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Article

Gene Ontology Analysis Highlights Biological Processes Influencing Non-Response to Anti-TNF Therapy in Rheumatoid Arthritis

by
Gregor Jezernik
1,
Mario Gorenjak
1 and
Uroš Potočnik
1,2,3,*
1
Faculty of Medicine, University of Maribor, Taborska ulica 8, 2000 Maribor, Slovenia
2
Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia
3
Department for Science and Research, University Medical Centre Maribor, Ljubljanska ulica 5, 2000 Maribor, Slovenia
*
Author to whom correspondence should be addressed.
Biomedicines 2022, 10(8), 1808; https://0-doi-org.brum.beds.ac.uk/10.3390/biomedicines10081808
Submission received: 1 July 2022 / Revised: 22 July 2022 / Accepted: 26 July 2022 / Published: 27 July 2022
(This article belongs to the Section Molecular Genetics and Genetic Diseases)
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Abstract

:
Anti-TNF therapy has significantly improved disease control in rheumatoid arthritis, but a fraction of rheumatoid arthritis patients do not respond to anti-TNF therapy or lose response over time. Moreover, the mechanisms underlying non-response to anti-TNF therapy remain largely unknown. To date, many single biomarkers of response to anti-TNF therapy have been published but they have not yet been analyzed as a system of interacting nodes. The aim of our study is to systematically elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis using the gene ontologies of previously published predictive biomarkers. Gene networks were constructed based on published biomarkers and then enriched gene ontology terms were elucidated in subgroups using gene ontology software tools. Our results highlight the novel role of proteasome-mediated protein catabolic processes (p = 2.91 × 10−15) and plasma lipoproteins (p = 4.55 × 10−11) in anti-TNF therapy response. The results of our gene ontology analysis help elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis and encourage further study of the highlighted processes.

1. Introduction

Rheumatoid arthritis (RA) is a common complex autoimmune disease characterized by chronic and progressive joint inflammation. Currently, first-line therapeutic approaches in rheumatoid arthritis focus on minimizing disease activity using, primarily, corticosteroids with or without disease-modifying antirheumatic drugs (DMARDs). The development of biological drugs such as monoclonal antibodies against key inflammatory cytokines has significantly improved symptom control [1] in severe rheumatoid arthritis and chronic patients failing first-line therapy. Etanercept [2] and infliximab, inhibitors of proinflammatory cytokine tumor necrosis factor alpha (anti-TNF) [3], were the first anti-TNF biological drugs indicated for rheumatoid arthritis, and later more biological drugs against TNFα were developed, including adalimumab [4], certulizumab pegol [5] and golimumab [6]. In recent years, the emergence of biosimilars of anti-TNF biological drugs has also somewhat reduced the initially high cost of anti-TNF therapy while maintaining efficacy levels comparable to those of the originator biological drugs [7].
However, despite the immense therapeutic power of anti-TNF therapy, 10–30% of patients do not respond to anti-TNF biological drugs upon therapy initiation (i.e., primary non-response) and 23–46% of responders lose response to anti-TNF therapy over time (i.e., secondary non-response) [8]. Non-response to anti-TNF therapy usually represents loss of disease control in patients with severe rheumatoid arthritis, as well as unnecessary exposure to potentially severe adverse effects of anti-TNF drugs and inefficient use of expansive biological therapeutics. Patients who fail to respond to anti-TNF drugs may switch to a different biological drug, such as anakinra, rituximab or sarilumab [9]. Even so, other biological drugs face similar challenges to anti-TNF drugs in terms of non-response [1,10,11]. Therefore, disease-modifying antirheumatic drugs (DMARDs) remain the long-term therapy of choice alongside corticosteroids for disease flares, both of which are known to have significant long-term adverse effects [12].
Predicting non-response to anti-TNF therapy based on the patient’s clinical and biological data would allow targeted therapy with higher efficacy and fewer adverse effects, as well as cost-efficient use of therapeutics. Physicians could determine if and when to switch anti-TNF therapeutics or whether it would be more effective to switch to biological drugs with different therapeutic targets. To date, response to anti-TNF therapy has been intensively studied and several DNA, RNA and protein response biomarkers with low to moderate predictive accuracy have been identified. However, despite the many published anti-TNF response biomarkers, the biological processes underlying non-response to anti-TNF therapy in RA remain largely unknown. Improving the understanding of the mechanisms underlying non-response to anti-TNF drugs on a molecular level would allow the development of novel therapeutic strategies to prevent non-response or the discovery of novel pharmaceutical targets for drug development. To this end, we reviewed already published genomic, transcriptomic and proteomic markers of response and non-response to anti-TNF biological drugs in rheumatoid arthritis and performed a gene ontology analysis to help elucidate biological processes linked to response and non-response to anti-TNF therapy.

2. Materials and Methods

2.1. Literature Search

To perform a comprehensive review of the literature on anti-TNF therapy response biomarkers, we searched the PubMed database using a combination of terms defining disease, drug, response, biomarker type and exclusion criteria. To prevent Mesh terms missing synonyms, we employed a combination of both Mesh terms and equivalent non-Mesh keywords. The final search query was defined as a combination of the following term groups:
  • Disease terms: “Arthritis, Rheumatoid” (Mesh) OR (“rheumatoid” AND “arthritis”);
  • Drug terms: “infliximab” OR “adalimumab” OR “etanercept” OR “golimumab” OR “certolizumab pegol” OR “Tumor Necrosis Factor-alpha/antagonists and inhibitors” (Mesh) OR “TNFA inhibitor” OR “TNF inhibitor” OR “anti-TNF therapy” OR “anti-TNFA therapy” OR “Treatment Outcome” (Mesh);
  • Response terms: “predictor” OR “responder” OR “nonresponder” OR “non-responder” OR “therapy outcome” OR “therapy response” OR “response biomarker” OR “outcome biomarker” OR “response predictor” OR “outcome predictor”;
  • Biomarker terms: genetics OR genomics OR transcriptomics OR proteomics OR metabolomics OR “DNA methylation”;
  • Exclusion terms: NOT (“tocilizumab” OR dose OR dosing).
Studies were included based on the following inclusion criteria:
  • Published between the years 2002 and 2022;
  • The study used well-defined response criteria (e.g., those included in the Disease Activity Score in 28 Joints, also known as ΔDAS28);
  • Biomarkers were analyzed prior to therapy initiation and, if applicable, after therapy (e.g., gene expression and serum protein levels);
  • Quantitative biomarkers were reported with a clearly defined direction of association (e.g., gene expression defined as up-regulated or down-regulated, not merely “associated”).
In this gene ontology study, we did not make any additional distinctions based on the anti-TNF drugs used or on whether patients were anti-TNF naive or not.

