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. 2025 Oct 17;6(1):100422. doi: 10.1016/j.xjidi.2025.100422

Nanostring Transcriptomic Analysis Highlights IL-6 Family and TGF-β Pathways in the Pathogenesis of Prurigo Nodularis

Yagiz Matthew Akiska 1,2,5, Shrey Bhatt 1,2,5, Kavita Vats 1,2, Selina M Yossef 1,2,3, Davies Gage 1,2, Shahin Shahsavari 1,2, Louis J Born 1,2, Perya Bhagchandani 1,2, Deena Fayyad 1,2, Hannah Cornman 1,2, Thomas Pritchard 1,2, Jessica E Teague 4, Rachael A Clark 4, Madan M Kwatra 3,6, Shawn G Kwatra 1,2,6,∗
PMCID: PMC12666349  PMID: 41333131

Abstract

Prurigo nodularis (PN) is a chronic, neuroimmune-driven skin disease characterized by intensely pruritic nodules and marked impairment in QOL. Although PN shares inflammatory features with atopic dermatitis and psoriasis, its underlying pathogenesis remains poorly defined. In this study, we performed NanoString-based transcriptomic profiling on lesional skin biopsies from patients with PN (n = 26), those with atopic dermatitis (n = 25), those with psoriasis (n = 15), and healthy controls (n = 12) using a custom neuroinflammation-focused panel. PN demonstrated a unique molecular signature involving neuroimmune activation (TMEM119, S100A10), extracellular matrix remodeling (matrix metalloproteinase 14 gene MMP14, COL6A3), and fibrotic signaling (TGFBR1, ITGB5), with IL-6 and MAPK12 as key inflammatory mediators. Compared with atopic dermatitis, PN displayed reduced T helper 2 signaling but greater neuroinflammatory and fibrotic activity. Relative to psoriasis, PN lacked T helper 17–driven hyperproliferation but showed macrophage and extracellular matrix activation. STRING network analysis also revealed IL-6 and TGF-β signaling as central hubs linking neuroinflammation and fibrosis. These findings establish PN as a distinct inflammatory skin disease at the intersection of neuroimmune dysregulation and tissue remodeling. Our results highlight key pathways that may guide precision therapies beyond current T helper 2–targeted treatments, supporting the development of IL-6, TGF-β, and Jak/signal transducer and activator of transcription–directed strategies in PN. This transcriptomic analysis establishes a framework to guide mechanistic and therapeutic investigations in PN.

Keywords: Extracellular matrix remodeling, Fibrosis, Itch, Prurigo nodularis, Transcriptomics

Introduction

Prurigo nodularis (PN) is a chronic inflammatory and neuroimmune-mediated skin disease characterized by intensely pruritic nodular lesions, primarily affecting the trunk and extremities (Kwatra et al, 2025). PN significantly impairs QOL, contributing to sleep disturbances, psychological distress, and reduced daily functioning (Rodriguez et al, 2023). Emerging evidence highlights PN’s complex pathophysiology, integrating neuroinflammation, type 2 (T helper [Th] 2) inflammation, and profibrotic processes, with additional contributions from Th1 and Th17 pathways (Belzberg et al, 2021). Prior studies have revealed elevated neurovascular, extracellular matrix (ECM) remodeling, and fibroproliferative molecular signatures in PN compared with those in atopic dermatitis (AD) and psoriasis (PS), underscoring its unique molecular drivers (Sutaria et al, 2022). Neuroimmune, profibrotic, and barrier-associated plasma protein levels have also been shown to decrease with clinical improvement after treatment, supporting a link between PN’s molecular pathways and disease activity (Bao et al, 2024).

In contrast, AD and PS are well-characterized chronic inflammatory skin diseases, with AD primarily driven by Th2-mediated barrier dysfunction and PS dominated by Th17/Th1-mediated epidermal hyperplasia (Facheris et al, 2023; Kwatra et al, 2025). Although overlapping immune pathways exist, PN’s distinct neuroimmune, fibrotic, and ECM remodeling mechanisms differentiate it from these conditions. A deeper understanding of PN’s pathogenesis is essential to refine therapeutic approaches. Comparative studies not only elucidate shared pathophysiological mechanisms that may enable the repurposing of AD or PS therapies for PN but also highlight PN-specific pathways that could inform innovative, tailored treatments.

In this exploratory study, we utilize a custom neuroinflammation-focused NanoString panel to perform a comprehensive analysis of gene expression profiles from patient biopsies. This study aims to define the unique molecular signature of PN and identify key pathways that distinguish it from AD and PS.

Results

Patient and sample characteristics

Transcriptome analysis was performed on a total of 26 lesional skin biopsies from patients with PN (mean age = 52.5 years, 77% female, and 65% African American), 25 lesional skin biopsies from patients with AD (mean age = 48.5 years, 68% female, and 56% African American), and 15 lesional biopsies from patients with PS (mean age = 54.1 years, 40% female, and 47% African American) as well as 12 matched healthy controls (HCs) (Figure 1a). Demographic characteristics of all participants are displayed in Table 1. Representative clinical and histological pictures of PN, AD, and PS lesions are shown (Figure 1b and c).

