Diagnostic and prognostic utility of inflammatory biomarkers in dermatology: A narrative review

Vidyadhar R. Sardesai; Sahana Ojha

doi:10.25259/IJDVL_350_2025

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Review Article

ARTICLE IN PRESS

doi:

10.25259/IJDVL_350_2025

Diagnostic and prognostic utility of inflammatory biomarkers in dermatology: A narrative review

Vidyadhar R. Sardesai¹, Sahana Ojha^1,

1Department of Dermatology, Venereology and Leprosy, Bharati Vidyapeeth (Deemed To Be University) Medical College, Pune, Maharashtra, India

Corresponding author: Dr. Sahana Ojha, Department of Dermatology, Venereology and Leprosy, Bharati Vidyapeeth Medical College, Pune, Maharashtra, India. sahanaojha@gmail.com

Received: 2025-02-27, Accepted: 2025-06-16, Epub ahead of print: 2025-08-19,

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This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

How to cite this article: Sardesai VR, Ojha S. Diagnostic and prognostic utility of inflammatory biomarkers in dermatology: A narrative review. Indian J Dermatol Venereol Leprol. doi: 10.25259/IJDVL_350_2025

Abstract

Dermatological diseases are characterised by a wide array of clinical features that often overlap, complicating their diagnosis. The use of readily available drugs can further modify these features, necessitating early invasive interventions for accurate diagnosis. Recent advancements in the identification of biomarkers specific to various dermatologic disorders have significantly improved diagnostic accuracy, severity grading, and prognosis. For instance, the assessment of cytokines and chemokines has been explored as a complementary diagnostic tool for inflammatory skin diseases, revealing distinct inflammatory signatures. The integration of precision medicine in dermatology, driven by molecular biomarkers, has facilitated personalised treatment approaches. Biomarkers in conditions like hidradenitis suppurativa and psoriasis have guided the use of targeted therapies, enhancing treatment efficacy. Moreover, non-invasive imaging modalities are being investigated as potential biomarkers, offering a promising avenue for assessing disease severity and therapeutic response without the need for invasive procedures. The exploration of proteomic signatures in inflammatory skin diseases, has further enriched our understanding of disease pathogenesis and systemic inflammation, identifying novel biomarkers that correlate with clinical parameters, thus paving the way for more precise and individualised treatment strategies. We describe the different validated and emerging biomarkers in various dermatological disorders in this narrative review, along with a discussion on their clinical utility and future directions.

Keywords

Acute phase reactant

biomarker

inflammatory marker

diagnostic markers

prognostic markers.

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Introduction

Dermatological diseases represent a heterogeneous group of disorders affecting the integumentary system- the skin, hair, and nails. These conditions arise from diverse aetiologies, encompassing inflammatory responses, autoimmune dysfunctions, and infections. Inflammatory diseases, like atopic dermatitis (AD) and psoriasis, result from exaggerated immune responses that lead to erythema, pruritus, and scaling. Autoimmune disorders, such as vitiligo and systemic lupus erythematosus (SLE), involve aberrant immune activity against self-antigens, causing a variety of cutaneous presentations, including hyperpigmentation, hypopigmentation, and mucocutaneous lesions. Infectious diseases, caused by bacteria, viruses, and fungi, manifest as pustules, vesicles, or macules. This broad spectrum of aetiologies and clinical manifestations underscores the complexity of dermatological diseases and necessitates a comprehensive approach to diagnosis and management. Diagnosing these conditions is complex due to overlapping clinical presentations, which are often altered by prior treatment and by their atypical manifestations. This obligates invasive procedures like skin biopsies, or costly and time-consuming laboratory tests. The emergence of biomarker tests offers a promising avenue to address these diagnostic challenges by providing objective, reproducible and non-invasive assessments of disease activity, as adjuncts to clinical examination, facilitating precise and timely diagnoses and improving patient outcomes.¹ We present a narrative review aimed at summarising the utility of inflammatory biomarkers across dermatological conditions, with emphasis on their clinical relevance and emerging tools.

Biomarkers

Definition: Biomarkers are objectively measurable indicators of normal or pathological biological processes, or responses to a therapeutic intervention. In dermatology, they serve as surrogate markers that reflect disease activity, severity, prognosis, or treatment response. Among these, inflammatory markers, such as cytokines, chemokines, and acute phase reactants (APRs), are especially relevant due to their key role in the pathogenesis of several dermatoses.

Value of Biomarkers: Given the clinical heterogeneity and overlapping presentations of many dermatological conditions, making an accurate diagnosis is often challenging. Biomarkers offer a reliable, quantitative adjunct to clinical evaluation, improving diagnostic precision, facilitating timely interventions, and enabling stratification of disease severity. Their integration supports the principles of precision dermatology by guiding therapeutic decisions, monitoring response to treatment, and predicting prognosis.