2.2. Subset Definition

Subsets for gene ontology (GO) analysis were defined based on biomarker type. Preliminary subset analysis revealed no significant differences between the gene ontology terms of biomarkers measured in synovial fluid and those measured in sera. For this reason, we did not make any distinctions based on biomarker measurement locations.
Potential therapeutic targets can be either stimulated or blocked. In general, processes that are up-regulated in responders or down-regulated in non-responders could be stimulated to achieve better response or even restore response. Similarly, processes that are down-regulated in responders or up-regulated in non-responders can be blocked. Following this reasoning, we created two additional separate groups for RNA and protein biomarkers. The first group (_UP_R_DO_N) contains biomarkers reported either as up-regulated in responders or down-regulated in non-responders; the second group (_DO_R_UP_N) contain biomarkers down-regulated in responders or up-regulated in non-responders.
To enhance biological process discovery with gene ontology analysis, gene networks were constructed. In this study, “gene network” refers to a set of interacting biomarkers produced from a list of biomarkers of interest (i.e., previously published anti-TNF response biomarkers). Biomarkers interacting with at least two biomarkers of interest were obtained from BIOGRID [13,14] using the biogridR package [15] for R (version 4.1.1, R Core Team, Vienna, Austria) [16].
Subset names are defined in Table 1.

2.3. Gene Ontology Analysis

Gene ontology analysis was performed using the software package CytoScape (v3.8.2., CytoScape Team) [17] with the integrated application ClueGO (v2.5.8, Laboratory of Integrative Cancer Immunology (Team 15), Paris, France) [18]. ClueGO analysis was performed using the following parameters and selected options:
  • Ontology/pathways selected:
    ○
    Biological Process (13 May 2021);
    ○
    Cellular Component (13 May 2021);
    ○
    Molecular Function (13 May 2021);
  • Evidence selected: only All_Experimental.
Moreover, comparative gene ontology analysis was employed to estimate GO term specificity between different subsets (e.g., _UP_RE_DO_NR vs. _UP_NR_DO_RE).
Statistical significance was defined as a p-value lower than 5 × 10−2 after Bonferonni step-down correction (the default selection in ClueGO v2.5.8).
Gene ontology analysis results were visualized using default CytoScape settings and freely available style options.

3. Results

3.1. Literature Search

Using the defined search query (see Materials and Methods—Literature Search), we obtained 185 results in the PubMed database. Based on the inclusion criteria, 125 studies were included in the gene ontology analysis. Among the 125 studies, 61 studies reported DNA biomarkers, 15 studies reported RNA biomarkers, 39 studies reported protein biomarkers, while 10 studies reported response biomarkers that could not be categorized as DNA, RNA or protein biomarkers as they were cell counts, nuclear magnetic resonance (NMR) spectra or metabolomic markers. In addition, five studies reported biomarkers at several molecular levels.
Use of technologies to comprehensively study the genome, transcriptome and proteome remains uncommon, but it has become more common in recent years. Among the 61 DNA biomarker studies, 8 employed next-generation sequencing (NGS) technology and 3 out of 15 RNA biomarker studies employed RNA sequencing (RNAseq). Similarly, 7 out of 39 protein biomarker studies used liquid chromatography with mass spectrometry (LC–MS/MS) for biomarker discovery.

3.2. Biomarker Collection

The biomarkers extracted from the studies gathered from the literature are shown in Table 2 (DNA biomarkers), Table 3 (RNA biomarkers) and Table 4 (protein biomarkers). For gene ontology (GO) analysis, only biomarkers indexed in GO datasets can be processed. To remove potential duplicate biomarkers and obsolete gene names, we used the g:Convert Gene ID Converter tool [19] to update the biomarker names to the most recent ones. Finally, biomarkers that could not be reliably assigned to a gene with GO definitions were excluded (e.g., intergenic genetic variants).
Studies reporting biomarkers that could not be categorized as DNA, RNA or protein biomarkers are displayed below in Table 5.

3.3. Gene Ontology Analysis Results

The DNA subset has enriched GO terms related to the definition of non-response, while the DNA gene network only expanded upon the terms NF-κB signaling and TNF-α processes.
Gene ontology analysis of DNA biomarkers revealed terms already known to be associated with anti-TNF therapy non-response in rheumatoid arthritis, namely, terms connected to the definition of non-response or anti-TNF therapy, such as inflammation, tumor necrosis factor alpha, NF-κB signaling, IL-1, IL-2, IL-6 and IL-27. A subset of the terms related to NF-κB signaling is displayed in Figure 1.
RNA biomarker subsets revealed several enriched GO terms that were not previously directly associated with anti-TNF therapy response in rheumatoid arthritis. Such enriched terms in RNA subsets include prostaglandin synthesis, response to lipopolysaccharide (LPS), interferon gamma and macrophage chemotaxis. Gene networks based on RNA biomarkers and their BIOGRID interactors revealed novel significantly enriched GO terms related to the proteasome; the term proteasome-mediated ubiquitin-dependent protein catabolic process (p = 2.91 × 10−15) is a significant novel hyponym. The gene ontology terms related to the proteasome and others identified in the BIOGRID RNA biomarker network are illustrated in Figure 2.
Similarly, protein subsets also revealed several enriched GO terms that were not previously directly associated with anti-TNF therapy response in rheumatoid arthritis. Gene ontology analysis revealed several enriched blood lipoprotein (HDL, VLDL and cholesterol) terms, illustrated in Figure 3.
The full results of the gene ontology subset analysis are available in Table S1.
BIOGRID data gene networks based on DNA and protein biomarkers did not reveal any novel enriched GO terms but expanded the associated hyponyms of leading GO terms.
Comparative GO analysis of DNA, RNA and protein biomarkers showed no novel differences between analyzed subsets based on biomarker type. NF-κB signaling terms are specific to DNA, MHC protein complex terms are specific for RNA, while lipoprotein terms are specific to protein biomarkers.

