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CLC number: Q522

On-line Access: 2018-09-04

Received: 2017-06-25

Revision Accepted: 2018-02-26

Crosschecked: 2018-08-14

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Citations:  Bibtex RefMan EndNote GB/T7714


Mei-ting Wang


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Journal of Zhejiang University SCIENCE B 2018 Vol.19 No.9 P.674-688


Transcriptomic analysis reveals key lncRNAs associated with ribosomal biogenesis and epidermis differentiation in head and neck squamous cell carcinoma

Author(s):  Yu-zhu Guo, Hui-hui Sun, Xiang-ting Wang, Mei-ting Wang

Affiliation(s):  School of Life Sciences, University of Science and Technology of China, Hefei 230026, China; more

Corresponding email(s):   stucomputer0@163.com

Key Words:  Head and neck squamous cell carcinoma, Long noncoding RNA (lncRNA), Weighted gene co-expression network analysis (WGCNA), Clinicopathological feature, Multivariate Cox regression model

Yu-zhu Guo, Hui-hui Sun, Xiang-ting Wang, Mei-ting Wang. Transcriptomic analysis reveals key lncRNAs associated with ribosomal biogenesis and epidermis differentiation in head and neck squamous cell carcinoma[J]. Journal of Zhejiang University Science B, 2018, 19(9): 674-688.

@article{title="Transcriptomic analysis reveals key lncRNAs associated with ribosomal biogenesis and epidermis differentiation in head and neck squamous cell carcinoma",
author="Yu-zhu Guo, Hui-hui Sun, Xiang-ting Wang, Mei-ting Wang",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Transcriptomic analysis reveals key lncRNAs associated with ribosomal biogenesis and epidermis differentiation in head and neck squamous cell carcinoma
%A Yu-zhu Guo
%A Hui-hui Sun
%A Xiang-ting Wang
%A Mei-ting Wang
%J Journal of Zhejiang University SCIENCE B
%V 19
%N 9
%P 674-688
%@ 1673-1581
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1700319

T1 - Transcriptomic analysis reveals key lncRNAs associated with ribosomal biogenesis and epidermis differentiation in head and neck squamous cell carcinoma
A1 - Yu-zhu Guo
A1 - Hui-hui Sun
A1 - Xiang-ting Wang
A1 - Mei-ting Wang
J0 - Journal of Zhejiang University Science B
VL - 19
IS - 9
SP - 674
EP - 688
%@ 1673-1581
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B1700319

Objective: In this study, we aimed to expand current knowledge of head and neck squamous cell carcinoma (HNSCC)-associated long noncoding RNAs (lncRNAs), and to discover potential lncRNA prognostic biomarkers for HNSCC based on next-generation RNA-seq. Methods: RNA-seq data of 546 samples from patients with HNSCC were downloaded from The Cancer Genome Atlas (TCGA), including 43 paired samples of tumor tissue and adjacent normal tissue. An integrated analysis incorporating differential expression, weighted gene co-expression networks, functional enrichment, clinical parameters, and survival analysis was conducted to discover HNSCC-associated lncRNAs. The function of CYTOR was verified by cell-based experiments. To further identify lncRNAs with prognostic significance, a multivariate Cox proportional hazard regression analysis was performed. The identified lncRNAs were validated with an independent cohort using clinical feature relevance analysis and multivariate Cox regression analysis. Results: We identified nine HNSCC-relevant lncRNAs likely to play pivotal roles in HNSCC onset and development. By functional enrichment analysis, we revealed that CYTOR might participate in the multistep pathological processes of cancer, such as ribosome biogenesis and maintenance of genomic stability. CYTOR was identified to be positively correlated with lymph node metastasis, and significantly negatively correlated with overall survival (OS) and disease free survival (DFS) of HNSCC patients. Moreover, CYTOR inhibited cell apoptosis following treatment with the chemotherapeutic drug diamminedichloroplatinum (DDP). HCG22, the most dramatically down-regulated lncRNA in tumor tissue, may function in epidermis differentiation. It was also significantly associated with several clinical features of patients with HNSCC, and positively correlated with patient survival. CYTOR and HCG22 maintained their prognostic values independent of several clinical features in multivariate Cox hazards analysis. Notably, validation either based on an independent HNSCC cohort or by laboratory experiments confirmed these findings. Conclusions: Our transcriptomic analysis suggested that dysregulation of these HNSCC-associated lncRNAs might be involved in HNSCC oncogenesis and progression. Moreover, CYTOR and HCG22 were confirmed as two independent prognostic factors for HNSCC patient survival, providing new insights into the roles of these lncRNAs in HNSCC as well as clinical applications.


方法:从癌症基因组数据集(The Cancer Genome Atlas)中获得RNA-seq数据.结合差异表达分析和共表达网络分析的方法发掘出与头颈部鳞状细胞癌相关的lncRNA,探讨其与头颈部肿瘤临床病理变化和预后的关系,进一步利用外部数据集以及细胞水平进行验证.


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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[76]List of electronic supplementary materials

[77]Fig. S1 Determination of parameter β of the adjacency function in the WGCNA algorithm

[78]Fig. S2 Hierarchical clustering of samples based on the gene expression profile

[79]Fig. S3 Overview of the workflow of the study design

[80]Fig. S4 Functional profiling of modules identified by WGCNA

[81]Fig. S5 Co-expression of six hub lncRNAs visualized using Cytoscape software based on the top 50 most connected genes of each given lncRNA

[82]Fig. S6 Kaplan-Meier analysis of disease free survival in HNSCC patients

[83]Fig. S7 Effects of CYTOR knockdown on cell apoptosis, proliferation, and migration in TSCC15 cells

[84]Table S1 Results of differential expression analysis

[85]Table S2 GO term enrichment analysis for WGCNA modules and HNSCC-associated lncRNAs

[86]Table S3 Cancer hallmarks enrichment analysis for HNSCC-associated lncRNAs

[87]Table S4 GO term enrichment analysis for HNSCC-associated lncRNAs

[88]Table S5 KEGG pathway enrichment analysis for HNSCC-associated lncRNAs

[89]Table S6 OR analysis and multivariate Cox regression analysis in HNSCC

[90]Table S7 WGCNA network of six lncRNAs

[91]Table S8 OR and multivariate Cox regression analysis based on Oncomine dataset

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