Purpose
The objective of this study was to investigate the key glycolysis-related genes linked to immune cell infiltration in endometriosis and to develop a new endometriosis (EMS) predictive model.
Methods
A training set and a test set were created from the Gene Expression Omnibus (GEO) public database. We identified five glycolysis-related genes using least absolute shrinkage and selection operator (LASSO) regression and the random forest method. Then, we developed and tested a prediction model for EMS diagnosis. The CIBERSORT method was used to compare the infiltration of 22 different immune cells. We examined the relationship between key glycolysis-related genes and immune factors in the eutopic endometrium of women with endometriosis. In addition, Gene Ontology (GO)-based semantic similarity and logistic regression model analyses were used to investigate core genes. Reverse real-time quantitative PCR (RT-qPCR) of 5 target genes was analysed.
Results
The five glycolysis-related hub genes (CHPF, CITED2, GPC3, PDK3, ADH6) were used to establish a predictive model for EMS. In the training and test sets, the area under the curve (AUC) of the receiver operating characteristic curve (ROC) prediction model was 0.777, 0.824, and 0.774. Additionally, there was a remarkable difference in the immune environment between the EMS and control groups. Eventually, the five target genes were verified by RT-qPCR.
Conclusion
The glycolysis-immune-based predictive model was established to forecast EMS patients’ diagnosis, and a detailed comprehension of the interactions between endometriosis, glycolysis, and the immune system may be vital for the recognition of potential novel therapeutic approaches and targets for EMS patients.
Polycystic ovary syndrome(PCOS)is one of the most common reproductive endocrine disorders affecting approximately 5-20% of women in the reproductive age. Patients with PCOS also have chronic inflammation and oxidative stress, which can lead to abnormalities in the follicular development microenvironment, resulting in the accumulation of small follicles in the ovary, polycystic ovarian morphology, and ovulatory dysfunction. Some studies have shown that CD24 has multiple immune functions and plays an important role in the development of autoimmune diseases, inflammatory responses, and tumors. Moreover, recent studies indicated that CD24 plays a critical role in ovulation and may be related to PCOS. However, there is a lack of clinical data support, and the mechanism by which CD24 affects PCOS remains unclear. In this study, we explored CD24 differential expression in ovarian granulosa cells of patients with PCOS infertility by SCRB-Seq (single cell RNA barcoding and sequencing). Furthermore, increased CD24 mRNA level correlated with serum AMH in ovarian granulosa cells and BMI index. In addition, there was a significant positive correlation between granulosa cell CD24 mRNA expression and numbers of retrieved oocytes, two-pronuclear zygotes (2PN), transferable embryos, good quality embryos and cleaved embryos. At the same time, we found that CD24 mRNA is significantly higher in pregnant patients than in non-pregnant ones in granulosa cells, suggesting that CD24 is associated with PCOS, and it may influence the clinical outcome of PCOS patients undergoing IVF.
In this paper, through literature collection, combined with the economic environment and the actual situation in the field of enterprise financial risk, 15 obstacle factors related to enterprise financial risk are selected, and the multi-level structure diagram of the influencing factors is constructed by using the Interpretative Structural Model (ISM), and then the hierarchical structure of each factor and the path of influencing enterprise financial risk are analyzed, and the driving force and dependence of 15 influencing factors are analyzed by using Matrix Impacts Cross-reference Multiplication Applied to a Classification(MICMAC).
Purpose: The objective of this study was to investigate the key glycolysis-related genes linked to immune cell infiltration in endometriosis and to develop a new endometriosis(EMS) predictive model.Methods: A training set and a test set were created from the NCBI GEO public database. We identified five glycolysis-related genes using LASSO and the Random Forest method. Then we developed and tested a prediction model for EMS diagnosis. The method CIBERSORT was used to compare the infiltration of 22 different immune cells. We looked into the relationship between key glycolysis-related genes and immune factors in eutopic endometrial of women with endometriosis. Besides, GO-based semantic similarity and logistic regression model analyses were used to investigate core genes.Results: The five glycolysis-related hub genes (CHPF, CITED2, GPC3, PDK3, ADH6) were used to establish a predictive model for EMS. In the training and test set, the AUC of the ROC prediction model was 0.777, 0.824, and 0.774, respectively. Additionally, there was a remarkable difference in the immune environment between EMS and control. Conclusion: The glycolysis-immune-based predictive model was established to forecast EMS patients’ diagnosis, and a detailed comprehension of the interactions between endometriosis, glycolysis, and the immune system, may be vital for the recognition of potential novel therapeutic approaches and targets for EMS patients.
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