Figure 1.

Figure 1

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Overview of study design and representative clinical images of PN, AD, and PS lesional skin.(a) Schematic of study design and statistical analysis. Lesional skin biopsies and nonlesional skin biopsies were collected from patients with PN (PN L [n = 26] and PN NL [n = 26]) and AD (AD L [n =25] and AD NL [n = 15]), lesional skin biopsies were collected from patients with PS (n = 15), and nonlesional skin biopsies were collected from healthy controls (n = 12). To analyze the neuroimmune signature of these different conditions, RNA was extracted from each sample, and Nanostring transcriptional profiling was conducted using a custom neuroinflammation-focused Nanostring panel of 770 genes. Subsequent statistical analysis was conducted, comparing differentially expressed genes between lesional and nonlesional samples across conditions. (b) Representative clinical images of lesional skin from patients with PN, AD, or PS. (c) H&E staining of lesional skin from patients with PN, AD, and PS. Representative images highlight key histopathological features characteristic of each disease, including epidermal hyperplasia, dermal inflammation, and tissue remodeling. Bar = 200 μm. Consent for this study was obtained from the Johns Hopkins Institutional Review Board (IRB00231694). Written informed consent was obtained from each participant and is on file. Consent for publication of the clinical images was also obtained from the participant(s) or their parent(s)/guardian(s), as applicable. L denotes lesional, and NL denotes nonlesional. AD, atopic dermatitis; PN, prurigo nodularis; PS, psoriasis.

Table 1.

Study Cohort Demographic Information of Patients Recruited to Provide Skin Samples for NanoString Transcriptional Profiling

Demographic Information AD (n = 25) HC (n = 12) PN (n = 26) PS (n = 15)
Age, y, mean (SD) 48.5 (20.5) 53 (15.6) 52.5 (9.6) 54.1 (17.0)
Sex (%)
Female 17 (68) 10 (83) 20 (77) 6 (40)
Male 8 (32) 2 (17) 6 (23) 9 (60)
Race, n (%)
Black/African American 14 (56) 7 (58) 17 (65) 7 (47)
Other 0 0 1 (4) 0
White 11 (44) 5 (42) 8 (31) 8 (53)
WI-NRS, mean (SD) 8.3 (1.8) 0 8.4 (1.3) N/A
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Abbreviations: AD, atopic dermatitis; HC, healthy control; N/A, not available; PN, prurigo nodularis; PS, psoriasis; WI-NRS, Worst-Itch Numeric Rating Score.

Shared features in PN, AD, and PS

Hierarchical clustering of differentially expressed genes (DEGs) across PN, AD, PS, and HCs demonstrates distinct transcriptional signatures for each condition (Figure 2a). DEG overlap analysis highlights 71 genes shared between PN, AD, and PS (Figure 2b). Pathway enrichment analysis reveals significant upregulation of IL-4 and IL-13 signaling, neuroinflammation signaling pathway, and ECM organization in PN, aligning with its distinct neuroimmune and fibrotic pathogenesis, which differs from the Th2-driven barrier dysfunction in AD and the Th17-mediated hyperproliferation in PS (Figure 2c). Shared DEGs between PN, AD, and PS lesional skin and HC include neuroinflammation (APOE, GRIA4), ECM interaction (matrix metalloproteinase [MMP] genes MMP9 and MMP12), skin barrier function (TGM1, S100A12), macrophage activation (CD68, C1QC, FCGR1A), Th1 immune activity (IRF7, signal transducer and activator of transcription [STAT] gene STAT1), and Th2 immune activity (CCL2) (Figure 2d).

Figure 2.

Figure 2

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Shared features between PN, AD, and PS transcriptomes.(a) Heatmap showing cutaneous mRNA expression of nCounter’s Human Neuroinflammation Panel (770 genes) together with a custom panel of Mrgpr genes and cytokines (30 genes) for all lesional samples (PN lesional [n = 26], AD lesional [n = 25], PS lesional [n = 15] and HC samples (n = 12). Red indicates higher expression (+z-score), and blue indicates lower expression (−z-score). (b) Venn diagram of DEGs for PN lesional samples (n = 26) compared with those in HCs (n = 12), AD lesional samples (n = 25) compared with HCs (n = 12), and PS lesional samples (n = 15) compared with HCs (n = 12). DEGs are defined as genes with an FC > 1.5 or < −1.5, BH-adjusted P < .05, and FDR < 0.05. (c) IPA plot of enriched pathways for DEGs of PN L (n = 26) versus HC (n = 12) (square), DEGs of AD L (n = 25) versus HC (n = 12) (circle), and DEGs of PS L (n = 15) versus HC (n = 12) DEGs. The x-axis represents the negative log of the BH-adjusted P-value (−log10 BH-adjusted P-value), indicating pathway enrichment significance; the y-axis lists relevant pathways. Relevant pathways are shown, with red bars representing upregulation of the pathway (+z-score) and blue bars representing downregulation of the pathway (−z-score) within the respective comparisons. (d) Volcano plots showing relevant genes for PN L (n = 26) versus HC (n = 12), AD L (n = 25) versus HC (n = 12), and PS L (n = 15) versus HC (n = 12) comparisons (red, neuroinflammation; blue, fibroblast activation; gray, macrophage activation; brown, ECM interaction; pink, skin barrier; green, Th1 immune activity; orange, Th2 immune activity; and yellow, Th17 immune activity). L denotes lesional. AD, atopic dermatitis; BH, Benjamini–Hochberg; DEG, differentially expressed gene; ECM, extracellular matrix; FC, fold change; FDR, false discovery rate; HC, healthy control; IPA, Ingenuity Pathway Analysis; PN, prurigo nodularis; PS, psoriasis; Th, T helper.

