Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways.

ABSTRACT: Small cell lung cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysi

SUBMITTER: Ozen M 

PROVIDER: S-EPMC10618210 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature