Differentiation of tumour-stage mycosis fungoides, psoriasis vulgaris and normal controls in a pilot study using serum proteomic analysis

Multi-Omics Approach to Improved Diagnosis and Treatment of Atopic Dermatitis and Psoriasis

Psoriasis and atopic dermatitis fall within the category of cutaneous immune-mediated inflammatory diseases (IMIDs). The prevalence of IMIDs is increasing in industrialized societies, influenced by both environmental changes and a genetic predisposition. However, the exact immune factors driving these chronic, progressive diseases are not fully understood. By using multi-omics techniques in cutaneous IMIDs, it is expected to advance the understanding of skin biology, uncover the underlying mechanisms of skin conditions, and potentially devise precise and personalized approaches to diagnosis and treatment. We provide a narrative review of the current knowledge in genomics, epigenomics, and proteomics of atopic dermatitis and psoriasis. A literature search was performed for articles published until 30 November 2023. Although there is still much to uncover, recent evidence has already provided valuable insights, such as proteomic profiles that permit differentiating psoriasis from mycosis fungoides and β-defensin 2 correlation to PASI and its drop due to secukinumab first injection, among others.

Analytical approaches to assess metabolic changes in psoriasis

Psoriasis is one of the most common human skin diseases, although its development is not limited to one tissue, but is associated with autoimmune reactions throughout the body. Overproduction of pro-inflammatory cytokines and growth factors systemically stimulates the proliferation of skin cells, which manifests as excessive exfoliation of the epidermis, and/or arthritis, as well as other comorbidities such as insulin resistance, metabolic syndrome, hypertension, and depression. Thus, there is a great need for a thorough analysis of the pathophysiology of psoriatic patients, including classical methods, such as spectrophotometry, chromatography, or Western blot, and also novel omics approaches such as lipidomics and proteomics. Moreover, the extensive pathophysiology forces increased research examining biological changes in both skin cells, and systemically. A wide range of techniques involved in lipidomic research based on a combination of mass spectrometry and different types of chromatography (RP-LC-QTOF-MS/MS, HILIC-QTOF-MS/MS or RP-LC-QTRAP-MS/MS), have allowed comprehensive assessment of lipid modification in psoriatic skin and provided new insight into the role of lipids and their mechanism of action in psoriasis. Moreover, proteomic analysis using gel-nanoLC-OrbiTrap-MS/MS, as well as MALDI-TOF/TOF techniques facilitates the description of panels of enzymes involved in lipidome modifications, and the response of the endocannabinoid system to metabolic changes. Psoriasis is known to alter the expression of proteins that are involved in the inflammatory and antioxidant response, as well as protein biosynthesis, degradation, as well as cell proliferation and apoptosis. Knowledge of changes in the lipidomic and proteomic profile will not only allow the understanding of psoriasis pathophysiology, but also facilitate proper and early diagnosis and effective pharmacotherapy.

Proteomic Profiling Change and Its Implies in the Early Mycosis Fungoides (MF) Using Isobaric Tags for Relative and Absolute Quantification (iTRAQ)

Purpose

Mycosis fungoides (MF) is the most common T-cell lymphoma, with indolent biologic behavior in the early stage and features of invasive in the tumor stage. The diagnosis of MF is still ambiguous and difficult. We focused on the proteomic profiling change in the pathogenesis of early MF and identified candidate biomarkers for early diagnosis.

Methods

We collected peripheral blood samples of MF patients and healthy individuals (HI) performed proteomic profiling analysis using isobaric tags for relative and absolute quantification (iTRAQ) platform. Differently expressed proteins (DEPs) were filtered, and involved biological functions were analyzed through Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA) software.

Results

We identified 78 DEPs including fifty proteins were upregulated and 28 proteins were downregulated in the MF group with HI as a control. Total DEPs were analyzed according to the biological regulation and metabolic process through GO analysis. The pathways of LXR/RXR activation and FXR/RXR activation were significantly activated, in which APOH, CLU, and ITIH4 were involved. The top annotated disease and function network was (Cancer, Organismal Injury and Abnormalities, Reproductive System Disease), with a key node CLU. These DEPs were involved in cancer, including thyroid carcinoma, head and neck carcinoma, and cancer of secretory structure, in which CLU, GNAS, and PKM played an indirect role in the occurrence and development of cancer. Relevant causal network was IL12 (family), which is related to GNAS, PKM, and other DEPs.

Conclusion

Proteomic profiling of early-stage MF provided candidate protein biomarkers such as CLU, GNAS, and PKM, which benefit the early diagnosis and understanding of the mechanism of MF development. Besides, lipid metabolism may be one of the pathogenesis of MF, and IL12 was a potential marker for the diagnosis and treatment of early MF.

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

Proteomics in Psoriasis

Psoriasis has been thought to be driven primarily by innate and adaptive immune systems that can be modified by genetic and environmental factors. Complex interplay between inflammatory cytokines and T-cells, especially Th1 and Th17 cells, leads to abnormal cell proliferation and psoriatic skin lesions. Nevertheless, such mechanisms do not entirely represent the pathogenesis of psoriasis. Moreover, earlier and better biomarkers in diagnostics, prognostics, and monitoring therapeutic outcomes of psoriasis are still needed. During the last two decades, proteomics (a systematic analysis of proteins for their identities, quantities, and functions) has been widely employed to psoriatic research. This review summarizes and discusses all of the previous studies that applied various modalities of proteomics technologies to psoriatic skin disease. The data obtained from such studies have led to (i) novel mechanisms and new hypotheses of the disease pathogenesis; (ii) biomarker discovery for diagnostics and prognostics; and (iii) proteome profiling for monitoring treatment efficacy and drug-induced toxicities.

