Bioinformatics Identification of Ferroptosis-related Genes and Therapeutic Drugs in Rheumatoid Arthritis

doi:10.21203/rs.3.rs-2380843/v1

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Research Article

Bioinformatics Identification of Ferroptosis-related Genes and Therapeutic Drugs in Rheumatoid Arthritis

https://doi.org/10.21203/rs.3.rs-2380843/v1

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Objectives

Rheumatoid arthritis (RA) is a chronic immune disease characterized by synovial inflammation and bone destruction, with a largely unclear etiology. Evidences have indicated that ferroptosis may play an increasingly important role in the onset and development RA. However, ferroptosis-related genes are still largely unexplored in RA. Therefore, this work focused on identifying and validating the potential ferroptosis-related genes involved in RA through bioinformatics analysis.

Methods

We screened differentially expressed ferroptosis-related genes (DEFGs) between RA patients and healthy individuals based on GSE55235 dataset. Subsequently, correlation analysis, protein-protein interaction (PPI) network analysis, GO, and KEGG enrichment analyses were performed using these DEFGs. Finally, our results were validated by GSE12021 dataset.

Results

We discovered 34 potential DEFGs in RA based on bioinformatics analysis. According to functional enrichment analysis, these genes were mainly enriched in HIF-1 signaling pathway, FoxO signaling pathway, and Ferroptosis pathway. Four genes (GABARPL1, DUSP1, JUN, and MAPK8) were validated to be downregulated by GSE12021 dataset and may be possible diagnostic biomarkers and therapeutic targets for RA via the regulation of ferroptosis.

Conclusions

Our results may help shed more light on the pathogenesis of RA. Ferroptosis-related genes in RA could be valuable diagnostic biomarkers and they will be exploited clinically as therapeutic targets in the future.

Rheumatoid arthritis

Ferroptosis

Bioinformatics analysis

Therapeutic targets

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease characterized by inflammation and affects about 1% of the world population [1]. As a result, RA can lead to joint deformity and disability, severely reducing the quality of life for RA patients. Although the pathogenesis of RA is not been entirely clarified, genetic and environmental factors have been shown to be involved [2]. Several studies also demonstrated that the pathogenesis of RA was associated with certain biological functions such as osteoclast differentiation, inflammatory cell infiltration, angiogenesis, and ferroptosis [3, 4]. Among these biological functions, ferroptosis is an interesting topic because of its potential links to cell death, suggesting a possible crucial role in the occurrence of RA.

Ferroptosis is a newly identified cell death form, which is characterized by iron-dependent accumulation and lipid peroxidation [5, 6]. Evidences have reported that ferroptosis can trigger undesirable inflammatory responses [7, 8], and its inhibitor has an anti-inflammatory effect on different inflammatory diseases [9, 10]. Various evidences have indicated that several pathways or compounds are related to ferroptosis in the pathogenesis of RA [4, 11, 12]. Ferroptosis has been shown to be associated with RA, osteoarthritis, gout arthritis, and ankylosing spondylitis [13]. However, the exact mechanism between ferroptosis and RA remains largely unclear, and it is necessary and urgent to elucidate the role in RA pathogenesis. The identification of potential ferroptosis-related genes in RA may help to derive potential biomarkers for treatment. Woetzel et al. [14] leveraged the GSE55235 dataset to obtain DEGs between RA patients and healthy individuals, their study sought to distinguish RA patients and healthy individuals by applying rule-based classifiers. However, they neglect the effect of ferroptosis on RA. Therefore, it is essential to analyze the relationship between ferroptosis and synovial inflammation, which may provide potential therapeutic targets for RA.

In this paper, we attempted to investigate the pathogenesis of RA from the ferroptosis perspective. To functionally identify and characterize the DEFGs in RA, we screened DEFGs based on the GSE55235 dataset and further validated our results in the GSE12021 dataset. Finally, this work may provide potential biomarkers for RA therapy and benefit the understanding of RA pathogenesis.

