Background: Proteins play a central role in psoriasis as they are involved in the structural phenotypic changes and inflammation that characterize the disease. This systematic review aimed to assess which proteins have been consistently reported as upregulated or downregulated in the skin and blood from patients with psoriasis. Methods: We included proteomic studies reporting differentially expressed proteins (DEPs) in at least one of four predefined comparisons using a standardized procedure to extract and align data. Network analysis of functional protein associations was made with StringApp in Cytoscape. A protocol for this review was registered in the PROSPERO database (ref:CRD42022363226). Results: We identified and assessed 772 studies published between December 2, 1996, and April 28, 2023, among which 30 studies met the inclusion and data availability criteria for analysis that together reported a sum of 5,314 DEPs. The majority of consistently reported upregulated and downregulated proteins were found in lesional versus non-lesional skin (n = 313), followed by lesional versus healthy skin (n = 185), blood from patients with psoriasis versus blood from healthy individuals (n = 140), and non-lesional versus healthy skin (n = 1). Network analysis of upregulated proteins revealed different functional clusters with interleukin (IL)-6, IL-8, IL-17A, C-C motif chemokine (CCL) 20, signal transducer and activator of transcription (STAT) 3, and interferon (IFN)-γ along with less well-studied proteins playing central roles. Some of the reported changes are associated with anti-inflammatory effects. Additionally, the proteomic dysregulation also included antimicrobial peptides, alarmins, angiogenic factors, and proteins related to protein synthesis. Conclusion: Our findings generally support current understandings of the pathological mechanisms in psoriasis. Importantly, some consistent findings have not been discussed before and deserve attention in future research.

Psoriasis is a chronic systemic inflammatory disease that involves a complex interplay between innate and adaptive immunity. The disease can erupt in genetically predisposed individuals exposed to various environmental triggers and affects a large proportion of the worldwide population [1]. The knowledge of implicated molecular mechanisms of psoriasis is still incomplete [2].

Proteomics is a term for large-scale measurements of proteins. Proteomic analyses can be targeted measurements of predefined panels of proteins or untargeted measurements of all proteins, the proteome, present in the investigated samples. Targeted approaches are often efficient in identifying low-abundant proteins, including many cytokines and other signaling molecules [3]. However, targeted approaches are limited to analyzing the selected protein panels. Conversely, untargeted methods allow agnostic analyses of proteomes in complex samples [4]. Given that proteins are central biomolecules carrying out structural, enzymatic, and signaling functions, proteomic studies thus have great potential to expand our understanding of disease pathology. This also holds true in psoriasis, where proteins central to the pathogenesis include cytokines, proteases, alarmins, antimicrobial peptides, growth factors, and autoantigens [2]. Differences in the proteome of blood-derived samples between individuals with and without psoriasis can yield information on the systemic components implicated in psoriasis and its comorbidities, while proteomic differences in skin samples between these individuals can provide insights into the local mechanisms underlying psoriasis. Two recent literature reviews of proteomic studies in psoriasis summarize main findings and methods applied in earlier studies, concluding that they relevantly contribute to knowledge of disease mechanisms, biomarker discovery, and prediction of response to treatment or side effects [5, 6]. However, proteomic research of biological samples is prone to biases due to differences in sample handling, as demonstrated by Geyer et al. [7]. Additionally, age, sex, body mass index, and lifestyle factors have been associated with proteomic differences [8‒11]. Thus, reproducibility and consistency in reports of proteins associated with disease are key in terms of generalizability of results.

In this systematic review, we extract results from proteomic studies identifying proteins that are differentially expressed in skin and blood from patients with psoriasis and healthy individuals. Furthermore, we identify DEPs overlapping across skin and blood-derived samples and analyze functional associations among the reported proteins.

The protocol for this review was registered in the PROSPERO database (ref:CRD42022363226). The study was conducted according to the preferred reporting system for systematic reviews (PRISMA) guideline [12].

Eligibility Criteria

We included peer-reviewed human studies published as original articles or letters with participants who had cutaneous psoriasis (i.e., not psoriatic arthritis without skin manifestations). To be eligible, studies had to identify at least 10 proteins and report data on significantly differentially expressed proteins in at least one of the four comparisons.

