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Integrative multi-omics analysis identifies a mitochondrial dysfunction-associated diagnostic signature, molecular
Xin Le1, Youfen Fan1, Sida Xu1
1Burn Department, Ningbo No. 2 Hospital, Wenzhou Medical University, Ningbo, Zhejiang Province 315010, China.
Background:
Keloid, a fibroproliferative disorder, has limited treatments and lacks reliable biomarkers. Mitochondrial dysfunction is implicated in fibrosis, but its transcriptomic role in keloid remains incompletely characterised.
Methods:
We integrated five bulk transcriptomic cohorts (core training set: 46 samples; independent diagnostic validation: 7 keloid/control samples) and a single-cell RNA-seq dataset (8592 cells) to profile mitochondrial-related genes. Differential expression, WGCNA, machine learning, NMF clustering, immune infiltration, single-cell scoring, and sensitivity analyses using a stricter mitochondrial energy-metabolism subset were performed.
Results:
We identified 648 mitochondrial-related differentially expressed genes, with downregulated genes enriched in cell cycle and mitotic pathways and upregulated genes enriched in immune-related pathways. A stricter mitochondrial energy-metabolism subset showed the same dominant downregulated direction (48 significant genes; 41 downregulated). WGCNA revealed ME11 as the keloid-associated module (r = 0.504, P = 3.58 ×10-4). LASSO selected a seven-gene transcriptomic signature (RAB3GAP2, NCOA6, NSF, HEBP1, IDS, MSX1, BRWD1) achieving AUC 0.881 in internal testing and 1.000 in the very small independent validation set (n = 7), which should be interpreted cautiously. The keloid-associated score (KAS), defined as an unsupervised PC1 score of these genes for stratification rather than as the supervised LASSO probability, was higher in keloid than controls (P = 1.27 × 10⁻⁵) but showed only moderate cross-cohort generalisation in GSE188952 (AUC = 0.667). Additional public GEO mining identified GSE218007 as a supportive sensitivity dataset (donor-mean AUC = 0.889), although fixed KAS performance remained heterogeneous across external datasets. Bulk immune correlations did not remain significant after FDR correction; single-cell KAS was highest in dendritic cells. Perturbation-signature analysis generated exploratory therapeutic hypotheses rather than validated drug candidates.
Conclusion:
This study provides a hypothesis-generating mitochondrial-related transcriptomic framework for keloid diagnosis and stratification. Validation in larger cohorts and functional tissue is required before the signature, KAS, or drug hypotheses are clinically actionable.
