Jones Lamont R, Greene Joshua, Chen Kang Mei, Divine George, Chitale Dhananjay, Shah Veena, Datta Indrani, Worsham Maria J
Department of Otolaryngology-Head and Neck Surgery, Henry Ford Hospital, Detroit, Michigan, U.S.A.
Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, U.S.A.
Laryngoscope. 2017 Jan;127(1):70-78. doi: 10.1002/lary.26063. Epub 2016 Jun 16.
OBJECTIVES/HYPOTHESIS: To obtain biological insight into keloid pathogenesis and treatment using pathway analysis of genome-wide differentially methylated gene profiles between keloid and normal skin.
Prospective cohort.
Genome-wide profiling was previously done, with institutional review board approval, on six fresh keloid and six fresh normal skin tissue samples, using the Infinium HumanMethylation450 BeadChip kit. Statistically significant differentially methylated cytosine-phosphodiester bond-guanines (CpGs, n = 197) between keloid and normal tissue mapped to 152 genes. These genes were uploaded into Ingenuity Pathway Analysis (IPA) software to identify biological functions or regulatory networks interacting. The pathways (or "network") with an enrichment probability value ≤ .01 were subjected to a heuristic filter of keywords associated with keloid pathogenesis.
Of the 197 CpGs, 191 were found in the IPA database and mapped to 152 unique genes. The top 10 hypermethylated genes were ACTR3C, LRRC61, PAQR4, C1orf109, SLCO2B1, CMKLR1, AHDC1, FYCO1, CCDC34, and CACNB2. The top 10 hypomethylated genes were GALNT3, SCML4, PPP1R13L, ANKRD11, WIPF1, MX2, IFFO1, DENND1C, CFH, and GHDC. IPA identified nine pathways with enrichment probability values ≤ .01, of which five (histidine degradation V1, phospholipase C signaling, colorectal cancer metastasis signaling, P2Y purinergic receptor signaling, and Gαi signaling) were associated with keloid keywords and contained "keloid genes" (P < .05).
Genes differentially methylated between keloid and normal skin reside in known bionetwork pathways involved in critical biological functioning and signaling events in the cell. This information could be used to refine screening processes for biological significance to better understand keloid pathogenesis and to develop molecular-targeted therapy.
NA Laryngoscope, 127:70-78, 2017.
目的/假设:通过对瘢痕疙瘩与正常皮肤之间全基因组差异甲基化基因谱进行通路分析,获得有关瘢痕疙瘩发病机制及治疗的生物学见解。
前瞻性队列研究。
此前已在机构审查委员会批准下,使用Infinium HumanMethylation450 BeadChip试剂盒对6份新鲜瘢痕疙瘩组织样本和6份新鲜正常皮肤组织样本进行全基因组分析。瘢痕疙瘩组织与正常组织之间具有统计学意义的差异甲基化胞嘧啶 - 磷酸二酯键 - 鸟嘌呤(CpG,n = 197)定位到152个基因。将这些基因上传至Ingenuity Pathway Analysis(IPA)软件,以识别相互作用的生物学功能或调控网络。对富集概率值≤0.01的通路(或“网络”)进行与瘢痕疙瘩发病机制相关的关键词启发式筛选。
在197个CpG中,有191个在IPA数据库中被发现,并定位到152个独特基因。甲基化程度最高的前10个基因是ACTR3C、LRRC61、PAQR4、C1orf109、SLCO2B1、CMKLR1、AHDC1、FYCO1、CCDC34和CACNB2。甲基化程度最低的前10个基因是GALNT3、SCML4、PPP1R13L、ANKRD11、WIPF1、MX2、IFFO1、DENND1C、CFH和GHDC。IPA识别出9条富集概率值≤0.01的通路,其中5条(组氨酸降解V1、磷脂酶C信号传导、结直肠癌转移信号传导、P2Y嘌呤能受体信号传导和Gαi信号传导)与瘢痕疙瘩关键词相关且包含“瘢痕疙瘩基因”(P < 0.05)。
瘢痕疙瘩与正常皮肤之间差异甲基化的基因存在于已知的生物网络通路中,这些通路参与细胞中的关键生物学功能和信号事件。该信息可用于优化生物学意义的筛选过程,以更好地理解瘢痕疙瘩发病机制并开发分子靶向治疗。
NA 《喉镜》,2017年,第127卷,第70 - 78页