 1 | 29 | 40.08 ± 4.83 | 23.20 ± 3.47 |
Group 2 | 29 | 42.17 ± 5.18 | 22.00 ± 2.16 |
Group 3 | 32 | 40.63 ± 4.55 | 22.32 ± 2.93 |
Group 4 | 40 | 40.64 ± 5.57 | 22.37 ± 1.62 |
Group 5 | 38 | 42.43 ± 5.31 | 22.88 ± 1.96 |
K-W test ( | 5.336 | 7.640 | |
K-W test ( | 0.255 | 0.106 |
Group 1 – healthy women; Group 2 – high-risk HPV infection; Group 3 – low-grade squamous intraepithelial lesion; Group 4 – high-grade squamous intraepithelial lesion; Group 5 – cervical cancer
To obtain normalized data for each sample, OTU sequences were aligned with MUSCLE (v.3.8.31) (Quast et al. 2013). To perform the alpha diversity analysis, QIIME (v.1.9.1) (Caporaso et al. 2010) was used to calculate the observed species index, Chao index, Shannon index, Simpson index, ACE index, and PD whole tree index, and R software (v.2.15.3) was used to draw dilution curves, rank abundance curves, and species accumulation curves. Bray-Curtis distance was also calculated using QIIME software (Version 1.9.1) to perform beta diversity analysis. The detection of different species between different groups was analyzed using a
Fig. 1.
Microbiome communities of five groups.

The dominant genera mainly included
Fig. 2.
Alpha diversity index of cervical microflora between groups. Group 1 – healthy group; Group 2 – HR-HPV infection group; Group 3 – LSIL group; Group 4 – HSIL group; Group 5 – cervical cancer group.

Fig. 3.
Beta diversity between five groups; A) beta diversity between groups; B) box plot based on weighted UniFrac beta diversity.

At the genus level, 75 significantly different species with five abundance change models were detected between different groups. The most abundant species continuously decreased, and seven genera showed LDA values larger than four. From the healthy group to the LSIL group, the abundance of
Fig. 4.
Abundance heatmap of different speciesat the genus level in phase 1.

A total of 63 significantly different species with four abundance change models were detected between other groups at the genus level. Among them, the abundances of most species gradually increased, and six genera showed LDA values larger than four. From the LSIL group to the cervical cancer group, the abundances of
Fig. 5.
Abundance heatmap of different species at the genus level in phase 2.

Fig. 6.
Co-occurrence network diagram.

Cervical cancer is the fourth most common cancer for women worldwide, but it is considered preventable because HR-HPV infection is necessary for cervical cancer (Borgdorff et al. 2014). The vaginal microenvironment plays an important role in HPV infection (Mitra et al. 2015). The vaginal environment is a comprehensive environment home to various microorganisms, in which the abundance of
Most investigations of vaginal microecology have only focused on disease and ignored the impact of physiological factors (Klein et al. 2019). However, vaginal microecology can be impacted by human hormone fluctuations, microbial biomass, vaginal operations, etc. (Silva et al. 2014). In this study, strict standards of sample selection were used to exclude pathological factors as much as possible. We performed homogenization analysis on the age and BMI of subjects to show comparability among these groups. In addition, the sequencing depth was sufficient based on the sparse sample curve. Therefore, the fundamentals of our analysis were adequate and credible.
There was no significant difference between the disease groups and the healthy group based on the alpha diversity results, which was inconsistent with previous studies showing that community diversity was higher in the disease group than in the healthy group (Audirac-Chalifour et al. 2016; Salas-Jara et al. 2016; Kyrgiou et al. 2017). Our results might be significantly influenced by the sample types and cervical secretions, in which community diversity was usually high, which would reduce the difference between different groups. At the same time, the strict criteria for subject selection decreased the influence of physiological factors on the vaginal microbiome and increased the difficulty in sample collection (168 cases in a year) (Łaniewski et al. 2018). Furthermore, there may be a temporal relationship between the microbiome and the progression of the infection. In the study of (Audirac-Chalifour et al. 2016), the sample’s age ranged from 22 to 61, which ignored women’s menopausal status, which can impact the composition of the vaginal microbiome (Di Paola et al. 2017) and affect the accuracy of the results. Additionally, in contrast to previous studies, the degrees of cervical lesions were divided in more detail. We first reported that the community composition of the LSIL group was closer to that of the healthy group, which was consistent with the clinical prognosis of the disease and further confirmed that the cervical microbiome is related to disease prognosis.
Compared with the healthy group, the HR-HPV infection group had more diversities at the genus level and showed an obvious disordered vaginal microenvironment based on the beta diversity results consistent with previous reports (Klein et al. 2019). Among these different genera, the genera
Moreover, the healthy group had no difference from the LSIL group in weighted UniFrac beta analysis, and 60% of LSIL group patients could heal themselves in clinical recorders. However, we could not speculate whether these changes in the microbiome were the result of immune regulation or the effect of microecological regulation, and it should be discussed in the future by expanding the sample size. Our study provided reference data on the cervical microbiome and could play a key role in further studies for blocking HR-HPV infection and preventing cervical cancer.
In conclusion, the most related genera to the healthy group were