Types of Biomarkers: Biomarkers in dermatology can be broadly categorised based on their function as¹:

Diagnostic biomarkers- which help confirm the presence of disease
Prognostic biomarkers- which predict disease course or complications
Predictive biomarkers- which estimate response to treatment
Monitoring biomarkers- used to track treatment efficacy or relapse

They can also be classified by origin:

Cytokines (e.g., interleukin-6, tumour necrosis factor-alpha)
Chemokines (e.g., CXCL8)
Enzymes (e.g., matrix metalloproteinases)
Proteins (e.g., C-reactive protein (CRP), fibrinogen, eosinophil cationic protein (ECP)
Human leukocyte antigen (HLA) markers
Epithelial derived factors like thymic stromal lymphopoietin (TSLP).¹

Many of these are produced by hepatocytes, but others originate from fibroblasts, macrophages, adipocytes, endothelial cells, and lesional keratinocytes. APRs, a subset of biomarkers, are defined by serum level changes of ≥25% during inflammation² and are further classified by response magnitude (major, moderate, minor) [Table 1] and direction (positive or negative) [Table 2].^2-4 The Classification of Biomarkers as per their type, sample source and clinical use is described in Table 3.

Table 1: Classification of APRs based on magnitude

Major APRs

Moderate APRs

Minor APRs

10-100-fold increase

Rise 4 hours after stimulus

Peak at 24-72 hours

2- to 10-fold increase

Rise 2-4 hours after stimulus

Peak at 2-3 days

Decline at 7-14 days

<2-fold increase

Rise is gradual and slow

C-reactive Protein

Serum Amyloid A

α-1 Acid Glycoproteins (AGP)

Fibrinogen

Haptoglobin

Ceruloplasmin

Table 2: Classification of APRs based on function and direction of change

Function	Positive APRs	Negative APRs
Complement system	C3 C4 C9 Factor B C1 inhibitor C4b-binding protein Mannose-binding lectin
Coagulation and fibrinolytic system	Fibrinogen Plasminogen Tissue plasminogen activator Urokinase Protein S Vitronectin Plasminogen-activator inhibitor 1	Factor XII Antithrombin
Antiproteases	a1-Protease inhibitor a1-Antichymotrypsin Pancreatic secretory trypsin inhibitor Inter-a-trypsin inhibitors
Transport proteins	Ceruloplasmin Haptoglobin Hemopexin	Transferrin Prealbumin (transthyretin)
Participants in inflammatory responses	Secreted phospholipase A2 Lipopolysaccharide-binding protein Interleukin-1–receptor antagonist Granulocyte colony-stimulating factor Procalcitonin
Others	C-reactive protein Serum amyloid A a1-Acid glycoprotein Fibronectin Ferritin Angiotensinogen Hepcidin Lactoferrin	Retinol binding protein Albumin α-fetoprotein thyroxine binding globulin Insulin-like growth factor 1

Table 3: Classification of Biomarkers mentioning type, sample and clinical use

Biomarker	Type	Sample source	Clinical utility
CRP	Non-specific APR (Monitoring)	Serum	Systemic inflammation in psoriasis, HS, acne, AD
SAA	Non-specific APR (Monitoring)	Serum	Correlates with severity; higher in psoriasis than in AD
Ferritin	Non-specific APR (Monitoring)	Serum	Elevated in severe psoriasis and acne; indicates systemic inflammation
CXC Chemokine Ligand 10 (CXCL10)	Diagnostic, Prognostic	Serum, Lesional skin	Disease activity in vitiligo, AA, SLE; relapse predictor
Interleukin 17A (IL- 17A)	Predictive, Monitoring	Serum, Tissue	Reflects Th17 activation; guides biologic therapy in psoriasis
Interleukin 36γ (IL 36γ)	Diagnostic	Lesional skin	Psoriasis-specific marker; indicates keratinocyte-driven inflammation
Periostin	Prognostic	Serum, Lesional skin	Tissue remodelling and chronicity in atopic dermatitis
Anti-dsDNA	Diagnostic, Prognostic	Serum	Specific for SLE; correlates with renal involvement and flare prediction
Anti Bullous Pemphigoid antigen 180	Diagnostic, Monitoring	Serum	Reflects disease activity in bullous pemphigoid
Tryptase	Diagnostic	Serum	Diagnostic marker in mastocytosis; indicates mast cell burden
S100B	Prognostic, Monitoring	Serum	Marker of melanocyte damage in vitiligo; associated with disease progression
Runt-related transcription factor 2 (RUNX2)	Prognostic	Lesional skin	Linked to disease progression in mycosis fungoides
Galectin-9	Prognostic, Monitoring	Serum	Disease activity marker in dermatomyositis and SLE
microRNA-4484	Diagnostic	Salivary exosomes	Non-invasive diagnostic marker in early detection of oral lichen planus
Haptoglobin	Non-specific APR (Monitoring)	Serum	Elevated in vitiligo; marker of systemic inflammation
Transcriptional co-activator with PDZ-binding motif (TAZ)	Predictive	Tissue	Implicated in lichen planus pathogenesis via the Hippo pathway; potential marker under evaluation

Advantages

Biomarkers provide an objective, reproducible, and non-invasive means of evaluating disease status. They reduce dependence on subjective clinical assessments and invasive procedures such as skin biopsies. Additionally, biomarkers can detect subclinical disease activity, enable early therapeutic intervention, assist in tailoring individualised treatment regimens, and facilitate monitoring of therapeutic response and relapse, ultimately improving clinical outcomes.

Despite their promise, several limitations hinder the widespread clinical application of biomarkers. Many lack disease specificity and are influenced by genetic, environmental, and comorbid factors.² Standardised reference values are often unavailable, and inter-laboratory variability can affect interpretation. High costs, limited availability in routine dermatology settings, and the need for specialised laboratory infrastructure further restrict use. Moreover, most biomarkers require validation in large-scale, population-based studies.

