ABSTRACTVulvovaginal candidiasis (VVC) can alter the vaginal microbiome composition and structure, and this may be correlated with its variable treatment efficacy. Integrated analysis of the mycobiome and bacteriome in VVC could facilitate accurate diagnosis of infected patients and further decipher the characterized bacteriome in different types of VVC. Our mycobiome analysis determined two common types of VVC, which were clustered into two community state types (CSTs) featured byCandida glabrata(CST I) andCandida albicans(CST II). Subsequently, we compared the vaginal bacteriome in two CSTs of VVC and two other types of reproductive tract infections (RTIs), bacterial vaginosis (BV) andUreaplasma urealyticum(UU) infection. The vaginal bacteriome in VVC patients was between the healthy and other RTIs (BV and UU) status, it bore the greatest resemblance to that of healthy subjects. While BV and UU patients have the unique vaginal microbiota community structure, which very different with healthy women. Compared with CST II, the vaginal bacteriome of CST I VVC was characterized byPrevotella,a key signature in BV. In comparison, CST II was featured byUreaplasma, the pathogen of UU. The findings of our study highlight the need for co-analysis and simultaneous consideration of vaginal mycobiome and bacteriome in the diagnosis and treatment of VVC to solve common clinical problems, such as unsatisfactory cure rates and recurrent symptoms.IMPORTANCEFungi headed byC. albicansplay a critical role in VVC but are not sufficient for its occurrence, indicating the involvement of other factors, such as the vaginal bacteriome. We found that different CST correspond to different bacterial composition in patients with VVC, and this could underlie the alteration of vaginal microorganism environment in VVC patients. We believe that this correlation should not be ignored, and it may be related to the unsatisfactory treatment outcomes and high recurrence rate of VVC. Here, we provided evidence for associations between vaginal bacteriome patterns and fungal infection. Screening specific biomarkers for three common RTIs paves a theoretical basis for further development of personalized precision treatment.