The role of miR-106p-5p in cervical cancer: from expression to molecular mechanism

Abstract

This study aims to investigate the role of miR-106b-5p in cervical cancer by performing a comprehensive analysis on its expression and identifying its putative molecular targets and pathways based on The Cancer Genome Atlas (TCGA) dataset, Gene Expression Omnibus (GEO) dataset, and literature review. Significant upregulation of miR-106b-5p in cervical cancer is confirmed by meta-analysis with the data from TCGA, GEO, and literature. Moreover, the expression of miR-106b-5p is significantly correlated with the number of metastatic lymph nodes. Our bioinformatics analyses show that miR-106b could promote cervical cancer progression by modulating the expression of GSK3B, VEGFA, and PTK2 genes. Importantly, these three genes play a crucial role in PI3K-Akt signaling, focal adhesion, and cancer. Both the expression of miR-106b-5p and key genes are upregulated in cervical cancer. Several explanations could be implemented for this upregulation. However, the specific mechanism needs to be investigated further.

Fig. 1. Expression of miR-106b-5p in cervical cancer from TCGA.

NT normal tissue, TP primary tumor. There are three samples in the NT group and 309 samples in the TP group. Student's t-test is used for the statistical analysis

Fig. 2

Searching workflow for the expression of miR-106b-5p between cervical cancer and non-cancerous tissue. a Searching strategy in GEO; bSearching strategy in literature review

Fig. 3. Meta-analysis of miR-106b-5p between healthy and cancerous cervical tissue based on TCGA and GEO.

a Forest plot of SMD. The expression of miR-106b-5p is significantly higher in cervical cancer tissue; b Funnel plot for four studies that are marked as circles. No significant publication bias is detected (P = 0.5187); c Influence analysis for four studies. No study had an impact on the overall SMD estimation. d Subgroup forest plot based on cancer type. As I² value is still relatively high, the cancer subtype is not the only source of heterogeneity

Fig. 4. Predication of miR-106b-5p target genes and candidate genes screening.

a The number of overlapped genes across 12 databases; 10,073 target genes which overlapped at least five databases are obtained. b Venn plot for the integration between DEGs and predicted target genes of miR-106b-5p

Fig. 5. The top 20 items of cellular component (CeC), biological process (BP) pathways, molecular function (MF), and pathways in Gene Ontology (GO) and pathway enrichment analysis for candidate target genes of miR-106b-5p in CC.

Values are expressed as −log10 (P-value)

Fig. 6. Protein–protein analysis (PPI) analysis of candidate genes and pathway crosstalk and gene-pathway analysis of hub genes.

a PPI network of candidate genes; b Subnetwork of PPI for main nodes extracted according to degree and betweenness being higher than average; c Pathway crosstalk analysis of hub genes. The thickness of lines between nodes are represented by the average value of Jaccard coefficient (JC) and overlapping coefficient (OC); d Subnetwork of pathway crosstalk extracted by MCODE; e Comprehensive gene-pathway network constructed by mapping the hub genes to the subnetwork. The arrow direction between gene and pathway is determined by KEGG. Red circle: genes; green square: pathway. f Subnetwork of gene-pathway collected according to the criteria that node’s degree > average

Fig. 7

The expression of main genes of cervical cancer across nine studies (a), Venn plot for the interaction between key genes and their pathways (b), and the binding sequence and location between miR-106b-5p and each critical gene (c). Orange square: coding sequence (CDS). Green square: binding site

Fig. 8. Work flow of the clinical significance evaluation and comprehensive analysis for miR-106b-5p in cervical cancer.

The modules of clinical value evaluation (green), meta-analysis (brown), and bioinformatics analyses (pink) are included