Diagnostic Utility of Serum and Urine Biomarkers in Idiopathic Membranous Nephropathy: a Systematic Review and Meta-analysis

Diagnostic Utility of Serum and Urine Biomarkers in Idiopathic Membranous Nephropathy: a Systematic Review and Meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diagnostic Utility of Serum and Urine Biomarkers in Idiopathic Membranous Nephropathy: a Systematic Review and Meta-analysis Dan Gao, Li-Ping Lu, Zhi-Guo Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1114255/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Membranous nephropathy is an autoimmune nephropathy that is one of the most common pathological types of nephrotic syndrome. It is important to find and apply specific biomarkers for the noninvasive diagnosis of idiopathic membranous nephropathy (IMN). However, there are limited data about their diagnostic value. Therefore, an overall meta-analysis helps to identify effective biomarkers for the clinical diagnosis of IMN. Methods A systematic literature search was carried out in PubMed, Embase, Cochrane and Web of Science from inception until December 31, 2020. Two researchers searched for studies that met the inclusion criteria. The results of the joint study were expressed in terms of sensitivity and specificity. Results The meta-analysis included 24 studies with biomarkers for the clinical diagnosis of IMN, including phospholipase A2 receptor (PLA2R), thrombospondin type I domain-containing 7A (THSD7A), lysosome membrane protein-2 (LIMP-2) and circular RNAs. The diagnostic efficiency of PLA2R for IMN had a combined sensitivity of 60% and a combined specificity of 100%. The diagnostic efficiency of THSD7A for IMN had a combined sensitivity of 3% and a combined specificity of 99%. The diagnostic efficiency of urinary LIMP-2 for IMN was 100%, and the specificity was 100%. The diagnostic efficiency of exosomal circRNAs for IMN was 100%, and the specificity was 100%. Conclusions This meta-analysis shows that PLA2R and THSD7A are of important diagnostic value for IMN. More studies are needed in the future to reveal the diagnostic value of LIMP-2 and circRNAs for IMN. At the same time, other new diagnostic biomarkers in IMN need to be found in the future. Urology & Nephrology Idiopathic membranous nephropathy Phospholipase A2 receptor Thrombospondin type I domain-containing 7A Lysosome membrane protein-2 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Membranous nephropathy (MN) is the most common cause of adult nephrotic syndrome [ 1 ]. Approximately 20% of MN patients will progress to end-stage renal disease, and approximately 10% of them will die within 5 to 10 years [ 2 , 3 ]. MN can be divided into idiopathic membranous nephropathy (IMN) and secondary membranous nephropathy (SMN). Approximately 75% of MN patients have idiopathic membranous nephopathy (IMN), while 20% - 25% of patients are secondary to different diseases, such as autoimmune diseases, infection, drugs, and malignancy [ 4 ]. In the past 10 years, the incidence of IMN has increased significantly, and it has been the main pathological type of primary glomerular disease [ 5 , 6 ]. At present, the diagnosis of IMN mainly depends on kidney biopsy. Although kidney biopsy is the gold standard for diagnosing IMN, there are many potential complications in this method, such as perirenal hematoma, infection and other organ damage. Second, some patients cannot undergo renal biopsy, including isolated kidneys, abnormal coagulation function, hypertension dissatisfied with drug control and mental illness. Therefore, we have been committed to finding reliable biomarkers to guide clinical diagnosis through simple and noninvasive technology. In recent years, several biomarkers in serum, THSD7A, PLA 2 R, and IgG4 antibodies, have been assessed for their clinical significance in diagnosing idiopathic membranous nephropathy [ 7 – 9 ]. However, the current research still has some limitations. PLA 2 R is the most commonly used method for the diagnosis of IMN, and the clinical value of other serum biomarkers still needs to be further explored. There are few studies on urine biomarkers, such as lysosome membrane protein-2 (LIMP-2) and circular RNAs [ 10 , 11 ], but they have broad prospects and need to be confirmed by large, multicenter studies. In this article, we performed the first systematic review and meta-analysis of serum and urine biomarkers in IMN patients, with the hope of promoting clinical diagnosis through noninvasive techniques. Methods Data sources and search strategy Two researchers, Gao and Zhao, conducted a systematic review of qualified articles on PubMed, Embase, Cochrane and Web of Science from the beginning until December 31, 2020. The search terms were “idiopathic membranous nephropathy and (phospholipase A2 receptor or PLA2R or the thrombospondin type I domain-containing 7A or THSD7A or IgG4 or lysosome membrane protein-2 or LIMP-2 or circular RNAs)”. The literature search was limited to human studies and was published in English. Study selection We will include reports of original observational studies of IMN biomarkers and healthy control groups. These were exclusion criteria: (1) IMN biomarkers measured in animal models; (2) cadaver specimens; (3) in vitro data; (4) no healthy controls; (5) complications with other serious diseases or complications. Data extraction and quality assessment Two researchers (D.G. and Z.Z.) extracted data independently from all eligible initial documents. Disagreements were discussed and resolved by a third person’s point of view (L.L.). The extracted information included the year of article publication, author, country, sample type, type of markers, experimental method, numbers of case groups, control groups, true positive (TP), false positive (FP), false negative (FN), and true negative (TN) results in each included study. Two authors (Gao and Lu) assessed the quality of the included studies using the updated Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool [ 12 ]. Statistical analysis All of the data were analyzed using Review Manager 5.3 and Stata MP 16.0 (Multiprocessor computers) software. TP, FP, FN and TN were used to describe various indicators in the studies. According to the Cochrane Handbook, I 2 is divided into 0.25, 0.50 and 0.75, representing mild, moderate and high heterogeneity, respectively [ 13 ]. When P 50%, we used the random effect model. When P >0.05 and I 2 <50%, we used the fixed effect model [ 14 ]. The results of the combination of the studies were expressed by sensitivity, specificity, PLR, NLR and DOR. Forest maps were used to describe the 95% CI (95% confidence interval) sensitivity and specificity in the study. Deeks’ Funnel Plot Asymmetry Test was used to reflect literature publication bias. Results Search results and study characteristics We obtained 1003 records from the PubMed database and 872 records from the Web of Science, Embase and Cochrane databases. Excluding duplicated articles, 1103 articles remained. We browsed the titles and abstracts of the articles, and 207 articles remained. 