Ferroptosis-associated genes and RA-related microarray dataset

The publicly available database FerrDb [15] (http://www.zhounan.org/ferrdb/) contains 259 ferroptosis-related genes. These downloaded data were used for subsequent analyses. Furthermore, we also collected gene expression profiles for 10 RA patients and 10 healthy individuals from the GSE55235 dataset to screen differentially expressed ferroptosis-related genes (DEFGs). The microarray data were quantified using the GPL96 platform from Affymetrix Human Genome U133A Array [14].

Screening of DEFGs

First, we used the ‘limma’ package to normalize the gene expression profile. Second, the probes were annotated using ‘annotation’ package. We applied principal component analysis (PCA) to evaluate the repeatability of the GSE55235 dataset. The bioconductor ‘limma’ package was subsequently utilized to screen DEFGs between RA patients and healthy individuals. |logFC|> 1 and adjusted p-value < 0.01 were set as the cutoff criteria of DEFGs. Lastly, we leveraged the ‘heatmap’ and ‘ggplot2’ packages to present the result of DEFGs.

Functional enrichment analysis of DEFGs

Gene ontology (GO) is the widely used tool for gene function annotations. The GO terms are classified into three types: molecular function (MF), biological process (BP), and cellular component (CC). Pathway enrichment analysis was conducted using KEGG pathway database. The ‘clusterprofile’ package [16] was utilized to perform the functional enrichment analyses of the DEFGs, with the cutoff criteria set at p < 0.01.

PPI network construction and correlation analysis of DEFGs

The STRING database was used to schematically represent functional relationship network of DEFGs, named PPI network. We considered SRING interactions that were of medium confidence (combined STRING score > 0.4), which were experimentally derived and curated interactions. The PPI networks of DEFGs were visualized using the STRING database. Correlation analysis was performed using Pearson correlation coefficient with the ‘corrplot’ package.

Validation of Gene Expression Associated with Ferroptosis

The GSE12021 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which included 9 RA patients and 12 healthy individuals. The differentially expressed genes were also identified using the “limma” package according to the adjusted p-value < 0.01 and |logFC| >1. Finally, we obtained the overlapping genes of 259 ferroptosis-associated genes, GSE55235 DEGs, and GSE12021 DEGs.

Statistical analysis

R software (version 3.6.2) was used for statistical analysis. We applied the ‘limma’ package to screen differentially expressed genes between RA patients and normal samples. The threshold was set at p < 0.01.

3.1 Identification of 34 DEFGs in RA

The result of PCA showed good biological replicate concordance of each biological sample in the GSE55235 dataset (Fig. 1A). We integrated 259 ferroptosis-related genes and DEGs between RA patients and healthy individuals from the GSE55235 database to screen DEFGs. We obtained a total of 34 DEFGs, of which 8 were upregulated and 26 were downregulated genes (Fig. 1B and C).

Furthermore, we generated a violin diagram to illustrate the expression patterns of the 34 DEFGs (Fig. 2). There are 8 upregulated genes (Fig. 2A) including SLC2A6, ALOX5, AURKA, CYBB, NCF2, CAPG, RRM2, BLOC1S5-TCNDC5 and 26 downregulated genes (Fig. 2B) included ANGPTL7, ATF3, BNIP3, CBS, DDIT4, DUSP1, GDF15, NNMT, PTGS2, SLC3A2, TF, VEGFA, CDO1, DUOX2, EGFR, GABARPL1, MAPK8, ZEB1, AKR1C2, CDKN1A, JUN, MT1G, PLIN2, SCD, ZFP36.

3.2 Construction of PPI network and correlation analysis of the 34 DEFGs

To elucidate the relationship of these 34 DEFGs, we performed PPI network analysis. The results demonstrated that 34 DEFGs were closely interconnected (Fig. 3). We find several hub genes (VEGFA, JUN, CDKN1A, DUSP1, MAPK8, and EGFR) that have high degree. In addition, to examine if there was a correlation between the expression levels of these DEFGs, we conducted correlation analysis. The results showed that the correlation relationship of most genes is positive, only a few have negative correlation relationship (Fig. 4).

