Abstract
BACKGROUND: Intratumor microbiomes can influence tumorigenesis and progression. The relationship between intratumor microbiomes and cervical cancer metastasis, however, remains unclear. METHODS: We examined 294 cervical cancer samples together with information on microbial expression, identified metastasis-associated microbiomes, and used machine learning methods to validate their predictive ability on tumor metastasis. The tumors were subsequently typed based on differences in microbial expression. Differentially expressed genes in different tumor types were combined to construct a tumor-prognostic risk score model and a multiparameter nomogram model. In addition, we performed a functional enrichment analysis of differentially expressed genes to infer the mechanism of action between microbiomes and tumor cells. RESULTS: Based on the 15 differentially expressed microbiomes, machine learning models were able to correctly predict the risk of cervical cancer metastasis. In addition, both the risk score and the nomogram model accurately predicted tumor prognosis. Differences in the expression of endogenous genes in tumors can influence the distribution of the intracellular microbiomes. CONCLUSIONS: Intratumoral microbiomes in cervical cancer are associated with tumor metastasis and influence disease prognosis. A change in gene expression within tumor cells is responsible for differences in the microbial populations within the tumor.