  • 1.

    Blood-derived samples from patients with psoriasis compared with those from healthy individuals.

  • 2.

    Lesional skin compared with non-lesional skin from patients with psoriasis.

  • 3.

    Lesional skin from patients with psoriasis compared with skin from healthy individuals.

  • 4.

    Non-lesional skin from patients with psoriasis compared with skin from healthy individuals.

Studies with less than three participants in each group were excluded. We applied no limits to publication years. Articles not written in English were excluded.

Literature Search

We systematically searched PubMed, EMBASE, and Web of Science from inception to May 16, 2023, for eligible studies with the search string: psoria* AND proteom*. In PubMed, we added MeSH terms for #Psoriasis and #Proteome.

Data Extraction

Search results were imported into the review tool Rayyan and deduplicated [13]. Two authors (B.K. and A.O.) independently screened titles and abstracts for eligibility. Both authors read potentially eligible papers in full text. Studies that met the inclusion criteria were selected for data extraction. Any discrepancies in this process were discussed to reach consensus. We sent data inquiries to corresponding authors of potentially eligible studies that did not specify the identified DEPs. B.K. and A.O. extracted meta-data and main findings from included articles. Lists of DEPs were extracted independently by two authors (B.K. and Y.M.Z.), and any discrepancies were discussed to reach consensus. To address the fact that both gene symbols and uniprot identifiers can vary over time, we decided to standardize and align our results to the Swiss-Prot format [14]. We used the ID mapping conversion tool (UniProtKB AC/ID to UniProtKB/Swiss-Prot) to identify proteins that were already listed in this format [15]. Next, we used the UniRef100 database cross-reference tool to search for reviewed entries with 100% amino acid sequence similarity when unreviewed entries were provided [16]. We converted gene symbols to Swiss-Prot identifiers, also by using ID mapping (gene name to UniProtKB/Swiss-Prot). We used manual curation when identifiers were provided in other formats. Non-canonical isoforms were converted to their canonical SwissProt identifier. The conversions are approximations due to historical changes in gene and protein nomenclature and identified isoforms. All information on the identifiers provided in the original studies was retained in separate columns and can be found in online supplementary Table 1 (for all online suppl. material, see https://doi.org/10.1159/000533981), where the identifiers that we could not match to a SwissProt ID are marked.

Data Items

The primary objective of this systemic review was to identify the DEPs reported in the included studies in each of the four comparisons of interest. Additionally, we extracted meta-data on applied methods, participant numbers, whether groups were matched or not, psoriasis subtype, treatment status, sample tissue, and the number of identified proteins. We also extracted data on the statistical criteria and fold-change cut-offs applied in each study to define when proteins were differentially expressed.

Analysis of Differentially Expressed Proteins

Analyses of the DEPs that were reported in multiple studies within a comparison without discrepancies regarding direction of change were performed with the StringApp in Cytoscape [17]. Proteins reported only once or proteins reported as both upregulated and downregulated were not included in the analyses but are listed in online supplementary Table 1.

Search Results

We conducted a search across PubMed, EMBASE, and Web of Science to identify proteomic studies that investigated differences in the protein expression in the skin and blood from patients with psoriasis compared with healthy controls. Overall, we identified 772 unique studies after removing duplicates from a total of 1,217 studies. We screened titles and abstracts of these 772 studies for eligibility and read the full text of 111 studies, which resulted in exclusion of 81 studies. A total of 30 studies were included in this systematic review (Fig. 1; online suppl. Table 2).

Fig. 1.

The inclusion chart is modified from the PRISMA template [12]. *Case report, cell culture study, not reporting on the number of participants, method article.

Fig. 1.

The inclusion chart is modified from the PRISMA template [12]. *Case report, cell culture study, not reporting on the number of participants, method article.

