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

Number of Access:26（2025-09-29 10:02 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/102621

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Title
Identification of the differences in molecular networks between idiopathic pulmonary fibrosis and lung squamous cell carcinoma using machine learning
Creator
NRID
1000030815717 1000030815717
ORCID
0000-0002-6821-0093 0000-0002-6821-0093
researchmap permalink
yosui_nojima yosui_nojima
Scopus Author ID
56533374600 56533374600
CreatorName
Nojima, Yosui
NRID
1000050450896 1000050450896
ORCID
0000-0003-3021-7078 0000-0003-3021-7078
researchmap permalink
kenji_mizuguchi kenji_mizuguchi
Scopus Author ID
57191477452 57191477452
CreatorName
Mizuguchi, Kenji
Subject
Idiopathic pulmonary fibrosis
Lung squamous cell carcinoma
Machine learning
Molecular networks
Public data
RNA sequencing
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.
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.
Publisher
Elsevier Ltd
Source Title
Computational Biology and Chemistry
Volume (Issue)
119
Page
108560
Date of Issued
2025-06-27
Language
English
Handle URL
https://hdl.handle.net/11094/102621
PISSN
14769271
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1016/j.compbiolchem.2025.108560
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
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Version of Record
Category
学術雑誌論文 Journal Article