The role of genetic analysis for predicting outcome of rheumatoid arthritis

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

A 9 mRNAs-based diagnostic signature for rheumatoid arthritis by integrating bioinformatic analysis and machine-learning

Background

Rheumatoid arthritis (RA) is an autoimmune rheumatic disease that carries a substantial burden for both patients and society. Early diagnosis of RA is essential to prevent disease progression and select an optimal therapeutic strategy. However, RA diagnosis is challenging, partly due to a lack of reliable biomarkers. Here, we aimed to explore the diagnostic signature and establish a predictive model of RA.

Methods

The mRNA expression profiling data of GSE17755, containing blood samples of 112 RA patients and 53 healthy control patients, were obtained from the Gene Expression Omnibus (GEO) database, followed by differential expression, GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. A PPI network was constructed to select candidate hub genes, then logistic regression and random forest models were established based on the identified genes.

Results

Significantly, we identified 52 differentially expressed genes (DEGs), including 16 upregulated genes and 36 downregulated genes in RA samples compared with control samples. GO and KEGG analysis showed that several immune-related cellular processes were particularly enriched. We identified nine hub genes in the PPI network, including CFL1, COTL1, ACTG1, PFN1, LCP1, LCK, HLA-E, FYN, and HLA-DRA. The logistic regression and random forest models based on the nine identified genes reliably distinguished the RA samples from the healthy samples with substantially high AUC.

Conclusion

The diagnostic logistic regression and random forest models based on nine hub genes reliably predicted the occurrence of RA. Our findings could provide new insights into RA diagnostics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-020-02180-w.

Common trace metals in rheumatoid arthritis: A systematic review and meta-analysis

INTRODUCTION

Environmental risk factors regrading rheumatoid arthritis (RA) have not been explored extensively. Selenium (Se), zinc (Zn) and copper (Cu) nutrients were reported to associate with RA, but the results were inconsistent. Therefore, we conduct present study to meta-analyze the relationship between serum Se, Zn and Cu and RA and review the potential mechanisms.

METHODS

PubMed, Web of Science and Cochrane Library were comprehensively searched till October 1, 2018 for pertinent studies. Standard mean differences (SMDs) and 95% confident intervals (CIs) were calculated according to random effects model.

RESULTS

Finally 41 literatures were included. Meta-analysis of 16 studies involving 806 RA patients and 959 health controls showed that serum Se (SMD = -1.04, 95% CI = -1.58 to -0.50) was decreased in RA patients, and 23 literatures with 1398 patients and 1299 controls reported serum Zn (SMD = -1.20, 95% CI = -1.74 to -0.67) was decreased. But serum Cu (SMD = 1.26, 95% CI = 0.63 to -1.89) was increased with 26 studies including 1723 patients and 1451 controls. Meta-regression reported that steroid use was positively related to serum level of Se in RA (β = 0.041, 95% CI = 0.002 to 0.079). Differences in serum Se, Zn and Cu between rheumatoid arthritis patients and controls were all related with the geographical distribution.

CONCLUSIONS

Patients with RA have significant decreased serum Se and Zn and increased serum Cu than health controls, suggesting potential roles of Se, Zn and Cu in the pathogenesis of RA. Patients and rheumatologist should give enough attention to the monitor of these elements during follow up.