A review of current biomarkers in chronic rhinosinusitis with or without nasal polyps

ABSTRACT

Introduction

Chronic rhinosinusitis (CRS) is a heterogeneous disease with a variety of cellular and molecular pathophysiologic mechanisms. Biomarkers have been explored in CRS using various phenotypes, such as polyp recurrence after surgery. Recently, the presence of regiotype in CRS with nasal polyps (CRSwNP) and the introduction of biologics for the treatment of CRSwNP has indicated the importance of endotypes, and there is a need to elucidate endotype-based biomarkers.

Areas covered

Biomarkers for eosinophilic CRS, nasal polyps, disease severity, and polyp recurrence have been identified. Additionally, endotypes are being identified for CRSwNP and CRS without nasal polyps using cluster analysis, an unsupervised learning technique.

Expert opinion

Endotypes in CRS have still being established, and biomarkers capable of identifying endotypes of CRS are not yet clear. When identifying endotype-based biomarkers, it is necessary to first identify endotypes clarified by cluster analysis for outcomes. With the application of machine learning, the idea of predicting outcomes using a combination of multiple integrated biomarkers, rather than a single biomarker, will become mainstream.

KEYWORDS:

• Biomarker
• chronic rhinosinusitis
• endotype
• machine learning
• precision medicine
• type 2 inflammation

Article highlights

• Tissue eosinophil counts are readily available data in routine clinical practice and can predict various phenotypes, but precise cutoff values and counting methods are unclear. Deep learning, a machine learning approach, has the potential to address these measurement biases.

• IL-4, IL-5, and IL-13, representative cytokines of type 2 inflammation, are present in low levels in tissues and nasal secretion of CRS and may not be appropriate as biomarkers.

• POSTN/Periostin, CST1/Cystatin SN, and CCL26/eotaxin-3 have the potential to be endotype-based biomarkers, as it is expressed at sufficient levels in control patients. Identifying biomarkers that share similar characteristics as periostin could aid in the development of endotype-specific diagnostic and therapeutic approaches.

• Machine learning revealed that a combination of C-C motif chemokine ligand 13 (CCL13), CCL18, and Cystatin SN (CST1) can predict type 2 CRSwNP with high probability by using multi-biomarkers rather than a single biomarker.

• The polygenic risk score, which scores the risk of multiple single nucleotide variants to predict an individual’s genetic risk, may reveal biomarkers for variants involved in endotypes.

Declaration of interest

T Nakayama reports lecture fees and was supported by a research grant from Sanofi and SI Haruna reports lecture fees from Sanofi. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.