Background Breast cancer is the mostly diagnosed malignance in female worldwide. However, the mechanisms of its pathogenesis remain largely unknown. Methods In this study, we used weighted gene co-expression network analysis (WGCNA) to identify novel biomarkers associated with the prognosis of breast cancer. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Results A total of 5 modules were identified via the average linkage hierarchical clustering. And a module significantly with the pathological grade was screened out. 33 genes with high connectivity in the clinically significant module were identified as hub genes. Among them, CASC5 and RAD51 were negatively associated with the overall survival and disease-specific survival. Similar results were observed in the validation dataset. Protein levels of CACS5 and RAD51 were also significantly higher in tumor tissues compared with normal tissues based on the analysis of the Human Protein Atlas. Convincingly, qRT-PCR analysis of breast cancer tissues and matched paracancerous tissue demonstrated that CACS5 and RAD51 were significantly upregulated in in breast cancer compared to paracancerous tissues. Further cell proliferation assay indicated that CACS5 and RAD51 depletion decreased cell proliferation capability. Conclusion In conclusion, our findings suggested that CASC5 and RAD51 could serve as biomarkers related to the prognosis of breast cancer and may be helpful for revealing pathogenic mechanism and developing further research.
Background: Bladder cancer (BLCA) is a common malignancy from urinary tract. Although the diagnosis and treatment of bladder cancer has made great progress in the past few decades, the effects of existing treatment methods are still limited. Therefore, it is still necessary to develop new methods to assist in the disease management and treatment. Tumor antigens are tumor-specific surface molecules and are generally considered to be the main components of a typical cancer vaccine, which could initiate and active immune cells to recognize and eliminate cancer cells. In the context of the COVID-19 pandemic, mRNA vaccines have re-entered people's vision. Methods: The genomic and clinical data of 411 BLCA and 19 normal tissues were acquired from The Cancer Genome Atlas (TCGA) and GSE13507 cohorts. Differential expression genes and mutation analysis were performed to screen out potential antigens, Kaplan-Meier curves were carried out to investigate the correlation between the level of potential antigens and OS of patients. Immuno-phenotyping of 411 tumor samples was based on the single-sample gene sets enrichment analysis (ssGSEA). The tumor immune microenvironment characteristics was explored in each immune subtype. Weighted gene co-expression network analysis (WGCNA) was used to clusterimmune-related genes and screen the hub genes, and pathway enrichment analyses were performed on the hub modules related to immune subtypes in the WGCNA.Results: Through genetic and transcriptional analysis on TCGA and GSE13507 datasets, we have identified 6 genes as potential candidate genes for BLCA specific tumor antigens. We also identified 3 immune subtypes of BLCA, which displayed distinct clinical, molecular and immune-related characteristics. In addition, we have constructed immune landscape to identify the immune cell components of each BLCA patient, which could predict clinical outcome of the patients, and assist in the development of personalized mRNA vaccines. Conclusions: our findings indicated that 6 genes such as PTPN6 may be potential tumor antigens, and provide a reliable reference for the further development and management of cancer vaccines.
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