# CT imaging characteristics analysis of bone erosion in rheumatoid arthritis and bioinformatics study of inflammation-related gene rG4s

## TL;DR

This study uses CT imaging and deep learning to assess bone erosion in rheumatoid arthritis and identifies inflammation-related genes regulated by rG4s, offering new insights into disease mechanisms.

## Contribution

The novel contribution is the integration of deep learning for bone erosion assessment with bioinformatics analysis of rG4-regulated genes in rheumatoid arthritis.

## Key findings

- 67 RA-related inflammation-related differentially expressed genes (irDEGs) were identified, with 42 containing potential rG4 structures.
- The U-Net CNN model achieved high accuracy in bone erosion segmentation and correlated with clinical disease activity scores.
- CT imaging features of bone erosion were closely linked to the expression of rG4-regulated irDEGs.

## Abstract

This work aimed to collect joint computed tomography (CT) imaging and peripheral blood transcriptome data from patients with rheumatoid arthritis (RA), and construct a deep learning model for the automatic and precise assessment of bone erosion (BE). It was to screen RA-related inflammation genes regulated by rG4s through bioinformatics methods, explore potential associations between BE imaging phenotypes and molecular regulatory features, and provide hypotheses and clues for investigating the post-transcriptional regulatory mechanisms of RA bone destruction.

Clinical data, joint CT images, and peripheral blood RNA sequencing data were collected from the RA group (AG, 148 cases) and the healthy control group (BG, 49 cases) at Yancheng Third People’s Hospital. DESeq2 software was used for differential expression analysis of RNA-seq data. Combined with an inflammation core gene set integrated from multiple databases, RA-related inflammation-related Differentially Expressed Genes (irDEGs) were screened. The rG4detector tool was used to predict rG4s structures in target genes. The Metascape database was used for functional enrichment analysis to identify core candidate genes. An optimized U-Net CNN model was constructed based on the PyTorch framework to achieve automatic segmentation and severity quantification of BE in CT images. Multiple metrics were used to evaluate model performance, and the correlation between candidate gene expression levels and imaging scores was analyzed.

A total of 67 RA-related irDEGs were screened, of which 42 contained potential rG4s structures. The U-Net CNN model performed excellently in BE segmentation, with pixel-level accuracy, Dice Similarity Coefficient (DSC), sensitivity, and specificity on the test set all at high levels. The model’s quantitative score was significantly correlated with the clinical disease activity score (DAS28).

CT imaging characteristics of BE in RA patients were closely associated with the expression of rG4s-regulated irDEGs. The deep learning model constructed in this study enabled precise quantification of BE, providing an efficient method for the clinical assessment of RA bone erosion. It also offered a new research perspective and candidate targets for understanding the molecular mechanisms of RA bone destruction at the post-transcriptional regulatory level.