| Background:Coal worker’s pneumoconiosis(CWP)is a chronic occupational disease mainly caused by coal dust inhalation in miners.This disease is a fibrotic pulmonary disease characterized by chronic inflammatory response caused by the activation of macrophages and endothelial cells in the lung.Despite decades of prevention efforts,CWP remains a public health problem worldwide.China is a country with a high incidence of occupational diseases.By the end of 2018,there were more than 975,000 cases of occupational diseases,of which 873,000 were pneumoconiosis,accounting for about 90%of the total reported occupational diseases.The new cases diagnosed pneumoconiosis was 19468 in 2018.CWP accounted for almost 60%of the newly reported cases of occupational pneumoconiosis in China in 2016.Although the specific pathogenesis of CWP remains unclear,oxidative stress,inflammatory response and pulmonary fibrosis play an important role in the progression of CWP.With the progress of the disease,CWP patients will gradually feel dyspnea and fatigue,eventually leading to respiratory failure.Interestingly,even if exposure factors are removed,the disease may continue to deteriorate.Therefore,exploring biomarkers for early diagnosis of the disease is of great significance for the control and prognosis of the disease.This study aimed to investigate the clinical value of Osteopontin,KL-6,Syndecan-4 and Gremlin-1 as serum biomarkers in CWP.Patients and methods:We integrated reported lung tissues transcriptome data in pneumoconiosis patients with silica-exposed alveolar macrophage microarray data to identify four CWP-associated serum biomarkers.The serum concentrations of Osteopontin(OPN),Krebs von den Lungen-6(KL-6),Syndecan-4 and Gremlin-1 were measured in 100 healthy controls(HC),100 dust-exposed workers(DEW)and 200 patients of CWP.Logistic analysis was used to analyze the association between biomarkers and the risk of CWP,while linear regression was applied to analyze the association between biomarkers and pulmonary function parameters.Receiver operating characteristic(ROC)curve analysis was used to determine the sensitivity,specificity,cut-off value and area under the curve(AUC)value of biomarkers.Results:1.The pulmonary function parameters decreased sequentially among the HC,DEW and CWP groups.As expected,the CWP patients had the lowest FEV1 observed,FVC observed,FEV1%predicted,FVC%predicted,and FEV1/FVC ratio which was significantly different from DEWs or HCs(p<0.05).2.The serum OPN,KL-6,Syndecan-4 and Gremlin-1 concentrations were increased sequentially among the three groups,and were higher in the DEW group,highest in the CWP group.These four biomarkers’ concentrations in CWP patients showed remarkable difference from those in HC(p<0.001).When comparing with DEW group,level of OPN,KL-6,Syndecan-4 were significantly higher in CWP group(p<0.001).The serum concentration of Gremlin-1 was higher in CWP group,although it did not reach statistical significance(p=0.158).3.Multivariable analysis revealed that these four biomarkers were negatively correlated with the pulmonary function parameters(FVC observed,FEV1 observed and FEV1/FVC ratio)among all participants(all p<0.001).4.Compared with HC,patients with higher OPN,KL-6,Syndecan-4 and Gremlin-1 had higher risk for CWP.The AUC for OPN,KL-6,Syndecan-4 and Gremlin-1 were 0.970,0.924,0.917,0.868,respectively.The cut-off values for OPN,KL-6,Syndecan-4 and Gremlin-1 were 10.58ng/ml,674.83 U/ml,5.76 ng/ml,and 777.3 ng/ml,respectively.5.Compared with DEW,patients with higher OPN,KL-6 and Syndecan-4 had higher risk for CWP.The AUC for OPN,KL-6,and Syndecan-4 were 0.788,0.823,and 0.643,respectively.The cut-off values were 12.16 ng/ml,745.63 U/ml,6.06 ng/ml,respectively.6.The combination of OPN,KL-6,and Syndecan-4 can improve the diagnostic sensitivity and specificity of CWP patients differentiated from HC or DEW.Conclusions:OPN,KL-6 and Syndecan-4 are novel biomarkers that can be used for CWP auxiliary diagnosis.The combination of three biomarkers