
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39289490
73146
10.1038/s41598-024-73146-9
Article
Analysis of vaginal flora diversity and study on the role of Porphyromonas asaccharolytica in promoting IL-1β in regulating cervical cancer
Bai Bing 1
Tuerxun Gulixian 2
Tuerdi Awahan 3
Maimaiti Rexianguli 2
Sun Yuping Sunyuping@xjmu.edu.cn

456
Abudukerimu Azierguli arzu64@xjmu.edu.cn

456
1 https://ror.org/01p455v08 grid.13394.3c 0000 0004 1799 3993 School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, 830017 China
2 https://ror.org/01p455v08 grid.13394.3c 0000 0004 1799 3993 Xinjiang Medical University Cancer Hospital, Urumqi, 830000 China
3 Urumqi Shayibake District Maternal and Child Health Service Centre, Urumqi, 830000 China
4 https://ror.org/01p455v08 grid.13394.3c 0000 0004 1799 3993 Department of Microbiology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, 830017 China
5 Key Laboratory of Xinjiang Uygur Autonomous Region, Laboratory of Molecular Biology of Endemic Diseases, Urumqi, 830017 China
6 State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, 830017 China
17 9 2024
17 9 2024
2024
14 2173110 4 2024
13 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Cervical cancer, a prevalent malignancy in the female reproductive tract, exhibits a high incidence. Existing evidence indicates a robust correlation between alterations in vaginal flora composition and the progression of cervical cancer. Nevertheless, there is a lack of clarity concerning the specific microorganisms within the vaginal microbiota that are linked to the onset and development of cervical cancer, as well as the mechanisms through which they exert carcinogenic effects. The 16 S ribosomal (rRNA) and metagenomic sequencing technology were used to analyze vaginal microorganisms, and screening for human papillomavirus (HPV) positive cervical cancer-associated microbial markers using fold change in mean bacterial abundance. Moreover, vaginal microenvironmental factors were detected, and the local vaginal inflammatory state in patients with cervical cancer was subjected to assay via qRT-PCR and ELISA. The hub inflammatory genes were screened by transcriptome sequencing after co-culture of bacteria and normal cervical epithelial cells, and an in vitro model was utilized to assess the impacts of inflammatory factors on cervical cancer. Both cervical cancer patients and HPV-positive patients showed significant changes in the composition of the vaginal flora, characterised by a decrease in the abundance of Lactobacillus and an increase in the abundance of a variety of anaerobic bacteria; The microbial sequencing identified Porphyromonas, Porphyromonas_asaccharolytica, and Porphyromonas_uenonis as microbial markers for HPV-associated cervical cancer. Vaginal inflammatory factors in patients with cervical cancer were overexpressed. After Porphyromonas_asaccharolytica intervention on cervical epithelial H8 cells, interleukin (IL)-1β, a hub differential gene, markedly promoted tumor-associated biological behaviors at the in vitro cytological level in cervical cancer. This study for the first demonstrated that Porphyromonas, Porphyromonas_asaccharolytica, and Porphyromonas_uenonis could serve as novel microbial markers for cervical cancer. Moreover, Porphyromonas_asaccharolytica was identified as having the ability to induce the overexpression of inflammatory genes in cervical epithelial cells to create a favorable microenvironment for the onset and development of cervical cancer. The effects of dysbacteriosis on cervical cancer were microbiologically elucidated.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-73146-9.

Keywords

Cervical cancer
Inflammation
Porphyromonas
Subject terms

Cancer
Microbiology
Natural Science Foundation of the Xinjiang Uygur Autonomous Region2022D01C185 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Globally, cervical cancer stands as the fourth principal cause of female mortality, ranking as the second most prevalent malignant tumor among Chinese women1,2. The incidence of cervical cancer exhibits considerable variation across nations, with a recorded 604,000 new cases and 342,000 deaths in 2020. Notably, a majority of these cases are concentrated in low- and middle-income countries, a trend possibly linked to lower human papillomavirus (HPV) vaccination coverage, poor personal hygiene and insufficient cervical screening in these areas3–5. Epidemiologic data have suggested that HPV infection is essential for the occurrence of cervical cancer1. While the immune system can spontaneously clear HPV infection in women, persistent infections may occur in some cases. In these instances, the virus binds to specific receptors on the cell surface and integrates its genome into the host genome within the nucleus, facilitated by breaks in the nuclear membrane. This integration process leads to irregular and uncontrolled growth of host cells, ultimately resulting in the carcinogenesis6. Currently, there is no effective treatment for HPV persistence7, and prevention of HPV-associated cervical cancer relies on expensive HPV vaccines and repeat cervical screening8. Although the role of vaccines has shown promising results in recent years9, implementing a universal HPV vaccination policy remains costly for less developed countries and multivalent vaccines do not provide complete coverage of all major HPV infection types10,11. In addition, all commercially available vaccines are prophylactic and not curative for existing infections7. Similar to various other cancer types, the primary treatment modalities for cervical cancer encompass surgical excision, radiotherapy, chemotherapy, and other therapeutic approaches. These interventions, while essential for managing the disease, can impose significant physical and economic burdens on patients. Despite these treatments, high rates of relapse and metastasis persist12,13. Thus, strategies to stimulate the self-cleaning capacity of the body and remove persistent HPV infection are particularly critical for preventing the onset and development of cervical cancer.

