Screening key lncRNAs with diagnostic and prognostic value for head and neck squamous cell carcinoma based on machine learning and mRNA-lncRNA co-expression network analysis
Screening key lncRNAs with diagnostic and prognostic value for head and neck squamous cell carcinoma based on machine learning and mRNA-lncRNA co-expression network analysis
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DOI：
10.3233/cbm-190694
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发表时间：
2020-01-01
影响因子：
3.1
通讯作者：
Tan, Pingqing
中科院分区：
文献类型：
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作者：
Hu, Ying;Guo, Geyang;Tan, Pingqing
背景：头颈部鳞状细胞癌(HNSCC)是世界第七大常见癌症类型。本研究旨在寻找作为HNSCC诊断和预后生物标志物的长非编码RNAs(LncRNAs)。方法：基于TCGA数据集，鉴定HNSCC与正常组织之间差异表达的mRNAs(DEmRNAs)和LncRNAs(DElncRNAs)。通过机器学习和生存分析，评估lncRNAs对HNSCC的潜在诊断和预后价值。我们还构建了共表达网络和功能标注。结果：共获得3363个DEmRNAs(1822个下调和1541个上调的mRNAs)和32个DElncRNAs(13个下调和19个上调的lncRNAs)。共有13个lncRNAs(IL12A.AS1、RP11.159F24.6、RP11.863P13.3、LINC00941、FOXCUT、RNF144A.AS1、RP11.218E20.3、HCG22、HAGLROS、LINC01615、RP11.351J23.1、AC024592.9和MIR9.3HG)被定义为诊断HNSCC的最佳生物标志物。支持向量机模型、决策树模型和随机森林模型的曲线下面积分别为0.983、0.842和0.983，其特异度和敏感度分别为95.5%和96.2%、77.3%和97.6%和93.2%和97.8%。其中，AC024592.9、LINC00941、LINC01615和MIR9-3HG不仅是诊断LncRNAs的理想标记，而且与生存时间有关。焦点黏附、ECM-受体相互作用、肿瘤途径和细胞因子-细胞因子受体相互作用是DEmRNAs与已确定的最佳诊断lncRNAs共表达的四条显著丰富的途径。但所选择的大多数DEmRNAs和DElncRNAs的表达与我们的综合分析结果一致，包括LINC00941、LINC01615、FOXCUT、TGA6和MMP13。结论：AC024592.9、LINC00941、LINC01615和MIR9-3HG不仅是一个最好的诊断InncRNAs的生物标志物，而且是一个预测IncRNAs的生物标志物。
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer around the world. The aim of this study was to seek the long non-coding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of HNSCC.METHODS: Base on TCGA dataset, the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified between HNSCC and normal tissue. The machine learning and survival analysis were performed to estimate the potential diagnostic and prognostic value of lncRNAs for HNSCC. We also build the co-expression network and functional annotation. The expression of selected candidate mRNAs and lncRNAs were validated by Quantitative real time polymerase chain reaction (qRT-PCR).RESULTS: A total of 3363 DEmRNAs (1822 down-regulated and 1541 up-regulated mRNAs) and 32 DElncRNAs (13 downregulated and 19 up-regulated lncRNAs) between HNSCC and normal tissue were obtained. A total of 13 lncRNAs (IL12A.AS1, RP11.159F24.6, RP11.863P13.3, LINC00941, FOXCUT, RNF144A.AS1, RP11.218E20.3, HCG22, HAGLROS, LINC01615, RP11.351J23.1, AC024592.9 and MIR9.3HG) were defined as optimal diagnostic lncRNAs biomarkers for HNSCC. The area under curve (AUC) of the support vector machine (SVM) model, decision tree model and random forests model and were 0.983, 0.842 and 0.983, and the specificity and sensitivity of the three model were 95.5% and 96.2%, 77.3% and 97.6% and 93.2% and 97.8%, respectively. Among them, AC024592.9, LINC00941, LINC01615 and MIR9-3HG was not only an optimal diagnostic lncRNAs biomarkers, but also related to survival time. The focal adhesion, ECM-receptor interaction, pathways in cancer and cytokine-cytokine receptor interaction were four significantly enriched pathways in DEmRNAs co-expressed with the identified optimal diagnostic lncRNAs. But for most of the selected DEmRNAs and DElncRNAs, the expression was consistent with our integrated analysis results, including LINC00941, LINC01615, FOXCUT, TGA6 and MMP13.CONCLUSION: AC024592.9, LINC00941, LINC01615 and MIR9-3HG was not only an optimal diagnostic lncRNAs biomarkers, but also were a prognostic lncRNAs biomarkers.