Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis from Multi-Omics Analysis

A list of candidate biomarkers for idiopathic pulmonary fibrosis identified through integrated transcriptomic and machine-learning analysis. The dataset contains three genes—CD247, IL7R, and RETN—validated by RT-qPCR in an independent clinical cohort. It was published by Yongbin Wang on figshare in April 2026 under a CC-BY-4.0 license.

Use Cases

• Validate candidate biomarkers for idiopathic pulmonary fibrosis based on the identified genes CD247, IL7R, and RETN.
• Train diagnostic machine learning models based on the feature genes selected from 101 model combinations described in the study.
• Investigate mitochondrial dynamics and spliceosome-related pathways in fibrotic lung disease based on the enrichment analyses mentioned.
• Construct regulatory networks involving transcription factors and non-coding RNAs based on the described multilayer analysis.
• Analyze single-cell expression patterns in monocyte-associated compartments based on the referenced scRNA-seq data.

Strengths

• Identifies three specific candidate biomarkers (CD247, IL7R, RETN) with diagnostic AUC > 0.7.
• Validation includes results from an artificial neural network model with training AUC of 0.91 and validation AUC of 0.82.
• Analysis integrates multiple data types: bulk transcriptomics, single-cell RNA-seq, and literature-derived gene sets.
• Independent validation was performed using RT-qPCR on a clinical cohort.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 25.1 KB, indicating limited scope, likely containing only the key gene list and related analysis results.

Provenance

Source

Integrated data from the Gene Expression Omnibus (GEO) and single-cell RNA sequencing sources, as described.

Collection Method

Candidate genes identified through differential expression analysis, consensus clustering, and machine learning model combinations.

Time Range

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Freshness

Last updated 2026-04-23 17:24:12; freshness should be verified.

Geography

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