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Identification and Validation of ATF3 Serving as a Potential Biomarker and Correlating With Pharmacotherapy Response and Immune Infiltration Characteristics in Rheumatoid Arthritis.

ABSTRACT: Background: Although disease-modifying antirheumatic drugs (DMARDs) have significantly improved the prognosis of patients with rheumatoid arthritis (RA), approximately 40% of RA patients have limited response. Therefore, it was essential to explore new biomarkers to improve the therapeutic effects on RA. This study aimed to develop a new biomarker and validate it by an in vitro study. Methods: The RNA-seq and the clinicopathologic data of RA patients were downloaded from Gene Expression Omnibus (GEO) databases. Differentially expressed genes were screened in the GPL96 and GPL570 databases. Then, weighted gene co-expression network analysis (WGCNA) was used to explore the most correlated gene modules to normal and RA synovium in the GPL96 and GPL570 databases. After that, the differentially expressed genes were intersected with the correlated gene modules to find the potential biomarkers. The CIBERSORT tool was applied to investigate the relationship between activated transcription factor 3 (ATF3) expression and the immune cell infiltration, and Gene Set Enrichment Analysis (GSEA) was used to investigate the related signaling pathways of differentially expressed genes in the high and low ATF3 groups. Furthermore, the relationships between ATF3 expression and clinical parameters were also explored in the GEO database. Finally, the role of ATF3 was verified by in vitro cell experiments. Results: We intersected the differentially expressed genes and the most correlated gene modules in the GPL570 and GPL96 databases and identified that ATF3 is a significant potential biomarker and correlates with some clinical-pharmacological variables. Immune infiltration analysis showed that activated mast cells had a significant infiltration in the high ATF3 group in the two databases. GSEA showed that metabolism-associated pathways belonged to the high ATF3 groups and that inflammation and immunoregulation pathways were enriched in the low ATF3 group. Finally, we validated that ATF3 could promote the proliferation, migration, and invasion of RA fibroblast-like synoviocyte (FLS) and MH7A. Flow cytometry showed that ATF3 expression could decrease the proportion of apoptotic cells and increase the proportion of S and G2/M phase cells. Conclusion: We successfully identified and validated that ATF3 could serve as a novel biomarker in RA, which correlated with pharmacotherapy response and immune cell infiltration.

SUBMITTER: Hu H

PROVIDER: S-EPMC8710747 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRheumatoid arthritis (RA) patients have increased risk for cardiovascular disease (CVD). Accurate CVD risk prediction could improve care for RA patients. Our goal is to develop and validate a biomarker-based model for predicting CVD risk in RA patients.MethodsMedicare claims data were linked to multi-biomarker disease activity (MBDA) test results to create an RA patient cohort with age ??40?years that was split 2:1 for training and internal validation. Clinical and RA-related variables, MBDA score, and its 12 biomarkers were evaluated as predictors of a composite CVD outcome: myocardial infarction (MI), stroke, or fatal CVD within 3?years. Model building used Cox proportional hazard regression with backward elimination. The final MBDA-based CVD risk score was internally validated and compared to four clinical CVD risk prediction models.Results30,751 RA patients (904 CVD events) were analyzed. Covariates in the final MBDA-based CVD risk score were age, diabetes, hypertension, tobacco use, history of CVD (excluding MI/stroke), MBDA score, leptin, MMP-3 and TNF-R1. In internal validation, the MBDA-based CVD risk score was a strong predictor of 3-year risk for a CVD event, with hazard ratio (95% CI) of 2.89 (2.46-3.41). The predicted 3-year CVD risk was low for 9.4% of patients, borderline for 10.2%, intermediate for 52.2%, and high for 28.2%. Model fit was good, with mean predicted versus observed 3-year CVD risks of 4.5% versus 4.4%. The MBDA-based CVD risk score significantly improved risk discrimination by the likelihood ratio test, compared to four clinical models. The risk score also improved prediction, reclassifying 42% of patients versus the simplest clinical model (age + sex), with a net reclassification index (NRI) (95% CI) of 0.19 (0.10-0.27); and 28% of patients versus the most comprehensive clinical model (age + sex + diabetes + hypertension + tobacco use + history of CVD + CRP), with an NRI of 0.07 (0.001-0.13). C-index was 0.715 versus 0.661 to 0.696 for the four clinical models.ConclusionA prognostic score has been developed to predict 3-year CVD risk for RA patients by using clinical data, three serum biomarkers and the MBDA score. In internal validation, it had good accuracy and outperformed clinical models with and without CRP. The MBDA-based CVD risk prediction score may improve RA patient care by offering a risk stratification tool that incorporates the effect of RA inflammation.

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Identification and Validation of ATF3 Serving as a Potential Biomarker and Correlating With Pharmacotherapy Response and Immune Infiltration Characteristics in Rheumatoid Arthritis.

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