Network diffusion-based approach for survival prediction and identification of biomarkers using multi-omics data of papillary renal cell carcinoma.

Identification of cancer subtypes based on molecular knowledge is crucial for improving the patient diagnosis, prognosis, and treatment. In this work, we integrated copy number variations (CNVs) and transcriptomic data of Kidney Papillary Renal Cell Carcinoma (KIRP) using a network diffusion strategy to stratify cancers into clinically and biologically relevant subtypes. We constructed GeneNet, a KIRP specific gene expression network from RNA-seq data. The copy number variation data was projected onto GeneNet and propagated on the network for clustering. We identified robust subtypes that are biologically informative and significantly associated with patient survival, tumor stage and clinical subtypes of KIRP. We performed a Singular Value Decomposition (SVD) analysis of KIRP subtypes, which revealed the genes/silent players related to poor survival. A differential gene expression analysis between subtypes showed that genes related to immune, extracellular matrix organization, and genomic instability are upregulated in the poor survival group. Overall, the network-based approach revealed the molecular subtypes of KIRP and captured the relationship between gene expression and CNVs. This framework can be further expanded to integrate other omics data.