RESUMEN
MicroRNAs (miRNAs) play a crucial role in the early diagnosis of autoinflammatory diseases, with Hidradenitis Suppurativa (HS) being a notable example. HS, an autoinflammatory skin disease affecting the pilosebaceous unit, profoundly impacts patients' quality of life. Its hidden nature, with insidious initial symptoms and patient reluctance to seek medical consultation, often leads to a diagnostic delay of up to 7 years. Recognizing the urgency for early diagnostic tools, recent research identified significant differences in circulating miRNA expression, including miR-24-1-5p, miR-146a-5p, miR26a-5p, miR-206, miR338-3p, and miR-338-5p, between HS patients and healthy controls. These miRNAs serve as potential biomarkers for earlier disease detection. Traditional molecular biology techniques, like reverse transcription quantitative-polymerase chain reaction (RT-qPCR), are employed for their detection using specific primers and probes. Alternatively, short peptides offer a versatile and effective means for capturing miRNAs, providing specificity, ease of synthesis, stability, and multiplexing potential. In this context, we present a computational simulation pipeline designed for crafting peptide sequences that can capture circulating miRNAs in the blood of patients with autoinflammatory skin diseases, including HS. This innovative approach aims to expedite early diagnosis and enhance therapeutic follow-up, addressing the critical need for timely intervention in HS and similar conditions.
