Plasma Metabolomics Identifies Lipid and Amino Acid Markers of Weight Loss in Patients with Upper Gastrointestinal Cancer

Inter-organ cross-talk in human cancer cachexia revealed by spatial metabolomics

BACKGROUND

Cancer cachexia (CCx) presents a multifaceted challenge characterized by negative protein and energy balance and systemic inflammatory response activation. While previous CCx studies predominantly focused on mouse models or human body fluids, there's an unmet need to elucidate the molecular inter-organ cross-talk underlying the pathophysiology of human CCx.

METHODS

Spatial metabolomics were conducted on liver, skeletal muscle, subcutaneous and visceral adipose tissue, and serum from cachectic and control cancer patients. Organ-wise comparisons were performed using component, pathway enrichment and correlation network analyses. Inter-organ correlations in CCx altered pathways were assessed using Circos. Machine learning on tissues and serum established classifiers as potential diagnostic biomarkers for CCx.

RESULTS

Distinct metabolic pathway alteration was detected in CCx, with adipose tissues and liver displaying the most significant (P ≤ 0.05) metabolic disturbances. CCx patients exhibited increased metabolic activity in visceral and subcutaneous adipose tissues and liver, contrasting with decreased activity in muscle and serum compared to control patients. Carbohydrate, lipid, amino acid, and vitamin metabolism emerged as highly interacting pathways across different organ systems in CCx. Muscle tissue showed decreased (P ≤ 0.001) energy charge in CCx patients, while liver and adipose tissues displayed increased energy charge (P ≤ 0.001). We stratified CCx patients by severity and metabolic changes, finding that visceral adipose tissue is most affected, especially in cases of severe cachexia. Morphometric analysis showed smaller (P ≤ 0.05) adipocyte size in visceral adipose tissue, indicating catabolic processes. We developed tissue-based classifiers for cancer cachexia specific to individual organs, facilitating the transfer of patient serum as minimally invasive diagnostic markers of CCx in the constitution of the organs.

CONCLUSIONS

These findings support the concept of CCx as a multi-organ syndrome with diverse metabolic alterations, providing insights into the pathophysiology and organ cross-talk of human CCx. This study pioneers spatial metabolomics for CCx, demonstrating the feasibility of distinguishing cachexia status at the organ level using serum.

Serum metabolomics analysis of malnutrition in patients with gastric cancer: a cross sectional study

BACKGROUND

Although malnutrition is common in cancer patients, its molecular mechanisms has not been fully clarified. This study aims to identify significantly differential metabolites, match the corresponding metabolic pathways, and develop a predictive model of malnutrition in patients with gastric cancer.

METHODS

In this cross-sectional study, we applied non-targeted metabolomics using liquid chromatography-mass spectrometry to explore the serum fingerprinting of malnutrition in patients with gastric cancer. Malnutrition-specific differential metabolites were identified by orthogonal partial least-squares discriminant analysis and t-test and matched with the Human Metabolome Database and the LIPID Metabolites and Pathways Strategy. We matched the corresponding metabolic pathways of malnutrition using pathway analysis at the MetaboAnalyst 5.0. We used random forest analyses to establish the predictive model.

RESULTS

We recruited 220 malnourished and 198 non-malnourished patients with gastric cancer. The intensities of 25 annotated significantly differential metabolites were lower in patients with malnutrition than those without, while two others were higher in patients with malnutrition than those without, including newly identified significantly differential metabolites such as indoleacrylic acid and lysophosphatidylcholine(18:3/0:0). We matched eight metabolic pathways associated with malnutrition, including aminoacyl-tRNA biosynthesis, tryptophan metabolism, and glycerophospholipid metabolism. We established a predictive model with an area under the curve of 0.702 (95% CI: 0.651-0.768) based on four annotated significantly differential metabolites, namely indoleacrylic acid, lysophosphatidylcholine(18:3/0:0), L-tryptophan, and lysophosphatidylcholine(20:3/0:0).

CONCLUSIONS

We identified 27 specific differential metabolites of malnutrition in malnourished compared to non-malnourished patients with gastric cancer. We also matched eight corresponding metabolic pathways and developed a predictive model. These findings provide supportive data to better understand molecular mechanisms of malnutrition in patients with gastric cancer and new strategies for the prediction, diagnosis, prevention, and treatment for those malnourished.

