Untargeted LC-HRMS-based metabolomics to identify novel biomarkers of metastatic colorectal cancer

Exploiting the metabolic vulnerability of circulating tumour cells

Metastasis has a major part in the severity of disease and lethality of cancer. Circulating tumour cells (CTCs) represent a reservoir of metastatic precursors in circulation, most of which cannot survive due to hostile conditions in the bloodstream. Surviving cells colonise a secondary site based on a combination of physical, metabolic, and oxidative stress protection states required for that environment. Recent advances in CTC isolation methods and high-resolution 'omics technologies are revealing specific metabolic pathways that support this selection of CTCs. In this review, we discuss recent advances in our understanding of CTC biology and discoveries of adaptations in metabolic pathways during their selection. Understanding these traits and delineating mechanisms by which they confer acquired resistance or vulnerability in CTCs is crucial for developing successful prognostic and therapeutic strategies in cancer.

Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study

BACKGROUND

Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge.

OBJECTIVES

To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management.

METHODS

In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM.

RESULTS

We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample.

CONCLUSIONS

These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.

Linking MS1 and MS2 signals in positive and negative modes of LC-HRMS in untargeted metabolomics using the ROIMCR approach

Data-independent acquisition (DIA) mode in liquid chromatography (LC) high-resolution mass spectrometry (HRMS) has emerged as a powerful strategy in untargeted metabolomics for detecting a broad range of metabolites. However, the use of this approach also represents a challenge in the analysis of the large datasets generated. The regions of interest (ROI) multivariate curve resolution (MCR) approach can help in the identification and characterization of unknown metabolites in their mixtures by linking their MS1 and MS2 DIA spectral signals. In this study, it is proposed for the first time the analysis of MS1 and MS2 DIA signals in positive and negative electrospray ionization modes simultaneously to increase the coverage of possible metabolites present in biological systems. In this work, this approach has been tested for the detection and identification of the amino acids present in a standard mixture solution and in fish embryo samples. The ROIMCR analysis allowed for the identification of all amino acids present in the analyzed mixtures in both positive and negative modes. The methodology allowed for the direct linking and correspondence between the MS signals in their different acquisition modes. Overall, this approach confirmed the advantages and possibilities of performing the proposed ROIMCR simultaneous analysis of mass spectrometry signals in their differing acquisition modes in untargeted metabolomics studies.

UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC-MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.

Methods and biomarkers for early detection, prediction, and diagnosis of colorectal cancer

Colorectal cancer (CRC) is one of the most common digestive diseases worldwide. It has steadily ascended to the top three cancers in terms of incidence and mortality. The primary cause is the inability to diagnose it at an early stage. Therefore, early detection and diagnosis are essential for colorectal cancer prevention. Although there are now various methods for CRC early detection, in addition to recent developments in surgical and multimodal therapy, the poor prognosis and late detection of CRC still remain significant. Thus, it is important to investigate novel technologies and biomarkers to improve the sensitization and specification of CRC diagnosis. Here, we present some common methods and biomarkers for early detection and diagnosis of CRC, we hope this review will encourage the adoption of screening programs and the clinical use of these potential molecules as biomarkers for CRC early detection and prognosis.

From adenoma to CRC stages: the oral-gut microbiome axis as a source of potential microbial and metabolic biomarkers of malignancy

BACKGROUND

Approximately 95% of Colorectal cancers (CRC) consist of adenocarcinomas originating from colonic Adenomatous polyps (AP). Increasing importance in CRC occurrence and progression has been attributed to the gut microbiota; however, a huge proportion of microorganisms inhabit the human digestive system. So, to comprehensively study the microbial spatial variations and their role in CRC progression, from AP to the different CRC phases, a holistic vision is imperative, including the simultaneous evaluation of multiple niches from the gastrointestinal system. Through an integrated approach, we identified potential microbial and metabolic biomarkers, able to discriminate human CRC from AP and/or also the different Tumor node metastasis (TNM) staging. In addition, as the microbiota contributes to the production of essential metabolic products detectable in fecal samples, we analysed and compared metabolites obtained from CRC and AP patients by using a Nuclear magnetic resonance (NMR) approach.

METHODS

In this observational study, saliva, tissue and stool samples from 61 patients, have been collected, including 46 CRC and 15 AP patients, age and sex-matched, undergoing surgery in 2018 at the Careggi University Hospital (Florence, Italy). First, the microbiota in the three-district between CRC and AP patients has been characterized, as well as in different CRC TNM stages. Subsequently, proton NMR spectroscopy has been used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profile of a restricted group of CRC and AP patients.