Investigations on the diversity and compositions of numerous microorganisms pave the way for extensive application of biomedicine. Microbiota parasitising with human barrier tissues, particularly in the gastrointestinal, oral, and genitourinary tracts14–16. Ecosystems created by resident bacteria and fungi profoundly impact health and disease17,18. Changes in the compositions of vaginal microbiota are currently thought to be closely associated with cervical cancer19. However, identifying specific microorganisms linked to the occurrence and progression of the disease remains a challenge, complicating the establishment of a foundation for potential causality. For example, vaginal Atopobium, Gardnerella, and Prevotella have all been identified as potential risk factors for cervical cancer20,21. Moreover, there is limited understanding of how imbalances in vaginal microecology contribute to carcinogenic effects. Research indicates that dysbacteriosis leads to a reduction in the abundance of the dominant probiotic Lactobacillus, diminishing its advantageous effects and promoting the prevalence of pathogenic microorganisms. Further, co-infection involving viruses and microorganisms substantially heightens the risk of cancers associated with HPV22,23. Based on the above, our hypothesis posited that the disruption of vaginal flora in cervical cancer patients leads to an increase in key pathogenic bacteria, which in turn stimulates normal epithelial cells of the cervix and upregulates the expression of inflammatory factors. This inflammatory microenvironment in the vagina, characterized by specific inflammatory factors, enhances the oncogenicity of HPV, thereby promoting invasion and metastasis of tumor cells.

Our study identified Porphyromonas, Porphyromonas asaccharolytica (P. asaccharolytica) and Porphyromonas uenonis (P. uenonis) as vaginal microbial markers of HPV-positive cervical cancer. Specifically, P. asaccharolytica was found to induce the upregulation of various inflammatory genes in cervical epithelial cells, with IL-1β playing a key role in promoting the development of cervical cancer.

Materials and methods

Human participants

Sample collection occurred at the Cancer Hospital of Xinjiang Medical University and in general communities during August 2022 and January-May 2023. Inclusion and exclusion criteria for study participants were as follows: (1) age: 20–75 years; (2) having a history of sexual life; (3) no history of vaginal douching or drug application within one week before sampling; (4) not in pregnancy, breastfeeding, or menstruation; (5) absence of HIV or hepatitis B or C infections, other malignancies, or autoimmune diseases; (6) no recent (within six weeks) treatment with antimicrobials and microbial products; (7) more than one year of long-term residency in Xinjiang. Patients with cervical cancer and other diseases were confirmed by clinicians after pathologic biopsy or other tests. Following the inclusion and exclusion criteria, 146 adult females were enrolled in this research. Two allocation methods were employed: participants were assigned to the health control group (HC), Other gynaecological diseases group (OD) (specific diseases were shown in Table S1), and cervical cancer group (CC) based on health status or diseases of study participants; Alternatively, they were categorized into the HPV positive (HPV.P) group and HPV negative (HPV.N) group based on HPV infections. This research received approval from the Xinjiang Medical University institutional review board (Ethical approval number XJYKDXR20211015003) and adhered strictly to the Declaration of Helsinki principles. Informed consent was obtained from all participants.

Sample collection

Secretions were collected using a disposable vaginal dilator to gently stretch the vagina. A sterile swab was used to scrape the deep vagina to obtain secretions in the posterior vaginal fornix or the upper third of the vaginal lateral wall, based on the visible presence of secretions on the swab. After sample collection, the swab was placed in sterile test tubes and kept in a refrigerator at -80 °C. For vaginal lavage fluid collection, 5 ml of 0.9% sodium chloride injection was used to repeatedly wash the upper vaginal segment and cervical surface for about 30 s. Subsequently, 3 ml of lavage fluid without blood components was drawn from the posterior vaginal fornix and underwent centrifugation at 3000 g/min for 5 min for collecting the liquid supernatant. The supernatant was then transferred in a sterile test tube and kept in a refrigerator at -80 °C.Postoperative tissue samples were collected and stored in liquid nitrogen.

PCR amplification of vaginal secretions, 16 S rRNA and metagenomic sequencing

Vaginal secretion DNA extraction was performed utilizing a DNA extraction kit (Tiangen, China). The concentration and integrity of the extracted DNA were assessed by means of Nano-Drop 2000 (Thermo Fisher Scientific, USA) and agarose gel electrophoresis. The hypervariable region V3-V4 of 16 S rRNA was amplified utilizing primers 341 F (CCTAYGGGRBGCASCAG) and 806R (GGACTACNNGGGTATCTAAT). Subsequent sequencing was carried out using NovaSeq6000 PE250. The sample data fractionation was conducted according to the Barcode sequence and PCR amplification primer sequence. Following Barcode and primer sequence truncation, FLASH (Version 1.2.11) was utilized to splice the reads of each sample, generating Raw Tags. The next step involved using fastp software (Version 0.23.1) to filter Raw Tags, resulting in Clean Tags and the removal of chimera sequences to obtain Effective Tags.Metagenomic sequencing was performed using Illumina PE150. Readfq (V8) was used to process the Raw Data, eliminating low-quality bases to obtain Clean Data. Subsequently, MEGAHIT software (v1.0.4-beta) conducted assembly analysis on Clean Data, while MetaGeneMark (V2.10) predicted the open reading frame (ORF) of Scaftigs (≥ 500 bp) for each sample, with information less than 100nt filtered out. CD-HIT software (V4.5.8) was then utilized to eliminate redundancy from the ORF results, resulting in a gene catalog. Bowtie2 (Bowtie2.2.4) compared the Clean Data of each sample to the gene catalog, generating final Unigenes for subsequent analysis. Finally, DIAMOND software (V0.9.9.110) was utilized to compare Unigenes sequences in the National Center of Biotechnology Information (NCBI) NR database (version 2023.03), and the lowest common ancestor (LCA) algorithm was used to assess the species annotation information of the sequence (Novogene, China).

