Recent Metabolomics Analysis in Tumor Metabolism Reprogramming

Investigation on the mechanisms of scorpion venom in hepatocellular carcinoma model mice via untargeted metabolomics profiling

Metabolic reprogramming is frequently accompanied by hepatocellular carcinoma (HCC) progression. Disrupted metabolites act as potential biomarkers and drug therapeutic targets for HCC. Peptide extract of scorpion venom (PESV) induces cytotoxic anti-proliferative effects and apoptosis in tumors. However, the action mechanisms of PESV remain unknown. This study aimed to explore the serum metabolic profiles of tumor-bearing mouse model. We generated an orthotopic HCC xenograft mouse model by implanting H22 cells into the left hepatic lobe of male C57BL/6 mice. After surgery, the mice were assigned to two groups randomly: PESV (PESV-treated 40 mg/kg daily, i.g.; n = 6) and control (treated with the solvent equally for 14 d, n = 6) groups. Based on an untargeted metabolomics approach using ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry, differential metabolites were screened via univariate and multivariate data analyses. A total of 48 differential metabolites in negative ion mode and 63 in positive ion mode were identified in the serum samples. Furthermore, metabolic pathway analysis revealed that aminoacyl-tRNA biosynthesis, amino acid pathway, glutathione metabolism, protein transports, protein digestion and absorption, and cAMP signaling pathways play vital roles in PESV-induced inhibition of tumors. These findings highlight the distinct changes in the metabolic profiles of HCC-bearing mice after PESV treatment, suggesting the potential of the identified metabolic molecules as therapeutic targets for HCC.

Metabolomics and cellular altered pathways in cancer biology: A review

Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

Qi Wei Anti-burn Tincture Remodels Liver Metabolic Pathways and Treats Burn Wounds Efficiently

This work aims to elucidate the molecular mechanism of Qi Wei anti-burn Tincture (QW) on wound healing in burnt mice using metabolomics and molecular biology techniques. A scald model was first established in Kunming mice. After treatment, biochemical indicators for liver function and burnt skin tissues were then evaluated via biochemical detection and HE staining, respectively. Liver tissues were further analyzed for differential metabolites, inflammatory factors, and mRNA levels of cytokines using metabolomics and molecular biology techniques. Involved metabolic pathways were also identified using software. QW treatment did promote the healing of the burn wounds in Kunming mice with a downregulation of ALP, ALT, and AST to normal levels. In mouse liver tissue, the contents of glutamine, aspartic acid, and citrulline were significantly reduced, while the contents of 5-hydroxyproline, taurine, hypotaurine, and glutamic acid significantly increased. These major differential compounds are involved in the arginine metabolic pathway, nitrogen excretion, and the metabolism of taurine and hypotaurine, suggesting that QW reprogramed the above metabolic processes in the liver. Furthermore, the application of QW increased the expression of TGF-β1 and FGF-2 and reduced the levels of TNF-α, IL-1β, IL-6, and reactive oxygen species in the liver of mice induced by burn injury. This study found that QW treatment promoted metabolic pathway remodeling in the liver, which might be a potential mechanism for QW to treat burn wounds.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Heterogeneous changes in gut and tumor microbiota in patients with pancreatic cancer: insights from clinical evidence

BACKGROUND

Pancreatic cancer is the foremost contributor to cancer-related deaths globally, and its prevalence continues to rise annually. Nevertheless, the underlying mechanisms behind its development remain unclear and necessitate comprehensive investigation.

METHODS

In this study, a total of 29 fresh stool samples were collected from patients diagnosed with pancreatic cancer. The gut microbial data of healthy controls were obtained from the SRA database (SRA data number: SRP150089). Additionally, 28 serum samples and diseased tissues were collected from 14 patients with confirmed pancreatic cancer and 14 patients with chronic pancreatitis. Informed consent was obtained from both groups of patients. Microbial sequencing was performed using 16s rRNA.

RESULTS

The results showed that compared with healthy controls, the species abundance index of intestinal flora in patients with pancreatic cancer was increased (P < 0.05), and the number of beneficial bacteria at the genus level was reduced (P < 0.05). Compared with patients with chronic pancreatitis, the expression levels of CA242 and CA199 in the serum of patients with pancreatic cancer were increased (P < 0.05). The bacterial richness index of tumor microorganisms in patients with pancreatic cancer increased, while the diversity index decreased(P < 0.05). Furthermore, there was a change in the species composition at the genus level. Additionally, the expression level of CA242 was found to be significantly positively correlated with the relative abundance of Acinetobacter(P < 0.05).

CONCLUSION

Over all, the expression levels of serum tumor markers CA242 and CA19-9 in patients with pancreatic cancer are increased, while the beneficial bacteria in the intestine and tumor microenvironment are reduced and pathogenic bacteria are increased. Acinetobacter is a specific bacterial genus highly expressed in pancreatic cancer tissue.

Profiling of serum metabolome of breast cancer: multi-cancer features discriminate between healthy women and patients with breast cancer

Introduction

The progression of solid cancers is manifested at the systemic level as molecular changes in the metabolome of body fluids, an emerging source of cancer biomarkers.

Methods

We analyzed quantitatively the serum metabolite profile using high-resolution mass spectrometry. Metabolic profiles were compared between breast cancer patients (n=112) and two groups of healthy women (from Poland and Norway; n=95 and n=112, respectively) with similar age distributions.

Results

Despite differences between both cohorts of controls, a set of 43 metabolites and lipids uniformly discriminated against breast cancer patients and healthy women. Moreover, smaller groups of female patients with other types of solid cancers (colorectal, head and neck, and lung cancers) were analyzed, which revealed a set of 42 metabolites and lipids that uniformly differentiated all three cancer types from both cohorts of healthy women. A common part of both sets, which could be called a multi-cancer signature, contained 23 compounds, which included reduced levels of a few amino acids (alanine, aspartate, glutamine, histidine, phenylalanine, and leucine/isoleucine), lysophosphatidylcholines (exemplified by LPC(18:0)), and diglycerides. Interestingly, a reduced concentration of the most abundant cholesteryl ester (CE(18:2)) typical for other cancers was the least significant in the serum of breast cancer patients. Components present in a multi-cancer signature enabled the establishment of a well-performing breast cancer classifier, which predicted cancer with a very high precision in independent groups of women (AUC>0.95).

Discussion

In conclusion, metabolites critical for discriminating breast cancer patients from controls included components of hypothetical multi-cancer signature, which indicated wider potential applicability of a general serum metabolome cancer biomarker.

Metabolomic Profiling of Blood Plasma in Females with Hyperplasia and Endometrial Cancer

Uterine cancer is the most prevalent gynecologic malignancy in women worldwide. Endometrial cancer (EC) has an 81% five-year survival rate, depending on disease stage and time of diagnosis. While endometrial cancer is largely treatable when detected early, no established screening techniques are available in clinical practice. As a result, one of the most significant issues in the medical field is the development of novel ways for early cancer identification, which could boost treatment success rates. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based metabolomics was employed to explore the metabolomic markers and pathways unique to this cancer type and link them to the benign endometrial hyperplasia that may progress to cancer in 5% to 25% of patients. The study involved 59 postmenopausal participants, 20 with EC type 1, 20 with benign hyperplasia, and 19 healthy participants. Metabolite distribution changes were analyzed, and 338 of these features were dysregulated and significant. The first two main components, PC1 and PC2, were responsible for 11.5% and 12.2% of the total metabolites, respectively. Compared with the control group (CO), EC samples had 203 differentially expressed metabolites (180 upregulated and 23 downregulated); in hyperplasia (HP), 157 metabolites were dysregulated (127 upregulated and 30 downregulated) compared to the CO group while 21 metabolites exhibited differential regulation (16 upregulated and 5 downregulated) in EC plasma samples compared to the HP group. Hyperplasia samples exhibited similar metabolic changes to those reported in cancer, except for alterations in triglyceride levels, 7a,12 b-dihydroxy-5b-Cholan-24-oic acid, and Hept-2-enedioyl carnitine levels. The metabolites N-heptanoyl glycine and -(Methylthio)-2,3-isopentyl phosphate and formimino glutamic acid can be specific markers for hyperplasia conditions and dimethyl phosphatidyl ethanolamine and 8-isoprostaglandin E2 can be specific markers for EC conditions. Metabolic activities rely on mitochondrial oxidative phosphorylation for energy generation. The changes in metabolites identified in our study indicate that endometrial cancer cells adopt alternative strategies to increase energy production to meet the energy demand, thereby supporting proliferation.

Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine

Colorectal cancer (CRC) is one of the most heterogeneous and deadly diseases, with a global incidence of 1.5 million cases per year. Genomics has revolutionized the clinical management of CRC by enabling comprehensive molecular profiling of cancer. However, a deeper understanding of the molecular factors is needed to identify new prognostic and predictive markers that can assist in designing more effective therapeutic regimens for the improved management of CRC. Recent breakthroughs in single-cell analysis have identified new cell subtypes that play a critical role in tumor progression and could serve as potential therapeutic targets. Spatial analysis of the transcriptome and proteome holds the key to unlocking pathogenic cellular interactions, while liquid biopsy profiling of molecular variables from serum holds great potential for monitoring therapy resistance. Furthermore, gene expression signatures from various pathways have emerged as promising prognostic indicators in colorectal cancer and have the potential to enhance the development of equitable medicine. The advancement of these technologies for identifying new markers, particularly in the domain of predictive and personalized medicine, has the potential to improve the management of patients with CRC. Further investigations utilizing similar methods could uncover molecular subtypes specific to emerging therapies, potentially strengthening the development of personalized medicine for CRC patients.

Propionate reinforces epithelial identity and reduces aggressiveness of lung carcinoma

The epithelial-to-mesenchymal transition (EMT) plays a central role in the development of cancer metastasis and resistance to chemotherapy. However, its pharmacological treatment remains challenging. Here, we used an EMT-focused integrative functional genomic approach and identified an inverse association between short-chain fatty acids (propionate and butanoate) and EMT in non-small cell lung cancer (NSCLC) patients. Remarkably, treatment with propionate in vitro reinforced the epithelial transcriptional program promoting cell-to-cell contact and cell adhesion, while reducing the aggressive and chemo-resistant EMT phenotype in lung cancer cell lines. Propionate treatment also decreased the metastatic potential and limited lymph node spread in both nude mice and a genetic NSCLC mouse model. Further analysis revealed that chromatin remodeling through H3K27 acetylation (mediated by p300) is the mechanism underlying the shift toward an epithelial state upon propionate treatment. The results suggest that propionate administration has therapeutic potential in reducing NSCLC aggressiveness and warrants further clinical testing.

m6A-Modified circTET2 Interacting with HNRNPC Regulates Fatty Acid Oxidation to Promote the Proliferation of Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) is a hematological malignancy with high metabolic heterogeneity. N6-methyladenosine (m6A) modification plays an important role in metabolism through regulating circular RNAs (circRNAs). However, the underlying mechanism is not yet fully understood in CLL. Herein, an m6A scoring system and an m6A-related circRNA prognostic signature are established, and circTET2 as a potential prognostic biomarker for CLL is identified. The level of m6A modification is found to affect the transport of circTET2 out of the nucleus. By interacting with the RNA-binding protein (RBP) heterogeneous nuclear ribonucleoprotein C (HNRNPC), circTET2 regulates the stability of CPT1A and participates in the lipid metabolism and proliferation of CLL cells through mTORC1 signaling pathway. The mTOR inhibitor dactolisib and FAO inhibitor perhexiline exert a synergistic effect on CLL cells. In addition, the biogenesis of circTET2 can be affected by the splicing process and the RBPs RBMX and YTHDC1. CP028, a splicing inhibitor, modulates the expression of circTET2 and shows pronounced inhibitory effects. In summary, circTET2 plays an important role in the modulation of lipid metabolism and cell proliferation in CLL. This study demonstrates the clinical value of circTET2 as a prognostic indicator as well as provides novel insights in targeting treatment for CLL.

