Identification of miRNA-mRNA Regulatory Network and Construction of Prognostic Signature in Cervical Cancer
Identification of miRNA-mRNA Regulatory Network and Construction of Prognostic Signature in Cervical Cancer
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DOI：
10.1089/dna.2020.5452
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发表时间：
2020-04-28
影响因子：
3.1
通讯作者：
Zhang, Jinsong
中科院分区：
文献类型：
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作者：
Mei, Yong;Jiang, Pinping;Zhang, Jinsong
子宫颈癌（cervical cancer，CC）是世界范围内最常见的女性恶性肿瘤之一，但目前用于诊断和预后判断的生物标志物较少。本研究的目的是找到可靠和有效的生物标志物CC的发展。从基因表达综合数据库（GEO）下载微阵列数据集以搜索CC中潜在的miRNA-mRNA。为了揭示差异表达基因（DEG）的潜在功能和途径，进行了基因本体论术语富集和基因与基因组京都百科全书（KEGG）途径分析。采用单变量考克斯、多变量考克斯和风险评分方法确定预后模型。共确定了209个社区中心的DEG。在蛋白质-蛋白质相互作用网络中，枢纽模块和枢纽基因被识别。基于DEG，筛选出三种小分子（硫代鸟苷、芹菜素和曲马斯他丁A）作为潜在药物。两种miRNAs（hsa-mir-101- 3 p和hsa-mir-6507- 5 p）和一些转录因子与CC的预后相关。构建了5个候选基因签名（APOBEC 3B、DSG 2、CXCL 8、ABCA 8和PLAGL 1），以分层CC患者的风险亚组。还发现预后模型的风险评分与免疫细胞浸润相关，包括肥大细胞活化、静息自然杀伤细胞、静息树突状细胞、调节性T细胞（Tcells regulatory，Tcells）和滤泡辅助性T细胞。miRNA-mRNA调控网络和预后模型对促进CC的预后预测和治疗具有重要的临床意义。
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.