By identifying molecular signatures unique to specific dermatological conditions, biomarkers enable more accurate diagnoses and reproducible assessments of disease activity and treatment efficacy.⁵ This shift toward data-driven, individualised care marks a significant step forward in dermatology and holds promise for improving long-term patient outcomes.

Biomarkers in specific dermatological conditions

A. Inflammatory skin diseases [Table 4]

Table 4: Biomarkers in inflammatory dermatoses

Disease	Key biomarkers	Type	Sample source	Clinical utility
Psoriasis	CRP, SAA, IL-17A, IL-22, IL-36γ, Ferritin, CXCL8	Diagnostic, Predictive, Monitoring	Serum, Lesional skin	Assess severity, systemic inflammation, guides biologics (e.g., IL-17 inhibitors)
Atopic dermatitis	IL-4, IL-13, IL-17, IL-22, Periostin, Eotaxin, IgE	Diagnostic, Prognostic, Predictive	Serum, Lesional skin	Th2/Th17 profiling, severity stratification, biologic targeting (e.g., Dupilumab)
Acne vulgaris	CRP, IL-1β, TNF-α, Hepcidin, Ferritin, IGF-1	Diagnostic, Monitoring	Serum	Indicates severity, inflammatory burden, endocrine-inflammation link guiding treatment direction
Hidradenitis suppurativa	CRP, IL-1β, TNF-α, IL-17	Diagnostic, Predictive, Monitoring	Serum, Lesional tissue	Reflects disease severity, guides anti-TNF/IL-17 therapy, monitors response

Atopic Dermatitis

In AD, various biomarkers and APRs significantly influence disease pathogenesis and clinical severity. CRP and Serum Amyloid A (SAA), though non-specific, are often elevated in inflammatory conditions, including AD.^6,7 Kaminishi et al. identified T helper 2 (Th2) cytokines and Interleukins 4, 5 and 13 (IL-4, IL-5, IL-13) as central to AD pathogenesis, with IL-13 correlating with elevated serum IgE, a hallmark of the disease.⁸

Additionally, periostin, an extracellular matrix protein induced by IL-13 and IL-4, plays a role in epidermal remodelling and chronicity in AD.⁹ Studies by Ungar (2020) and Nakajima (2014) highlighted contributions from Th17 cytokines, Interleukins 17, 22 (IL-17, IL-22), with IL-17 enhancing Th2 responses and IL-22 correlating with disease severity.^10,11 Elevated chemokines like eotaxin (CCL11) recruits eosinophils, intensifying inflammation.^12,13 Nitric oxide synthase 2, human beta-defensin-2, and matrix metalloproteinases are other biomarkers which aid in diagnosis, monitor disease severity and treatment response.¹³ Collectively, these biomarkers reveal the complex Th2/Th17- driven immunological landscape, supporting targeted therapeutic strategies.

Psoriasis

Biomarkers such as CRP and SAA reflect systemic inflammation and correlate with psoriasis severity. These are driven by T helper 1 (Th1) cytokines, interferon-gamma (IFN-γ), tumour necrosis factor-alpha (TNF-α), and Th17 cytokines (IL-17, IL-22).^14,15 IL-17A stimulates SAA synthesis, explaining its higher expression in psoriasis compared to AD.^16,17 Severe psoriasis is associated with increased ferritin-iron ratios, indicating systemic inflammation. CXCL8 further amplifies neutrophil recruitment and keratinocyte activation.¹⁸

Importantly, IL-36γ has emerged as a psoriasis-specific biomarker, promoting keratinocyte-mediated inflammation and correlating with lesion activity. Its expression helps distinguish psoriasis from other inflammatory dermatoses and supports its potential as a therapeutic target.¹⁹ These biomarkers are valuable in stratifying disease severity, monitoring therapeutic response, and selecting biologic therapy.

Acne Vulgaris

CRP positively correlates with acne severity, serving as a progression marker.²⁰ Hepcidin, an APR, is also elevated in acne vulgaris and may contribute to post-inflammatory hyperpigmentation, indicating its potential role in the inflammatory process of acne according to El-Taweel et al., 2019.²¹ Serum ferritin, elevated in nodulocystic acne, further emphasises the inflammatory nature of severe cases.²¹ Pro-inflammatory cytokines like TNF-α and Interleukin 1-beta (IL-1β) drive CRP production and inflammatory cascades. Additionally, hormonal factors such as androgens and insulin like growth factor- 1 (IGF-1) modulate inflammatory markers and sebaceous gland activity.²² Together, these biomarkers reflect the endocrine-inflammatory axis in acne and may guide systemic therapy decisions.

