Application of metabolomics technologies toward cancer prognosis and therapy

Glycosylation-related genes mediated prognostic signature contribute to prognostic prediction and treatment options in ovarian cancer: based on bulk and single‑cell RNA sequencing data

BACKGROUND

Ovarian cancer (OC) is a complex disease with significant tumor heterogeneity with the worst prognosis and highest mortality among all gynecological cancers. Glycosylation is a specific post-translational modification that plays an important role in tumor progression, immune escape and metastatic spread. The aim of this work was to identify the major glycosylation-related genes (GRGs) in OC and construct an effective GRGs signature to predict prognosis and immunotherapy.

METHODS

AUCell algorithm was used to identify glycosylation-related genes (GRGs) based on the scRNA-seq and bulk RNA-seq data. An effective GRGs signature was conducted using COX and LASSO regression algorithm. The texting dataset and clinical sample data were used to assessed the accuracy of GRGs signature. We evaluated the differences in immune cell infiltration, enrichment of immune checkpoints, immunotherapy response, and gene mutation status among different risk groups. Finally, RT-qPCR, Wound-healing assay, Transwell assay were performed to verify the effect of the CYBRD1 on OC.

RESULTS

A total of 1187 GRGs were obtained and a GRGs signature including 16 genes was established. The OC patients were divided into high- and low- risk group based on the median riskscore and the patients in high-risk group have poor outcome. We also found that the patients in low-risk group have higher immune cell infiltration, enrichment of immune checkpoints and immunotherapy response. The results of laboratory test showed that CYBRD1 can promote the invasion, and migration of OC and is closely related to the poor prognosis of OC patients.

CONCLUSIONS

Our study established a GRGs signature consisting of 16 genes based on the scRNA-seq and bulk RNA-seq data, which provides a new perspective on the prognosis prediction and treatment strategy for OC.

Multilevel omics for the discovery of biomarkers in pediatric sepsis

Severe sepsis causes organ dysfunction and continues to be the leading reason for pediatric death worldwide. Early recognition of sepsis could substantially promote precision treatment and reduce the risk of pediatric death. The host cellular response to infection during sepsis between adults and pediatrics could be significantly different. A growing body of studies focused on finding markers in pediatric sepsis in recent years using multi-omics approaches. This narrative review summarized the progress in studying pediatric sepsis biomarkers from genome, transcript, protein, and metabolite levels according to the omics technique that has been applied for biomarker screening. It is most likely not a single biomarker could work for precision diagnosis of sepsis, but a panel of markers and probably a combination of markers detected at multi-levels. Importantly, we emphasize the importance of group distinction of infectious agents in sepsis patients for biomarker identification, because the host response to infection of bacteria, virus, or fungus could be substantially different and thus the results of biomarker screening. Further studies on the investigation of sepsis biomarkers that were caused by a specific group of infectious agents should be encouraged in the future, which will better improve the clinical execution of personalized medicine for pediatric sepsis.

UPLC-MS based serum metabolomics for early diagnosis of refractory tumor-induced osteomalacia: a case-control study

CONTEXT

Nearly 20% patients with Tumor-induced osteomalacia (TIO) experienced recurrence or nonrecovery after surgery. Serum FGF23 and phosphate concentrations are not capable for prognosis in such cases. Despite its importance for understanding of prognosis and underlying pathogenesis, the alteration of systemic metabolism in refractory TIO remains unclear.

OBJECTIVE

We aimed to find the metabolomic characteristics of refractory TIO, and establish a novel predictive model for early discriminating refractory TIO based on their serum metabolomics.

DESIGN AND SETTING

Cross-section study for comparison of metabolomic profile between TIO and normal control, and longitudinal study for identifying prognostic model.

METHODS

Based on liquid chromatography-tandem mass spectrometry, we analyzed the global metabolomes of preoperative sera from 86 samples (32 TIO recovery patients, 11 non-remission patients and 43 matched controls). Statistical analyses, pathway enrichment and receiver operating characteristic analysis were performed to identified and evaluate potential markers.

RESULTS

Sparse partial least squares discriminant analysis indicated a clear separation of metabolomic profiles between Healthy controls and TIO patients. The serum metabolites altered in different prognostic groups. L-Pipecolic acid, 2-Dodecylbenzenesulfonic acid and 2-Deoxygalactopyranose were the top 3 metabolites that were significantly perturbed. A combination of L-Pipecolic acid and 2-Dodecylbenzenesulfonic acid demonstrated a high-performance panel for TIO prognosis evaluated by random forest algorithm (AUC=0.921, 95% confidence interval of 0.787-0.995).

CONCLUSIONS

We investigate the global metabolomes of refractory TIO and identify potential prognostic biomarkers preliminarily. A high sensitivity and specificity panel were identified as promising discriminating predictor, which need to be verified in more patients. This work may demonstrate novel insights into TIO prognosis and pathogenesis.

Non-Invasive Biomarkers for Early Lung Cancer Detection

Worldwide, lung cancer (LC) is the most common cause of cancer death, and any delay in the detection of new and relapsed disease serves as a major factor for a significant proportion of LC morbidity and mortality. Though invasive methods such as tissue biopsy are considered the gold standard for diagnosis and disease monitoring, they have several limitations. Therefore, there is an urgent need to identify and validate non-invasive biomarkers for the early diagnosis, prognosis, and treatment of lung cancer for improved patient management. Despite recent progress in the identification of non-invasive biomarkers, currently, there is a shortage of reliable and accessible biomarkers demonstrating high sensitivity and specificity for LC detection. In this review, we aim to cover the latest developments in the field, including the utility of biomarkers that are currently used in LC screening and diagnosis. We comment on their limitations and summarise the findings and developmental stages of potential molecular contenders such as microRNAs, circulating tumour DNA, and methylation markers. Furthermore, we summarise research challenges in the development of biomarkers used for screening purposes and the potential clinical applications of newly discovered biomarkers.