can improve the diagnostic values of CWP.Background:Coal workers’ pneumoconiosis(CWP)is a chronic lung disease caused by long-term exposure to and inhalation of coal dust,which results in inflammation and fibrosis.In developed countries such as the United States,the incidence of CWP is relatively low because effective prevention measures have been implemented for coal miners.However,in developing countries such as China,the prevalence of CWP is still high due to inadequate dust prevention measures.It should be noted that,the risk of pulmonary inflammation and fibrosis in patients with pneumoconiosis persists even after escaping from the dust environment.The purpose of this study is to explore plasma biomarkers for the diagnosis of CWP through proteiomics combined with machine learning strategy.Methods:In this study,liquid chromatography tandem mass spectrometry(LC-MS/MS)was used to sequence the proteomics of 160 enrolled patients(30 healthy controls,30 dust exposed controls,40 CWP patients with stage I,30 CWP patients with stage II,and 30 CWP patients with stage III).Data independent acquisition(DIA)mode and data independent acquisition(DDA)method were used to identify the differentially expressed proteins.The sequencing results were analyzed using Venn diagrams,heat maps,volcanic maps,and PCA diagrams.Functional annotation of differentially expressed proteins was performed using GO,KEGG,and Reactome enrichment analysis.Random forest machine learning models were used to mine biomarkers for diagnosis of CWP.Finally,the target biomarkers were validated.Results:A total of 9150 peptide segments and 580 proteins were identified in the DIA mode.After KNN filling and standardization,378 proteins were quantitatively identified and analyzed.Compared with the healthy controls(HC),19 differential proteins were identified in the dust exposed workers(DEW),of which 7 proteins were up-regulated and 12 proteins were down-regulated;Compared with the HC,81 differential proteins were identified in the CWP group,of which 26 proteins were up-regulated and 55 proteins were down-regulated;Compared with the DEW,55 differential proteins were identified in stage I of CWP,of which 23 proteins were upregulated and 32 proteins were downregulated.Functional annotation of differentially expressed proteins(DEPs)through GO,KEGG,and Reactome enrichment analysis revealed that they are mainly involved in immune reactions,complement reactions,and lipid metabolism.The top five proteins for CWP diagnosis using a random forest machine learning model are Apolipoprotein A-Ⅳ(APOA4),Leucine-rich alpha-2-glycoprotein(LRG1)、von Willebrand factor(VWF)、Pregnancy zone protein(PZP)、Serum amyloid A-1 protein(SAA1).The Area Under Curve(AUC)of APOA4 is 0.745,with a sensitivity of 0.70 and a specificity of 0.77;The AUC of LRG1 is 0.736,with a sensitivity of 0.77 and a specificity of 0.63;The AUC of VWF is 0.695,with a sensitivity of 0.59 and a specificity of 0.80;The AUC of PZP is 0.624,with a sensitivity of 0.27 and a specificity of 0.97;The AUC of SAA1 is 0.599,with a sensitivity of 0.42 and a specificity of 0.93.The top five proteins selected for early diagnosis of CWP are Trypsin-3(PRSS3)、Apolipoprotein A-Ⅳ(APOA4)、Apolipoprotein A-Ⅱ(APOA2)、Leucine-rich alpha-2-glycoprotein(LRG1)、Immunoglobulin lambda variable 1-36(IGLV1-36).The AUC of PRSS3 is 0.946,with a sensitivity of 0.80 and a specificity of 1.00;The AUC of APOA4 is 0.858,with a sensitivity of 0.87 and a specificity of 0.80;The AUC of APOA2 is 0.812,with a sensitivity of 0.75 and a specificity of 0.83 The AUC of LRG1 is 0.732,with a sensitivity of 0.85 and a specificity of 0.53;The AUC of IGLV1-36 is 0.659,with a sensitivity of 0.52 and a specificity of 0.83.Conclusion:The top five proteins of diagnosis of CWP were APOA4,LRG1,VWF,PZP,SAA1;The top five proteins for early diagnosis of CWP were PRSS3,APOA4,APOA2,LRG1 and IGLV1-36.APOA4 and LRG1 can be used as potential biomarkers for the diagnosis of CWP. |
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