A patient-specific functional module and path identification technique from RNA-seq data

With the advancement of new technologies, a huge amount of high dimensional data is being generated which is opening new opportunities and challenges to the study of cancer and diseases. In particular, distinguishing the patient-specific key components and modules which drive tumorigenesis is necessary to analyze. A complex disease generally does not initiate from the dysregulation of a single component but it is the result of the dysfunction of a group of components and networks which differs from patient to patient. However, a patientspecific network is required to understand the disease and its molecular mechanism. We address this requirement by constructing a patient-specific network by sample-specific network theory with integrating cancerspecific differentially expressed genes and elite genes. By elucidating patient-specific networks, it can identify the regulatory modules, driver genes as well as personalized disease networks which can lead to personalized drug design. This method can provide insight into how genes are associating with each other and characterized the patient-specific disease subtypes. The results show that this method can be beneficial for the detection of patient-specific differential modules and interaction between genes. Extensive analysis using existing literature, gene enrichment and survival analysis for three cancer types STAD, PAAD and LUAD shows the effectiveness of this method over other existing methods. In addition, this method can be useful for personalized therapeutics and drug design. This methodology is implemented in the R language and is available at https://github.com/riasata zim/PatientSpecificRNANetwork.

Automated summary

With the advancement of new technologies, the study of cancer and diseases faces new opportunities and challenges due to the large amount of high dimensional data being generated. By constructing a patient-specific network, this method can identify regulatory modules, driver genes, and personalized disease networks, offering insights for personalized drug design.