Scholarlink

Background <p>Coal-dust, a persistent airborne pollutant, induces dose-related pulmonary fibrosis; however, plasma biomarkers for pre-clinical toxicity remain lacking.</p> Methods <p>We enrolled 158 participants, including 28 healthy controls (HCs), 30 dust-exposed workers (DEWs), and 100 patients with coal workers’ pneumoconiosis (CWP) at different stages (n <sub>CWP−I</sub>=40, n <sub>CWP−II</sub>=30, n <sub>CWP−III</sub>=30). Plasma proteomic profiling was performed via data-independent acquisition (DIA) mass spectrometry. Differentially expressed proteins were identified and functionally annotated. Key proteins were selected and multiple machine learning algorithms were employed to construct and validate predictive models.</p> Results <p>We identified 1,239 plasma proteins, including 645 high-confidence candidates. Functional enrichment revealed significant associations between disease progression and pathways such as PPAR signaling, cholesterol metabolism, Epstein-Barr virus infection, and the pentose phosphate pathway. These alterations converge on dysregulated lipid metabolism, chronic inflammatory signaling and virus-induced immune evasion, suggesting a metabolic-immune axis that orchestrates early fibrotic progression. We successfully constructed the first plasma proteomics-based machine learning models for pneumoconiosis grading and early screening. Notably, a single biomarker, PRSS3, demonstrated exceptional performance in distinguishing DEW patients from early-stage pneumoconiosis patients (CWP-I), achieving an area under the curve (AUC) of 1.00 and an accuracy of 1.00 in the training set and an AUC of 1.00 with an accuracy between 0.93 and 1.00 in the validation set.</p> Conclusion <p>This study establishes innovative machine learning-based models for the grading and early screening of pneumoconiosis via plasma proteomics. The identification of PRSS3 as a potential biomarker highlights the clinical utility of our approach. These findings provide a foundation for noninvasive diagnostic strategies and future translational research in occupational lung diseases.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Noninvasive early detection and grading of pneumoconiosis via plasma proteomics and machine learning: PRSS3 as a potential biomarker

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月15日

查看全文
  • Yizhuo Tian,
  • Mingyao Wang,
  • Zhifei Hou,
  • Wenfeng Zhu,
  • Yong Gao,
  • Jing Geng,
  • Xinran Zhang,
  • Kaiyuan Min,
  • Jiangfeng Liu,
  • Juntao Yang,
  • Huaping Dai

摘要

Background

Coal-dust, a persistent airborne pollutant, induces dose-related pulmonary fibrosis; however, plasma biomarkers for pre-clinical toxicity remain lacking.

Methods

We enrolled 158 participants, including 28 healthy controls (HCs), 30 dust-exposed workers (DEWs), and 100 patients with coal workers’ pneumoconiosis (CWP) at different stages (n CWP−I=40, n CWP−II=30, n CWP−III=30). Plasma proteomic profiling was performed via data-independent acquisition (DIA) mass spectrometry. Differentially expressed proteins were identified and functionally annotated. Key proteins were selected and multiple machine learning algorithms were employed to construct and validate predictive models.

Results

We identified 1,239 plasma proteins, including 645 high-confidence candidates. Functional enrichment revealed significant associations between disease progression and pathways such as PPAR signaling, cholesterol metabolism, Epstein-Barr virus infection, and the pentose phosphate pathway. These alterations converge on dysregulated lipid metabolism, chronic inflammatory signaling and virus-induced immune evasion, suggesting a metabolic-immune axis that orchestrates early fibrotic progression. We successfully constructed the first plasma proteomics-based machine learning models for pneumoconiosis grading and early screening. Notably, a single biomarker, PRSS3, demonstrated exceptional performance in distinguishing DEW patients from early-stage pneumoconiosis patients (CWP-I), achieving an area under the curve (AUC) of 1.00 and an accuracy of 1.00 in the training set and an AUC of 1.00 with an accuracy between 0.93 and 1.00 in the validation set.

Conclusion

This study establishes innovative machine learning-based models for the grading and early screening of pneumoconiosis via plasma proteomics. The identification of PRSS3 as a potential biomarker highlights the clinical utility of our approach. These findings provide a foundation for noninvasive diagnostic strategies and future translational research in occupational lung diseases.