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March 15, 2026MedicineOpen Access

Identification of mitochondrial dynamics-related biomarkers in psoriasis using bioinformatics and Mendelian randomization

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Bioinformatics reveals potential biomarkers in psoriasis and their immune responses, indicating new avenues for therapeutic targets.

The research seeks to identify mitochondrial dynamics-related biomarkers in psoriasis using bioinformatics and Mendelian randomization techniques.
Used datasets from psoriasis cases to analyze mitochondrial dynamics-related genes
Employed weighted gene co-expression network analysis to identify significant module genes
Implemented Mendelian randomization to determine causal relationships and candidate genes
Applied protein–protein interaction networks to refine potential biomarkers and validate using machine learning techniques
Conducted gene set enrichment analysis to explore the biological implications of identified biomarkers.
Identified C1orf43, SNF8, STOML2, and MRPS16 as key biomarkers linked to mitochondrial dynamics in psoriasis
Different immune responses and cell types show significant differential expression between psoriasis patients and controls
Selected 6 feature genes with good predictive ability for distinction using machine learning techniques
Explored associations of biomarkers with pathways such as pyrimidine metabolism, DNA replication, and proteasome.

Zhang et al. (2026) studied this question.

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