Neoplasms

Urinary Tract, Sexual Organs, and Pregnancy Conditions

All Conditions

Uterine Cervical Neoplasms

Uterine Neoplasms

Genital Neoplasms, Female

Urogenital Neoplasms

Uterine Cervical Diseases

Uterine Diseases

Genital Diseases, Female

Genital Diseases

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Patients with normal cervix and HPV negative

Patients with normal cervix and HPV positive

Patients with low-grade squamous Intraepithelial lesion

Patients with high-grade squamous Intraepithelial lesion

Patients newly diagnosed invasive cervical cancer

This project is a clinical observational study. No additional medication or surgical interventions are performed on the subjects.

Rao Qunxian

The disorder of vaginal microflora and its metabolites is considered to be a facilitating factor to human papillomavirus-mediated cervical cancer. However, the mechanism is still unclear. This study intends to carry out a cross-sectional study and a cohort study. The cross-sectional study intends to recruit 300 premenopausal non-pregnant women, dividing them into five groups, with 60 in each group: HPV negative \[Ctrl HPV (-)\], HPV positive \[Ctrl HPV (+)\], low-grade squamous Intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL) and newly diagnosed invasive cervical cancer (ICC). Obtain basic information through the questionnaire, and collect vaginal secretion and blood samples. At the same time, patients who are diagnosed with cervical cancer for the first time will be included in the cohort study. Collect the same kind of information. The follow-up period is set to be 3 years, and samples will be collected every six months. If any condition changes within the 3 years, samples should be collected. If new treatments are taken, samples should be taken before and after treatment. And if the lesion turns negative after treatment within the 3 years, complete the follow-up. Using 16S rRNA gene sequencing, metabolomics, and immunological methods to determine the vaginal microbiota and its metabolites and inflammation condition, select biomarkers related to the onset of cervical cancer. construct a cervical cancer risk model and outcome prediction model, and reveal the mechanism of vaginal flora and its metabolites in the pathogenesis and development of cervical cancer. Therefore provides a new direction for the prevention and treatment of cervical cancer.

The disorder of vaginal microflora and its metabolites is considered to be a facilitating factor to human papillomavirus-mediated cervical cancer. However, the mechanism is still unclear.

This study intends to carry out a cross-sectional study and a cohort study. The cross-sectional study intends to recruit 300 premenopausal non-pregnant women, dividing them into five groups, with 60 in each group: HPV negative \[Ctrl HPV (-)\], HPV positive \[Ctrl HPV (+)\], low-grade squamous Intraepithelial lesion (LSIL group), high-grade squamous intraepithelial lesion (HSIL group) and newly diagnosed invasive cervical cancer (ICC group).

Obtain basic information through the questionnaire, and collect vaginal secretion and blood samples every time the patients review the clincal department as scheduled. At the same time, patients who are diagnosed with cervical cancer for the first time will be included in the cohort study. Collect the same kind of information. The follow-up period is set to be 3 years, and samples will be collected every six months. If any condition changes within the 3 years, samples should be collected. If new treatments are taken, samples should be taken before and after treatment. And if the lesion turns negative after treatment within the 3 years, complete the follow-up.

Using 16S rRNA gene sequencing, metabolomics, and immunological methods to determine the vaginal microbiota and its metabolites and inflammation condition, select biomarkers related to the onset of cervical cancer.

Carry out the genital tract inflammation score calculating, blood inflammatory factors testing, biological information analyzing, and metabolite composition and content in vaginal secretions analyzing.

The purpose of this study is to construct a cervical cancer risk model and outcome prediction model, and reveal the mechanism of vaginal flora and its metabolites in the pathogenesis and development of cervical cancer. Therefore provides a new direction for the prevention and treatment of cervical cancer.

Extract the total DNA of the bacteria from the vaginal discharge of the patients enrolled.

The Mechanism of Vaginal Flora and Its Metabolites in the Pathogenesis of Cervical Cancer

ELISA kit is used to detect the expression levels of 7 cytokines (IL-1α, IL-1β, IL-8, MIP-1β, CCL20, RANTES and TNF-α, etc.) in the vaginal secretions, and determine a cumulative score according to the level of each cytokine. If 3 or more than 3 of the 7 cytokines ranks in the upper quartile of all participants, it's considered high-level reproductive tract inflammation. A score of 5 to 7 is considered high-grade genital tract inflammation, 1 to 5 is low-grade genital tract inflammation, and a score of 0 is no inflammation.

Use ELISA kit to detect 7 kinds of inflammatory factors (IL-1α, IL-1β, IL-8, MIP-1β, CCL20, RANTES and TNF-α.) in the blood sample.

Extract DNA with a total bacterial DNA extraction kit, using bacterial DNA as a template, bacterial 16S rDNA V3\~V4 variable regions as targets, and barcode-equipped universal primers for PCR amplification. The PCR products will be sequenced using Illumina NovaSeq sequencing technology. After quality control, trimming, denoising, splicing, and chimera removal of the obtained raw data and reads, the high-throughput original base sequence is obtained, and the data will be analyzed using Qiime2 software. Data analysis includes operational unit (OTU) clustering, genetic enrichment analysis, principal component analysis (PCoA), community structure diversity (α and β diversity), and analysis of bacterial genus differences between groups (using linear discriminant effect analysis of LefSe ), correlation analysis, intestinal flora prediction model (random forest model).

The non-targeted metabolomics method is used to detect the metabolite composition and content in vaginal secretions. Quantitative analysis of metabolomics in each group, principal component analysis (PCOA, group analysis), differential metabolite spectrum analysis (increased/decreased metabolites in each group), correlation analysis (correlation analysis of inflammatory factors and metabolites). Correlation analysis between microbiology and metabolomics (including correlation analysis between different species and different metabolites, Scatter plot analysis, etc).

The content of the questionnaire includes:

1. General information: name, place of origin, age, nationality, occupation, education level, family income, etc.
2. Marriage and childbirth history: menstruation, marital status, pregnancy and parity, history of sexual life, contraceptive methods, etc.
3. Lifestyle: smoking history, drinking history, sleep time, moderate to high-intensity physical activity, etc.
4. Disease history and family history: diabetes, hypertension, venereal history, history of gynecological tumors.
5. Testing and laboratory examinations: height, weight, gynecological examinations, HPV testing, TCT or cervical pathological diagnosis, etc.

Spots Global Cancer Trial Database for metaboliomics