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Study Characteristics

The included studies had 3 to 45 participants in each group. Most studies reported a disease severity indicating moderate-to-severe disease. The patients were predominantly untreated or had a washout period before samples were obtained, but three studies included patients with active treatment, and 10 studies did not report treatment status. Most studies analyzed paired samples, matched samples, or adjusted for potential confounders; however, six studies did not report on this. Approximately half of the studies (17 of 30) used fold-change as a criterion in addition to the statistical test for differences between groups in their definition of DEPs. Between 13 and up to more than 7,000 proteins were identified, while the number of DEPs ranged from 1 to 748. Approximately two-thirds (19 of 30) of the studies did not adjust for multiple testing (online suppl. Table 1). The use of targeted and untargeted methods varied across tissues with an even distribution in studies measuring proteins in blood-derived samples and more untargeted mass spectrometry-based methods in studies of the skin (Table 1).

Table 1.

Distribution of methods in relation to the tissue comparisons

Method\tissueBlood, psoriasis versus healthy (n = 17)Lesional versus healthy skin (n = 8)Lesional versus non-lesional skin (n = 9)Non-lesional versus healthy skin (n = 4)
Targeted (e.g., antibody-based), n (%) 8 (47) 3 (37.5) 2 (22) 2 (50) 
Untargeted (mass spectrometry-based), n (%) 8 (47) 5 (62.5) 7 (78) 2 (50) 
A combination of targeted and untargeted methods, n (%) 1 (6) 
Method\tissueBlood, psoriasis versus healthy (n = 17)Lesional versus healthy skin (n = 8)Lesional versus non-lesional skin (n = 9)Non-lesional versus healthy skin (n = 4)
Targeted (e.g., antibody-based), n (%) 8 (47) 3 (37.5) 2 (22) 2 (50) 
Untargeted (mass spectrometry-based), n (%) 8 (47) 5 (62.5) 7 (78) 2 (50) 
A combination of targeted and untargeted methods, n (%) 1 (6) 

Differentially expressed proteins were reported in each of the four comparisons. We collected and curated lists of DEPs based on a standard operating procedure where we standardized all protein names to SwissProt IDs. The combined lists of DEPs from all studies are available in online supplementary Table 2. In general, proteins were mostly reported as being upregulated in psoriatic tissues. The number of DEPs in each step of the data filtration process and the number of studies for each tissue are shown in Table 2.

Table 2.

Number of differentially expressed proteins in each comparison through the data filtration process

Tissue (number of studies)Blood, psoriasis versus healthy (n = 17)Lesional versus healthy skin (n = 8)Lesional versus non-lesional skin (n = 9)Non-lesional versus healthy skin (n = 4)
DEPs reported 1,692 1,589 1,952 81 
DEPs assigned to canonical swiss-prot ID via UniRef100 1,583 1,365 1,852 59 
Unique/non-redundant DEPs 1,322 1,103 1,319 58 
Unique proteins reported as DEPs in >1 study 213 199 358 
Unique proteins consistently (non-divergently) reported as up-/downregulated in >1 study 128 ↑/12 ↓ 154 ↑/31 ↓ 256 ↑/57 ↓ 1 ↑/0 ↓ 
Tissue (number of studies)Blood, psoriasis versus healthy (n = 17)Lesional versus healthy skin (n = 8)Lesional versus non-lesional skin (n = 9)Non-lesional versus healthy skin (n = 4)
DEPs reported 1,692 1,589 1,952 81 
DEPs assigned to canonical swiss-prot ID via UniRef100 1,583 1,365 1,852 59 
Unique/non-redundant DEPs 1,322 1,103 1,319 58 
Unique proteins reported as DEPs in >1 study 213 199 358 
Unique proteins consistently (non-divergently) reported as up-/downregulated in >1 study 128 ↑/12 ↓ 154 ↑/31 ↓ 256 ↑/57 ↓ 1 ↑/0 ↓ 

Differentially Expressed Proteins in Skin

We queried the data for proteins that were consistently reported to be upregulated or downregulated in at least two separate studies comparing psoriatic lesional to healthy skin or lesional versus non-lesional skin. Among the eight studies comparing lesional and healthy skin, a total of 154 proteins consistently exhibited upregulation, and 31 proteins showed downregulation in more than one study. In the nine studies of lesional versus non-lesional skin, 256 and 57 proteins were upregulated and downregulated, respectively, in more than one study. Only one upregulated protein (S100-A7) and no downregulated protein were reported in more than one study of non-lesional versus healthy skin (Table 2).