Metabolic linkages between zinc exposure and lung cancer risk: A nested case-control study

Epidemiologic studies have suggested that elevated concentrations of zinc are associated with a decreased risk of lung cancer, but the underlying mechanisms remain to be investigated. The metabolites are highly sensitive to environmental stress, which will help to reveal the linkages between zinc exposure and lung cancer risk. We designed a nested case-control study including 101 incident lung cancer cases and 1:2 age- and sex-frequency-matched 202 healthy controls from the Dongfeng-Tongji (DFTJ) cohort. Their plasma level of zinc was determined by using inductively coupled plasma-mass spectrometry (ICP-MS) and plasma profiles of metabolites were detected by using an untargeted metabolomics approach. The generalized linear models (GLM) were applied to assess the associations of plasma zinc with metabolites, and the mediation effects of zinc-related metabolites on zinc-lung cancer association were further testified. The concentrations of 55 metabolites had linear dose-response relationships with plasma zinc at a false discovery rate (FDR) < 0.05, among which L-proline, phosphatidylcholine (PC, 34:2), phosphatidylethanolamine (PE, O-36:5), L-altrose, and sphingomyelin (SM, 40:3) showed different levels between lung cancer cases and healthy controls (fold change = 0.92, 0.95, 1.07, 0.90, and 1.08, respectively, and all P < 0.05). The plasma concentration of SM(40:3) was negatively associated with incident risk of lung cancer [OR(95%CI) = 0.71(0.55, 0.91), P = 0.007] and could mediate 41.7% of the association between zinc and lung cancer risk (P = 0.004). Moreover, compared to the traditional factors, addition of SM(40:3) exerted improved prediction performance for incident risk of lung cancer [AUC(95%CIs) = 0.714(0.654, 0.775) vs. 0.663(0.600, 0.727), P = 0.030]. Our findings revealed metabolic profiles with zinc exposure and provide new insight into the alternations of metabolites underpinning the links between zinc exposure and lung cancer development.

A Metabolomic Approach and Traditional Physical Assessments to Compare U22 Soccer Players According to Their Competitive Level

The purpose of this study was to use traditional physical assessments combined with a metabolomic approach to compare the anthropometric, physical fitness level, and serum fasting metabolic profile among U22 soccer players at different competitive levels. In the experimental design, two teams of male U22 soccer were evaluated (non-elite = 20 athletes, competing in a regional division; elite = 16 athletes, competing in the first division of the national U22 youth league). Earlobe blood samples were collected, and metabolites were extracted after overnight fasting (12 h). Untargeted metabolomics through Liquid Chromatograph Mass Spectrometry (LC-MS) analysis and anthropometric evaluation were performed. Critical velocity was applied to determine aerobic (CV) and anaerobic (ARC) capacity. Height (non-elite = 174.4 ± 7.0 cm; elite = 176.5 ± 7.0 cm), body mass index (non-elite = 22.1 ± 2.4 kg/m2; elite = 21.9 ± 2.3 kg/m2), body mass (non-elite = 67.1 ± 8.8 kg; elite = 68.5 ± 10.1 kg), lean body mass (non-elite = 59.3 ± 7.1 kg; elite = 61.1 ± 7.9 kg), body fat (non-elite = 7.8 ± 2.4 kg; elite = 7.3 ± 2.4 kg), body fat percentage (non-elite = 11.4 ± 2.4%; elite = 10.5 ± 1.7%), hematocrit (non-elite = 50.2 ± 4.0%; elite = 51.0 ± 4.0%), CV (non-elite = 3.1 ± 0.4 m/s; elite = 3.0 ± 0.2 m/s), and ARC (non-elite = 129.6 ± 55.7 m; elite = 161.5 ± 61.0 m) showed no significant differences between the elite and non-elite teams, while the multivariate Partial Least Squares Discriminant Analysis (PLS-DA) model revealed a separation between the elite and non-elite athletes. Nineteen metabolites with importance for projection (VIP) >1.0 were annotated as belonging to the glycerolipid, sterol lipid, fatty acyl, flavonoid, and glycerophospholipid classes. Metabolites with a high relative abundance in the elite group were related in the literature to a better level of aerobic power, greater efficiency in the recovery process, and improvement of mood, immunity, decision making, and accuracy, in addition to acting in mitochondrial preservation and electron transport chain maintenance. In conclusion, although classical physical assessments were not able to distinguish the teams at different competitive levels, the metabolomics approach successfully indicated differences between the fasting metabolic profiles of elite and non-elite teams.

Medicinal Plants for the Treatment of Gastrointestinal Cancers From the Metabolomics Perspective

Gastrointestinal cancer (GIC), primarily including colorectal cancer, gastric cancer, liver cancer, pancreatic cancer, and esophageal cancer, is one of the most common causes of cancer-related deaths with increasing prevalence and poor prognosis. Medicinal plants have been shown to be a great resource for the treatment of GIC. Due to their complex manifestations of multi-component and multi-target, the underlying mechanisms how they function against GIC remain to be completely deciphered. Cell metabolism is of primary importance in the initialization and development of GIC, which is reported to be a potential target. As an essential supplement to the newest “omics” sciences, metabolomics focuses on the systematic study of the small exogenous and endogenous metabolites involved in extensive biochemical metabolic pathways of living system. In good agreement with the systemic perspective of medicinal plants, metabolomics offers a new insight into the efficacy assessment and action mechanism investigation of medicinal plants as adjuvant therapeutics for GIC therapy. In this review, the metabolomics investigations on metabolism-targeting therapies for GIC in the recent 10 years were systematically reviewed from five aspects of carbohydrate, lipid, amino acid, and nucleotide metabolisms, as well as other altered metabolisms (microbial metabolism, inflammation, and oxidation), with particular attention to the potential of active compounds, extracts, and formulae from medicinal plants. Meanwhile, the current perspectives and future challenges of metabolism-targeting therapies of medicinal plants for GIC were also discussed. In conclusion, the understanding of the action mechanisms of medicinal plants in GIC from the metabolomics perspective will contribute to the clinical application of potential candidates from the resourceful medicinal plants as novel and efficient adjuvant therapeutics for GIC therapy.

