| Background: Recurrent spontaneous abortion（RSA）can cause recurrent pregnancy loss,and its mechanism is still unclear.The presence of microinflammatory state can block placental angiogenesis,which makes ischemic and hypoxic in the embryo and triggering the occurrence of miscarriage;at the same time,inflammatory mediators are released in large quantities and neutrophils are activated,producing large amounts of oxygen free radicals,which can trigger oxidative stress,thus further strengthening the chain reaction between oxidation and inflammation,leading to the occurrence of miscarriage.Ferroptosis is a non-apoptotic and regulated cell death method driven by iron accumulation and lipid peroxidation,which can lead to imbalance of oxidative and antioxidant systems,massive release of inflammatory factors and abnormal innate immune system.There are few studies associated with ferroptosis in RSA.In this paper,firstly,analyzing the serological indicators related to inflammation,blood lipid and oxidative stress in RSA women based on a clinical retrospective study;secondly,the bioinformatics method was used to explore the relevant pathways and molecular mechanisms of ferroptosis in RSA,construct a diagnostic model and analyze the immune content.The above research provides a theoretical basis for the mechanism exploration and provides new ideas for the diagnosis and treatment of RSA in clinical practice.Chapter 1: Comparison of inflammatory,blood lipids and uric acid levels in RSA and control patients based on retrospective studiesObjective: To compare whether there are differences in the levels of inflammation,blood lipids and uric acid levels between RSA and control patients,and to determine whether inflammation,blood lipids and oxidative stress are related to the RSA.Methods: From January 2019 to December 2021,458 patients who underwent in vitro fertilization（IVF/ICSI）assisted pregnancy in our hospital were collected,including 214 RSA patients.The clinical medical records of the two groups were collected and analyzed retrospectively.First,the general conditions,inflammatory index,blood lipid levels and uric acid levels of the patients were compared;Secondly,the two groups of patients were divided into two age groups: age≤35 years old and age >35 years old.BMI levels are divided into four categories: thin（BMI<18.5 kg/m~2）,normal（18.5≤BMI<24.0 kg/m~2）,overweight（24.0≤BMI<27.9 kg/m~2）,and obese（BMI≥28.0 kg/m~2）.The blood lipid levels of the two groups were analyzed and compared through correlation analysis and stratified analysis.Binary logistic regression was used to determine whether inflammation,blood lipids and uric acid were related to the risk of RSA,and the ROC curve was used to determine whether blood lipids,inflammation and uric acid were related to RSA;Finally,the patients were divided into4 groups using the quartile method according to the uric acid level,the first quantile（～P25,UA≤230umol/L）was recorded as the U1,the second and fourth quantile（P25-P50,230300umol/L）was recorded as U4.According to BMI and uric acid stratification,we divided all patients into 8 groups B1-B8,non-obese and four uric acid levels in U1,U2,U3,U4 range of patients were divided into B1,B2,B3,B4 group,obesity and four uric acid levels in U1,U2,U3,U4 range of patients were divided into B5,B6,B7,B8 group respectively,using binary logistic regression and restricted cubic spline plots to explore the relationship between serum uric acid levels and the risk of RSA.Result:1.Compared with the control group,WBC,NEUT#,NLR and SII of RSA patients were significantly increased（P<0.05）.The risk of RSA was positively correlated with WBC,NEUT#,NLR and SII（P<0.05）;There was a positive correlation between NLR and age in RSA patients（P<0.05）;There was a negative correlation between WBC,NEUT# and age in control group（P<0.05）,but no correlation between BMI and inflammatory index.Binary logistic regression was used to take age,NLR,and SII as independent variables,and the risk of RSA as the dependent variable.Binary logistic regression analysis showed that SII significantly increased the risk of RSA in women aged≤35 years [OR=1.002,95% CI（1.001-1.003）],NLR significantly increased the risk of RSA in women aged>35 years old[OR=2.686,95% CI（1.062-6.795）];AUC of predicting RSA was 73.44% combined with age,which was higher than the predictive value of SII and NLR alone.2.Compared with the control group,TC,TG and LDL were dramatically advanced in RSA（P < 0.05）and there was no significant difference between the HDL groups（P>0.05）.The risk of RSA was positively correlated with TC,TG and LDL（P <0.05）.There was a positive correlation between TC,TG and age,same as TG and BMI in RSA patients（P<0.05）.There was a positive correlation with TC,LDL and age,same as TG,LDL and BMI in control patients（P<0.05）;HDL was dramatically advanced in RSA patients aged ≤35 years old（P<0.01）.HDL was slightly higher,TG and HDL were higher,TC and LDL were lower in RSA patients aged>35 years old,but the difference was not statistically significant（P>0.05）;Compared with the control group with the same BMI level,lower TC and LDL,TG and HDL were higher in overweight RSA patients,and LDL was significantly increased in obese RSA patients（P<0.05）.The binary logistic regression analysis was used to take age,BMI,TC,TG and LDL as independent variables,and the risk of RSA as the dependent variable.Binary logistic regression analysis showed that LDL significantly increased the risk of RSA[ OR=2.206,95% CI（1.208～4.027）];The area under curve of predicting RSA was73.92% combined with BMI and age,which was higher than the predictive value of LDL alone.3.Compared with the control group,the uric acid was significantly increased in the RSA patients（P<0.05）,the distribution of uric acid in the two groups was roughly similar（the quartiles were close）,but there were more outliers in the RSA patients.There was a positive correlation between the risk of RSA and uric acid（P<0.05）;The constituent ratios of the four levels of UA were 27.1%,21.4%,21.4%,and 30% in RSA,and the constituent ratios of RSA in the high-level uric acid group was greater than that in the low-level uric acid group,and the chi-square test indicated that the differences between