RESULTS

CRC patients display a different profile of tissue and fecal microbiota with respect to AP patients. Significant differences have been observed in CRC tissue microbial clades, with a rise of the Fusobacterium genus. In addition, significant taxa increase at the genus level has been observed in stool samples of CRC patients. Furthermore, Fusobacterium found in intestinal tissue has been positively correlated with fecal Parvimonas, for the first time. Moreover, as predicted by metagenomics pathway analysis, a significant increase of lactate (p=0.037) has been observed in the CRC fecal metabolic profiles, and positively correlated with Bifidobacterium (p=0.036). Finally, minor bacterial differences in CRC patients at stage T2 (TNM classification) have been detected, with a raise of the Spirochaetota phylum in CRC samples, with a slight increase of the Alphaproteobacteria class in fecal samples.

CONCLUSION

Our results suggest the importance of microbiota communities and oncometabolites in CRC development. Further studies on CRC/AP management with a focus on CRC assessment are needed to investigate novel microbial-related diagnostic tools aimed to improve therapeutic interventions.

Serum nuclear magnetic resonance metabolomics analysis of human metastatic colorectal cancer: Biomarkers and pathway analysis

We describe the use of nuclear magnetic resonance metabolomics to analyze blood serum samples from healthy individuals (n = 26) and those with metastatic colorectal cancer (CRC; n = 57). The assessment, employing both linear and nonlinear multivariate data analysis techniques, revealed specific metabolite changes associated with metastatic CRC, including increased levels of lactate, glutamate, and pyruvate, and decreased levels of certain amino acids and total fatty acids. Biomarker ratios such as glutamate-to-glutamine and pyruvate-to-alanine were also found to be related to CRC. The study also found that glutamate was linked to progression-free survival and that both glutamate and 3-hydroxybutyrate were risk factors for metastatic CRC. Additionally, gas chromatography coupled to flame-ionization detection was utilized to analyze the fatty acid profile and pathway analysis was performed on the profiled metabolites to understand the metabolic processes involved in CRC. A correlation was also found between the presence of certain metabolites in the blood of CRC patients and certain clinical features.

Untargeted serum metabolomics analysis of Trichinella spiralis-infected mouse

BACKGROUND

Trichinellosis, caused by a parasitic nematode of the genus Trichinella, is a zoonosis that affects people worldwide. After ingesting raw meat containing Trichinella spp. larvae, patients show signs of myalgia, headaches, and facial and periorbital edema, and severe cases may die from myocarditis and heart failure. The molecular mechanisms of trichinellosis are unclear, and the sensitivity of the diagnostic methods used for this disease are unsatisfactory. Metabolomics is an excellent tool for studying disease progression and biomarkers; however, it has never been applied to trichinellosis. We aimed to elucidate the impacts of Trichinella infection on the host body and identify potential biomarkers using metabolomics.

METHODOLOGY/PRINCIPAL FINDINGS

Mice were infected with T. spiralis larvae, and sera were collected before and 2, 4, and 8 weeks after infection. Metabolites in the sera were extracted and identified using untargeted mass spectrometry. Metabolomic data were annotated via the XCMS online platform and analyzed with Metaboanalyst version 5.0. A total of 10,221 metabolomic features were identified, and the levels of 566, 330, and 418 features were significantly changed at 2-, 4-, and 8-weeks post-infection, respectively. The altered metabolites were used for further pathway analysis and biomarker selection. A major pathway affected by Trichinella infection was glycerophospholipid metabolism, and glycerophospholipids comprised the main metabolite class identified. Receiver operating characteristic revealed 244 molecules with diagnostic power for trichinellosis, with phosphatidylserines (PS) being the primary lipid class. Some lipid molecules, e.g., PS (18:0/19:0)[U] and PA (O-16:0/21:0), were not present in metabolome databases of humans and mice, thus they may have been secreted by the parasites.

CONCLUSIONS/SIGNIFICANCE

Our study highlighted glycerophospholipid metabolism as the major pathway affected by trichinellosis, hence glycerophospholipid species are potential markers of trichinellosis. The findings of this study represent the initial steps in biomarker discovery that may benefit future trichinellosis diagnosis.

Untargeted Metabolomics by Liquid Chromatography-Mass Spectrometry in Biomedical Research

Metabolomics, alone or in combination with other omics sciences, has shown great relevance in a large number of investigations in different branches of biomedicine, often providing novel discoveries and helping to expand the knowledge. Metabolomics analyses are carried out using different techniques, but in this chapter, we focus on liquid chromatography coupled to high-resolution mass spectrometry. The designated methodology consists of an untargeted approach for the analysis of plasma samples. The use of this method, with a reverse-phase column and electrospray ionization in positive mode, covers the detection of a broad range of metabolites, mainly of nonpolar and of intermediate polarity. This chapter also reviews the mass fragmentation spectra for the identification of bile acids, acylcarnitines, and glycerophospholipids.

Microbial metabolites in colorectal tumorigenesis and cancer therapy

Trillions of microbes are indigenous to the human gastrointestinal tract, together forming an ecological community known as the gut microbiota. The gut microbiota is involved in dietary digestion to produce various metabolites. In healthy condition, microbial metabolites have unneglectable roles in regulating host physiology and intestinal homeostasis. However, increasing studies have reported the correlation between metabolites and the development of colorectal cancer (CRC), with the identification of oncometabolites. Meanwhile, metabolites can also influence the efficacy of cancer treatments. In this review, metabolites derived from microbes-mediated metabolism of dietary carbohydrates, proteins, and cholesterol, are introduced. The roles of pro-tumorigenic (secondary bile acids and polyamines) and anti-tumorigenic (short-chain fatty acids and indole derivatives) metabolites in CRC development are then discussed. The impacts of metabolites on chemotherapy and immunotherapy are further elucidated. Collectively, given the importance of microbial metabolites in CRC, therapeutic approaches that target metabolites may be promising to improve patient outcome.

Comprehensive serum metabolomics and network analysis to reveal the mechanism of gypenosides in treating lung cancer and enhancing the pharmacological effects of cisplatin

Gypenosides (GYP) exerted anticancer activity against various cancers. However, the mechanism of GYP against lung cancer (LC) in vivo remains unclear. This study aims to reveal the potential mechanism of GYP against LC and enhancing cisplatin efficacy using a comprehensive analysis of metabolomics, network analysis. Pharmacodynamic results showed that GYP inhibited tumor growth, reduced tumor volume and tumor weight, and alleviated pathological symptoms in Lewis tumor-bearing mice, and GYP could enhance the anti-LC effects of cisplatin. Using serum metabolomics methods, 53 metabolites were found to be significantly altered in the model group, and the levels of 23 biomarkers were significantly restored after GYP treatment. GYP-related metabolic pathways involved six pathways, including alpha-linolenic acid metabolism, glutathione metabolism, sphingolipid metabolism, glycerophospholipid metabolism, tryptophan metabolism, and primary bile acid biosynthesis. 57 genes associated with differential metabolites of GYP recovery and 7 genes of 11 saponins of GYP against LC were screened by network analysis, the STRING database was used to find the association between 57 genes and 7 genes, and a compound-intersection gene-metabolite related gene-metabolite-pathway network was constructed, and STAT3, MAPK14, EGFR and TYMS might be the crucial targets of GYP against LC. Western blot results showed that GYP restored the levels of STA3, MAPK14, EGFR, and TYMS in the model group, and GYP also restored the levels of STAT3 and MAPK14 in the cisplatin group, indicating that GYP might exert anti-LC effects and enhance the pharmacological effects of cisplatin through MAPK14/STAT3 signaling pathway. Our method revealed the effect and mechanism of GYP on LC and the pharmacological effects of GYP-enhanced chemotherapeutic agent cisplatin, which provided some reference for the development of anti-cancer drugs.

Sánchez B, León C, Nozal L, Mora-Rodríguez JM, Castro F, Crego AL, Díaz-Laviada I. Metabolic fingerprinting of chemotherapy-resistant prostate cancer stem cells. An untargeted metabolomic approach by liquid chromatography-mass spectrometry

Chemoresistance is one of the most important challenges in cancer therapy. The presence of cancer stem cells within the tumor may contribute to chemotherapy resistance since these cells express high levels of extrusion pumps and xenobiotic metabolizing enzymes that inactivate the therapeutic drug. Despite the recent advances in cancer cell metabolism adaptations, little is known about the metabolic adaptations of the cancer stem cells resistant to chemotherapy. In this study, we have undertaken an untargeted metabolomic analysis by liquid chromatography–high-resolution spectrometry combined with cytotoxicity assay, western blot, quantitative real-time polymerase chain reaction (qPCR), and fatty acid oxidation in a prostate cancer cell line resistant to the antiandrogen 2-hydroxiflutamide with features of cancer stem cells, compared to its parental androgen-sensitive cell line. Metabolic fingerprinting revealed 106 out of the 850 metabolites in ESI+ and 67 out of 446 in ESI- with significant differences between the sensitive and the resistant cell lines. Pathway analysis performed with the unequivocally identified metabolites, revealed changes in pathways involved in energy metabolism as well as posttranscriptional regulation. Validation by enzyme expression analysis indicated that the chemotherapy-resistant prostate cancer stem cells were metabolically dormant with decreased fatty acid oxidation, methionine metabolism and ADP-ribosylation. Our results shed light on the pathways underlying the entry of cancer cells into dormancy that might contribute to the mechanisms of drug resistance.

Design, synthesis and cytotoxic evaluation of novel betulonic acid-diazine derivatives as potential antitumor agents

With the purpose to improve antiproliferative activity, 26 new betulonic acid-diazine derivatives were designed and synthesized from betulinic acid. The anticancer activity of these semi-synthetic compounds was evaluated by MTT assay in both tumor cell lines and normal cell line. The results indicated that majority of new compounds exhibited improved antitumor activity compared with the parent compound betulonic acid. Compound BoA2C, in particular, had the most significant action with IC50 value of 3.39 μM against MCF-7 cells, while it showed lower cytotoxicity on MDCK cell line than cisplatin. Furthermore, we discovered that BoA2C strongly increased MCF-7 cell damage mostly by influencing arginine and fatty acid metabolism. In addition, the structure-activity relationships were briefly discussed. The results of this study suggested that the introduction of different diazines at C-28 could selectively inhibit different kinds of cancer cells and might be an effective way to synthesize potent anticancer lead compound from betulonic acid.

A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients

Accurate diagnosis of colorectal cancer (CRC) still relies on invasive colonoscopy. Noninvasive methods are less sensitive in detecting the disease, particularly in the early stage. In the current work, a metabolomics analysis of fecal samples was carried out by ultra-high-performance liquid chromatography–tandem mass spectroscopy (UPLC-MS/MS). A total of 1380 metabolites were analyzed in a cohort of 120 fecal samples from patients with normal colonoscopy, advanced adenoma (AA) and CRC. Multivariate analysis revealed that metabolic profiles of CRC and AA patients were similar and could be clearly separated from control individuals. Among the 25 significant metabolites, sphingomyelins (SM), lactosylceramides (LacCer), secondary bile acids, polypeptides, formiminoglutamate, heme and cytidine-containing pyrimidines were found to be dysregulated in CRC patients. Supervised random forest (RF) and logistic regression algorithms were employed to build a CRC accurate predicted model consisting of the combination of hemoglobin (Hgb) and bilirubin E,E, lactosyl-N-palmitoyl-sphingosine, glycocholenate sulfate and STLVT with an accuracy, sensitivity and specificity of 91.67% (95% Confidence Interval (CI) 0.7753–0.9825), 0.7 and 1, respectively.

Bioprospecting the Metabolome of Plant Urtica dioica L.: A Fast Dereplication and Annotation Workflow in Plant Metabolomics

Plants have a pivotal role in ethnopharmacology, and their preparations are in use globally. However, getting down to the structure requires an effective workflow and mostly requires a time-consuming isolation process. Although bioassay-guided approaches are widely popular, they face a massive problem of rediscovery in recent times, especially in plant metabolomics. Mass spectrometry (MS)-based approach incorporated molecular networking via Global Natural Product Social Molecular Networking (GNPS) is considered here for the benefit of the fast screening of secondary metabolites. This study uses direct crude extracts obtained from various parts of the Urtica dioica plant for the characterization of secondary metabolites. The crude extract of the plant initially displayed promising antioxidant and anti-diabetic activities. Then, we employed mass spectrometry-based dereplication to identify the phytochemical components in the extracts. This led to the discovery of 7 unknown and 17 known secondary metabolites, which were further verified with the SIRIUS 4 platform, a computational tool for the annotation of compounds using tandem MS data. On the other hand, chasing the antioxidant activity of methanolic extract of U. dioica leaves, we employed a bioassay-guided isolation approach. With this method, we isolated and characterized compound 13, a known molecule, which possessed strong antioxidant activity without showing much toxicity in the brine shrimp lethality test at the test concentration of 1 mg/mL. With our results, we advocate the MS-based approach as a good starting point for the dereplication of compounds from the complex crude extracts of plants.

Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach

Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography−high‐resolution mass spectrometry (LC‐HRMS)‐based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA–simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple‐negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted‐based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow‐up in the clinical practice.

The Application of Metabolomics in Recent Colorectal Cancer Studies: A State-of-the-Art Review

Simple Summary

Colorectal Cancer (CRC) is one of the leading causes of cancer-related death in the United States. Current diagnosis techniques are either highly invasive or lack sensitivity, suggesting the need for alternative techniques for biomarker detection. Metabolomics represents one such technique with great promise in identifying CRC biomarkers with high sensitivity and specificity, but thus far is rarely employed in a clinical setting. In order to provide a framework for future clinical usage, we characterized dysregulated metabolites across recent literature, identifying metabolites dysregulated across a variety of biospecimens. We additionally put special focus on the interplay of the gut microbiome and perturbed metabolites in CRC. We were able to identify many metabolites showing consistent dysregulation in CRC, demonstrating the value of metabolomics as a promising diagnostic technique.