93 of the full-text articles were assessed for eligibility, but 76 articles were excluded for reasons (49 were mainly about IMN therapy, 8 did not provide enough data, 6 did not meet the accuracy of the test, 13 were without healthy controls). Finally, the meta-analysis included 17 articles, including 24 studies. Article selection flow chart is shown in Figure 1 . Table 1 Informations of the qualified studies. Year Study Country Sample Biomarker Method Test group Control group1 Control group2 Control group3 Control group4 TP FP1 FP2 FP3 FP4 FN TN1 TN2 TN3 TN4 2009 Beck [ 15 ] USA Serum PLA 2 R WB 37 8 15 30 7 26 0 0 0 0 11 8 15 30 7 2011 Hoxha [ 16 ] Germany Serum PLA 2 R IFT 100 17 90 153 52 0 0 0 48 17 90 153 2012 Murtas [ 17 ] Italy Serum PLA 2 R WB 186 92 96 111 0 0 75 92 96 2013 Behnert [ 18 ] Germany Serum PLA 2 R IIF-CBA 165 50 50 85 0 0 80 50 50 2014 Tomas [ 19 ] France Serum THSD7A WB 118 35 76 44 6 1 0 0 112 34 76 44 2015 Kim [ 20 ] Korea Serum PLA 2 R ELISA 93 14 41 12 41 0 0 0 52 14 41 12 2015 Rood [ 21 ] The Netherlands Urine LIMP-2 Proteomics 5 5 3 5 0 0 0 5 3 2015 Yang [ 22 ] China Serum PLA 2 R IIF 20 10 5 12 2 0 8 8 5 2016 Li1 [ 23 ] China Serum PLA 2 R ELISA 82 22 40 20 51 7 0 0 31 15 40 20 2016 Li2 [ 23 ] China Serum PLA 2 R IIF-CBA 82 22 40 20 53 8 0 0 29 14 40 20 2017 Wang1 [ 24 ] China Serum PLA 2 R WB 578 114 64 20 394 29 0 0 184 85 64 20 2017 Wang2 [ 24 ] China Serum THSD7A WB 578 114 64 20 8 1 0 0 570 113 64 20 2017 Zhang [ 25 ] China Serum PLA 2 R TRFIA 69 9 94 286 49 0 0 0 20 9 94 286 2018 Radice [ 26 ] Italy Serum PLA 2 R IIF 252 32 80 43 72 178 9 1 0 0 74 23 79 43 72 2019 Cheng [ 27 ] China Serum PLA 2 R ELISA 146 51 62 102 0 0 44 51 62 2019 Ma1 [ 28 ] China Serum circRNAs in exosomes RT-PCR qPCR 10 10 10 0 0 10 2019 Ma2 [ 28 ] China Urine circRNAs in exosomes RT-PCR qPCR 10 10 10 0 0 10 2019 Zaghrini1[ 29 ] France Serum PLA 2 R ELISA 1012 52 687 0 325 52 2019 Zaghrini2[ 29 ] France Serum THSD7A ELISA 1012 52 28 0 984 52 2020 Huang [ 30 ] China Serum PLA 2 R ELISA 142 187 40 110 0 0 32 187 40 2020 Maifata1 [ 31 ] Malaysia Serum PLA 2 R ELISA 47 22 24 13 1 0 34 21 24 2020 Maifata2 [ 31 ] Malaysia Urine PLA 2 R ELISA 47 22 24 13 1 0 34 21 24 2020 Maifata3 [ 31 ] Malaysia Serum THSD7A ELISA 47 22 24 4 2 0 43 20 24 2020 Maifata4 [ 31 ] Malaysia Urine THSD7A ELISA 47 22 24 0 0 0 47 22 24 1: SMN control group, 2: other glomerular disease control group, 3: healthy control group, 4: other immune disease control group. Li1 indicates the study with ELISA method. Li2 indicates the study with IIF-CBA method. Wang1 indicates the study of biomarker PLA 2 R. Wang2 indicates the study of biomarker THSD7A. Ma1 indicates the study of circRNAs in exosomes from serum. Ma2 indicates the study of circRNAs in exosomes from urine. Zaghrini1 indicates the study of biomarker PLA 2 R. Zaghrini2 indicates the study of biomarker THSD7A. Maifata1 indicates the study of PLA 2 R from serum. Maifata2 indicates the study of PLA 2 R from urine. Maifata3 indicates the study of THSD7A from serum. Maifata4 indicates the study of THSD7A from urine. The characteristics of the included studies are shown in Table 1 . The studies included 20 serum samples and 4 urine samples. Biomarkers included PLA 2 R, THSD7A, LIMP-2 and circular RNA in exosomes. PLA 2 R was detected by Western blotting (WB) in three studies, by enzyme-linked immunosorbent assay (ELISA) in seven studies, by immunofluorescence test (IFT) in one study, by indirect immunofluorescence cell-based assay (IIF-CBA) in two studies, by indirect immunofluorescence (IIF) in two studies and by time-resolved fluoroimmunoassay (TRFIA) in one study. THSD7A was detected by WB in two studies and by ELISA in three studies. LIMP-2 was detected by Proteomics. Circular RNAs in exosomes were detected by reverse transcription polymerase chain reaction (RT–PCR) followed by quantitative PCR (qPCR) in two studies. 15 studies used SMN patients as controls, and 14 studies used other glomerular disease patients as controls. Quality evaluation The quality evaluation of the selected studies was based on the QUADAS-2, which is shown in Figure 2 . Overall, the quality evaluation of the included studies was reliable, but 11 studies had unclear risks in terms of flow and timing, and 2 studies had higher risks in terms of flow and timing. At the same time, 6 studies were unclear on the risks of the index test. Diagnostic value of PLA 2 R in IMN As shown in Figure 3 a, in our meta-analysis, the random-effect model was chosen because I 2 was 88.47% (P<0.01), implying a high degree of heterogeneity in the study sample. The combined sensitivity was 60% (95% CI: 53%-67%), and the combined specificity was 100% (95% CI: 97%-100%). The combined PLR was 153.30 (95% CI: 21.80-1076.30), the combined NLR was 0.40 (95% CI: 0.34-0.48), and the combined DOR was 382.00 (95% CI: 53.00-2777.00). Figure 3 b shows a summary of the receiver operating characteristics (SROC) of the 95% confidence profile and the 95% predicted profile, with an AUC of 0.81 (95% CI: 0.77-0.84), indicating that the diagnostic accuracy of PLA 2 R in IMN is relatively acceptable. Diagnostic value of THSD7A in IMN As shown in Figure 4 a, in our meta-analysis, the random-effect model was chosen because I 2 had a combined sensitivity of 72.08% (P<0.05), which implies a high degree of heterogeneity in the study sample. The combined sensitivity was 3% (95% CI: 1%-5%), and the combined specificity was 99% (95% CI: 97%-100%). The combined PLR was 4.00 (95% CI: 1.20-13.90), the combined NLR was 0.98 (95% CI: 0.96-1.00), and the combined DOR was 4.00 (95% CI: 1.00-14.00). The SROC summary chart with 95% confidence contour and 95% prediction contour is shown in Figure 4 b. The AUC was 0.52 (95% CI: 0.48-0.57), indicating that THSD7A has a relatively low level of influence on the diagnostic accuracy of IMN. Diagnostic value of other biomarkers in IMN There was one study testing LIMP-2 in urine with proteomics. The sensitivity was 100% (95% CI: 48%-100%), and the specificity was 100% (95% CI: 63%-100%). There were two studies testing circRNAs in exosomes in serum and urine. The sensitivity was 100% (95% CI: 69%-100%), and the specificity was 100% (95% CI: 69%-100%). Predicted posterior probability of PLA 2 R and THSD7A in IMN As shown in Figure 5 , the pre-test probability of PLA 2 R was 20%, and the post-test probability of PLA 2 R was 97%. The pre-test probability of THSD7A was 20%, and the post-test probability of THSD7A was 50%. This means that PLA 2 R and THSD7A can improve the diagnosis of IMN. Subgroup and sensitivity analysis of PLA 2 R The causes of heterogeneity were analyzed by subgroup analysis. As shown in Table 2 , the diagnostic accuracy rate of PLA 2 R testing in Asia was higher than that in Europe. There were also other factors, such as method, sample, controls and sample size. Table 2 Subgroup analysis of PLA 2 R in the diagnosis of IMN Subgroup N Sensitivity Specificity PLR NLR AUC Region America 1 - - - - - Europe 5 0.61(0.54-0.68) 1.00(0.57-1.00) 1853.5(0.8- 4.1e+0.6) 0.39(0.32-0.47) 0.76(0.72-0.80) Asia 10 0.58(0.47-0.69) 0.99(0.93-1.00) 51.1(8.20-317.10) 0.42(0.32-0.55) 0.82(0.79-0.85) Method WB 3 - - - - - IFT 1 - - - - - IIF-CBA 2 - - - - - TRFIA 1 - - - - - IIF 2 - - - - - ELISA 7 0.55(0.40-0.69) 1.00(0.97-1.00) 151.60(9.00-2559.30) 0.45(0.32-0.63) 0.88(0.85-0.91) Sample serum 15 0.62(0.56-0.68) 1.00(0.97-1.00) 206.90(22.20-1930.60) 0.38(0.32-0.45) 0.79(0.76-0.83) urine 1 - - - - - Control SMN 11 0.57(0.47-0.66) 0.98(0.94-1.00) 36.50(9.30-143.80) 0.44(0.35-0.54) 0.81(0.77-0.84) SMN+other glomerular disease 8 0.63(0.57-0.68) 1.00(0.82-1.00) 131.00(3.20-5442.00) 0.38(0.32-0.44) 0.73(0.69-0.76) Other immune disease 3 - - - - - Sample size ≤300 10 0.53(0.44-0.62) 0.99(0.95-1.00) 69.80(10.80-451.60) 0.47(0.38-0.58) 0.79(0.75-0.82) >300 6 0.69(0.65-0.72) 1.00(0.88-1.00) 678.40(5.00-91324.20) 0.31(0.28-0.35) 0.74(0.70-0.77) Subgroup and sensitivity analysis of THSD7A Table 3 shows that the diagnostic accuracy rate of THSD7A in serum is higher than that in urine. Table 3 Subgroup analysis of THSD7A in the IMN diagnosis Subgroup N Sensitivity Specificity PLR NLR AUC serum 4 0.03(0.02-0.06) 0.99(0.97-1.00) 3.7(1.2-11.7) 0.98(0.96-1.00) 0.55(0.5-0.59) urine 1 - - - - - Publication bias evaluation The publication bias of the included studies was