Identification of the differences in molecular networks between idiopathic pulmonary fibrosis and lung squamous cell carcinoma using machine learning

Nojima, Yosui; Mizuguchi, Kenji

doi:https://doi.org/10.1016/j.compbiolchem.2025.108560

Identification of the differences in molecular networks between idiopathic pulmonary fibrosis and lung squamous cell carcinoma using machine learning

Nojima, Yosui; Mizuguchi, Kenji

Computational Biology and Chemistry, 2025, 119, 108560

アクセス数:46件（2025-11-04 00:09 集計）

固定URL: https://hdl.handle.net/11094/102621

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タイトル	Identification of the differences in molecular networks between idiopathic pulmonary fibrosis and lung squamous cell carcinoma using machine learning
著者	NRID 1000030815717 1000030815717 ORCID 0000-0002-6821-0093 0000-0002-6821-0093 researchmap permalink yosui_nojima yosui_nojima Scopus Author ID 56533374600 56533374600 著者名 Nojima, Yosui
著者	NRID 1000050450896 1000050450896 ORCID 0000-0003-3021-7078 0000-0003-3021-7078 researchmap permalink kenji_mizuguchi kenji_mizuguchi Scopus Author ID 57191477452 57191477452 著者名 Mizuguchi, Kenji
キーワード等	Idiopathic pulmonary fibrosis
	Lung squamous cell carcinoma
	Machine learning
	Molecular networks
	Public data
	RNA sequencing
内容	Nojima Y., Mizuguchi K.. Identification of the differences in molecular networks between idiopathic pulmonary fibrosis and lung squamous cell carcinoma using machine learning. Computational Biology and Chemistry 119, 108560 (2025); https://doi.org/10.1016/j.compbiolchem.2025.108560.
抄録	Idiopathic pulmonary fibrosis (IPF) is an independent risk factor for lung cancer, especially squamous cell carcinoma (SCC). The prognosis of patients with both IPF and SCC is poorer than that of patients with only IPF, and preventive measures against SCC in patients with IPF remain elusive. Understanding the distinct mechanisms that induce both diseases is crucial for mitigating SCC onset in patients with IPF. We developed highly accurate machine learning (ML) models to classify patients with IPF or SCC using public RNA sequencing data. To construct the ML models, a random restart technique was applied to the five algorithms. To identify the differentially expressed genes (DEGs) between IPF and SCC, feature importance was calculated in the classification models. Furthermore, we detected somatic mutations affecting gene expression using SCC data. The ML models identified VCX2, TMPRSS11B, PRUNE2, PRG4, PZP, SCARA5, DES, HPSE2, HOXD11, S100A7A, and PLA2G2A as DEGs. Somatic mutations were detected in four transcription factors, BHLHE40, MYC, STAT1, and E2F4, which regulate the expression of these 11 genes. Furthermore, a molecular network comprising four transcription factors and 11 downstream genes was discovered. This newly identified molecular network enhances our understanding of the distinct mechanisms underlying IPF and SCC onset, and provides new insights into preventing SCC complications in patients with IPF.
出版者	Elsevier Ltd
収録物名	Computational Biology and Chemistry
巻(号)	119
ページ	108560
刊行年月	2025-06-27
言語	英語
ハンドルURL	https://hdl.handle.net/11094/102621
PISSN	14769271
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1016/j.compbiolchem.2025.108560
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Computational Biology and Chemistry. 2025, 119, p. 108560
DC.title	Identification of the differences in molecular networks between idiopathic pulmonary fibrosis and lung squamous cell carcinoma using machine learning
DC.creator	Nojima, Yosui
DC.creator	Mizuguchi, Kenji
DC.publisher	Elsevier Ltd
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-06-27
DC.identifier	https://doi.org/10.1016/j.compbiolchem.2025.108560
DC.subject	Idiopathic pulmonary fibrosis
	Lung squamous cell carcinoma
	Machine learning
	Molecular networks
	Public data
	RNA sequencing
DC.description	Nojima Y., Mizuguchi K.. Identification of the differences in molecular networks between idiopathic pulmonary fibrosis and lung squamous cell carcinoma using machine learning. Computational Biology and Chemistry 119, 108560 (2025); https://doi.org/10.1016/j.compbiolchem.2025.108560.
DCTERMS.abstract	Idiopathic pulmonary fibrosis (IPF) is an independent risk factor for lung cancer, especially squamous cell carcinoma (SCC). The prognosis of patients with both IPF and SCC is poorer than that of patients with only IPF, and preventive measures against SCC in patients with IPF remain elusive. Understanding the distinct mechanisms that induce both diseases is crucial for mitigating SCC onset in patients with IPF. We developed highly accurate machine learning (ML) models to classify patients with IPF or SCC using public RNA sequencing data. To construct the ML models, a random restart technique was applied to the five algorithms. To identify the differentially expressed genes (DEGs) between IPF and SCC, feature importance was calculated in the classification models. Furthermore, we detected somatic mutations affecting gene expression using SCC data. The ML models identified VCX2, TMPRSS11B, PRUNE2, PRG4, PZP, SCARA5, DES, HPSE2, HOXD11, S100A7A, and PLA2G2A as DEGs. Somatic mutations were detected in four transcription factors, BHLHE40, MYC, STAT1, and E2F4, which regulate the expression of these 11 genes. Furthermore, a molecular network comprising four transcription factors and 11 downstream genes was discovered. This newly identified molecular network enhances our understanding of the distinct mechanisms underlying IPF and SCC onset, and provides new insights into preventing SCC complications in patients with IPF.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	102621
citation_title	Identification of the differences in molecular networks between idiopathic pulmonary fibrosis and lung squamous cell carcinoma using machine learning
citation_author	Nojima, Yosui
citation_author	Mizuguchi, Kenji
citation_publisher	Elsevier Ltd
citation_language	英語
citation_date	2025-06-27
citation_journal_title	Computational Biology and Chemistry
citation_volume	119
citation_firstpage	108560
citation_doi	https://doi.org/10.1016/j.compbiolchem.2025.108560
citation_public_url	https://hdl.handle.net/11094/102621

Identification of the differences in molecular networks between idiopathic pulmonary fibrosis and lung squamous cell carcinoma using machine learning

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