Analysis Of Risk Factors And Differential Expression Of Mrna/lncrna In Diabetic Peripheral Neuropathy With Blood Stasis Syndrome

Objective:We took the DPN blood stasis syndrome population as the research object,and used the method of retrospective analysis to screen out the clinical risk factors of DPN blood stasis syndrome;Differential expression profiles of m RNA and lnc RNA in peripheral blood of people with stasis syndrome,comprehensively discuss the clinical risk factors and biological markers of DPN blood stasis syndrome,and provide theoretical and experimental basis for the diagnosis and treatment of DPN by TCM syndrome differentiation,so as to achieve precise treatment of TCM syndromes.Methods:Part IWe adopted a case-control study design,collected and included clinical data of 532DPN patients,and analyzed the clinical characteristics of the DPN population with blood stasis syndrome.We used Lasso regression method to preliminarily screen the clinical characteristics of DPN blood stasis syndrome,and used Logistic regression analysis to analyze the influencing factors of DPN blood stasis syndrome by univariate and multivariate analysis,and screened out the clinical risk factors of DPN blood stasis syndrome.Finally,we comprehensively evaluate the reliability and validity of risk factors through discrimination test,calibration test and decision curve analysis.Part IIWe selected DPN patients with blood stasis syndrome（A）,DPN non-blood stasis syndrome patients（D）,and 4 healthy control groups（C）,respectively extracted peripheral blood mononuclear cells（PBMCs）,and detected m RNA and lnc RNA using a high-throughput sequencing platform The differentially expressed m RNA/lnc RNA was screened out by bioinformatics method,the PPI network and hub gene of the differentially expressed m RNA were constructed,the target gene of the differentially expressed lnc RNA was predicted,and the lnc RNA-m RNA co-expression network was constructed.At the same time,we further analyzed the signaling pathways and biological processes involved in the differential m RNAs and lnc RNAs,and found the core m RNAs and lnc RNAs related to the occurrence and development of DPN blood stasis syndrome.Part IIIBased on the results of the comprehensive analysis in the second part,we screened out the core m RNAs and lnc RNAs related to DPN blood stasis syndrome,expanded the sample size for verification,and used the 2^-△△CT method to calculate the relative expression of target genes;We used Pearson correlation to analyze the correlation between core m RNA and lnc RNA and clinical physical and chemical indicators;we used general linear regression to analyze the correlation between core m RNA and lnc RNA and DPN blood stasis syndrome;we evaluated core m RNA and lnc RNA by AUC area of ROC curve diagnostic value in DPN blood stasis syndrome.Results:Part I（1）The results of Lasso analysis showed that the optimal lambda yielded 20variables coefficients,namely age,BMI,duration of diabetes,complications of diabetic retinopathy,complications of cerebral infarction,systolic blood pressure（SBP）,diastolic blood pressure（DBP）,fasting blood glucose（FPG）,two-hour postprandial blood glucose（2HPG）,glycosylated hemoglobin（Hb A1c）,triglyceride（TG）,total cholesterol（T-CHOL）,high-density lipoprotein cholesterol（HDL-C）,hemoglobin（HGB）,platelet（PLT）,prothrombin time（PT）,activated partial prothrombin time（APTT）,fibrinogen（FBG）,thrombin time（TT）,erythrocyte sedimentation rate（ESR）.（2）Univariate Logistic regression analysis showed that 11 variables were screened out to be related to the occurrence of DPN blood stasis syndrome,namely the duration of diabetes,two-hour postprandial blood glucose（2HPG）,glycosylated hemoglobin（Hb A1c）,triglyceride（TG）,total cholesterol（T-CHOL）,hemoglobin（HGB）,platelet（PLT）,prothrombin time（PT）,activated partial prothrombin time（APTT）,fibrinogen（FBG）,erythrocyte sedimentation rate（ESR）.