evaluated by Deeks' funnel plot asymmetry test. As shown in Figure 6 , the results showed that the PLA 2 R ( P =0.80) and THSD7A ( P =0.61) studies had no publication bias. P <0.05 indicates publication bias. Discussion This systematic review and meta-analysis focused on the diagnostic value of serum and urine biomarkers in IMN. At the same time, this is the first meta-analysis for the diagnostic value of different biomarkers of IMN. There was a meta-analysis of the diagnostic value of PLA 2 R and THSD7A separately. In this meta-analysis, the study group included healthy controls, and the criteria for inclusion in the literature were different from those of previous meta-analyses. The specimen type was obtained from serum and urine. There were several biomarkers (PLA 2 R, THSD7A, LIMP-2 and circRNAs) that met the inclusion criteria of the study. In 2009, Beck [ 15 ] found that PLA 2 R is specific to the antigen of adult MN, and its specific PLA 2 R antibody was a serum biomarker for detecting IMN, with high sensitivity and specificity. We included 16 studies about the diagnostic value of PLA 2 R that met the inclusion criteria. The sensitivity was 60% (95% CI: 53%-67%), and the specificity was 100% (95% CI: 97%-100%). The AUC was 0.81 (95%CI: 0.77-0.84). Serum PLA 2 R antibody testing is an important clinical diagnostic value of IMN. That is consistent with the research of Hu [ 32 ]. Therefore, there was a high level of heterogeneity in the sensitivity of our meta-analysis ( I 2 =88.47%), probably due to the region of studies, test method, specimen type, control group classification and sample size. Then, subgroup analysis further explored the source of heterogeneity. In our meta-analysis, studies were mainly distributed in Asia, followed by Europe and only America. More studies that meet the inclusion criteria are needed in the future. Then, detection methods and the grouping of studies can lead to sources of heterogeneity. Regarding the control group of the studies, we were included in the IMN and contained healthy controls, which was different from the control group of other studies. Studies in other meta-analyses may not have a healthy control group. However, we think it is necessary to include a healthy control group in the study and play the role of disease screening [ 33 ]. THSD7A is structurally similar to PLA 2 R, which has been determined to be the second autoantigen of IMN in adults [ 19 ]. We included 5 studies about the diagnostic value of THSD7A that met the inclusion criteria. The sensitivity was 3% (95% CI: 1%-5%), and the specificity was 99% (95% CI: 97%-100%). The results are consistent with the research of Liu [ 34 ]. Although not sensitive enough, the diagnosis of IMN is very specific. The prevalence of THSD7A in PLA 2 R-negative patients was higher than that in IMN patients [ 35 ]. THSD7A testing is important for the clinical diagnostic value of IMN. In this meta-analysis, the small number of studies on THSD7A explored the source of heterogeneity. We need more research on the diagnostic value of THSD7A in IMN. Noninvasive diagnosis of IMN was performed according to the actual clinical needs of patients, we first conducted a systematic meta-analysis, and reviewed the diagnostic efficiency of PLA2R and THSD7A for IMN patients without publication bias. In our meta-analysis, LIMP-2 in urine and circular RNAs in exosomes had important clinical value in the diagnosis of IMN, although there were few articles included. They were highly specific and sensitive by proteomics. In conclusion, this meta-analysis shows that PLA 2 R and THSD7A are of important diagnostic value for IMN. Future studies are needed to uncover the diagnostic value of LIMP-2 and circular RNAs for IMN. At the same time, other new diagnostic biomarkers in IMN need to be found and applied as noninvasive diagnostic methods for IMN in the future. Declarations Author contrubutions Designed the experiments: Z.Zhao. Screened literature: (D. Gao and Z.Zhao). Extracted data: (D. Gao, Z. Zhao and L.Lu). Assessed the quality of the included studies: (D. Gao and L.Lu). Contributed to statistical analysis: (D. Gao and Z.Zhao). Wrote the manuscript: D.Gao. Funding We received no financial support from any individual or organization. Competing interests On behalf of all authors, the corresponding author states that there are no competing interests. References Bobkova, I.N., Kamyshova, E.S. [Modern view on treatment of membranous nephropathy]. Ter Arkh. 92 ,99-104 (2020). Sim, J. J. et al . 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Ultrasensitive Quantitation of Anti-Phospholipase A2 Receptor Antibody as A Diagnostic and Prognostic Indicator of Idiopathic Membranous Nephropathy. Sci Rep . 7 ,12049 (2017). Radice, A. et al . Diagnostic specificity of autoantibodies to M-type phospholipase A2 receptor (PLA2R) in differentiating idiopathic membranous nephropathy (IMN) from secondary forms and other glomerular diseases. J Nephrol. 31 ,271-278 (2018). Cheng, G. et al . Serum phospholipase A2 receptor antibodies and immunoglobulin G subtypes in adult idiopathic membranous nephropathy: Clinical value assessment. Clin Chim Acta. 490 ,135-141(2019). Ma, H. et al . Differential expression study of circular RNAs in exosomes from serum and urine in patients with idiopathic membranous nephropathy. Arch Med Sci . 15 ,738-753 (2019). Zaghrini, C. et al . Novel ELISA for thrombospondin type 1 domain-containing 7A autoantibodies in membranous nephropathy. Kidney Int . 95 , 666-679 (2019). 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An update on clinical significance of use of THSD7A in diagnosing idiopathic membranous nephropathy: a systematic review and meta-analysis of THSD7A in IMN. Ren Fail . 40 , 306-313 (2018). Additional Declarations No competing interests reported. Supplementary Files PRISMAchecklist.docx PRISMAflowchart.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1114255","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":68458199,"identity":"329b0de7-cea7-41ad-b1ec-c8ee02ca5758","order_by":0,"name":"Dan Gao","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Gao","suffix":""},{"id":68458200,"identity":"462bea4a-1419-41c4-9ec6-b2f348f460b9","order_by":1,"name":"Li-Ping Lu","email":"","orcid":"","institution":"Shengjing Hospital of China Medical 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12:59:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1114255/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1114255/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":16284223,"identity":"a304d8f4-5030-4ffb-ad68-15021d0c7d9f","added_by":"auto","created_at":"2021-12-08 15:50:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53069,"visible":true,"origin":"","legend":"Article selection flowchart","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1114255/v1/acfdabb86a13e0d376901c19.jpg"},{"id":16284219,"identity":"46472fb6-1d54-46b9-87b8-415231b21217","added_by":"auto","created_at":"2021-12-08 15:50:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":143025,"visible":true,"origin":"","legend":"The quality evaluation results of included studies","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1114255/v1/9fecbad29ded6018ff218ac6.jpg"},{"id":16284796,"identity":"31c16376-bd43-44c0-802c-17ad33822d0e","added_by":"auto","created_at":"2021-12-08 15:53:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102759,"visible":true,"origin":"","legend":"Forest map (a) and AUC (b) of the diagnostic accuracy of PLA2R in IMN","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1114255/v1/27282ff4ca90db3b1325efad.jpg"},{"id":16284221,"identity":"64939c18-5fbc-4d02-84c0-d2c4047c1a91","added_by":"auto","created_at":"2021-12-08 