Systematic analyses of glutamine and glutamate metabolisms across different cancer types

Background: Glutamine and glutamate are known to play important roles in cancer biology. However, no detailed information is available in terms of their levels of involvement in various biological processes across different cancer types, whereas such knowledge could be critical for understanding the distinct characteristics of different cancer types. Our computational study aimed to examine the functional roles of glutamine and glutamate across different cancer types.Methods: We conducted a comparative analysis of gene expression data of cancer tissues versus normal control tissues of 11 cancer types to understand glutamine and glutamate metabolisms in cancer. Specifically, we developed a linear regression model to assess differential contributions by glutamine and/or glutamate to each of seven biological processes in cancer versus control tissues.Results: While our computational predictions were consistent with some of the previous observations, multiple novel predictions were made:(1) glutamine is generally not involved in purine synthesis in cancer except for breast cancer, and is similarly not involved in pyridine synthesis except for kidney cancer;(2) glutamine is generally not involved in ATP production in cancer;(3) glutamine's contribution to nucleotide synthesis is minimal if any in cancer;(4) glutamine is not involved in asparagine synthesis in cancer except for bladder and lung cancers; and(5) glutamate does not contribute to serine synthesis except for bladder cancer.Conclusions: We comprehensively predicted the roles of glutamine and glutamate metabolisms in selected metabolic pathways in cancer tissues versus control tissues, which may lead to novel approaches to therapeutic development targeted at glutamine and/or glutamate metabolism. However, our predictions need further functional validation.

Warburg Effects in Cancer and Normal Proliferating Cells:Two Tales of the Same Name

It has been observed that both cancer tissue cells and normal proliferating cells(NPCs)have the Warburg effect.Our goal here is to demonstrate that they do this for different reasons.To accomplish this,we have analyzed the transcriptomic data of over 7000 cancer and control tissues of 14 cancer types in TCGA and data of five NPC types in GEO.Our analyses reveal that NPCs accumulate large quantities of ATPs produced by the respiration process before starting the Warburg effect,to raise the intracellular pH from 6.8 to 7.2 and to prepare for cell division energetically.Once cell cycle starts,the cells start to rely on glycolysis for ATP generation followed by ATP hydrolysis and lactic acid release,to maintain the elevated intracellular pH as needed by cell division since together the three processes are pH neutral.The cells go back to the normal respirationbased ATP production once the cell division phase ends.In comparison,cancer cells have reached their intracellular pH at 7.4 from top down as multiple acid-loading transporters are up-regulated and most acid-extruding ones except for lactic acid exporters are repressed.Cancer cells use continuous glycolysis for ATP production as way to acidify the intracellular space since the lactic acid secretion is decoupled from glycolysis-based ATP generation and is pH balanced by increased expressions of acid-loading transporters.Co-expression analyses suggest that lactic acid secretion is regulated by external,non-pH related signals.Overall,our data strongly suggest that the two cell types have the Warburg effect for very different reasons.

Fenton reactions drive nucleotide and ATP syntheses in cancer

We present a computational study of tissue transcriptomic data of 14 cancer types to address： what may drive cancer cell division？ Our analyses point to that persistent disruption of the intraceUular pH by Fenton reactions may be at the root of cancer development. Specifically, we have statistically demonstrated that Fenton reactions take place in cancer cytosoi and mitochondria across all the 14 cancer types, based on cancer tissue gene-expression data integrated via the Michaelis-Menten equation. In addition, we have shown that （i） Fenton reactions in cytosol of the disease cells will continuously increase their pH, to which the cells respond by generating net protons to keep the pH stable through a combination of synthesizing glycolytic ATPs and consuming them by nucleotide syntheses, which may drive cell division to rid of the continuously synthesized nucleotides; and （ii） Fenton reactions in mitochondria give rise to novel ways for ATP synthesis with electrons ultimately coming from H2O2, largely originated from immune cells. A model is developed to link these to cancer development, where some mutations may be selected to facilitate cell division at rates dictated by Fenton reactions.