L-norvaline affects the proliferation of breast cancer cells based on the microbiome and metabolome analysis

AIMS

The altered fecal metabolites and microbiota might be involved in the development of breast cancer. We aimed to investigate the effect of differential metabolites on the proliferative activity of breast cancer cells.

METHODS AND RESULTS

We collected fecal samples from 14 breast cancer patients and 14 healthy subjects. Untargeted metabolomics analysis, short-chain fatty acid (SCFA) targeted analysis, and 16S rDNA sequencing was performed. The gut metabolite composition of patients changed significantly. Levels of norvaline, glucuronate, and galacturonate were lower in the Cancer group than in the Control (p < 0.05). 4-Methylcatechol and guaiacol increased (p < 0.05). Acetic acid and butyric acid were lower in the Cancer group than in the Control group (p < 0.05). Isobutyric acid and pentanoic acid were higher in the Cancer group than in the Control (p < 0.05). In the genus, the abundance of Rothia and Actinomyces increased in the Cancer group, compared with the Control group (p < 0.05). The differential microbiotas were clearly associated with differential metabolites but weakly with SCFAs. The abundance of Rothia and Actinomyces was markedly positively correlated with 4-methylcatechol and guaiacol (p < 0.05) and negatively correlated with norvaline (p < 0.05). L-norvaline inhibited the content of Arg-1 in a concentration-dependent manner. Compared with the L-norvaline or doxorubicin hydrochloride (DOX) group, the proliferation abilities of 4T1 cells were the lowest in the L-norvaline combined with DOX (p < 0.05). The apoptosis rate increased (p < 0.05).

CONCLUSIONS

Fecal metabolites and microbiota were significantly altered in breast cancer. Levels of differential metabolites (i.e., Norvaline) were significantly correlated with the abundance of differential microbiota. L-norvaline combined with DOX could clearly inhibit the proliferation activity of breast cancer cells.

SIGNIFICANCE AND IMPACT OF STUDY

This might provide clues to uncover potential biomarkers for breast cancer diagnosis and treatment.

Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review

OBJECTIVE

To determine the accuracy of metabolomics in predicting hypertensive disorders in pregnancy.

DESIGN

Systematic review of observational studies.

DATA SOURCES AND STUDY ELIGIBILITY CRITERIA

An electronic literature search was performed in June 2019 and February 2022. Two researchers independently selected studies published between 1998 and 2022 on metabolomic techniques applied to predict the condition; subsequently, they extracted data and performed quality assessment. Discrepancies were dealt with a third reviewer. The primary outcome was pre-eclampsia. Cohort or case-control studies were eligible when maternal samples were taken before diagnosis of the hypertensive disorder.

STUDY APPRAISAL AND SYNTHESIS METHODS

Data on study design, maternal characteristics, how hypertension was diagnosed, metabolomics details and metabolites, and accuracy were independently extracted by two authors.

RESULTS

Among 4613 initially identified studies on metabolomics, 68 were read in full text and 32 articles were included. Studies were excluded due to duplicated data, study design or lack of identification of metabolites. Metabolomics was applied mainly in the second trimester; the most common technique was liquid-chromatography coupled to mass spectrometry. Among the 122 different metabolites found, there were 23 amino acids and 21 fatty acids. Most of the metabolites were involved with ammonia recycling; amino acid metabolism; arachidonic acid metabolism; lipid transport, metabolism and peroxidation; fatty acid metabolism; cell signalling; galactose metabolism; nucleotide sugars metabolism; lactose degradation; and glycerolipid metabolism. Only citrate was a common metabolite for prediction of early-onset and late-onset pre-eclampsia. Vitamin D was the only metabolite in common for pre-eclampsia and gestational hypertension prediction. Meta-analysis was not performed due to lack of appropriate standardised data.

CONCLUSIONS AND IMPLICATIONS

Metabolite signatures may contribute to further insights into the pathogenesis of pre-eclampsia and support screening tests. Nevertheless, it is mandatory to validate such methods in larger studies with a heterogeneous population to ascertain the potential for their use in clinical practice.

PROSPERO REGISTRATION NUMBER

CRD42018097409.

Identifying potential metabolic tissue biomarkers for papillary thyroid cancer in different iodine nutrient regions

PURPOSE

To investigate the applicability of metabolomics to select thyroid cancer-associated biomarkers and discover the effects of iodine on metabolic changes in thyroid cancer.

METHODS

In this study, a total of 33 papillary thyroid cancer (PTC) patients from areas with iodine excess and 32 PTC patients from areas with adequate iodine were recruited, and their cancerous tissue and paracancerous tissue were collected. These specimens were analyzed by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/QTOF/MS) in conjunction with multivariate statistical analysis.

RESULTS

Good separations were obtained for PTC tissue vs. paracancerous tissue, and 15 metabolites, including L-octanoylcarnitine, N-arachidonoylglycine, and others were found to be disturbed in PTC tissue. Moreover, the metabolic profile presented considerable separation between PTC tissue from different iodine areas, and 15 metabolomic biomarkers were found to be differentially expressed. Among them, 10 metabolites, including arachidonoylcarnitine and LysoPCs, were related to thyroid cancer and excess iodine. These biomarkers play a role in arachidonic acid metabolism pathways and others. In addition, biomarkers such as 3,5-tetradecadiencarnitine and oxidized glutathione were significantly correlated with thyroid function, and biomarkers such as L-octanoylcarnitine and arachidonic acid were significantly correlated with the clinical characteristics of PTC.

