Bioinformatic Analyzes of the Association Between Upregulated Expression of JUN Gene via APOBEC-Induced FLG Gene Mutation and Prognosis of Cervical Cancer

Abstract

Globally, cervical cancer (CC) is the most common malignant tumor of the female reproductive system and its incidence is only second after breast cancer. Although screening and advanced treatment strategies have improved the rates of survival, some patients with CC still die due to metastasis and drug resistance. It is considered that cancer is driven by somatic mutations, such as single nucleotide, small insertions/deletions, copy number, and structural variations, as well as epigenetic changes. Previous studies have shown that cervical intraepithelial neoplasia is associated with copy number variants (CNVs) and/or mutations in cancer-related genes. Further, CC is also related to genetic mutations. The present study analyzed the data on somatic mutations of cervical squamous cell carcinoma (CESC) in the Cancer Genome Atlas database. It was evident that the Apolipoprotein B mRNA editing enzyme-catalyzed polypeptide-like (APOBEC)-related mutation of the FLG gene can upregulate the expression of the JUN gene and ultimately lead to poor prognosis for patients with CC. Therefore, the findings of the current study provide a new direction for future treatment of CC.

Keywords: APOBEC; FLG; JUN; cervical cancer; mutation.

Figure 1

The workflow of the study. TCGA, The Cancer Genome Atlas; CESC, cervical squamous cell carcinoma; Maftools, a package in R language; MuTarget, an online platform, which could help us to identify the gene(s) showing altered expression in samples harboring a mutated input gene or identify mutations resulting in the expression change in the input gene (https://mutarget.com/); GO, gene oncology; KEGG, Kyoto Encyclopedia of Genes and Genomes; STRING, functional protein association networks platform (https://cn.string-db.org); PPI, protein–protein interaction; Cytoscape, an open-source software platform for dynamic, graphical visualization, manipulation, and analysis of networks; Gepia2, an online analysis platform based on TCGA database (http://gepia.cancer-pku.cn/); HPA, human protein atlas (https://www.proteinatlas.org) GEO, Gene Expression Omnibus; IHC, Immunohistochemistry.

Figure 2

The summary of the TCGA CESC maf file. (A) The most common mutation was missense mutation, followed by nonsense mutation, frame shift del, frame shift ins, splice site, in-frame ins, in-frame del, and translation start site. (B) The most common variant type was single nucleotide polymorphism (SNP), followed by INS and DEL. (C) The most common SNV variant was C>T, followed by C>G, C>A, T>C, T>G, and T>A. (D) The number of variants per sample was 85. (E) The median missense mutation per sample was 74, followed by nonsense mutation, frame shift del, frame shift ins, splice site, in-frame ins, in-frame del, and translation start site. (F) The top 10 mutant genes were TNN, MUC4, PIK3CA, MUC16, KMT2C, KMT2D, SYNE1, FLG, EP300, and DMD.

Figure 3

Summary of the TCGA CESC mutations. There were 55,768 mutations in all 289 samples. The top 10 mutant genes were TNN, MUC4, PIK3CA, MUC16, KMT2C, KMT2D, SYNE1, FLG, EP300, and DMD.

Figure 4

Summary of Apolipoprotein B mRNA editing enzyme-catalyzed polypeptide-like (APOBEC)-induced mutation. Apolipoprotein B mRNA editing enzyme-catalyzed polypeptide-like -enriched and non-enriched sample sizes were 211 and 78, respectively. The top 10 of 192 differentially APOBEC-induced mutant genes were FOLH1, FLG, ADCY6, ALPK1, ARHGEF1, LRRIQ3, PCGF5, QSOX1, SLC5A2, and TEAD2. **, *** means significant difference.

Figure 5

The GO analysis and KEGG pathway enrichment of hub mutant gene's targeted genes. (A) Biological processes (GO-BP). (B) Cellular components (GO-CC). (C) Molecular functions (GO-MF). (D) KEGG pathway of hub mutant gene's targeted genes.

Figure 6

Multiple gene expression analysis of the APOBEC family in CESC. Among the APOBEC gene family, the expression of APOBEC3A and APOBEC3B was significantly higher in tumor tissues than in normal tissues.

Figure 7

Correlation between the expression of APOBEC3B and overall survival (OS) and disease-free survival (DFS). (A) Overall survival was inferior in APOBEC3B-low patients; (B) DFS was inferior in APOBEC3B-low patients.

Figure 8

The expression of APOBEC3B in validation datasets. (A) The expression of APOBEC3B in GSE63678, (B) the expression of APOBEC3B in GSE63541, and (C) the expression of APOBEC3B in GES6791.

Figure 9

Kaplan–Meier survival plot for FLG wild-type and patients with CESC mutants. Patients with CESC and FLG mutations had significant poor survival.

Figure 10

Correlation between the expression of JUN and OS. Overall survival was inferior in patients with high JUN expression.

Figure 11

Immunohistochemical images of the JUN gene in cervical tissue. (A–D) Normal cervix JUN protein was not shown in normal cervical tissues; (E–H) JUN protein showed moderate to high levels of staining in CESC (Image available from v21.0. proteinatlas.org).

Figure 12

Diagram of the mechanism of action of FLG gene mutation. Human papilloma virus infection stimulates the expression of APOBEC3B, which induces the APOBEC-related mutation of FLG ssDNA. The mutated FLG protein acts on JUN, resulting in its upregulation, and ultimately leading to the development and progression of CC.