4. Discussion

The results of our study help to elucidate the mechanisms underlying response and non-response to anti-TNF therapy in rheumatoid arthritis. Biological markers linked to mechanisms associated with response and/or non-response to anti-TNF therapy have potential clinical applications as response predictors before or during anti-TNF therapy or even as potential novel therapeutic targets.
First, there was significant enrichment of protein metabolism terms in gene network subsets based on RNA biomarkers (specifically, RNA_UP_R_DO_N_BIO). The leading GO term was the hypernym positive regulation of protein metabolic process (p = 3.63 × 10−37). Specifically, several enriched hyponyms under this leading term are associated with the proteasome, such as proteasome-mediated ubiquitin-dependent protein catabolic process (p = 2.91 × 10−15). To our best knowledge, proteasome processes have not yet been implicated in anti-TNF therapy response in rheumatoid arthritis. In RA, the autophagy and proteasome protein degradation pathways are key processes for synovial fibroblast survival [141]. In response to TNFα, the autophagy pathway, but not the proteasome, is consistently stimulated, yet there is an increased dependence on the proteasome for cell viability [141]. If autophagy is blocked in the presence of TNFα, an increase in proteasome activity occurs in some RA synovial fibroblasts but decreases in healthy synovial fibroblasts [141]. Targeting the proteasome complex thus represents a therapeutic opportunity to decrease synovial fibroblast survival, pannus growth and inflammation in RA [142,143,144]. Bortezomib, a proteasome inhibitor indicated for hematological cancers, was shown to decrease bone loss in an animal model of RA [145] and inflammatory cytokine production in an ex vivo study of activated T cells of healthy controls and RA patients [146]. In a recent study, delanzomib, a novel proteasome inhibitor, was successfully used together with adalimumab in a rat model of rheumatoid arthritis [147]. Moreover, two case reports showed remission of rheumatoid arthritis complicated with multiple myeloma [148] or TEMPI syndrome [149] after administration of bortezomib.
Second, several terms related to lipoproteins were found to be significantly enriched in protein biomarker subsets. In the subset containing all protein biomarkers, the leading lipoprotein terms were lipoprotein particle receptor binding (p = 8.81 × 10−12) and plasma lipoprotein particle (p = 4.55 × 10−11). Interestingly, the hyponyms very-low-density lipoprotein particle (p = 1.83 × 10−10) and spherical high-density lipoprotein particle (p = 5.22 × 10−8) suggest the role of very-low-density lipoproteins (VLDLs) and high-density lipoproteins (HDLs) in response. Comparative GO analysis showed VLDL to be specific for protein biomarkers down-regulated in responders (or up-regulated in non-responders), and HDL was shown to be up-regulated in responders (or down-regulated in non-responders). These findings confirm clinical observations of increased HDL [150,151] as well as triglyceride and total cholesterol levels [152] after anti-TNF therapy initiation. Moreover, low baseline VLDL has been linked with a better response to anti-TNF therapy [153], which coincides with our finding of VLDLs being down-regulated in responders. Although blood lipid profiles may only reflect systemic inflammation and thus also disease severity, their role in anti-TNF therapy response is not yet understood. Blood lipid profiles are potential accessible and affordable anti-TNF response biomarkers that could be integrated into clinical routine.
Third, our results show a significant enrichment of GO terms related to leukocyte chemotaxis in RNA subsets, with the leading term being negative regulation of leukocyte chemotaxis (p = 3.26 × 10−4). Hyponym investigation in a comparative analysis of RNA biomarkers up-regulated and down-regulated in responders showed the term negative regulation of macrophage chemotaxis (p = 3.00 × 10−3) to be up-regulated in responders (or down-regulated in non-responders). This finding suggests that good responders have lower macrophage infiltration than non-responders. Macrophage chemotaxis thus represents both an opportunity for response biomarker discovery as well as a therapeutic target. An example of a leukocyte chemotaxis reducing drug is montelukast, a cysteinyl leukotriene receptor antagonist used to treat asthma and allergic rhinitis. Although montelukast is mainly used to block leukotriene-dependent human airway smooth muscle contractions, it also blocks up-regulation of vascular permeability and leukocyte chemotaxis. A study has shown that montelukast decreases inflammatory cytokine production in RA and thus represents a novel therapeutic strategy [154].
Finally, our review of anti-TNF therapy response biomarkers has revealed that many response biomarkers have been reported at several levels of biological data (DNA, RNA, proteins, etc.), but only 12 biomarkers were reported by more than one study. Biomarkers reported by more than one study include the DNA biomarkers CCL4 and IL1B; the RNA biomarkers FCGR2A, FCGR3A, IL10, IL6, PTPRC and TNF; and the protein biomarkers IL6, ITIH1, S100A8 and S100A9. Recently, a Japanese cohort has demonstrated the use of interferon signatures and their dynamics for use in long-term anti-TNF drug response prediction, which validates previously reported biomarkers related to interferon proteins [155]. Interestingly, results from another recent study showed that interferon-related chemokine levels (e.g., CXCL10) correlated with disease activity but not with short-term response to anti-TNF therapy (certolizumab pegol) in a Swedish cohort [156]. These studies highlight the difficulties of biomarker replication, especially with cohorts from different ethnic backgrounds and with different study designs.
Our GO analysis of anti-TNF therapy response biomarkers highlighted several biological processes as significantly enriched in response and/or non-response to anti-TNF therapy. Our results encourage targeted analysis of these biological processes for novel biomarker discovery but also the development of novel therapeutic strategies in the treatment of RA. The highlighted therapeutic targets could be useful either as alternatives for anti-TNF therapy non-responders, as co-therapies with anti-TNF treatment or as novel maintenance strategies. Moreover, our study’s review of anti-TNF response biomarkers revealed that although response biomarkers have been extensively studied, there is a generally low rate of overlap and biomarker validation between studies.