Cancer cachexia: biomarkers and the influence of age

Cancer cachexia (Ccx) is a complex metabolic condition characterized by pronounced muscle and fat wasting, systemic inflammation, weakness and fatigue. Up to 30% of cancer patients succumb directly to Ccx, yet therapies that effectively address this perturbed metabolic state are rare. In recent decades, several characteristics of Ccx have been established in mice and humans, of which we here highlight adipose tissue dysfunction, muscle wasting and systemic inflammation, as they are directly linked to biomarker discovery. To counteract cachexia pathogenesis as early as possible and mitigate its detrimental impact on anti-cancer treatments, identification and validation of clinically endorsed biomarkers assume paramount importance. Ageing was recently shown to affect both the validity of Ccx biomarkers and Ccx development, but the underlying mechanisms are still unknown. Thus, unravelling the intricate interplay between ageing and Ccx can help to counteract Ccx pathogenesis and tailor diagnostic and treatment strategies to individual needs.

A molecular pathway for cancer cachexia-induced muscle atrophy revealed at single-nucleus resolution

Cancer cachexia is a prevalent and often fatal wasting condition that cannot be fully reversed with nutritional interventions. Muscle atrophy is a central component of the syndrome, but the mechanisms whereby cancer leads to skeletal muscle atrophy are not well understood. We performed single-nucleus multi-omics on skeletal muscles from a mouse model of cancer cachexia and profiled the molecular changes in cachexic muscle. Our results revealed the activation of a denervation-dependent gene program that upregulates the transcription factor myogenin. Further studies showed that a myogenin-myostatin pathway promotes muscle atrophy in response to cancer cachexia. Short hairpin RNA inhibition of myogenin or inhibition of myostatin through overexpression of its endogenous inhibitor follistatin prevented cancer cachexia-induced muscle atrophy in mice. Our findings uncover a molecular basis of muscle atrophy associated with cancer cachexia and highlight potential therapeutic targets for this disorder.

Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration

For many patients, the cancer continuum includes a syndrome known as cancer-associated cachexia (CAC), which encompasses the unintended loss of body weight and muscle mass, and is often associated with fat loss, decreased appetite, lower tolerance and poorer response to treatment, poor quality of life, and reduced survival. Unfortunately, there are no effective therapeutic interventions to completely reverse cancer cachexia and no FDA-approved pharmacologic agents; hence, new approaches are urgently needed. In May of 2022, researchers and clinicians from Moffitt Cancer Center held an inaugural retreat on CAC that aimed to review the state of the science, identify knowledge gaps and research priorities, and foster transdisciplinary collaborative research projects. This review summarizes research priorities that emerged from the retreat, examples of ongoing collaborations, and opportunities to move science forward. The highest priorities identified include the need to (1) evaluate patient-reported outcome (PRO) measures obtained in clinical practice and assess their use in improving CAC-related outcomes; (2) identify biomarkers (imaging, molecular, and/or behavioral) and novel analytic approaches to accurately predict the early onset of CAC and its progression; and (3) develop and test interventions (pharmacologic, nutritional, exercise-based, and through mathematical modeling) to prevent CAC progression and improve associated symptoms and outcomes.

Phosphatidylethanolamine (18:2e/18:2) may inhibit adipose tissue wasting in patients with cancer cachexia by increasing lysophosphatidic acid receptor 6

BACKGROUND

Cancer associated cachexia is characterized by the significant loss of adipose tissue, leading to devastating weight loss and muscle wasting in the majority of cancer patients. The effects and underlying mechanisms of degradation metabolites on adipocytes in cachectic patients remain poorly understood. To address this knowledge gap, we conducted a comprehensive study combining lipidomic analysis of subcutaneous and visceral adipose tissue with transcriptomics data from the database to investigate the mechanisms of lipid regulation in adipocytes.

METHODS

We collected subcutaneous and visceral adipose tissue samples from cachectic and noncachectic cancer patients. Lipidomic analysis was performed to identify differentially expressed lipids in both types of adipose tissue. Additionally, transcriptomics data from the GEO database were analyzed to explore gene expression patterns in adipocytes. Bioinformatics analysis was employed to determine the enrichment of differentially expressed genes in specific pathways. Furthermore, molecular docking studies were conducted to predict potential protein targets of specific lipids, with a focus on the PI3K-Akt signaling pathway. Western blot analysis was used to validate protein levels of the identified target gene, lysophosphatidic acid receptor 6 (LPAR6), in subcutaneous and visceral adipose tissue from cachectic and noncachectic patients.

