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Profiling Blood Serum Extracellular Vesicles in Plaque Psoriasis and Psoriatic Arthritis Patients Reveals Potential Disease Biomarkers.

ABSTRACT: Psoriasis vulgaris (PsV) and psoriatic arthritis (PsA) are inflammatory diseases with unresolved pathophysiological aspects. Extracellular vesicles (EVs) play an important role in intercellular communication. We compared the miRNA contents and surface proteome of the EVs in the blood serum of PsV and PsA patients to healthy controls. Size-exclusion chromatography was used to isolate EVs from the blood serum of 12 PsV patients, 12 PsA patients and 12 healthy control subjects. EV samples were characterized and RNA sequencing was used to identify differentially enriched EV-bound miRNAs. We found 212 differentially enriched EV-bound miRNAs present in both PsV and PsA groups-a total of 13 miRNAs at FDR ≤ 0.05. The predicted target genes of these miRNAs were significantly related to lesser known but potentially disease-relevant pathways. The EV array revealed that PsV patient EV samples were significantly enriched with CD9 EV-marker compared to controls. Analysis of EV-bound miRNAs suggests that signaling via EVs in the blood serum could play a role in the pathophysiological processes of PsV and PsA. EVs may be able to fill the void in clinically applicable diagnostic and prognostic biomarkers for PsV and PsA.

SUBMITTER: Lattekivi F

PROVIDER: S-EPMC9000144 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Profiling Blood Serum Extracellular Vesicles in Plaque Psoriasis and Psoriatic Arthritis Patients Reveals Potential Disease Biomarkers.

Lättekivi Freddy F Guljavina Irina I Midekessa Getnet G Viil Janeli J Heath Paul R PR Bæk Rikke R Jørgensen Malene Møller MM Andronowska Aneta A Kingo Kulli K Fazeli Alireza A

International journal of molecular sciences 20220404 7

Psoriasis vulgaris (PsV) and psoriatic arthritis (PsA) are inflammatory diseases with unresolved pathophysiological aspects. Extracellular vesicles (EVs) play an important role in intercellular communication. We compared the miRNA contents and surface proteome of the EVs in the blood serum of PsV and PsA patients to healthy controls. Size-exclusion chromatography was used to isolate EVs from the blood serum of 12 PsV patients, 12 PsA patients and 12 healthy control subjects. EV samples were char ...[more]

PMID: 35409365

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Project description:BackgroundPsoriatic arthritis (PSA) is an inflammatory joint disease associated with psoriasis (PSO) that can be easily missed. Existing PSA screening tools ignore objective serologic indicators. The aim of this study was to develop a disease screening model and the Psoriatic Arthritis Inflammation Index (PSAII) based on serologic data to enhance the efficiency of PSA screening.MethodA total of 719 PSO and PSA patients from the National Health and Nutrition Examination Survey (NHANES) (as training set and test set) and 135 PSO and PSA patients who were seen at The First Affiliated Hospital of Zhejiang Chinese Medical University (as external validation set) were selected, 31 indicators for these patients were collected as potential input features for the model. Least Absolute Shrinkage and Selection Operator (LASSO) was used to identify PSA-related features. Five models of logistic regression (LR), random forest, k-nearest neighbor, gradient augmentation and neural network were developed in the training set using quintuple cross validation. And we developed PSAII based on the results of LASSO regression and weights of logistic model parameters. All performance metrics are derived on the test set and the external validation set.ResultsFive variables were selected to build models, including age, lymphocyte percentage, neutrophil count, eosinophilic count, and C-reactive protein. In all established models, the LR model performed the best, with an Area Under Curve (AUC) of 0.87 (95% confidence interval (CI): 0.83-0.90) on the test set; on the external validation set the AUC was 0.82 (95%CI: 0.74-0.90). The PSAII formula was PSAII = percentage of lymphocytes × C-reactive protein/(neutrophil count × eosinophilic count × 10). The AUC of PSAII in the test is 0.93 (95%CI: 0.88-0.97), and the cutoff value is 18. The AUC of the external validation set is 0.81 (95%CI: 0.72-0.89).ConclusionsThis study developed and validated five models to assist screening for PSA by analyzing serum data from NHANES and Chinese populations. The LR model demonstrated the best performance. We created PSAII for PSA screening. However, the high false positive rate of PSAII makes it necessary to combine it with other PSA screening tools when applied.

Dataset Information

Profiling Blood Serum Extracellular Vesicles in Plaque Psoriasis and Psoriatic Arthritis Patients Reveals Potential Disease Biomarkers.

Publications

Profiling Blood Serum Extracellular Vesicles in Plaque Psoriasis and Psoriatic Arthritis Patients Reveals Potential Disease Biomarkers.

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