Pathway analysis based on the STRING database revealed no discernible functional pattern to distinguish between the two comparisons of lesional skin to healthy skin and to non-lesional skin [18]. We therefore present the results from these two comparisons together.

Most of the upregulated proteins in lesional skin were involved in transcription of genes, translation of RNA, and protein turnover. In addition, 97 of the reported upregulated proteins in lesional skin were annotated in the immune system pathway in the Reactome database, more than half of which were associated with neutrophil degranulation, including several S100A-proteins, serin protease inhibitors (SERPINs), and neutrophil granule proteins [19, 20]. Twelve upregulated proteins were present downstream in the IL12-family signaling pathway, including plastin-2 involved in T-cell activation and proteasome activator complex subunit 2 implicated in immunoproteasome assembly [21]. Among other upregulated immune system proteins were STAT1 and STAT3 involved in multiple inflammatory pathways, including the IL-23/IL-17/IL-22 axis and signaling by interferons [22‒24]. Moreover, several platelet degranulation and antigen processing proteins were consistently upregulated.

We found 78 proteins that were reported to be downregulated in at least two studies in lesional skin compared with non-lesional or healthy skin. These downregulated proteins were mainly related to skin barrier and integrity and included some important barrier proteins such as filaggrin, filaggrin-2, and keratin 10. Cathepsins G and V, proteases involved in antigen processing and activation of certain cytokines, such as IL-36γ, were also downregulated [25]. A group of six collagens and other collagen-associated proteins were downregulated as well.

Differentially Expressed Proteins in Blood

We analyzed data from 17 studies comparing blood-derived samples from patients with psoriasis and blood samples from healthy individuals and found 140 DEPs reported in two or more studies with the same direction of change. Most (128) DEPs were upregulated and were primarily involved in immune responses. Cytokines IL-17A, IL-17C, IL-6, IL-8 and CCL20 from the IL-17 signaling pathway (KEGG) were upregulated along with the anti-microbial proteins S100-A8 and S100-A9, which are known to form the pro-inflammatory heterodimer calprotectin [26]. Moreover, IL-16, a chemoattractant and growth factor for CD4+ cells, and IL-20, an angiogenic factor and regulator of keratinocyte differentiation, consistently showed increased expression [27, 28]. The apoptosis related CASP-8, several tumor necrosis factor (TNF) receptor superfamily proteins were also upregulated [29]. Additionally, cathepsins A, B, D, L, and Z, which are proteases involved in apoptosis, antigen processing and activation of inflammatory pro-proteins, were upregulated [30]. CD74, which can stabilize cathepsin L and MHC II-complexes or act as an autoantigen itself, showed increased expression as well [31]. A large proportion of the upregulated immune system-related proteins were associated with innate immune processes, particularly neutrophils, including proteins involved in neutrophil chemotaxis, activation and degranulation such as LBP, CD14, selectins, elafin, and CCL24 [20]. Interestingly, a number of proteins with anti-inflammatory properties, including IL-1RN, IL-2Ra and NF-κ-BIε, were also reported to be upregulated in blood-derived samples [32‒34]. Additionally, LDL receptor and proprotein convertase subtilisin/kexin type 9 (PCSK9), important players in the cholesterol metabolism, consistently exhibited increased expression [35].

Among the 12 proteins reported to be downregulated in at least two studies was CD99, a membrane protein that presents on all human T cells and is known to be involved in T cell extravasation and T cell death [36]. In addition, mitochondrial antiviral signaling protein, mannan binding lectin serine peptidase 1, apolipoprotein F, and eight proteins mainly involved in cellular transport processes and basic metabolism were also downregulated.

Shared Proteins and Functional Overlaps among the Comparisons

Furthermore, we investigated DEPs shared among the comparisons. As expected, we identified a large overlap (91 upregulated and nine downregulated proteins) between the comparisons of lesional skin to healthy skin and to non-lesional skin. In contrast, only 14 proteins (3.3%) were upregulated in both blood and lesional skin from patients with psoriasis, and no downregulated proteins were identified consistently in the skin and blood (Fig. 2). The DEPs present in both skin and blood were implicated in transcription and translation processes or immunity-related proteins represented by IL-1RN, S100-A8, S100-A9, and elafin.

