Identification Of Potential Biomarkers For Premature Birth Based On Proteomic Analysis And Development Of A Predictive Model

Objectives:Preterm birth is one of the most common complications in the perinatal period,which is an important cause of perinatal and neonatal mortality and growth and developmental disabilities,and also seriously affects the postpartum physical and mental recovery of pregnant women after delivery.However,the mechanism of premature birth is still not clear,and there is a lack of effective prevention and treatment measures,Predicting preterm birth in advance is of great significance.Due to the limitations of existing prediction techniques in terms of convenience,accuracy,and safety,the early prediction of premature birth still faces enormous difficulties and challenges.Therefore,there is an urgent need for new specific biomarkers to assist in the prediction of premature birth.The purpose of this topic is to explore potential specific biomarkers in the blood of pregnant women by means of proteomics technology,and construct a prediction model for premature birth,so as to provide theoretical basis for early prediction and precise prevention of premature birth.Methods:For this research,nested case-control study design was adopted.Blood samples of 24 preterm pregnant women and 24 full-term pregnant women who visited the obstetrics department of the First Affiliated Hospital of Kunming Medical University from September 2019 to December 2020 and eventually gave birth during the second trimester were selected for proteomic detection.GO analysis,KEGG analysis and PPI interaction network analysis system were used to analyze the functions of differential proteins,clarify the gene expression,metabolic pathway and the interrelationship between proteins involved in differential proteins,and further understand the molecular biological processes and biological functions involved in differential proteins.Finally,LASSO algorithm and RF random forest method were used to select the most important differentially expressed proteins as candidate biomarkers.The performance of the protein markers was evaluated by receiver operating characteristic curve(ROC),clinical decision curve(DCA),calibration degree and differentiation degree,and the prediction model of premature birth was established.Kaplan-Meier curve was used to analyze the relationship between protein expression level and preterm birth.The subject data were analyzed using software R language(Version4.2.0)and Graph Pad Prism(Version 9.0.0).Results:1.After matching the age and gestational age of blood collection,a total of 48pregnant women meeting the criteria were included in this study,and after definite diagnosis of premature birth,including 24 pregnant women in the premature birth group(gestational age at delivery<37 weeks)and 24 pregnant women in the normal group(gestational age at delivery≥37 weeks).The pregnant women ranged in age from 25 to 40 years old,the mean age was(29.6±4.1)years old,and the mean post-pregnancy BMI was 23.1±3.5 years old.There were no significant differences in other factors,including post-pregnancy BMI,education level,residence,nationality,occupation,fetal sex and pregnancy planning(P>0.05).2.Our criteria of FC>1.5 or FC≤1/1.5(0.67)and P?<?0.05 were applied to the protein quantification and qualitative results,which revealed that,A comparison of the blood of pregnant women who delivered preterm to those who delivered at term revealed 28 distinct proteins expressed.There were 11 proteins with up‐regulated expression level and 17 proteins with down‐regulated expression level.3.The results of bioinformatics analysis showed that the differentially expressed proteins in the blood of the two groups of pregnant women were mainly distributed in the cytoplasm,nucleus,and cell membrane;GO functional enrichment analysis showed that differential proteins were mainly involved in biological processes such as leukocyte activation and mediated immune response,response to organic nitrogen compounds,and neutrophil degranulation;KEGG analysis showed that tyrosine metabolism,glycolysis/gluconeogenesis,cytochrome P450 metabolic pathways,and other signaling pathways are highly enriched in differentially expressed proteins(P<0.01).4.ROC results indicated that the AUC values of the differential proteins PLA2G15,PPP1R7,and AGRN when used as single indicators for the prediction of preterm labor were 0.76(95%CI:0.63-0.90)、0.78(95%CI:0.64-0.92)、0.81(95%CI:0.68-0.94);The combined index predictive model composed of(PLA2G15+PPP1R7+AGRN)had an AUC value of 0.91(95%CI:0.83-1.00)in discriminating between the preterm and normal term birth groups,with a sensitivity of 0.92 and specificity of 0.83.The discrimination of 0.913,R~2=0.624 and calibration curves showed that the model had good performance.The results of DCA curve analysis suggested that the model had high clinical applicability.5.Results of internal validation of the combined marker model effectiveness are shown that,the average AUC of Bootstrap method was 0.92(95%CI:0.91-0.92),and the average C-Index was 0.92(95%CI:0.91-0.92).The average AUC of cross-validation was 0.90(95%CI:0.87-0.92),and the average C-Index was 0.89(95%CI:0.86-0.91),suggesting that the combined marker model had good stability.6.A Kaplan Meier survival analysis revealed that PPP1R7 expression levels were low PPP1R7(HR=6.02,95%CI:1.69-14.40,P<0.001)、PLA2G15(HR=2.70,95%CI:0.70-5.50,P=0.005)、AGRN(HR=2.57,95%CI:0.87-5.40,P<0.001).The risk of premature birth appears to be linked to them.7.Proteins PLA2G15,PPP1R7,and AGRN participate in the protein dephosphorylation process and regulate cell cycle,neuromuscular development,lipid homeostasis,and energy metabolism,respectively,and jointly regulate the physiological environment of the mother during pregnancy and the growth and development of the fetus,thereby affecting the maternal and infant outcomes.Conclusions:1.Through proteomic techniques,we have identified 28 proteins that are significantly differentially expressed in the blood of preterm pregnant women during the second trimester of pregnancy,which may participate in the potential regulatory mechanism of preterm birth as an interconnected whole.2.Low expression of PPP1R7,PLA2G15,and AGRN is associated with an increased risk of premature birth.When the expression levels of three proteins in pregnant women decrease,it is important to be alert to the occurrence of premature birth.3.Proteins PLA2G15,PPP1R7,and AGRN are expected to become biomarkers of premature birth.Their combined models have certain value in predicting premature birth,but further research is still needed.