NMR metabolomic profiles associated with long-term risk of prostate cancer

Causal associations of histidine and 12 site-specific cancers: a bidirectional Mendelian randomization study

An increasing number of studies indicate that cancer patients' histidine (HIS) circulating levels have changed. However, the causality between HIS and cancer is still not well established. Thus, to ascertain the causal link between HIS and cancers, we performed a bidirectional Mendelian randomization (MR) analysis. Summary-level data are derived from publicly available genome-wide association studies (GWAS). The causal effects were mainly estimated using the inverse-variance weighted method (IVW). The weighted-median (WM) method and MR-Egger regression were conducted as sensitivity analyses. In the forward-MR, we found malignant neoplasm of respiratory system and intrathoracic organs (OR: 1.020; 95% CI: 1.006-1.035; pIVW = 0.007) genetically associated with circulating HIS. And there was no significant genetic correlation between HIS and another 11 site-specific cancers using IVW method. In the reversed-MR, we did not observe the causal relationship between HIS and 12 site-specific cancers. Our findings help clarify that HIS, as a biomarker for malignant neoplasms of respiratory system and intrathoracic organs, is causal rather than a secondary biomarker of the cancerous progression. The mechanism between histidine and cancer progression deserves further investigation.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

Identification of metabolic biomarkers for diagnosis of epithelial ovarian cancer using internal extraction electrospray ionization mass spectrometry (iEESI-MS)

BACKGROUND

Epithelial ovarian cancer (EOC) is the leading cause of death from gynecologic malignancies. The poor prognosis of EOC is mainly due to its asymptomatic early stage, lack of effective screening methods, and a late diagnosis in the advanced stages of the disease.

OBJECTIVE

This study investigated metabolomic abnormalities in epithelial ovarian cancers.

METHODS

Our study developed a novel strategy to rapidly identify the metabolic biomarkers in the plasma of the EOC patients using Internal Extraction Electrospray Ionization Mass Spectrometry (IEESI-MS) and Liquid Chromatography-mass Spectrometry (HPLC-MS), which could distinguish the differential metabolites in between plasma samples collected from 98 patients with epithelial ovarian cancer, including 78 cases with original (P), and 20 cases with self-configuration (ZP), as well as 60 healthy subjects, including 30 cases in the original sample (H), 30 cases in self-configuration (ZH), and 6 cases in a blind sample (B).

RESULTS

Our study detected 880 metabolites based on criteria variable importance in projection (VIP) > 1, among which 26 metabolites were selected for further identification. They are mainly metabolism-related lipids, amino acids, nucleic acids, and others. The metabolic pathways associated with the differential metabolites were explored by the KEGG analysis, a comprehensive database that integrates genome, chemistry, and system function information. The abnormal metabolites of EOC patients identified by IEESI-MS and HPLC-MS included Lysophosphatidylcholine (16:0) [Lyso PC (16:0)], L-Phenylalanine, L-Leucine, Phenylpyruvic acid, L-Tryptophan, and L-Histidine.

CONCLUSIONS

Identifying the abnormal metabolites of EOC patients through metabolomics analyses could provide a new strategy to identify valuable potential biomarkers for the screening and early diagnosis of EOC.

Cancer insights from magnetic resonance spectroscopy of cells and excised tumors

Multinuclear ex vivo magnetic resonance spectroscopy (MRS) of cancer cells, xenografts, human cancer tissue, and biofluids is a rapidly expanding field that is providing unique insights into cancer. Starting from the 1970s, the field has continued to evolve as a stand-alone technology or as a complement to in vivo MRS to characterize the metabolome of cancer cells, cancer-associated stromal cells, immune cells, tumors, biofluids and, more recently, changes in the metabolome of organs induced by cancers. Here, we review some of the insights into cancer obtained with ex vivo MRS and provide a perspective of future directions. Ex vivo MRS of cells and tumors provides opportunities to understand the role of metabolism in cancer immune surveillance and immunotherapy. With advances in computational capabilities, the integration of artificial intelligence to identify differences in multinuclear spectral patterns, especially in easily accessible biofluids, is providing exciting advances in detection and monitoring response to treatment. Metabolotheranostics to target cancers and to normalize metabolic changes in organs induced by cancers to prevent cancer-induced morbidity are other areas of future development.