CONCLUSIONS

Distinct differences in metabolic profiles were found to exist between PTCs from areas with different levels of iodine nutrition. The identified biomarkers have significant potential for diagnosing PTC and investigating its underlying mechanisms.

Differential Glycosylation Levels in Saliva from Patients with Lung or Breast Cancer: A Preliminary Assessment for Early Diagnostic Purposes

Glycans play a fundamental role in several biological processes, such as cell-cell adhesion, signaling, and recognition. Similarly, abnormal glycosylation is involved in many pathological processes, among which include tumor growth and progression. Several highly glycosylated proteins found in blood are currently used in clinical practice as cancer biomarkers (e.g., CA125, PSA, and CA19-9). The development of novel non-invasive diagnostic procedures would greatly simplify the screening and discovery of pathologies at an early stage, thus also allowing for simpler treatment and a higher success rate. In this observational study carried out on 68 subjects diagnosed with either breast or lung cancer and 34 healthy volunteers, we hydrolyzed the glycoproteins in saliva and quantified the obtained free sugars (fucose, mannose, galactose, glucosamine, and galactosamine) by using high-performance anion-exchange chromatography with pulsed-amperometric detection (HPAEC-PAD). The glycosidic profiles were compared by using multivariate statistical analysis, showing differential glycosylation patterns among the three categories. Furthermore, Receiver Operating Characteristics (ROC) analysis allowed obtaining a reliable and minimally invasive protocol able to discriminate between healthy and pathological subjects.

Optimization of Sample Preparation for Metabolomics Exploration of Urine, Feces, Blood and Saliva in Humans Using Combined NMR and UHPLC-HRMS Platforms

Currently, most clinical studies in metabolomics only consider a single type of sample such as urine, plasma, or feces and use a single analytical platform, either NMR or MS. Although some studies have already investigated metabolomics data from multiple fluids, the information is limited to a unique analytical platform. On the other hand, clinical studies investigating the human metabolome that combine multi-analytical platforms have focused on a single biofluid. Combining data from multiple sample types for one patient using a multimodal analytical approach (NMR and MS) should extend the metabolome coverage. Pre-analytical and analytical phases are time consuming. These steps need to be improved in order to move into clinical studies that deal with a large number of patient samples. Our study describes a standard operating procedure for biological specimens (urine, blood, saliva, and feces) using multiple platforms (¹H-NMR, RP-UHPLC-MS, and HILIC-UHPLC-MS). Each sample type follows a unique sample preparation procedure for analysis on a multi-platform basis. Our method was evaluated for its robustness and was able to generate a representative metabolic map.

Inhibition of glutaminolysis in combination with other therapies to improve cancer treatment

Targeting glutamine catabolism has been attracting more research attention on the development of successful cancer therapy. Catalytic enzymes such as glutaminase (GLS) in glutaminolysis, a series of biochemical reactions by which glutamine is converted to glutamate and then alpha-ketoglutarate, an intermediate of the tricarboxylic acid (TCA) cycle, can be targeted by small molecule inhibitors, some of which are undergoing early phase clinical trials and exhibiting promising safety profiles. However, resistance to glutaminolysis targeting treatments has been observed, necessitating the development of treatments to combat this resistance. One option is to use synergy drug combinations, which improve tumor chemotherapy's effectiveness and diminish drug resistance and side effects. This review will focus on studies involving the glutaminolysis pathway and diverse combination therapies with therapeutic implications.

The Heterogeneity of Liver Cancer Metabolism

Primary liver cancer is the fourth leading cause of cancer death around the world. Histologically, it can be divided into two major groups, hepatocellular carcinoma (75% of all liver cancer) and intrahepatic cholangiocarcinoma (15% of all liver cancer) [1, 2]. Primary liver cancer usually happens in liver disease or cirrhosis patients [1], and the risk factors for developing HCC depend on the etiology [3] and the country of provenance [1]. There is an urgent need for an accurate diagnostic test given the high proportion of false positives and false negatives for alpha-fetoprotein (AFP), a common HCC biomarker [4]. Due to often being diagnosed in advanced stages, HCCrelated deaths per year have doubled since 1999 [3]. With the use of metabolomics technologies [5], the aberrant metabolism characteristics of cancer tissues can be discovered and exploited for the new biomarkers and new therapies to treat HCC [6, 7].

Glutamine Metabolism in Cancer

Metabolism is a fundamental process for all cellular functions. For decades, there has been growing evidence of a relationship between metabolism and malignant cell proliferation. Unlike normal differentiated cells, cancer cells have reprogrammed metabolism in order to fulfill their energy requirements. These cells display crucial modifications in many metabolic pathways, such as glycolysis and glutaminolysis, which include the tricarboxylic acid (TCA) cycle, the electron transport chain (ETC), and the pentose phosphate pathway (PPP) [1]. Since the discovery of the Warburg effect, it has been shown that the metabolism of cancer cells plays a critical role in cancer survival and growth. More recent research suggests that the involvement of glutamine in cancer metabolism is more significant than previously thought. Glutamine, a nonessential amino acid with both amine and amide functional groups, is the most abundant amino acid circulating in the bloodstream [2]. This chapter discusses the characteristic features of glutamine metabolism in cancers and the therapeutic options to target glutamine metabolism for cancer treatment.

The Intricate Metabolism of Pancreatic Cancers

Currently, approximately 95% of pancreatic cancers are pancreatic ductal adenocarcinomas (PDAC), which are the most aggressive form and the fourth leading cause of cancer death with extremely poor prognosis [1]. Poor prognosis is primarily attributed to the late diagnosis of the disease when patients are no longer candidates for surgical resection [2]. Cancer cells are dependent on the oncogenes that allow them to proliferate limitlessly. Thus, targeting the expression of known oncogenes in pancreatic cancer has been shown to lead to more effective treatment [3]. This chapter discusses the complexity of metabolic features in pancreatic cancers. In order to comprehend the heterogeneous nature of cancer metabolism fully, we need to take into account the close relationship between cancer metabolism and genetics. Gene expression varies tremendously, not only among different types of cancers but also within the same type of cancer among different patients. Cancer metabolism heterogeneity is often prompted and perpetuated not only by mutations in oncogenes and tumor-suppressor genes but also by the innate diversity of the tumor microenvironment. Much effort has been focused on elucidating the genetic alterations that correlate with disease progression and treatment response [4, 5]. However, the precise mechanisms by which tumor metabolism contributes to cancer growth, survival, mobility, and aggressiveness represent a functional readout of tumor progression (Fig. 1).

The Multifaceted Glioblastoma: From Genomic Alterations to Metabolic Adaptations

Glioblastoma multiforme (GBM) develops on glial cells and is the most common as well as the deadliest form of brain cancer. As in other cancers, distinct combinations of genetic alterations in GBM subtypes induce a diversity of metabolic phenotypes, which explains the variability of GBM sensitivity to current therapies targeting its reprogrammed metabolism. Therefore, it is becoming imperative for cancer researchers to account for the temporal and spatial heterogeneity within this cancer type before making generalized conclusions about a particular treatment's efficacy. Standard therapies for GBM have shown little success as the disease is almost always lethal; however, researchers are making progress and learning how to combine therapeutic strategies most effectively. GBMs can be classified initially into two subsets consisting of primary and secondary GBMs, and this categorization stems from cancer development. GBM is the highest grade of gliomas, which includes glioma I (low proliferative potential), glioma II (low proliferative potential with some capacity for infiltration and recurrence), glioma III (evidence of malignancy), and glioma IV (GBM) (malignant with features of necrosis and microvascular proliferation). Secondary GBM develops from a low-grade glioma to an advanced-stage cancer, while primary GBM provides no signs of progression and is identified as an advanced-stage glioma from the onset. The differences in prognosis and histology correlated with each classification are generally negligible, but the demographics of individuals affected and the accompanying genetic/metabolic properties show distinct differentiation [3].

Cancer Stem Cell Metabolism

Cancer stem cells (CSCs), also known as tumorinitiating cells (TICs), are a group of cells found within cancer cells. Like normal stem cells, CSCs can proliferate, engage in self-renewal, and are often implicated in the recurrence of tumors after therapy [1, 2]. The existence of CSCs in various types of cancer has been proven, such as in acute myeloid leukemia (AML) [3], breast [4], pancreatic [5], and lung cancers [6], to name a few. There are two theories regarding the origin of CSCs. First, CSCs may have arisen from normal stem/progenitor cells that experienced changes in their environment or genetic mutations. On the other hand, CSCs may also have originated from differentiated cells that underwent genetic and/or heterotypic modifications [7]. Either way, CSCs reprogram their metabolism in order to support tumorigenesis.

The Intratumoral Heterogeneity of Cancer Metabolism

Cancer is one of the deadliest diseases in the world, causing over half a million deaths a year in the USA alone. Despite recent advances made in the field of cancer biology and the therapies that have been developed [1, 2], it is clear that more advances are necessary for us to classify cancer as curable. The logical question that arises is simple: Why, despite all the technologies and medical innovations of our time, has a complete cure eluded us? This chapter sheds light on one of cancer's most impactful attributes: its heterogeneity and, more specifically, the intratumoral heterogeneity of cancer metabolism. Simply put, what makes cancer one of the deadliest diseases is its ability to change and adapt. Cancer cells' rapid evolution, coupled with their irrepressible ability to divide, gives most of them the advantage over our immune systems. In this chapter, we delve into the complexities of this adaptability and the vital role that metabolism plays in the rise and progression of this heterogeneity.

Diabetes and Cancer: The Epidemiological and Metabolic Associations

Diabetes mellitus, commonly known as diabetes, and cancer are two of the most common diseases plaguing the world today. According to the Centers for Disease Control and Prevention (CDC), there are currently more than 20 million people with diabetes in the United States [1]. According to the International Agency for Research on Cancer (IARC), there were around 18 million people diagnosed with cancer, with approximately ten million deaths globally in 2018 [2]. Given the prevalence and deadliness of diabetes and cancer, these two diseases have long been the focus of many researchers with the goal of improving treatment outcomes. While diabetes and cancer may seem to be two very different diseases at first glance, they share several similarities, especially regarding their metabolic characteristics. This chapter discusses the similarities and relationships between the metabolism of diabetes, especially type 2 diabetes (T2D), and cancer, including their abnormal glucose and amino acid metabolism, the contribution of hyperglycemia to oncogenic mutation, and the contribution of hyperinsulinemia to cancer progression. Investigating the metabolic interplay between diabetes and cancer in an effort to exploit this connection for cancer treatment has the potential to significantly improve clinical efficacy.