15:50:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78555,"visible":true,"origin":"","legend":"Forest map (a) and AUC (b) of THSD7A in diagnosing IMN","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1114255/v1/345d49f2a22c4b4119f737d4.jpg"},{"id":16284226,"identity":"39bb31a2-f7a0-4485-bef9-206a9bf92476","added_by":"auto","created_at":"2021-12-08 15:50:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":142171,"visible":true,"origin":"","legend":"Predicted posterior probability of PLA2R(a) and THSD7A(b) in IMN","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1114255/v1/b1c78604e903c41df24245ed.jpg"},{"id":16284925,"identity":"4a1a60a4-2e15-442e-a0f4-086c51c55fb2","added_by":"auto","created_at":"2021-12-08 15:56:06","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":48529,"visible":true,"origin":"","legend":"The publication bias of PLA2R(a) and THSD7A(b)","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1114255/v1/4af3b2ef919c70f41f5203d8.jpg"},{"id":18055687,"identity":"c4cbe85f-5a65-4ec1-9a46-d193db1da4e5","added_by":"auto","created_at":"2022-02-09 08:14:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":773552,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1114255/v1/0dc7e7b8-a4f1-4018-a604-6c220e463cf3.pdf"},{"id":16284220,"identity":"e2c63ce3-ddbc-4b13-94a7-22cdcea6c9e1","added_by":"auto","created_at":"2021-12-08 15:50:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":33213,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMAchecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-1114255/v1/cf7fb8a34174ff5e9ebb1b32.docx"},{"id":16284804,"identity":"c870f76d-40a8-4e94-abd1-81f31d17299a","added_by":"auto","created_at":"2021-12-08 15:53:06","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":53215,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMAflowchart.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1114255/v1/df26bbd0e744c5db65030ceb.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDiagnostic Utility of Serum and Urine Biomarkers in Idiopathic Membranous Nephropathy: a Systematic Review and Meta-analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMembranous nephropathy (MN) is the most common cause of adult nephrotic syndrome [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Approximately 20% of MN patients will progress to end-stage renal disease, and approximately 10% of them will die within 5 to 10 years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. MN can be divided into idiopathic membranous nephropathy (IMN) and secondary membranous nephropathy (SMN). Approximately 75% of MN patients have idiopathic membranous nephopathy (IMN), while 20% - 25% of patients are secondary to different diseases, such as autoimmune diseases, infection, drugs, and malignancy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the past 10 years, the incidence of IMN has increased significantly, and it has been the main pathological type of primary glomerular disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. At present, the diagnosis of IMN mainly depends on kidney biopsy. Although kidney biopsy is the gold standard for diagnosing IMN, there are many potential complications in this method, such as perirenal hematoma, infection and other organ damage. Second, some patients cannot undergo renal biopsy, including isolated kidneys, abnormal coagulation function, hypertension dissatisfied with drug control and mental illness. Therefore, we have been committed to finding reliable biomarkers to guide clinical diagnosis through simple and noninvasive technology.\u003c/p\u003e \u003cp\u003eIn recent years, several biomarkers in serum, THSD7A, PLA\u003csub\u003e2\u003c/sub\u003eR, and IgG4 antibodies, have been assessed for their clinical significance in diagnosing idiopathic membranous nephropathy [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the current research still has some limitations. PLA\u003csub\u003e2\u003c/sub\u003eR is the most commonly used method for the diagnosis of IMN, and the clinical value of other serum biomarkers still needs to be further explored. There are few studies on urine biomarkers, such as lysosome membrane protein-2 (LIMP-2) and circular RNAs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], but they have broad prospects and need to be confirmed by large, multicenter studies.\u003c/p\u003e \u003cp\u003eIn this article, we performed the first systematic review and meta-analysis of serum and urine biomarkers in IMN patients, with the hope of promoting clinical diagnosis through noninvasive techniques.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eData sources and search strategy\u003c/h2\u003e\n\u003cp\u003eTwo researchers, Gao and Zhao, conducted a systematic review of qualified articles on PubMed, Embase, Cochrane and Web of Science from the beginning until December 31, 2020. The search terms were \u0026ldquo;idiopathic membranous nephropathy and (phospholipase A2 receptor or PLA2R or the thrombospondin type I domain-containing 7A or THSD7A or IgG4 or lysosome membrane protein-2 or LIMP-2 or circular RNAs)\u0026rdquo;. The literature search was limited to human studies and was published in English.\u003c/p\u003e\n\u003ch2\u003eStudy selection\u003c/h2\u003e\n\u003cp\u003eWe will include reports of original observational studies of IMN biomarkers and healthy control groups. These were exclusion criteria: (1) IMN biomarkers measured in animal models; (2) cadaver specimens; (3) in vitro data; (4) no healthy controls; (5) complications with other serious diseases or complications.\u003c/p\u003e\n\u003ch2\u003eData extraction and quality assessment\u003c/h2\u003e\n\u003cp\u003eTwo researchers (D.G. and Z.Z.) extracted data independently from all eligible initial documents. Disagreements were discussed and resolved by a third person\u0026rsquo;s point of view (L.L.). The extracted information included the year of article publication, author, country, sample type, type of markers, experimental method, numbers of case groups, control groups, true positive (TP), false positive (FP), false negative (FN), and true negative (TN) results in each included study. Two authors (Gao and Lu) assessed the quality of the included studies using the updated Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAll of the data were analyzed using Review Manager 5.3 and Stata MP 16.0 (Multiprocessor computers) software. TP, FP, FN and TN were used to describe various indicators in the studies. According to the Cochrane Handbook, \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e is divided into 0.25, 0.50 and 0.75, representing mild, moderate and high heterogeneity, respectively [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. When \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 and \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u0026gt;50%, we used the random effect model. When \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05 and \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u0026lt;50%, we used the fixed effect model [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. The results of the combination of the studies were expressed by sensitivity, specificity, PLR, NLR and DOR. Forest maps were used to describe the 95% CI (95% confidence interval) sensitivity and specificity in the study. Deeks\u0026rsquo; Funnel Plot Asymmetry Test was used to reflect literature publication bias.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eSearch results and study characteristics\u003c/h2\u003e\n\u003cp\u003eWe obtained 1003 records from the PubMed database and 872 records from the Web of Science, Embase and Cochrane databases. Excluding duplicated articles, 1103 articles remained. We browsed the titles and abstracts of the articles, and 207 articles remained. 