3.3 Functional enrichment analyses of the 34 DEFGs

To elucidate the biological functions of the 34 DEFGs, GO and KEGG enrichment analyses were performed using the ‘clusterprofiler’ package to identify significantly enriched terms. The most significantly enriched GO terms of BP were cellular response to oxidative stress, response to extracellular stimulus, and reactive oxygen species metabolic process (Fig. 5A). In the CC category, they were involved in nicotinamide adenine dinucleotide phosphate (NADPH) oxidase complex, oxidoreductase complex, and nuclear envelope (Fig. 5B). In the MF category, they were involved in oxidoreductase activity, superoxide generating NADPH oxidase activity, and oxidoreductase activity acting on nicotinamide adenine dinucleotide phosphate (NADPH) (Fig. 5C). KEGG enrichment was mainly distributed in HIF-1 signaling pathway, FoxO signaling pathway, and Ferroptosis pathway (Fig. 5D).

3.4 Validation of Gene Expression Associated with Ferroptosis

To validate the reliability of 34 DEFGs expression levels, we used another dataset (GSE12021) [19] to verify expression levels. Compared with the results of the GSE55235 dataset, the expression levels of GABARPL1, DUSP1, JUN, and MAPK8 were decreased in the RA samples (-1.75-, -4.47-, -3.44-, and − 1.28-fold, respectively) compared to normal samples (Fig. 6). It indicated that the expression levels of four genes were consistent with the results of GSE55235.

3.5 Prediction of Potential Therapeutic Drugs

DGIbd [17] and CMAP database [18] were utilized to find potential candidate agents for GABARPL1, DUSP1, JUN and MAPK8. In the DGIbd or CMap database, Albuterol, Hydroxyurea, or Vasopressin was found to be the targeted medicine of DUSP1, Irisolidone, Holacanthone, Sergeolide or Irbesartan was found to be the targeted medicine of JUN, and Cardamomin, CC-401, Tanzisertib or BI-78D3 was found to be the targeted medicine of MAPK8 (Table 1).

Table 1

Prediction of potential therapeutic drugs.
Drug	Gene	Source	PubChem	Ref
Albuterol	DUSP1	DGIdb	182176	[19]
Hydroxyurea	DUSP1	DGIdb	3657	[20]
Vasopressin	DUSP1	DGIdb	123131941	[21]
Irisolidone	JUN	DGIdb	5281781	[22]
Holacanthone	JUN	DGIdb	158475	[23]
Sergeolide	JUN	DGIdb	134025	[23]
Irbesartan	JUN	CMAP	3749	[24]
Cardamomin	MAPK8	DGIdb	641785	[25]
CC-401	MAPK8	DGIdb/CMAP	10430360	[26]
Tanzisertib	MAPK8	DGIdb	11597537	[27]
BI-78D3	MAPK8	CMAP	2747117	[28]

Although great progress has been made in exploring new treatments for RA. To date, elucidating the pathogenesis of RA remains a major challenge, thus it is urgent to explore the molecular mechanisms of these diseases. Moreover, several studies have illustrated that ferroptosis may be involved in various tumor types [29, 30]. However, there have been few studies focused specifically on ferroptosis-associated genes in RA.

In our study, there were 34 potential ferroptosis-related genes were identified between RA patients and healthy individuals by using bioinformatic analyses. Furthermore, GO and KEGG enrichment analyses were conducted to investigate the biological functions of the 34 DEFGs. The oxidative stress, oxidoreductase complex, and NADPH oxidase were significantly enriched by these DEFGs, indicating that these DEFGs were associated with oxidation, which was consistent with previous reports [31].

Based on KEGG pathway enrichment, we found that these genes were significantly enriched in FoxO signaling pathway, HIF-1 signaling pathway, and Ferroptosis pathway. Lee et al. [32] identified FoxO3 as a biomarker of RA severity, and its haplotype was associated with erosion scores of RA. Kok et al. [33] reported that SIRT-1/FoxO3a signaling played a crucial role in the occurrence and development of RA. Besides, FoxO signaling was also vital in OA. Abnormal expression of FoxO had been reported to be closely associated with osteoarthritis [34]. HIF-1α is a very important transcription factor that regulates developmental and cellular responses to hypoxia. A deficiency of HIF-1α has been reported to exacerbate MMP13 expression levels to lead to degrading cartilage tissue [35]. Our results represented these RA-related pathways, which might enhance our understanding of RA pathogenesis.

