Department of Oral and Maxillofacial Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
PLoS One. 2019 May 29;14(5):e0216834. doi: 10.1371/journal.pone.0216834. eCollection 2019.
The study aimed to investigate the ceRNA network in biological development of Tongue Squamous Cell Carcinoma (TSCC) and to identify novel molecular subtypes of TSCC to screen potential biomarkers for target therapy and prognosis by using integrated genomic analysis based on The Cancer Genome Atlas (TCGA) database.
Data on gene expressions were downloaded from TCGA and GEO database. Differentially expressed RNAs(DERNAs) were shown by DESeq2 package in R. Functional enrichment analysis of DEmRNAs was performed using clusterprofilers in R. PPI network was established by referring to String website. Survival analysis of DERNAs was carried out by survival package in R. Interactions among mRNAs, miRNAs and lncRNAs were obtained from Starbase v3.0 and used to construct ceRNA network. Consensus Cluster Plus package was applied to identify molecular subtypes. All key genes were validated by comparing them with GEO microarray data. Statistical analyses of clinical features among different subtypes were performed using SPSS 22.0.
A total of 2907 mRNAs (1366 up-regulated and 1541 down-regulated), 191miRNAs (98 up-regulated and 93 down-regulated) and 1831 lncRNAs (1151 up-regulated and 680 down-regulated) were identified from tumor and normal tissues. A ceRNA network was successfully constructed and 15 DEmRNAs, 1 DEmiRNA, 2 DElncRNAs associated with prognosis were employed. Furthermore, we firstly identified 2 molecular subtypes, basal and differentiated, and found that differentiated subtype consumed less alcohol and was related to a better overall survival.
The study constructed a ceRNA network and identified molecular subtypes of TSCC, and our findings provided a novel insight into this intractable cancer and potential therapeutic targets and prognostic indicators.
本研究旨在通过基于癌症基因组图谱(TCGA)数据库的综合基因组分析,探讨舌鳞状细胞癌(TSCC)生物学发育中的 ceRNA 网络,并鉴定 TSCC 的新型分子亚型,以筛选潜在的靶向治疗和预后的生物标志物。
从 TCGA 和 GEO 数据库下载基因表达数据。使用 R 中的 DESeq2 包显示差异表达的 RNA(DERNAs)。使用 R 中的 clusterprofilers 对 DEmRNAs 进行功能富集分析。通过参考 String 网站构建 PPI 网络。使用 R 中的 survival 包进行 DERNAs 的生存分析。从 Starbase v3.0 获取 mRNAs、miRNAs 和 lncRNAs 之间的相互作用,用于构建 ceRNA 网络。应用 Consensus Cluster Plus 包识别分子亚型。通过与 GEO 微阵列数据比较验证所有关键基因。使用 SPSS 22.0 对不同亚型之间的临床特征进行统计学分析。
从肿瘤和正常组织中鉴定出 2907 个 mRNAs(1366 个上调和 1541 个下调)、191 个 miRNAs(98 个上调和 93 个下调)和 1831 个 lncRNAs(1151 个上调和 680 个下调)。成功构建了 ceRNA 网络,并选用了 15 个与预后相关的 DEmRNAs、1 个 DEmiRNA 和 2 个 DElncRNAs。此外,我们首次鉴定出 2 个分子亚型,即基底型和分化型,并发现分化型亚型消耗的酒精较少,且与整体生存预后较好相关。
本研究构建了 TSCC 的 ceRNA 网络和鉴定了分子亚型,为这种难治性癌症提供了新的见解和潜在的治疗靶点和预后指标。