5. Conclusions

Biological processes related to the proteasome and blood lipids could affect response to anti-TNF therapy according to gene ontology of existing anti-TNF therapy response biomarkers in RA. Our study encourages further investigation of proteasome and blood lipid processes in RA anti-TNF response.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/biomedicines10081808/s1, Table S1: Full gene ontology analysis results.

Author Contributions

Conceptualization, U.P.; data curation, G.J. and M.G.; formal Analysis, G.J.; investigation, G.J., M.G. and U.P.; methodology, G.J., M.G. and U.P.; project administration, U.P.; software, M.G.; supervision, U.P.; validation, G.J.; visualization, G.J.; writing—original draft preparation, G.J.; writing—review and editing, M.G. and U.P. All authors have read and agreed to the published version of the manuscript.

Funding

The authors acknowledge the financial support of the Slovenian Research Agency research core funding No. P3-0067 and P3-0427 and research grant No. J3-9258.

Institutional Review Board Statement

No humans or animals were involved in this study.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article or the Supplementary Materials.

Acknowledgments

The authors would like to thank Boris Gole for providing support with gene ontology software protocols.

Conflicts of Interest

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Figure 1. Extended network of gene ontology term nodes related to NF-κB signaling, as identified in the DNA biomarker subset.
Figure 1. Extended network of gene ontology term nodes related to NF-κB signaling, as identified in the DNA biomarker subset.
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Figure 2. Network of gene ontology term nodes related to the proteasome, as identified in RNA biomarker subsets with BIOGRID data.
Figure 2. Network of gene ontology term nodes related to the proteasome, as identified in RNA biomarker subsets with BIOGRID data.
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Figure 3. Extended network of gene ontology term nodes related to lipids, as identified in the protein biomarker subset.
Figure 3. Extended network of gene ontology term nodes related to lipids, as identified in the protein biomarker subset.
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Table
Table 1. Biomarker subsets. Subset names are constructed using biomarker type (DNA, RNA or PRO for protein) followed by association type (_UP_R_DO_N or _DO_R_UP_N) and indicate whether or not a given subset is a gene network derived from BIOGRID data (_BIO).
Table 1. Biomarker subsets. Subset names are constructed using biomarker type (DNA, RNA or PRO for protein) followed by association type (_UP_R_DO_N or _DO_R_UP_N) and indicate whether or not a given subset is a gene network derived from BIOGRID data (_BIO).
Subset NameBiomarkers Included in Subset
DNAAll DNA biomarkers
RNAAll RNA biomarkers
RNA_UP_R_DO_NRNA biomarkers up-regulated in responders or down-regulated in non-responders
RNA_DO_R_UP_NRNA biomarkers up-regulated in non-responders or down-regulated in responders
PROAll protein biomarkers
PRO_UP_R_DO_NProtein biomarkers up-regulated in responders or down-regulated in non-responders
PRO_DO_R_UP_NProtein biomarkers up-regulated in non-responders or down-regulated in responders
DNA_BIOBIOGRID network based on DNA biomarkers
RNA_BIOBIOGRID network based on RNA biomarkers
RNA_UP_R_DO_N_BIOBIOGRID network based on RNA biomarkers up-regulated in responders or down-regulated in non-responders
RNA_DO_R_UP_N_BIOBIOGRID network based on RNA biomarkers up-regulated in non-responders or down-regulated in responders
PRO_BIOBIOGRID network based on protein biomarkers
PRO_UP_R_DO_N_BIOBIOGRID network based on protein biomarkers up-regulated in responders or down-regulated in non-responders
PRO_DO_R_UP_N_BIOBIOGRID network based on protein biomarkers up-regulated in non-responders or down-regulated in responders
Table
Table 2. DNA biomarkers of response to anti-TNF therapy in RA.
Table 2. DNA biomarkers of response to anti-TNF therapy in RA.
StudyAssociated Gene
Criswell, L.A. et al., 2004 [20]TNF
LTA
HLA-DRB1
Lee, Y.H. et al., 2006 [21]TNF
Ongaro, A. et al., 2008 [22]TNFSFR1B
Jančić, I. et al., 2013 [23]IL6
Lee, Y.H. et al., 2014 [24]IL6
Lee, Y.H. et al., 2016 [25]PTPRC
FCGR2A
Schotte, H. et al., 2015 [26]IL6
Pappas, D.A. et al., 2013 [27]CCL21
CD28
Morales-Lara, M.J. et al., 2012 [28]TRAILR1
TNFR1A
Pers, Y.M. et al., 2014 [29]TNFSFR1B
Iwaszko, M. et al., 2016 [30]KLRD1
KLRC1
O’Rielly, D.D. et al., 2009 [31]TNF
Ferreiro-Iglesias, A. et al., 2016 [32]PTPRC
IL10
CHUK
Julià, A. et al., 2016 [33]MED15
Kang, C.P. et al., 2005 [34]TNF
Seitz, M. et al., 2007 [35]TNF
Iannaccone, C.K. et al., 2011 [36]PTPRC
Dávila-Fajardo, C.L. et al., 2014 [37]IL6