Abstract

Colorectal cancer (CRC) is a highly prevalent disease with poor prognostic outcomes if not diagnosed in early stages. Current diagnosis techniques are either highly invasive or lack sufficient sensitivity. Thus, identifying diagnostic biomarkers of CRC with high sensitivity and specificity is desirable. Metabolomics represents an analytical profiling technique with great promise in identifying such biomarkers and typically represents a close tie with the phenotype of a specific disease. We thus conducted a systematic review of studies reported from January 2012 to July 2021 relating to the detection of CRC biomarkers through metabolomics to provide a collection of knowledge for future diagnostic development. We identified thirty-seven metabolomics studies characterizing CRC, many of which provided metabolites/metabolic profile-based diagnostic models with high sensitivity and specificity. These studies demonstrated that a great number of metabolites can be differentially regulated in CRC patients compared to healthy controls, adenomatous polyps, or across stages of CRC. Among these metabolite biomarkers, especially dysregulated were certain amino acids, fatty acids, and lysophosphatidylcholines. Additionally, we discussed the contribution of the gut bacterial population to pathogenesis of CRC through their modulation to fecal metabolite pools and summarized the established links in the literature between certain microbial genera and altered metabolite levels in CRC patients. Taken together, we conclude that metabolomics presents itself as a promising and effective method of CRC biomarker detection.

Metabolomics Profiling Discriminates Prostate Cancer From Benign Prostatic Hyperplasia Within the Prostate-Specific Antigen Gray Zone

Objective

Prostate cancer (PCa) is the second most common male malignancy globally. Prostate-specific antigen (PSA) is an important biomarker for PCa diagnosis. However, it is not accurate in the diagnostic gray zone of 4–10 ng/ml of PSA. In the current study, the performance of serum metabolomics profiling in discriminating PCa patients from benign prostatic hyperplasia (BPH) individuals with a PSA concentration in the range of 4–10 ng/ml was explored.

Methods

A total of 220 individuals, including patients diagnosed with PCa and BPH within PSA levels in the range of 4–10 ng/ml and healthy controls, were enrolled in the study. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomics method was utilized to characterize serum metabolic profiles of participants. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods were used for multivariate analysis. Receiver operating characteristic (ROC) curve analysis was performed to explore the diagnostic value of candidate metabolites in differentiating PCa from BPH. Correlation analysis was conducted to explore the relationship between serum metabolites and common clinically used fasting lipid profiles.

Results

Several differential metabolites were identified. The top enriched pathways in PCa subjects such as glycerophospholipid and glycerolipid metabolisms were associated with lipid metabolism. Lipids and lipid-like compounds were the predominant metabolites within the top 50 differential metabolites selected using fold-change threshold >1.5 or <2/3, variable importance in projection (VIP) > 1, and Student’s t-test threshold p < 0.05. Eighteen lipid or lipid-related metabolites were selected including 4-oxoretinol, anandamide, palmitic acid, glycerol 1-hexadecanoate, dl-dihydrosphingosine, 2-methoxy-6Z-hexadecenoic acid, 3-oxo-nonadecanoic acid, 2-hydroxy-nonadecanoic acid, N-palmitoyl glycine, 2-palmitoylglycerol, hexadecenal, d-erythro-sphingosine C-15, N-methyl arachidonoyl amine, 9-octadecenal, hexadecyl acetyl glycerol, 1-(9Z-pentadecenoyl)-2-eicosanoyl-glycero-3-phosphate, 3Z,6Z,9Z-octadecatriene, and glycidyl stearate. Selected metabolites effectively discriminated PCa from BPH when PSA levels were in the range of 4–10 ng/ml (area under the curve (AUC) > 0.80). Notably, the 18 identified metabolites were negatively corrected with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and Apo-B levels in PCa patients; and some were negatively correlated with high-density lipoprotein cholesterol (HDL-C) and Apo-A levels. However, the metabolites were not correlated with triglycerides (TG).

Conclusion

The findings of the present study indicate that metabolic reprogramming, mainly lipid metabolism, is a key signature of PCa. The 18 lipid or lipid-associated metabolites identified in this study are potential diagnostic markers for differential diagnosis of PCa patients and BPH individuals within a PSA level in the gray zone of 4–10 ng/ml.

Identification of potential serum metabolic biomarkers for patient with keratoconus using untargeted metabolomics approach

This study aimed to investigate the metabolite differences between patients with keratoconus and control subjects and identify potential serum biomarkers for keratoconus using a non-targeted metabolomics approach. Venous blood samples were obtained from patients with keratoconus (n = 20) as well as from age-, gender- and race-matched control subjects (n = 20). Metabolites extracted from serum were separated and analyzed by liquid chromatography/quadrupole time-of-flight mass spectrometer. Processing of raw data and analysis of the data files was performed using Agilent Mass Hunter Qualitative software. The identified metabolites were subjected to a principal component and hierarchical cluster analysis. Appropriate statistical tests were used to analyze the metabolomic profiling data. Together, the analysis revealed that the dehydroepiandrosterone sulfate from the steroidal hormone synthesis pathway was significantly upregulated in patients with keratoconus (p < 0.05). Also, a combination of eicosanoids from the arachidonic acid pathway, mainly prostaglandin F2α, prostaglandin A2, 16,16-dimethyl prostaglandin E2, and 5-hydroxyeicosatetraenoic acid were collectively up-regulated as a group in keratoconus patients (p < 0.05). On the other hand, glycerophospholipid PS(17:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z)) was found to be significantly upregulated in the metabolomics profiles of control subjects (p < 0.05). The differently regulated metabolites provide insights into the pathophysiology of keratoconus and could potentially be used as biomarkers for keratoconus to aid in screening for individuals at risk hence, enabling early diagnosis and timely monitoring of disease.

