Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease.
Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease.
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DOI：
10.1038/s41598-023-35840-y
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发表时间：
2023-05-27
影响因子：
4.6
通讯作者：
Zaman, Tanzira
中科院分区：
文献类型：
--
作者：
Cerro-Chiang, Giuliana;Ayres, Matthew;Rivas, Alejandro;Romero, Tahmineh;Parker, Sarah J.;Mastali, Mitra;Elashoff, David;Chen, Peter;Van Eyk, Jennifer E.;Wolters, Paul J.;Boin, Francesco;Zaman, Tanzira
系统性硬化症是一种罕见的结缔组织疾病;间质性肺病（SSc-ILD）与显著的发病率和死亡率相关。没有临床、放射学特征或生物标志物可以确定患者处于进展风险的特定时间，在该时间治疗获益超过风险。我们的研究旨在使用无偏倚、高通量方法鉴定与SSc-ILD患者间质性肺病进展相关的血液蛋白生物标志物。我们根据用力肺活量在12个月或更短时间内的变化将SSc-ILD分为进行性或稳定性。我们通过定量质谱分析血清蛋白，并通过logistic回归分析蛋白水平与SSc-ILD进展之间的相关性。在独创性途径分析（IPA）软件中查询p值<0.1的相关蛋白质，以鉴定相互作用网络、信号传导和代谢途径。通过主成分分析，前10个主成分之间的关系进行了评估和进展。使用热图进行无监督分层聚类以定义独特的组。该队列包括72例患者，其中32例为进展性SSc-ILD，40例为具有相似基线特征的稳定疾病。在总共794种蛋白质中，29种与疾病进展相关。经过多次测试调整后，这些关联并不显著。IPA确定了五个上游调节因子，靶向与进展相关的蛋白质，以及在进展组中具有更高信号的经典途径。主成分分析表明，具有最高特征值的10个成分代表了样本变异性的41%。无监督聚类分析显示，受试者之间没有显着的异质性。我们鉴定了29种与进行性SSc-ILD相关的蛋白质。虽然这些关联在解释多次测试后并不显著，但其中一些蛋白质是与自身免疫和纤维化相关的途径的一部分。局限性包括样本量小和队列中免疫抑制剂的使用比例，这可能改变了炎症和免疫蛋白的表达。未来的方向包括在另一个SSc-ILD队列中对这些蛋白质进行靶向评价，或将本研究设计应用于初治人群。
Systemic sclerosis is a rare connective tissue disease; and interstitial lung disease (SSc–ILD) is associated with significant morbidity and mortality. There are no clinical, radiologic features, nor biomarkers that identify the specific time when patients are at risk for progression at which the benefits from treatment outweigh the risks. Our study aimed to identify blood protein biomarkers associated with progression of interstitial lung disease in patients with SSc–ILD using an unbiased, high-throughput approach. We classified SSc–ILD as progressive or stable based on change in forced vital capacity over 12 months or less. We profiled serum proteins by quantitative mass spectrometry and analyzed the association between protein levels and progression of SSc–ILD via logistic regression. The proteins associated with at a p value of < 0.1 were queried in the ingenuity pathway analysis (IPA) software to identify interaction networks, signaling, and metabolic pathways. Through principal component analysis, the relationship between the top 10 principal components and progression was evaluated. Unsupervised hierarchical clustering with heatmapping was done to define unique groups. The cohort consisted of 72 patients, 32 with progressive SSc–ILD and 40 with stable disease with similar baseline characteristics. Of a total of 794 proteins, 29 were associated with disease progression. After adjusting for multiple testing, these associations did not remain significant. IPA identified five upstream regulators that targeted proteins associated with progression, as well as a canonical pathway with a higher signal in the progression group. Principal component analysis showed that the ten components with the highest Eigenvalues represented 41% of the variability of the sample. Unsupervised clustering analysis revealed no significant heterogeneity between the subjects. We identified 29 proteins associated with progressive SSc–ILD. While these associations did not remain significant after accounting for multiple testing, some of these proteins are part of pathways relevant to autoimmunity and fibrogenesis. Limitations included a small sample size and a proportion of immunosuppressant use in the cohort, which could have altered the expression of inflammatory and immunologic proteins. Future directions include a targeted evaluation of these proteins in another SSc–ILD cohort or application of this study design to a treatment naïve population.
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DOI：
10.3899/jrheum.170518
发表时间：
2018-08
期刊：
The Journal of rheumatology
影响因子：
--
作者：
Salazar GA;Kuwana M;Wu M;Estrada-Y-Martin RM;Ying J;Charles J;Mayes MD;Assassi S
通讯作者：
Assassi S
影响因子：
27.4
作者：
Hoffmann-Vold AM;Allanore Y;Alves M;Brunborg C;Airó P;Ananieva LP;Czirják L;Guiducci S;Hachulla E;Li M;Mihai C;Riemekasten G;Sfikakis PP;Kowal-Bielecka O;Riccardi A;Distler O;EUSTAR collaborators
通讯作者：
EUSTAR collaborators
DOI：
10.1080/03610918.2016.1230216
发表时间：
2017
期刊：
Communications in statistics: Simulation and computation
影响因子：
--
作者：
Grogan TR;Elashoff DA
通讯作者：
Elashoff DA
影响因子：
27.4
作者：
van den Hoogen, Frank;Khanna, Dinesh;Pope, Janet E.
通讯作者：
Pope, Janet E.
影响因子：
9.3
作者：
Mc Ardle, Angela;Binek, Aleksandra;Van Eyk, Jennifer E.
通讯作者：
Van Eyk, Jennifer E.