ONCOchannelome: A computational framework to investigate altered ion channels across tumor types
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Conventional approaches in analyzing ion channels in cancer primarily focus on detection of membrane potential to record the polarization states of the cellular membrane. Ion channels are known to exhibit context dependent activity that often depends on cancer type and metabolic state of the tumor. Here, we developed a computational framework – ONCOchannelome that allows identification of potential ion channels using transcriptomic datasets. We collected publicly available RNA-Seq datasets corresponding to 2421 normal, 5442 primary tumors 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 tumor and metastatic states aiding in determination of ion channels in the different tumor states. Subsequently, we developed strategies to determine the correlation of altered ion channels with epithelial to mesenchymal transition and to identify differentially co-expressed ion channel networks along with their possible transcription factors. The ONCOchannelome can be utilized to compare the alterations in ion channels in histological subtypes of lung cancer. The ONCOchannelome supports visualizing the altered ion channels in tumor and metastatic states in addition to providing the changes resulting in biological processes and molecular functions as the tumor progresses. The ONCOchannelome can be used to identify altered ion channels significant in various tumors that may aid in understanding tumor behaviour in turn facilitating exploration of their role in hallmarks of cancers.
Author Summary
Studying ion channels using current available methods often rely on monitoring membrane voltage using patch clamp technique or computationally simulating channel behaviour using models. Using these methods in the initial stages are considered to be time-consuming, difficult to scale and dependent on models that may or may not reflect actual tumor environment. However, gene expression patterns of the ion channels could be studied to obtain significant insights on their role and importance in a particular tumor type. In view of this, we developed ONCOchannelome, a computational framework to explore alterations in ion channels across tumors by utilizing publicly available transcriptome profiles of patients with different tumor types. ONCOchannelome would enhance our understanding of the dependence of ion channels in the different tumors in addition to providing their observed alterations on progressing from primary tumor state to metastatic state.