Integration of global metabolomics and lipidomics approaches reveals the molecular mechanisms and the potential biomarkers for postoperative recurrence in early-stage cholangiocarcinoma

Background

Cholangiocarcioma (CCA) treatment is challenging because most of the patients are diagnosed when the disease is advanced, and cancer recurrence is the main problem after treatment, leading to low survival rates. Therefore, our understanding of the mechanism underlying CCA recurrence is essential in order to prevent CCA recurrence and improve patient outcomes.

Methods

We performed ¹H-NMR and UPLC-MS-based metabolomics on the CCA serum. The differential metabolites were further analyzed using pathway analysis and potential biomarker identification.

Results

At an early stage, the metabolites involved in energy metabolisms, such as pyruvate metabolism, and the TCA cycle, are downregulated, while most lipids, including TGs, PCs, PEs, and PAs, are upregulated in recurrence patients. This metabolic feature has been described in cancer stem-like cell (CSC) metabolism. In addition, the CSC markers CD44v6 and CD44v8-10 are associated with CD36 (a protein involved in lipid uptake) as well as with recurrence-free survival. We also found that citrate, sarcosine, succinate, creatine, creatinine and pyruvate, and TGs have good predictive values for CCA recurrence.

Conclusion

Our study demonstrates the possible molecular mechanisms underlying CCA recurrence, and these may associate with the existence of CSCs. The metabolic change involved in the recurrence pathway might be used to determine biomarkers for predicting CCA recurrence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40170-021-00266-5.

Systems-level biomarkers identification and drug repositioning in colorectal cancer

Colorectal cancer (CRC) is the most commonly diagnosed fatal cancer in both women and men worldwide. CRC ranked second in mortality and third in incidence in 2020. It is difficult to diagnose CRC at an early stage as there are no clinical symptoms. Despite advances in molecular biology, only a limited number of biomarkers have been translated into routine clinical practice to predict risk, prognosis and response to treatment. In the last decades, systems biology approaches at the omics level have gained importance. Over the years, several biomarkers for CRC have been discovered in terms of disease diagnosis and prognosis. On the other hand, a few drugs are being developed and used in clinics for the treatment of CRC. However, the development of new drugs is very costly and time-consuming as the research and development takes about 10 years and more than $1 billion. Therefore, drug repositioning (DR) could save time and money by establishing new indications for existing drugs. In this review, we aim to provide an overview of biomarkers for the diagnosis and prognosis of CRC from the systems biology perspective and insights into DR approaches for the prevention or treatment of CRC.

Comprehensive Plasma Metabolomic Profile of Patients with Advanced Neuroendocrine Tumors (NETs). Diagnostic and Biological Relevance

Simple Summary

Metabolic flexibility is one of the key hallmarks of cancer and metabolites are the final products of this adaptation, reflecting the aberrant changes of tumors. However, the metabolic plasticity of each cancer type is still unknown, and specifically to date, there are no data on metabolic profile in neuroendocrine tumors. The aim of our retrospective study was to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. We provided, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use, selecting a reduced set of metabolites of high diagnostic accuracy. We have identified 32 novel enriched metabolic pathways in NETs related with the TCA cycle, and with arginine, pyruvate or glutathione metabolism, which have distinct implications in oncogenesis and may open innovative avenues of clinical research.

Abstract

Purpose: High-throughput “-omic” technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. Methods: Plasma samples from 77 NETs and 68 controls were profiled by GC−MS, CE−MS and LC−MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential. Results: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential. Conclusions: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research.

Molecular and Cellular Biomarkers of COVID-19 Prognosis: Protocol for the Prospective Cohort TARGET Study

Background

Since the beginning of the COVID-19 pandemic, the world’s attention has been focused on better understanding the relation between the human host and the SARS-CoV-2 virus, as its action has led to hundreds of thousands of deaths.

Objective

In this context, we decided to study certain consequences of the abundant cytokine release over the innate and adaptive immune systems, inflammation, and hemostasis, comparing mild and severe forms of COVID-19.