RESULTS

Significant lipid differences in subcutaneous and visceral adipose tissue between cachectic and noncachectic patients were identified by multivariate statistical analysis. Cachectic patients exhibited elevated Ceramides levels and reduced CerG2GNAc1 levels (P < 0.05). A total of 10 shared lipids correlated with weight loss and IL-6 levels, enriched in Sphingolipid metabolism, GPI-anchor biosynthesis, and Glyceropholipid metabolism pathways. LPAR6 expression was significantly elevated in both adipose tissues of cachectic patients (P < 0.05). Molecular docking analysis indicated strong binding of Phosphatidylethanolamine (PE) (18:2e/18:2) to LPAR6.

CONCLUSIONS

Our findings suggest that specific lipids, including PE(18:2e/18:2), may mitigate adipose tissue wasting in cachexia by modulating the expression of LPAR6 through the PI3K-Akt signaling pathway. The identification of these potential targets and mechanisms provides a foundation for future investigations and therapeutic strategies to combat cachexia. By understanding the underlying lipid regulation in adipocytes, we aim to develop targeted interventions to ameliorate the devastating impact of cachexia on patient outcomes and quality of life. Nevertheless, further studies and validation are warranted to fully elucidate the intricate mechanisms involved and translate these findings into effective clinical interventions.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Oncometabolite D-2-hydroxyglutarate-dependent metabolic reprogramming induces skeletal muscle atrophy during cancer cachexia

Cancer cachexia is characterized by weight loss and skeletal muscle wasting. Based on the up-regulation of catabolism and down-regulation of anabolism, here we showed genetic mutation-mediated metabolic reprogramming in the progression of cancer cachexia by screening for metabolites and investigating their direct effect on muscle atrophy. Treatment with 93 μM D-2-hydroxyglutarate (D2HG) resulted in reduced myotube width and increased expression of E3 ubiquitin ligases. Isocitrate Dehydrogenase 1 (IDH1) mutant patients had higher D2HG than non-mutant patients. In the in vivo murine cancer cachexia model, mutant IDH1 in CT26 cancer cells accelerated cachexia progression and worsened overall survival. Transcriptomics and metabolomics revealed a distinct D2HG-induced metabolic imbalance. Treatment with the IDH1 inhibitor ivosidenib delayed the progression of cancer cachexia in murine GL261 glioma model and CT26 colorectal carcinoma models. These data demonstrate the contribution of IDH1 mutation mediated D2HG accumulation to the progression of cancer cachexia and highlight the individualized treatment of IDH1 mutation associated cancer cachexia.

Cachexia: A systemic consequence of progressive, unresolved disease

Cachexia, a systemic wasting condition, is considered a late consequence of diseases, including cancer, organ failure, or infections, and contributes to significant morbidity and mortality. The induction process and mechanistic progression of cachexia are incompletely understood. Refocusing academic efforts away from advanced cachexia to the etiology of cachexia may enable discoveries of new therapeutic approaches. Here, we review drivers, mechanisms, organismal predispositions, evidence for multi-organ interaction, model systems, clinical research, trials, and care provision from early onset to late cachexia. Evidence is emerging that distinct inflammatory, metabolic, and neuro-modulatory drivers can initiate processes that ultimately converge on advanced cachexia.

Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers

BACKGROUND AND AIMS

Two of the most lethal gastrointestinal (GI) cancers, gastric cancer (GC) and colon cancer (CC), are ranked in the top five cancers that cause deaths worldwide. Most GI cancer deaths can be reduced by earlier detection and more appropriate medical treatment. Unlike the current "gold standard" techniques, non-invasive and highly sensitive screening tests are required for GI cancer diagnosis. Here, we explored the potential of metabolomics for GI cancer detection and the classification of tissue-of-origin, and even the prognosis management.

METHODS

Plasma samples from 37 gastric cancer (GC), 17 colon cancer (CC), and 27 non-cancer (NC) patients were prepared for metabolomics and lipidomics analysis by three MS-based platforms. Univariate, multivariate, and clustering analyses were used for selecting significant metabolic features. ROC curve analysis was based on a series of different binary classifications as well as the true-positive rate (sensitivity) and the false-positive rate (1-specificity).

