Dataset Information

Plasma Proteomics and Machine Learning Deliver Non-invasive Distinction between Fibrotic Hypersensitivity Pneumonitis and Idiopathic Pulmonary Fibrosis

ABSTRACT: A total of 119 subjects were enrolled from the Chinese Interstitial Lung Disease (ILD) National Cohort and the PORTRAY IPF Cohort between July 2018 and June 2022, including 32 healthy controls (HCs), 31 non-fibrotic HP (NFHP), 28 FHP, and 28 IPF cases. Plasma samples underwent quantitative proteomic profiling, weighted gene co-expression network analysis (WGCNA), and bioinformatics analysis to identify differentially expressed proteins, core pathways, and co-expression modules. Key proteins were selected to construct and validate disease differentiation models using machine learning algorithms. The proteomic dataset comprised 813 identified proteins, with 493 reliably quantified. Pathway analysis implicated nitrogen metabolism, alcohol addiction signaling, and thyroid hormone biosynthesis in disease pathogenesis. A machine learning-based proteomic model was developed for differentiating HP subtypes from IPF. The support vector machine (SVM) algorithm demonstrated the most robust and balanced performance on an independent test set, achieving an accuracy of 71.4% with equal sensitivity and specificity. This model provides a non-invasive tool for preliminary case differentiation and suggests potential biomarkers for precision medicine in fibrotic lung diseases.

ORGANISM(S): Homo Sapiens

SUBMITTER: Juntao Yang

PROVIDER: PXD069424 | iProX | Tue Oct 14 00:00:00 BST 2025

REPOSITORIES: iProX

ACCESS DATA

Similar Datasets

Project description:Objectives Our goal was to evaluate the diagnostic value of DNA methylation analysis in combination with machine learning to differentiate pleural mesothelioma (PM) from important histopathological mimics. Material and methods DNA methylation data of PM, lung adenocarcinomas, lung squamous cell carcinomas and chronic pleuritis was used to train a random forest as well as a support vector machine. These classifiers were validated using an independent validation cohort including pleural carcinosis and pleomorphic variants of lung adeno- and squamous cell carcinomas. Furthermore, we used a deconvolution method to estimate the composition of the tumor microenvironment. Results T-distributed stochastic neighbor embedding clearly separated PM from lung adenocarcinomas and squamous cell carcinomas, but there was a considerable overlap between chronic pleuritis specimens and PM with low tumor cell content. While both machine learning algorithms achieved comparable accuracies in a nested cross validation on the training cohort (random forest: 94.9%; support vector machine: 95.5%), the support vector machine outperformed the random forest in distinguishing PM from chronic pleuritis. Differential methylation analysis revealed promoter hypermethylation in PM specimens, including the tumor suppressor genes BCL11B, EBF1, FOXA1, and WNK2. Furthermore, we observed comparable accuracies for the support vector machine on the validation cohort (97.1%) while the random forest performed considerably worse (89.9%). Deconvolution of the stromal and immune cell composition revealed higher rates of regulatory T-cells and endothelial cells in tumor specimens and a heterogenous inflammation including macrophages, B-cells and natural killer cells in chronic pleuritis. Conclusion DNA methylation in combination with machine learning is a promising tool to reliably differentiate PM from chronic pleuritis and lung cancer, including pleomorphic carcinomas. Furthermore, our study highlights new candidate genes for PM carcinogenesis and shows that deconvolution of DNA methylation data can provide reasonable insights into the composition of the tumor microenvironment.

Dataset Information

Plasma Proteomics and Machine Learning Deliver Non-invasive Distinction between Fibrotic Hypersensitivity Pneumonitis and Idiopathic Pulmonary Fibrosis

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets