A metabolomics-based biomarker signature discriminates pancreatic cancer from chronic pancreatitis

Lipid Identification of Biomarkers in Esophageal Squamous Cell Carcinoma by Lipidomic Analysis

Lipids participate in many important biological functions through energy storage, membrane structure stabilization, signal transduction, and molecular recognition. Previous studies have shown that patients with esophageal squamous cell carcinoma (ESCC) have abnormal lipid metabolism. However, studies characterizing lipid metabolism in ESCC patients through lipidomics are limited. Plasma lipid profiles of 65 ESCC patients and 42 healthy controls (HC) were characterized by lipidomics-based ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Single-factor and multi-factor statistical analysis were used to screen the differences in blood lipids between groups, and combined with component ratio analysis and receiver operating characteristic (ROC) curve diagnostic efficiency assessment, to reveal the potential mechanisms and biomarkers of ESCC. There were significant differences in lipid profiles between the ESCC and HC groups. Thirty-six differential lipids (11 up-regulated and 25 down-regulated) were selected based on the criteria of p < .05 and fold change > 1.3 or < 0.77. Glycerophospholipids were the major differential lipids, suggesting that these lipid metabolic pathways exhibit a significant imbalance that may contribute to the development of esophageal squamous cell carcinoma. Among them, the seven candidate biomarkers for esophageal squamous cell carcinoma with the highest diagnostic value are three phosphatidylserine (PS), three fatty acids (FA) and one phosphatidylcholine (PC).

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Independent Validation and Assay Standardization of Improved Metabolic Biomarker Signature to Differentiate Pancreatic Ductal Adenocarcinoma From Chronic Pancreatitis

BACKGROUND & AIMS

Pancreatic ductal adenocarcinoma cancer (PDAC) is a highly lethal malignancy requiring efficient detection when the primary tumor is still resectable. We previously developed the MxPancreasScore comprising 9 analytes and serum carbohydrate antigen 19-9 (CA19-9), achieving an accuracy of 90.6%. The necessity for 5 different analytical platforms and multiple analytical runs, however, hindered clinical applicability. We therefore aimed to develop a simpler single-analytical run, single-platform diagnostic signature.

METHODS

We evaluated 941 patients (PDAC, 356; chronic pancreatitis [CP], 304; nonpancreatic disease, 281) in 3 multicenter independent tests, and identification (ID) and validation cohort 1 (VD1) and 2 (VD2) were evaluated. Targeted quantitative plasma metabolite analysis was performed on a liquid chromatography-tandem mass spectrometry platform. A machine learning-aided algorithm identified an improved (i-Metabolic) and minimalistic metabolic (m-Metabolic) signatures, and compared them for performance.

RESULTS

The i-Metabolic Signature, (12 analytes plus CA19-9) distinguished PDAC from CP with area under the curve (95% confidence interval) of 97.2% (97.1%-97.3%), 93.5% (93.4%-93.7%), and 92.2% (92.1%-92.3%) in the ID, VD1, and VD2 cohorts, respectively. In the VD2 cohort, the m-Metabolic signature (4 analytes plus CA19-9) discriminated PDAC from CP with a sensitivity of 77.3% and specificity of 89.6%, with an overall accuracy of 82.4%. For the subset of 45 patients with PDAC with resectable stages IA-IIB tumors, the sensitivity, specificity, and accuracy were 73.2%, 89.6%, and 82.7%, respectively; for those with detectable CA19-9 >2 U/mL, 81.6%, 88.7%, and 84.5%, respectively; and for those with CA19-9 <37 U/mL, 39.7%, 94.1%, and 76.3%, respectively.

CONCLUSIONS

The single-platform, single-run, m-Metabolic signature of just 4 metabolites used in combination with serum CA19-9 levels is an innovative accurate diagnostic tool for PDAC at the time of clinical presentation, warranting further large-scale evaluation.

Nanomaterials-assisted metabolic analysis toward in vitro diagnostics

In vitro diagnostics (IVD) has played an indispensable role in healthcare system by providing necessary information to indicate disease condition and guide therapeutic decision. Metabolic analysis can be the primary choice to facilitate the IVD since it characterizes the downstream metabolites and offers real-time feedback of the human body. Nanomaterials with well-designed composition and nanostructure have been developed for the construction of high-performance detection platforms toward metabolic analysis. Herein, we summarize the recent progress of nanomaterials-assisted metabolic analysis and the related applications in IVD. We first introduce the important role that nanomaterials play in metabolic analysis when coupled with different detection platforms, including electrochemical sensors, optical spectrometry, and mass spectrometry. We further highlight the nanomaterials-assisted metabolic analysis toward IVD applications, from the perspectives of both the targeted biomarker quantitation and untargeted fingerprint extraction. This review provides fundamental insights into the function of nanomaterials in metabolic analysis, thus facilitating the design of next-generation diagnostic devices in clinical practice.

Ruscogenin Alleviates Myocardial Ischemia-Induced Ferroptosis through the Activation of BCAT1/BCAT2

Ruscogenin (RUS), a natural steroidal sapogenin, exerts various biological activities. However, its effectiveness for preventing myocardial ischemia (MI) and its molecular mechanisms need further clarification. The model of MI mice and oxygen-glucose deprivation-induced cardiomyocytes injury was performed. RUS significantly alleviated MI, as evidenced by decreased infarct size, ameliorated biochemical indicators and cardiac pathological features, and markedly inhibited ferroptosis by means of the up-regulation of GPX4 and down-regulation of ACSL4 and FLC. Simultaneously, RUS notably mitigated cell injury and oxidative stress, and ameliorated ferroptosis in vitro. Subsequently, HPLC-Q-TOF/MS-based metabolomics identified BCAT1/BCAT2 as possible regulatory enzymes responsible for the cardioprotection of RUS. Importantly, RUS treatment significantly increased the expression of BCAT1 and BCAT2 in MI. Furthermore, we found that BCAT1 or BCAT2 siRNA significantly decreased cell viability, promoted ferroptosis, and increased Keap1 expression, and induced Nrf2 and HO-1 degradation in cardiomyocytes. Conversely, cardiac overexpression of BCAT1 or BCAT2 in MI mice activated the Keap1/Nrf2/HO-1 pathway. Moreover, RUS significantly activated the Keap1/Nrf2/HO-1 pathway in MI, whereas BCAT1 or BCAT2 siRNA partially weakened the protective effects of RUS, suggesting that RUS might suppress myocardial injury through BCAT1 and BCAT2. Overall, this study demonstrated that BCAT1/BCAT2 could alleviate MI-induced ferroptosis through the activation of the Keap1/Nrf2/HO-1 pathway and RUS exerted cardioprotective effects via BCAT1/BCAT2.

Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning

BACKGROUND

Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients.

METHODS

A total of 525 serum samples were subjected to lipidomic analysis. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. A diagnostic model using a panel of selected features was developed and evaluated. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation.

RESULTS

Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analysis revealed matched variation trend of genes encoding key enzymes in lipid metabolism.

CONCLUSIONS

We have identified a panel of 12 lipid biomarkers for diagnostic modelling and potential mechanisms of lipid dysregulation in the serum of ESCC patients. This is a reliable, rapid and non-invasive tumour-diagnostic approach for clinical application.

Dietary and Lifestyle Factors Associated with Gastric and Pancreatic Cancers: A Case-Control Study

Gastric cancer (GC) and pancreatic cancer (PC) are the third and seventh most likely cancers to cause death worldwide. We aimed to determine the dietary and lifestyle factors of patients with GC or PC and their associated risk among Jordanians. This case-control study enrolled 587 adults (patients with PC, 101; patients with GC, 172; healthy controls, 314) between March 2015 and August 2018, who were assessed using interview-based personal and physical activity questionnaires. Multivariable logistic regression models were taken as measures for predictors of GC and PC risk. We showed that GC and PC patients had higher pre-diagnosis body-mass indexes, a greater proportion smoked and had a family history of cancer than controls. Furthermore, consumption of two snacks [odds ratios (OR)=0.44, 95% confidence intervals (CI): 0.23~0.85], three snacks (OR=0.04, 95% CI: 0.01~0.23) and no meals eaten outside (OR=0.31, 95% CI: 0.09~0.99) showed a protective effect against GC, and consumption of three snacks (OR=0.08, 95% CI: 0.02~0.40) reduced significantly the risk of PC. These results suggest that bodyweight, physical activity, smoking, and family history of cancer are among factors that affect GC and PC risk among Jordanians.

