ArticleGenes2024
Comparison of B-Cell Lupus and Lymphoma Using a Novel Immune Imbalance Transcriptomics Algorithm Reveals Potential Therapeutic Targets.
Article in Genes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Sarcoma in patients with systemic lupus erythematosus. A systematic review.Reumatologia · 2026Review
- Histone-, Receptor-, and Integrin-Related Gene Products and ADAM28 as Relevant to B-Cell Acute Lymphoblastic Leukemia (B-ALL).Current issues in molecular biology · 2025Article
- Article
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11 authors.
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Abstract
BACKGROUND/
objectivesSystemic lupus erythematosus (lupus) and B-cell lymphoma (lymphoma) co-occur at higher-than-expected rates and primarily depend on B cells for their pathology. These observations implicate shared inflammation-related B cell molecular mechanisms as a potential cause of co-occurrence.
methodsWe consequently implemented a novel Immune Imbalance Transcriptomics (IIT) algorithm and applied IIT to lupus, lymphoma, and healthy B cell RNA-sequencing (RNA-seq) data to find shared and contrasting mechanisms that are potential therapeutic targets.
resultsWe observed 7143 significantly dysregulated genes in both lupus and lymphoma. Of those genes, we found 5137 to have a significant immune imbalance, defined as a significant dysregulation by both diseases, as analyzed by IIT. Gene Ontology (GO) term and pathway enrichment of the IIT genes yielded immune-related "Neutrophil Degranulation" and "Adaptive Immune System", which validates that the IIT algorithm isolates biologically relevant genes in immunity and inflammation. We found that 344 IIT gene products are known targets for established and/or repurposed drugs. Among our results, we found 48 known and 296 novel lupus targets, along with 151 known and 193 novel lymphoma targets. Known disease drug targets in our IIT results further validate that IIT isolates genes with disease-relevant mechanisms.
conclusionsWe anticipate the IIT algorithm, together with the shared and contrasting gene mechanisms uncovered here, will contribute to the development of immune-related therapeutic options for lupus and lymphoma patients.
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