Positive regulation of oxidative phosphorylation by nuclear myosin 1 protects cells from metabolic reprogramming and tumorigenesis in mice

Metabolic reprogramming is one of the hallmarks of tumorigenesis. Here, we show that nuclear myosin 1 (NM1) serves as a key regulator of cellular metabolism. NM1 directly affects mitochondrial oxidative phosphorylation (OXPHOS) by regulating mitochondrial transcription factors TFAM and PGC1α, and its deletion leads to underdeveloped mitochondria inner cristae and mitochondrial redistribution within the cell. These changes are associated with reduced OXPHOS gene expression, decreased mitochondrial DNA copy number, and deregulated mitochondrial dynamics, which lead to metabolic reprogramming of NM1 KO cells from OXPHOS to aerobic glycolysis.This, in turn, is associated with a metabolomic profile typical for cancer cells, namely increased amino acid-, fatty acid-, and sugar metabolism, and increased glucose uptake, lactate production, and intracellular acidity. NM1 KO cells form solid tumors in a mouse model, suggesting that the metabolic switch towards aerobic glycolysis provides a sufficient carcinogenic signal. We suggest that NM1 plays a role as a tumor suppressor and that NM1 depletion may contribute to the Warburg effect at the onset of tumorigenesis.

Suppression of hepatocellular carcinoma by Ulva lactuca ulvan via gut microbiota and metabolite interactions

INTRODUCTION

Ulva lactuca polysaccharide (ULP) is green algae extract with numerous biological activities, including anticoagulant, anti-inflammatory, and antiviral effects. However, the inhibitory ability of ULP in the development of hepatocellular carcinoma warrants further studies.

OBJECTIVES

To elucidate the anti-tumor mechanism of ULP action and evaluate its regulatory effect on gut microbiota and metabolism in H22 hepatocellular carcinoma tumor-bearing mice.

METHODS

An H22 tumor-bearing mouse model was established by subcutaneously injecting H22 hepatoma cells. The gut microbiota composition in cecal feces was assessed and subjected to untargeted metabolomic sequencing. The antitumor activity of ULP was verified further by western blot, RT-qPCR, and reactive oxygen species (ROS) assays.

RESULTS

Administration of ULP alleviated tumor growth by modulating the compositions of the gut microbial communities (Tenericutes, Agathobacter, Ruminiclostridium, Parabacteroides, Lactobacillus, and Holdemania) and metabolites (docosahexaenoic acid, uric acid, N-Oleoyl Dopamine, and L-Kynurenine). Mechanistically, ULP promoted ROS production by inhibiting the protein levels of JNK, c-JUN, PI3K, Akt, and Bcl-6, thereby delaying the growth of HepG2 cells.

CONCLUSION

ULP attenuates tumor growth in H22 tumor-bearing mice by modulating gut microbial composition and metabolism. ULP inhibits tumor growth mainly by promoting ROS generation.

Metabolic Clues to Bile Acid Patterns and Prolonged Survival in Patients with Metastatic Soft-Tissue Sarcoma Treated with Trabectedin

