Novel Diagnostic Value of Driver Gene Transcription Signatures to Characterise Clear Cell Renal Cell Carcinoma, ccRCC

Routine molecular tumour diagnostics are augmented by DNA-based qualitative and quantitative molecular techniques detecting mutations of DNA. However, in the past decade, it has been unravelled that the phenotype of cancer, as it’s an extremely complex disease, cannot be fully described and explained by single or multiple genetic variants affecting only the coding regions of the genes. Moreover, studying the manifestation of these somatic mutations and the altered transcription programming—driven by genomic rearrangements, dysregulation of DNA methylation and epigenetic landscape—standing behind the tumorigenesis and detecting these changes could provide a more detailed characterisation of the tumour phenotype. Consequently, novel comparative cancer diagnostic pipelines, including DNA- and RNA-based approaches, are needed for a global assessment of cancer patients. Here we report, that by monitoring the expression patterns of key tumour driver genes by qPCR, the normal and the tumorous samples can be separated into distinct categories. Furthermore, we also prove that by examining the transcription signatures of frequently affected genes at 3p25, 3p21 and 9p21.3 genomic regions, the ccRCC (clear cell renal cell carcinoma) and non-tumorous kidney tissues can be distinguished based on the mRNA level of the selected genes. Our results open new diagnostics possibilities where the mRNA signatures of tumour drivers can supplement the DNA-based approaches providing a more precise diagnostics opportunity leading to determine more precise therapeutic protocols

BC-miR: Monitoring Breast Cancer-Related miRNA Profile in Blood Sera—A Prosperous Approach for Tumor Detection

Breast cancer is the most frequent cancer with a high fatality rate amongst women worldwide. Diagnosing at an early stage is challenging, and due to the limitations of the currently used techniques, including mammography and imaging diagnostics, it still remains unascertained. Serum biomarkers can be a solution for this as they can be isolated in a less painful, more cost-effective, and minimally invasive manner. In this study, we shed light on the relevant role of multiple microRNAs (miRNAs) as potential biomarkers in breast cancer diagnosis. We monitored the expressional changes of 15 pre-selected miRNAs in a large cohort, including 65 patients with breast cancer and 42 healthy individuals. We performed thorough statistical analyses on the cohort sample set and determined the diagnostic accuracy of individual and multiple miRNAs. Our study reveals a potential improvement in diagnostics by implicating the monitoring of miR-15a+miR-16+miR-221 expression in breast cancer management