4. Hidradenitis Suppuritiva

Hidradenitis suppuritiva (HS) patients demonstrate elevated CRP, correlating with disease severity.²² TNF-α, IL-1β, and IL-17 are central inflammatory drivers, with TNF-α being a validated target for biologics like adalimumab.^23,24 Witte-Händel et al., 2019 showed that IL-1β facilitates immune cell infiltration and tissue destruction, while IL-17 contributes to chronic inflammation and treatment resistance.²⁵ Anti-IL-17 biologics have shown encouraging results in early trials. Monitoring these cytokines aids in evaluating disease severity, guiding treatment, and predicting therapeutic outcomes,^25-27

B. Autoimmune mucocutaneous blistering disorders [Table 5]

Table 5: Biomarkers in autoimmune bullous disorders

Disease	Key Biomarkers	Type	Sample Source	Clinical Utility
Pemphigus vulgaris/foliaceus	Anti-Dsg1, Anti-Dsg3	Diagnostic, Monitoring	Serum	Diagnosis confirmation, correlates with severity and relapse risk
Bullous pemphigoid	Anti-BP180, Anti-BP230	Diagnostic, Monitoring	Serum	Tracks disease activity, treatment response, flare prediction

Pemphigus group

Pemphigus group of disorders, including pemphigus vulgaris and foliaceus, are characterised by the autoantibodies targeting desmoglein 3 (Dsg3) and desmoglein 1 (Dsg1), which are essential adhesion proteins in keratinocytes.^28,29 Autoantibody titres correlate with disease activity, serving as diagnostic and monitoring tools.³⁰ Inflammatory markers such as CRP and erythrocyte sedimentation rate (ESR) are often elevated, but lack specificity. The quantification of Dsg antibody titres via ELISA aids in determining disease severity and therapeutic response and may serve as a relapse predictor during follow-up.³¹

Pemphigoid group of disorders

In bullous pemphigoid, autoantibodies against the NC16A domain of BP180 (a transmembrane hemidesmosomal protein) and BP230 (an intracellular plakin) mediate subepidermal blistering.^32,33 Anti-BP180 antibody levels correlate with disease severity and treatment response, and are useful in tracking disease flares.^34,35 While CRP and ESR may support the inflammatory profile, anti-BP180 ELISA remains the primary biomarker for diagnosis and disease monitoring.³⁶

C. Connective tissue diseases [Table 6]

Table 6: Biomarkers in connective tissue diseases

Disease	Key biomarkers	Type	Sample source	Clinical utility
Systemic lupus erythematosus (SLE)	ANA, Anti-dsDNA, C3/C4, Galectin-3, CXCL10	Diagnostic, Prognostic, Monitoring	Serum	Diagnosis, renal involvement prediction, disease activity tracking
Systemic sclerosis (SSc)	Anti-Scl-70, ACA, Anti-RNA Pol III, SPRY4-IT1	Diagnostic, Prognostic	Serum	Stratifies disease subtype, organ involvement prediction
Dermatomyositis (DM)	Anti-Mi-2, Anti-MDA5, Anti-TIF1-γ, CXCL10, Galectin-9	Diagnostic, Prognostic, Monitoring	Serum	Diagnosis and subtype differentiation, ILD risk prediction, flare monitoring

Systemic Lupus Erythematosus

Antinuclear antibodies (ANA) serve as primary diagnostic markers in SLE.³⁷ Anti-double-stranded DNA (anti-dsDNA) antibodies offer greater specificity, particularly in lupus nephritis.³⁸ Complement 3 and 4 levels (C3, C4) are reduced during active disease phases due to complement consumption.³⁹ Elevated ESR and CRP support systemic inflammation but are non-specific.^40,41 Emerging markers such as serum TNF-α and galectin-3 may provide additional insights into disease activity.⁴²

Systemic Sclerosis

In scleroderma, or systemic sclerosis (SSc), autoantibodies like anti-topoisomerase I (anti-Scl-70), anti-centromere antibodies (ACA), and anti-RNA polymerase III (Anti-RNA Pol III) help stratify disease subtypes and predict organ involvement. Anti-Scl-70 is associated with diffuse SSc and interstitial lung disease (ILD), while ACA is linked to limited SSc and a lower risk of pulmonary hypertension.^43-46 Inflammatory markers like CRP and ESR provide a general inflammatory context but lack disease specificity.⁴⁷ A 2020 study on emerging biomarkers included long noncoding ribonucleic acids like SPRY4-IT1 and demonstrated their potential in diagnosing SSc and differentiating its subtypes.⁴⁶

Dermatomyositis

In dermatomyositis, high CRP, SAA, and elevated ferritin characterise the classical biomarker profile.⁴⁸ Myositis-specific autoantibodies (MSAs), anti-Mi-2, anti-NXP2, anti-TIF1-γ, anti-SAE, and anti-MDA5, guide diagnosis and prognosis.^49,50 Anti-Mi-2 correlates with classic cutaneous features and favourable outcomes, whereas anti-MDA5 is associated with rapidly progressive ILD. Other promising biomarkers include soluble CD163, CD206, neopterin, galectin-3/9, and circulating T-cell subsets like CD4+CXCR5+CCR7(lo)PD-1(hi) and TIGIT+CD226+CD4 T-cells, which are associated with disease activity.⁵¹ Galectin-9 and CXCL10 have been validated as biomarkers for disease activity in juvenile dermatomyositis.⁵² These biomarkers provide tools for assessing disease activity thereby guiding treatment strategies in dermatomyositis.

