Does the Core Position of Cervical Microbial Community Analysis in Predicting Cervical Intraepithelial Neoplasia Malignant Transformation?

Abstract Objective：Cervical microbial community in the cervical intraepithelial neoplasia and cervical cancer patients was analysed to study its composition, diversity and signalling pathways by high-throughput 16S rDNA sequencing,and the candidate genes associated with occurrence and progression of cervical intraepithelial neoplasia were screened out and the model was established to predict the evolution of cervical intraepithelial neoplasia malignant transformation from the cervical microbial genes aspect. Methods：Cervical tissues of normal, cervical intraepithelial neoplasia and cervical cancer patients without receiving any treatment were collected. The correlation between candidate genes and cervical intraepithelial neoplasia progression was initially determined by analyzing the microbial flora. Real-time fluorescence quantitative PCR was used to detect the expression of candidate genes in different cervical tissues, ROC curve and logistic regression was used to analyse and predict the risk factors related to the occurrence and progression of cervical intraepithelial neoplasia. Finally, the early warning model of cervical intraepithelial neoplasia occurrence and progression is established. Results：Cervical tissues from normal, cervical intraepithelial neoplasia and cervical cancer patients were collected for microbial community high-throughput 16S rDNA sequencing. The analysis revealed five different pathways related to cervical intraepithelial neoplasia. 10 candidate genes were selected by further bioinformatics analysis and preliminary screening. Real time PCR, ROC curve and Logistic regression analysis showed that human papillomavirus infection, TCT severity, ABCG2, TDG, PCNA were independent risk factors for cervical intraepithelial neoplasia. We used these indicators to establish a random forest model. Seven models were built through different combinations.The model 4 (ABCG2 + PCNA + TDG) was the best early warning model for the occurrence and progression of CIN. Conclusions: A total of 5 differential pathways and 10 candidate genes related to occurrence and progression of cervical intraepithelial neoplasia were found in cervical microbial community. This study firstly identified the genes from cervical microbial community that play an important role in the occurrence and progression of cervical intraepithelial neoplasia. At the same time, the early warning model including ABCG2+PCNA+TDG genes provided a new idea and target for clinical prediction and blocking the evolution of cervical intraepithelial neoplasia malignant transformation from the aspect of cervical microbiological related genes.