Acid–base Homeostasis and Implications to the Phenotypic Behaviors of Cancer

Acid–base homeostasis is a fundamental property of living cells,and its persistent disruption in human cells can lead to a wide range of diseases.In this study,we conducted a computational modeling analysis of transcriptomic data of 4750 human tissue samples of 9 cancer types in The Cancer Genome Atlas(TCGA)database.Built on our previous study,we quantitatively estimated the average production rate of OHby cytosolic Fenton reactions,which continuously disrupt the intracellular pH(pHi)homeostasis.Our predictions indicate that all or at least a subset of 43 reprogrammed metabolisms(RMs)are induced to produce net protons(H+)at comparable rates of Fenton reactions to keep the pHi stable.We then discovered that a number of wellknown phenotypes of cancers,including increased growth rate,metastasis rate,and local immune cell composition,can be naturally explained in terms of the Fenton reaction level and the induced RMs.This study strongly suggests the possibility to have a unified framework for studies of cancerinducing stressors,adaptive metabolic reprogramming,and cancerous behaviors.In addition,strong evidence is provided to demonstrate that a popular view that Na+/H+exchangers along with lactic acid exporters and carbonic anhydrases are responsible for the intracellular alkalization and extracellular acidification in cancer may not be justified.

Differential Splicing of Skipped Exons Predicts Drug Response in Cancer Cell Lines

Alternative splicing of pre-mRNA transcripts is an important regulatory mechanism that increases the diversity of gene products in eukaryotes.Various studies have linked specific transcript isoforms to altered drug response in cancer;however,few algorithms have incorporated splicing information into drug response prediction.In this study,we evaluated whether basal-level splicing information could be used to predict drug sensitivity by constructing doxorubicin-sensitivity classification models with splicing and expression data.We detailed splicing differences between sensitive and resistant cell lines by implementing quasi-binomial generalized linear modeling(QBGLM)and found altered inclusion of 277 skipped exons.We additionally conducted RNA-binding protein(RBP)binding motif enrichment and differential ex-pression analysis to characterize cis-and trans-acting elements that potentially influence doxorubicin response-mediating splicing alterations.Our results showed that a classification model built with skipped exon data exhibited strong predictive power.We discovered an association between differentially spliced events and epithelial-mesenchymal transition(EMT)and observed motif enrichment,as well as differential expression of RBFOX and ELAVL RBP family members.Our work demonstrates the potential of incorporating splicing data into drug response algorithms and the utility of a QBGLM approach for fast,scalable identification of relevant splicing differences between large groups of samples.