Furthermore, we identified 4 DEFGs (GABARPL1, DUSP1, JUN, and MAPK8) and observed that the expression levels of these genes in the GSE55235 were consistent with the GSE12021 dataset. Several genes had been shown to be closely related to RA. For example, DUSP1 is a phosphatase with dual specificity for tyrosine and threonine, which involves cellular processes by regulating MAPK1/ERK2. Vattakuzhi et al. [36] found that DUSP1 can regulate MAPK signaling and its low expression may be related to osteolytic lesions in arthritis. JUN is the putative transforming gene of avian sarcoma virus 17. It regulated gene expression by interacting with specific target DNA sequences [37]. Huber et al. [38] revealed that Jun/Fos proto-oncogene was significantly decreased at the mRNA level in RA. MAPK8, also known as Jun nuclear kinase (JNK), was a member of the MAP kinase family, which was involved in various cellular processes (proliferation, differentiation, transcription regulation, and development) [39]. Ding et al. [40] reported that abnormal activation of MAPKs in synovial tissues of RA patients promoted pannus formation. Thus, MAPK is considered a promising potential target in the treatment of RA.

However, this study still has some limitations. First, this work utilized a small sample size, which might lead to bias. Second. More in-vivo and in-vitro studies were required to verify the reliability and significance of the results. Finally, it is necessary to understand the hub genes of RA for diagnosis and treatment.

In conclusion, we derived 34 potential ferroptosis-related genes between RA patients and healthy individuals using bioinformatics method. There were four genes (GABARPL1, DUSP1, JUN, and MAPK8) were verified to be differentially expressed and may serve as important diagnostic markers and new potential therapeutic targets for RA through the regulation of ferroptosis. Our study may be benefit to enhance the understanding of RA pathogenesis and potential for clinical use in the future.

Author contribution Xianbin Li designed the study. Xianbin Li and Andong He supervised the study. Xianbin Li, Andong He, Yue Liu, Yuye Huang and Xueli Zhang analyze the data. Xianbin Li and Yue Liu did the digital visualization. Xianbin Li and Yue Liu wrote and revised the manuscript. All authors read and approved the final manuscript. All the authors agreed to publish the article. Xianbin Li and Andong He contributed equally to this work and should be regarded as co-first authors.

Funding This work was supported by the National Natural Science Foundation of China (62202115), China Postdoctoral Science Foundation (2022M722746), and Zhejiang Province Natural Science Foundation (Q23H010006).

Availability of data and materials

The datasets generated and/or analyzed during the current study are openly and permanently available in the [GSE55235] repository, [https://www.ncbi.nlm.nih.gov/

geo/query/acc.cgi?acc=GSE55235], and [GSE12021] repository, [https://www.ncbi.

nlm.nih.gov/geo/query/acc.cgi?acc=GSE12021]

Ethics approval and consent to participate

Not applicable

Consent for publication

All authors have consented this study to publication

Competing interests

The authors declare no completing interests.

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No competing interests reported.

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Bioinformatics Identification of Ferroptosis-related Genes and Therapeutic Drugs in Rheumatoid Arthritis

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Abstract

Objectives

Methods

Results

Conclusions

Figures

Introduction

Materials And Methods

Ferroptosis-associated genes and RA-related microarray dataset

Screening of DEFGs

Functional enrichment analysis of DEFGs

PPI network construction and correlation analysis of DEFGs

Validation of Gene Expression Associated with Ferroptosis

Statistical analysis

Results

3.1 Identification of 34 DEFGs in RA

3.2 Construction of PPI network and correlation analysis of the 34 DEFGs

3.3 Functional enrichment analyses of the 34 DEFGs

3.4 Validation of Gene Expression Associated with Ferroptosis

3.5 Prediction of Potential Therapeutic Drugs

Discussion

Declarations

References

Additional Declarations

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