93 of the full-text articles were assessed for eligibility, but 76 articles were excluded for reasons (49 were mainly about IMN therapy, 8 did not provide enough data, 6 did not meet the accuracy of the test, 13 were without healthy controls). Finally, the meta-analysis included 17 articles, including 24 studies. Article selection flow chart is shown in Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eInformations of the qualified studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eYear\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStudy\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCountry\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSample\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBiomarker\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMethod\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTest\u003c/p\u003e\n\u003cp\u003egroup\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003cp\u003egroup1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eControl group2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eControl group3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eControl group4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFP1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFP2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFP3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFP4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth 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align=\"char\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHoxha [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGermany\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIFT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMurtas [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItaly\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e186\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBehnert [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGermany\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIIF-CBA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTomas [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTHSD7A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKim [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKorea\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRood [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe Netherlands\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLIMP-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProteomics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYang [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIIF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLi1 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLi2 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIIF-CBA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWang1 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e578\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e114\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e394\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWang2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTHSD7A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e578\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e114\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e570\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZhang [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRFIA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e286\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e286\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRadice [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItaly\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIIF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e72\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCheng [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMa1 [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecircRNAs in exosomes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRT-PCR\u003c/p\u003e\n\u003cp\u003eqPCR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMa2 [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecircRNAs in exosomes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRT-PCR\u003c/p\u003e\n\u003cp\u003eqPCR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZaghrini1[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e687\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e325\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZaghrini2[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTHSD7A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e984\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHuang [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e187\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e187\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaifata1 [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMalaysia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaifata2 [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMalaysia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLA\u003csub\u003e2\u003c/sub\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaifata3 [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMalaysia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTHSD7A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaifata4 [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMalaysia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTHSD7A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"21\"\u003e1: SMN control group, 2: other glomerular disease control group, 3: healthy control group, 4: other immune disease control group. Li1 indicates the study with ELISA method. Li2 indicates the study with IIF-CBA method. Wang1 indicates the study of biomarker PLA\u003csub\u003e2\u003c/sub\u003eR. Wang2 indicates the study of biomarker THSD7A. Ma1 indicates the study of circRNAs in exosomes from serum. Ma2 indicates the study of circRNAs in exosomes from urine. Zaghrini1 indicates the study of biomarker PLA\u003csub\u003e2\u003c/sub\u003eR. Zaghrini2 indicates the study of biomarker THSD7A. Maifata1 indicates the study of PLA\u003csub\u003e2\u003c/sub\u003eR from serum. Maifata2 indicates the study of PLA\u003csub\u003e2\u003c/sub\u003eR from urine. Maifata3 indicates the study of THSD7A from serum. Maifata4 indicates the study of THSD7A from urine.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe characteristics of the included studies are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The studies included 20 serum samples and 4 urine samples. Biomarkers included PLA\u003csub\u003e2\u003c/sub\u003eR, THSD7A, LIMP-2 and circular RNA in exosomes. PLA\u003csub\u003e2\u003c/sub\u003eR was detected by Western blotting (WB) in three studies, by enzyme-linked immunosorbent assay (ELISA) in seven studies, by immunofluorescence test (IFT) in one study, by indirect immunofluorescence cell-based assay (IIF-CBA) in two studies, by indirect immunofluorescence (IIF) in two studies and by time-resolved fluoroimmunoassay (TRFIA) in one study. THSD7A was detected by WB in two studies and by ELISA in three studies. LIMP-2 was detected by Proteomics. Circular RNAs in exosomes were detected by reverse transcription polymerase chain reaction (RT\u0026ndash;PCR) followed by quantitative PCR (qPCR) in two studies. 15 studies used SMN patients as controls, and 14 studies used other glomerular disease patients as controls.