Bridging the Metabolic Parallels Between Neurological Diseases and Cancer

Despite the many recent breakthroughs in cancer research, oncology has traditionally been seen as a distinct field from other diseases. Recently, more attention has been paid to repurposing established therapeutic strategies and targets of other diseases towards cancer treatment, with some of these attempts generating promising outcomes [1, 2]. Recent studies using advanced metabolomics technologies [3] have shown evidence of close metabolic similarities between cancer and neurological diseases. These studies have unveiled several metabolic characteristics shared by these two categories of diseases, including metabolism of glutamine, gamma-aminobutyric acid (GABA), and N-acetyl-aspartyl-glutamate (NAAG) [4-6]. The striking metabolic overlap between cancer and neurological diseases sheds light on novel therapeutic strategies for cancer treatment. For example, 2-(phosphonomethyl) pentanedioic acid (2-PMPA), one of the glutamate carboxypeptidase II (GCP II) inhibitors that prevent the conversion of NAAG to glutamate, has been shown to suppress cancer growth [6, 7]. These promising results have led to an increased interest in integrating this metabolic overlap between cancer and neurological diseases into the study of cancer metabolism. The advantages of studying this metabolic overlap include not only drug repurposing but also translating existing knowledge from neurological diseases to the field of cancer research. This chapter discusses the specific overlapping metabolic features between cancer and neurological diseases, focusing on glutamine, GABA, and NAAG metabolisms. Understanding the interconnections between cancer and neurological diseases will guide researchers and clinicians to find more effective cancer treatments.

Metabolic Intersection of Cancer and Cardiovascular Diseases: Opportunities for Cancer Therapy

According to data from the World Health Organization, cardiovascular diseases and cancer are the two leading causes of mortality in the world [1]. Despite the immense effort to study these diseases and the constant innovation in treatment modalities, the number of deaths associated with cardiovascular diseases and cancer is predicted to increase in the coming decades [1]. From 2008 to 2030, due to population growth and population aging in many parts of the world, the number of deaths caused by cancer globally is projected to increase by 45%, corresponding to an annual increase of around four million people [1]. For cardiovascular diseases, this number is six million people [1]. In the United States, treatments for these two diseases are among the most costly and result in a disproportionate impact on low- and middleincome people. As the fight against these fatal diseases continues, it is crucial that we continue our investigation and broaden our understanding of cancer and cardiovascular diseases to innovate our prognostic and treatment approaches. Even though cardiovascular diseases and cancer are usually studied independently [2-12], there are some striking overlaps between their metabolic behaviors and therapeutic targets, suggesting the potential application of cardiovascular disease treatments for cancer therapy. More specifically, both cancer and many cardiovascular diseases have an upregulated glutaminolysis pathway, resulting in low glutamine and high glutamate circulating levels. Similar treatment modalities, such as glutaminase (GLS) inhibition and glutamine supplementation, have been identified to target glutamine metabolism in both cancer and some cardiovascular diseases. Studies have also found similarities in lipid metabolism, specifically fatty acid oxidation (FAO) and synthesis. Pharmacological inhibition of FAO and fatty acid synthesis have proven effective against many cancer types as well as specific cardiovascular conditions. Many of these treatments have been tested in clinical trials, and some have been medically prescribed to patients to treat certain diseases, such as angina pectoris [13, 14]. Other metabolic pathways, such as tryptophan catabolism and pyruvate metabolism, were also dysregulated in both diseases, making them promising treatment targets. Understanding the overlapping traits exhibited by both cancer metabolism and cardiovascular disease metabolism can give us a more holistic view of how important metabolic dysregulation is in the progression of diseases. Using established links between these illnesses, researchers can take advantage of the discoveries from one field and potentially apply them to the other. In this chapter, we highlight some promising therapeutic discoveries that can support our fight against cancer, based on common metabolic traits displayed in both cancer and cardiovascular diseases.

Non-Hodgkin Lymphoma Metabolism

Non-Hodgkin lymphomas (NHLs) are a heterogeneous group of lymphoid neoplasms with different biological characteristics. About 90% of all lymphomas in the United States originate from B lymphocytes, while the remaining originate from T cells [1]. The treatment of NHLs depends on the neoplastic histology and stage of the tumor, which will indicate whether radiotherapy, chemotherapy, or a combination is the best suitable treatment [2]. The American Cancer Society describes the staging of lymphoma as follows: Stage I is lymphoma in a single node or area. Stage II is when that lymphoma has spread to another node or organ tissue. Stage III is when it has spread to lymph nodes on two sides of the diaphragm. Stage IV is when cancer has significantly spread to organs outside the lymph system. Radiation therapy is the traditional therapeutic route for localized follicular and mucosa-associated lymphomas. Chemotherapy is utilized for the treatment of large-cell lymphomas and high-grade lymphomas [2]. However, the treatment of indolent lymphomas remains problematic as the patients often have metastasis, for which no standard approach exists [2].

The Heterogeneity of Breast Cancer Metabolism

Despite advances in screening, therapy, and surveillance that have improved patient survival rates, breast cancer is still the most commonly diagnosed cancer and the second leading cause of cancer mortality among women [1]. Breast cancer is a highly heterogeneous disease rooted in a genetic basis, influenced by extrinsic stimuli, and reflected in clinical behavior. The diversity of breast cancer hormone receptor status and the expression of surface molecules have guided therapy decisions for decades; however, subtype-specific treatment often yields diverse responses due to varying tumor evolution and malignant potential. Although the mechanisms behind breast cancer heterogeneity is not well understood, available evidence suggests that studying breast cancer metabolism has the potential to provide valuable insights into the causes of these variations as well as viable targets for intervention.

