ObjectiveTo investigate the Interaction between chronic endometritis (CE) caused endometrial microbiota disorder and endometrial immune environment change in recurrent implantation failure (RIF).MethodTranscriptome sequencing analysis of the endometrial of 112 patients was preform by using High-Throughput Sequencing. The endometrial microbiota of 43 patients was analyzed by using 16s rRNA sequencing technology.ResultIn host endometrium, CD4 T cell and macrophage exhibited significant differences abundance between CE and non-CE patients. The enrichment analysis indicated differentially expressed genes mainly enriched in immune-related functional terms. Phyllobacterium and Sphingomonas were significantly high infiltration in CE patients, and active in pathways related to carbohydrate metabolism and/or fat metabolism. The increased synthesis of lipopolysaccharide, an important immunomodulator, was the result of microbial disorders in the endometrium.ConclusionThe composition of endometrial microorganisms in CE and non-CE patients were significantly different. Phyllobacterium and Sphingomonas mainly regulated immune cells by interfering with the process of carbohydrate metabolism and/or fat metabolism in the endometrium. CE endometrial microorganisms might regulate Th17 response and the ratio of Th1 to Th17 through lipopolysaccharide (LPS).
Ovarian cancer (OvCa) is an intractable gynecological malignancy due to the high recurrence rate. Several molecular biomarkers have been previously screened for early identifying patients with a high recurrence risk and poor prognosis. However, all the known studies focused on a single type of RNAs, not integrating various types. This study was to construct a new multi-RNA-based model to predict the recurrence and prognosis for OvCa patients by using the messenger RNA (mRNA, including long noncoding RNA (lncRNA)) and microRNA (miRNA) sequencing data of The Cancer Genome Atlas database. After univariate Cox regression and least absolute shrinkage and selection operator analyses, a multi-RNA-based signature (2 miRNAs: hsa-miR-508, hsa-miR-506; 1 lncRNA: TM4SF1-AS1; 11 mRNAs: MAGI3, SLAMF7, GLI2, PDK1, ARID3A, PLEKHG4B, TNFAIP8L3, C1QTNF3, NDUFAF1, CH25H, TMEM129) was generated and used to establish a risk score model. The high- and low-risk patients classified by the median risk score exhibited significantly different recurrence risks (89% versus 61%, p<0.001) and survival time (the area under the receiver operating characteristic curve (AUC) = 0.901 for 5-year disease-free survival (DFS)). This risk model was independent of other clinical features and superior to pathologic staging for DFS prediction (AUC, 0.906 versus 0.524; C-index, 0.633 versus 0.510). Furthermore, some new interaction axes were revealed to explain the possible functions of these RNAs (competing endogenous RNA: TM4SF1-AS1-miR-186-STEAP2, LINC00536-miR-508-STEAP2, LINC00475-miR-506-TMEM129; coexpression: LINC00598-PLEKHG4B). In conclusion, this multi-RNA-based risk model may be clinically useful to stratify OvCa patients with different recurrence risks and survival outcomes and included RNAs may be potential therapeutic targets.
Objective: In this study, we mainly explored two questions: Which microorganisms were functionally active in the endometrium of patients with endometrial cancer (EC)? What kind of response did the human host respond to functionally active microorganisms?Methods: Nine endometrial cancer patients and eight normal subjects were included in this study. HMP Unified Metabolic Analysis Network 3 (HUMAnN3) was used to obtain functional information of microorganisms. In addition, metaCyc-based GSEA functional enrichment analysis was used to obtain information on the metabolic pathways of the human host. At the same time, the O2PLS model and Spearman correlation analysis were used to analyze the microorganisms–host interaction.Results: With the novel metatranscriptome analysis pipeline, we described the composition of more than 5,000 functionally active microorganisms and analyzed the difference in microorganisms between the EC and the normal group. Our research found that these microorganisms were involved in part of the metabolic process of endometrial cancer, such as 6-sulfo-sialyl Lewis x epitope, N-acetyl-beta-glucosaminyl. In addition, the host–microbiota crosstalk of EC endometrium also included many biological processes, mainly functions related to tumor migration and the Apelin signaling pathway.Conclusion: The functionally active microorganisms in the EC endometrium played an essential role in the occurrence and migration of tumors. This meant that functionally active microorganisms could not be ignored in the treatment of endometrial cancer. This study helped to better understand the possible role of endometrial functional, active microorganisms in the occurrence and development of EC in patients with endometrial cancer and provided new information for new attempts to treat EC.
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