Microbiological analysis

The DADA2 module in QIIME2 (Version QIIME2-202006) was used to denoise Effective Tags, resulting in the final Amplicon Sequence Variants (ASVs), equivalent to 100% similarity clustering24.Subsequently, the classify-sklearn algorithm of QIIME2 was used for species annotation25, comparing the results with the Silva 138.1 database. Finally, each sample was homogenized to calculate the ASV number along with the Chao1, Shannon, and Simpson indices. Based on the presence or absence of species and changes in species abundance, Weighted Unifrac distance was selected for principal coordinates analysis (PCOA) analysis and non-metric multidimensional scaling (NMDS) analysis (R version 4.0.3). The fold change of relative species abundance was calculated through ASV number.To assess the performance of the screened species in identifying diseases, a receiver operating curve (ROC) analysis was executed. LEfSe software linear discriminant analysis (LDA) score 4 was used in metagenomic sequencing to screen species with characteristic significance. Sequencing data have been submitted to NCBI (study ID: SRP472321).

Vaginal microenvironmental factor testing

The Vaginitis Multi Test Kit (Bioperferfectus, China) was used to detect vaginal microenvironmental factors. The swab with vaginal secretions attached was placed in the reagent diluent, squeezed, and rotated several times to fully release the secretions into the diluent. Subsequently, it was dropwise added to the reaction plate. Dry chemical enzyme technology was used to develop color according to enzymolysis-specific substrates, and the qualitative assessment of results was conducted by interpreting the observed color changes. The detected vaginal microenvironmental factors included hydrogen ion concentration (pH), acetylglucosaminidase (NAG), β-glucuronidase (GUS), sialidase (SIA), leukocyte esterase (LE), and hydrogen peroxide (H2O2).

ELISA assay

Tissue samples were ground in normal saline with subsequent centrifugation at 3000 g/min for 10 min to collect the supernatant. The levels of interleukin-6 (IL-6), interleukin-1β (IL-1β), interleukin-12 (IL-12), tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), and chemokines 8 (CXCL8) in the supernatant obtained from the ground tissue samples and vaginal lavage fluid were detected according to the kit instruction manual (Sinobestbio, China).

Culture of P. asaccharolytica

P. asaccharolytica (DSM 20707) was purchased from the Beijing BeNa Culture Collection, inoculated into brain heart infusion (BHI) medium (Solarbio, China), and cultured anaerobically at 37 °C. The bacteria underwent 3–5 passages for the assay, and the optical density (OD 600 nm) of the bacterial suspension was gauged utilizing a spectrophotometer (Thermo Fisher Scientific). The colony forming units per milliliter were then calculated. Subsequently, centrifugation was performed at 3000 g/min for 5 min at 4 °C, followed by two rinses with phosphate buffer saline (PBS). The bacteria in the logarithmic growth phase were then collected for assay.

Cell culture

The human HPV16 + cervical cancer cell line SiHa and normal cervical epithelial cell line H8 cell were obtained form the Cell Bank of the Chinese Academy of Science. Cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM, Gibco, USA) supplemented with 10% heat-inactivated fetal bovine serum (FBS, Sigma, USA) and 1% Penicillin–Streptomycin (Hyclone, USA) in 5% CO2 atmosphere at 37 °C.

RNA extraction and qRT-PCR assay

Total RNA extraction from pathological tissues and cells was executed utilizing TRIzol reagent (Solarbio), and the mRNA reverse transcription kit and PCR kit (Transgen Biotech, China) were utilized as per the instruction manual. The synthesized cDNA was amplified via real-time polymerase chain reaction (qPCR) (ABI Q1, USA). The primers utilized for PCR are available in Table S2. GAPDH was utilized as an internal reference gene, and the relative expressions of mRNAs were computed utilizing the 2−∆∆Ct method.

P. asaccharolytica intervention in H8 cells and transcriptome sequencing

P. asaccharolytica in the logarithmic growth phase was dissolved in DMEM, diluted to a concentration of bacteria: cells (10:1) through the complete medium without Penicillin–Streptomycin. H8 cells were subjected to intervention for 2.5 h, followed by the collection of cell pellets and subsequent extraction of cellular RNA. Qubit2.0 was used to detect RNA concentration, and agarose gel electrophoresis detected RNA integrity. Library construction and RNA sequencing were conducted by Sangon Biotech (China) using the Illumina NovaSeq platform, with each group comprising three biological replicates. RNAseq data have been submitted to NCBI (accession ID: PRJNA1040786). DESeq2 (Version 1.12.4) was used for differential expression analysis, designating transcripts with P-value < 0.05 and Foldchange > 2 as differentially expressed genes (DEGs). Genecards was used to obtain inflammation-related genes. DAVID software was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs. The PPI (Protein-Protein Interaction) network was established utilizing STRING (Interaction score 0.4), and Cytoscape was employed for visualization. The CytoHubba Degree algorithm was applied to identify hub genes.