Fig. 2.

We investigated differentially expressed proteins shared among the comparisons. a Fourteen proteins were reported to be upregulated in the blood and lesional skin from patients with psoriasis. One hundred fifty-four and 54 proteins were upregulated in lesional skin compared with non-lesional skin and compared with healthy skin, respectively, and 91 proteins were upregulated in the lesional skin in both comparisons but not in blood. One hundred and fourteen proteins were exclusively upregulated in the blood from patients with psoriasis. b Forty-eight and 21 proteins were reported to be downregulated in lesional skin compared with non-lesional skin and with healthy skin, respectively, and nine proteins were downregulated in both skin comparisons. In blood from patients with psoriasis compared with blood from healthy individuals, 12 proteins were consistently reported as downregulated with no overlapping proteins between blood and skin comparisons. Non-lesional versus healthy skin is not represented in this figure. Abbreviations: DEP, differentially expressed protein; PSO, psoriasis; HC, healthy control individuals; L, lesional; NL, non-lesional.

Fig. 2.

We investigated differentially expressed proteins shared among the comparisons. a Fourteen proteins were reported to be upregulated in the blood and lesional skin from patients with psoriasis. One hundred fifty-four and 54 proteins were upregulated in lesional skin compared with non-lesional skin and compared with healthy skin, respectively, and 91 proteins were upregulated in the lesional skin in both comparisons but not in blood. One hundred and fourteen proteins were exclusively upregulated in the blood from patients with psoriasis. b Forty-eight and 21 proteins were reported to be downregulated in lesional skin compared with non-lesional skin and with healthy skin, respectively, and nine proteins were downregulated in both skin comparisons. In blood from patients with psoriasis compared with blood from healthy individuals, 12 proteins were consistently reported as downregulated with no overlapping proteins between blood and skin comparisons. Non-lesional versus healthy skin is not represented in this figure. Abbreviations: DEP, differentially expressed protein; PSO, psoriasis; HC, healthy control individuals; L, lesional; NL, non-lesional.

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In contrast to the limited overlap of specific proteins, we found several functional associations among upregulated proteins based on network analysis of functional interactions, including those only reported in either studies of blood-derived samples or studies of skin (Fig. 3). There were only a few associations among downregulated proteins. Figure 3 shows functional networks of the reported DEPs with different colors based on which tissue the proteins were reported in. The largest central cluster in the network of upregulated proteins exemplifies the functional overlap between dysregulated proteins in skin and blood. In this cluster, STAT3 and STAT1 connect several inflammatory proteins upregulated in the blood from patients with psoriasis to the upregulation of proteins involved in gene transcription and RNA binding as well as heat shock proteins associated with increased cellular stress in psoriatic lesions.

Fig. 3.

We performed network analysis of upregulated and downregulated proteins with the StringApp in Cytoscape. Proteins are annotated by their corresponding gene symbols. String score cutoff for confidence in functional protein association: 0.4 (medium). Wider edges equal higher confidence in functional associations. Colors: O = dysregulated in one skin comparison, O = dysregulated in both skin comparisons, O = dysregulated in blood, O = dysregulated in blood and at least one skin comparison. a Upregulated proteins. b Downregulated proteins.

Fig. 3.

We performed network analysis of upregulated and downregulated proteins with the StringApp in Cytoscape. Proteins are annotated by their corresponding gene symbols. String score cutoff for confidence in functional protein association: 0.4 (medium). Wider edges equal higher confidence in functional associations. Colors: O = dysregulated in one skin comparison, O = dysregulated in both skin comparisons, O = dysregulated in blood, O = dysregulated in blood and at least one skin comparison. a Upregulated proteins. b Downregulated proteins.