Comparative transcriptome analysis of AD and PN

A volcano plot illustrates DEGs between PN and AD, highlighting ECM-related genes (MMP14, COL6A3, IGF1) upregulated in PN and Th2 cytokines (IL4, IL13) elevated in AD (Figure 3a). Compared with HCs, PN and AD lesional skin shared 72 upregulated and 5 downregulated DEGs (Figure 3b).

Figure 3.

Figure 3

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PN transcriptome compared with that of AD.(a) Volcano plots showing relevant genes for PN L (n = 26) versus AD L (n = 25) comparison (red, neuroinflammation; blue, fibroblast activation; gray, macrophage activation; brown, ECM interaction; pink, skin barrier; green, Th1 immune activity; and orange, Th2 immune activity). (b) Venn diagram of DEGs for PN lesional samples (n = 26) compared with those for HCs (n = 12) and AD lesional samples (n = 25) compared with HCs (n = 12). DEGs are defined as genes with an FC > 1.5 or < −1.5, BH-adjusted P < .05, and FDR < 0.05. (c) IPA plot of enriched pathways for AD L (n = 25) versus HC (n = 12) DEGs. The x-axis represents the negative log of the BH-adjusted P-value (−log10 BH-adjusted P-value), indicating the significance of pathway enrichment; the y-axis lists the relevant pathways. Relevant pathways are shown, with red bars representing upregulation of the pathway (+z-score) and blue bars representing downregulation of the pathway (−z-score). (d) IPA plot of enriched pathways for DEGs of PN L (n = 26) versus AD L (n = 25). The x-axis represents the negative log of the BH-adjusted P-value (−log10 BH-adjusted P-value), indicating pathway enrichment significance; the y-axis lists relevant pathways. Relevant pathways are shown, with red bars representing upregulation of the pathway (+z-score, PN L upregulated relative to AD L) and blue bars representing downregulation of the pathway (−z-score, PN L downregulated relative to AD L). (e) STRING PPI network analysis of AD L (n = 25) versus HC (n = 12) DEGs. Network nodes represent proteins, and edges represent protein–protein associations (cyan, curated databases; purple, experimentally determined; green, gene neighborhood; red, gene fusions; dark blue, gene co-occurrence; yellow, text mining; black, coexpression; and light blue, protein homology). Edge thickness corresponds to interaction confidence scores. L denotes lesional. AD, atopic dermatitis; BH, Benjamini–Hochberg; DEG, differentially expressed gene; ECM, extracellular matrix; FC, fold change; FDR, false discovery rate; HC, healthy control; IPA, Ingenuity Pathway Analysis; PN, prurigo nodularis; PPI, protein–protein interaction; Th, T helper.

Pathway enrichment analysis (Figure 3c) reveals that AD lesional skin, compared with HC, is significantly enriched in IL-4 and IL-13 signaling as well as macrophage classical activation signaling, reflecting pathways associated with Th2 inflammation. Comparative pathway analysis (Figure 3d) shows that PN lesional skin exhibits increased activation of ECM organization and neuroinflammation pathways relative to AD lesional skin (z > 0). In contrast, Th2 signaling and adaptive immune pathways are more enriched in AD (z < 0). STRING network mapping depicts key protein–protein interactions in AD, highlighting Th2 markers (IL-4 and IL-13) as central markers (Figure 3e).

Comparative transcriptome analysis of PS and PN

A volcano plot (Figure 4a) highlights some of the ECM, macrophage activation, and Th1/Th2-related genes (MMP14, ITGB5, CHST8, TMEM119, COL6A3, TNFRSF12A, CD109, IGF-31) upregulated in PN. In contrast, type 1 (TNFSF10, TGFA, STAT1) and type 3 (IL36A, IL17A, STAT3) inflammation-related genes were upregulated in PS. Overall, compared with HCs, PN and PS lesional skin shared 96 upregulated and 17 downregulated DEGs (Figure 4b).

Figure 4.