Western blotting

Total protein extraction was conducted utilizing RIPA lysis buffer (Beyotime) comprising 1% phenylmethylsulfonyl fluoride (PMSF), and the protein concentration was determined utilizing the BCA protein quantification kit (Beyotime). Separation was achieved through 10% and 15% SDS-PAGE gel electrophoresis with subsequent transfer of proteins to a polyvinylidene fluoride (PVDF, Sigma) membrane. Blocking was conducted with 5% skim milk at room temperature for 2 h, followed by overnight incubation with primary antibody at 4 °C. Then, the secondary antibody underwent incubation for 1 h at room temperature, and visualization was carried out by means of the Western Bright ECL detection system (Bio-Rad, USA). The primary antibodies included anti-MMP-2(sc-1359), anti-cyclin D1(sc-8396), anti-HPV-E7(sc-6981), anti-IL-1β (sc-12742), anti-CXCL8(sc-376750) from Santa Cruz (USA), anti-E-cadherin (ab4077), anti-N-cadherin (ab76011) from Abcam (UK), anti-HPV-E6 (YF-3640746, Invitrogen, USA), anti-Cleaved-Caspase3(9661, Cell Signaling Technology), and anti-β-Actin (66009-1-Ig, Proteintech, China).

In vitro cell proliferation assay

The Cell Counting Kit-8(CCK-8) (Elabscience, China) assay was employed to evaluate cell proliferation. SiHa cells in complete medium were seeded onto a 96-well plate at a density of 1500 cells/well and allowed to adhere for 12 h. Subsequently, the complete medium was replaced with DMEM, and the cells were starved for 12 h. IL-1β and CXCL8 (Med Chem Express, USA) was diluted with complete medium to 10 ng/mL, 40 ng/mL, and 80 ng/mL for interventions on SiHa cells at 0, 12, 24, and 48 h, respectively. Cells without IL-1β and CXCL8 stimulation were used as the control group (0 ng/mL). 10 µl CCK-8 solution was introduced to all wells and incubated at 37 °C for 2 h. The absorbance at OD450 nm was then measured by a photometer.

Wound healing assay

SiHa cells were inoculated in a 6-well plate at a density of 50*104 cells/well. Following a 12-h adhesion period and a subsequent 12-h starvation period, a sterile pipette tip was utilized to create a vertical scratch on the cell monolayer. The DMEM was then aspirated, rinsed twice with PBS, and replaced with a complete medium containing IL-1β and CXCL8(0 ng/mL, 10 ng/mL, 40 ng/mL, and 80n g/mL). After 0, 24, and 48 h of interventions, a light microscope (Olympus, Japan) was employed to photograph the scratches and compute the cell migration rate. The migration rate (MR) was quantified by the formula: MR=(scratch area at 0 h-scratch area at 24 h and 48 h)/scratch area at 0 h)×100%.

Transwell assay

Transwell assay was executed on 24-well plates. Matrigel mix (BD Biosciences, USA) was used to detect invasion. Transwell chambers (8-µm pore size, Corning, USA) were utilized to resuspend 5 × 104 SiHa cells in an FBS-free culture medium containing 0 ng/ mL and 80 ng/mL IL-1β. The lower chamber was filled with 600 µl of medium containing 20% FBS. Following 48 h of incubation, cells underwent fixation with 4% paraformaldehyde for 30 min, with subsequent staining with 0.1% crystal violet for 20 min. Following a PBS wash, the upper chamber of the Transwell was swabbed with a cotton swab and captured utilizing a light microscope.

5-Ethynyl-20deoxyuridine (EdU) assay

The BeyoClick™ EdU-555 cell proliferation assay kit (Beyotime, China) was used, and SiHa cells were inoculated on a 24-well plate at a density of 3.0*104 cells/well. After a 12-h adhesion period and a subsequent 12-h starvation, DMEM was replaced with a complete medium containing IL-1β (0 ng/mL and 80 ng/mL) for a 48 h culture. EdU staining was then performed following the reagent instructions. Red fluorescent labeling indicated EdU proliferative cells, and images were captured utilizing a fluorescence microscope (Leica, Germany).

Colony formation assay

SiHa cells were inoculated on a 24-well plate at a density of 500 cells/well. Following a 12-h adhesion period and subsequent 12-h starvation, DMEM was replaced with a complete medium containing IL-1β (0 ng/mL and 80 ng/mL) for culture. The culture medium was replaced every three days until visible colonies formed. Subsequently, the culture medium was aspirated, and the cells underwent fixation with methanol for 15 min. Following fixation, they were rinsed twice with PBS, followed by staining with crystal violet for 15 min after which they were photographed.

Statistical analysis

Statistical analyses were executed utilizing SPSS 26.0 software (IBM, USA) and Graphpad Prism 9.1.0 (Graphpad, USA). Student’s t-test was utilized to compare variations between the two groups, a one-way analysis of variance or Manwhitney U test was utilized for comparison among multiple groups. The chi-square test was applied to count data. P < 0.05 was deemed as a statistically significant value.

Results

Participant information

Of the 146 participants, seven were excluded due to their vaginal secretion PCR amplification products failing to meet the criteria for library construction. Finally, 139 were included in this study. Based on the first allocation method, the distribution included 21 individuals in the HC group, 77 in the OD group, and 41 in the CC group. Based on the second method, there were 35 in the HPV.P group and 95 in the HPV.N group, with nine cases having unknown HPV results. For the above allocations, general information of participants, such as age, BMI, and marital status, were compared, revealing no statistical differences and indicating a balanced and comparable distribution (Table S3).

Analysis of the vaginal microbiological composition of the study population (16 S rRNA)

High-quality data were obtained from 16 S rRNA amplification analysis of vaginal secretions from 139 cases (Figure. S1).