Montes, A. et al., 2014 [38]FCGR2A
Bowes, J.D. et al., 2009 [39]MAP3K1
MAP3K14
Miceli-Richard, C. et al., 2008 [40]HLA-DRB1
Tsukahara, S. et al., 2008 [41]FCGR3A
Cañete, J.D. et al., 2009 [42]FCGR2A
FCGR3A
Potter, C. et al., 2010 [43]MYD88
CHUK
Coulthard, L.R. et al., 2011 [44]MAP2K6
MSK1
MSK2
MAPK14
Acosta-Colman, I. et al., 2013 [45]PDE3A
Dávila-Fajardo, C.L. et al., 2015 [46]FCGR2A
Sun, Y. et al., 2017 [47]FCGR2A
FCGR3A
Morales-Lara, M.J. et al., 2010 [48]FCGR3A
Lee, Y.H. et al., 2010 [49]TNF
Liu, C. et al., 2008 [50]LMO4
GBP6
CERS6
ARAP2
QKI
PON1
IFNK
MOB3B
C9orf72
MAFB
CST5
Tan, R.J. et al., 2010 [51]AFF3
CD226
Plant, D. et al., 2011 [52]EYA4
PDZD2
McGeough, C.M. et al., 2012 [53]HLA-C
Krintel, S.B. et al., 2012 [54]CD19
STXBP6
Plant, D. et al., 2012 [55]PTPRC
Cui, J. et al., 2013 [56]CD84
Cui, J. et al., 2010 [57]PTPRC
Sode, J. et al., 2014 [58]NLRP3
Umiċeviċ Mirkov, M. et al., 2013 [59]CNTN5
NUBPL
Canhão, H. et al., 2015 [60]TRAF1
Avila-Pedretti, G. et al., 2015 [61]FCGR2A
Schotte, H. et al., 2015 [62]IL10
Sode, J. et al., 2015 [63]TLR1
TLR5
NLRP3
Honne, K. et al., 2016 [64]MAP3K7
BACH2
WDR27
GFRA1
Jančić, I. et al., 2015 [65]TNF
IL6
Folkersen, L. et al., 2016 [66]MAFB
Gębura, K. et al., 2017 [67]TLR9
NFKB1
Nishimoto, T. et al., 2014 [68]TRAF1
Sarsour, K. et al., 2013 [69]FCGR3A
Vasilopoulos, Y. et al., 2011 [70]TNFRSF1B
TNF
TNFRSF1A
Rooryck, C. et al., 2008 [71]TNFRSF1B
Cuchacovich, M. et al., 2006 [72]TNF
Tutuncu, Z. et al., 2005 [73]FCGR3A
Sode, J. et al., 2018 [74]IRAK3
CHUK
MYD88
NFKBIB
NLRP3
Iwaszko, M. et al., 2018 [75]NKG2D
Skapenko, A. et al., 2019 [76]HLA-DRB1
IL4R
FCGR2B
Spiliopoulou, A. et al., 2019 [77]CD40
ENTPD1
Wielińska, J. et al., 2020 [78]RANK
RANKL
Gibson, D.S. et al., 2021 [79]CD226
HLA-DRB1
Iwaszko, M. et al., 2021 [80]IL33
Table
Table 3. RNA biomarkers of response to anti-TNF therapy in RA.
Table 3. RNA biomarkers of response to anti-TNF therapy in RA.
StudyGeneAssociation Direction
Stuhlmüller, B. et al., 2010 [81]CD11CUp-regulated in responders
Sekiguchi, N. et al., 2008 [82]HLA-DQA1Down-regulated in non-responders
IGHMDown-regulated in non-responders
AP1S2Up-regulated in non-responders
Wright, H.L. et al., 2015 [83]IFNGUp-regulated in responders
Wright, H.L. et al., 2016 [84]CMPK2Up-regulated in responders
IFIT1BUp-regulated in responders
RNASE3Up-regulated in responders
Tsuzaka, K. et al., 2010 [85]ADAMTS5Down-regulated in responders
Oliveira, R.D. et al., 2012 [86]CCL4Up-regulated in responders
CD83Up-regulated in responders
BCL2A1Up-regulated in responders
Lequerré, T. et al., 2006 [87]CYP3A4Down-regulated in responders
AKAP9Down-regulated in responders
LAMR1Down-regulated in responders
FBXO5Down-regulated in responders
RASGRP3Down-regulated in responders
PFKFB4Down-regulated in responders
HLA-DPB1Down-regulated in responders
PSMB9Down-regulated in responders
EPS15Down-regulated in responders
MTCBP-1Down-regulated in responders
MRPL22Up-regulated in responders
MCPUp-regulated in responders
KNG1Up-regulated in responders
AADATUp-regulated in responders
Koczan, D. et al., 2008 [88]TNFAIP3Down-regulated in responders
NFKBIADown-regulated in responders
RUNX1Up-regulated in responders
ZFP36L2Down-regulated in responders
IL1BDown-regulated in responders
IL1BDown-regulated in responders
CCL4Down-regulated in responders
CCL3Down-regulated in responders
CXCL2Down-regulated in responders
ADAM12Down-regulated in responders
SCN2BUp-regulated in responders
PDE4BDown-regulated in responders
RAPGEF1Down-regulated in responders
MYO10Down-regulated in responders
PTPRDUp-regulated in responders
PDE4BDown-regulated in responders
LGALS13Up-regulated in responders
CHST3Down-regulated in responders
LUC7L3Up-regulated in responders
PPP1R15ADown-regulated in responders
ADMDown-regulated in responders
CHRNDDown-regulated in responders
PIGODown-regulated in responders
RNF19BDown-regulated in responders
FSD1Down-regulated in responders
van Baarsen, L.G. et al., 2010 [89]OAS1Up-regulated in non-responders
LGALS3BPUp-regulated in non-responders
MX2Up-regulated in non-responders
OAS2Up-regulated in non-responders
SERPING1Up-regulated in non-responders
Toonen, E.J. et al., 2012 [90]HIRIP3Down-regulated in responders
TPM1Up-regulated in responders
NPRL2Down-regulated in responders
CLIC3Down-regulated in responders
PTGS2Up-regulated in responders
G0S2Up-regulated in responders
PIGVDown-regulated in responders
HIF1AUp-regulated in responders
ZBTB6Down-regulated in responders
RANBP17Up-regulated in responders
PCGF5Up-regulated in responders
SESTD1Up-regulated in responders
GPD2Up-regulated in responders
HERPUD2Up-regulated in responders
DND1Down-regulated in responders
SH2D2ADown-regulated in responders
EIF4E2Down-regulated in responders
GTPBP2Up-regulated in responders
TPRA1Down-regulated in responders
GRAMD1BUp-regulated in responders