Figure 4

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PN transcriptome compared with that of PS.(a) Volcano plots showing relevant genes for PN L (n = 26) versus PS L (n = 15) comparison (red, neuroinflammation; blue, fibroblast activation; gray, macrophage activation; brown, ECM interaction; pink, skin barrier; yellow, Th17 immune activity; and orange, Th2 immune activity). (b) Venn diagram of DEGs for PN lesional samples (n = 26) compared with those of the HCs (n = 12) and PS lesional samples (n = 15) compared with HCs (n = 12). DEGs are defined as genes with an FC > 1.5 or < −1.5, BH-adjusted P < .05, and FDR < 0.05. (c) IPA plot of enriched pathways for DEGs of PS L (n = 15) versus HC (n = 12). The x-axis represents the negative log of the BH-adjusted P-value (−log10 BH-adjusted P-value), indicating the significance of pathway enrichment; the y-axis lists the relevant pathways. Relevant pathways are shown, with red bars representing upregulation of the pathway (+z-score) and blue bars representing downregulation of the pathway (−z-score). (d) IPA plot of enriched pathways for DEGs of PN L (n = 26) versus PS L (n = 15). The x-axis represents the negative log of the BH-adjusted P-value (−log10 BH-adjusted P-value), indicating the significance of pathway enrichment; the y-axis lists the relevant pathways. Relevant pathways are shown, with red bars representing upregulation of the pathway (+z-score, PN L upregulated relative to PS L) and blue bars representing downregulation of the pathway (−z-score, PN L downregulated relative to PS L). (e) STRING PPI network analysis of PS L (n = 15) versus HC (n = 12) DEGs. Network nodes represent proteins, and edges represent protein–protein associations (cyan, curated databases; purple, experimentally determined; green, gene neighborhood; red, gene fusions; dark blue, gene co-occurrence; yellow, text mining; black, coexpression; and light blue, protein homology). Edge thickness corresponds to interaction confidence scores. L denotes lesional. BH, Benjamini–Hochberg; DEG, differentially expressed gene; ECM, extracellular matrix; FC, fold change; FDR, false discovery rate; HC, healthy control; IPA, Ingenuity Pathway Analysis; PN, prurigo nodularis; PPI, protein–protein interaction; PS, psoriasis; Th, T helper.

Pathway enrichment analysis (Figure 4c) shows that PS lesional skin was significantly enriched in IL-4 and IL-13 signaling, neuroinflammation signaling, and Th17 activation pathways compared with HC. Comparative pathway analysis (Figure 4d) reveals that PN lesional skin had higher activation of ECM organization and collagen-related pathways (z > 0), whereas IL-17 signaling and macrophage classical activation were more enriched in PS (z < 0). STRING network mapping (Figure 4e) depicts molecular interactions of PS, with STAT1 and SOCS3 central to PS-associated Th17 polarization.

Unique molecular signatures in PN

A volcano plot (Figure 5a) highlights some genes uniquely upregulated in PN, with enrichment in remodeling (MMP14, ITGB5, COL6A3), neuroinflammation (TMEM119, IGF1), and fibrosis-related pathways (TGFB1, TNFRSF12A). Pathway enrichment analysis (Figure 5b) shows that these molecules are primarily associated with activation of MMPs, insulin-like GF transport, apoptosis, and MYC-mediated anergy signaling.

Figure 5.

Figure 5

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The unique transcriptional landscape of PN lesional skin.(a) Volcano plots showing relevant genes for PN L (n = 26) versus HC (n = 12) comparison (red, neuroinflammation; blue, fibroblast activation; gray, macrophage activation; brown, ECM interaction; pink, skin barrier; yellow, Th17 immune activity; and orange, Th2 immune activity). (b) IPA plot of enriched pathways for unique DEGs of PN L (n = 26) versus HC (n = 12). The x-axis represents the negative log of the BH-adjusted P-value (−log10 BH-adjusted P-value), indicating the significance of pathway enrichment; the y-axis lists the relevant pathways. Relevant pathways are shown, with red bars representing upregulation of the pathway (+z-score) and blue bars representing downregulation of the pathway (−z-score). (c) IPA plot of enriched pathways for all DEGs of PN L (n = 26) versus HC (n = 12). The x-axis represents the negative log of the BH-adjusted P-value (−log10 BH-adjusted P-value), indicating pathway enrichment significance; the y-axis lists relevant pathways. Relevant pathways are shown, with red bars representing upregulation of the pathway (+z-score) and blue bars representing downregulation of the pathway (−z-score). (d) String PPI network analysis of PN L (n = 26) versus HC (n = 12) all DEGs. Network nodes represent proteins, and edges represent protein–protein associations (cyan, curated databases; purple, experimentally determined; green, gene neighborhood; red, gene fusions; dark blue, gene co-occurrence; yellow, text mining; black, coexpression; and light blue, protein homology). Edge thickness corresponds to interaction confidence scores. (e) STRING PPI network analysis of PN L (n = 26) versus HC (n = 12) unique DEGs. Network nodes represent proteins, and edges represent protein–protein associations (cyan, curated databases; purple, experimentally determined; green, gene neighborhood; red, gene fusions; dark blue, gene co-occurrence; yellow, text mining; black, co-expression; and light blue, protein homology). Edge thickness corresponds to interaction confidence scores. BH, Benjamini–Hochberg; DEG, differentially expressed gene; ECM, extracellular matrix; HC, healthy control; IPA, Ingenuity Pathway Analysis; PN, prurigo nodularis; PPI, protein–protein interaction.

