Evidence map›Paper›PMID 39589706›Full record

ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2025

Single-cell transcriptome analysis identifies subclusters and signature with N-glycosylation in endometrial cancer.

Min Zhou, Yuefeng Zhang, Wei Song

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Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Min Zhou *Department of Gynecology and Obstetrics, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
Yuefeng Zhang *Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
Wei SongDepartment of Gastrointestinal Surgery II, Renmin Hospital of Wuhan University, No. 238, Jiefang Road, Wuhan, 430060, China. whdxsw@whu.edu.cn.ORCID http://orcid.org/0000-0003-4576-1949

Funding

Fundamental Research Funds for the Central Universities 2042023kf0031Fundamental Research Funds for the Central Universities 2042023kf0048Natural Science Foundation of Hubei Province 2023AFB251
6 · The paper itself

Abstract

introductionEndometrial cancer (EC) is a prevalent gynecologic cancer, with worldwide increasing incidence and disease-associated mortality. N-glycosylation, a critical post-translational modification, has been implicated in cancer progression and immune response modulation. We aimed to elucidate the role of N-glycosylation-related genes on EC cell heterogeneity, prognosis, and immunotherapy response.

methodsData from single-cell RNA sequencing (scRNA) of five patients with EC were acquired from the Gene Expression Omnibus (GEO) database. Nonnegative matrix factorization (NMF) was used to identify cell subtypes related to N-glycosylation from a scRNA matrix. Subsequently, a consensus prognostic signature by integrating 101 combinations of 10 machine learning algorithms. The response to immunotherapy in EC was further examined by multiple algorithms.

resultsOur findings identified 11,020 differentially expressed genes (DEGs), of which 34 N-glycosylation-related DEGs were remarkably associated with overall survival (OS) in EC. Single-cell RNA sequencing analysis revealed 30,233 cells divided into eight clusters, with T cells and epithelial cells showing distinct functional characteristics. NMF clustering further classified malignant cells into four subtypes: N-glycosylation-C0, Glycosphingolipid-C1, O-GalNAc-C2, and Elongation-C3. The O-GalNAc-C2 subtype exhibited the highest metabolic pathway activity and activation of transcription factors SOX4, JUND, and FOS. Additionally, cell-cell interaction networks highlighted the MK signaling pathway as a critical mediator of intercellular communication. An integrated machine learning framework generated a prognostic model comprising eight DEGs (LAMC2, KRT7, IL32, KRT18, SERPINA1, PGR, AKAP12, EDN2), achieving an average C-index of 0.712 in training and validation cohorts. A low-risk score implies more significant immune cell infiltration and better response to immunotherapy.

conclusionsOur study underscores the role of N-glycosylation-related genes in EC prognosis and immunotherapy response prediction, and may provide a basis for the development of targeted therapies and personalized treatment strategies.

Indexed as

Endometrial NeoplasmsGene Expression ProfilingSingle-Cell AnalysisTranscriptomeBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticGlycosylationHumansImmunotherapyMachine LearningPrognosisSingle-Cell Gene Expression AnalysisBiomarkers, TumorEndometrial cancerImmunotherapyN-glycosylationScRNA-seqTumor microenvironment

Identifiers

PMID39589706

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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.