Anti-CCND1 monoclonal antibody, clone BG3H6

Background Ovarian cancer (OC) is a major cause of death among women due to the lack of early screening methods and its complex pathological progression. Increasing evidence has indicated that microRNAs regulate gene expression in tumours by interacting with mRNAs. Although the research regarding OC and microRNAs is extensive, the vital role ofMIR502in OC remains unclear. Methods We integrated two microRNA expression arrays from GEO to identify differentially expressed genes. The Kaplan-Meier method was used to screen for miRNAs that had an influence on survival outcome. Upstream regulators ofMIR502were predicted by JASPAR and verified by ChIP-seq data. The LinkedOmics database was used to study genes that were correlated withMIR502. Gene Set Enrichment Analysis (GSEA) was conducted for functional annotation with GO and KEGG pathway enrichment analyses by using the open access WebGestalt tool. We constructed a PPI network by using STRING to further explore the core proteins. Results We found that the expression level ofMIR502was significantly downregulated in OC, which was related to poor overall survival. NRF1, as an upstream regulator ofMIR502, was predicted by JASPAR and verified by ChIP-seq data. In addition, anti-apoptosis and pro-proliferation genes in the Hippo signalling pathway, including CCND1, MYC, FGF1 and GLI2, were negatively regulated byMIR502, as shown in the GO and KEGG pathway enrichment results. The PPI network further demonstrated that CCND1 and MYCN were at core positions in the development of ovarian cancer. Conclusions MIR502, which is regulated by NRF1, acts as a tumour suppressor gene to accelerate apoptosis and suppress proliferation by targeting the Hippo signalling pathway in ovarian cancer.

While the incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been increasing in these two decades, primarily due to human papillomavirus (HPV), stratification of OPSCC into molecular subgroups showing different clinicopathological features has not been fully investigated. We performed DNA methylome analysis using Infinium 450k for 170 OPSCC cases, including 89 cases in our cohort and 81 cases reported by The Cancer Genome Atlas, together with targeted exon sequencing analysis. We stratified OPSCC by hierarchical clustering analysis using methylome data. Methylation levels of classifier markers were validated quantitatively using pyrosequencing, and area under the curve (AUC) values of receiver operating characteristics (ROC) curves were calculated. OPSCC was stratified into four epigenotypes: HPV(+) high-methylation (OP1), HPV(+) intermediate-methylation (OP2), HPV(-) intermediate-methylation (OP3) and HPV(-) low-methylation (OP4). Ten methylation marker genes were generated: five to classify HPV(+) cases into OP1 and OP2, and five to classify HPV(-) cases into OP3 and OP4. AUC values of ROC curves were 0.969 and 0.952 for the two marker panels, respectively. While significantly higher TP53 mutation and CCND1 copy number gains were observed in HPV(-) than in HPV(+) groups (p < 0.01), no significant difference of genomic aberrations was observed between OP1 and OP2, or OP3 and OP4. The four epigenotypes showed significantly different prognosis (p = 0.0006), distinguishing the most favorable OPSCC subgroup (OP1) among generally favorable HPV(+) cases, and the most unfavorable OPSCC subgroup (OP3) among generally unfavorable HPV(-) cases. HPV(+) and HPV(-) OPSCC are further divided into distinct DNA methylation epigenotypes, showing significantly different prognosis.