Prognostic 4‑lncRNA‑based risk model predicts survival time of patients with head and neck squamous cell carcinoma

Xing,Lu; Zhang,Xiaoqian; Chen,Anwei

doi:10.3892/ol.2019.10670

Prognostic 4‑lncRNA‑based risk model predicts survival time of patients with head and neck squamous cell carcinoma

Authors:
- Lu Xing
- Xiaoqian Zhang
- Anwei Chen
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Affiliations: School of Stomatology, Shandong University, Shandong Provincial Key Laboratory of Oral Tissue Regeneration, Jinan, Shandong 250012, P.R. China, Department of Stomatology, Haiyuan College of Kunming Medical University, Kunming, Yunnan 650000, P.R. China, Department of Oral and Maxillofacial Surgery, Qilu Hospital, Institute of Stomatology, Shandong University, Jinan, Shandong 250000, P.R. China
Published online on: July 26, 2019 https://doi.org/10.3892/ol.2019.10670
Pages: 3304-3316
Copyright: © Xing et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Head and neck squamous cell carcinoma (HNSCC) is a common malignant disease with high mortality rates. Recently, long non‑coding RNAs (lncRNAs) have been demonstrated to participate in a number of important biological functions and could serve as prognostic biomarkers in the field of oncology. Therefore, the present study aimed to identify an lncRNA‑based model that was associated with prognosis. RNA‑sequencing data was downloaded from The Cancer Genome Atlas and R software was used to analyze the data. Univariate analyses, robust likelihood analyses and multivariate analyses were performed to screen out key lncRNA candidates associated with prognosis and construct a risk model. A Kaplan‑Meier plot was constructed for survival analysis. LncBase and Starbase were used to identify the miRNA and protein targets. Gene set enrichment analysis was used for functional analysis. As a result, a 4‑lncRNA (ALMS1‑IT1, RP11‑359J14.2, CTB‑178M22.2 and RP11‑347C18.5) based risk model was identified and patients in the high‑risk group were revealed to have a lower survival rate than patients in the low‑risk group. A nomogram that could predict the survival of patients was plotted. A total of 79 target miRNAs and 61 target proteins were identified. The gene set enrichment analysis results revealed that nutrient metabolism pathways were enriched in the high‑risk group and immune regulation pathways were enriched in the low‑risk group. In summary, a 4‑lncRNA based risk model was identified that was associated with prognosis, which may serve as a prognosis prediction biomarker for HNSCC.

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