Burns : journal of the International Society for Burn Injuries · 2026
Transcriptome profile of keloid patients in Zunyi, China via multiple bioinformatic analyses.
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Sun Nana, Liu Bo, Zeng Ni, Wen Yang, Cai Yuan, Xu Guangchao, Liu Jie, Liu Jianguo
Аннотация
BACKGROUND: Keloids are pathological scars. There is a genetic predisposition for keloids, and the molecular events associated with keloid need to be profiled.
METHODS: This study used whole-genome RNA-Seq to analyze aberrant gene expression between 17 keloid and 8 normal skin samples at the Affiliated Hospital of Zunyi Medical University, followed by comprehensive bioinformatic analysis.
RESULTS: RNA-Seq revealed 2610 differentially expressed genes (DEGs) with FDR < 0.01, and RT-qPCR verified selected 13 DEGs. Principal component analysis using Partek Flow revealed distinct gene expression patterns between keloid and normal skin samples. Ingenuity Pathway Analysis (IPA) analyses revealed that "Oxidative phosphorylation", "Mitochondria dysfunction", and "Sirtuin signaling pathway" were the most highly affected canonical pathways, consistent with KEGG analysis. Upstream regulator analysis of IPA revealed that "TP53", "tumor microenvironment TLE3", "mitochondrial protein UQCC3 and ClpP", and "TGFβ1" were upregulated, while the metabolism-related molecules "CPT1B", "Insulin", "INSR", and "PPARGC1A" were downregulated, along with endocrine disruption, consistent with Gene Ontology (GO). The DEG profiles were highly correlated with the gene expression of skin diseases in the GEO database using Illumina BaseSpace Correlation Engine (BSCE).
CONCLUSIONS: This study integrated RNA-Seq with Partek Flow, IPA, KEGG, GO and BSCE Bioinformatics to comprehensively reveal the molecular events associated with keloid development and progression.
METHODS: This study used whole-genome RNA-Seq to analyze aberrant gene expression between 17 keloid and 8 normal skin samples at the Affiliated Hospital of Zunyi Medical University, followed by comprehensive bioinformatic analysis.
RESULTS: RNA-Seq revealed 2610 differentially expressed genes (DEGs) with FDR < 0.01, and RT-qPCR verified selected 13 DEGs. Principal component analysis using Partek Flow revealed distinct gene expression patterns between keloid and normal skin samples. Ingenuity Pathway Analysis (IPA) analyses revealed that "Oxidative phosphorylation", "Mitochondria dysfunction", and "Sirtuin signaling pathway" were the most highly affected canonical pathways, consistent with KEGG analysis. Upstream regulator analysis of IPA revealed that "TP53", "tumor microenvironment TLE3", "mitochondrial protein UQCC3 and ClpP", and "TGFβ1" were upregulated, while the metabolism-related molecules "CPT1B", "Insulin", "INSR", and "PPARGC1A" were downregulated, along with endocrine disruption, consistent with Gene Ontology (GO). The DEG profiles were highly correlated with the gene expression of skin diseases in the GEO database using Illumina BaseSpace Correlation Engine (BSCE).
CONCLUSIONS: This study integrated RNA-Seq with Partek Flow, IPA, KEGG, GO and BSCE Bioinformatics to comprehensively reveal the molecular events associated with keloid development and progression.