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We conducted a systematic review of proteomic changes in skin and blood from patients with psoriasis compared with healthy individuals and from lesional psoriatic skin compared with non-lesional skin. Based on data from 30 studies and focusing on proteins that are reported in at least two studies comparing either skin or blood-derived samples, we find 3,622 skin-associated and 1,692 blood-associated DEPs. In total, 312 proteins and 128 proteins are consistently reported to be upregulated in psoriatic skin and in the blood of patients with psoriasis, respectively, among which 14 proteins are upregulated in both skin and blood. Additionally, 78 and 12 proteins are consistently downregulated in psoriatic skin and in the blood of patients with psoriasis, respectively.

Results of the network analysis of the functional associations among these reported DEPs reflect the histological changes seen in lesional psoriatic skin, such as thickened epidermis with acanthosis and increased cell turnover, indicated by the upregulation of cytosolic proteins, of which many are involved in protein synthesis. Several proteins involved in immunity and, especially, neutrophil degranulation are upregulated in lesional skin as well. The combined protein signature reported in studies of blood-derived samples is dominated by the IL-17 signaling pathway, apoptosis, antigen processing, and neutrophil inflammation. There is a substantial functional overlap between the dysregulated proteins across tissues, supporting our current understanding of psoriasis as a disease characterized by functionally connected local and systemic inflammation. Several of the consistently upregulated proteins are known key players in the pathogenesis of psoriasis and include a wide palette of inflammatory cytokines, antimicrobial peptides, and growth factors. Some of the central and interconnected roles of key cytokines, such as IL-17A, IL-6, IL-8, CCL20, and IFN-γ, are displayed in the network analysis (Fig. 3) [2]. Furthermore, the network analysis shows the direct and indirect functional interactions with other proteins involved in the regulation of inflammation, such as IL1-family cytokines (IL-36A, IL-36γ, IL-36Ra, IL-1RN), IL-2Ra, IL-16, CXCL9, alarmins (keratins 6B, 16 and 17), SERPINs, STAT1 and STAT3, cathepsins, and the neutrophil adhesion molecules E-selectin and P-selectin. In contrast to the strong functional overlap, we find only 14 upregulated proteins and no downregulated proteins in both skin and blood. Several neutrophil-related proteins are represented, most likely due to an increased presence of neutrophils in both psoriatic skin and blood [37]. The limited overlap in proteins is likely a consequence of differences in tissue composition and the applied proteomic methodology. S100-A7, also known as Psoriasin because it was first discovered in lesional psoriatic keratinocytes [38], is the only protein upregulated in both lesional skin and non-lesional skin. S100-A7 is well established to be upregulated in lesional skin, and its upregulation in non-lesional skin has earlier been suggested to be a result of increased IL-17 signaling in psoriatic skin, even where the inflammation is not visible to the eye [39].

Importantly, our systematic approach revealed dysregulation of several proteins that are less described in the context of psoriasis. We note that IL-17C is upregulated in the blood serum from patients with psoriasis in three included studies, whereas another study measuring IL-17C in serum with ELISA reported it to be downregulated in patients with psoriatic arthritis compared with healthy controls [40]. IL-17C is well-established as being upregulated in psoriatic lesions, whereas its upregulation in blood and potential consequences are not well described. IL-17C is structurally distinct from other IL-17-family cytokines and, in contrast to these other IL-17-family cytokines being mainly produced by CD4+ T cells, is recognized as an autocrine and paracrine cytokine produced almost exclusively by epithelial cells. IL-17C exerts its functions through a heterodimeric receptor consisting of IL-17RA and IL-17RE. The IL-17RE subunit is unique to IL-17C signaling and is expressed by epithelial cells, where it induces the expression of barrier proteins in epithelial cells and Th17 cells, where it induces inflammatory signaling [41]. Contributions of IL-17C in systemic inflammation and its potential involvement in comorbidities are relatively uncharacterized. Wang et al. find that IL-17C in combination with elafin levels in blood plasma exhibit a strong correlation with the Psoriasis Area and Severity Index (PASI) and conclude that IL-17C (in combination with elafin) could represent a biomarker of effective systemic treatment [42]. The level of IL-17C in the blood, in combination with other proteins, has also been suggested as a potential predictor of treatment response in patients with psoriasis [43]. Furthermore, IL-17C has been linked to atherosclerosis in mice [44], which may be relevant in the context of cardiovascular comorbidity in psoriasis. However, the atherogenic effect of IL-17C has not yet been demonstrated in humans.