α diversity index

When comparing the ASV number and Chao1 index, which measure species abundance, between the two allocations, no statistical difference in the α diversity index was observed. However, there were notable variations in the comparison of the Shannon and Simpson indices, which assess species diversity. Notably, the mean index values gradually increased from the HC group to the OD group and finally to the CC group. Additionally, the Shannon and Simpson indices of the HPV.P group were elevated relative to those in the HPV.N group (Fig. 1a-b).

Fig. 1 Microbial species α diversity analysis. (a and b) α diversity analysis among groups (Shannon: HC 2.154 ± 0.885, OD 3.588 ± 1.336, CC 4.202 ± 1.138, HPV.P 4.023 ± 1.048, HPV.N 3.335 ± 1.465. Simpson: HC 0.426 ± 0.186, OD 0.726 ± 0.205, CC 0.823 ± 0.128, HPV.P 0.802 ± 0.143, HPV.N 0.665 ± 0.236).

β diversity index

Both allocations showed statistical differences in reflecting the community structure among groups (Adonis, Weighted-unifrac, P = 0.001). In PCOA and NMDS, which reflect the β diversity index, it was observed that the HC group had the smallest distance matrix interval. As the distance matrix range from the OD group to the CC group and the HPV.N group to the HPV.P group gradually increased, this result suggested that the HC group and the HPV.N group had a more homogeneous community structure compared to the other groups. With the severity of the disease, the interval range of the community structure continued to expand (Fig. 2a-d).

Fig. 2 Microbial species β diversity analysis. (a and b) principal coordinates analysis (PCOA) analysis among groups (Weighted unifrac). (c and d) non-metric multidimensional scaling (NMDS) analysis among groups.

At the phylum level

The abundance of Firmicutes was HC > OD > CC, respectively, with a gradual decrease. Actinobacteriota had the highest abundance in OD, and the abundance of the remaining phylum levels was HC < OD < CC, respectively, with a gradual increase. In the second allocation, the HPV.P group displayed a reduction in the abundance of Firmicutes, Acidobacteriota, and Patescibacteria relative to the HPV.N group, while the abundance of other phyla increased (Fig. 3a-b, Table S4).

Fig. 3 Results for relative species abundance. (a and b) Relative species abundance at the phylum level among groups (TOP 7).

At the genus level

The abundance of Lactobacillus was HC > OD > CC, respectively, with a gradual decrease. Gardnerella had the highest abundance in the OD group, and the abundance of Streptococcus and Porphyromonas was HC < OD < CC, respectively, with a gradual increase. The second allocation suggested that the abundance of Lactobacillus in the HPV.P group notably decreased (Fig. 4a-b, Table S4). The findings of α diversity indicated that, despite no statistical difference in species abundance, changes in bacterial abundance in each group might be at the expense of decreasing other major bacteria. These data suggested that the diversity and compositions of vaginal microorganisms in patients with cervical cancer have undergone significant alterations.

Fig. 4 Results for relative species abundance. (a and b) Relative species abundance among groups at the genus level (TOP 15).

Comparison of mean species abundance

An elevation in the abundance of various microorganisms was noted in both the CC group and HPV.P group. Therefore, the abundance analysis of microorganism ASV number in each group was performed. The bacterial abundance means at the genus and species level in each group were calculated and sorted (Table S5).

At the genus level

The results showed that the fold change of Porphyromonas abundance mean increased most significantly in the CC/HC group comparison and HPV.P/HPV.N comparison, reaching 141.47 times and 3.25 times, respectively. In the OD group, Porphyromonas did not rank among the top ten bacterial genera, and its mean abundance value was only 22.22 times higher than that of the HC group. In the CC/OD group comparison, the fold change of the mean abundance value of Porphyromonas reached 6.368 times. (Figure. 5a-e). This result indicated that Porphyromonas has the highest fold change of the abundance mean in the CC-HC group and the HPV.P-HPV.N group, representing differential comparisons. It also exhibited the highest fold change of the abundance means in the CC-OD group, signifying a specific comparison (Table S6). This suggested that Porphyromonas could be used as a predictive microbial marker closely linked to the onset and development of HPV-positive cervical cancer.

Fig. 5 Fold change results of mean species abundance. (a) Visualization of mean species abundance of the HC group (TOP 10). (b and c) Visualization results for fold change of mean species abundance of the CC/HC and OD/HC groups (CC and OD TOP 10). (d) Visualization results for fold change of mean species abundance of the HPV.N group (TOP 10). (e) Visualization results for fold change of mean species abundance of the HPV.P/HPV.N group (HPV.P, TOP 10). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

At the species level

In the sub-analysis of bacterial amplification at the genus level for each group, the findings strongly suggested a close association between Porphyromonas and HPV-positive cervical cancer. Consequently, a more detailed exploration of Porphyromonas at the species level was undertaken using 16 S rRNA to identify bacteria at the smallest taxonomic level. Similarly, the mean ASV number of Porphyromonas at the species level was calculated according to the above analysis method. The fold change calculation of abundance means at the species level in the Porphyromonas showed that P. asaccharolytica and P. uenonis had the most significant increase in fold change in the CC/HC group and HPV.P/HPV.N group. Porphyromonas_somerae also displayed a noteworthy fold change, but its trend was opposite in the HPV.P/HPV.N group, where the mean abundance value of this strain in the HPV.N group was higher than that in the HPV.P group (Fig. 6a-e, Table S6). For 16 S rRNA sequencing, any one or more hypervariable regions, although highly specific, may not be within the amplified region for species at lower taxonomic levels. Therefore, Porphyromonas not identify in analysed results. The ROC was utilized to test the capability of Porphyromonas, P. asaccharolytica, and P. uenonis to identify cervical cancer. The results showed that they all exhibited a commendable ability to identify cervical cancer (Fig. 6f-h).

