Evidence map›Paper›PMID 42899174›Full record

ArticleJournal of inflammation research2026

Nasal Secretion Multi-Omics Reveals Candidate Signatures for Noninvasive Endotyping of Chronic Rhinosinusitis with Nasal Polyps.

Yunfei Wang, Jianchao Cong, Hao Lv, Yulie Xie, Mengting Guan, Weiwei Lei, Mengcheng Yu, Hui Peng, Yibin Sun, Peiqiang Liu and 1 more

Abstract read
In one paragraph

Article in Journal of inflammation research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yunfei Wang *Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.ORCID 0009-0007-9744-4482
Jianchao Cong *Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Hao LvDepartment of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Yulie XieDepartment of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Mengting GuanDepartment of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.ORCID 0009-0002-3347-2699
Weiwei LeiDepartment of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Mengcheng YuDepartment of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Hui PengDepartment of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Yibin SunDepartment of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Peiqiang LiuDepartment of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Yu XuDepartment of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.ORCID 0000-0001-7751-6345

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is biologically heterogeneous, yet practical noninvasive markers that distinguish eosinophilic CRSwNP (eCRSwNP) from non-eosinophilic CRSwNP (neCRSwNP) remain limited. We investigated nasal-secretion proteomic and metabolic features associated with tissue-defined endotypes in a small pilot cohort. Methods: Nasal secretions from controls (n = 5), eCRSwNP (n = 5), and neCRSwNP (n = 5) were analyzed by four-dimensional data-independent acquisition (4D-DIA) proteomics and untargeted metabolomics. An intersection-exclusion strategy was used to prioritize eCRSwNP-associated features across three pairwise comparisons. Candidate proteins were further ranked using apparent receiver operating characteristic (ROC) performance, effect size, exploratory multiple-testing support, and repeated leave-one-out cross-validation (LOOCV)-based penalized regression. Three proteins were measured by enzyme-linked immunosorbent assay (ELISA) in a separate same-center replication set (n = 9). Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) was performed in nine matched eCRSwNP/neCRSwNP samples retained after metabolomic quality control. Results: Of 5874 proteins, 4609 passed filtering, and global principal component analysis showed limited separation among groups. The screening strategy prioritized 94 protein features and 28 putatively annotated metabolic features. A score based on tumor protein D52 (TPD52), eosinophil peroxidase (EPX), and transmembrane protein 263 (TMEM263) was higher in eCRSwNP than in neCRSwNP and controls, with concordant directional trends in the ELISA set. DIABLO retained these three proteins together with features putatively annotated as guanosine 3',5'-cyclic monophosphate and UDP-D-galactose. In the nine matched samples, the integrated model yielded an apparent area under the ROC curve (AUC) of 0.95 and a LOOCV error of 0.167; these estimates remain vulnerable to selection bias and overfitting. Conclusion: In this small, single-center study, nasal-secretion multi-omics identified molecular features associated with tissue-defined eCRSwNP. These findings are preliminary and require confirmation in larger independent cohorts using targeted quantification and paired tissue-level biological validation.

Indexed as

eosinophilic CRSwNPmulti-omics integrationnasal secretionsproteomicsuntargeted metabolomics

Identifiers

PMID42899174
PMCPMC13679523

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.