ONCOchannelome: A computational framework for investigating altered ion channels across tumor types

Abstract

Conventional approaches for analyzing ion channels in cancer primarily focus on the detection of membrane potential to record the polarization states of the cellular membrane. Although these approaches provide meaningful insights during validation, they face challenges in discovery, especially in identifying the ion channels that govern the membrane potential. Moreover, ion channels are known to exhibit context-dependent activity that often depends on the cancer type and metabolic state of the tumor. Here, we developed a computational framework, ONCOchannelome, that allows the identification of potential ion channels via transcriptomic datasets. We collected publicly available RNA-Seq datasets corresponding to 2421 normal, 5442 primary tumor and 588 metastatic samples of 15 tumor types. Using the data, we designed a computational strategy to merge the datasets, identifying differentially expressed ion channels in the tumor and metastatic states and aiding in the determination of ion channels in different tumor states. We subsequently developed strategies to determine the correlation of altered ion channels with epithelial-to-mesenchymal transition and to identify differentially coexpressed ion channel networks along with their possible transcription factors. The ONCOchannelome allows visualization of altered ion channels in the tumor and metastatic states in addition to revealing changes resulting in changes in biological processes and molecular functions as the tumor progresses. The ONCOchannelome enhances our understanding of the dependence of ion channels in different tumors in addition to revealing their observed alterations in progressing from the primary tumor state to the metastatic state.