（3）Multivariate Logistic regression analysis showed that 8 variables were associated with the occurrence of DPN blood stasis syndrome,namely the duration of diabetes（OR:1.06,95%CI:1.02-1.09）,glycosylated hemoglobin（OR:1.40,95）%CI:1.24-1.59),triglycerides（OR:1.62,95%CI:1.32-1.98）,platelet（OR:1.01,95%CI:1.00-1.01）,prothrombin time（OR:0.41,95%CI:0.32-0.51）,activated partial prothrombin time（OR:0.67,95%CI:0.59-0.77）,fibrinogen（OR:3.67,95%CI:2.50-5.40）,erythrocyte sedimentation rate（OR:1.03）,95%CI:1.00-1.07).（4）The results of discrimination test,calibration test and decision curve analysis showed that the duration of diabetes,glycosylated hemoglobin,triglyceride,platelet,prothrombin time,activated partial prothrombin time,fibrinogen and erythrocyte sedimentation rate were included.The eight variables of DPN have good specificity,sensitivity and clinical net benefit rate,and have good predictive ability,which can provide reference for the risk assessment of DPN blood stasis syndrome.Part II（1）With q-value<0.05 and|log2FC|≥2 as the threshold criteria,the differentially expressed m RNAs and lnc RNAs in each group were screened out.By comparing the DPN blood stasis syndrome with the healthy controls,we obtained a total of 980 differentially expressed m RNAs,among which the m RNAs were up-regulated The number of m RNAs was 593 and the number of down-regulated m RNAs was 397.We obtained a total of 1648differential lnc RNAs,including 997 up-regulated lnc RNAs and 651 down-regulated lnc RNAs.Through DPN blood stasis syndrome and non-blood stasis syndrome control,we obtained a total of 615 differential m RNAs,including 380 up-regulated m RNAs and235 down-regulated m RNAs;we obtained a total of 1026 differential lnc RNAs,of which678 were up-regulated lnc RNAs,the number of down-regulated lnc RNAs was 348.The common parts of the two gene sets were selected,and the differentially expressed gene profiles related to DPN blood stasis syndrome were finally screened out by joint analysis.We obtained a total of 396 differential m RNAs,including 281 up-regulated m RNAs and115 down-regulated m RNAs;413 differential lnc RNAs,including 290 up-regulated lnc RNAs and 123 down-regulated lnc RNAs.（2）The differentially expressed m RNAs were involved in 274 biological processes（BP）,23 cellular components（CC）,34 molecular functions（MF）and 90 significantly enriched KEGG pathways.The differentially expressed lnc RNAs were involved in 364biological processes（BP）,118 cellular components（CC）,19 molecular functions（MF）and 29 significantly enriched KEGG pathways.Differential lnc RNAs and differential m RNAs were jointly involved in 163 biological processes（BP）,25 cellular components（CC）,38 molecular functions（MF）and 25 significantly enriched KEGG pathways.（3）Differentially expressed m RNAs and lnc RNAs together are mainly involved in biological processes such as response to molecules of bacterial origin,response to lipopolysaccharide,neutrophil activation,neutrophil-mediated immunity,neutrophil degranulation,and neutrophil activation involved in immune response.It is mainly enriched in cellular structural locations such as specific granules,specific granule lumen,cytoplasmic vesicle lumen,vesicle lumen,and tertiary granulocyte lumen.Mainly involved in molecular functions such as binding of haptoglobin,oxygen carrier activity,peroxidase activity,oxidoreductase activity,and acting as receptor by peroxides.It is mainly enriched in IL-17 signaling pathway,cytokine-cytokine receptor interaction,transcriptional dysregulation in malaria,rheumatoid arthritis,cancer,interaction of viral proteins with cytokines and cytokines,NF-κB signaling pathway,TNF signaling pathway,lipids and atherosclerosis.