Targeting Metabolic Cross Talk Between Cancer Cells and Cancer-Associated Fibroblasts

Although cancer has classically been regarded as a genetic disease of uncontrolled cell growth, the importance of the tumor microenvironment (TME) [1, 2] is continuously emphasized by the accumulating evidence that cancer growth is not simply dependent on the cancer cells themselves [3, 4] but also dependent on angiogenesis [5-8], inflammation [9, 10], and the supporting roles of cancer-associated fibroblasts (CAFs) [11-13]. After the discovery that CAFs are able to remodel the tumor matrix within the TME and provide the nutrients and chemicals to promote cancer cell growth [14], many studies have aimed to uncover the cross talk between cancer cells and CAFs. Moreover, a new paradigm in cancer metabolism shows how cancer cells act like "metabolic parasites" to take up the high-energy metabolites, such as lactate, ketone bodies, free fatty acids, and glutamine from supporting cells, including CAFs and cancer-associated adipocytes (CAAs) [15, 16]. This chapter provides an overview of the metabolic coupling between CAFs and cancer cells to further define the therapeutic options to disrupt the CAF-cancer cell interactions.

Metabolic Relationship Between Cancer-Associated Fibroblasts and Cancer Cells

Cancer-associated fibroblasts (CAFs), a major component of the tumor microenvironment (TME), play an important role in cancer initiation, progression, and metastasis. Recent findings have demonstrated that the TME not only provides physical support for cancer cells but also directs cell-to-cell interactions (in this case, the interaction between cancer cells and CAFs). As cancer progresses, the CAFs also coevolve, transitioning from an inactivated state to an activated state. The elucidation and understanding of the interaction between cancer cells and CAFs will pave the way for new cancer therapies [1-3].

Metabolism of Immune Cells in the Tumor Microenvironment

The tumor microenvironment (TME) is a complex biological structure surrounding tumor cells and includes blood vessels, immune cells, fibroblasts, adipocytes, and extracellular matrix (ECM) [1, 2]. These heterogeneous surrounding structures provide nutrients, metabolites, and signaling molecules to provide a cancer-friendly environment. The metabolic interplay between immune cells and cancer cells in the TME is a key feature not only for understanding tumor biology but also for discovering cancer cells' vulnerability. As cancer immunotherapy to treat cancer patients and the use of metabolomics technologies become more and more common [3], the importance of the interplay between cancer cells and immune cells in the TME is emerging with respect to not only cell-to-cell interactions but also metabolic pathways. This interaction between immune cells and cancer cells is a complex and dynamic process in which immune cells act as a determinant factor of cancer cells' fate and vice versa. In this chapter, we provide an overview of the metabolic interplay between immune cells and cancer cells and discuss the therapeutic opportunities as a result of this interplay in order to define targets for cancer treatment. It is important to understand and identify therapeutic targets that interrupt this cancerpromoting relationship between cancer cells and the surrounding immune cells, allowing for maximum efficacy of immune checkpoint inhibitors as well as other genetic and cellular therapies.

Different Tumor Microenvironments Lead to Different Metabolic Phenotypes

The beginning of the twenty-first century offered new advances in cancer research, including new knowledge about the tumor microenvironment (TME). Because TMEs provide the niches in which cancer cells, fibroblasts, lymphocytes, and immune cells reside, they play a crucial role in cancer cell development, differentiation, survival, and proliferation. Throughout cancer progression, the TME constantly evolves, causing cancer cells to adapt to the new conditions. The heterogeneity of cancer, evidenced by diverse proliferation rates, cellular structures, metabolisms, and gene expressions, presents challenges for cancer treatment despite the advances in research. This chapter discusses how different TMEs lead to specific metabolic adaptations that drive cancer progression.

The Heterogeneity Metabolism of Renal Cell Carcinomas

According to data from the American Cancer Society, cancer is one of the deadliest health problems globally. Annually, renal cell carcinoma (RCC) causes more than 100,000 deaths worldwide [1-4], posing an urgent need to develop effective treatments to increase patient survival outcomes. New therapies are expected to address a major factor contributing to cancer's resistance to standard therapies: oncogenic heterogeneity. Gene expression can vary tremendously among different types of cancers, different patients of the same tumor type, and even within individual tumors; various metabolic phenotypes can emerge, making singletherapy approaches insufficient. Novel strategies targeting the diverse metabolism of cancers aim to overcome this obstacle. Though some have yielded positive results, it remains a challenge to uncover all of the distinct metabolic profiles of RCC. In the quest to overcome this obstacle, the metabolic oriented research focusing on these cancers has offered freshly new perspectives, which are expected to contribute heavily to the development of new treatments.

Glucose Metabolism in Cancer: The Warburg Effect and Beyond

Otto Warburg observed a peculiar phenomenon in 1924, unknowingly laying the foundation for the field of cancer metabolism. While his contemporaries hypothesized that tumor cells derived the energy required for uncontrolled replication from proteolysis and lipolysis, Warburg instead found them to rapidly consume glucose, converting it to lactate even in the presence of oxygen. The significance of this finding, later termed the Warburg effect, went unnoticed by the broader scientific community at that time. The field of cancer metabolism lay dormant for almost a century awaiting advances in molecular biology and genetics, which would later open the doors to new cancer therapies [2, 3].

Capillary Electrophoresis-Mass Spectrometry for Cancer Metabolomics

This chapter presents the fundamentals, instrumentation, methodology, and applications of capillary electrophoresis-mass spectrometry (CE-MS) for cancer metabolomics. CE offers fast and high-resolution separation of charged analytes from a very small amount of sample. When coupled to MS, it represents a powerful analytical technique enabling identification and quantification of metabolites in biological samples. Several issues need to be addressed when combining CE with MS, especially the interface between CE and MS and the selection of a proper separation methodology, sample pretreatment, and capillary coatings. We will discuss these aspects of CE-MS and detail representative applications for cancer metabolomic analysis.

