Screening and Identification of Key Biomarkers for Bladder Cancer: A Study Based on TCGA and GEO Data.
Yingkun XuGuangzhen WuJianyi LiJiatong LiNingke RuanLiye MaXiaoyang HanYanjun WeiLiang LiHongge ZhangYougen ChenQinghua XiaPublished in: BioMed research international (2020)
Bladder cancer (BLCA) is a common malignant cancer, and it is the most common genitourinary cancer in the world. The recurrence rate is the highest of all cancers, and the treatment of BLCA has only slightly improved over the past 30 years. Genetic and environmental factors play an important role in the development and progression of BLCA. However, the mechanism of cancer development remains to be proven. Therefore, the identification of potential oncogenes is urgent for developing new therapeutic directions and designing novel biomarkers for the diagnosis and prognosis of BLCA. Based on this need, we screened overlapping differentially expressed genes (DEG) from the GSE7476, GSE13507, and TCGA BLCA datasets. To identify the central genes from these DEGs, we performed a protein-protein interaction network analysis. To investigate the role of DEGs and the underlying mechanisms in BLCA, we performed Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) analysis; we identified the hub genes via different evaluation methods in cytoHubba and then selected the target genes by performing survival analysis. Finally, the relationship between these target genes and tumour immunity was analysed to explore the roles of these genes. In summary, our current studies indicate that both cell division cycle 20 (CDC20) and abnormal spindle microtubule assembly (ASPM) genes are potential prognostic biomarkers for BLCA. It may also be a potential immunotherapeutic target with future clinical significance.
Keyphrases
- genome wide
- bioinformatics analysis
- genome wide identification
- genome wide analysis
- network analysis
- copy number
- papillary thyroid
- dna methylation
- protein protein
- transcription factor
- squamous cell
- squamous cell carcinoma
- machine learning
- small molecule
- cell therapy
- deep learning
- single cell
- childhood cancer
- rna seq
- muscle invasive bladder cancer