Dataset Information

Cutaneous T-cell-attracting chemokine as a novel biomarker for predicting prognosis of idiopathic pulmonary fibrosis: a prospective observational study.

ABSTRACT:

Background

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic lung disease that leads to respiratory failure and death. Although there is a greater understanding of the etiology of this disease, accurately predicting the disease course in individual patients is still not possible. This study aimed to evaluate serum cytokines/chemokines as potential biomarkers that can predict outcomes in IPF patients.

Methods

A multi-institutional prospective two-stage discovery and validation design using two independent cohorts was adopted. For the discovery analysis, serum samples from 100 IPF patients and 32 healthy controls were examined using an unbiased, multiplex immunoassay of 48 cytokines/chemokines. The serum cytokine/chemokine values were compared between IPF patients and controls; the association between multiplex measurements and survival time was evaluated in IPF patients. In the validation analysis, the cytokines/chemokines identified in the discovery analysis were examined in serum samples from another 81 IPF patients to verify the ability of these cytokines/chemokines to predict survival. Immunohistochemical assessment of IPF-derived lung samples was also performed to determine where this novel biomarker is expressed.

Results

In the discovery cohort, 18 cytokines/chemokines were significantly elevated in sera from IPF patients compared with those from controls. Interleukin-1 receptor alpha (IL-1Rα), interleukin-8 (IL-8), macrophage inflammatory protein 1 alpha (MIP-1α), and cutaneous T-cell-attracting chemokine (CTACK) were associated with survival: IL-1Rα, hazard ratio (HR) = 1.04 per 10 units, 95% confidence interval (95% CI) 1.01-1.07; IL-8, HR = 1.04, 95% CI 1.01-1.08; MIP-1α, HR = 1.19, 95% CI 1.00-1.36; and CTACK, HR = 1.12 per 100 units, 95% CI 1.02-1.21. A replication analysis was performed only for CTACK because others were previously reported to be potential biomarkers of interstitial lung diseases. In the validation cohort, CTACK was associated with survival: HR = 1.14 per 100 units, 95% CI 1.01-1.28. Immunohistochemistry revealed the expression of CTACK and CC chemokine receptor 10 (a ligand of CTACK) in airway and type II alveolar epithelial cells of IPF patients but not in those of controls.

Conclusions

CTACK is a novel prognostic biomarker of IPF. Trial registration None (because of no healthcare intervention).

SUBMITTER: Niwamoto T

PROVIDER: S-EPMC8218397 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF.MethodsTotal RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p < 0.05); 2) Differentially expressed between observed "good" vs. "poor" prognosis with fold change (FC) >1.5 and false discovery rate (FDR) < 2%; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. "Survival risk group prediction" was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis.ResultsA set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling.ConclusionsUsing supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients.

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