the groups were statistically significant（P<0.05）.Binary logistic regression analysis showed that compared with the B4 group,the low-level UA group was lower risk of RSA in the non-obese group,and UA between 260-300umol/L had the lowest risk of RSA.AUC of predicting RSA reached 60.02% combined with BMI.Restricted cubic spline showed that uric acid at 260-290umol/L did not increase the risk of RSA.Conclusion: 1.NEUT#,NLR and SII were significantly different in two groups,and NEUT# were significantly different in RSA patients,NLR and SII were significantly increased,SII and NLR can predict the risk of RSA combined with age;2.Compared with the control group,TC,TG and LDL are significantly different in the RSA patients.LDL is significantly increased in RSA patients,which is more significant in RSA patients aged≤35 years old and normal and thin weight.LDL has better predictive value,and the predictive value is greater with age and BMI in RSA;3.Uric acid was significantly different in two groups,and uric acid in 260-290umol/L will not increase the risk of RSA.Chapter 2: Preliminary exploration of the mechanisms associated with ferroptosis in recurrent spontaneous abortion and construction of a diagnostic model based on bioinformaticsObjective: 1.The aim of this study is to explore potential diagnosis and therapeutic targets gene that associated with ferroptosis of RSA and analyze its possible pathways and molecular mechanisms;2.Identify key genes associated with ferroptosis in RSA and analyze its possible pathways,using key genes to construct diagnostic models and verify the stability;3.Analyzing the immune infiltration of RSA and the relationship between immune cell content and key genes;4.Construction of a network about miRNA-mRNA.Methods: 1.The raw data of GSE76862 was obtained from the GEO website（http://www.ncbi.nlm.nih.gov/geo/）,the differentially expressed genes（DEGs）between the RSA and control samples were analyzed using the limma package in R,which were those genes with a P<0.05 and |Log2FC|>0.585;Gene database（Gene Cards）extracted from ferroptosis related genes,260 ferroptosis related genes.The intersection of DEGs and ferroptosis genes is specific ferroptosis-related genes of RSA.The enrichment analysis was carried out according to the specific ferroptosis-related genes of RSA to explore different molecular mechanisms;2.Lasso regression was used to characterize the specific ferroptosis-related genes of RSA to identify key genes,which were used to construct a diagnostic model,and the ROC curve and GSE22490 date were used to verify the accuracy of the diagnostic model;The pathways analysis of GSEA was carried out according to the high-expression and low-expression to explore different molecular mechanisms;3.CIBERSORT was used to evaluate the ratio of the immune-stromal component in the RSA microenvironment and use Pearson correlation analysis on gene expression and immune cell content;4.Combined with the mircode website（http://mircode.org）to reversely predict the interaction between mRNA and miRNA,establish a miRNA-mRNA network,and visualize it by cytoscape.Result:1.A total of 1102 DEGs were identified in GSE76862 data,444 up-regulated genes and 658 down-regulated genes,260 ferroptosis related genes from Gene database（Gene Cards）,The intersection of the DEGs and ferroptosis genes is specific ferroptosisrelated genes of RSA,22 specific ferroptosis-related genes of RSA were identified.GO and KEGG enrichment analyses illustrated that the differential functions and pathways between specific ferroptosis-related genes of RSA were mainly concentrated on ubiquitin protein ligase binding、ferroptosis、transforming growth factor beta receptor signaling pathway、response to oxidative stress、regulation of RNA splicing、ATP-dependent activity、nuclear division、positive regulation of organelle organization.2.The obtained key genes are NCOA4,PRDX2 and MAP1LC3 B.Compared with the control group,MAP1LC3 B is highly expressed in RSA,while NCOA4 and PRDX2 are lowly expressed in RSA.The prediction model formula constructed using the three key genes is: score=NCOA4×（-0.185）+PRDX2×（-0.145）+ MAP1LC3B×0.0167,ROC validation using internal data shows that the prediction model constructed by the three genes has good diagnostic performance,the GSE22490 data is used as the validation set to verify the diagnostic model,and the model has strong stability;GSEA analysis shows that the high expression of MAP1LC3 B gene is mainly involved in m TOR signaling pathway,leukocyte transendothelial migration,p53 signaling pathway,glycerolipid metabolism,purine metabolism,regulation of autophagy.Low expression of NOCX4 gene is mainly involved in arachidonic acid metabolism,Hedgehog signaling pathway,TGF beta signaling pathway,TOLL like receptor signaling pathway.Low expression of PRDX2 gene is mainly involved in linoleic acid metabolism,fatty acid metabolism,NOD like receptor signaling pathway,B cell receptor signaling pathway,leukocyte transendothelial migration.Key genes may cause RSA through lipid metabolism,oxidative stress,inflammation,autophagy,immunity,etc.3.Compared with normal patients,resting dendritic cells in RSA patients were significantly increased,and the difference was statistically significant.Resting and activated memory CD4+T cells,follicular helper T cells and γδT cells were no expression in the control group.Further analysis of the three key genes NCOA4,PRDX2,MAP1LC3 B and immune cell content have a strong correlation;4.3 key genes were reversely predicted,154 miRNAs were predicted with a total of 241 mRNA-miRNA relationship pairs and a miRNA network related to key genes was constructed;Conclusions: 1.A total of 22 ferroptosis-related genes in RSA and 3 key genes,which may be diagnostic and therapeutic targets related to ferroptosis in RSA;2.The diagnostic model constructed by internal and external data has strong stability based on3 key genes（NCOA4,PRDX2,MAP1LC3B）;3.The resting dendritic cells in RSA were significantly higher than the control group,which play an important role in the occurrence of RSA;4.Key genes may cause RSA through lipid metabolism,oxidative stress,inflammation,autophagy,immunity,etc. |
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