J L Zhu1, L T Tran2, M Smith2, F Zheng2, L Cai3, J A James2, J M Guthridge2, B F Chong1. 1. Department of Dermatology, University of Texas at Southwestern Medical Center, Dallas, TX, USA. 2. Arthritis and Clinical Research Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA. 3. Department of Population and Data Sciences, Quantitative Biomedical Research Center, Dallas, TX, USA.
Abstract
BACKGROUND: Cutaneous lupus erythematosus (CLE) is a heterogeneous autoimmune disease with clinical sequelae such as itching, dyspigmentation and scarring. OBJECTIVES: We applied a previously described modular analysis approach to assess the molecular heterogeneity of patients with CLE. METHODS: Whole-blood transcriptomes of RNA sequencing data from a racially and ethnically diverse group of patients with CLE (n = 62) were used to calculate gene co-expression module scores. An unsupervised cluster analysis and k-means clustering based on these module scores were then performed. We used Fisher's exact tests and Kruskal-Wallis tests to compare characteristics between patient clusters. RESULTS: Six unique clusters of patients with CLE were identified from the cluster analysis. We observed that seven inflammation modules were elevated in two clusters of patients with CLE. Additionally, these clusters were characterized by interferon, neutrophil and cell-death signatures, suggesting that interferon-related proteins, neutrophils and cell-death processes could be driving the inflammatory response in these subgroups. Three different clusters had a predominant T-cell signature, which were supported by lymphocyte counts. CONCLUSIONS: Our data support a diverse molecular profile in CLE that further adds to the clinical variations of this skin disease, and may affect disease course and treatment selection. Future studies with a larger and diverse cohort of patients with CLE are warranted to confirm these findings.
BACKGROUND: Cutaneous lupus erythematosus (CLE) is a heterogeneous autoimmune disease with clinical sequelae such as itching, dyspigmentation and scarring. OBJECTIVES: We applied a previously described modular analysis approach to assess the molecular heterogeneity of patients with CLE. METHODS: Whole-blood transcriptomes of RNA sequencing data from a racially and ethnically diverse group of patients with CLE (n = 62) were used to calculate gene co-expression module scores. An unsupervised cluster analysis and k-means clustering based on these module scores were then performed. We used Fisher's exact tests and Kruskal-Wallis tests to compare characteristics between patient clusters. RESULTS: Six unique clusters of patients with CLE were identified from the cluster analysis. We observed that seven inflammation modules were elevated in two clusters of patients with CLE. Additionally, these clusters were characterized by interferon, neutrophil and cell-death signatures, suggesting that interferon-related proteins, neutrophils and cell-death processes could be driving the inflammatory response in these subgroups. Three different clusters had a predominant T-cell signature, which were supported by lymphocyte counts. CONCLUSIONS: Our data support a diverse molecular profile in CLE that further adds to the clinical variations of this skin disease, and may affect disease course and treatment selection. Future studies with a larger and diverse cohort of patients with CLE are warranted to confirm these findings.
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