ANLN functions as a key candidate gene in cervical cancer as determined by integrated bioinformatic analysis

Abstract

Background: Cervical cancer, one of the leading causes of female deaths, remains a top cause of mortality in gynecologic oncology and tends to affect younger individuals. However, the pathogenesis of cervical cancer is still far from clear. Given the high incidence and mortality of cervical cancer, uncovering the causes and pathogenesis as well as identifying novel biomarkers are of great significance and are desperately needed.

Materials and methods: First, raw data were downloaded from the Gene Expression Omnibus database. The Robuse Multi-Array Average algorithm and combat function of the sva package were subsequently applied to preprocess and remove batch effects. Differentially expressed genes (DEGs) analyzed with the limma package were followed by gene ontology and pathway analysis, and a protein-protein interaction (PPI) network based on the STRING website and the Cytoscape software was constructed. Weighted Correlation Network Analysis (WGCNA) was utilized to build the coexpression network. Subsequently, UALCAN websites were employed to conduct survival analysis. Finally, the oncomine database was used to validate the expression of ANLN in other datasets.

Results: GSE29570 and GSE89657, including 49 cervical cancer tissues and 20 normal cervical tissues, were screened as the datasets. Three-hundred-twenty-four DEGs were identified and, among them, 123 were upregulated, while 201 were downregulated. The DEGs PPI network complex, contained 305 nodes and 4,962 edges, and 8 clusters were calculated according to k-core =2. Among them, cluster 1, which had 65 nodes and 1,780 edges, had the highest score in these clusters. In coexpression analysis, there were 86 hubgenes from the Brown modules that were chosen for further analysis. Sixty-one key genes were identified as the intersecting genes of the Brown module of WGCNA and DEGs. In survival analysis, only ANLN was a prognostic factor, and the survival was significantly better in the low-expression ANLN group.

Conclusion: Our study suggested that ANLN may be a potential tumor oncogene and could serve as a biomarker for predicting the prognosis of cervical cancer patients.

Keywords: ANLN; WGCNA; bioinformatics analysis; cervical cancer.

Figure 1

DEGs identified in GSE29570 and GSE89657. Notes: (A) Volcano map of differently expressed genes between cervical cancer tissues and normal cervical tissues. (B) Heatmap of the top 100 DEGs according to the value of |logFC|. (C) The top 20 GO terms in the enrichment analysis of the upregulated and downregulated genes. (D) The top 20 KEGG pathways in the enrichment analysis of upregulated and downregulated genes. Abbreviations: DEG, differentially expressed genes; GO, Gene Ontology; HTLV-I, human T-cell leukemia virus type 1; KEGG, Kyoto Encyclopedia of Genes and Genomes ; Dw, down; NoDiff, no significant difference.

Figure 2

Cluster analysis of the PPI network. Notes: (A) Three-hundred-five DEGs were filtered into the DEGs PPI network complex that contained 305 nodes and 4,962 edges. (B) Cluster 1 consists of 65 nodes and 1,780 edges and has the highest score in those clusters. Abbreviations: DEG, differentially expressed genes; PPI, protein–protein interaction.

Figure 3

WGCNA of GSE29570 and GSE89657. Notes: (A) Six-thousand-sixty genes were assigned to one of 13 modules, including Module gray, with a cutoff of powers =6. The top image shows a gene dendrogram, and the bottom image shows the gene modules with different colors. (B) Correlation between modules and traits. The upper number in each cell refers to the correlation coefficient of each module in the trait, and the lower number is the corresponding p-value. Among them, the Brown and Turquoise modules were the most relevant modules with cancer traits. (C) A heatmap of 1,000 genes was selected at random. The intensity of the red color indicates the strength of the correlation between pairs of modules on a linear scale. (D) A scatter plot of GS for cervical cancer versus the MM in the Brown module. Intramodular analysis of the genes found in the Brown module, which contains genes that have a high correlation with cervical cancer, with p<1e-200 and correlation =0.92. Abbreviations: GS, gene significance; MM, module membership; WGCNA, Weighted gene coexpression network analysis.

Figure 4

ANLN is a biomarker and prognostic factor in cervical cancer. Notes: (A) A Venn diagram of the DEGs and hubgenes in the Brown module revealed 61 key genes. (B) Survival analysis indicated that ANLN is a poor prognosis factor in cervical cancer, while patients with a higher expression of ANLN have significantly shorter overall survival compared to those with lower expression (p=0.013). (C–E) Validation of ANLN expression both from the oncomine database (C) and GEO databases (D, E). Three datasets showed higher expression of ANLN in cervical cancer tissues compared with normal cervical tissues (p<0.05). (F) PPI network of ANLN based on the STRING website. Many genes involved in the progression of cervical cancer have interactions with ANLN, including MKI67 and FOXM1. ^***p<0.001. Abbreviations: DEG, differentially expressed genes; GEO, Gene Expression Omnibus; PPI, protein–protein interaction.