Squamous Cell Carcinoma-related Antigen (SCCr-Ag), sICAM-1 and β2-Microglobulin are Useful Markers of Disease Activity in Psoriasis

Pità, Ornella De; Frezzolini, Alessandra; Cianetti, A.; Sanctis, G. De; Fontana, Luis; Bottari, V.

doi:10.1080/000155599750011354

Cited by 18 publications

(1 citation statement)

References 6 publications

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“…In addition, upregulation of SERPINB3 is associated with benign hyperplasia [ 32 ]. SERPINB3 is highly expressed in psoriasis [ 33 ], cutaneous SCC [ 34 ], and all SCC specimens as well as psoriasis [ 32 ]. In our predictions, we found that SERPINB3 is associated with malignant tumors of the cervix and malignant neoplasms of the skin.…”

Section: Resultsmentioning

confidence: 99%

Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions

Suratanee

Plaimas

2018

PLoS ONE

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Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several studies have attempted to identify disease-gene associations, the number of possible disease-gene associations is very small. High-throughput technologies have been established experimentally to identify the association between genes and diseases. However, these techniques are still quite expensive, time consuming, and even difficult to perform. Thus, based on currently available data and knowledge, computational methods have served as alternatives to provide more possible associations to increase our understanding of disease mechanisms. Here, a new network-based algorithm, namely, Disease-Gene Association (DGA), was developed to calculate the association score of a query gene to a new possible set of diseases. First, a large-scale protein interaction network was constructed, and the relationship between two interacting proteins was calculated with regard to the disease relationship. Novel plausible disease-gene pairs were identified and statistically scored by our algorithm using neighboring protein information. The results yielded high performance for disease-gene prediction, with an F-measure of 0.78 and an AUC of 0.86. To identify promising candidates of disease-gene associations, the association coverage of genes and diseases were calculated and used with the association score to perform gene and disease selection. Based on gene selection, we identified promising pairs that exhibited evidence related to several important diseases, e.g., inflammation, lipid metabolism, inborn errors, xanthomatosis, cerebellar ataxia, cognitive deterioration, malignant neoplasms of the skin and malignant tumors of the cervix. Focusing on disease selection, we identified target genes that were important to blistering skin diseases and muscular dystrophy. In summary, our developed algorithm is simple, efficiently identifies disease–gene associations in the protein-protein interaction network and provides additional knowledge regarding disease-gene associations. This method can be generalized to other association studies to further advance biomedical science.

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Section: Resultsmentioning

confidence: 99%

Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions

Suratanee

Plaimas

2018

PLoS ONE

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Inflammation

Steinhoff¹,

Griffiths²,

Church³

et al. 2004

Rook's Textbook of Dermatology

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Inflammation, Immunology and Allergy

Steinhoff

2016

Rook's Textbook of Dermatology, Ninth Edition

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Inflammation can be defined as a complex body defence mechanism responding to dangerous endogenous or exogenous stimuli in order to restore body homeostasis. Thus, the skin is continuously challenged to prevent inflammation, and inflammatory skin diseases are very common worldwide. Similarly, allergic reactions (types 1–4) are inflammatory processes created by the body system to respond to ‘danger signals’ of various origins. Very early in phylogenesis, inflammation is associated with the host defence against infectious, allergic or toxic agents, UV radiation, noxious heat and cold, or other injury. Innate and later adaptive immune mechanisms have been established that, uncontrolled, potentially lead to chronic disease or death, as in anaphylactic shock. The body system responds with the induction of various inflammatory pathways such as radical oxygen species, nitric oxide derivatives, complement compounds, adenosine monophosphate, antimicrobial peptides, cytokines, chemokines, prostaglandins, leukotrienes, proteases, neuropeptides and growth factors, just to name a few. This chapter systematically covers the various mechanisms that regulate inflammatory and allergic responses and refers in a translational setting to the diseases that are involved in order better to understand the clinical characteristics of a skin disease, its differential diagnoses and optimal treatment options.

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Squamous Cell Carcinoma-related Antigen (SCCr-Ag), sICAM-1 and β2-Microglobulin are Useful Markers of Disease Activity in Psoriasis

Cited by 18 publications

References 6 publications

Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions

Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions

Inflammation

Inflammation, Immunology and Allergy

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