Methods

To accomplish these aims, we will analyze demographic characteristics, biochemical tests, immune biomarkers, leukocyte phenotyping, immunoglobulin profile, hormonal release (cortisol and prolactin), gene expression, thromboelastometry, neutralizing antibodies, metabolic profile, and neutrophil function (reactive oxygen species production, neutrophil extracellular trap production, phagocytosis, migration, gene expression, and proteomics). A total of 200 reverse transcription polymerase chain reaction–confirmed patients will be enrolled and divided into two groups: mild/moderate or severe/critical forms of COVID-19. Blood samples will be collected at different times: at inclusion and after 9 and 18 days, with an additional 3-day sample for severe patients. We believe that this information will provide more knowledge for future studies that will provide more robust and useful clinical information that may allow for better decisions at the front lines of health care.

Results

The recruitment began in June 2020 and is still in progress. It is expected to continue until February 2021. Data analysis is scheduled to start after all data have been collected. The coagulation study branch is complete and is already in the analysis phase.

Conclusions

This study is original in terms of the different parameters analyzed in the same sample of patients with COVID-19. The project, which is currently in the data collection phase, was approved by the Brazilian Committee of Ethics in Human Research (CAAE 30846920.7.0000.0008).

Trial Registration

Brazilian Registry of Clinical Trials RBR-62zdkk; https://ensaiosclinicos.gov.br/rg/RBR-62zdkk

International Registered Report Identifier (IRRID)

DERR1-10.2196/24211

Colorectal cancer screening and diagnosis: omics-based technologies for development of a non-invasive blood-based method

Introduction: Colorectal cancer (CRC) is one of the most important health problems in the Western world. In order to reduce the burden of the disease, two strategies are proposed: screening and prompt detection in symptomatic patients. Although diagnosis and prevention are mainly based on colonoscopy, fecal hemoglobin detection has been widely implemented as a noninvasive strategy. Various studies aiming to discover blood-based biomarkers have recently emerged.Areas covered: The burgeoning omics field provides diverse high-throughput approaches for CRC blood-based biomarker discovery. In this review, we appraise the most robust and commonly used technologies within the fields of genomics, transcriptomics, epigenomics, proteomics, and metabolomics, together with their targeted validation approaches. We summarize the transference process from the discovery phase until clinical translation. Finally, we review the best candidate biomarkers and their potential clinical applicability.Expert opinion: Some available biomarkers are promising, especially in the field of epigenomics: DNA methylation and microRNA. Transference requires the joint collaboration of basic researchers, intellectual property experts, technology transfer officers and clinicians. Blood-based biomarkers will be selected not only based on their diagnostic accuracy and cost but also on their reliability, applicability to clinical analysis laboratories and their acceptance by the population.

Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer

Simple Summary

Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and tailored monitoring are in critical demand. Thus, metabolomics appears to be a growing tool in order to detect molecular differences between distinct groups. In this case, an untargeted analytical approach was used to identify metabolomics differences between molecular subtypes of breast cancer in comparison with healthy matched controls. Footprints for each breast cancer subtype provided diagnostic capacities with an area under the receiver-operating characteristic curve above 0.85, which suggests that our results may represent a major advance towards the improvement of personalized medicine and precise targeted therapies for the various breast cancer phenotypes. To validate these molecular profiling as potential therapeutic strategies for the different breast cancer subtypes, further analysis and larger cohorts would be necessary in the near future.

Abstract

Purpose: The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure. Methods: Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. Results: We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85. Conclusion: Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

Metabolic regulation of prostate cancer heterogeneity and plasticity

Metabolic reprogramming is one of the main hallmarks of cancer cells. It refers to the metabolic adaptations of tumor cells in response to nutrient deficiency, microenvironmental insults, and anti-cancer therapies. Metabolic transformation during tumor development plays a critical role in the continued tumor growth and progression and is driven by a complex interplay between the tumor mutational landscape, epigenetic modifications, and microenvironmental influences. Understanding the tumor metabolic vulnerabilities might open novel diagnostic and therapeutic approaches with the potential to improve the efficacy of current tumor treatments. Prostate cancer is a highly heterogeneous disease harboring different mutations and tumor cell phenotypes. While the increase of intra-tumor genetic and epigenetic heterogeneity is associated with tumor progression, less is known about metabolic regulation of prostate cancer cell heterogeneity and plasticity. This review summarizes the central metabolic adaptations in prostate tumors, state-of-the-art technologies for metabolic analysis, and the perspectives for metabolic targeting and diagnostic implications.

