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

Article type: Research Article

Authors: Hu, Ying^{a; *} | Guo, Geyang^a | Li, Junjun^b | Chen, Jie^c | Tan, Pingqing^{c; *}

Affiliations: [a] Department of Radiotherapy, Hunan Cancer Hospital and the Affliated Cancer Hospital of Xiangya School of Medicine, Central South University, Hunan, China | [b] Department of Pathology, Hunan Cancer Hospital and the Affliated Cancer Hospital of Xiangya School of Medicine, Central South University, Hunan, China | [c] Department of Head and Neck Surgery, Hunan Cancer Hospital and the Affliated Cancer Hospital of Xiangya School of Medicine, Central South University, Hunan, China

Correspondence: [*] Corresponding authors: Ying Hu, Department of Radiotherapy, Hunan Cancer Hospital and the Affliated Cancer Hospital of Xiangya School of Medicine, Central South University, No. 283, Tong Zi Po, Yuelu District, Changsha, Hunan 410013, China. E-mail: [email protected]; Pingqing Tan, Department of Head and Neck Surgery, Hunan Cancer Hospital and the Affliated Cancer Hospital of Xiangya School of Medicine, Central South University, No. 283, Tong Zi Po, Yuelu District, Changsha, Hunan 410013, China. E-mail: [email protected].

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.

Keywords: Head and neck squamous cell carcinoma, diagnostic, prognostic, machine learning

DOI: 10.3233/CBM-190694

Journal: Cancer Biomarkers, vol. 27, no. 2, pp. 195-206, 2020