Robust rank aggregation and cibersort algorithm applied to the identification of key genes in head and neck squamous cell cancer

Tingting Chen; Wei Hua; Bing Xu; Hui Chen; Minhao Xie; Xinchen Sun; Xiaolin Ge; Tingting Chen; Wei Hua; Bing Xu; Hui Chen; Minhao Xie; Xinchen Sun; Xiaolin Ge

doi:10.3934/mbe.2021228

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 4491-4507. doi: 10.3934/mbe.2021228

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Robust rank aggregation and cibersort algorithm applied to the identification of key genes in head and neck squamous cell cancer

1.
Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China
2.
Department of Oncology, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu 225000, China
3.
The First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu 210000, China

† The authors contributed equally to this work.
Academic Editor: Kelvin K.L. Wong

Received: 26 March 2021 Accepted: 18 May 2021 Published: 25 May 2021

Objective
Although multiple hub genes have been identified in head and neck squamous cell cancer (HNSCC) in recent years, because of the limited sample size and inconsistent bioinformatics analysis methods, the results are not reliable. Therefore, it is urgent to use reliable algorithms to find new prognostic markers of HNSCC.
Method
The Robust Rank Aggregation (RRA) method was used to integrate 8 microarray datasets of HNSCC downloaded from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs). Later, Gene Ontology (GO) functional annotation together with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was carried out to discover functions of those discovered DEGs. According to the KEGG results, those discovered DEGs showed tight association with the occurrence and development of HNSCC. Then cibersort algorithm was used to analyze the infiltration of immune cells of HNSCC and we found that the main infiltrated immune cells were B cells, dendritic cells and macrophages. A protein-protein interaction (PPI) network was established; moreover, key modules were also constructed to select 5 hub genes from the whole network using cytoHubba. 3 hub genes showed significant relationship with prognosis for TCGA-derived HNSCC patients.
Result
The potent DEGs along with hub genes were selected by the combined bioinformatic approach. AURKA, BIRC5 and UBE2C genes may be the potential prognostic biomarker and therapeutic targets of HNSCC.
Conclusions
The Robust Rank Aggregation method and cibersort algorithm method can accurately predict the potential prognostic biomarker and therapeutic targets of HNSCC through multiple GEO datasets.
- bioinformatics,
- differentially expressed genes,
- biomarkers,
- robust rank aggregation method,
- cibersort algorithm
Citation: Tingting Chen, Wei Hua, Bing Xu, Hui Chen, Minhao Xie, Xinchen Sun, Xiaolin Ge. Robust rank aggregation and cibersort algorithm applied to the identification of key genes in head and neck squamous cell cancer[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4491-4507. doi: 10.3934/mbe.2021228

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Abstract

Objective

Although multiple hub genes have been identified in head and neck squamous cell cancer (HNSCC) in recent years, because of the limited sample size and inconsistent bioinformatics analysis methods, the results are not reliable. Therefore, it is urgent to use reliable algorithms to find new prognostic markers of HNSCC.

Method

The Robust Rank Aggregation (RRA) method was used to integrate 8 microarray datasets of HNSCC downloaded from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs). Later, Gene Ontology (GO) functional annotation together with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was carried out to discover functions of those discovered DEGs. According to the KEGG results, those discovered DEGs showed tight association with the occurrence and development of HNSCC. Then cibersort algorithm was used to analyze the infiltration of immune cells of HNSCC and we found that the main infiltrated immune cells were B cells, dendritic cells and macrophages. A protein-protein interaction (PPI) network was established; moreover, key modules were also constructed to select 5 hub genes from the whole network using cytoHubba. 3 hub genes showed significant relationship with prognosis for TCGA-derived HNSCC patients.

Result

The potent DEGs along with hub genes were selected by the combined bioinformatic approach. AURKA, BIRC5 and UBE2C genes may be the potential prognostic biomarker and therapeutic targets of HNSCC.

Conclusions

The Robust Rank Aggregation method and cibersort algorithm method can accurately predict the potential prognostic biomarker and therapeutic targets of HNSCC through multiple GEO datasets.

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