Metastatic soft-tissue sarcomas (mSTS) encompass a highly heterogeneous group of rare tumours characterized by different clinical behaviours and outcomes. Currently, prognostic factors for mSTS are very limited, posing significant challenges in predicting patient survival. Within a cohort of 39 mSTS patients undergoing trabectedin treatment, it was remarkable to find one patient who underwent 73 cycles of trabectedin achieving an unforeseen clinical outcome. To identify contributing factors to her exceptional long-term survival, we have explored circulation metabolomics and biohumoral biomarkers to uncover a potential distinct host biochemical phenotype. The long-term survival patient compared with the other mSTS patients exhibited a distinctive metabolic profile characterized by remarkably higher levels of ursodeoxycholic acid (UDCA) derivatives and vitamin D and lower levels of lithocholic acid (LCA) derivatives, as well as reduced levels of inflammatory C-Reactive Protein 4 (C-RP4) biomarker. Despite its exploratory nature, this study reveals a potential association between specific bile acid metabolic profiles and mSTS patients' prognosis. Enhanced clinical understanding of the interplay between bile acid metabolism and disease progression could pave the way for new targeted therapeutic interventions which may improve the overall survival of mSTS patients.

Comprehensive analyses of fatty acid metabolism-related lncRNA for ovarian cancer patients

Ovarian cancer (OC) is a disease with difficult early diagnosis and treatment and poor prognosis. OC data profiles were downloaded from The Cancer Genome Atlas. Eight key fatty acid metabolism-related long non-coding RNAs (lncRNAs) were finally screened for building a risk scoring model by univariate/ multifactor and least absolute shrinkage and selection operator (LASSO) Cox regression. To make this risk scoring model more applicable to clinical work, we established a nomogram containing the clinical characteristics of OC patients after confirming that the model has good reliability and validity and the ability to distinguish patient prognosis. To further explore how these key lncRNAs are involved in OC progression, we explored their relationship with LUAD immune signatures and tumor drug resistance. The structure shows that the risk scoring model established based on these 8 fatty acid metabolism-related lncRNAs has good reliability and validity and can better predict the prognosis of patients with different risks of OC, and LINC00861in these key RNAs may be a hub gene that affects the progression of OC and closely related to the sensitivity of current OC chemotherapy drugs. In addition, combined with immune signature analysis, we found that patients in the high-risk group are in a state of immunosuppression, and Tfh cells may play an important role in it. We innovatively established a prognostic prediction model with excellent reliability and validity from the perspective of OC fatty acid metabolism reprogramming and lncRNA regulation and found new molecular/cellular targets for future OC treatment.

Prognostic Implication of Plasma Metabolites in Gastric Cancer

Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients' quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan-Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients.

Exploring Metabolomic Patterns in Type 2 Diabetes Mellitus and Response to Glucose-Lowering Medications-Review

The spectrum of information related to precision medicine in diabetes generally includes clinical data, genetics, and omics-based biomarkers that can guide personalized decisions on diabetes care. Given the remarkable progress in patient risk characterization, there is particular interest in using molecular biomarkers to guide diabetes management. Metabolomics is an emerging molecular approach that helps better understand the etiology and promises the identification of novel biomarkers for complex diseases. Both targeted or untargeted metabolites extracted from cells, biofluids, or tissues can be investigated by established high-throughput platforms, like nuclear magnetic resonance (NMR) and mass spectrometry (MS) techniques. Metabolomics is proposed as a valuable tool in precision diabetes medicine to discover biomarkers for diagnosis, prognosis, and management of the progress of diabetes through personalized phenotyping and individualized drug-response monitoring. This review offers an overview of metabolomics knowledge as potential biomarkers in type 2 diabetes mellitus (T2D) diagnosis and the response to glucose-lowering medications.

Untargeted urinary metabolomics for bladder cancer biomarker screening with ultrahigh-resolution mass spectrometry

Bladder cancer (BC) is a common urological malignancy with a high probability of death and recurrence. Cystoscopy is used as a routine examination for diagnosis and following patient monitoring for recurrence. Repeated costly and intrusive treatments may discourage patients from having frequent follow-up screenings. Hence, exploring novel non-invasive ways to help identify recurrent and/or primary BC is critical. In this work, 200 human urine samples were profiled using ultra-high-performance liquid chromatography and ultra-high-resolution mass spectrometry (UHPLC-UHRMS) to uncover molecular markers differentiating BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation identified metabolites that distinguish BC patients from NCs disease. More detailed divisions for the stage, grade, age, and gender are also discussed. Findings indicate that monitoring urine metabolites may provide a non-invasive and more straightforward diagnostic method for identifying BC and treating recurrent diseases.

Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review

In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and replicable over time. This revolutionary approach can be complementary and may, in the future, replace tissue biopsy, which is still considered the gold standard for cancer diagnosis. “Classical” tissue biopsy is invasive, often cannot provide sufficient bioptic material for advanced screening, and can provide isolated information about disease evolution and heterogeneity. Recent literature highlighted how liquid biopsy is informative of proteomic, genomic, epigenetic, and metabolic alterations. These biomarkers can be detected and investigated using single-omic and, recently, in combination through multi-omic approaches. This review will provide an overview of the most suitable techniques to thoroughly characterize tumor biomarkers and their potential clinical applications, highlighting the importance of an integrated multi-omic, multi-analyte approach. Personalized medical investigations will soon allow patients to receive predictable prognostic evaluations, early disease diagnosis, and subsequent ad hoc treatments.

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.

The Role of Genetic, Metabolic, Inflammatory, and Immunologic Mediators in the Progression of Intraductal Papillary Mucinous Neoplasms to Pancreatic Adenocarcinoma

Intraductal papillary mucinous neoplasms (IPMN) have the potential to progress to pancreatic ductal adenocarcinoma (PDAC). As with any progression to malignancy, there are a variety of genetic and metabolic changes, as well as other disruptions to the cellular microenvironment including immune alterations and inflammation, that can contribute to tumorigenesis. Previous studies further characterized these alterations, revealing changes in lipid and glucose metabolism, and signaling pathways that mediate the progression of IPMN to PDAC. With the increased diagnosis of IPMNs and pancreatic cysts on imaging, the opportunity to attenuate risk with the removal of high-risk lesions is possible with the understanding of what factors accelerate malignant progression and how they can be clinically utilized to determine the level of dysplasia and stratify the risk of progression. Here, we reviewed the genetic, metabolic, inflammatory, and immunologic pathways regulating the progression of IPMN to PDAC.

Metabolomics: A New Era in the Diagnosis or Prognosis of B-Cell Non-Hodgkin's Lymphoma

A wide range of histological as well as clinical properties are exhibited by B-cell non-Hodgkin's lymphomas. These properties could make the diagnostics process complicated. The diagnosis of lymphomas at an initial stage is essential because early remedial actions taken against destructive subtypes are commonly deliberated as successful and restorative. Therefore, better protective action is needed to improve the condition of those patients who are extensively affected by cancer when diagnosed for the first time. The development of new and efficient methods for early detection of cancer has become crucial nowadays. Biomarkers are urgently needed for diagnosing B-cell non-Hodgkin's lymphoma and assessing the severity of the disease and its prognosis. New possibilities are now open for diagnosing cancer with the help of metabolomics. The study of all the metabolites synthesised in the human body is called "metabolomics." A patient's phenotype is directly linked with metabolomics, which can help in providing some clinically beneficial biomarkers and is applied in the diagnostics of B-cell non-Hodgkin's lymphoma. In cancer research, it can analyse the cancerous metabolome to identify the metabolic biomarkers. This review provides an understanding of B-cell non-Hodgkin's lymphoma metabolism and its applications in medical diagnostics. A description of the workflow based on metabolomics is also provided, along with the benefits and drawbacks of various techniques. The use of predictive metabolic biomarkers for the diagnosis and prognosis of B-cell non-Hodgkin's lymphoma is also explored. Thus, we can say that abnormalities related to metabolic processes can occur in a vast range of B-cell non-Hodgkin's lymphomas. The metabolic biomarkers could only be discovered and identified as innovative therapeutic objects if we explored and researched them. In the near future, the innovations involving metabolomics could prove fruitful for predicting outcomes and bringing out novel remedial approaches.