D. Infectious diseases [Table 7]

Table 7: Biomarkers in infectious dermatoses

Disease	Key biomarkers	Type	Sample source	Clinical utility
Leprosy	CRP, ESR, α1-antitrypsin, C3, Pentraxin-3	Monitoring	Serum	Reflects systemic inflammation; pentraxin-3 useful in ENL reaction monitoring
Cutaneous tuberculosis	CFP-10, ESAT-6, Ag85, PPE18	Diagnostic	Blood, Lesional tissue	Immune-based diagnosis confirmation; supports clinical and histopathological findings
Viral infections (HSV, HPV)	HSV DNA, HSV IgG/IgM, Viral load	Diagnostic	Serum, Tissue swabs	Differentiates primary vs recurrent infection, assesses immunosuppression status
Bacterial infections	CRP, PCT, HNL	Diagnostic, Monitoring	Serum	Differentiates bacterial vs viral etiology, assesses acute inflammation
Fungal infections	Beta-D-glucan (BDG), Galactomannan (GM), Fungal DNA	Diagnostic	Serum, BAL, PCR	Detects systemic fungal infections; useful in immunocompromised patients

Viral infections

Most cutaneous viral infections can be diagnosed by clinical examination or bedside tests. However, in unusual, chronic cases, biomarkers and APRs play a crucial role in ttheir diagnosis. Herpes Simplex Virus (HSV) biomarkers include viral DNA and HSV antibodies (IgM, IgG). HSV DNA can be quantitatively analysed in infected tissues, such as ganglia, to assess viral latency and replication.⁵³ The detection of HSV-specific immunoglobulin M and G antibodies (IgM, IgG), against viral envelope and capsid antigens, is significant in identifying primary and recurrent infections.⁵⁴ In Human Papillomavirus (HPV), viral DNA is detected in cervical specimens using polymerase chain reaction (PCR) methods, helping identification of different oncogenic serotypes.⁵⁵ Additionally, low-avidity IgG antibodies to HSV in HPV-positive patients can indicate immunosuppression and are considered a diagnostic marker for HPV-induced precancerous conditions.⁵⁵ Beyond these specific markers, CRP and SAA are elevated in viral infections, representing general indicators of inflammation and immune response.^56,57

Bacterial infections

SAA, CRP, procalcitonin (PCT), and neutrophil lipocalin (HNL) differentiate bacterial from viral infections.^57,58 Streptococcus pyogenes infections are identified through bacterial culture and anti-streptolysin O (ASO) titres, which reflect the body’s immune response.^59,60 These biomarkers, along with traditional methods, enable accurate diagnosis and treatment of bacterial infections.

Mycobacterial infections

In lepromatous leprosy and erythema nodosum leprosum (ENL), elevated ESR, CRP, α1-antitrypsin, C3, and circulating immune complexes are observed, reflecting systemic inflammation.^61,62 Pentraxin-3 has been highlighted as a potential biomarker for monitoring disease progression and reactive states in multibacillary leprosy, as serum levels are notably higher in patients before and during acute ENL reactions.⁶² In cutaneous tuberculosis, biomarkers like Culture Filtrate Protein 10 (CFP 10), Early Secreted Antigenic Target 6 kDa (ESAT 6), Antigen 85 A and B (Ag85A, B), Pro-Pro-Glu 18 (PPE18) have diagnostic significance and are integral to understanding the immune response.^63,64

Fungal infections

For superficial fungal infections like dermatophytosis, clinical examination suffices, though nonspecific reactants like CRP and SAA can indicate inflammation.⁶⁵ In deep invasive fungal infections, combining biomarkers like galactomannan (GM) and β-D-glucan (BDG) improves diagnostic accuracy, especially in immunocompromised patients.^66,67 PCR assays provide high specificity for detecting fungal DNA.⁶⁸ Despite advancements, clinical judgment remains vital alongside these biomarkers due to their variable sensitivity and specificity.^69,70,71

E. Immune mediated dermatologic diseases [Table 8]

Table 8: Biomarkers in immune mediated dermatoses

Disease	Key biomarkers	Type	Sample source	Clinical utility
Vitiligo	IFN-γ, CXCL9, CXCL10, S100B, hs-CRP, Haptoglobin, Ceruloplasmin	Diagnostic, Prognostic, Monitoring	Serum, Lesional skin	Reflects disease activity, progression; guides immunotherapy
Alopecia areata (AA)	Interleukins 2, 6, 15, CXCL10, MDA, IMA, Granulysin, IL-6, MPO	Diagnostic, Prognostic, Monitoring	Serum, Lesional skin	Indicates acute vs chronic activity, relapse risk, systemic inflammation
Lichen planus (LP)	Cortisol, IgA, Adiponectin, TAZ, miR-4484, Metabolites	Diagnostic, Monitoring	Serum, Saliva, Tissue	Reflects inflammatory, stress-mediated and molecular changes; early detection

Alopecia Areata

Alopecia Areata (AA), an autoimmune disorder causing non-scarring hair loss, involves key cytokines including interleukin 2 (IL-2), interleukin 2 receptor antagonist (IL-2RA), Janus Kinase 3 (JAK3), Interleukin 15 (IL-15), CXC Chemokine Ligand 9 and 10 (CXCL9), (CXCL10), linked to Th1/Th2 response imbalance.⁷² CXCL10, produced by hair follicles, plays a role in Th1 and Tc1 cell infiltration during the acute phase of AA.⁷³ Systemic inflammation markers such as interleukins 6, 8 (IL-6, IL-8), oxidised low density lipoprotein receptor 1 (OLR1), and myeloperoxidase (MPO) indicate a broader systemic immune dysregulation in AA patients.⁷⁴ Oxidative stress markers like malondialdehyde (MDA), ischaemic-modified albumin (IMA) and granulysin reflect a pro-oxidative state in AA correlating with disease severity.^75,76 These biomarkers elucidate AA’s pathophysiology and suggest targets for therapeutic interventions.

