基于机器学习和 mRNA-lncRNA 共表达网络分析筛选具有诊断和预后价值的头颈鳞状细胞癌关键 lncRNAs。

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.

机构信息

Department of Radiotherapy, Hunan Cancer Hospital and the Affliated Cancer Hospital of Xiangya School of Medicine, Central South University, Hunan, China.

Department of Pathology, Hunan Cancer Hospital and the Affliated Cancer Hospital of Xiangya School of Medicine, Central South University, Hunan, China.

出版信息

Cancer Biomark. 2020;27(2):195-206. doi: 10.3233/CBM-190694.

DOI:10.3233/CBM-190694

PMID:31815689

Abstract

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 down-regulated 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.

摘要

背景

头颈部鳞状细胞癌（HNSCC）是全球第七大常见癌症类型。本研究旨在寻找可作为 HNSCC 诊断和预后生物标志物的长非编码 RNA（lncRNA）。

方法

基于 TCGA 数据集，鉴定 HNSCC 与正常组织之间差异表达的信使 RNA（DEmRNAs）和长非编码 RNA（DElncRNAs）。通过机器学习和生存分析评估 lncRNA 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注释。通过定量实时聚合酶链反应（qRT-PCR）验证选定候选 mRNAs 和 lncRNAs 的表达。

结果

共获得 HNSCC 与正常组织之间的 3363 个 DEmRNAs（1822 个下调和 1541 个上调 mRNAs）和 32 个 DElncRNAs（13 个下调和 19 个上调 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 HNSCC 的最佳诊断 lncRNA 生物标志物。支持向量机（SVM）模型、决策树模型和随机森林模型的曲线下面积（AUC）分别为 0.983、0.842 和 0.983，三个模型的特异性和灵敏度分别为 95.5%和 96.2%、77.3%和 97.6%以及 93.2%和 97.8%。其中，AC024592.9、LINC00941、LINC01615 和 MIR9-3HG 不仅是最佳诊断 lncRNA 生物标志物，而且与生存时间相关。在与鉴定出的最佳诊断 lncRNA 共表达的 DEmRNAs 中，有四个显著富集的通路：黏附斑、ECM-受体相互作用、癌症通路和细胞因子-细胞因子受体相互作用。但对于大多数选定的 DEmRNAs 和 DElncRNAs，其表达与我们的综合分析结果一致，包括 LINC00941、LINC01615、FOXCUT、TGA6 和 MMP13。