Metabonomic analysis of tumor microenvironments: a mini-review

Metabolomic analysis is a vital part of studying cancer progression. Metabonomic crosstalk, such as nutrient availability, physicochemical transformation, and intercellular interactions can affect tumor metabolism. Many original studies have demonstrated that metabolomics is important in some aspects of tumor metabolism. In this mini-review, we summarize the definition of metabolomics and how it can help change a tumor microenvironment, especially in pathways of three metabonomic tumors. Just as non-invasive biofluids have been identified as early biomarkers of tumor development, metabolomics can also predict differences in tumor drug response, drug resistance, and efficacy. Therefore, metabolomics is important for tumor metabolism and how it can affect oncology drugs in cancer therapy.

Untargeted stable isotope-resolved metabolomics to assess the effect of PI3Kβ inhibition on metabolic pathway activities in a PTEN null breast cancer cell line

The combination of high-resolution LC-MS untargeted metabolomics with stable isotope-resolved tracing is a promising approach for the global exploration of metabolic pathway activities. In our established workflow we combine targeted isotopologue feature extraction with the non-targeted X¹³CMS routine. Metabolites, detected by X¹³CMS as differentially labeled between two biological conditions are subsequently integrated into the original targeted library. This strategy enables monitoring of changes in known pathways as well as the discovery of hitherto unknown metabolic alterations. Here, we demonstrate this workflow in a PTEN (phosphatase and tensin homolog) null breast cancer cell line (MDA-MB-468) exploring metabolic pathway activities in the absence and presence of the selective PI3Kβ inhibitor AZD8186. Cells were fed with [U-¹³C] glucose and treated for 1, 3, 6, and 24 h with 0.5 µM AZD8186 or vehicle, extracted by an optimized sample preparation protocol and analyzed by LC-QTOF-MS. Untargeted differential tracing of labels revealed 286 isotope-enriched features that were significantly altered between control and treatment conditions, of which 19 features could be attributed to known compounds from targeted pathways. Other 11 features were unambiguously identified based on data-dependent MS/MS spectra and reference substances. Notably, only a minority of the significantly altered features (11 and 16, respectively) were identified when preprocessing of the same data set (treatment vs. control in 24 h unlabeled samples) was performed with tools commonly used for label-free (i.e. w/o isotopic tracer) non-targeted metabolomics experiments (Profinder´s batch recursive feature extraction and XCMS). The structurally identified metabolites were integrated into the existing targeted isotopologue feature extraction workflow to enable natural abundance correction, evaluation of assay performance and assessment of drug-induced changes in pathway activities. Label incorporation was highly reproducible for the majority of isotopologues in technical replicates with a RSD below 10%. Furthermore, inter-day repeatability of a second label experiment showed strong correlation (Pearson R² > 0.99) between tracer incorporation on different days. Finally, we could identify prominent pathway activity alterations upon PI3Kβ inhibition. Besides pathways in central metabolism, known to be changed our workflow revealed additional pathways, like pyrimidine metabolism or hexosamine pathway. All pathways identified represent key metabolic processes associated with cancer metabolism and therapy.

On the Relevance of Soft Tissue Sarcomas Metabolic Landscape Mapping

Soft tissue sarcomas (STS) prognosis is disappointing, with current treatment strategies being based on a "fit for all" principle and not taking distinct sarcoma subtypes specificities and genetic/metabolic differences into consideration. The paucity of precision therapies in STS reflects the shortage of studies that seek to decipher the sarcomagenesis mechanisms. There is an urge to improve STS diagnosis precision, refine STS classification criteria, and increase the capability of identifying STS prognostic biomarkers. Single-omics and multi-omics studies may play a key role on decodifying sarcomagenesis. Metabolomics provides a singular insight, either as a single-omics approach or as part of a multi-omics strategy, into the metabolic adaptations that support sarcomagenesis. Although STS metabolome is scarcely characterized, untargeted and targeted metabolomics approaches employing different data acquisition methods such as mass spectrometry (MS), MS imaging, and nuclear magnetic resonance (NMR) spectroscopy provided important information, warranting further studies. New chromatographic, MS, NMR-based, and flow cytometry-based methods will offer opportunities to therapeutically target metabolic pathways and to monitorize the response to such metabolic targeting therapies. Here we provide a comprehensive review of STS omics applications, comprising a detailed analysis of studies focused on the metabolic landscape of these tumors.