Vitiligo

Elevated haptoglobin and ceruloplasmin, which are APRs associated with inflammation, are noted in vitiligo.⁷⁷ Additionally, increased high-sensitivity CRP (hs-CRP) has been linked to generalised vitiligo.^78,79 Cytokines IFN-γ, CXCL9, and CXCL10 are highly expressed in the skin and plasma of vitiligo patients and correlate with disease activity.^80-82 S100B, linked to melanocyte cytotoxicity, is elevated in nonsegmental vitiligo, correlating with disease progression.⁸³ These biomarkers enhance understanding of vitiligo pathogenesis and promote targeted therapy development.

Lichen Planus

Traditional biomarkers such as cortisol, immunoglobulin A (IgA), and adiponectin have been studied in the context of oral lichen planus (OLP), suggesting their role in the disease’s inflammatory and neuroendocrine involvement.^84,85 Emerging evidence has also implicated transcriptional coactivator with PDZ-binding motif (TAZ), a component of the Hippo signalling pathway, in disease pathogenesis.⁸⁶ Additionally, metabolic profiling using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (HRMS) has identified specific metabolites that offer promising diagnostic accuracy.⁸⁷ Non-invasive biomarkers such as salivary exosomal microRNAs, notably miR-4484, further contribute to understanding the molecular basis of OLP and may facilitate early detection.⁸⁸ Together, these advancements expand the biomarker repertoire and hold potential for enhancing the clinical management of lichen planus.

F. Cutaneous Neoplasms and Cell Proliferation Disorders [Table 9]

Table 9: Biomarkers in cutaneous neoplasms and cell proliferation disorders

Disease	Key biomarkers	Type	Sample source	Clinical utility
Mycosis fungoides (MF)	RUNX2, α1-acid glycoprotein, miR-155, miR-146a, NEAT-1	Diagnostic, Prognostic	Lesional skin, Serum	Correlates with disease progression and subtype stratification
Mastocytosis	Tryptase, Galectin-3, Allergin-1	Diagnostic, Prognostic	Serum	Confirms diagnosis; stratifies risk of anaphylaxis and mediator-related symptoms
Histiocytosis	CRP, IL-6, SAA, VWF, Factor VIII	Monitoring	Serum	Reflects systemic inflammation and endothelial dysfunction
BCC	FGF20, KIF23, NCAPG, PTGES2, CD133, Cornulin	Diagnostic, Prognostic	Lesional skin	Differentiates BCC from trichoblastoma and SCC; correlates with immune infiltration
SCC	Dsg2, CD73, MYBL2, C5aR1, circRNA-CYP24A1	Diagnostic, Prognostic	Lesional skin	Indicates tumor progression and metastatic potential

Mycosis Fungoides

In mycosis fungoides (MF), a cutaneous T-cell malignancy, elevated α1-acid glycoprotein (AGP) and α1-antichymotrypsin levels indicate inflammation across most stages.⁸⁹ Additionally, RUNX2 can identify patients likely to progress to advanced stages of MF, as it is upregulated in malignant T-cells.⁸⁹ The miR-34a/NEAT-1/p53 axis shows potential as significant elevations are seen in the nuclear enriched abundant transcript-1 (NEAT-1) and tumour protein p53 levels in MF patients, suggesting their role in disease pathogenesis.⁹⁰ Dysregulation of miR-146a and miR-155, with proteomic analysis, underscores insights into the disease’s molecular interplay.^91,92 These biomarkers facilitate diagnosis and therapeutic strategies.

Mastocytosis

In mastocytosis, marked by clonal mast cell proliferation, tryptase is used as a primary marker of mast cell activation and proliferation to make a diagnosis.^93,94 Hereditary alpha tryptasemia (HαT) is a genetic biomarker associated with elevated serum tryptase levels and an increased risk of severe anaphylaxis in mastocytosis patients.⁹⁵ Novel biomarkers like E-selectin, adrenomedullin, T-cell immunoglobulin, mucin domain 1, and CUB domain-containing protein 1/CD138 help differentiate between clinical subtypes.⁹³ These markers refine diagnostic accuracy and symptom management. Furthermore, Gülen et al. in 2021 showed that Allergin-1, pregnancy-associated plasma protein-A, and Galectin-3 have been identified as potential markers to differentiate mastocytosis patients with anaphylaxis from those without.⁹³ These biomarkers assist in confirming the diagnosis of mastocytosis and assessing severe mediator-related symptoms, improving patient management.

Histiocytosis

CRP and IL-6 are prominent APRs in Histiocytosis.^96,97 SAA holds diagnostic relevance⁵⁷ and Von Willebrand factor (VWF) and factor VIII suggest endothelial dysfunction.⁹⁸

Basal cell carcinoma

Recent research has identified diagnostic biomarkers, including fibroblast growth factor 20 (FGF20), kinesin family member 23 (KIF23), and non-SMC condensing I complex subunit G (NCAPG), linked to immune cell infiltration, particularly T-cells, macrophages, and natural killer cells, for basal cell carcinoma (BCC).⁹⁹ Additionally, prostaglandin E synthetase 2 protein (PTGES2) and Ribonuclease T2 (RNASET2) have been highlighted as novel biomarkers and therapeutic targets for BCC. CD133 has been suggested as a biomarker for differentiating BCC from trichoblastomas.¹⁰⁰ Cornulin has been identified as a discriminant biomarker between BCC and squamous cell carcinoma.¹⁰¹ CRP and IL-6 highlight inflammation but require further specificity studies.⁹⁶