Pathway analysis of PN versus HC (Figure 5c) demonstrates significant upregulation of IL-4 and IL-13 signaling, Jak/STAT signaling, and pathways related to macrophage activation and ECM remodeling. STRING network analysis (Figure 5d) maps all molecules differentially expressed in PN, revealing key interactions between fibrotic, neuroinflammatory, and ECM regulatory pathways. STRING network analysis (Figure 5e) identifies distinct molecular clusters unique to PN, with IL-6 serving as a key hub that interacts with TGFBR1, TIMP1, MMP14, IGF1, and IL1R. These findings suggest a coordinated regulation of fibrosis, ECM remodeling, and neuroinflammation in PN.

Discussion

PN is a complex dermatological disease characterized by clinical heterogeneity and distinct molecular mechanisms. Previous studies have demonstrated the cutaneous and systemic inflammatory processes that contribute to the intricate pathogenesis behind PN (Belzberg et al, 2021). However, there is a need for further characterization of the underlying neuroinflammatory, fibrotic, and immune-modulating pathways that are unique to PN’s pathogenesis. In this study, we employed a custom, neuroinflammation-focused panel to identify gene expression differences, including altered transcriptional profiles and pathway disruptions in PN compared with those in AD, PS, and HCs. Our transcriptomic analysis positions PN at the intersection of neuroimmune activation, fibrosis, and ECM dysregulation, highlighting the need for distinct therapeutic approaches.

A defining feature of PN-lesional skin is its pronounced neuroinflammatory signature, characterized by the dysregulation of GRIA4 and the upregulation of TMEM119 and S100A10. GRIA4, a glutamate receptor subunit, was downregulated in PN, consistent with evidence that altered keratinocyte GRIA4 expression contributes to sensory abnormalities (Cabañero et al, 2016). Moreover, the significant upregulation of TMEM119, a microglia and peripheral nerve–resident macrophage marker, suggests a role for tissue-specific neuroimmune interactions in PN (Satoh et al, 2016). Recent studies have shown that peripheral nerve macrophages share transcriptional profiles with activated microglia, linking TMEM119 to neuroimmune signaling in chronic inflammatory and neuropathic conditions (Gao et al, 2023; Wang et al, 2020). The increased expression of TMEM119 in PN may suggest a potential contribution of these macrophage populations to sustained neuroinflammation and pruritus.

In addition, S100A10, a calcium-binding protein involved in macrophage migration and ECM remodeling, was uniquely elevated in PN compared with that in AD and PS, suggesting a role in both neural sensitization and fibrosis. Although additional members of the S100 family, such as S100A7, are associated with AD, and S100A8/9 and S100A12 are upregulated in PS, our finding that S100A10 is uniquely upregulated in PN compared with that in these diseases underscores its distinct role in PN’s neuroimmune and fibrotic pathophysiology (Saito-Sasaki and Sawada, 2023). The role of S100A10 in PN combines the neuroinflammatory role behind pruritus by influencing macrophage migration and subsequent ECM remodeling through plasmin-induced MMP activation, possibly contributing to the fibrotic lesions seen in PN (O’Connell et al, 2010; Phipps et al, 2011). Although previous studies have demonstrated the importance of various S100 proteins, the recent finding of S100A10 expression in PN warrants further exploration.

Beyond neuroinflammation, PN exhibited a distinct fibrotic and ECM remodeling signature, setting it apart from the barrier dysfunction of AD and the keratinocyte hyperplasia of PS. MMP14, COL6A3, and TGFBR1 were highly upregulated in PN, reflecting an active fibrotic process. MMP14, an MMP essential for ECM turnover and fibroblast survival, was elevated in PN compared with that in PS, consistent with previous findings that link MMP14 to pathological fibrosis and collagen remodeling (Joo and Seomun, 2008; Sabeh et al, 2024). Similarly, COL6A3, which encodes a subunit of collagen VI, was upregulated in PN, reinforcing ECM stabilization and nodular fibrosis formation (Ma et al, 2024). A particularly notable finding was the increased expression of TGFBR1, a key mediator of TGF-β–driven fibrosis (Liarte et al, 2020). TGF-β signaling has been implicated in numerous fibrotic skin disorders, and its positive feedback loop with MMP14, where TGF-β promotes MMP14 expression, which in turn activates latent TGF-β, may drive the extensive collagen deposition observed in PN lesions (Liarte et al, 2020; Sounni et al, 2010). These findings highlight PN’s distinction as a fibrotic inflammatory disease with potential therapeutic targets within TGF-β and MMP-mediated ECM remodeling pathways.