We find that IL-16 is reported to be increased in the blood from patient with psoriasis in two studies. IL-16 is a chemoattractant for and activator of T helper cells, dendritic cells, and other CD4+ cells, and it induces expression of IL-1-β, IL-6, and TNF in monocytes [45‒48]. IL-16 has been found to be increased in sera from patients with psoriasis and correlate with disease severity and treatment response; however, treatment targeted against IL-16 has not been tried in psoriasis [49, 50]. The C-X-C motif chemokine (CXCL) 9 is increased in sera of patients with psoriasis in three included studies, in contrast to an earlier report that serum levels of CXCL9 between patients with psoriasis and healthy individuals are not different [51]. CXCL9 is known to be upregulated in psoriatic lesions and is possibly linked to LL37, a potential autoantigen in psoriasis [52].

CCL24 can be produced by mononuclear cells and is known as a chemokine for resting T lymphocytes [53, 54]. It signals through the receptor CCR3 that is expressed on multiple cell types, especially keratinocytes in inflamed skin [55]. Kusumoto et al. find the CCL24 mRNA to be highly upregulated in skin samples from patients with psoriasis who respond well to TNF inhibition compared with non-responders. This indicates that CCL24 might play a role in some, but not all, patients with psoriasis and has possible implications regarding response to TNF inhibition [56]. Two studies included in this systematic review find CCL24 to be upregulated in psoriatic blood, supporting that this chemokine could be implicated in psoriasis pathogenesis.

Several cathepsins are reported to be upregulated in the blood serum/plasma and in peripheral blood mononuclear cells in the included studies. Cathepsins are proteases and serve various functions, including intracellular protein degradation, antigen (and autoantigen) processing, cleavage of pro-interleukins, and promotion of CD4+ cell transmigration and proliferation. Cathepsins are involved in inflammasome signaling associated with psoriasis, cardiovascular disease, and other inflammatory conditions [57, 58]. PCSK9 is another protein that is consistently upregulated in psoriatic blood in two Olink-based studies; it is genetically associated and likely causally related to both psoriasis and cardiovascular disease, according to a recent study applying Mendelian randomization [59]. The concurrent finding of upregulated LDL receptor seems counterintuitive because PCSK9 degrades the LDL receptor, which in turn would increase plasma LDL levels. A possible explanation could be that the used Olink antibodies bind to fragments of degraded LDL receptor molecules or that the concentration of LDL receptor molecules is increased due to other factors such as increased LDL – a common finding in patients with psoriasis [60].

We also find DEPs that might counteract some of the negative effects from the elevated inflammation. IL-36Ra is consistently reported as upregulated in lesional skin in seven of the included studies. Furthermore, cathepsin G, which is the most important activator of pro-IL-36γ, is downregulated in lesional versus healthy skin, indicating that multiple compensatory mechanisms may act to prevent the potent attraction of neutrophils by IL-36γ. Consistent downregulation of cathepsin G is a surprising finding, considering results from other studies. Cathepsin G mRNA has recently been found upregulated in lesional skin, and its level decreases with disease improvement after UVB treatment [61]. In addition, cathepsin G-like enzymatic activity has been found to be increased in lesional psoriatic skin [26]. It should be considered that in healthy skin, cathepsin G is most abundant in the dermis, which is likely underrepresented in lesional psoriatic skin samples due to epidermal thickening [62]. Adding to the complexity regarding the role of cathepsin G in psoriatic skin, SERPINB4 (an inhibitor of cathepsin G) is consistently upregulated in lesional skin. However, fragments from SERPINB4 and SERPINB3 (also consistently upregulated) are known to aggregate to form Pso p27, an autoantigen in psoriasis, and are likely contributing to the chronicity of the disease [63]. Thus, it would be important to further investigate the localization and proteoform distribution of both cathepsin G and SERPIN B4 in psoriatic skin.