Transcription regulation by DNA methylation under stressful conditions in human cancer

Background： We aim to address one question： do cancer vs. normal tissue cells execute their transcription regulation essentially the same or differently, and why？ Methods： We utilized an integrated computational study of cancer epigenomes and transcriptomes of 10 cancer types, by using penalized linear regression models to evaluate the regulatory effects of DNA methylations on gene expressions. Results： Our main discoveries are： （i） 56 genes have their expressions consistently regulated by DNA methylation specifically in cancer, which enrich pathways associated with micro-environmental stresses and responses, particularly oxidative stress; （ii） the level of involvement by DNA methylation in transcription regulation increases as a cancer advances for majority of the cancer types examined; （iii） transcription regulation in cancer vs. control tissue cells are substantially different, with the former being largely done through direct DNA methylation and the latter mainly done via transcriptional factors; （iv） the altered DNA methylation landscapes in cancer vs. control are predominantly accomplished by DNMTI, TET3 and CBX2, which are predicted to be the result of persistent stresses present in the intracellular and micro-environments of cancer cells, which is consistent with the general understanding about epigenomic functions. Conclusions： Our integrative analyses discovered that a large class of genes is regulated via direct DNA methylation of the genes in cancer, comparing to TFs in normal cells. Such genes fall into a few stress and response pathways. As a cancer advances, the level of involvement by direct DNA methylation in transcription regulation increases for majority of the cancer types examined.

Population dynamics inside cancer biomass driven by repeated hypoxia-reoxygenation cycles

A computational analysis of genome-scale transcriptomic data collected on -1,700 tissue samples of three cancer types： breast carcinoma, colon adenocarcinoma and lung adenocarcinoma, revealed that each tissue consists of （at least） two major subpopulations of cancer cells with different capabilities to handle fluctuating Oz levels. The two populations have distinct genomic and transcriptomic characteristics, one accelerating its proliferation under hypoxic conditions and the other proliferating faster with higher O2 levels, referred to as the hypoxia and the reoxygenation subpopulations, respectively. The proportions of the two subpopulations within a cancer tissue change as the average 02 level changes. They both contribute to cancer development but in a complementary manner. The hypoxia subpopulation tends to have higher proliferation rates than the reoxygenation one as well as higher apoptosis rates; and it is largely responsible for the acidic environment that enables tissue invasion and provides protection against attacks from T-cells. In comparison, the reoxygenation subpopulation generates new extracellular matrices in support of further growth of the tumor and strengthens cell-cell adhesion to provide scaffolds to keep all the cells connected. This subpopulation also serves as the major source of growth factors for tissue growth. These data and observations strongly suggest that these two major subpopulations within each tumor work together in a conjugative relationship to allow the tumor to overcome stresses associated with the constantly changing Oz level due to repeated growth and angiogenesis. The analysis results not only reveal new insights about the population dynamics within a tumor but also have implications to our understanding of possible causes of different cancer phenotypes such as diffused versus more tightly connected tumor tissues.

Elucidation of drivers of high-level production of lactates throughout a cancer development

Lactates play key roles in facilitating or protecting the development of a cancer in most cancer types.While its beneficial effects to cancer development have been extensively studied,very little is known about what derives the high-level production of lactates in a cancer throughout its entire development.Here we present a novel computational analysis of transcriptomic data of nine primary cancer types,plus a few precancerous and metastatic cancer,to address this issue.Our approach is to identify stress types,which are known to play key roles in cancer development and show strong co-expressions with lactate dehydrogenase-A(LDHA),at different stages of cancer development.A number of interesting observations are made through our analyses,including(i)all nine primary cancer types show similar association patterns between stresses and LDHA,namely the strengths of the associations increase from early-to intermediate-stage cancer tissues but then make a substantial down turn at the most advanced stage;(ii)while the detailed stress types associated with LDHA may vary across different cancer types,stresses induced by apoptosis and adaptive immune responses are present universally,suggesting that these two stresses are possibly two key drivers to keep the high-level production of lactates;and(iii)there is a clear distinction between stress types associated with LDHA in precancerous tissues vs.cancer and metastasis tissues.We anticipate that the analyses can provide highly useful information for designing personalized treatments for different cancers at different stages,as stopping lactate production could have devastating effects on a cancer development.

MRHCA： a nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network

Background： Gene co-expression and differential co-expression analysis has been increasingly used to study co- functional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods： In this study, we develop a nonparametric approach to identify hub genes and modules in a large co- expression network with low computational and memory cost, namely MRHCA. Results： We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co- expression analysis to E. coil and TCGA cancer data, we have identified significant condition specific activated genes in E. coil and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations. Conclusion： Our analysis has demonstrated MRItCA can （i） deal with large association networks, （ii） rigorously assess statistical significance for hubs and module sizes, （iii） identify co-expression modules with low associations, （iv） detect small and significant modules, and （v） allow genes to be present in more than one modules, compared with existing methods.