Tissue metabolomics identified new biomarkers for the diagnosis and prognosis prediction of pancreatic cancer

WTAP contributes to platinum resistance in high-grade serous ovarian cancer by up-regulating malic acid: insights from liquid chromatography and mass spectrometry analysis

High-grade serous cancer (HGSC) is the most prevalent and aggressive subtype of ovarian cancer. In this study, we utilized liquid chromatography and mass spectrometry analysis to investigate metabolic alterations in HGSC. Among the 1353 metabolites identified, 140 were significantly differed between HGSC and normal ovarian tissue. KEGG pathway enrichment analysis revealed 23 distinct metabolic pathways, including the alanine/aspartate/glutamate metabolism, pyruvate metabolism, biosynthesis of amino acids, and citrate cycle, etc. Of the significantly differentiated metabolites, malic acid, fumarate, and phosphoenolpyruvate were found in the citrate cycle and glycolysis. In further analysis, 22 differentially expressed genes (DEGs) of glucose metabolism were found between HGSC and normal controls. Multivariate Cox analysis of the 22 DEGs showed that ME1, ALDOC, and RANBP2 were associated with overall survival in the TCGA cohort.Bioinformatic analysis indicated WTAP is strongly correlated to the expression of ME1, which is a rate-limiting enzyme that regulates the shuttle of malic acid in mitochondria and cytoplasm. After the knockdown of WTAP in A2780 and OVCAR-3 cells, the activity of the malic enzyme decreased which led to the accumulation of malic acid and citric acid, and the reduction of pyruvate and lactic acid. In A2780 and OVCAR-3 cells, the IC50 to platinum was increased after the knockdown of WTAP. After the knockdown of WTAP, the expression of ME1 was down-regulated and the m6A modification was down-regulated in ovarian cell lines. On the SRAMP website, there were two binding sites with high m6A scores at the 5 '-UTR 177 and 970 of ME1 mRNA. WTAP contributes to the platinum resistance through regulating the conversion from aerobic glycolysis to OXPHOS by upregulating the expression of ME1.

Genetically predicted gut bacteria, circulating bacteria-associated metabolites and pancreatic ductal adenocarcinoma: a Mendelian randomisation study

Pancreatic ductal adenocarcinoma (PDAC) has high mortality and rising incidence rates. Recent data indicate that the gut microbiome and associated metabolites may play a role in the development of PDAC. To complement and inform observational studies, we investigated associations of genetically predicted abundances of individual gut bacteria and genetically predicted circulating concentrations of microbiome-associated metabolites with PDAC using Mendelian randomisation (MR). Gut microbiome-associated metabolites were identified through a comprehensive search of Pubmed, Exposome Explorer and Human Metabolome Database. Single Nucleotide Polymorphisms (SNPs) associated by Genome-Wide Association Studies (GWAS) with circulating levels of 109 of these metabolites were collated from Pubmed and the GWAS catalogue. SNPs for 119 taxonomically defined gut genera were selected from a meta-analysis performed by the MiBioGen consortium. Two-sample MR was conducted using GWAS summary statistics from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), including a total of 8,769 cases and 7,055 controls. Inverse variance-weighted MR analyses were performed along with sensitivity analyses to assess potential violations of MR assumptions. Nominally significant associations were noted for genetically predicted circulating concentrations of mannitol (odds ratio per standard deviation [ORSD] = 0.97; 95% confidence interval [CI]: 0.95-0.99, p = 0.006), methionine (ORSD= 0.97; 95%CI: 0.94-1.00, p = 0.031), stearic acid (ORSD= 0.93; 95%CI: 0.87-0.99, p = 0.027), carnitine = (ORSD=1.01; 95% CI: 1.00-1.03, p = 0.027), hippuric acid (ORSD= 1.02; 95%CI: 1.00-1.04, p = 0.038) and 3-methylhistidine (ORSD= 1.05; 95%CI: 1.01-1.10, p = 0.02). Two gut microbiome genera were associated with reduced PDAC risk; Clostridium sensu stricto 1 (OR: 0.88; 95%CI: 0.78-0.99, p = 0.027) and Romboutsia (OR: 0.87; 95%CI: 0.80-0.96, p = 0.004). These results, though based only on genetically predicted gut microbiome characteristics and circulating bacteria-related metabolite concentrations, provide evidence for causal associations with pancreatic carcinogenesis.