Integrin signaling further emerged as a key differentiator in PN, linking ECM remodeling to macrophage activation and fibrosis. ITGB5, an integrin subunit known to mediate TGF-β activation and fibroblast adhesion, was significantly upregulated in PN, suggesting increased integrin-mediated TGF-β signaling (Wipff and Hinz, 2008). Prior studies (Munger and Sheppard, 2011) have demonstrated that integrin–TGF-β interactions contribute to persistent fibrotic remodeling, a process distinct from the Th2-driven barrier dysfunction of AD or the hyperproliferative epidermis of PS. Moreover, TNFRSF12A (Fn14), a receptor linked to integrin signaling and fibrotic responses, was uniquely elevated in PN, supporting a pathway through which integrins and TNF superfamily receptors promote sustained ECM remodeling (Campbell et al, 2004; Nishimura, 2009; Wang et al, 2023; Winkles, 2008). These findings underscore the role of integrin–ECM interactions as key drivers of PN fibrosis, suggesting that targeting integrin-mediated TGF-β activation could provide additional therapeutic strategies (Nishimura, 2009; Pang et al, 2023; Wang et al, 2023).

The upregulation of IL-6, MAPK12, and IL-31 further supported neuroimmune dysregulation in PN. IL-6 is a key inflammatory mediator that amplifies nociceptive signaling in dorsal root ganglia neurons, thereby perpetuating chronic itch (Mitchell et al, 2024. Our data support prior studies implicating IL-6 in PN-associated pruritus, and its upregulation aligns with the observed increase in MAPK12, a component of the p38 MAPK pathway activated by IL-6 (Zarubin et al, 2005). Furthermore, IL-31, a well-established pruritogenic cytokine, was significantly upregulated in PN (Hashimoto et al, 2021; Kwatra et al, 2023). The IL-6/MAPK axis may represent a crucial targetable pathway in PN, with IL-6 blockade (eg, tocilizumab) or p38 MAPK inhibitors serving as potential therapeutic strategies (Meng et al, 2005). Moreover, the interaction between IL-6 and TGFBR1 is known to vary on the basis of cell type and signaling environment. Still, this pathway may also contribute to the formation and maintenance of PN’s characteristic nodules (Liarte et al, 2020; Zhang et al, 2005). Future investigations into these signaling interactions could refine therapeutic approaches and improve disease outcomes.

Our findings complement and extend those of prior transcriptomic studies of PN using RNA-sequencing platforms. Previous work, such as that by Shao et al (2023), Sutaria et al (2022), and Tsoi et al (2022), has demonstrated IL-6 and ECM pathway activation in PN relative to that in AD and PS, highlighting inflammatory and fibrotic processes in PN pathogenesis.

However, this study leverages a targeted neuroinflammation-focused NanoString panel, which provides enhanced sensitivity for detecting low-abundance neuroimmune transcripts and immune-fibrotic regulators with reduced background variability compared with genome-wide RNA sequencing (Veldman-Jones et al, 2015; Wang et al, 2016). This approach allowed us to identify and validate, to our knowledge, previously unreported PN-associated molecular signatures, including S100A10, a calcium-binding protein linked to macrophage migration and ECM remodeling, and TMEM119, a microglia/peripheral nerve macrophage marker not previously implicated in PN. In addition, we identified the upregulation of ITGB5, an integrin subunit involved in TGF-β activation, providing further evidence of integrin–ECM signaling in PN that was not previously emphasized in RNA-sequencing studies. These findings underscore the added value of pathway-focused profiling platforms in uncovering neuroimmune and fibrotic regulators that are uniquely enriched in PN and offer additional precision-targetable candidates beyond what has been captured by broad RNA-sequencing comparisons.

Despite shared inflammatory mediators with AD and PS, PN’s immune activation pattern remains distinct, integrating Th1, Th2, and Th17 pathways while also engaging IFN signaling, macrophage activation, and ECM remodeling. Current therapies, such as dupilumab (IL-4Rα blockade) and nemolizumab (IL-31Rα blockade), demonstrate efficacy in Th2-dominant diseases; however, partial responses in many patients suggest the existence of additional mechanisms beyond Th2 inflammation. Our data indicate that IL-4/IL-13 and IL-31 signaling converge on downstream mediators such as IL-6 and TGF-β to drive macrophage activation, fibroblast remodeling, and fibrosis. Oncostatin M, an IL-6 family cytokine that signals through the IL-31 receptor subunit OSMRβ, further links Th2/IL-31 pathways to neuroimmune activation and fibrotic remodeling, and dual IL-31/oncostatin M blockade (vixarelimab) has shown early efficacy in PN (Licata et al, 2025). IL-6, through STAT3 activation, may amplify Th2-driven tissue remodeling, positioning it as a potential therapeutic target. Although IL-6 inhibition (eg, tocilizumab) does not directly block upstream signaling, it may attenuate downstream profibrotic cascades, supporting the exploration of IL-6–directed therapies as a complement to current Th2-targeted approaches. Taken together, targeting IL-6, TGF-β, or Jak/STAT signaling may be effective in subsets of PN, whereas neuromodulators or p38 MAPK inhibitors hold promise for addressing neuroimmune activation and sensory hypersensitivity (Licata et al, 2025).

