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Identification of miRNA-mRNA Regulatory Network and Construction of Prognostic Signature in Cervical Cancer

机译:miRNA-mRNA监管网络的鉴定及宫颈癌预后签名的构建

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Cervical cancer (CC) remains a most prevalent female cancer worldwide, but there are few biomarkers used in diagnosis and prognosis of CC. The aim of this study is to find reliable and effective biomarkers regarding CC development. Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database to search potential miRNA-mRNA in CC. The gene ontology term enrichment and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were conducted to reveal the underlying functions and pathways of differently expressed genes (DEGs). Univariate Cox, multivariate Cox, and risk scoring methods were performed to identify a prognostic model. A total of 209 DEGs of CC were identified. In the protein-protein interaction network, hub module, and hub genes were recognized. Based on DEGs, three small molecules (thioguanosine, apigenin, and trichostatin A) were screened out as potential drugs. Two miRNAs (hsa-mir-101-3p and hsa-mir-6507-5p) and some transcription factors were found to be associated with prognosis of CC. A five-candidate gene signature (APOBEC3B, DSG2, CXCL8, ABCA8, and PLAGL1) was constructed to stratify risk subgroups for patients with CC. The risk score of the prognostic model was also found to be associated with immune cells infiltration, including mast cell activation, natural killer cells resting, dendritic cells resting, T cells regulatory (Tregs), and T cells follicular helper. The miRNA-mRNA regulatory network and the prognostic model are of great clinical significance in promoting prognosis prediction and treatment of CC.
机译:宫颈癌(CC)仍然是全世界最普遍的女性癌症,但只有很少用于CC的诊断和预后的生物标志物。本研究的目的是找到关于CC开发的可靠和有效的生物标志物。微阵列数据集从基因表达omnibus(Geo)数据库下载以搜索CC中的潜在miRNA-mRNA。进行基因本体术语富集和基因组(KEGG)途径分析的富集和京都富核分析以揭示不同表达基因(DEGS)的潜在功能和途径。进行单变量COX,多元COX和风险评分方法以鉴定预后模型。共鉴定了总共209℃。在蛋白质 - 蛋白质相互作用网络中,认识到集线器模块和轮毂基因。基于DEGS,筛选出三个小分子(Thioguanosine,Apigenin和Trichostatina)作为潜在的药物。发现两个miRNA(HSA-MIR-101-3P和HSA-MIR-6507-5P)和一些转录因子与CC的预后有关。构建了五种候选基因签名(apobec3b,dsg2,cxcl8,abca8和plagl1)以对CC患者分层风险亚组。还发现预后模型的风险得分与免疫细胞浸润有关,包括肥大细胞活化,自然杀伤细胞休息,树突细胞静息,T细胞调节(Tregs)和T细胞滤泡助手。 miRNA-mRNA调节网络和预后模型对促进CC预后预测和治疗具有很大的临床意义。

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