Fig. 6 Mean abundance fold change and receiver operating characteristic (ROC) results of Porphyromonas at the species level. (a) Visualization of the mean Porphyromonas abundance at the species level in the HC group (TOP 10). (b and c) Visualization results of fold change of abundance mean of Porphyromonas at the species level in the CC/HC and OD/HC groups (CC and OD TOP 10). (d) Visualization results of mean Porphyromonas abundance at the species level in the HPV.N group (TOP 10). (e) Visualization results of the fold change of abundance mean of Porphyromonas at the species level in the HPV.P/HPV.N group (HPV.P, TOP 10). (f, g, and h) ROC results among groups. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Metagenomic sequencing technology further probes Porphyromonas species-level results

Metagenomic sequencing technology assembles small fragments into longer sequences, which has high advantages in species identification. Therefore, this technique can be used to further validate whether P.asaccharolytica and P.uenonis have biological characteristics in cervical cancer diseases. Considering that Lactobacillus has been established as the predominant bacterial genus in the female vagina in prior studies, with its abundance closely linked to the health of the female vaginal environment, the top six secretion samples in abundance from Lactobacillus in the HC group and Porphyromonas in the CC group were selected. Metagenomic sequencing and relative species abundance results showed high rankings in abundance for P.uenonis and P. asaccharolytica. Among Lactobacillus, Lactobacillus iners, and Lactobacillus crispatus were bacteria with higher abundance (Fig. 7a-b). LDA showed that P.uenonis and P.asaccharolytica exhibited biological characteristics in the CC group, while Lactobacillus crispatus demonstrated biological characteristics in the HC group (Fig. 7c).

Fig. 7 Metagenomic sequencing results. (a) Relative species abundance of the Cervical cancer group (CC) and Health control group (HC). (b) Heat map of species in CC and HC groups (TOP 20). (c) Intergroup linear discriminant analysis (LDA) of the CC and HC groups (LDA score 4).

Vaginal microenvironmental factor test results

According to the first allocation method (based on health status or diseases of study participants), vaginal microenvironmental factor test results revealed that pH, H2O2, LE, and NAG differed significantly among the three groups. According to the second allocation method (based on HPV infections), vaginal microenvironmental factor test results suggested that pH value and LE differed significantly among the two groups (Table S7).

Detection of inflammatory factors in cervical cancer tissue and vaginal lavage fluid

Randomly selected vaginal irrigations from 6 healthy people and 6 patients with cervical cancer. Additionally, six samples of normal cervical tissue (cervical tissue confirmed pathologically without lesions after total resection of uterine leiomyoma) and six samples of cervical cancer tissue were included for comparative analysis of inflammatory factors. The ELISA assay results of vaginal lavage fluid showed statistical differences between the two groups in the inflammatory indicators of IL-1β and TNF-α (Fig. 8a). The results of ELISA and qRT-PCR assays on tissue specimens showed statistical differences between the two groups in six inflammatory indicators, including IL-12 and IL-6 (Fig. 8b-c).

Fig. 8 Detection of inflammatory factors. (a and b) Enzyme-linked immunosorbent assay (ELISA) results for vaginal lavage and tissue samples. (c) Expression results for mRNA from tissue samples.

Transcriptome sequencing results of P. asaccharolytica intervention in H8 cells

In the logarithmic growth phase, P. asaccharolytica (OD 0.85 ≈ 11.05*108 CFU/mL) was co-cultured with H8 cells for 2.5 h. H8 The morphology and growth of the cells is normal (Fig. 9a-b). H8 cells without intervention were used as the control group. Transcriptome sequencing was performed, revealing 184 upregulated genes and 1467 downregulated genes (Table S8, Fig. 9c).

Fig. 9 (a) Growth curves of P. asaccharolytica. (b) Morphological display of H8 cells after P. asaccharolytica intervention. (c) Volcanic map of differential genes.

Inflammation-related genes, sourced from Genecards (n = 15161, Table S8), were intersected with the differential genes of the experimental group resulting in a total of 860 differential genes related to inflammation (Fig. 10a, Table S8). GO and KEGG analysis results of 860 intersection genes showed that in biological process enrichment was predominantly observed in the regulation of transcription from RNA polymerase II promoter and positive regulation of transcription from RNA polymerase II promoter, among others. In cellular component, enrichment was observed in cytoplasm and cytosol, while in molecular function, enrichment was observed in protein binding and metal ion binding, among others. In terms of KEGG pathways, enrichment was observed in “pathways in cancer “, “Herpes simplex virus 1 infection,” “Human papillomavirus infection,” etc. (Figure. 10b-c). PPI network analysis was performed on 860 intersection genes. Unrelated genes in the network were excluded, and the TOP10 hub genes were obtained using CytoHubba’s Degree (Fig. 10d). IL-1β, CXCL8 and JUN are up-regulated inflammatory genes of TOP10 hub genes, but our study requires exogenous inflammatory factors to create an inflammatory microenvironmental state, and JUN is not suitable for exogenous intervention approach. Ultimately IL-1β and CXCL8 served as inflammatory factors of interest for our subsequent experiments (Table S8).