Although this study provides critical molecular insights, it has several limitations. As a bulk transcriptomic analysis, our data cannot directly assign gene expression changes to specific cell types; therefore, validation with immunohistochemistry or protein-level assays will be necessary. Paired lesional and nonlesional samples as well as an independent validation cohort were not included, which limited the ability to distinguish disease-specific alterations and generalize the results. The cross-sectional design further precludes assessment of temporal or treatment-associated changes. In addition, modest sample sizes and demographic imbalances may have introduced confounding variables. Posthoc power calculations indicated that the study was powered to detect large-magnitude differences but underpowered for smaller effects, particularly in PS versus HC contrasts. As such, findings should be interpreted as highlighting robust, large-scale alterations, whereas more subtle changes may have gone undetected. Finally, the use of a neuroinflammation-focused NanoString panel may not capture all relevant pathways.

Despite these limitations, the study provides insights into the neuroimmune and fibrotic mechanisms in PN and establishes a foundation for future work. Longitudinal studies incorporating spatial transcriptomics, single-cell RNA sequencing, and proteomics will be crucial for validating molecular drivers and refining therapeutic strategies. Building on the identification of IL-6, MAPK12, and integrin-mediated TGF-β activation as central pathways, future studies should investigate whether targeting these downstream mediators can complement existing therapies targeting IL-4Rα and IL-31Rα.

By integrating neuroimmune activation, fibrosis, and broad immune dysregulation, our exploratory study highlights PN as a distinct inflammatory skin disease that extends beyond traditional Th2-driven paradigms. This transcriptomic analysis provides a foundation for precision medicine approaches, highlighting unique molecular pathways that could inform targeted therapies to address pruritus, inflammation, and fibrosis, ultimately improving patient outcomes.

Materials and Methods

Sample collection

NanoString transcriptional profiling using a custom neuroinflammation-focused NanoString panel was performed on skin punch biopsies from lesional areas of patients with PN, patients with AD, and patients with PS with moderate-to-severe pruritus as well as matched HCs. Lesional skin biopsies were collected from 26 patients with PN, 25 patients with AD, 15 patients with PS, and 12 HCs matched by age, sex, and race. PS diagnoses were confirmed by histopathologic evaluation, whereas PN and AD were diagnosed on the basis of standard clinical criteria by a board-certified dermatologist (Davis et al, 2024; Kwatra et al, 2023). Lesional biopsies were collected from the most pruritic skin areas. Consent for this study was obtained from the Johns Hopkins Institutional Review Board (IRB00231694). Written informed consent was obtained from each participant and is on file. Race and ethnicity data were collected to assess demographic representation and potential confounding effects in transcriptomic analyses, consistent with National Institutes of Health reporting standards. These categories were not funder mandated but were included to ensure appropriate interpretation of molecular differences across diverse populations. Race and ethnicity were obtained by self-report at the time of clinical enrollment and recorded in the study database. These data were later verified against the electronic health record where available. Participants were classified as Black/African American, White, or other, consistent with National Institutes of Health and United States Census definitions.

Gene expression

RNA was extracted from lesional samples using the RNeasy FFPE Kit (Qiagen) as per the manufacturer’s instructions. Gene expression analysis was conducted for 770 neuroinflammatory-related genes using the nCounter Human Neuroinflammation Panel and a panel of 30 genes focused on specific cytokines. Nsolver Analysis Software 4.0 was used to perform quality control (QC), background thresholding, and normalization of raw expression data. RLF files containing patient gene expression data were uploaded to nSolver, and QC was completed, testing for general QC and individual QC factors (imaging QC, binding density QC, positive control linearity QC, limit of detection QC). No samples were flagged for low quality across the general and specific QC measures. Background thresholding was completed using the geometric mean of the average counts for 8 negative control samples. Positive control normalization was calculated using the geometric mean of the average counts for 6 synthetic single-stranded DNA control samples; housekeeping normalization was calculated using the geometric mean of the average counts for 9 selected housekeeping genes (ASB7, CCDC127, CNOT10, FAM104A, MTO1, SUPT7L, TADA2B, TBP, and XPNPEP1). The average percent coefficient of variation (%CV) for code set probes was 67.79%, whereas the average %CV for the 9 housekeeping probes was 46.78%, indicating generally high global variability for the genes. These %CV levels are not uncommon for bulk RNA analysis of complex heterogeneous tissue samples (GTEx Consortium, 2015; Gustafsson et al, 2020). One AD lesional sample was flagged for mRNA content; thus, 78 samples were used for subsequent statistical analysis.

