Evidence map›Paper›PMID 40461517›Full record

ArticleScientific reports2025

Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic rhinosinusitis with nasal polyps.

Zhaonan Xu, Qing Hao, Bingrui Yan, Qiuying Li, Xuan Kan, Qin Wu, Hongtian Yi, Xianji Shen, Lingmei Qu, Peng Wang and 1 more

Erratum issuedAbstract read
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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Translational andrology and urology · 2026
    Article
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Zhaonan XuDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Qing HaoDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Bingrui YanDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Qiuying LiDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Xuan KanDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Qin WuDepartment of Otolaryngology, Head and Neck Surgery, The Fifth Affiliated Hospital of Harbin Medical University, Daqing, China.
Hongtian YiDepartment of Otolaryngology, Head and Neck Surgery, The Fifth Affiliated Hospital of Harbin Medical University, Daqing, China.
Xianji ShenDepartment of Otolaryngology, Head and Neck Surgery, The Fifth Affiliated Hospital of Harbin Medical University, Daqing, China.
Lingmei QuDepartment of Otolaryngology, Head and Neck Surgery, The Fifth Affiliated Hospital of Harbin Medical University, Daqing, China. qlm8977976@163.com.
Peng WangDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China. 350239591@qq.com.
Yanan SunDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China. h04015@hrbmu.edu.cn.

Funding

Heilongjiang Provincial Health Commission Science and Technology Plan 20220707011051National Natural Science Foundation of China 82473035Natural Science Foundation of Heilongjiang Province of China LH2022H009
6 · The paper itself

Abstract

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent inflammatory disease where immunomodulation plays a pivotal role. However, immuno-transcriptomic characteristics and its clinical relevance remains largely known. We analyzed transcriptome data of 48 patients with CRSwNP and 34 healthy control subjects from different cohorts and investigated the immuno-transcriptomic characteristics. Differential immune-related genes (DIRGs) were identified and subjected to enrichment analysis. Protein-protein interaction (PPI) networks were constructed to identify hub genes. The least absolute shrinkage and selection operator (LASSO) regression model and multivariate support vector machine recursive feature elimination (mSVM-RFE) were used to identify potential biomarkers, which were validated using the real time quantitative polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC). Infiltration abundance of immune cells in the microenvironment were estimated using CIBERSORT algorithm. Our study identified a total of 660 differentially expressed genes (DEGs) and 81 differentially immune-related genes (DIRGs) in CRSwNP compared to controls. Functional enrichment analysis revealed that the DIRGs were primarily associated with cell chemotaxis and leukocyte migration, and cytokine-cytokine receptor interaction. Through machine learning, we further identified five candidate genes, CXCR1, CCL13, CCR3, PPBP, and MMP9. These five potential CRSwNP biomarkers were experimentally verified in our in-house cohort. Analysis of immune cell infiltration landscape revealed significant variations in the abundance of macrophages and mast cells between CRSwNP and healthy control. Our findings illuminate the significance of immune characteristics in CRSwNP pathogenesis. Future studies focusing on these candidate genes can help elucidate the underlying mechanisms and identify potential therapeutic targets for CRSwNP.

Indexed as

Machine LearningNasal PolypsRhinitisSinusitisTranscriptomeAdultBiomarkersChronic DiseaseFemaleGene Expression ProfilingGene Regulatory NetworksHumansMaleMiddle AgedProtein Interaction MapsRhinosinusitisBiomarkersBioinformaticsChronic rhinosinusitis with nasal polypsDifferential immune-related genesImmune infiltrationMachine learning

Identifiers

PMID40461517
PMCPMC12134379

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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 Socratic graph.