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Updated: Oct 31, 2025
Development of Compendium for Esophageal Squamous Cell Carcinoma
Published on: April 12, 2024
Robust rank aggregation and cibersort algorithm applied to the identification of key genes in head and neck squamous
Tingting Chen1,2, Wei Hua2, Bing Xu1
1Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China.
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.
