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Identifying oxidative stress-related biomarkers in idiopathic pulmonary fibrosis in the context of predictive, preventive, and personalized medicine using integrative omics approaches and machine-learning strategies

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Abstract

Background

Idiopathic pulmonary fibrosis (IPF) is a rare interstitial lung disease with a poor prognosis that currently lacks effective treatment methods. Preventing the acute exacerbation of IPF, identifying the molecular subtypes of patients, providing personalized treatment, and developing individualized drugs are guidelines for predictive, preventive, and personalized medicine (PPPM / 3PM) to promote the development of IPF. Oxidative stress (OS) is an important pathological process of IPF. However, the relationship between the expression levels of oxidative stress-related genes (OSRGs) and clinical indices in patients with IPF is unclear; therefore, it is still a challenge to identify potential beneficiaries of antioxidant therapy. Because PPPM aims to recognize and manage diseases by integrating multiple methods, patient stratification and analysis based on OSRGs and identifying biomarkers can help achieve the above goals.

Methods

Transcriptome data from 250 IPF patients were divided into training and validation sets. Core OSRGs were identified in the training set and subsequently clustered to identify oxidative stress-related subtypes. The oxidative stress scores, clinical characteristics, and expression levels of senescence-associated secretory phenotypes (SASPs) of different subtypes were compared to identify patients who were sensitive to antioxidant therapy to conduct differential gene functional enrichment analysis and predict potential therapeutic drugs. Diagnostic markers between subtypes were obtained by integrating multiple machine learning methods, their expression levels were tested in rat models with different degrees of pulmonary fibrosis and validation sets, and nomogram models were constructed. CIBERSORT, single-cell RNA sequencing, and immunofluorescence staining were used to explore the effects of OSRGs on the immune microenvironment.

Results

Core OSRGs classified IPF into two subtypes. Patients classified into subtypes with low oxidative stress levels had better clinical scores, less severe fibrosis, and lower expression of SASP-related molecules. A reliable nomogram model based on five diagnostic markers was constructed, and these markers' expression stability was verified in animal experiments. The number of neutrophils in the immune microenvironment was significantly different between the two subtypes and was closely related to the degree of fibrosis.

Conclusion

Within the framework of PPPM, this work comprehensively explored the role of OSRGs and their mediated cellular senescence and immune processes in the progress of IPF and assessed their capabilities as

  • predictors of high oxidative stress and disease progression,

  • targets of the vicious loop between regulated pulmonary fibrosis and OS for targeted secondary and tertiary prevention, and

  • references for personalized antioxidant and antifibrotic therapies.

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Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

BALF:

Bronchoalveolar lavage fluid

CCR2:

C-C motif chemokine receptor-2

DEGs:

Differentially expressed genes

EMT:

Epithelial mesenchymal transition

FPR2:

Formyl peptide receptor 2

GSVA:

Gene set variation analysis

HRCT:

High-resolution computed tomography

ILD:

Interstitial lung disease

IPF:

Idiopathic pulmonary fibrosis

KLF9:

KLF transcription factor 9

LASSO:

Least absolute shrinkage and selection operator

NOX4:

NADPH oxidase 4

OS:

Oxidative stress

OSRGs:

OS-related genes

PDK4:

Pyruvate dehydrogenase kinase 4

PPAR:

Proliferator-activated receptor

PPPM:

Predictive, preventive, and personalized medicine

PTGS2:

Prostaglandin-endoperoxide synthase 2

RF:

Random forest

ROC:

Receiver operating characteristic

ROS:

Reactive oxygen species

SASPs:

Senescence-associated secretory phenotypes

SVM-RFE:

Support vector machine-recursive feature elimination

THBS1:

Thrombospondin 1

UIP:

Usual interstitial pneumonia

WGCNA:

Weighted gene co-expression network analysis

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Funding

This work was supported by the National Key R&D Program of China (2020YFC2003100, 2020YFC2003101), Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (No. ZYYCXTD-C-202001).

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Author notes

  1. Fan Yang and Wendusubilige have contributed equally to this work.

Authors and Affiliations

  1. College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China

    Fan Yang,  Wendusubilige, Jingwei Kong, Yuhan Zong, Manting Wang & Zhaotian Ma

  2. National Institute of TCM Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China

    Fan Yang, Jingwei Kong, Yuhan Zong, Manting Wang & Ji Wang

  3. Institute of Ethnic Medicine, Beijing University of Chinese Medicine, Beijing, China

    Wendusubilige & Zhaotian Ma

  4. The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China

    Chuanqing Jing

  5. Department of Clinical Nutrition, Chinese Academy of Medical Sciences - Peking Union Medical College, Peking Union Medical College Hospital (Dongdan campus), Beijing, China

    Wanyang Li

  6. College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China

    Renshuang Cao, Shuwen Jing, Jie Gao & Wenxin Li

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  1. Fan Yang
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Contributions

FY, Wen, and JW designed and conducted the whole research. CJ, WL and RC carried out animal experiments and molecular biological analysis. JK, YZ, MW and ZM applied for the GEO dataset analysis of IPF. FY, Wen, SJ, JG, and WeL completed the data analysis and drafted the manuscript. JW revised and finalized the manuscript. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Ji Wang.

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Our laboratory followed the National Institute of Health Guide for the Care and Use of Laboratory Animals when handling and caring for animals. The study protocol was approved by the Research Ethics Committee of the Affiliated Hospital of Shandong University of Traditional Chinese Medicine (Approval No. AWE2019046).

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The authors declare no competing interests.

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Yang, F., Wendusubilige, Kong, J. et al. Identifying oxidative stress-related biomarkers in idiopathic pulmonary fibrosis in the context of predictive, preventive, and personalized medicine using integrative omics approaches and machine-learning strategies. EPMA Journal 14, 417–442 (2023). https://doi.org/10.1007/s13167-023-00334-4

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