IL-1RN is consistently upregulated in both skin and blood. IL-1RN inhibits inflammation by blocking the interleukin 1 receptor to prevent IL1-α/β from binding to the receptor, thereby affecting multiple inflammatory cascades. Loss-of-function mutations in IL1RN have been associated with perinatal-onset pustular dermatitis, resembling generalized pustular dermatitis, and other severe inflammatory conditions predominantly in skin and bone [64]. A beneficial function of IL-1RN upregulation in psoriasis may be to prevent further aggravation of the disease [65].

This study has several limitations. The study does not include proteomic studies with less than 10 identified proteins. Furthermore, the methodological limitations in the included studies also apply to our analysis, although the combination of targeted and untargeted methods is complementary in terms of the total number of proteins covered. Although the sensitivity of proteomic methods constantly improves, proteomic research has generally suffered from abundance bias, which makes it easier to detect significant differences in more abundant proteins. We chose a pragmatic approach to cover large amounts of data. Thus, our analysis is not a meta-analysis, nor did we consider different isoforms, unknown proteins, or proteins reported to be upregulated and downregulated in different studies. The quantitative nature of the proteomic data allows for interpretation of upregulation and downregulation in the reported comparisons, but they do not include information of active or inactive proteoforms. Furthermore, data from the included studies are observational. Therefore, we do not know whether the dysregulated proteins are causally related to psoriasis. Not all studies adjusted for multiple testing, thus increasing the risk of type 1 errors, which is especially relevant when measuring high numbers of proteins. Some studies do not match patients to healthy individuals, potentially resulting in significant findings not related to psoriasis. Lastly, patients in the included studies suffered from either psoriasis vulgaris, including plaque psoriasis, or unspecified psoriasis; therefore, these results cannot be generalized to other disease subtypes such as pustular variants.

In conclusion, the results of this systematic review underline the central role of known inflammatory mediators in psoriasis pathogenesis. Network analysis demonstrates a multi-faceted interplay between pro- and anti-inflammatory proteins and how these might relate to processes leading to phenotypic traits in psoriatic disease. The presence of inflammatory markers in both skin and blood supports the notion of psoriasis as a systemic inflammatory condition. Some consistently upregulated proteins in the included studies have not or only to a very limited extent been discussed before in the context of psoriasis and could represent novel and interesting topics for future research. The present study is the first to identify proteins consistently reported in proteomic studies as upregulated or downregulated in the skin and blood from patients with psoriasis. Our approach demonstrates that summarizing proteome-wide research is indeed capable of revealing central aspects of the disease pathology and might serve as a framework for future studies of less well-investigated diseases where proteomic studies have been published.

Consistently reported proteomic changes in psoriasis illustrate central pathogenic aspects of the disease.

We thank Julie Sølberg and Nikolai Nguyen Loft for valuable academic discussions.

An ethics statement is not applicable because this study is based exclusively on published literature. Informed consent was not required.

L.S. has received research funding from Novartis, Bristol-Myers Squibb, AbbVie, Janssen Pharmaceuticals, Sanofi, Almirall, the Danish National Psoriasis Foundation, the LEO Foundation and honoraria as consultant and/or speaker for AbbVie, Eli Lilly, Novartis, Pfizer, and LEO Pharma, Janssen, UCB, Almirall, Boehringer Ingelheim, Bristol-Myers Squibb, and Sanofi. B.K., A.O., Y.M.Z., M.B.L., and B.D.-A. have no conflicts of interest to declare.

This work was supported by grants from the Novo Nordisk Foundation (NNF21OC0066694) and Leo Foundation.

B.K. and A.O. searched databases and assessed studies for eligibility. B.K., A.O., and Y.M.Z. extracted meta-data from included studies. B.K. and Y.M.Z. extracted lists of DEPs. from included studies. B.K. and B.D.A. drafted the manuscript. B.K., A.O., Y.M.Z., M.B.L., L.S., and B.D.A. critically read and revised the manuscript and contributed to study concept and design.

The list of DEPs identified in the included studies is available in online supplementary Table 1. Further inquiries can be directed to the corresponding author.

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