Key result
Gene expression profiling accurately identifies cardiac sarcoidosis with an AUC up to 0.94.
Why the study?
Does a gene expression profile from endomyocardial biopsies improve the diagnostic accuracy of cardiac sarcoidosis compared to other inflammatory cardiomyopathies?
Case-Control (n=133)
Does a gene expression profile from endomyocardial biopsies improve the diagnostic accuracy of cardiac sarcoidosis compared to other inflammatory cardiomyopathies?
Effect estimate: AUC 0.94
A novel gene expression profile using 3 to 10 genes from endomyocardial biopsies can accurately distinguish cardiac sarcoidosis from inflammatory cardiomyopathy and giant cell myocarditis, potentially overcoming histological sampling errors.
Background Cardiac sarcoidosis (CS) is an inflammatory disease of the heart which is present in around a quarter of patients with systemic sarcoidosis. Diagnosis of CS remains challenging due to unspecific symptoms of the condition. Even indications for CS can be obtained by non-invasive measurements, it can only be reliably identified by histological examination of endomyocardial biopsies (EMBs). However, due to the focal nature of CS, diagnosis via histological examination still contains a degree of uncertainty due to a possible sampling error. This inaccuracy raises the need for improved diagnostic methods. Here, we investigate gene expression changes characteristic for CS with the goal of developing a gene expression profile (GEP) to clearly diagnose the presence of CS. Methods Expression of 112 inflammatory genes were analyzed in EMBs from N = 20 patients with CS and compared to N = 57 patients with inflammatory cardiomyopathy (DCMi) and N = 56 patients with giant cell myocarditis (GCMC). We identified the top 50 genes with the highest discriminatory power between DCMi/GCMC and CS via analysis of the area under the receiver operator curve (AUC). Top genes were used to develop GEPs based on a decision-tree approach and a linear model with varying numbers of predictors. Results We aim to find the smallest possible gene sets which allow for a highly accurate diagnosis of CS. Individual marker genes such as CXCL9 (AUC = 0.85), CCL17 (AUC = 0.83) and GSN (AUC = 0.82) were highly accurate to discriminate CS from DCMi controls. Other genes such as CYBB (AUC = 0.82), TLR2 (AUC = 0.81) and TIMP1 (AUC = 0.81) were suitable to discriminate between CS and GCMC. Different GEPs using 3 to 10 genes were developed on the basis of decision tree models and linear regression and achieve AUCs of up to 0.94 (Sensitivity: 90.9%, Specificity: 84.8%) [GW1]in a test data set. Decision trees require 7 genes to resolve all patients in test data both in relation to DCMi and GCMC. Conclusion The GEPs presented here enables the diagnosis of CS on a molecular level with a high diagnostic accuracy and can thus circumvent the sampling error in EMB analyses between inflammatory cardiomyopathy and sarcoidosis. This is highly relevant as it significantly improves diagnostics and the resulting therapeutic decisions.
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Wiegleb et al. (2025) conducted a case-control in Cardiac sarcoidosis (n=133). Gene expression profiling vs. Patients with inflammatory cardiomyopathy or giant cell myocarditis was evaluated on Discriminatory power (AUC) for diagnosing cardiac sarcoidosis (AUC 0.94). A gene expression profile using 3 to 10 genes accurately discriminated cardiac sarcoidosis from inflammatory cardiomyopathy and giant cell myocarditis with an AUC of up to 0.94.
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