Proteomic Approaches to Biomarker Discovery in Cutaneous T-Cell Lymphoma

Cutaneous T-cell lymphoma (CTCL) is the most frequently encountered type of skin lymphoma in humans. CTCL encompasses multiple variants, but the most common types are mycosis fungoides (MF) and Sezary syndrome (SS). While most cases of MF run a mild course over a period of many years, other subtypes of CTCL are very aggressive. The rapidly expanding fields of proteomics and genomics have not only helped increase knowledge concerning the carcinogenesis and tumor biology of CTCL but also led to the discovery of novel markers for targeted therapy. Although multiple biomarkers linked to CTCL have been known for a relatively long time (e.g., CD25, CD45, CD45RA, and CD45R0), compared to other cancers (lymphoma, melanoma, colon carcinoma, head and neck cancer, renal cancer, and cutaneous B-cell lymphoma), information about the antigenicity of CTCL remains relatively limited and no dependable protein marker for CTCL has been discovered. Considering the aggressive nature of some types of CTCL, it is necessary to identify circulating molecules that can help in the early diagnosis, differentiation from inflammatory skin diseases (psoriasis, nummular eczema), and aid in predicting the prognosis and evolution of this pathology. This review aims to bring together some of the information concerning protein markers linked to CTCL, in an effort to further the understanding of the convolute processes involved in this complex pathology.

Proteome technologies in studying the pathogenesisof psoriasis

Psoriasis is one of the most prevalent inflammatory dermatoses. At the same time, its pathogenesis has not been studied in full. Proteome profiling is currently a promising method to study the biological mechanisms of developing different diseases. Proteome technologies make it possible to detect changes in the skin protein profile in psoriatic patients and identify the revealed proteins. The proteins serve as potential targets for drugs or biomarkers for assessing the patients individual drug response. The article describes the key achievements in the field of studying the pathogenesis of psoriasis using proteome technologies (two-dimensional electrophoresis and mass spectrometry) set out in the latest literature sources, and also analyzes the results of the authors studies aimed at detecting proteins being markers of the patients response to the infliximab therapy.

Post-genomics and skin inflammation

Atopic dermatitis and psoriasis are two chronic skin inflammatory diseases that have so far received a greater attention within the scientific community through different post-genomic approaches; on the contrary, acne, which is undoubtedly one of the most common skin disorders involving inflammatory processes, seems to be still quite neglected under the post-genomic point of view. In this paper, we will review how post-genomic technologies have provided new fundamental tools for the analysis of these three conditions and we will cast light on their potential in addressing future research challenges.

Search for potential biomarkers of inveterate dermatoses by means of proteomic analysis

The review covers the key achievements of proteome studies using mass spectrometry and two-dimensional electrophoresis methods in the field of dermatology Works studying the most prevalent chronic dermatoses such as psoriasis, atopic dermatitis, acne vulgaris and mycosis fungoidea are examined. Proteome analysis in dermatology is a promising technique today because it makes it possible to study molecular pathogenic mechanisms of skin chronic diseases in a greater detail. In addition, proteome technologies are aimed at searching for potential disease biomarkers and targets for drugs.

A proteomic workflow for discovery of serum carrier protein-bound biomarker candidates of alcohol abuse using LC-MS/MS

The diagnosis and care of patients with alcohol abuse and dependence is hampered by a lack of sensitive and specific screening and monitoring tests. Proteomics is a good approach to search for biomarkers of alcohol abuse. Serum carrier protein-bound proteins have attracted significant interest because they remain a relatively un-mined region of the proteome. In the present study, a proteomic workflow including LC-MS/MS with enrichment of serum carrier protein-bound biomarkers technique was applied to profile the changes in quality and quantity of serum carrier protein-bound proteins for the discovery of novel biomarker candidates of alcohol abuse. In total, 311 proteins identified with high confidence were discovered to be bound to serum carrier proteins. Complement isoforms, Ig fragments, and apolipoprotein family proteins are the main serum carrier-bound proteins. Protein quantification analysis with and without concern as to gender revealed that gender is a critical consideration for biomarker development in alcohol abuse. Identified proteins not previously associated with alcohol abuse include gelsolin, selenoprotein P, serotransferrin, tetranectin, hemopexin, histidine-rich glycoprotein, plasma kallikrein, and vitronectin. Altered abundance of these proteins suggests that they may be potential novel biomarkers for alcohol abuse.

QUADOMICS: an adaptation of the Quality Assessment of Diagnostic Accuracy Assessment (QUADAS) for the evaluation of the methodological quality of studies on the diagnostic accuracy of '-omics'-based technologies

OBJECTIVES

To adapt the Quality Assessment of Diagnostic Accuracy Assessment (QUADAS) to the particular methodological challenges posed by research on '-omics'-based diagnostic tests.

DESIGN AND METHODS

We generated new guidelines by appraising the suitability of each criterion from QUADAS to '-omics'-based diagnostic research, and by adding new items that addressed specific sources of error. In addition, we defined four phases in the evaluation of a diagnostic test.

RESULTS

Twelve of the 14 criteria from QUADAS were retained in the new tool. The items relating to selection criteria and the description of the test were reformulated, and the criteria about external validation and the availability of clinical data were applied only in studies in the last research phase. Four new items were incorporated to QUADOMICS related to pre-analytical conditions and methods to avoid overfitting.

CONCLUSIONS

QUADOMICS is an adaptation of QUADAS to the special nature of '-omics'-based diagnostic research. The tool adds new items that assess quality issues specific to this research, and may enhance the application of '-omics'-based discoveries to clinical \and public health practice.