Kidney Week

Abstract: SA-PO635

Establishment of a Quantitative Evaluation System for Interferon Pathways in Patients with Lupus Nephritis

Session Information

• Genetic Kidney Diseases: Models, Mechanisms, and Therapies
  October 26, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Genetic Diseases of the Kidneys

• 1202 Genetic Diseases of the Kidneys: Non-Cystic

Authors

• Zhang, Jiahui, National Clinical Research Center of Kidney Diseases, Jingling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, China

Jiahui Zhang, MS

• Zhang, Yangyang, National Clinical Research Center of Kidney Diseases, Jingling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, China

Yangyang Zhang, MD

• Jin, Ying, National Clinical Research Center of Kidney Diseases, Jingling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, China

Ying Jin, MD

• Zhang, Changming, National Clinical Research Center of Kidney Diseases, Jingling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, China

Changming Zhang

• Liu, Zhihong, National Clinical Research Center of Kidney Diseases, Jingling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, China

Zhihong Liu, MD

Background

The interferon (IFN) pathway is one of the important pathogenic factors of lupus nephritis (LN), however, there is currently a lack of unified and clear quantitative assessment scoring system for its expression level, making difficulties in its applications in clinical practice.

Methods

RNAseq of 271 LN patients (before IFN-inhibition: n=261; after IFN-inhibition: n=10) and 94 healthy controls were conducted. ROC analysis were used to analyze the best performing scoring model. Data distribution analysis were made by R-mclust.

Results

Pivotal IFN genes in LN were highly expressed in patients, and decreased after JAK inhibitor therapy (Fig 1A). The analyses based on different datasets of LN patients resulted in two sets of genes, named 11-gene and 14-gene (Fig 1B). The z-score algorithm was used to calculate IFN scores based on each gene sets. The set of 11-gene had the best performance as LN biomarkers, followed by 14-gene set. And the previous reported gene sets generated from wider spectrum of autoimmune diseases had relatively lower AUC values (Fig 1C). Besides, the 11-gene score (IFN11) had bimodality of expression with different data distribution ranges and trends in different cells (Fig 1D), indicating the necessity to establish different thresholds for whole blood and PBMCs, two common materials for clinical tests, and the bimodality can provide basis for threshold delineation.

Conclusion

It is very feasible to develop a specialized IFN scoring system for LN patients, and IFN11 has good clinical application prospects in assessing disease status, guiding treatment, and predicting the risk of recurrence in clinical practise.

Fig 1. Comparison of LN-specific IFN scores.
A. The preliminary screening of characteristic genes in the IFN pathway through differential expression analysis. B. IFN gene sets previously reported (28IRS and score A) and obtained from this study (11-gene and 14-gene). C. ROC analysis of several IFN gene sets. D. The bimodality of 11-gene IFN score.

Funding

• Government Support – Non-U.S.