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Research
Weizhong Li
Southern Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0001-7188-0904
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Tongue squamous cell carcinoma (TSCC) is one of the most common oral squamous cell carcinoma (OSCC) with a high occurrence and a poor prognosis, yet its molecular mechanisms are largely unknown and novel drug candidates are needed. The purpose of this study was to construct gene co-expression networks to identify hub proteins significantly associated with tumor grades and the overall survival (OS) of TSCC patients and provide potential drug candidates.
Methods
The mRNA-sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) dataset. Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the tumor grade of TSCC. The hub proteins were selected by the interaction number with other proteins, and their correlations with prognosis and tumor grades were calculated. Virtual screening of compounds by the hub protein structures was used to identify the drug candidates.
Results
WGCNA identified ten co-expression modules, in which the brown module consisted of 163 genes was most significantly correlated with the tumor grade. Six hub genes/proteins (BUB1, CCNB2, CDC6, CDC20, CDK1, and MCM2) tended to be in the central hub of the network. And higher expression levels of these hub genes were associated with tumor grades and worse overall survival. Three compounds, targeting hub proteins, demonstrated high binding affinities, favorable pharmacologic properties, and low toxicity.
Conclusion
The gene co-expression network-based study could provide additional insight into tumorigenesis and progression of TSCC, and our study might provide novel drug candidates.
Keywords
TSCC, WGCNA, hub proteins, virtual screening, drug candidates
Figure 1
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This is a list of supplementary files associated with this preprint. Click to download.
- FigS1.pdf
(a) Clustering of samples and removal of outliers. (b) Scatter diagram for module membership vs. gene significance for DEGs in the brown module. Abbreviation: DEGs, differentially expressed genes.
- FigS2.pdf
The scattered plots for the expression level of BUB1, CCNB2, CDC6, CDC20, CDK1, and MCM2 in normal and tumor group samples (TCGA dataset). Horizontal lines represent medians and dispersions. Abbreviation: TCGA, The Cancer Genome Atlas.
- FigS3.pdf
The scattered plots for the expression level of BUB1, CCNB2, CDC6, CDC20, CDK1, and MCM2 across different tumor grades in TCGA dataset. Horizontal lines represent medians and dispersions. Abbreviation: TCGA, The Cancer Genome Atlas.
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This is a preprint. It has not completed peer review.
Research
Weizhong Li
Southern Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0001-7188-0904
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 02 Feb, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Tongue squamous cell carcinoma (TSCC) is one of the most common oral squamous cell carcinoma (OSCC) with a high occurrence and a poor prognosis, yet its molecular mechanisms are largely unknown and novel drug candidates are needed. The purpose of this study was to construct gene co-expression networks to identify hub proteins significantly associated with tumor grades and the overall survival (OS) of TSCC patients and provide potential drug candidates.
Methods
The mRNA-sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) dataset. Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the tumor grade of TSCC. The hub proteins were selected by the interaction number with other proteins, and their correlations with prognosis and tumor grades were calculated. Virtual screening of compounds by the hub protein structures was used to identify the drug candidates.
Results
WGCNA identified ten co-expression modules, in which the brown module consisted of 163 genes was most significantly correlated with the tumor grade. Six hub genes/proteins (BUB1, CCNB2, CDC6, CDC20, CDK1, and MCM2) tended to be in the central hub of the network. And higher expression levels of these hub genes were associated with tumor grades and worse overall survival. Three compounds, targeting hub proteins, demonstrated high binding affinities, favorable pharmacologic properties, and low toxicity.
Conclusion
The gene co-expression network-based study could provide additional insight into tumorigenesis and progression of TSCC, and our study might provide novel drug candidates.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
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