Fig. 10 (a) Venn diagram of the intersection of differential genes and inflammation-related genes. (b) GO analysis of 860 genes (TOP 8). (c) KEGG analysis of 860 genes (TOP 20). (d) Hub genes (TOP 10).

The qRT-PCR and Western blot results showed that the mRNA and protein expression of IL-1β and CXCL8 was significantly increased after P. asaccharolytica intervention in H8 cells (Figure S3a-b).

Effect of exogenous IL-1β and CXCL8 on tumour-associated biological behaviour of cervical cancer SiHa cells

Subsequently, the impact of IL-1β and CXCL8 inflammatory factor on the tumor-associated biological behavior of cervical squamous cell carcinoma SiHa cells was examined. Functional experiments such as cell proliferation, migration, invasion, and the detection of related biological tumor indicators were conducted.

CCK-8 findings highlighted statistically significant differences in the 24-h proliferation of SiHa cells following intervention with three concentrations of IL-1β relative to the control group. After 48 h, only the 80-ng/mL concentration still maintained an increased difference relative to the control group. (Figure. 11a). Wound healing assay outcomes demonstrated a statistical difference between the intervention group of 80 ng/mL and the control group at 24 h, and both the 40-ng/mL and 80-ng/mL intervention groups at 48 h differed from the control group (Fig. 11b-c).

Fig. 11 (a) Experimental results of Cell Counting Kit-8 (CCK-8). (b and c) Quantitative and experimental results of Wound healing, scale bar 200 μm.

The results of CCK-8 and scratch assay showed that the three concentrations of CXCL8 did not exhibit statistical differences from the control group after intervening in SiHa cells (P > 0.05) (Figure. S2a-c). Based on these experiments, it was decided that subsequent cell experiments should be performed with an 80 ng/mL IL-1β intervention for 48 h.

Transwell results showed that 80-ng/mL IL-1β intervention significantly enhanced the invasive ability of SiHa cells (Fig. 12a-b). EDU and colony formation assay further revealed that 80-ng/mL IL-1β intervention significantly increased the proliferation and tumorigenic abilities of SiHa cells (Fig. 12c and d-e). Western blotting results showed that the 80-ng/mL IL-1β intervention altered the tumor-associated biological behavior indicators of SiHa cells (Fig. 13).

Fig. 12 (a and b) Experimental and quantitative results of Transwell, scale bar 100 μm. (c) Experimental results of 5-Ethynyl-2′-deoxyuridine (EdU), scale bar 200 μm. (d and e) Experimental and quantitative results for Colony formation assay.

Fig. 13 Tumor-related biology-proliferative indicator Cyclin D1. Apoptotic indicators Caspase-3 and Cleaved-Caspase3. Invasive migration indicators E-cadherin, N-cadherin, and matrix metalloproteinase (MMP)-2. Oncogenic indicators human papillomavirus (HPV)16-E6 and HPV16-E7. Inflammatory indicators interleukin IL-1β.

Discussion

Despite recent data suggesting an association between various diseases and changes in microbiological compositions in the human body, studies involving specific bacteria and related mechanisms exhibit inconsistency. This variability may be attributed to factors such as the region and number of participants, differences in the analysis methods of sequencing results, and limited specificity of microbial markers. For vaginal microbiota, female Lactobacillus is known to play a role in combating diseases as a probiotic genus, which is consistent with numerous research findings26,27. Studies from diverse countries, including Europe, the United States, Brazil, and India, consistently demonstrates that Lactobacillus crispatus can serve as a broad-spectrum probiotic for vagina in women, contributing to the prevention of adverse reproductive diseases. This consistency aligns with the LDA analysis results of this study28–30, suggesting that the enrolled participants are well-suited to accomplish the study objectives.

Cervical cancer is mainly characterized by HPV infection, with approximately 90% of patients testing positive for HPV31,32. Therefore, two allocation methods were employed in this study: based on health status or diseases of study participants or HPV infections. Microbial analysis of vaginal microorganisms was conducted based on disease and etiology allocations to identify microbial markers associated with HPV-associated cervical cancer. In the comparison of microorganisms in CC-HC and HPV.P-HPV.N groups, significant differences in abundance levels were observed for specific microorganisms, namely Porphyromonas, P. asaccharolytica, and P. uenonis, in the context of cervical cancer. However, the criteria for screening disease markers should encompass aspects like diversity, specificity, and reproducibility33. Any further attempt to infer causality requires interventional studies34. To validate the specificity of the aforementioned microorganisms, the OD group was introduced based on the inclusion and exclusion criteria of this study and the actual admission of patients in the clinical department during specimen collection. The findings demonstrated that the abundance of Porphyromonas, P. asaccharolytica, and P. uenonis in the OD group was considerably reduced than that in the CC group, with the abundance fold change of CC/OD being the highest. Therefore, Porphyromonas, P. asaccharolytica, and P. uenonis exhibit notable differences and specificity in identifying cervical cancer. Gardnerella has been considered to be closely associated with cervical cancer in previous studies35, but it is the second most abundant bacterium after Lactobacillus vaginalis, and it may be reasonable to infer that when Lactobacillus abundance decreases, Gardnerella abundance shows a significant increase. However, in our results Gardnerella abundance showed a significant increase in the OD group, and its differential and specific performance was not considered to be closely related to cervical cancer, which is the difference between our results and those of previous studies. Subsequently, based on the results of 16 S rRNA, 12 samples were selected and subjected to metagenomic sequencing to explore the results of Porphyromonas at the species level. This process facilitated the identification of microbial markers for HPV-positive cervical cancer based on established screening criteria.

