1. Biomarkers of Disease Progression in People with Psoriasis: A Scoping Review. Ramessur R, Corbett M, Marshall D, Acencio ML, Barbosa IA, et al. Br J Dermatol. 2022 Apr 28. doi: 10.1111/bjd.21627. Epub ahead of print. PMID: 35482474.
2. Combined Single-Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning. Liu J, Kumar S, Hong J, Huang ZM, et al. Front Immunol. 2022 Mar 2;13:835760. doi: 10.3389/fimmu.2022.835760. PMID: 35309349; PMCID: PMC8924042.
3. Comparative Genetic Analysis of Psoriatic Arthritis and Psoriasis for the Discovery of Genetic Risk Factors and Risk Prediction Modelling. Soomro M, Stadler M, Dand N, et al. Arthritis Rheumatol. 2022 May 4. doi: 10.1002/art.42154. Epub ahead of print. PMID: 35507331.
Why these articles were chosen
Identifying biomarkers related to disease progression in psoriasis (PsO) represents a field of research that has been extensively explored. Clinical implications are dramatically relevant as the identification of biomarkers would help select those patients at risk of disease progression, defined as progression in disease severity and/or development of comorbidities, such as type II diabetes and arthritis (PsA).
Notwithstanding the efforts in identifying valuable candidates, no biomarkers are part of the routine practice as they are not supported by sufficient evidence. However, a scoping review1 analyzing 61 studies proposed a panel of 22 potential biomarkers of PsO progression, identified at the genomic, proteomic, and metabolomic levels. Most of them are involved in pathogenically relevant signaling pathways (i.e., IL-23/IL-17 pathway), antigen processing, and presentation (HLA-C*06:02, HLA-B*27, HLA-B*38, HLA-B*08), leucocyte recruitment (i.e., CXCL10) and activation (IL-13).
Eleven and fourteen candidates demonstrated potential biomarkers for disease severity and PsA development. A recent transcriptomic study attempted to identify additional potential biomarkers of PsA to facilitate early diagnosis via a machine-learning-based model.2 Overall, the study shed light on the differences between PsO and PsA in terms of circulating immune cell profile. Still, the machine-learning approach did not capture early or ephemeral biomarkers of disease progression in PsO patients who develop PsA.2 Other genetic risk factors predicting PsA development were investigated by Soomro M. et al.3 Their study proposed a novel susceptibility locus for PsA mapping on chromosome 22q11 and defined genes differentiating PsA from PsO (genes encoding members for NF- kB signaling and WNT signaling) through genome-wide meta-analyses. Nevertheless, the prediction model did not reveal any genomic risk factor for PsA, highlighting the modest performance of a risk model based on the currently available datasets.3 This finding confirmed the occurrence of multiple biases and methodological limitations in studies exploring the identification of biomarkers in psoriasis. Indeed, the ideal study is longitudinal in design, with a primary objective that should consist of the investigation of biomarkers related to disease progression. Information on key patient cohort characteristics (including participants’ ethnicity, age, psoriasis age-of-onset, and psoriasis subtypes) should be included. Confounding factors such as that ongoing therapies that could modify the course of disease and the accuracy in diagnosing comorbidities should be considered. These aspects were underlined by Ramessur R. et al., suggesting hints for designing and conducting future biomarker studies.1
Nowadays, a list of candidate biomarkers might be proposed, though they do not own sufficient evidence for clinical use without further validation. Thereby, identifying biomarkers predicting the risk of disease progression and/or development of comorbid conditions, in particular PsA, remains an important research question still unanswered.