Pancreatic stellate cells regulate branched-chain amino acid metabolism in pancreatic cancer

Background

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal malignancy: it has a 5-year survival rate of less than 9%. Although surgical resection is an effective treatment for PDAC, only a small number of patients can have their tumors surgically removed. Thus, an urgent need to find new therapeutic targets for PDAC exists. Understanding the molecular mechanism of PDAC development is essential for the treatment of this malignancy. This research aimed to study the mechanisms of pancreatic stellate cells (PSCs), which regulate branched-chain amino acid (BCAA) metabolism in PDAC.

Methods

Differentially expressed proteins were detected via nanoliquid chromatography coupled to mass spectrometry (nano-LC-MS/MS). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment methods were used to find the valine-leucine-isoleucine (BCAA) degradation pathway. The levels of BCAAs in the sera and tissues of patients with PDAC were measured by using nuclear magnetic resonance (NMR). The functions of BCAA concentrations and the effects of activated pancreatic stellate cells (aPSCs) were also evaluated by performing Cell Counting Kit-8, colony formation, and wound healing assays.

Results

A total of 1,519 proteins with significantly differential expression were discovered in PDAC and adjacent tissues by using nano-LC-MS/MS. KEGG pathway enrichment analysis identified the BCAA degradation pathway. The content of BCAA in PDAC clinical samples was up-regulated. However, the addition of different concentrations of BCAA to PDAC cell culture medium failed to promote the proliferation and migration of PDAC cells. Given that analysis based on The Cancer Genome Atlas database showed that the number of aPSCs gradually increased with the progression of PDAC, the effects of aPSCs on PDAC cells were explored. After coculture with aPSCs, PDAC cell proliferation showed a significant increase, and the proteins involved in the BCAA degradation pathway in PDAC cells had also changed.

Conclusions

aPSCs could regulate BCAA metabolism to enhance the progression of PDAC, indicating that the regulation of BCAA metabolism may serve as a new therapeutic direction for PDAC.

Novel Metabolomics Serum Biomarkers for Pancreatic Ductal Adenocarcinoma by the Comparison of Pre-, Postoperative and Normal Samples

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive human malignancies. The metabolomic approaches are developed to discover the novel biomarkers of PDAC. Methods: 550 preoperative, postoperative PDAC and normal controls (NCs) serums were employed to characterize metabolic alterations in training and validation sets by LC-MS. Results: The results of PLS-DA analysis indicated that three groups could be distinguished clearly and the post-PDAC group is adjacent to a normal group as compared with pre-PDAC group. Further results showed that histidinyl-lysine significantly increased whereas docosahexaenoic acid and LysoPC (14:0) decreased in pre-PDAC patients as compared with NCs. And these three markers had a significant tendency to recover after tumor resection. The validation set results revealed that for CA19-9 negative patients, 92.3% (12/13) of them can be screened using these three metabolites. The combination of these markers could significantly improve the diagnostic performance for PDAC, with higher sensitivity (0.93), specificity (0.92) and AUC (0.97). Moreover, network and pathways analyses explored the latent relationship among differential metabolites. The glycerolipid metabolism and primary bile acid synthesis showed variation in network and pathway analysis. Conclusions: These three markers combined with CA199 displayed high sensitivity and specificity for detecting PDAC patients from NCs. The results indicated that these three metabolites could be regarded as potential biomarkers to distinguish PDAC from NCs.