Identification of prediagnostic metabolites associated with prostate cancer risk by untargeted mass spectrometry-based metabolomics: A case-control study nested in the Northern Sweden Health and Disease Study

Prostate cancer (PCa) is the most common cancer form in males in many European and American countries, but there are still open questions regarding its etiology. Untargeted metabolomics can produce an unbiased global metabolic profile, with the opportunity for uncovering new plasma metabolites prospectively associated with risk of PCa, providing insights into disease etiology. We conducted a prospective untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis using prediagnostic fasting plasma samples from 752 PCa case-control pairs nested within the Northern Sweden Health and Disease Study (NSHDS). The pairs were matched by age, BMI, and sample storage time. Discriminating features were identified by a combination of orthogonal projection to latent structures-effect projections (OPLS-EP) and Wilcoxon signed-rank tests. Their prospective associations with PCa risk were investigated by conditional logistic regression. Subgroup analyses based on stratification by disease aggressiveness and baseline age were also conducted. Various free fatty acids and phospholipids were positively associated with overall risk of PCa and in various stratification subgroups. Aromatic amino acids were positively associated with overall risk of PCa. Uric acid was positively, and glucose negatively, associated with risk of PCa in the older subgroup. This is the largest untargeted LC-MS based metabolomics study to date on plasma metabolites prospectively associated with risk of developing PCa. Different subgroups of disease aggressiveness and baseline age showed different associations with metabolites. The findings suggest that shifts in plasma concentrations of metabolites in lipid, aromatic amino acid, and glucose metabolism are associated with risk of developing PCa during the following two decades.

Potential of nuclear magnetic resonance metabolomics in the study of prostate cancer

Nuclear magnetic resonance (NMR) metabolomics is a powerful analytical technique and a tool which has unique characteristics and capabilities for the evaluation of a number of biochemicals/metabolites of cancer and other disease processes that are present in biofluids (urine and blood) and tissues. The potential of NMR metabolomics in prostate cancer (PCa) has been explored by researchers and its usefulness has been documented. A large number of metabolites such as citrate, choline, and sarcosine were detected by NMR metabolomics from biofluids and tissues related to PCa and their levels were compared with controls and benign prostatic hyperplasia. The changes in the levels of these metabolites aid in the diagnosis and help to understand the dysregulated metabolic pathways in PCa. We review recent studies on in vitro and ex vivo NMR spectroscopy-based PCa metabolomics and its possible role as a diagnostic tool.

Plasma Metabolomics for Discovery of Early Metabolic Markers of Prostate Cancer Based on Ultra-High-Performance Liquid Chromatography-High Resolution Mass Spectrometry

BACKGROUND

The prevention and early screening of PCa is highly dependent on the identification of new biomarkers. In this study, we investigated whether plasma metabolic profiles from healthy males provide novel early biomarkers associated with future risk of PCa.

METHODS

Using the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, we identified plasma samples collected from 146 PCa cases up to 13 years prior to diagnosis and 272 matched controls. Plasma metabolic profiles were characterized using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS).

RESULTS

Orthogonal partial least squares discriminant analysis (OPLS-DA) discriminated PCa cases from controls, with a median area under the receiver operating characteristic curve (AU-ROC) of 0.92 using a 1000-time repeated random sub-sampling validation. Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) identified the top 10 most important metabolites (p < 0.001) discriminating PCa cases from controls. Among them, phosphate, ethyl oleate, eicosadienoic acid were higher in individuals that developed PCa than in the controls during the follow-up. In contrast, 2-hydroxyadenine, sphinganine, L-glutamic acid, serotonin, 7-keto cholesterol, tiglyl carnitine, and sphingosine were lower.

CONCLUSION

Our results support the dysregulation of amino acids and sphingolipid metabolism during the development of PCa. After validation in an independent cohort, these signatures may promote the development of new prevention and screening strategies to identify males at future risk of PCa.