PPP1R15AUp-regulated in responders
PMAIP1Up-regulated in responders
RAPGEF1Up-regulated in responders
CSRNP1Up-regulated in responders
TMOD2Up-regulated in responders
EGR2Up-regulated in responders
DUSP1Up-regulated in responders
MTURNUp-regulated in responders
EGR3Up-regulated in responders
SQSTM1Up-regulated in responders
RAMP3Down-regulated in responders
PDE3AUp-regulated in responders
VEPH1Up-regulated in responders
GBP7Up-regulated in responders
PSTPIP2Up-regulated in responders
FAM221ADown-regulated in responders
ZNF2Down-regulated in responders
MED12LUp-regulated in responders
OSMDown-regulated in responders
TMEM186Down-regulated in responders
PKHD1L1Up-regulated in responders
OR6C74Down-regulated in responders
GPN2Down-regulated in responders
DDX39BDown-regulated in responders
UNQ5840Down-regulated in responders
C15ORF40Down-regulated in responders
CMIPUp-regulated in responders
KCNJ13Down-regulated in responders
SLC7A6OSDown-regulated in responders
ELOVL4Down-regulated in responders
UQCRFS1Down-regulated in responders
NBNUp-regulated in responders
BEX2Down-regulated in responders
YPEL5Up-regulated in responders
FAIMDown-regulated in responders
STAT1Up-regulated in responders
CXCL8Down-regulated in responders
PIH1D2Down-regulated in responders
EDC3Down-regulated in responders
TNFAIP3Up-regulated in responders
FSCN1Down-regulated in responders
MGLLUp-regulated in responders
GCNT2Up-regulated in responders
EGFUp-regulated in responders
COLGALT2Down-regulated in responders
HOPXDown-regulated in responders
NT5C3AUp-regulated in responders
RNF11Up-regulated in responders
SLKUp-regulated in responders
TAP2Up-regulated in responders
GBP1Up-regulated in responders
GBP5Up-regulated in responders
XRN1Up-regulated in responders
PTGDSDown-regulated in responders
TAS2R50Up-regulated in responders
HSPC159Up-regulated in responders
ARL6Down-regulated in responders
PDE4BUp-regulated in responders
OR2L3Down-regulated in responders
NR4A2Up-regulated in responders
PALD1Down-regulated in responders
OGG1Down-regulated in responders
ADGRE5Up-regulated in responders
FRMD3Up-regulated in responders
LRRIQ3Down-regulated in responders
RAD23ADown-regulated in responders
APPUp-regulated in responders
PXT1Down-regulated in responders
MPP7Up-regulated in responders
NEXNUp-regulated in responders
GMPRUp-regulated in responders
UVRAGUp-regulated in responders
ADAMTS1Down-regulated in responders
ATP6V0A2Down-regulated in responders
CATSPER3Down-regulated in responders
C5Up-regulated in responders
MAP4K2Up-regulated in responders
GCH1Up-regulated in responders
ATP6V0E2Down-regulated in responders
FBXO10Down-regulated in responders
ZNF425Down-regulated in responders
HSCBDown-regulated in responders
GTF2F2Up-regulated in responders
PGK1Down-regulated in responders
STAT2Up-regulated in responders
PCSK6Up-regulated in responders
TMEM268Up-regulated in responders
PPCDCUp-regulated in responders
GSX1Down-regulated in responders
Cui, J. et al., 2013 [56]CD84Up-regulated in responders
Thomson, T.M. et al., 2015 [91]FOXA2Up-regulated in non-responders
ERBB2Up-regulated in non-responders
IL11Up-regulated in non-responders
MAP2K3Up-regulated in non-responders
NF1Down-regulated in non-responders
S100A9Down-regulated in non-responders
S100A8Down-regulated in non-responders
MST1RDown-regulated in non-responders
NOS2Down-regulated in non-responders
NR2F6Down-regulated in non-responders
PPARGUp-regulated in non-responders
MEIS1Up-regulated in non-responders
DPPA4Up-regulated in non-responders
MBD1Down-regulated in non-responders
CDK2Up-regulated in non-responders
Folkersen, L. et al., 2016 [66]SORBS3Down-regulated in responders
AKAP9Down-regulated in responders
Póliska, S. et al., 2019 [92]TMEM176AUp-regulated in responders
TMEM176BUp-regulated in responders
PLSCR1Up-regulated in responders
IFI44Up-regulated in responders
Oliver, J. et al., 2021 [93]LIN7ADown-regulated in responders
CREB5Down-regulated in responders
ENTPD1Down-regulated in responders
ITGB7Up-regulated in responders
HLA-DMAUp-regulated in responders
IL6RDown-regulated in responders
SLC8A1Down-regulated in responders
IL1BDown-regulated in responders
HLA-DOBUp-regulated in responders
MGAMDown-regulated in responders
TRAF5Up-regulated in responders
AESUp-regulated in responders
E2F5Up-regulated in responders
ZFYVE16Down-regulated in responders
HLA-DOAUp-regulated in responders
TLR8Down-regulated in responders
STAP1Up-regulated in responders
TGM3Down-regulated in responders
PI3Down-regulated in responders
ARG1Down-regulated in responders
MMP9Down-regulated in responders
MGAMDown-regulated in responders
CA4Down-regulated in responders
KAZNDown-regulated in responders
PGLYRP1Down-regulated in responders
FCARDown-regulated in responders
PROK2Down-regulated in responders
MANSC1Down-regulated in responders
TRPM6Down-regulated in responders
