Five genes as diagnostic biomarkers of dermatomyositis and their correlation with immune cell infiltration

Clinical and immunological characteristics and prognosis of patients with autoantibody negative dermatomyositis: a case control study

OBJECTIVES

Myositis-specific antibodies (MSAs) and myositis-associated antibodies (MAAs) are associated with distinctive dermatomyositis (DM) clinical phenotypes. The aim of this study is to explicate the clinical and immunological features of MSAs-negative DM patients.

METHODS

A total of 515 individuals diagnosed with DM was screened from 2013 to 2022 and 220 DM patients were enrolled in this retrospective cohort. Clinical and laboratory data of these patients were analyzed.

RESULTS

MSAs-negative DM patients were categorized into two groups: MAAs-negative (MSAs (-)/MAAs (-)) group and MAAs-positive (MSAs (-)/MAAs (+)) group. The percentage of Raynaud's phenomenon (P=0.026) was higher in the MSAs (-)/MAAs (+) DM patients than the MSAs-positive DM patients and MSAs (-)/MAAs (-) DM patients. The proportion of rapidly progressive interstitial lung disease (RP-ILD) in the MSAs-negative DM patients was lower than that in the MSAs-positive group. The MSAs (-)/MAAs (+) group had a higher proportion of organizing pneumonia and usual interstitial pneumonia (P=0.011), and elevated eosinophils in their bronchoalveolar lavage fluid (P=0.008). Counts of lymphocytes (P=0.001) and CD16+CD56+ natural killer (NK) cells (P=0.012) were higher in the MSAs-negative group. Additionally, the percentage of CD4+TNFα+ (P=0.040), CD4+IFNγ+ (P=0.037), and CD4+IL-2+ (P=0.018) cells among total CD4+ T cells were higher in the MSA-negative DM patients compared with the MSAs-positive DM patients. Besides, MSAs-negative patients demonstrated a more favorable prognosis than MSAs-positive patients. Multivariable regression analysis identified advanced onset age, higher level of carcinoembryonic antigen (CEA), and RP-ILD as risk factors for mortality in DM patients.

CONCLUSIONS

Compared with MSAs-positive group, MSAs-negative DM patients suffered less from organ involvement compared with MSAs-positive group and tend to have better prognosis. Key Points MSAs-negative DM patients exhibited distinct characteristics in comparison with MSAs-positive DM patients:   • The MSAs (-)/MAAs (+) DM patients demonstrated a higher prevalence of organizing pneumonia (OP) and usual interstitial pneumonia (UIP), and elevated eosinophil counts in bronchoalveolar lavage fluid.   • CEA levels were lower in MSAs-negative patients compared with MSAs-positive patients.   • Elevated counts of lymphocytes and CD16+CD56+ NK cells were identified in the MSAs-negative patients. Additionally, proportions of CD4+TNFα+, CD4+IFNγ+, and CD4+IL-2+ cells among total CD4+ T cells were higher in the MSAs-negative DM patients compared with DM MSAs-positive DM patients.   • MSAs-negative DM patients had a more favorable prognosis than MSAs-positive DM patients. A multivariable regression analysis revealed the advanced onset age, high CEA levels, and RP-ILD were risk factors for mortality in DM patients.

Novel endotypes of antisynthetase syndrome identified independent of anti-aminoacyl transfer RNA synthetase antibody specificity that improve prognostic stratification

OBJECTIVES

To systemically analyse the heterogeneity in the clinical manifestations and prognoses of patients with antisynthetase syndrome (ASS) and evaluate the transcriptional signatures related to different clinical phenotypes.

METHODS

A total of 701 patients with ASS were retrospectively enrolled. The clinical presentation and prognosis were assessed in association with four anti-aminoacyl transfer RNA synthetase (ARS) antibodies: anti-Jo1, anti-PL7, anti-PL12 and anti-EJ. Unsupervised machine learning was performed for patient clustering independent of anti-ARS antibodies. Transcriptome sequencing was conducted in clustered ASS patients and healthy controls.

RESULTS

Patients with four different anti-ARS antibody subtypes demonstrated no significant differences in the incidence of rapidly progressive interstitial lung disease (RP-ILD) or prognoses. Unsupervised machine learning, independent of anti-ARS specificity, identified three endotypes with distinct clinical features and outcomes. Endotype 1 (RP-ILD cluster, 23.7%) was characterised by a high incidence of RP-ILD and a high mortality rate. Endotype 2 (dermatomyositis (DM)-like cluster, 14.5%) corresponded to patients with DM-like skin and muscle symptoms with an intermediate prognosis. Endotype 3 (arthritis cluster, 61.8%) was characterised by arthritis and mechanic's hands, with a good prognosis. Transcriptome sequencing revealed that the different endotypes had distinct gene signatures and biological processes.

CONCLUSIONS

Anti-ARS antibodies were not significant in stratifying ASS patients into subgroups with greater homogeneity in RP-ILD and prognoses. Novel ASS endotypes were identified independent of anti-ARS specificity and differed in clinical outcomes and transcriptional signatures, providing new insights into the pathogenesis of ASS.

Predicting the Progress of Tuberculosis by Inflammatory Response-Related Genes Based on Multiple Machine Learning Comprehensive Analysis

Background

Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, affects approximately one-quarter of the global population and is considered one of the most lethal infectious diseases worldwide. The prevention of latent tuberculosis infection (LTBI) from progressing into active tuberculosis (ATB) is crucial for controlling and eradicating TB. Unfortunately, currently available biomarkers have limited effectiveness in identifying subpopulations that are at risk of developing ATB. Hence, it is imperative to develop advanced molecular tools for TB risk stratification.

Methods

The TB datasets were downloaded from the GEO database. Three machine learning models, namely LASSO, RF, and SVM-RFE, were used to identify the key characteristic genes related to inflammation during the progression of LTBI to ATB. The expression and diagnostic accuracy of these characteristic genes were subsequently verified. These genes were then used to develop diagnostic nomograms. In addition, single-cell expression clustering analysis, immune cell expression clustering analysis, GSVA analysis, immune cell correlation, and immune checkpoint correlation of characteristic genes were conducted. Furthermore, the upstream shared miRNA was predicted, and a miRNA-genes network was constructed. Candidate drugs were also analyzed and predicted.

Results

In comparison to LTBI, a total of 96 upregulated and 26 downregulated genes related to the inflammatory response were identified in ATB. These characteristic genes have demonstrated excellent diagnostic performance and significant correlation with many immune cells and immune sites. The results of the miRNA-genes network analysis suggested a potential role of hsa-miR-3163 in the molecular mechanism of LTBI progressing into ATB. Moreover, retinoic acid may offer a potential avenue for the prevention of LTBI progression to ATB and for the treatment of ATB.

Conclusion

Our research has identified key inflammatory response-related genes that are characteristic of LTBI progression to ATB and hsa-miR-3163 as a significant node in the molecular mechanism of this progression. Our analyses have demonstrated the excellent diagnostic performance of these characteristic genes and their significant correlation with many immune cells and immune checkpoints. The CD274 immune checkpoint presents a promising target for the prevention and treatment of ATB. Furthermore, our findings suggest that retinoic acid may have a role in preventing LTBI from progressing to ATB and in treating ATB. This study provides a new perspective for differential diagnosis of LTBI and ATB and may uncover potential inflammatory immune mechanisms, biomarkers, therapeutic targets, and effective drugs in the progression of LTBI into ATB.