AIM:The aim of our study is to investigate the metabolic profile differences during cervical lesion progression and evaluate their potential clinical value in assisting the diagnosis of cervical cancer(CC).METHODS:Ul-tra-high-performance liquid chromatography coupled with high-resolution mass spectrometry(UHPLC-HRMS)was em-ployed to conduct non-targeted metabolomic analysis of cervical swab samples from 43 CC patients,34 high-grade squa-mous intraepithelial lesion(HSIL)patients,and 43 healthy controls.Based on the distinct features among the three groups,principal component analysis(PCA)was used to identify the metabolic differences among CC,HSIL and healthy groups.MetaboAnalyst 5.0 was then employed to perform KEGG pathway enrichment analysis on the differential metabo-lites.Finally,random forest machine learning algorithm was used to construct classification prediction models for distin-guishing CC from healthy,HSIL from healthy,and CC from HSIL.The performance of these models was evaluated using receiver operating characteristic(ROC)curve analysis.RESULTS:A total of 1 543 metabolites were identified across the healthy,HSIL and CC groups after filtration,with 407 metabolites differing between the groups.The study found that metabolite PGE2 was present in all three groups,with its expression levels progressively increasing with the progression of cervical lesions.Differential metabolite enrichment analysis demonstrated that CC is associated with specific cancer-relat-ed metabolic pathways,including the tricarboxylic acid cycle,tyrosine metabolism,tryptophan metabolism,and the pen-tose phosphate pathways.Additionally,the study developed three prediction models based on metabolic products for diag-nosing HSIL and CC:the full model,the simplified model,and the PGE2 model.The results indicated that metabolites ex-hibited strong diagnostic efficiency.Both the full model and the simplified model effectively distinguished CC from HSIL,CC from healthy,and HSIL from healthy.The AUC values for the full model were 0.90,0.92 and 0.84,respectively,while those for the simplified model were 0.81,0.95 and 0.85,respectively.Furthermore,the PEG2 model achieved AUC values of 0.74 and 0.80 for distinguishing CC from healthy and HSIL from healthy,respectively.CONCLUSION:The metabolic profiles of cervical cancer exhibit significant differences during the progression of cervical cancer,and these metabolites hold potential clinical value as biomarkers for cervical lesions.

AIM:The aim of our study is to investigate the metabolic profile differences during cervical lesion progression and evaluate their potential clinical value in assisting the diagnosis of cervical cancer(CC).METHODS:Ul-tra-high-performance liquid chromatography coupled with high-resolution mass spectrometry(UHPLC-HRMS)was em-ployed to conduct non-targeted metabolomic analysis of cervical swab samples from 43 CC patients,34 high-grade squa-mous intraepithelial lesion(HSIL)patients,and 43 healthy controls.Based on the distinct features among the three groups,principal component analysis(PCA)was used to identify the metabolic differences among CC,HSIL and healthy groups.MetaboAnalyst 5.0 was then employed to perform KEGG pathway enrichment analysis on the differential metabo-lites.Finally,random forest machine learning algorithm was used to construct classification prediction models for distin-guishing CC from healthy,HSIL from healthy,and CC from HSIL.The performance of these models was evaluated using receiver operating characteristic(ROC)curve analysis.RESULTS:A total of 1 543 metabolites were identified across the healthy,HSIL and CC groups after filtration,with 407 metabolites differing between the groups.The study found that metabolite PGE2 was present in all three groups,with its expression levels progressively increasing with the progression of cervical lesions.Differential metabolite enrichment analysis demonstrated that CC is associated with specific cancer-relat-ed metabolic pathways,including the tricarboxylic acid cycle,tyrosine metabolism,tryptophan metabolism,and the pen-tose phosphate pathways.Additionally,the study developed three prediction models based on metabolic products for diag-nosing HSIL and CC:the full model,the simplified model,and the PGE2 model.The results indicated that metabolites ex-hibited strong diagnostic efficiency.Both the full model and the simplified model effectively distinguished CC from HSIL,CC from healthy,and HSIL from healthy.The AUC values for the full model were 0.90,0.92 and 0.84,respectively,while those for the simplified model were 0.81,0.95 and 0.85,respectively.Furthermore,the PEG2 model achieved AUC values of 0.74 and 0.80 for distinguishing CC from healthy and HSIL from healthy,respectively.CONCLUSION:The metabolic profiles of cervical cancer exhibit significant differences during the progression of cervical cancer,and these metabolites hold potential clinical value as biomarkers for cervical lesions.

A comprehensive profiling of the vaginal microbial communities and their variability enables an accurate description of the microbiome in women.However,there is a lack of studies available on Chinese women.In the present study,the composition of the vaginal microbiota during pregnancy and the 6-week postpartum period of 454 Chinese women was characterized by sequenc-ing the V3-V4 region of the 16S ribosomal RNA(rRNA)gene.The vaginal microbiome showed variations during pregnancy and the postpartum period based on the abortion history,hypertensive disorders,delivery mode,and maternal age.Co-variation of 22 bacterial taxa,including the Lacto-bacillus genus and two of its species,may account for the common characteristics of the vaginal microbiome under scenarios of different medical histories and pregnancy outcomes.In contrast,dis-criminant bacterial species were significantly different between women who had preterm birth(PTB)with and without premature rupture of membranes(PROM),and the community state type(CST)Ⅳ-A without any predominant Lactobacillus species in the microbiota was more prevalent during pregnancy in the PROM-PTB cases,suggesting that specific bacterial species could be considered to distinguish between different types of PTB.By providing data on Chinese women,this study will enrich the knowledge of the human microbiome and contribute to a better understanding of the association between the vaginal microbiome and reproductive health.

OBJECTIVE: To analyze the characteristics of cervical microecology in late reproductive-age women with cervical lesions and explore new methods for preventing cervical lesions. METHODS: Cervical smears were obtained from a total of 147 women of late reproductive age, including 24 with high-risk HPV infection (HR-HPV), 27 with low-grade squamous intra-epithelial lesions (LSIL), 36 with high-grade squamous intra-epithelial lesions (HSIL), 35 with cervical cancer (CC) and 25 healthy women. llumina MiSeq sequencing of V3-V4 region of the 16S rRNA gene amplicons was used to characterize the vaginal microbiota of the women. OTUs analysis of the valid data was performed, and the α-diversity (Chao1, Simpson's Index and Shannon Index) and β-diversity (T-test, weighted UniFrac β diversity, and MetaStat analysis) were evaluated. RESULTS: Dilution curve and species accumulation boxplot validated the quality of the samples. OTUs analysis of the 5 groups demonstrated that cervical bacterial genus consisted primarily of CONCLUSIONS The abundance of
Female ; Humans ; Microbiota ; Papillomaviridae ; Papillomavirus Infections ; RNA, Ribosomal, 16S/genetics* ; Uterine Cervical Neoplasms ; Vaginal Smears