STUDY QUESTION Can a panel of plasma protein biomarkers be identified to accurately and specifically diagnose endometriosis? SUMMARY ANSWER A novel panel of 10 plasma protein biomarkers was identified and validated, demonstrating strong predictive accuracy for the diagnosis of endometriosis. WHAT IS KNOWN ALREADY Endometriosis poses intricate medical challenges for affected individuals and their physicians, yet diagnosis currently takes an average of 7 years and normally requires invasive laparoscopy. Consequently, the need for a simple, accurate non-invasive diagnostic tool is paramount. STUDY DESIGN, SIZE, DURATION This study compared 805 participants across two independent clinical populations, with the status of all endometriosis and symptomatic control samples confirmed by laparoscopy. A proteomics workflow was used to identify and validate plasma protein biomarkers for the diagnosis of endometriosis. PARTICIPANTS/MATERIALS, SETTING, METHODS A proteomics discovery experiment identified candidate biomarkers before a targeted mass spectrometry assay was developed and used to compare plasma samples from 464 endometriosis cases, 153 general population controls, and 132 symptomatic controls. Three multivariate models were developed: Model 1 (logistic regression) for endometriosis cases versus general population controls, Model 2 (logistic regression) for rASRM stage II to IV (mild to severe) endometriosis cases versus symptomatic controls, and Model 3 (random forest) for stage IV (severe) endometriosis cases versus symptomatic controls. MAIN RESULTS AND THE ROLE OF CHANCE A panel of 10 protein biomarkers were identified across the three models which added significant value to clinical factors. Model 3 (severe endometriosis vs symptomatic controls) performed the best with an area under the receiver operating characteristic curve (AUC) of 0.997 (95% CI 0.994–1.000). This model could also accurately distinguish symptomatic controls from early-stage endometriosis when applied to the remaining dataset (AUCs ≥0.85 for stage I to III endometriosis). Model 1 also demonstrated strong predictive performance with an AUC of 0.993 (95% CI 0.988–0.998), while Model 2 achieved an AUC of 0.729 (95% CI 0.676–0.783). LIMITATIONS, REASONS FOR CAUTION The study participants were mostly of European ethnicity and the results may be biased from undiagnosed endometriosis in controls. Further analysis is required to enable the generalizability of the findings to other populations and settings. WIDER IMPLICATIONS OF THE FINDINGS In combination, these plasma protein biomarkers and resulting diagnostic models represent a potential new tool for the non-invasive diagnosis of endometriosis. STUDY FUNDING/COMPETING INTEREST(S) Subject recruitment at The Royal Women’s Hospital, Melbourne, was supported in part by funding from the Australian National Health and Medical Research Council (NHMRC) project grants GNT1105321 and GNT1026033 and Australian Medical Research Future Fund grant no. MRF1199715 (P.A.W.R., S.H.-C., and M.H.). Proteomics International has filed patent WO 2021/184060 A1 that relates to endometriosis biomarkers described in this manuscript; S.B., R.L., and T.C. declare an interest in this patent. J.I., S.B., C.L., D.I., H.L., K.P., M.D., M.M., M.R., P.T., R.L., and T.C. are shareholders in Proteomics International. Otherwise, the authors have no conflicts of interest. TRIAL REGISTRATION NUMBER N/A.

中文翻译：

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确定子宫内膜异位症的血浆蛋白生物标志物和开发用于疾病诊断的统计模型


研究问题 能否确定一组血浆蛋白生物标志物来准确和特异性地诊断子宫内膜异位症？总结答案 确定并验证了一组由 10 种血浆蛋白生物标志物组成的新型组，证明对子宫内膜异位症的诊断具有很强的预测准确性。已知的子宫内膜异位症给受影响的个体及其医生带来了错综复杂的医学挑战，但目前的诊断平均需要 7 年，通常需要侵入性腹腔镜检查。因此，对简单、准确的非侵入性诊断工具的需求至关重要。研究设计、规模、持续时间 本研究比较了两个独立临床人群的 805 名参与者，所有子宫内膜异位症和症状对照样本的状态均通过腹腔镜检查确认。使用蛋白质组学工作流程来识别和验证用于诊断子宫内膜异位症的血浆蛋白生物标志物。参与者/材料、设置、方法 蛋白质组学发现实验在开发靶向质谱测定法之前确定了候选生物标志物，并用于比较来自 464 例子宫内膜异位症病例、153 例一般人群对照和 132 例症状对照的血浆样本。开发了三个多变量模型：子宫内膜异位症病例与一般人群对照的模型 1 （logistic 回归），rASRM II 至 IV 期 （轻度至重度） 子宫内膜异位症病例与症状对照的模型 2 （logistic 回归），以及 IV 期 （重度） 子宫内膜异位症病例与症状对照的模型 3 （随机森林）。主要结果和机会的作用 在三个模型中确定了一组 10 种蛋白质生物标志物，这为临床因素增加了显着价值。 模型 3 （重度子宫内膜异位症与症状对照组） 表现最佳，受试者工作特征曲线下面积 （AUC） 为 0.997 （95% CI 0.994–1.000）。当应用于其余数据集时，该模型还可以准确区分症状对照和早期子宫内膜异位症 （I 至 III 期子宫内膜异位症的 AUCs ≥0.85）。模型 1 也表现出强大的预测性能，AUC 为 0.993 （95% CI 0.988-0.998），而模型 2 的 AUC 为 0.729 （95% CI 0.676-0.783）。局限性，谨慎的原因 研究参与者大多是欧洲人种，结果可能因对照组未确诊的子宫内膜异位症而产生偏倚。需要进一步分析以使研究结果能够推广到其他人群和环境。研究结果的更广泛意义这些血浆蛋白生物标志物和由此产生的诊断模型相结合，代表了子宫内膜异位症无创诊断的潜在新工具。研究资金/利益争夺 墨尔本皇家妇女医院的受试者招募部分得到了澳大利亚国家健康与医学研究委员会 （NHMRC） 项目赠款 GNT1105321 和 GNT1026033 以及澳大利亚医学研究未来基金赠款号的资助。MRF1199715 （P.A.W.R.， S.H.-C. 和 M.H.）。Proteomics International 已提交专利 WO 2021/184060 A1，该专利涉及本手稿中描述的子宫内膜异位症生物标志物;S.B.、R.L. 和 T.C. 声明对此专利感兴趣。J.I.， S.B.， C.L.， D.I.， H.L.， K.P.， M.D.， M.M.， M.R.， P.T.， R.L. 和 T.C. 是 Proteomics International 的股东。否则，作者没有利益冲突。试验注册号 N/A。