Statistical analysis

Differential expression was conducted using the normalized counts. Fold change and log2 fold change were calculated for each gene across PN(L) versus HC, AD(L) versus HC, PS(L) versus HC, PN(L) versus AD(L), and PN(L) versus PS(L) comparisons. A Welch 2-sample, unpaired t-test (assuming unequal variances) was performed after normalization and log2 transformation, and P-values were adjusted for multiple gene comparisons using the Benjamini–Hochberg false discovery rate method. DEGs were defined as genes with a fold change > 1.5 or < −1.5 and false discovery rate–adjusted P < .05. Power analysis—2-sample t-test, 2 sided, log2-normalized expression, α = 0.05 and Bonferroni α = 0.05/800—was computed. To examine pathway-level comparisons across conditions, Ingenuity Pathway Analysis was conducted with DEGs calculated from normalized counts consistent with Ingenuity Pathway Analysis’s linear calculations. Genes of interest from select significant pathways (P < .05 and z-score > 1.5) were mapped using STRING to assess potential association networks.

Ethics Statement

Consent for this study was obtained from the Johns Hopkins Institutional Review Board (IRB00231694). Written informed consent was obtained from each participant and is on file. Consent for publication of the clinical images was also obtained from the participant(s) or their parent(s)/guardian(s), as applicable.

Data Availability Statement

The raw and processed sequencing data generated in this study have been deposited in the Gene Expression Omnibus under accession GSE261704 and are publicly accessible at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE261704. All data supporting the findings of this study are included in this Gene Expression Omnibus record and associated metadata. No ethical or commercial restrictions apply.

ORCIDs

Yagiz Matthew Akiska: http://orcid.org/0000-0002-9851-8523

Shrey Bhatt: http://orcid.org/0009-0000-8070-3117

Kavita Vats: http://orcid.org/0000-0002-1491-348X

Selina M. Yossef: http://orcid.org/0009-0008-2463-9438

Davies Gage: http://orcid.org/0009-0008-5230-4109

Shahin Shahsavari: http://orcid.org/0000-0001-9774-4839

Perya Bhagchandani: http://orcid.org/0009-0003-8331-9374

Deena Fayyad: http://orcid.org/0000-0002-0553-4867

Hannah Cornman: http://orcid.org/0000-0003-1462-2479

Thomas Pritchard: http://orcid.org/0000-0001-7425-4642

Jessica E. Teague: http://orcid.org/0000-0002-4357-9142

Rachael A. Clark: http://orcid.org/0000-0002-6105-5764

Madan M. Kwatra: http://orcid.org/0000-0002-6547-8852

Shawn G. Kwatra: http://orcid.org/0000-0003-3736-1515

Conflict of Interest

SGK is an advisory board member/consultant for Abbvie, ASLAN Pharmaceuticals, Arcutis Biotherapeutics, Celldex Therapeutics, Castle Biosciences, Galderma, Genzada Pharmaceuticals, Incyte, Johnson & Johnson, Leo Pharma, Novartis Pharmaceuticals, Pfizer, Regeneron Pharmaceuticals, and Sanofi and has served as an investigator for Galderma, Incyte, Pfizer, and Sanofi. The remaining authors state no conflict of interest.

Acknowledgments

This work was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under award number K23AR077073, awarded to SGK. Additional support was provided by a Dermatology Foundation Career Development award to SGK. We would also like to thank the Human Skin Disease Resource Center of Brigham and Women’s Hospital and Harvard Medical School, which provided a Translation Accelerator grant, samples, and NanoString services. The Human Skin Disease Resource Center is supported in part by National Institute of Arthritis and Musculoskeletal and Skin Diseases Resource-based Center (grant number 1P30AR069625).

Author Contributions

Conceptualization: YMA, SB, MMK, SGK; Data Curation: YMA, SB, KV, HC, SMY; Formal Analysis: YMA, SB, KV, HC, SMY; Funding Acquisition: MMK, SGK; Investigation: YMA, DG, SS, LJB; Methodology: YMA, SB; Project Administration: MMK, SGK; Resources: MMK, SGK; Software: JET, RAC; Supervision: MMK, SGK; Visualization: YMA, SB, KV, HC, SMY; Writing - Original Draft Preparation: YMA, SB; Writing - Review and Editing: YMA, SB, KV, SMY, DG, SS, LJB, PB, DF, HC, TP, JET, RAC, MMK, SGK

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Declaration of Generative Artificial Intelligence (AI) or Large Language Models (LLMs)

The author(s) did not use AI/LLM in any part of the research process and/or manuscript preparation.

accepted manuscript published online XXX; corrected proof published online XXX

Footnotes

Cite this article as: JID Innovations 2025.100422

Supplementary Materials

Supplementary Data 1
mmc1.xlsx (124KB, xlsx)
Supplementary Data 2
mmc2.xlsx (206.5KB, xlsx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Data 1
mmc1.xlsx (124KB, xlsx)
Supplementary Data 2
mmc2.xlsx (206.5KB, xlsx)

Data Availability Statement

The raw and processed sequencing data generated in this study have been deposited in the Gene Expression Omnibus under accession GSE261704 and are publicly accessible at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE261704. All data supporting the findings of this study are included in this Gene Expression Omnibus record and associated metadata. No ethical or commercial restrictions apply.

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