Urinary charged metabolite profiling of colorectal cancer using capillary electrophoresis-mass spectrometry

Colorectal cancer (CRC) has increasing global prevalence and poor prognostic outcomes, and the development of low- or less invasive screening tests is urgently required. Urine is an ideal biofluid that can be collected non-invasively and contains various metabolite biomarkers. To understand the metabolomic profiles of different stages of CRC, we conducted metabolomic profiling of urinary samples. Capillary electrophoresis-time-of-flight mass spectrometry was used to quantify hydrophilic metabolites in 247 subjects with stage 0 to IV CRC or polyps, and healthy controls. The 154 identified and quantified metabolites included metabolites of glycolysis, TCA cycle, amino acids, urea cycle, and polyamine pathways. The concentrations of these metabolites gradually increased with the stage, and samples of CRC stage IV especially showed a large difference compared to other stages. Polyps and CRC also showed different concentration patterns. We also assessed the differentiation ability of these metabolites. A multiple logistic regression model using three metabolites was developed with a randomly designated training dataset and validated using the remaining data to differentiate CRC and polys from healthy controls based on a panel of urinary metabolites. These data highlight the changes in metabolites from early to late stage of CRC and also the differences between CRC and polyps.

Circulating osteocyte-related biomarkers (vitamin D, sclerostin, dickkopf-1), hepcidin, and oxidative stress markers in early breast cancer: Their impact in disease progression and outcome

Breast cancer (BC) is a major concern to female health worldwide. We assessed the circulating osteocyte-related biomarkers, hepcidin, and oxidative stress status among early-stage BC patients in aspects of clinical severity and impact on the outcome. The study incorporated 73 patients categorized into 57 early-stage BC and 16 benign breast diseases and 30 healthy controls. Serum 25-hydroxyvitamin D [25(OH)D], sclerostin (SOST), dickkopf-1(DKK1), and hepcidin were measured using ELISA, while, serum oxidative stress markers were assessed by spectrophotometry. Our results show that patients with BC showed significant increase in the mean levels of DKK1, SOST, hepcidin, and LPER and significant decrease in the mean levels of 25(OH)D, SOD, GPx, and Hb when compared with controls and benign breast diseases. Significantly higher DKK1, hepcidin, and SOD levels among benign breast diseases were found in comparison to control group. There were significantly lower levels of 25(OH)D, SOD, and Hb and significantly higher levels of SOST, DKK1, hepcidin, No, and LPER with advanced grade. Lower levels of 25(OH)D, SOD and higher levels of SOST, hepcidin were observed with increasing the malignant stage. Reduced levels of 25(OH)D, and SOD were significantly associated with poor prognosis and were strong predictors among BC. There were significant negative correlations between 25(OH)D with LPER, SOST, and hepicidin. We conclude that low 25(OH)D, high SOST, DKK1, and hepcidin, and dysregulated oxidative stress could be helpful in early detection and assessment of BC. 25(OH)D, and SOD were the most relevant to tumor progression and prognosis which indicate a significant role in the BC pathogenesis and could be promising targets in management. Our research paves the way to disrupt vicious circle between these biomarkers to obtain the best care of BC.

Omics technologies for improved diagnosis and treatment of colorectal cancer: Technical advancement and major perspectives

Colorectal cancer (CRC) ranks third among the most commonly occurring cancers worldwide, and it causes half a million deaths annually. Alongside mechanistic study for CRC detection and treatment by conventional techniques, new technologies have been developed to study CRC. These technologies include genomics, transcriptomics, proteomics, and metabolomics which elucidate DNA markers, RNA transcripts, protein and, metabolites produced inside the colon and rectum part of the gut. All these approaches form the omics arena, which presents a remarkable opportunity for the discovery of novel prognostic, diagnostic and therapeutic biomarkers and also delineate the underlying mechanism of CRC causation, which may further help in devising treatment strategies. This review also mentions the latest developments in metagenomics and culturomics as emerging evidence suggests that metagenomics of gut microbiota has profound implications in the causation, prognosis, and treatment of CRC. A majority of bacteria cannot be studied as they remain unculturable, so culturomics has also been strengthened to develop culture conditions suitable for the growth of unculturable bacteria and identify unknown bacteria. The overall purpose of this review is to succinctly evaluate the application of omics technologies in colorectal cancer research for improving the diagnosis and treatment strategies.

Deep Interrogation of Metabolism Using a Pathway-Targeted Click-Chemistry Approach

Untargeted metabolomics indicates that the number of unidentified small-molecule metabolites may exceed the number of protein-coding genes for many organisms, including humans, by orders of magnitude. Uncovering the underlying metabolic networks is essential for elucidating the physiological and ecological significance of these biogenic small molecules. Here we develop a click-chemistry-based enrichment strategy, DIMEN (deep interrogation of metabolism via enrichment), that we apply to investigate metabolism of the ascarosides, a family of signaling molecules in the model organism C. elegans. Using a single alkyne-modified metabolite and a solid-phase azide resin that installs a diagnostic moiety for MS/MS-based identification, DIMEN uncovered several hundred novel compounds originating from diverse biosynthetic transformations that reveal unexpected intersection with amino acid, carbohydrate, and energy metabolism. Many of the newly discovered transformations could not be identified or detected by conventional LC-MS analyses without enrichment, demonstrating the utility of DIMEN for deeply probing biochemical networks that generate extensive yet uncharacterized structure space.