Robust rank aggregation and cibersort algorithm applied to the identification of key genes in head and neck squamous cell cancer
Robust rank aggregation and cibersort algorithm applied to the identification of key genes in head and neck squamous cell cancer
复制标题
DOI：
10.3934/mbe.2021228
复制
发表时间：
2021-01-01
影响因子：
2.6
通讯作者：
Ge, Xiaolin
中科院分区：
文献类型：
--
作者：
Chen, Tingting;Hua, Wei;Ge, Xiaolin
目的：虽然近年来在头颈鳞状细胞癌（HNSCC）中发现了多个Hub基因，但由于样本量有限和生物信息学分析方法不一致，结果并不可靠。因此，迫切需要使用可靠的算法来寻找新的HNSCC预后标志物。方法：采用鲁棒排序聚合（RRA）方法整合从基因表达综合（GEO）数据库下载的8个HNSCC微阵列数据集，筛选差异表达基因（DEG）。随后，通过基因本体论（GO）功能注释以及京都基因和基因组百科全书（KEGG）分析来发现这些发现的DEG的功能。 KEGG结果显示，发现的DEGs与HNSCC的发生、发展密切相关。然后采用cibersort算法对HNSCC免疫细胞的浸润情况进行分析，发现主要浸润的免疫细胞是B细胞、树突状细胞和巨噬细胞。建立了蛋白质-蛋白质相互作用（PPI）网络；此外，还构建了关键模块，使用 cytoHubba 从整个网络中选择 5 个 hub 基因。 3 个枢纽基因与 TCGA 衍生的 HNSCC 患者的预后显着相关。结果：通过组合生物信息学方法选择了有效的 DEG 和 hub 基因。 AURKA、BIRC5和UBE2C基因可能是HNSCC潜在的预后生物标志物和治疗靶点。结论：Robust Rank Aggregation方法和cibersort算法方法可以通过多个GEO数据集准确预测HNSCC的潜在预后生物标志物和治疗靶点。
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 throughmultiple GEO datasets.