Hedan Yang,1,* Xiuzhen Li,1,* Xiaoli Zhang,1 Yiping Ge,1 Yin Yang,1 Tong Lin2 

1Department of Cosmetic Laser Surgery, Hospital of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, People’s Republic of China; 2Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs, Hospital of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Tong Lin, Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs, Hospital of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, People’s Republic of China, Email ddlin@hotmail.com Yin Yang, Department of Cosmetic Laser Surgery, Hospital of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, People’s Republic of China, Email yushiyy@163.com

Background: Vitiligo is an acquired depigmentary disorder caused by the loss of functional melanocytes. Increasing evidence suggests that competing endogenous RNA (ceRNA) interactions participate in this process, yet their global architecture in vitiligo remains unclear.
Objective: To delineate a long non-coding RNA (lncRNA)-microRNA (miRNA)-mRNA ceRNA network associated with vitiligo and to identify blood-borne RNA markers with diagnostic potential.
Methods: miRNA, mRNA, and lncRNA expression data from vitiligo patients and healthy controls were obtained from the GEO database (GSE141655 and GSE186928). Differentially expressed (DE) mRNAs, miRNAs and lncRNAs were screened (|log2 FC| > 0.5, adj. p< 0.05). Functional enrichment, STRING-based protein–protein interaction (PPI) mapping, and lncRNA–mRNA co-expression analysis (Pearson r > 0.9) was performed. miRNA–mRNA pairs were predicted with miRWalk 3.0, and miRNA–lncRNA pairs with miRanda v3.3a (score ≥ 140, energy ≤− 20 kcal mol−¹). Triplets that shared the same miRNA, displayed positive lncRNA––mRNA correlation, and showed inverse expression relative to the miRNA were combined into a ceRNA network; hub nodes were ranked by degree centrality. Candidate lncRNAs were validated by RT-qPCR in peripheral blood from 20 vitiligo patients and 20 matched controls.
Results: A total of 454 DE-mRNAs (341 down-, 113 up-regulated), 22 DE-miRNAs (6 down-, 16 up-regulated), and 281 DE-lncRNAs (112 down-, 169 up-regulated) were identified. Enrichment analysis highlighted pathways linked to melanogenesis, oxidative stress, PI3K–Akt, JAK–STAT and IL-17 signalling. The ceRNA network comprised 33 lncRNAs, 12 miRNAs and 58 mRNAs; SLC32A1, GRIA2, PRKACG and WNT1 were top hub proteins in the PPI sub-network. Blood validation confirmed up-regulation of CASC19, NUCB1-AS1 and LINC01485 and down-regulation of VAV3–AS1, SPATA13–AS1, ZNF350–AS1 and LINC00677 (all p< 0.05).
Conclusion: Our findings map a vitiligo-related ceRNA landscape and pinpoints seven circulating lncRNAs with diagnostic promise. The results provide a foundation for probing non-coding RNA-mediated mechanisms and developing targeted therapies for vitiligo.

Keywords: vitiligo, ceRNA, lncRNA, bioinformatics