Characterization of fatty acid metabolism-related lncRNAs in lung adenocarcinoma identifying potential novel prognostic targets

Lung adenocarcinoma (LUAD), a malignant respiratory tumor with an extremely poor prognosis, has troubled the medical community all over the world. According to recent studies, fatty acid metabolism (FAM) and long non-coding RNAs (lncRNAs) regulation have shown exciting results in tumor therapy. In this study, the original LUAD patient data was obtained from the TCGA database, and 12 FAM-related lncRNAs (AL390755.1, AC105020.6, TMPO-AS1, AC016737.2, AC127070.2, LINC01281, AL589986.2, GAS6-DT, AC078993.1, LINC02198, AC007032.1, and AL021026.1) that were highly related to the progression of LUAD were finally identified through bioinformatics analysis, and a risk score model for clinical reference was constructed. The window explores the immunology and molecular mechanism of LUAD, aiming to shed the hoping light on LUAD treatment.

Untargeted ultra-high-resolution mass spectrometry metabolomic profiling of blood serum in bladder cancer

Bladder cancer (BC) is a common urological cancer of high mortality and recurrence rates. Currently, cystoscopy is performed as standard examination for the diagnosis and subsequent monitoring for recurrence of the patients. Frequent expensive and invasive procedures may deterrent patients from regular follow-up screening, therefore it is important to look for new non-invasive methods to aid in the detection of recurrent and/or primary BC. In this study, ultra-high-performance liquid chromatography coupled with ultra-high-resolution mass spectrometry was employed for non-targeted metabolomic profiling of 200 human serum samples to identify biochemical signatures that differentiate BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation revealed twenty-seven metabolites that differentiate between BC patients from NCs. Abundances of these metabolites displayed statistically significant differences in two independent training and validation sets. Twenty-three serum metabolites were also found to be distinguishing between low- and high-grade of BC patients and controls. Thirty-seven serum metabolites were found to differentiate between different stages of BC. The results suggest that measurement of serum metabolites may provide more facile and less invasive diagnostic methodology for detection of bladder cancer and recurrent disease management.

SHMT2 Induces Stemness and Progression of Head and Neck Cancer

Various enzymes in the one-carbon metabolic pathway are closely related to the development of tumors, and they can all be potential targets for cancer therapy. Serine hydroxymethyltransferase2 (SHMT2), a key metabolic enzyme, is very important for the proliferation and growth of cancer cells. However, the function and mechanism of SHMT2 in head and neck cancer (HNC) are not clear. An analysis of The Cancer Genome Atlas (TCGA) data showed that the expression of SHMT2 was higher in tumor tissue than in normal tissue, and its expression was significantly associated with male sex, aggressive histological grade, lymph node metastasis, distant metastasis, advanced TNM stage, and lymphovascular invasion in HNC. SHMT2 knockdown in FADU and SNU1041 cell lines significantly inhibited cell proliferation, colony formation, migration, and invasion. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using TCGA data revealed that SHMT2 was closely related to cancer stem cell regulation and maintenance. Furthermore, we found that silencing SHMT2 inhibited the expression of stemness markers and tumor spheroid formation compared with a control group. On the contrary, stemness markers were significantly increased after SHMT2 overexpression in HEP-2 cells. Interestingly, we found that knocking down SHMT2 reduced the expression of genes related to the Notch and Wnt pathways. Finally, silencing SHMT2 significantly reduced tumor growth and decreased stemness markers in a xenograft model. Taken together, our study suggests that targeting SHMT2 may play an important role in inhibiting HNC progression.