Cancer Cell Metabolites: Updates on Current Tracing Methods

Cancer is the second leading cause of death-1 in 6 deaths globally is due to cancer. Cancer metabolism is a complex and one of the most actively researched area in cancer biology. Metabolic reprogramming in cancer cells entails activities that involve several enzymes and metabolites to convert nutrient into building blocks that alter energy metabolism to fuel rapid cell division. Metabolic dependencies in cancer generate signature metabolites that have key regulatory roles in tumorigenesis. In this minireview, we highlight recent advances in the popular methods ingrained in biochemistry research such as stable and flux isotope analysis, as well as radioisotope labeling, which are valuable in elucidating cancer metabolites. These methods together with analytical tools such as chromatography, nuclear magnetic resonance spectroscopy and mass spectrometry have helped to bring about exploratory work in understanding the role of important as well as obscure metabolites in cancer cells. Information obtained from these analyses significantly contribute in the diagnosis and prognosis of tumors leading to potential therapeutic targets for cancer therapy.

Detection of immunogenic cell death and its relevance for cancer therapy

Chemotherapy, radiation therapy, as well as targeted anticancer agents can induce clinically relevant tumor-targeting immune responses, which critically rely on the antigenicity of malignant cells and their capacity to generate adjuvant signals. In particular, immunogenic cell death (ICD) is accompanied by the exposure and release of numerous damage-associated molecular patterns (DAMPs), which altogether confer a robust adjuvanticity to dying cancer cells, as they favor the recruitment and activation of antigen-presenting cells. ICD-associated DAMPs include surface-exposed calreticulin (CALR) as well as secreted ATP, annexin A1 (ANXA1), type I interferon, and high-mobility group box 1 (HMGB1). Additional hallmarks of ICD encompass the phosphorylation of eukaryotic translation initiation factor 2 subunit-α (EIF2S1, better known as eIF2α), the activation of autophagy, and a global arrest in transcription and translation. Here, we outline methodological approaches for measuring ICD markers in vitro and ex vivo for the discovery of next-generation antineoplastic agents, the development of personalized anticancer regimens, and the identification of optimal therapeutic combinations for the clinical management of cancer.

MULTIOMIC PATTERNS IN BODY FLUIDS: TECHNOLOGICAL CHALLENGE WITH A GREAT POTENTIAL TO IMPLEMENT THE ADVANCED PARADIGM OF 3P MEDICINE

Liquid biopsy (LB) is defined as a sample of any of body fluids (blood, saliva, tear fluid, urine, sweat, amniotic, cerebrospinal and pleural fluids, cervicovaginal secretion, and wound efflux, amongst others), which can be ex vivo analysed to detect and quantity the target(s) of interest. LB represents diagnostic approach relevant for organ-specific changes and systemic health conditions including both manifested diseases and their prestages such as suboptimal health. Further, experts emphasise that DNA-based analysis alone does not provide sufficient information for optimal diagnostics and effective treatments. Consequently, of great scientific and clinical utility are molecular patterns detected by hybrid technologies such as metabolomic tools and molecular imaging. Future proposed strategies utilise multiomic pillars (generally genome, tanscriptome, proteome, metabolome, epigenome, radiome, and microbiome), system-biological approach, and multivariable algorithms for diagnostic, prognostic, and therapeutic purposes. Current article analyses pros and cons of the mass spectrometry-based technologies, provides eminent examples of a success story "from discovery to clinical application," and demonstrates a "road-map" for the technology-driven paradigm change from reactive to predictive, preventive and personalised medical services as the medicine of the future benefiting the patient and healthcare at large. © 2019 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

The effect of acupuncture on tumor growth and gut microbiota in mice inoculated with osteosarcoma cells

Background

Cancer is a complex systemic disease. As a key component of traditional Chinese medicine, acupuncture is a clinically proven medical treatment for many diseases, and it also has preventative effects as it balances the body, allowing it to self-regulate. For cancer patients, acupuncture is widely used as complementary therapy to boost the immune system and reduce the side effects of radiotherapy and chemotherapy. However, few studies have determined how acupuncture against cancer, especially in regulating the intestinal flora of the tumor-burdened mice.

Methods

We treated osteosarcoma tumor-burdened mice by using needling on different acupoints and acupoints combination, thereafter determined the effects of acupuncture on tumor growth by using imaging technology in vitro. In addition, intestinal bacteria were analyzed for further understanding the holistic and systemic treatment effects of acupuncture in osteosarcoma tumor-burdened mice.

Results

Acupuncture treatment can delay tumor growth and changes of intestinal bacteria in osteosarcoma tumor-burdened mice. In detail, the loss of body weight and the development of tumor volume of mice have been postposed by needling specific acupoints. In addition, acupuncture treatment has delayed the changes of the relative abundance of Bacteroidetes, Firmicutes and Candidatus Saccharibacteria at the phylum level. Moreover, the relative abundance of many bacteria (e.g., Catabacter, Acetatifactor and Aestuariispira) has been regulated by using acupuncture treatment, and the trend of structural changes of these bacteria at the genus level has also been postposed compared to that of the tumor-burdened mice model group.

Conclusion

Our results suggest that acupuncture may provide a systemic treatment for cancer. Our findings encourage new and extensive research into the effects of acupuncture on changes of the intestinal microbiome associated with the development of cancer.

The Metabolic Interplay between Cancer and Other Diseases

Over the past decade, knowledge of cancer metabolism has expanded exponentially and has provided several clinically relevant targets for cancer therapy. Although these current approaches have shown promise, there are very few studies showing how seemingly unrelated metabolic processes in other diseases can readily occur in cancer. Moreover, the striking metabolic overlap between cancer and other diseases such as diabetes, cardiovascular, neurological, obesity, and aging has provided key therapeutic strategies that have even begun to be translated into clinical trials. These promising results necessitate consideration of the interconnected metabolic network while studying the metabolism of cancer. This review article discusses how cancer metabolism is intertwined with systemic metabolism and how knowledge from other diseases can help to broaden therapeutic opportunities for cancer.