RESULTS

GI cancers exhibited obvious metabolic perturbation compared with benign diseases. The differentiated metabolites of gastric cancer (GC) and colon cancer (CC) were targeted to same pathways but with different degrees of cellular metabolism reprogramming. The cancer-specific metabolites distinguished the malignant and benign, and classified the cancer types. We also applied this test to before- and after-surgery samples, wherein surgical resection significantly altered the blood-metabolic patterns. There were 15 metabolites significantly altered in GC and CC patients who underwent surgical treatment, and partly returned to normal conditions.

CONCLUSION

Blood-based metabolomics analysis is an efficient strategy for GI cancer screening, especially for malignant and benign diagnoses. The cancer-specific metabolic patterns process the potential for classifying tissue-of-origin in multi-cancer screening. Besides, the circulating metabolites for prognosis management of GI cancer is a promising area of research.

Metabolomic profiles to explore biomarkers of severe sarcopenia in older men: A pilot study

BACKGROUND

The pathophysiology of sarcopenia is complex and multifactorial; however, it has not yet been fully elucidated. Identifying metabolomic profiles may help clarify the mechanisms underlying sarcopenia.

OBJECTIVE

This pilot study explored potential noninvasive biomarkers of severe sarcopenia through metabolomic analysis in community-dwelling older men.

METHODS

Twenty older men (mean age: 81.9 ± 2.8 years) were selected from the Korean Frailty and Aging Cohort Study. Participants with severe sarcopenia (n = 10) were compared with non-sarcopenic, age- and body mass index-matched controls (n = 10). Severe sarcopenia was defined as low muscle mass, low muscle strength, and low physical performance using the Asian Working Group for Sarcopenia 2019 criteria. Non-targeted metabolomic profiling of plasma metabolites was performed using capillary electrophoresis time-of-flight mass spectrometry and absolute quantification was performed in target metabolites.

RESULTS

Among 191 plasma metabolic peaks, the concentrations of 10 metabolites significantly differed between severe sarcopenia group and non-sarcopenic controls. The plasma concentrations of L-alanine, homocitrulline, N-acetylserine, gluconic acid, N-acetylalanine, proline, and sulfotyrosine were higher, while those of 4-methyl-2-oxovaleric acid, 3-methyl-2-oxovaleric acid, and tryptophan were lower in participants with severe sarcopenia than in non-sarcopenic controls (all, p < 0.05). Among the 53 metabolites quantified as target metabolites, L-alanine (area under the receiver operating characteristic curve [AUC] = 0.760; p = 0.049), gluconic acid (AUC = 0.800; p = 0.023), proline (AUC = 0.785; p = 0.031), and tryptophan (AUC = 0.800; p = 0.023) determined the presence of severe sarcopenia.

CONCLUSIONS

Plasma metabolomic analysis demonstrated that L-alanine, gluconic acid, proline, and tryptophan may be potential biomarkers of severe sarcopenia. The identified metabolites can provide new insights into the underlying pathophysiology of severe sarcopenia and serve as the basis for preventive interventions.

Identification of circulating plasma ceramides as a potential sexually dimorphic biomarker of pancreatic cancer-induced cachexia

Background

Cancer patients who exhibit cachexia lose weight and have low treatment tolerance and poor outcomes compared to cancer patients without weight loss. Despite the clear increased risk for patients, diagnosing cachexia still often relies on self-reported weight loss. A reliable biomarker to identify patients with cancer cachexia would be a valuable tool to improve clinical decision making and identification of patients at risk of adverse outcomes.

Methods

Targeted metabolomics, that included panels of amino acids, tricarboxylic acids, fatty acids, acylcarnitines, and sphingolipids, were conducted on plasma samples from patients with confirmed pancreatic ductal adenocarcinoma (PDAC) with and without cachexia and control patients without cancer (n=10/group, equally divided by sex). Additional patient samples were analyzed (total n=95) and Receiver Operating Characteristic (ROC) analyses were performed to establish if any metabolite could effectively serve as a biomarker of cachexia.

Results

Targeted profiling revealed that cachectic patients had decreased circulating levels of three sphingolipids compared to either non-cachectic PDAC patients or patients without cancer. The ratio of C18-ceramide to C24-ceramide (C18:C24) outperformed a number of other previously proposed biomarkers of cachexia (area under ROC = 0.810). It was notable that some biomarkers, including C18:C24, were only altered in cachectic males.