\u003c/p\u003e\n\u003ch2\u003eQuality evaluation\u003c/h2\u003e\n\u003cp\u003eThe quality evaluation of the selected studies was based on the QUADAS-2, which is shown in Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, the quality evaluation of the included studies was reliable, but 11 studies had unclear risks in terms of flow and timing, and 2 studies had higher risks in terms of flow and timing. At the same time, 6 studies were unclear on the risks of the index test.\u003c/p\u003e\n\u003ch2\u003eDiagnostic value of PLA\u003csub\u003e2\u003c/sub\u003eR in IMN\u003c/h2\u003e\n\u003cp\u003eAs shown in Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, in our meta-analysis, the random-effect model was chosen because \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e was 88.47% (P\u0026lt;0.01), implying a high degree of heterogeneity in the study sample. The combined sensitivity was 60% (95% CI: 53%-67%), and the combined specificity was 100% (95% CI: 97%-100%). The combined PLR was 153.30 (95% CI: 21.80-1076.30), the combined NLR was 0.40 (95% CI: 0.34-0.48), and the combined DOR was 382.00 (95% CI: 53.00-2777.00). Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb shows a summary of the receiver operating characteristics (SROC) of the 95% confidence profile and the 95% predicted profile, with an AUC of 0.81 (95% CI: 0.77-0.84), indicating that the diagnostic accuracy of PLA\u003csub\u003e2\u003c/sub\u003eR in IMN is relatively acceptable.\u003c/p\u003e\n\u003ch2\u003eDiagnostic value of THSD7A in IMN\u003c/h2\u003e\n\u003cp\u003eAs shown in Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, in our meta-analysis, the random-effect model was chosen because \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e had a combined sensitivity of 72.08% (P\u0026lt;0.05), which implies a high degree of heterogeneity in the study sample. The combined sensitivity was 3% (95% CI: 1%-5%), and the combined specificity was 99% (95% CI: 97%-100%). The combined PLR was 4.00 (95% CI: 1.20-13.90), the combined NLR was 0.98 (95% CI: 0.96-1.00), and the combined DOR was 4.00 (95% CI: 1.00-14.00). The SROC summary chart with 95% confidence contour and 95% prediction contour is shown in Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb. The AUC was 0.52 (95% CI: 0.48-0.57), indicating that THSD7A has a relatively low level of influence on the diagnostic accuracy of IMN.\u003c/p\u003e\n\u003ch2\u003eDiagnostic value of other biomarkers in IMN\u003c/h2\u003e\n\u003cp\u003eThere was one study testing LIMP-2 in urine with proteomics. The sensitivity was 100% (95% CI: 48%-100%), and the specificity was 100% (95% CI: 63%-100%). There were two studies testing circRNAs in exosomes in serum and urine. The sensitivity was 100% (95% CI: 69%-100%), and the specificity was 100% (95% CI: 69%-100%).\u003c/p\u003e\n\u003ch2\u003ePredicted posterior probability of PLA\u003csub\u003e2\u003c/sub\u003eR and THSD7A in IMN\u003c/h2\u003e\n\u003cp\u003eAs shown in Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the pre-test probability of PLA\u003csub\u003e2\u003c/sub\u003eR was 20%, and the post-test probability of PLA\u003csub\u003e2\u003c/sub\u003eR was 97%. The pre-test probability of THSD7A was 20%, and the post-test probability of THSD7A was 50%. This means that PLA\u003csub\u003e2\u003c/sub\u003eR and THSD7A can improve the diagnosis of IMN.\u003c/p\u003e\n\u003ch2\u003eSubgroup and sensitivity analysis of PLA\u003csub\u003e2\u003c/sub\u003eR\u003c/h2\u003e\n\u003cp\u003eThe causes of heterogeneity were analyzed by subgroup analysis. As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the diagnostic accuracy rate of PLA\u003csub\u003e2\u003c/sub\u003eR testing in Asia was higher than that in Europe. There were also other factors, such as method, sample, controls and sample size.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSubgroup analysis of PLA\u003csub\u003e2\u003c/sub\u003eR in the diagnosis of IMN\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSubgroup\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePLR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNLR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmerica\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEurope\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.61(0.54-0.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00(0.57-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1853.5(0.8- 4.1e+0.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.39(0.32-0.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76(0.72-0.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.58(0.47-0.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99(0.93-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.1(8.20-317.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.42(0.32-0.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82(0.79-0.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMethod\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIFT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIIF-CBA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRFIA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIIF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.55(0.40-0.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00(0.97-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151.60(9.00-2559.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.45(0.32-0.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88(0.85-0.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eserum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.62(0.56-0.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00(0.97-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206.90(22.20-1930.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.38(0.32-0.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79(0.76-0.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eurine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSMN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.57(0.47-0.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98(0.94-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.50(9.30-143.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.44(0.35-0.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81(0.77-0.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSMN+other glomerular disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.63(0.57-0.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00(0.82-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131.00(3.20-5442.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.38(0.32-0.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.73(0.69-0.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther immune disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.53(0.44-0.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99(0.95-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.80(10.80-451.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.47(0.38-0.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79(0.75-0.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.69(0.65-0.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00(0.88-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e678.40(5.00-91324.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.31(0.28-0.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74(0.70-0.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSubgroup and sensitivity analysis of THSD7A\u003c/h2\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the diagnostic accuracy rate of THSD7A in serum is higher than that in urine.