Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach

Abstract

Psoriasis is a chronic inflammatory skin disorder. Although it has been studied extensively, the molecular mechanisms driving the disease remain unclear. In this study, we utilized a tree-based machine learning approach to explore the gene regulatory networks underlying psoriasis. We then validated the regulators and their networks in an independent cohort. We identified some key regulators of psoriasis, which are candidates to serve as potential drug targets and disease severity biomarkers. According to the gene regulatory network that we identified, we suggest that interferon signaling represents a key pathway of psoriatic inflammation.

Sericin coated thin polymeric films reduce keratinocyte proliferation via the mTOR pathway and epidermal inflammation through IL17 signaling in psoriasis rat model

“…The mTOR signaling proteins are upregulated in psoriatic skin and proliferation keratinocytes 20 , 22 3* 19.3 120 22.50 TEP1_RAT Telomerase protein component 1 (TEP1) TEP1 is a component of the ribonucleoprotein complex responsible for telomerase activity enhancement in psoriasis 60 , 61 3* 14.5 106 6.00 K2C75_RAT Keratin, type II cytoskeletal 75 (KRT75) KRT75 is an essential component of keratin intermediate filaments in hair + . Upregulation of KRT75 may be associated with atopic dermatitis and psoriasis via peroxisome proliferator-activated receptors 62 1, 3, and 4* 13.3 279 3.20 K1C14_RAT Keratin, type I cytoskeletal 14 (KRT14) Alteration of the KRT14 gene may play a possible role in the pathogenesis of psoriasis and its severity 32 3 and 4* 50.7 932 2.80 K2C5_RAT Keratin, type II cytoskeletal 5 (KRT5) KRT5 differential expression is more relevant to atopic dermatitis than in psoriasis 63 3 and 4 ^ 33.5 805 2.76 S10A8_RAT Protein S100-A8 (S100A8) S100A8 is a calcium- and zinc-binding protein, which plays an important role in the regulation of inflammatory processes and immune responses + . S100A8-S100A9 are the most upregulated proteins expressed in the psoriatic skin 64 1 and 2* 55.1 311 2.65 ANXA1_RAT ...…”

Current developments and perspectives in psoriasis

“…Some of the above questions could also benefit from machine learning and artificial intelligence. 100,101 However, whether the various levels of complexity of chronic inflammation can be mapped in this way in the near future remains to be seen. As in many other areas, including medicine, the multicenter collection and analysis of large amounts of data (big data) on psoriasis patients will also have to address issues of data and privacy protection.…”

“…Current registry data clearly indicate a protective effect with respect to cardiovascular events 99 Some of the above questions could also benefit from machine learning and artificial intelligence 100,101 . However, whether the various levels of complexity of chronic inflammation can be mapped in this way in the near future remains to be seen.…”

Aktuelle Entwicklungen und Perspektiven bei Psoriasis

“…Manche der oben genannten Fragestellungen könnten auch von maschinellem Lernen und künstlicher Intelligenz profitieren. 100,101 Ob die verschiedenen Ebenen der Komplexität chronischer Entzündungen in naher Zukunft derart abgebildet werden können, bleibt allerdings abzuwarten. Wie in vielen anderen Bereichen, auch der Medizin, werden beim multizentrischen Sammeln und Auswerten großer Datenmengen (big data) zu Psoriasis-Patienten auch Fragen des Daten-und Persönlichkeitsschutzes zu lösen sein.…”

“…Aktuelle Registerdaten deuten klar auf einen protektiven Effekt in Hinblick auf kardiovaskuläre Ereignisse hin 99 Manche der oben genannten Fragestellungen könnten auch von maschinellem Lernen und künstlicher Intelligenz profitieren 100,101 . Ob die verschiedenen Ebenen der Komplexität chronischer Entzündungen in naher Zukunft derart abgebildet werden können, bleibt allerdings abzuwarten.…”