Cancer cells alter their metabolism for the production of precursors of macromolecules. However, the control mechanisms underlying this reprogramming are poorly understood. Here we show that metabolic reprogramming of colorectal cancer is caused chiefly by aberrant MYC expression. Multiomics-based analyses of paired normal and tumor tissues from 275 patients with colorectal cancer revealed that metabolic alterations occur at the adenoma stage of carcinogenesis, in a manner not associated with specific gene mutations involved in colorectal carcinogenesis. MYC expression induced at least 215 metabolic reactions by changing the expression levels of 121 metabolic genes and 39 transporter genes. Further, MYC negatively regulated the expression of genes involved in mitochondrial biogenesis and maintenance but positively regulated genes involved in DNA and histone methylation. Knockdown of MYC in colorectal cancer cells reset the altered metabolism and suppressed cell growth. Moreover, inhibition of MYC target pyrimidine synthesis genes such as CAD, UMPS, and CTPS blocked cell growth, and thus are potential targets for colorectal cancer therapy.ne of the prominent characteristics of rapidly growing tumor cells is their capacity to sustain high rates of glycolysis for ATP generation irrespective of oxygen availability, termed the Warburg effect (1). Recent studies have shown that cancer cells shift metabolic pathways to facilitate the uptake and incorporation of abundant nutrients, such as glucose and glutamine (2, 3), into cell building blocks, such as nucleotides, amino acids, and lipids, that are essential for highly proliferating cells (4). This seems to be a universal characteristic of highly malignant tumors (5), independent of their carcinogenetic origin (6). Understanding how cancer cells reprogram metabolism can stimulate the development of new approaches in cancer therapy.Although there is now substantial information about how these pathways are regulated, most existing studies on cancer metabolism have used in vitro cell lines. In addition to genetic and epigenetic alterations, altered tumor microenvironment (e.g., blood flow, oxygen and nutrient supply, pH distribution, redox state, and inflammation) plays a profound role in modulating tumor cell metabolism (7-9). Therefore, a systematic characterization of in vivo metabolic pathways was deemed necessary to understand how metabolic phenotypes are regulated in intact human tumors.Here we applied multiomics-based approaches [i.e., metabolomics, target sequencing of cancer-related genes, transcriptomics, and methylated DNA immunoprecipitation sequencing (MeDIPseq)] to paired normal and tumor tissues obtained from 275 patients with colorectal cancer (CRC) and uncovered the details of which factors contributed, and when they contributed, to metabolic reprogramming in colorectal cancer. The results were confirmed by analysis of colorectal tissue from Apc mutant mice and cancer cell lines.

The objective of this study was to explore salivary metabolite biomarkers by profiling both saliva and tumor tissue samples for oral cancer screening. Paired tumor and control tissues were obtained from oral cancer patients and whole unstimulated saliva samples were collected from patients and healthy controls. The comprehensive metabolomic analysis for profiling hydrophilic metabolites was conducted using capillary electrophoresis time-of-flight mass spectrometry. In total, 85 and 45 metabolites showed significant differences between tumor and matched control samples, and between salivary samples from oral cancer and controls, respectively (P < 0.05 correlated by false discovery rate); 17 metabolites showed consistent differences in both saliva and tissue-based comparisons. Of these, a combination of only two biomarkers yielded a high area under receiver operating characteristic curves (0.827; 95% confidence interval, 0.726–0.928, P < 0.0001) for discriminating oral cancers from controls. Various validation tests confirmed its high generalization ability. The demonstrated approach, integrating both saliva and tumor tissue metabolomics, helps eliminate pseudo-molecules that are coincidentally different between oral cancers and controls. These combined salivary metabolites could be the basis of a clinically feasible method of non-invasive oral cancer screening.

The development of high-throughput metabolite measurement technologies has enabled the use of metabolomics for epidemiologic studies by profiling metabolite concentrations in large cohorts of human blood samples. Standard protocols are necessary to obtain unbiased profiles through multiple runs over long periods of time and to allow reliable statistical analyses. This study assessed the effects of sampling procedures and storage conditions on the stability of metabolomic profiles in plasma and serum. Charged metabolomic profiles were determined by capillary electrophoresis-mass spectrometry (CE-MS) and compared by multivariate analyses. The effects of pre-analytical procedures, including times for clotting and incubation of serum and plasma, respectively; incubation temperatures; and number of freeze-thaw cycles, were assessed. Overall, inter-individual differences in profiles were larger than intra-individual differences, and profiles in plasma showed better stability than those in serum. These quantified datasets of metabolites, along with their stability and variation, may help in interpreting data from long-term cohort studies.

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The aim of this study is to evaluate the effect of duration after meals for saliva collections for oral cancer detection using metabolomics. Saliva samples were collected from oral cancer patients (n = 22) and controls (n = 44). Saliva from cancer patients was collected 12 h after dinner, and 1.5 and 3.5 h after breakfast. Control subjects fasted >1.5 h prior to saliva collection. Hydrophilic metabolites were analyzed using capillary electrophoresis mass spectrometry. Levels of 51 metabolites differed significantly in controls vs. oral cancer patients at the 12-h fasting time point (P < 0.05). Fifteen and ten metabolites differed significantly at the 1.5- and 3.5-h time points, respectively. The area of under receiver operating characteristic curve for discriminating oral cancer patients from controls was greatest at the 12-h fasting time point. The collection time after meals affects levels of salivary metabolites for oral cancer screening. The 12-h fasting after dinner time point is optimal. This study contributes to design of saliva collection protocols for metabolomics-based biomarker discovery.