SLC26A8Down-regulated in responders
SULT1B1Down-regulated in responders
IL1R1Down-regulated in responders
MAKDown-regulated in responders
ADMDown-regulated in responders
TMEM88Down-regulated in responders
CYP4F3Down-regulated in responders
REPS2Down-regulated in responders
ANXA3Down-regulated in responders
ABCA1Down-regulated in responders
F5Down-regulated in responders
ANPEPDown-regulated in responders
EPSTI1Up-regulated in responders
SERPING1Up-regulated in responders
MS4A1Up-regulated in responders
C1QAUp-regulated in responders
BATF2Up-regulated in responders
FCRLAUp-regulated in responders
IGLL5Up-regulated in responders
MZB1Up-regulated in responders
IGJUp-regulated in responders
Table
Table 4. Protein biomarkers of response to anti-TNF therapy in RA.
Table 4. Protein biomarkers of response to anti-TNF therapy in RA.
StudyProtein MarkerAssociation Direction
Straub, R.H. et al., 2008 [94]CortisolDown-regulated in responders
Ammitzbøll, C.G. et al., 2013 [95]FCN1Down-regulated in responders
Matsuyama, Y. et al., 2012 [96]IL33Down-regulated in responders
IL33Down-regulated in responders
Morozzi, G. et al., 2007 [97]COMPDown-regulated in responders
Kohno, M. et al., 2008 [98]IL17 to TNF ratioDown-regulated in responders
Ortea, I. et al., 2012 [99]GCUp-regulated in non-responders
CPUp-regulated in non-responders
APOBUp-regulated in non-responders
ITIH2Up-regulated in non-responders
THBS1Up-regulated in non-responders
C4BUp-regulated in non-responders
ITIH1Up-regulated in non-responders
GSNUp-regulated in non-responders
APOA2Up-regulated in non-responders
FN1Up-regulated in non-responders
CFHR4Up-regulated in non-responders
APOMUp-regulated in non-responders
APMAPUp-regulated in non-responders
MASP2Up-regulated in non-responders
Shi, R. et al., 2018 [100]BIRC5Down-regulated in responders
CRPUp-regulated in responders
IL6Up-regulated in responders
Cañete, J.D. et al., 2011 [101]TNFRSF1BUp-regulated in responders
Kayakabe, K. et al., 2012 [102]IL1BDown-regulated in non-responders
Sakthiswary, R. et al., 2014 [103]IgA rheumatoid factorUp-regulated in non-responders
Andersen, M. et al., 2017 [104]MC1RDown-regulated in responders
MC3RDown-regulated in responders
MC5RDown-regulated in responders
MC1RDown-regulated in responders
MC3RDown-regulated in responders
MC5RDown-regulated in responders
Choi, I.Y. et al., 2015 [105]S100A8/S100A9 complexUp-regulated in responders
La, D.T. et al., 2008 [106]TNFSF13BDown-regulated in responders
Odai, T. et al., 2009 [107]CX3CL1Down-regulated in responders
Kuuliala, A. et al., 2006 [108]IL2Down-regulated in responders
González-Alvaro, I. et al., 2007 [109]TNFSF11Down-regulated in responders
Fabre, S. et al., 2008 [110]CCL2Down-regulated in non-responders
EGFDown-regulated in non-responders
Wijbrandts, C.A. et al., 2008 [111]TNFUp-regulated in responders
Hueber, W. et al., 2009 [112]CSF2Up-regulated in responders
IL6Up-regulated in responders
FMODUp-regulated in responders
CLUUp-regulated in responders
APOEUp-regulated in responders
HIST1H2BMUp-regulated in responders
HSP58Up-regulated in responders
IL1AUp-regulated in responders
COMPUp-regulated in responders
CASTUp-regulated in responders
BGNUp-regulated in responders
OGNUp-regulated in responders
TMPRSS11AUp-regulated in responders
IL1BUp-regulated in responders
CCL11Up-regulated in responders
CXCL10Up-regulated in responders
FGF1Up-regulated in responders
CCL2Up-regulated in responders
IL12P70Up-regulated in responders
IL12P40Up-regulated in responders
IL15Up-regulated in responders
Lindberg, J. et al., 2010 [113]LGALS1Up-regulated in responders
SCNN1BDown-regulated in responders
GMNNDown-regulated in responders
PALLDDown-regulated in responders
TPPP3Up-regulated in responders
LGALS1Down-regulated in responders
NONODown-regulated in responders
ATP5HDown-regulated in responders
PGLSDown-regulated in responders
UBA52Down-regulated in responders
RPS12Down-regulated in responders
RPLP0P6Down-regulated in responders
ANAPC11Down-regulated in responders
PGA3Up-regulated in responders
WDR83OSDown-regulated in responders
MYO15ADown-regulated in responders
MRPL33Down-regulated in responders
FOXC2Down-regulated in responders
H3F3ADown-regulated in responders
FAPDown-regulated in responders
TRAF3IP2Down-regulated in responders
AGPAT4Down-regulated in responders
RPL36AUp-regulated in responders
RIN2Down-regulated in responders
RPL13ADown-regulated in responders
NEK5Down-regulated in responders
RPL7Down-regulated in responders
Trocmé, C. et al., 2009 [114]APOA1Up-regulated in responders
PF4Up-regulated in non-responders
Chen, D.Y. et al., 2011 [115]IL17Up-regulated in non-responders
Meusch, U. et al., 2013 [116]IL1R2Up-regulated in responders
Obry, A. et al., 2014 [117]S100A8Up-regulated in responders
S100A9Up-regulated in responders
Blaschke, S. et al., 2015 [118]Haptoglobin-α1Up-regulated in responders