（4）Through comprehensive analysis,the results showed that IL6,MMP9,CXCL1,CXCL10,MMP8,CAMP,CXCL8 and TLR4 are core m RNAs in the regulatory network,and MEG3,MALAT1,XIST,AC087239.1,AC00766.1 and AC009088.1 are core lnc RNAs,which may serve as gene-level biomarkers for DPN blood stasis evidence and need further experimental validation.Part III（1）Finally,3 lnc RNAs and 3 m RNAs were finally screened for expanded sample size validation.The results showed that the expression of MEG3,MALAT1,IL-6,MMP9 and CXCL1 was significantly higher and XIST expression was significantly lower in the DPN blood stasis syndrome group（A）compared with the healthy control group（C）,with statistically significant differences（P（27）0.05）.Compared with the DPN non-blood stasis syndrome group（D）,the expression of MEG3,MALAT1,IL-6,MMP9 and CXCL1 was significantly higher and XIST expression was significantly lower in the DPN blood stasis syndrome group（A）,with a statistically significant difference（P（27）0.05）.RT-q PCR was consistent with the expression results of high-throughput sequencing.It was verified that MEG3,MALAT1,XIST,IL-6,MMP9 and CXCL1 have important roles in the development of DPN blood stasis syndrome.（2）We conducted a correlation analysis of MEG3,MALAT1,XIST,IL-6,MMP9and CXCL1 with the risk factors screened in Study 1,and found that the relative expression of MEG3 was positively associated with glycosylated hemoglobin（Hb A1c）and fibrinogen.（FBG）and negatively correlated with prothrombin time（PT）and activated partial prothrombin time（APTT）.The relative expression of MALAT1 was positively correlated with the duration of diabetes,glycosylated hemoglobin（Hb A1c）,triglyceride（TG）and fibrinogen（FBG）.The relative expression of XIST was positively correlated with activated partial prothrombin time（APTT）and negatively correlated with glycosylated hemoglobin（Hb A1c）.The relative expression of IL-6 was positively correlated with the duration of diabetes,glycosylated hemoglobin（Hb A1c）,triglyceride（TG）,fibrinogen（FBG）and erythrocyte sedimentation rate（ESR）,and with activated partial prothrombin time（APTT）negatively correlated.The relative expression of MMP9was positively correlated with the duration of diabetes,glycosylated hemoglobin（Hb A1c）,triglyceride（TG）,platelet（PLT）,fibrinogen（FBG）and erythrocyte sedimentation rate（ESR）.CXCL1 was positively correlated with glycosylated hemoglobin（Hb A1c）and negatively correlated with prothrombin time（PT）.（3）The regression analysis of MEG3,MALAT1,XIST,IL-6,MMP9 and CXCL1and DPN blood stasis syndrome found that MEG3,MALAT1,XIST,IL-6 and MMP9 had a statistically significant risk level with DPN blood stasis syndrome.Arranged according to the size of the OR value influencing factors:MMP9（OR:1.78,95%CI:1.21,2.62）IL-6（OR:1.51,95%CI:1.09,2.08）,MALAT1（OR:1.27,95%CI:1.06,1.52）,MEG3（OR:1.16,95%CI:1.00,1.35）,XIST（OR:0.02,95%CI:0.00,0.26）.（4）The receiver operating characteristic curve of ROC showed that the AUC area of MEG3 was 0.7297,the specificity was 78.26%,and the sensitivity was 73.91%.The AUC area of MALAT1 was 0.8639,the specificity was 78.26%,and the sensitivity was 78.26%.The AUC area of XIST was 0.9244,the specificity was 78.26%,and the sensitivity was95.65%.The AUC area of IL-6 was 0.9433,the specificity was 100%,and the sensitivity was 91.30%.The AUC area of MMP9 was 0.8110,the specificity was 65.22%,and the sensitivity was 86.96%.The AUC area of CXCL1 was 0.7410,the specificity was 60.87%,and the sensitivity was 82.61%.Conclusion:（1）Diabetes duration,glycosylated hemoglobin,triglyceride,platelet,prothrombin time,activated partial prothrombin time,fibrinogen and erythrocyte sedimentation rate are important indicators for inducing DPN blood stasis syndrome.The occurrence and development of DPN blood stasis syndrome is related to the course of disease,blood glucose and lipids,platelet function,coagulation function,immune inflammation and other factors,and is the result of the comprehensive effect of many factors.（2）There are a large number of differentially expressed m RNAs and lnc RNAs in the peripheral blood of patients with DPN blood stasis syndrome,and their enriched pathways and biological processes are mainly involved in immune and inflammatory responses,suggesting that the occurrence and development of DPN blood stasis syndrome may be closely related to immune inflammation.（3）MEG3,MALAT1,XIST,IL-6,MMP9 and CXCL1 are key genes of DPN blood stasis syndrome and can be used as candidate biomarkers for DPN blood stasis syndrome.