THE TRANSITION FROM SINGLE-OMIC TO MULTI-OMIC IN THE REGULATORY ANALYSIS OF CLEAR CELL RENAL CARCINOMA
ccRCC; multiomics; master regulators; transcription factors; regulatory activity; aggressiveness.
Clear cell renal cell carcinoma (ccRCC) is the most frequently reported subtype among renal tissues, which has been presented as a tumor type with a growing wave of incidence and mortality in recent decades, being associated with the diffusion of the lifestyle adopted by Western culture. Despite presenting a well-defined characterization at the molecular level, there are gaps associated with the regulatory activity acting within the tumor environment of the pathology and how this activity is integrated with the different levels of gene information. This regulatory process can be performed by different types of regulatory elements, such as transcription factors and long non-coding RNAs, which act by modulating different processes associated with transcriptional expression, signaling pathways and control, and which are described as possible biomarkers and therapeutic targets. Therefore, the objective of this work is to understand how the regulatory activity of transcription factors occurs in ccRCC, identifying possible markers associated with the aggressiveness process of this cancer, as well as identifying processes and possible multiomic markers associated with the development and progression of the pathology. In part 2, transcriptome data from ccRCC samples were used to construct a regulatory network centered on transcription factors and identify master regulators (MRs) associated with the aggressive profile of the disease. Using feature selection techniques, 11 MRs associated with the most aggressive profile of the disease were identified, which presented a dichotomous and complementary regulatory activity, separating into two sets of MRs. In part 3, transcriptome, genome, epigenome and proteome data, shared between the same patients, are being integrated and analyzed together, to identify regulatory processes and activities associated with the development and progression of ccRCC. Preliminary results have shown a relationship between expression data, from miRNAseq and RNAseq, and protein abundance data, even in different integration approaches. Therefore, the results presented here provide insights into the regulatory activity of transcription factors in ccRCC, as well as indicative of the interaction between omics information.