ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1663487
This article is part of the Research TopicCommunity Series in Post-Translational Modifications of Proteins in Cancer Immunity and Immunotherapy, Volume IVView all 7 articles
Integrating scRNA-seq and Machine Learning Identifies MNAT1 as a Therapeutic Target in OSCC
Provisionally accepted
- 1First Affiliated Hospital of Huzhou University, huzhou, China
- 2The First People's Hospital of Huzhou, Huzhou, China
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Background :Oral squamous cell carcinoma, with high global incidence and mortality, requires improved early intervention strategies. Ubiquitination - a critical post-translational modification - has been strongly implicated in tumorigenesis, with particularly significant roles in T-cell regulation. We developed a T Cell-Related ubiquitination risk model that enhances prognostic prediction and immunotherapy response assessment, offering a framework for personalized OSCC manageme. Method:T cell-Related Ubiquitination genes were identified based on scRNA-seq analysis, and key genes were selected using WGCNA and LASSO algorithms to construct a prognostic model. Spearman correlation analysis revealed significant associations between riskScore and immune infiltration levels, checkpoint molecule expression, and MMR activity. Pseudotemporal trajectory and cell-cell communication analyses delineated dynamic gene expression patterns driving OSCC progression. Functional validation through colony formation and Transwell assays confirmed the tumor-suppressive effects of key model genes. Results:Given the high correlation between T cell-Related Ubiquitination genes and the prognosis of OSCC patients, a prognostic model based on patient scRNA-seq data was constructed and validated. The RiskScore derived from our model correlated significantly with expression levels of MMR genes, abundance of immune checkpoint proteins, and immunotherapy response. Cell-cell communication analysis further elucidated epithelial-macrophage crosstalk via MIF and IFN-II signaling, suggesting microenvironment-driven progression mechanisms. In vitro functional assays showed that depletion of MNAT1 impaired Cal27 cell proliferation and migration capacity. Conclusions: Collectively, integrating T cell-Related Ubiquitination genes through advanced computational analyses, we established a robust prognostic model for OSCC and identified MNAT1 as a promoter of malignant progression, highlighting its therapeutic potential
Keywords: oral squamous cell carcinoma, machine learning, Ubiquitination Modification, T cell, MNAT1
Received: 10 Jul 2025; Accepted: 13 Oct 2025.
Copyright: © 2025 Gao, Liu, Qian, Wu, Wang, Yang and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Weiping Yang, 1365407535@qq.com
Yinfang Shi, 52136@zjhu.edu.cn
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