Squamous cell carcinoma

Desmoglein 2 protein (Dsg2), Ct-SLCO1B3 mRNA, and CYP24A1 circular RNA (circRNA), found in tumour-derived extracellular vesicles, demonstrate potential clinical utility for diagnosing and predicting cSCC outcomes.¹⁰² MYB proto-oncogene like 2 (MYBL2) and tyrosine kinase 1 (TK1) are implicated in cSCC initiation and progression, linking cancer pathways and signaling.¹⁰³ CD73, associated with poor prognosis, particularly in patients with hematologic malignancies, fosters immunosuppressive environments.¹⁰⁴ Additionally, C5aR1, a biomarker and therapeutic target, correlates with metastasis risk and adverse outcomes.¹⁰⁵ APRs like CRP and interleukin-6 (IL-6) provide prognostic insights, reflecting systemic inflammatory responses.^96,106 Collectively, these biomarkers offer insights into cSCC pathophysiology, targeted therapies and diagnostic advancements.

Limitation, challenges and clinical integration of biomarkers

Biomarker testing in dermatology, though promising, faces limitations that impede routine use. Variability in specificity and sensitivity across biomarkers limits their reliability.^1,107 High costs, technological demands, and limited availability further restrict accessibility. A lack of standardised protocols complicates clinical adoption, as in imaging-based biomarkers for HS.¹⁰⁸ Varied aetiologies and disease heterogeneity contribute to significant intra-disease variability in biomarker levels, making it difficult to establish consistent diagnostic or prognostic criteria and limiting the universal applicability of biomarkers.^109,110 Moreover, the widespread integration of biomarkers into clinical practice is challenged by the requirement for extensive validation and regulatory approval.^107,111 Ethical concerns, such as potential misdiagnosis, over-reliance on surrogate markers, and unnecessary interventions, underscore the need for cautious and context-sensitive application.^112-114

Traditional diagnostic methods in dermatology, such as clinical examination, histopathology, and scoring indices, remain valuable for assessing disease morphology and severity. However, they are often subjective, invasive, and time-consuming. Biomarkers offer an objective, quantifiable, and frequently non-invasive alternative, particularly useful when clinical presentations are atypical or modified by prior treatments. Biomarker-based assessments can support early diagnosis, enable disease stratification, and facilitate longitudinal monitoring. Importantly, these tools should complement, rather than replace, traditional diagnostics, especially where histological confirmation or visual scoring remains essential.

The incorporation of biomarkers into routine dermatologic care allows for more precise and individualised treatment planning. By identifying specific immune pathways activated in individual patients, biomarkers can predict therapeutic responses and assist in selecting targeted therapies, such as IL-17 inhibitors in psoriasis or anti-IgE agents in AD.^8,14 Biomarkers also enable monitoring of disease activity and treatment efficacy, allowing clinicians to escalate or de-escalate therapy in a timely manner. In autoimmune and connective tissue disorders, longitudinal trends in biomarker levels can help anticipate disease flares, thereby enabling pre-emptive interventions.

The application of biomarkers in the Indian dermatological setting presents unique challenges. Access to specialised laboratories is limited, the cost of immunoassays remains high, and insurance coverage for such diagnostics is often lacking. These factors limit the routine use of biomarkers in public and private healthcare sectors. Additionally, the paucity of population-specific reference ranges and validation studies in Indian cohorts limits the generalisability of global findings. Addressing these gaps necessitates region-specific research aimed at establishing normative values, evaluating cost-effectiveness, and developing affordable, accessible diagnostic platforms tailored to India’s epidemiological landscape. Overcoming these barriers is crucial to enhancing the reliability, feasibility, and equity of biomarker integration in clinical dermatology.

To evaluate the real-world clinical utility of biomarkers, key parameters such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and their concordance with conventional diagnostic methods (e.g., histopathology, clinical scoring systems) must be considered. While many inflammatory markers show promise, only a subset are currently validated for clinical use. Table 10 summarises the diagnostic and prognostic performance of select biomarkers across common dermatological conditions, based on published literature.^{6,9,16,19,34,37,52,80,93}

Table 10: Diagnostic and prognostic utility of selected dermatological biomarkers, including sensitivity, specificity, predictive values, and correlation with standard diagnostic tools

Disease	Biomarker	Utility	Sensitivity (%)	Specificity (%)	PPV/NPV	Clinical significance
Atopic dermatitis	Periostin	Prognostic	60-75	60-70	Not available	Reflects chronicity of lesions
Psoriasis	IL-36γ	Diagnostic	∼85	Moderate	Experimental data only	Better than CRP for lesion activity
Psoriasis, hidradenitis suppuritiva, acne	CRP	Monitoring	60-80	Low	Low PPV	Adjunctive; not disease specific
Psoriasis	IL-17A	Predictive, Monitoring	70-80	70-85	Moderate	Correlates with PASI and response to Biologics
Bullous Pemphigoid	Anti- BP120	Diagnostic, Monitoring	90-95	80-90	High PPV	Correlates with flare ups
Systemic Lupus Erythematosus	Anti- dsDNA	Diagnostic, Prognostic	70-98	80-100	High PPV for nephritis	Correlates with flare ups and nephritis
Dermatomyositis, SLE	Galectin- 9	Monitoring	Not established	Not established	Not available	Associated with flare ups in JDM
Mastocytosis	Tryptase	Diagnostic	85-95	85-90	High PPV for systemic symptoms	Aligns with mast cell burden
Vitiligo	S100B	Monitoring	65-80	70-75	Moderate	Parallels melanocyte damage and disease
Vitiligo, Alopecia Areata, SLE	CXCL10	Disease Activity, Relapse	75-90	70-80	Moderate PPV	Correlates with active disease