\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSubgroup analysis of THSD7A in the IMN diagnosis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSubgroup\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePLR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNLR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eserum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.03(0.02-0.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.99(0.97-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e3.7(1.2-11.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.98(0.96-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.55(0.5-0.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eurine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePublication bias evaluation\u003c/h2\u003e\n\u003cp\u003eThe publication bias of the included studies was evaluated by Deeks' funnel plot asymmetry test. As shown in Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the results showed that the PLA\u003csub\u003e2\u003c/sub\u003eR (\u003cem\u003eP\u003c/em\u003e=0.80) and THSD7A (\u003cem\u003eP\u003c/em\u003e=0.61) studies had no publication bias. \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 indicates publication bias.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis systematic review and meta-analysis focused on the diagnostic value of serum and urine biomarkers in IMN. At the same time, this is the first meta-analysis for the diagnostic value of different biomarkers of IMN. There was a meta-analysis of the diagnostic value of PLA\u003csub\u003e2\u003c/sub\u003eR and THSD7A separately. In this meta-analysis, the study group included healthy controls, and the criteria for inclusion in the literature were different from those of previous meta-analyses. The specimen type was obtained from serum and urine. There were several biomarkers (PLA\u003csub\u003e2\u003c/sub\u003eR, THSD7A, LIMP-2 and circRNAs) that met the inclusion criteria of the study.\u003c/p\u003e \u003cp\u003eIn 2009, Beck [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] found that PLA\u003csub\u003e2\u003c/sub\u003eR is specific to the antigen of adult MN, and its specific PLA\u003csub\u003e2\u003c/sub\u003eR antibody was a serum biomarker for detecting IMN, with high sensitivity and specificity. We included 16 studies about the diagnostic value of PLA\u003csub\u003e2\u003c/sub\u003eR that met the inclusion criteria. The sensitivity was 60% (95% CI: 53%-67%), and the specificity was 100% (95% CI: 97%-100%). The AUC was 0.81 (95%CI: 0.77-0.84). Serum PLA\u003csub\u003e2\u003c/sub\u003eR antibody testing is an important clinical diagnostic value of IMN. That is consistent with the research of Hu [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, there was a high level of heterogeneity in the sensitivity of our meta-analysis (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e =88.47%), probably due to the region of studies, test method, specimen type, control group classification and sample size. Then, subgroup analysis further explored the source of heterogeneity. In our meta-analysis, studies were mainly distributed in Asia, followed by Europe and only America. More studies that meet the inclusion criteria are needed in the future. Then, detection methods and the grouping of studies can lead to sources of heterogeneity. Regarding the control group of the studies, we were included in the IMN and contained healthy controls, which was different from the control group of other studies. Studies in other meta-analyses may not have a healthy control group. However, we think it is necessary to include a healthy control group in the study and play the role of disease screening [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTHSD7A is structurally similar to PLA\u003csub\u003e2\u003c/sub\u003eR, which has been determined to be the second autoantigen of IMN in adults [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We included 5 studies about the diagnostic value of THSD7A that met the inclusion criteria. The sensitivity was 3% (95% CI: 1%-5%), and the specificity was 99% (95% CI: 97%-100%). The results are consistent with the research of Liu [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although not sensitive enough, the diagnosis of IMN is very specific. The prevalence of THSD7A in PLA\u003csub\u003e2\u003c/sub\u003eR-negative patients was higher than that in IMN patients [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. THSD7A testing is important for the clinical diagnostic value of IMN. In this meta-analysis, the small number of studies on THSD7A explored the source of heterogeneity. We need more research on the diagnostic value of THSD7A in IMN.\u003c/p\u003e \u003cp\u003eNoninvasive diagnosis of IMN was performed according to the actual clinical needs of patients, we first conducted a systematic meta-analysis, and reviewed the diagnostic efficiency of PLA2R and THSD7A for IMN patients without publication bias.\u003c/p\u003e \u003cp\u003eIn our meta-analysis, LIMP-2 in urine and circular RNAs in exosomes had important clinical value in the diagnosis of IMN, although there were few articles included. They were highly specific and sensitive by proteomics.\u003c/p\u003e \u003cp\u003eIn conclusion, this meta-analysis shows that PLA\u003csub\u003e2\u003c/sub\u003eR and THSD7A are of important diagnostic value for IMN. Future studies are needed to uncover the diagnostic value of LIMP-2 and circular RNAs for IMN. At the same time, other new diagnostic biomarkers in IMN need to be found and applied as noninvasive diagnostic methods for IMN in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contrubutions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDesigned the experiments: Z.Zhao. Screened literature: (D. Gao and Z.Zhao). Extracted data: (D. Gao, Z. Zhao and L.Lu). Assessed the quality of the included studies: (D. Gao and L.Lu). Contributed to statistical analysis: (D. Gao and Z.Zhao). Wrote the manuscript: D.Gao.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe received no financial support from any individual or organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn behalf of all authors, the corresponding author states that there are no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBobkova, I.N., Kamyshova, E.S. [Modern view on treatment of membranous nephropathy]. \u003cem\u003eTer Arkh.\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e,99-104 (2020).\u003c/li\u003e\n \u003cli\u003eSim, J. J. \u003cem\u003eet al\u003c/em\u003e. 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Coexistence of different circulating anti-podocyte antibodies in membranous nephropathy. \u003cem\u003eClin J Am Soc Nephrol\u003c/em\u003e. \u003cstrong\u003e7\u003c/strong\u003e,1394-400\u0026nbsp;(2012).\u003c/li\u003e\n \u003cli\u003eBehnert, A.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. An anti-phospholipase A2 receptor quantitative immunoassay and epitope analysis in membranous nephropathy reveals different antigenic domains of the receptor. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, e61669 (2013).\u003c/li\u003e\n \u003cli\u003eTomas, N. M.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Thrombospondin type-1 domain-containing 7A in idiopathic membranous nephropathy. \u003cem\u003eN Engl J Med\u003c/em\u003e. \u003cstrong\u003e371\u003c/strong\u003e,2277-2287 (2014).\u003c/li\u003e\n \u003cli\u003eKim, Y. G. \u003cem\u003eet al\u003c/em\u003e. Anti-Phospholipase A2 Receptor Antibody as Prognostic Indicator in Idiopathic Membranous Nephropathy. \u003cem\u003eAm J Nephrol.\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 250-7\u0026nbsp;(2015).\u003c/li\u003e\n \u003cli\u003eRood, I. M.