Limited information currently exists regarding Porphyromonas colonization in the human vagina, and none of the Porphyromonas species are considered unique to the reproductive tract36. Studies by Lithgow et al. suggested that P. asaccharolytica and P. uenonis possess the capability to secrete numerous extracellular matrix proteases, disrupting the coagulation system through collagen degradation and altering the balance of reproductive tissue homeostasis. Consequently, they are deemed key virulence factors in the pathogenesis of gynecological and reproductive diseases36. Among Porphyromonas species, the most extensively investigated to date is Porphyromonas gingivalis (P. gingivalis), which plays an important role in periodontitis by directly or indirectly destroying local tissues via inducing the inflammatory process37. Furthermore, P. gingivalis has been implicated in spreading to distant infection sites via the blood, causing cardiovascular diseases through the Toll-like receptors (TLR)-NF-κB pathway38,39. In cancer-associated diseases, P. gingivalis produces a pro-inflammatory microenvironment that is conducive to the progression of colon cancer by recruiting infiltrating myeloid cells and activating the NOD-like receptor protein 3 (NLRP3) inflammasome. Due to the affinity with microbial species, species in the same genus can perform similar functions40,41, and inflammation has been considered a core feature in tumor cell progression42. Therefore, in this study, dysbacteriosis caused a local inflammatory microenvironment in the female vagina. In addition, the reduced abundance of Lactobacillus was unable to exert a probiotic effect, leading to an increase in the pathogenic microorganisms, damaging the immune system, causing persistent HPV infection, and accelerating the onset and development of cervical cancer, forming the central ideas of this research (Fig. 14).

After identifying the target bacteria, test results of vaginal microenvironmental factors, vaginal lavage fluid, and inflammatory factors in tumor tissues suggested that the vagina of patients with cervical cancer was in an inflammatory state. In this regard, PH and LE showed statistical differences in the combined analysis of the two subgroups, Clarke43et al. found that elevated vaginal PH increased the risk of HPV infection by 30%, LE is an important biochemical indicator of response to bacterial vaginosis44, and the current study suggests that there is a significant correlation between BV and HPV infections and cervical lesions45. The reason for the variability in these indicators may be related to flora disorders, as Lactobacillus vaginalis produces high concentrations of lactic acid, which has the ability to acidify the vaginal environment, inhibit the reproduction of pathogenic bacteria, and have antiviral and immunomodulatory properties46. In contrast, the decreased abundance of vaginal Lactobacillu in patients with cervical cancer could not provide enough lactic acid to maintain the acidic environment of the vagina, resulting in the overgrowth of pathogenic bacteria, which manifested as a state of vaginal inflammation. And LE is an important biochemical indicator to respond to vaginal inflammation47, therefore, significant variability was observed in the comparison of the results.

In the transcriptome results, the results of KEGG demonstrated cancer pathways with HPV infection, etc., also presenting consistency with the central idea of this study.In prior studies, the major pathological tissue type in cervical cancer patients was squamous cell carcinoma, followed by adenocarcinoma48, aligning with the analysis results of general data of the participants in this research. In in vitro cell experiments, the cervical squamous cell carcinoma cell line SiHa was selected as the target cell, demonstrating that IL-1β could promote tumor cell-related biological behaviors, thereby elucidating the causality between changes in vaginal microbial compositions and cervical cancer.

Nevertheless, this study has several limitations. 1: In addition to the two important features of difference and specificity, disease markers should also have many features, such as reproducibility and prognosticability. Therefore, future research should broaden the scope and increase the number of sample collections to validate the research findings. Following up with study subjects over time would also be essential to determine whether vaginal microorganisms return to a healthy state during the recovery. 2: In vivo experiments should be conducted on P. asaccharolytica to validate the impact of the inflammatory microenvironment. Therefore, further research should focus on addressing the mentioned limitations to elucidate the molecular mechanisms between microorganisms and diseases. In conclusion, This study shows for the first time that Porphyromonas, P.asaccharolytica, and P.uenonis are vaginal microbial markers of HPV-positive cervical cancer. Porphyromonas_asaccharolytica induces up-regulation of the expression of several inflammatory genes in cervical epithelial cells, among which IL-1β as a key inflammatory factor has a role in promoting the development of cervical cancer.

Fig. 14 The mechanistic map of this study.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Supplementary Material 4

Supplementary Material 5

Supplementary Material 6

Acknowledgements

The authors would like to thank Xinjiang Medical University Cancer Hospital for providing clinical sample support.

Author contributions

BB designed the study; BB and AA performed the research; AA and YS contributed new reagents/analytical tools; GT, AT and RM collection of specimens; BB analyzed the data; and BB wrote the paper. All authors contributed to and approved the submitted manuscript.

Funding

This work was supported by grants from the Natural Science Foundation of the Xinjiang Uygur Autonomous Region (N2022D01C185).

Data availability

Microbial sequencing data supporting this study were stored in the National Center for Biotechnology Information (NCBI), study ID: SRP472321. Cell transcriptomics data are stored in the NCBI, accession ID: PRJNA1040786.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This research received approval from the Xinjiang Medical University institutional review board (Ethical approval number XJYKDXR20211015003. Informed consent was obtained from all participants.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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