Future directions

Personalised medicine

Novel biomarkers like microRNAs, circulating tumour DNA, and AI-enhanced predictive models enable precise diagnostics and treatment strategies. MicroRNAs offer non-invasive diagnostic potential in cancers like melanoma due to their stability in biological fluids and disease-related expression.^115,116 The challenge is to translate these findings into clinical practice, as microRNAs are not disease-specific and results can vary across studies.¹¹⁶ Personalised medicine tailored treatments based on individual biomarker profiles, optimize efficacy and safety.¹ This helps in selecting the appropriate therapy and predicting treatment responses and potential adverse events, minimising risks.^107,117 AI integration enhances biomarker analysis, allowing refined disease staging and prognosis.¹ The synergy of novel biomarkers, personalised approaches, and AI heralds improved patient outcomes and tailored therapeutic strategies, improving the quality of life for patients.¹⁴

Skin microbiome biomarkers

Dysbiosis, or the imbalance of microbial communities, is linked to the pathogenesis of atopic dermatitis, acne, and psoriasis.^15,16,113 Microbial metabolites, produced by skin bacteria, serve as biomarkers for disease monitoring, while therapies targeting microbiome balance- probiotics, microbial transplants, metabolite supplementation- offer promising interventions^15,118 Further exploration of host-microbiome interaction is essential to harness microbiome-based personalised dermatological treatments.¹⁸

Prognostication of disorders

Biomarkers predict disease progression and complications, guiding prognosis and management. They identify patients at high risk for severe disease or complications, such as in AD, where biomarkers like nitric oxide synthase 2 and human β-defensin-2 are pivotal for severity stratification and monitoring.¹⁰ Additionally, CCL17/TARC has shown robust correlation with AD clinical severity, predicting disease flares and relapses.⁵ They can flag systemic complications such as filaggrin mutations and allergen-specific IgE are associated with asthma risk in AD patients.¹⁰⁹ Early biomarker-based intervention allows personalisation, improving long-term outcomes.^5,10,109

Conclusion

Biomarkers enhance diagnostic precision, prognostication, and treatment outcomes in dermatology. They serve as molecular indicators that aid in identifying dermatological conditions and provide insights into disease staging and therapeutic monitoring.¹⁰ This means providing the right treatment to the right patient at the right time, which is particularly crucial in managing complex skin diseases with varied phenotypes.^6,8,107 They facilitate disease stratification, optimising therapy and minimising adverse effects.^6,107

Despite significant progress, challenges in validation, cost, and clinical implementation persist. Advances in non-invasive detection, AI, and multi-omics will further refine personalised care.^1,10

To summarise, while biomarkers have already transformed dermatological practice by enabling precise and treatment strategies, continued research is crucial to unlock their full clinical potential.

Declaration of patient consent

Patient’s consent not required as there are no patients in this study.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

Use of artificial intelligence (AI)-assisted technology for manuscript preparation

The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.

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Introduction

Biomarkers

Advantages

Biomarkers in specific dermatological conditions

A. Inflammatory skin diseases [Table 4]
B. Autoimmune mucocutaneous blistering disorders [Table 5]
C. Connective tissue diseases [Table 6]
D. Infectious diseases [Table 7]
E. Immune mediated dermatologic diseases [Table 8]
F. Cutaneous Neoplasms and Cell Proliferation Disorders [Table 9]

Limitation, challenges and clinical integration of biomarkers

Future directions

Conclusion

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Diagnostic and prognostic utility of inflammatory biomarkers in dermatology: A narrative review

Abstract

Keywords

Acute phase reactant

biomarker

inflammatory marker

diagnostic markers

prognostic markers.

Introduction

Biomarkers

Advantages

Biomarkers in specific dermatological conditions

A. Inflammatory skin diseases [Table 4]

Atopic Dermatitis

Psoriasis

Acne Vulgaris

4. Hidradenitis Suppuritiva

B. Autoimmune mucocutaneous blistering disorders [Table 5]

Pemphigus group

Pemphigoid group of disorders

C. Connective tissue diseases [Table 6]

Systemic Lupus Erythematosus

Systemic Sclerosis

Dermatomyositis

D. Infectious diseases [Table 7]

Viral infections

Bacterial infections

Mycobacterial infections

Fungal infections

E. Immune mediated dermatologic diseases [Table 8]

Alopecia Areata

Vitiligo

Lichen Planus

F. Cutaneous Neoplasms and Cell Proliferation Disorders [Table 9]

Mycosis Fungoides

Mastocytosis

Histiocytosis

Basal cell carcinoma

Squamous cell carcinoma

Limitation, challenges and clinical integration of biomarkers

Future directions

Personalised medicine

Skin microbiome biomarkers

Prognostication of disorders

Conclusion

Declaration of patient consent

Financial support and sponsorship

Conflicts of interest

Use of artificial intelligence (AI)-assisted technology for manuscript preparation

References

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