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.

中文翻译：

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miRNA-mRNA调控网络的鉴定和宫颈癌预后标记的构建。

宫颈癌（CC）仍然是全球最普遍的女性癌症，但很少有用于诊断和预后的生物标志物。这项研究的目的是找到有关CC发展的可靠和有效的生物标志物。从基因表达综合（GEO）数据库下载了微阵列数据集，以搜索CC中潜在的miRNA-mRNA。进行了基因本体术语富集和基因与基因组京都百科全书（KEGG）途径分析，以揭示不同表达基因（DEG）的潜在功能和途径。进行单变量Cox，多变量Cox和风险评分方法以鉴定预后模型。总共鉴定出209个CC的CC。在蛋白质间相互作用网络中，可以识别集线器模块和集线器基因。基于DEG，三个小分子（硫鸟嘌呤，芹菜素，和曲古抑菌素A）被筛选为潜在药物。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。五种候选基因签名（构建APOBEC3B，DSG2，CXCL8，ABCA8和PLAGL1）以对CC患者的危险亚组进行分层。还发现预后模型的风险评分与免疫细胞浸润有关，包括肥大细胞活化，自然杀伤细胞静止，树突细胞静止，T细胞调节（Tregs）和T细胞滤泡辅助。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。