Current Strategies and Future Perspectives for Precision Medicine in Pancreatic Cancer

Current standard-of-care for patients with pancreatic ductal adenocarcinoma (PDAC) focusses on chemotherapeutic regimens and pancreatic cancer surgery. However, limited treatment options, late diagnosis in advanced tumor stages and the aggressive behavior of PDAC contribute to the high mortality of the disease. Consequently, there is an urgent need of precision medicine for pancreatic cancer patients. All over the world, numerous initiatives started in recent years to translate novel scientific discoveries into prospective clinical trials. One major approach pursues the stratification of PDAC patients according the tumor transcriptome to predict treatment response. Other strategies concentrate on genomic alterations and the identification of individualized targeted therapies. Further experimental studies are ongoing to detect novel biomarkers for cancer diagnosis, subtyping, treatment response prediction or clinical outcome. However, the challenge remains to transfer the knowledge into clinical practice. In this review, we summarize current literature and knowledge and highlight novel concepts of basic and clinical research uncovering suitable biomarkers and targeted therapies. Thus, we provide an overview of preclinical and clinical efforts of precision medicine in pancreatic cancer.

Novel applications of mass spectrometry-based metabolomics in herbal medicines and its active ingredients: Current evidence

Current evidence shows that herbal medicines could be beneficial for the treatment of various diseases. However, the complexities present in chemical compositions of herbal medicines are currently an obstacle for the progression of herbal medicines, which involve unclear bioactive compounds, mechanisms of action, undetermined targets for therapy, non-specific features for drug metabolism, etc. To overcome those issues, metabolomics can be a great to improve and understand herbal medicines from the small-molecule metabolism level. Metabolomics could solve scientific difficulties with herbal medicines from a metabolic perspective, and promote drug discovery and development. In recent years, mass spectrometry-based metabolomics was widely applied for the analysis of herbal constituents in vivo and in vitro. In this review, we highlight the value of mass spectrometry-based metabolomics and metabolism to address the complexity of herbal medicines in systems pharmacology, and to enhance their biomedical value in biomedicine, to shed light on the aid that mass spectrometry-based metabolomics can offer to the investigation of its active ingredients, especially, to link phytochemical analysis with the assessment of pharmacological effect and therapeutic potential. © 2019 Wiley Periodicals, Inc. Mass Spec Rev.

Discovery and Validation of Clinical Biomarkers of Cancer: A Review Combining Metabolomics and Proteomics

Early detection and diagnosis of cancer can allow timely medical intervention, which greatly improves chances of survival and enhances quality of life. Biomarkers play an important role in assisting clinicians and health care providers in cancer diagnosis and treatment follow-up. In spite of years of research and the discovery of thousands of candidate cancer biomarkers, only a few have transitioned to routine usage in the clinic. This review highlights advances in proteomics technologies that have enabled high rates of discovery of candidate cancer biomarkers and evaluates integration with other omics technologies to improve their progress through to validation and clinical translation. Furthermore, it gauges the role of metabolomics technology in cancer biomarker research and assesses it as a complementary tool in aiding cancer biomarker discovery and validation.

Elevated Polyamines in Saliva of Pancreatic Cancer

Detection of pancreatic cancer (PC) at a resectable stage is still difficult because of the lack of accurate detection tests. The development of accurate biomarkers in low or non-invasive biofluids is essential to enable frequent tests, which would help increase the opportunity of PC detection in early stages. Polyamines have been reported as possible biomarkers in urine and saliva samples in various cancers. Here, we analyzed salivary metabolites, including polyamines, using capillary electrophoresis-mass spectrometry. Salivary samples were collected from patients with PC (n = 39), those with chronic pancreatitis (CP, n = 14), and controls (C, n = 26). Polyamines, such as spermine, N₁-acetylspermidine, and N₁-acetylspermine, showed a significant difference between patients with PC and those with C, and the combination of four metabolites including N₁-acetylspermidine showed high accuracy in discriminating PC from the other two groups. These data show the potential of saliva as a source for tests screening for PC.