Plant Metabolomics: The Future of Anticancer Drug Discovery

Drug development from medicinal plants constitutes an important strategy for finding natural anticancer therapies. While several plant secondary metabolites with potential antitumor activities have been identified, well-defined mechanisms of action remained uncovered. In fact, studies of medicinal plants have often focused on the genome, transcriptome, and proteome, dismissing the relevance of the metabolome for discovering effective plant-based drugs. Metabolomics has gained huge interest in cancer research as it facilitates the identification of potential anticancer metabolites and uncovers the metabolomic alterations that occur in cancer cells in response to treatment. This holds great promise for investigating the mode of action of target metabolites. Although metabolomics has made significant contributions to drug discovery, research in this area is still ongoing. In this review, we emphasize the significance of plant metabolomics in anticancer research, which continues to be a potential technique for the development of anticancer drugs in spite of all the challenges encountered. As well, we provide insights into the essential elements required for performing effective metabolomics analyses.

Multi-omics analyses based on genes associated with oxidative stress and phospholipid metabolism revealed the intrinsic molecular characteristics of pancreatic cancer

Oxidative stress (OS), which impacts lipid metabolic reprogramming, can affect the biological activities of cancer cells. How oxidative stress and phospholipid metabolism (OSPM) influence the prognosis of pancreatic cancer (PC) needs to be elucidated. The metabolic data of 35 pancreatic tumor samples, 34 para-carcinoma samples, and 31 normal pancreatic tissues were obtained from the previously published literature. Pan-cancer samples were obtained from The Cancer Genome Atlas (TCGA). And the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), ArrayExpress, and the Genotype-Tissue Expression (GTEx) databases were searched for more PC and normal pancreatic samples. The metabolites in PC were compared with normal and para-carcinoma tissues. The characteristics of the key OSPM genes were summarized in pan-cancer. The random survival forest analysis and multivariate Cox regression analysis were utilized to construct an OSPM-related signature. Based on this signature, PC samples were divided into high- and low-risk subgroups. The dysregulations of the tumor immune microenvironment were further investigated. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was conducted to investigate the expression of genes in the signature in PC and normal tissues. The protein levels of these genes were further demonstrated. In PC, metabolomic studies revealed the alteration of PM, while transcriptomic studies showed different expressions of OSPM-related genes. Then 930 PC samples were divided into three subtypes with different prognoses, and an OSPM-related signature including eight OSPM-related genes (i.e., SLC2A1, MMP14, TOP2A, MBOAT2, ANLN, ECT2, SLC22A3, and FGD6) was developed. High- and low-risk subgroups divided by the signature showed different prognoses, expression levels of immune checkpoint genes, immune cell infiltration, and tumor microenvironment. The risk score was negatively correlated with the proportion of TIL, pDC, Mast cell, and T cell co-stimulation. The expression levels of genes in the signature were verified in PC and normal samples. The protein levels of SLC2A1, MMP14, TOP2A, MBOAT2, ANLN, and SLC22A3 showed up-regulation in PC samples compared with normal tissues. After integrating metabolomics and transcriptomics data, the alterations in OSPM in PC were investigated, and an OSPM-related signature was developed, which was helpful for the prognostic assessment and individualized treatment for PC.

Plasma Metabolomics Predicts Chemotherapy Response in Advanced Pancreatic Cancer

Pancreatic cancer (PC) is one of the deadliest cancers. Developing biomarkers for chemotherapeutic response prediction is crucial for improving the dismal prognosis of advanced-PC patients (pts). To evaluate the potential of plasma metabolites as predictors of the response to chemotherapy for PC patients, we analyzed plasma metabolites using high-performance liquid chromatography-mass spectrometry from 31 cachectic, advanced-PC subjects enrolled into the PANCAX-1 (NCT02400398) prospective trial to receive a jejunal tube peptide-based diet for 12 weeks and who were planned for palliative chemotherapy. Overall, there were statistically significant differences in the levels of intermediates of multiple metabolic pathways in pts with a partial response (PR)/stable disease (SD) vs. progressive disease (PD) to chemotherapy. When stratified by the chemotherapy regimen, PD after 5-fluorouracil-based chemotherapy (e.g., FOLFIRINOX) was associated with decreased levels of amino acids (AAs). For gemcitabine-based chemotherapy (e.g., gemcitabine/nab-paclitaxel), PD was associated with increased levels of intermediates of glycolysis, the TCA cycle, nucleoside synthesis, and bile acid metabolism. These results demonstrate the feasibility of plasma metabolomics in a prospective cohort of advanced-PC patients for assessing the effect of enteral feeding as their primary source of nutrition. Metabolic signatures unique to FOLFIRINOX or gemcitabine/nab-paclitaxel may be predictive of a patient's response and warrant further study.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.