Psoriasis: Striving for Potential Biomarkers.

Psoriasis is a chronic disease that is caused by multiple factors and is identified by itchiness, unpleasant, red, or white scaly patches on the skin, particularly on regularly chafed body regions such as the lateral areas of the limbs. Reports suggest that globally around 2%-3% of the population suffers from psoriasis. In this review, we have discussed the clinical classification of psoriasis and also the ideal characteristics of the biomarkers. An overview regarding the discovery of the biomarker and method for validating the study has been discussed. A growing body of research suggests a link to certain other systemic symptoms such as cardiovascular disorder, metabolic syndrome, and few other comorbidities such as hypertension and nonalcoholic fatty liver disease. Natural killer (NK) cells are lymphocyte cells that concentrate on the destruction of virally infected and malignant cells; these tend to produce a wide range of inflammatory cytokines, some of which are associated with the etiology of psoriasis. Detailed information on the molecular pathogenesis of psoriasis in which interleukin (IL)-17, IL-23, tumor necrosis factor-α (TNF-α), and CCL20 play a very significant role in the development of psoriasis. In this review, we have discussed an overview of the recent state of the biomarkers available for the diagnosis and treatment of psoriasis by emphasizing on the available biomarkers such as epigenomic, transcriptomic, glycomic, and metabolomic. The most recent advancements in molecular-targeted therapy utilizing biologics and oral systemic therapy (methotrexate, apremilast) enable to adequately treat the most serious psoriatic symptoms and also the studies have validated the efficacy of biologic therapy such as TNF-α antagonist (infliximab, adalimumab), IL-23 antagonist (guselkumab, risankizumab), and IL-17 antagonist (secukinumab, ixekizumab). Finally, an overview about the technological opportunities as well as various challenges has been discussed.

Gender specificity improves the early-stage detection of clear cell renal cell carcinoma based on methylomic biomarkers.

AIM: The two genders are different ranging from the molecular to the phenotypic levels. But most studies did not use this important information. We hypothesize that the integration of gender information may improve the overall prediction accuracy. MATERIALS & METHODS: A comprehensive comparative study was carried out to test the hypothesis. The classification of the stages I + II versus III + IV of the clear cell renal cell carcinoma samples was formulated as an example. RESULTS & CONCLUSION: In most cases, female-specific model significantly outperformed both-gender model, as similarly for the male-specific model. Our data suggested that gender information is essential for building biomedical classification models and even a simple strategy of building two gender-specific models may outperform the gender-mixed model.