Haptoglobin-α2Up-regulated in responders
HPUp-regulated in responders
GCUp-regulated in responders
APOC3Up-regulated in non-responders
Zhang, F. et al., 2015 [119]IL34Down-regulated in responders
Meusch, U. et al., 2015 [120]TNFRSF1AUp-regulated in responders
IL1RAUp-regulated in responders
Obry, A. et al., 2015 [121]STUB1Up-regulated in responders
PROS1Up-regulated in responders
C1RUp-regulated in responders
CPN2Up-regulated in responders
CPUp-regulated in responders
ITIH1Up-regulated in responders
ITIH3Up-regulated in responders
DYNC1I1Up-regulated in responders
S100A9Up-regulated in responders
AZGP1Up-regulated in responders
TFDown-regulated in responders
PLGUp-regulated in responders
Nair, S.C. et al., 2016 [122]S100A8–S100A9 complexUp-regulated in responders
Ortea, I. et al., 2016 [123]ADAMTSL2Up-regulated in non-responders
A2MUp-regulated in non-responders
APOA1Down-regulated in non-responders
APOA2Up-regulated in non-responders
APOBUp-regulated in non-responders
APOC1Up-regulated in non-responders
APOC3Up-regulated in non-responders
APOMUp-regulated in non-responders
F9Up-regulated in non-responders
CFL1Up-regulated in non-responders
C3Up-regulated in non-responders
C4BUp-regulated in non-responders
C8AUp-regulated in non-responders
CFHR4Down-regulated in non-responders
LGALS3BPUp-regulated in non-responders
HPXUp-regulated in non-responders
ITIH1Up-regulated in non-responders
ITIH2Up-regulated in non-responders
TPM3Up-regulated in non-responders
FN1Up-regulated in non-responders
MASP2Up-regulated in non-responders
PF4Up-regulated in non-responders
SH3BGRL3Up-regulated in non-responders
ABI3BPDown-regulated in non-responders
TCFL5Down-regulated in non-responders
TPM4Up-regulated in non-responders
TAGLN2Up-regulated in non-responders
Wampler Muskardin, T. et al., 2016 [124]IFN-β–α activity ratioUp-regulated in non-responders
Folkersen, L. et al., 2016 [66]ICAM1Down-regulated in responders
CXCL13Up-regulated in responders
Nishimoto, T. et al., 2014 [68]TRAF1Up-regulated in non-responders
Koga, T. et al., 2011 [125]PLAUUp-regulated in responders
Down-regulated in non-responders
Gerli, R. et al., 2008 [126]CD30Up-regulated in responders
Braun-Moscovici, Y. et al., 2006 [127]IL6Down-regulated in responders
Nguyen, M.V.C. et al., 2018 [128]S100A12Down-regulated in responders
TTRUp-regulated in responders
PF4Up-regulated in responders
Otsubo, H. et al., 2018 [129]FOLR2Up-regulated in non-responders
Frostegård, J. et al., 2021 [130]PCSK9Down-regulated in responders
Table
Table 5. Markers which count not be categorized as DNA, RNA or protein biomarkers.
Table 5. Markers which count not be categorized as DNA, RNA or protein biomarkers.
StudyMarkerAssociation Direction
Citro, A. et al., 2015 [131]CD8+ T cellsUp-regulated in responders
Hull, D.N. et al., 2016 [132]Th17 cellsUp-regulated in non-responders
Plant, D. et al., 2016 [133]cg04857395Down-regulated in responders
cg26401028Down-regulated in responders
cg16426293Down-regulated in responders
cg03277049Down-regulated in responders
cg12226028Down-regulated in responders
Talotta, R. et al., 2015 [134]Th17 cellsUp-regulated in non-responders
Th1 cellsUp-regulated in non-responders
Cuppen, B.V. et al., 2016 [135]sn1-LPC (18:3-ω3/ω6)Down-regulated in responders
sn1-LPC (15:0)Up-regulated in responders
ethanolamineDown-regulated in responders
lysineUp-regulated in responders
Chara, L. et al., 2012 [136]CD14+highCD16−Up-regulated in non-responders
CD14+highCD16+Up-regulated in non-responders
CD14+lowCD16+Up-regulated in non-responders
Alzabin, S. et al., 2012 [137]Th17 cellsUp-regulated in non-responders
Klaasen, R. et. al., 2009 [138]lymphocyte aggregatesUp-regulated in responders
Talotta, R. et al., 2016 [139]MacrophagesUp-regulated in responders
Priori, R. et al., 2015 [140]NMR spectraResponder/non-responder specific
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Jezernik, G.; Gorenjak, M.; Potočnik, U. Gene Ontology Analysis Highlights Biological Processes Influencing Non-Response to Anti-TNF Therapy in Rheumatoid Arthritis. Biomedicines 2022, 10, 1808. https://0-doi-org.brum.beds.ac.uk/10.3390/biomedicines10081808

AMA Style

Jezernik G, Gorenjak M, Potočnik U. Gene Ontology Analysis Highlights Biological Processes Influencing Non-Response to Anti-TNF Therapy in Rheumatoid Arthritis. Biomedicines. 2022; 10(8):1808. https://0-doi-org.brum.beds.ac.uk/10.3390/biomedicines10081808

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Jezernik, Gregor, Mario Gorenjak, and Uroš Potočnik. 2022. "Gene Ontology Analysis Highlights Biological Processes Influencing Non-Response to Anti-TNF Therapy in Rheumatoid Arthritis" Biomedicines 10, no. 8: 1808. https://0-doi-org.brum.beds.ac.uk/10.3390/biomedicines10081808

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