ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1666240
This article is part of the Research TopicArtificial Intelligence in Multi-omics: Advancing Tumor Metastasis Prediction and Mechanism AnalysisView all 5 articles
Identification of MUC5B as a Lymph Node Metastasis-Associated Gene in Lung Adenocarcinoma through Integrated Transcriptomic and Machine Learning Approaches
Provisionally accepted- 1Department of General Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
- 2National Center for Respiratory Medicine, Beijing, China
- 3Medical school of Chinese PLA, Beijing, China
- 4Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- 5Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- 6Department of Laboratory Medicine, 1st Medical Center of Chinese PLA General Hospital, Beijing, China
- 7National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Background: Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer, with lymph node metastasis serving as a key prognostic factor. MUC5B, a member of the mucin family, has been implicated in the progression of various cancers, yet its specific role in LUAD metastasis remains underexplored. This study aimed to investigate the role of MUC5B in LUAD progression and its potential as a biomarker for lymph node metastasis. Methods: We integrated TCGA data, single-cell RNA-seq, and machine learning (LASSO, SVM-RFE) to identify MUC5B and associated metastatic markers. A 13-gene predictive model was constructed and validated using ROC analysis. Immunohistochemical staining confirmed the expression of MUC5B in the clinical case samples (n=65). In vitro experiments were performed using MUC5B-knockdown LUAD cell lines (A549, H1975) to assess changes in proliferation, migration, invasion, and colony formation. RNA sequencing was conducted to explore downstream molecular changes following MUC5B depletion. Results: MUC5B was significantly upregulated in LUAD with lymph node metastasis and associated with poor overall and progression-free survival. Knockdown of MUC5B suppressed LUAD cell proliferation, migration, and invasion. The 13-gene model showed high predictive accuracy (AUC > 0.9) for lymph node metastasis. GSVA analysis revealed most model genes correlated positively with Th2 cells and negatively with mast cells, type II interferons. Transcriptomic profiling revealed that MUC5B depletion led to significant downregulation of GINS1, GINS2, and GINS4—core components of the DNA replication GINS complex—suggesting a regulatory axis between MUC5B and cell cycle progression. Enrichment analyses further indicated that MUC5B promotes LUAD metastasis via pathways involved in DNA replication, cell cycle, and metabolic reprogramming. Conclusion: MUC5B facilitates LUAD lymph node metastasis, potentially by regulating the GINS complex and promoting oncogenic signaling. These findings highlight MUC5B as a promising biomarker and therapeutic target for advanced LUAD.
Keywords: MUC5B, Lung Adenocarcinoma, lymph node metastasis, prognosis, machine learning
Received: 15 Jul 2025; Accepted: 21 Oct 2025.
Copyright: © 2025 Song, Yang, Du, Wei, Zhou, S, Liang, Liu, Liang, Li and Gao. 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:
Mianyang Li, limianyang301@163.com
Yushun Gao, ysgaopumc@163.com
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