ArticleJournal of inflammation research2026
Nasal Secretion Multi-Omics Reveals Candidate Signatures for Noninvasive Endotyping of Chronic Rhinosinusitis with Nasal Polyps.
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.
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.
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.
Who cites it
1 citing paper in PubMed.
- Cordyceps as a multitarget therapeutic platform: biology, bioactive metabolites, and translational challenges.Archives of microbiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.