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Increased expression of lysosome membrane protein 2 in glomeruli of patients with idiopathic membranous nephropathy. \u003cem\u003eProteomics\u003c/em\u003e. \u003cstrong\u003e15\u003c/strong\u003e, 3722-30 (2015).\u003c/li\u003e\n \u003cli\u003eYang, Xuefen., Pan, Yangbin. \u0026amp; Ding Guohua. Correlation of Secreted Phospholipase A2-I B, Anti-phospholipase A2 Receptor Antibody with Idiopathic Membranous Nephropathy among Adult Patients.\u003cem\u003e\u0026nbsp;Chinese General Practice\u003c/em\u003e. \u003cstrong\u003e18\u003c/strong\u003e, 1018-1022,1028 (2015).\u003c/li\u003e\n \u003cli\u003eLi, X.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Anti-PLA2R Antibodies in Chinese Patients with Membranous Nephropathy. \u003cem\u003eMed Sci Monit\u003c/em\u003e. \u003cstrong\u003e22\u003c/strong\u003e,1630-6 (2016).\u003c/li\u003e\n \u003cli\u003eWang, J.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Circulating Antibodies against Thrombospondin Type-I Domain-Containing 7A in Chinese Patients with Idiopathic Membranous Nephropathy. \u003cem\u003eClin J Am Soc Nephrol.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e,1642-1651 (2017).\u003c/li\u003e\n \u003cli\u003eZhang, Q.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Ultrasensitive Quantitation of Anti-Phospholipase A2 Receptor Antibody as A Diagnostic and Prognostic Indicator of Idiopathic Membranous Nephropathy. \u003cem\u003eSci Rep\u003c/em\u003e.\u003cstrong\u003e\u0026nbsp;7\u003c/strong\u003e,12049 (2017).\u003c/li\u003e\n \u003cli\u003eRadice, A.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Diagnostic specificity of autoantibodies to M-type phospholipase A2 receptor (PLA2R) in differentiating idiopathic membranous nephropathy (IMN) from secondary forms and other glomerular diseases. \u003cem\u003eJ Nephrol.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e,271-278 (2018).\u003c/li\u003e\n \u003cli\u003eCheng, G.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Serum phospholipase A2 receptor antibodies and immunoglobulin G subtypes in adult idiopathic membranous nephropathy: Clinical value assessment.\u003cem\u003e\u0026nbsp;Clin Chim Acta.\u003c/em\u003e \u003cstrong\u003e490\u003c/strong\u003e,135-141(2019).\u003c/li\u003e\n \u003cli\u003eMa, H.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Differential expression study of circular RNAs in exosomes from serum and urine in patients with idiopathic membranous nephropathy. \u003cem\u003eArch Med Sci\u003c/em\u003e. \u003cstrong\u003e15\u003c/strong\u003e,738-753 (2019).\u003c/li\u003e\n \u003cli\u003eZaghrini, C.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Novel ELISA for thrombospondin type 1 domain-containing 7A autoantibodies in membranous nephropathy. \u003cem\u003eKidney Int\u003c/em\u003e. \u003cstrong\u003e95\u003c/strong\u003e, 666-679 (2019).\u003c/li\u003e\n \u003cli\u003eHuang, Zhenzhen., Fang, Yuan. \u0026amp; Chen Wei. Effects of the expression of serum PLA2R antibody on the diagnosis and immunological therapy of idiopathic membranous nephropathy. \u003cem\u003eInternational Journal of Clinical and Experimental Medicine\u003c/em\u003e.\u003cstrong\u003e\u0026nbsp;13\u003c/strong\u003e, 5959-5966 (2020).\u003c/li\u003e\n \u003cli\u003eMaifata, S. M., Hod, R., Zakaria, F. \u0026amp; Ghani, F. A. Role of Serum and Urine Biomarkers (PLA2R and THSD7A) in Diagnosis, Monitoring and Prognostication of Primary Membranous Glomerulonephritis. \u003cem\u003eBiomolecules\u003c/em\u003e. \u003cstrong\u003e10\u003c/strong\u003e, 319\u0026nbsp;(2020).\u003c/li\u003e\n \u003cli\u003eHu, S. L.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Diagnostic value of phospholipase A2 receptor in idiopathic membranous nephropathy: a systematic review and meta-analysis.\u003cem\u003e\u0026nbsp;J\u003c/em\u003e \u003cem\u003eNephrol\u003c/em\u003e. \u003cstrong\u003e27\u003c/strong\u003e,111-6 (2014).\u003c/li\u003e\n \u003cli\u003eBurbelo, P. D.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Detection of PLA2R Autoantibodies before the Diagnosis of Membranous Nephropathy.\u003cem\u003e\u0026nbsp;J Am Soc Nephrol\u003c/em\u003e.\u0026nbsp;\u003cstrong\u003e31\u003c/strong\u003e, 208-217 (2020).\u003c/li\u003e\n \u003cli\u003eLiu, Y.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Meta-Analysis of the Diagnostic Efficiency of THSD7A-AB for the Diagnosis of Idiopathic Membranous Nephropathy. \u003cem\u003eGlob Chall\u003c/em\u003e. \u003cstrong\u003e4\u003c/strong\u003e, 1900099 (2020).\u003c/li\u003e\n \u003cli\u003eRen, S.\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. An update on clinical significance of use of THSD7A in diagnosing idiopathic membranous nephropathy: a systematic review and meta-analysis of THSD7A in IMN. \u003cem\u003eRen Fail\u003c/em\u003e. \u003cstrong\u003e40\u003c/strong\u003e, 306-313 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Idiopathic membranous nephropathy, Phospholipase A2 receptor, Thrombospondin type I domain-containing 7A, Lysosome membrane protein-2","lastPublishedDoi":"10.21203/rs.3.rs-1114255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1114255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMembranous nephropathy is an autoimmune nephropathy that is one of the most common pathological types of nephrotic syndrome. It is important to find and apply specific biomarkers for the noninvasive diagnosis of idiopathic membranous nephropathy (IMN). However, there are limited data about their diagnostic value. Therefore, an overall meta-analysis helps to identify effective biomarkers for the clinical diagnosis of IMN.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA systematic literature search was carried out in PubMed, Embase, Cochrane and Web of Science from inception until December 31, 2020. Two researchers searched for studies that met the inclusion criteria. The results of the joint study were expressed in terms of sensitivity and specificity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe meta-analysis included 24 studies with biomarkers for the clinical diagnosis of IMN, including phospholipase A2 receptor (PLA2R), thrombospondin type I domain-containing 7A (THSD7A), lysosome membrane protein-2 (LIMP-2) and circular RNAs. The diagnostic efficiency of PLA2R for IMN had a combined sensitivity of 60% and a combined specificity of 100%. The diagnostic efficiency of THSD7A for IMN had a combined sensitivity of 3% and a combined specificity of 99%. The diagnostic efficiency of urinary LIMP-2 for IMN was 100%, and the specificity was 100%. The diagnostic efficiency of exosomal circRNAs for IMN was 100%, and the specificity was 100%.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis meta-analysis shows that PLA2R and THSD7A are of important diagnostic value for IMN. More studies are needed in the future to reveal the diagnostic value of LIMP-2 and circRNAs for IMN. At the same time, other new diagnostic biomarkers in IMN need to be found in the future.\u003c/p\u003e","manuscriptTitle":"Diagnostic Utility of Serum and Urine Biomarkers in Idiopathic Membranous Nephropathy: a Systematic Review and Meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-12-08 15:50:04","doi":"10.21203/rs.3.rs-1114255/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c0e9cde5-6eae-48ac-a09d-a97c11eeb1cd","owner":[],"postedDate":"December 8th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":9025014,"name":"Urology \u0026 Nephrology"}],"tags":[],"updatedAt":"2022-02-09T08:14:08+00:00","versionOfRecord":[],"versionCreatedAt":"2021-12-08 15:50:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1114255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1114255","identity":"rs-1114255","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}