Conclusions

Our findings identify C18:C24 as a potentially new biomarker of PDAC-induced cachexia that also highlight a previously unappreciated sexual dimorphism in cancer cachexia.

Cancer- and cardiac-induced cachexia: same fate through different inflammatory mediators?

BACKGROUND

Inflammation is widely recognized as the driving force of cachexia induced by chronic diseases; however, therapies targeting inflammation do not always reverse cachexia. Thus, whether inflammation per se plays an important role in the clinical course of cachectic patients is still a matter of debate.

AIMS

To give new insights into cachexia's pathogenesis and diagnosis, we performed a comprehensive literature search on the contribution of inflammatory markers to this syndrome, focusing on the noncommunicable diseases cancer and cardiovascular diseases.

METHODS

A systematic review was performed in PubMed using the keywords ("cancer" OR "cardiac" cachexia AND "human" OR "patient" AND "plasma" or "serum"). A total of 744 studies were retrieved and, from these, 206 were selected for full-text screening. In the end, 98 papers focusing on circulating biomarkers of cachexia were identified, which resulted in a list of 113 different mediators.

RESULTS

Data collected from the literature highlight the contribution of interleukin-6 (IL-6) and C-reactive protein (CRP) to cachexia, independently of the underlying condition. Despite not being specific, once the diagnosis of cachexia is established, CRP might help to monitor the effectiveness of anti-cachexia therapies. In cardiac diseases, B-type natriuretic peptide (BNP), renin, and obestatin might be putative markers of body wasting, whereas in cancer, growth differentiation factor (GDF) 15, transforming growth factor (TGF)-β1 and vascular endothelial growth factor (VEGF) C seem to be better markers of this syndrome. Independently of the circulating mediators, NF-κB and JAK/STAT signaling pathways play a key role in bridging inflammation with muscle wasting; however, therapies targeting these pathways were not proven effective for all cachectic patients.

CONCLUSION

The critical and integrative analysis performed herein will certainly feed future research focused on the better comprehension of cachexia pathogenesis toward the improvement of its diagnosis and the development of personalized therapies targeting specific cachexia phenotypes.

Quantitative Comparison of Statistical Methods for Analyzing Human Metabolomics Data

Emerging technologies now allow for mass spectrometry-based profiling of thousands of small molecule metabolites ('metabolomics') in an increasing number of biosamples. While offering great promise for insight into the pathogenesis of human disease, standard approaches have not yet been established for statistically analyzing increasingly complex, high-dimensional human metabolomics data in relation to clinical phenotypes, including disease outcomes. To determine optimal approaches for analysis, we formally compare traditional and newer statistical learning methods across a range of metabolomics dataset types. In simulated and experimental metabolomics data derived from large population-based human cohorts, we observe that with an increasing number of study subjects, univariate compared to multivariate methods result in an apparently higher false discovery rate as represented by substantial correlation between metabolites directly associated with the outcome and metabolites not associated with the outcome. Although the higher frequency of such associations would not be considered false in the strict statistical sense, it may be considered biologically less informative. In scenarios wherein the number of assayed metabolites increases, as in measures of nontargeted versus targeted metabolomics, multivariate methods performed especially favorably across a range of statistical operating characteristics. In nontargeted metabolomics datasets that included thousands of metabolite measures, sparse multivariate models demonstrated greater selectivity and lower potential for spurious relationships. When the number of metabolites was similar to or exceeded the number of study subjects, as is common with nontargeted metabolomics analysis of relatively small cohorts, sparse multivariate models exhibited the most-robust statistical power with more consistent results. These findings have important implications for metabolomics analysis in human disease.

Metabolomics studies on cachexia in patients with cancer: a scoping review protocol

INTRODUCTION

Cancer seriously threatens human health worldwide. Cancer cachexia is one of the life-threatening consequences that occurs commonly in patients with cancer, and severely worsens patient survival, prognosis and quality of life. Previous studies have demonstrated that cancer cachexia is closely related to differential metabolites and metabolic pathways based on metabolomics analysis. This scoping review protocol, therefore, aims to provide the strategy for a formal scoping review that will summarise the differential metabolites and related metabolic pathways of cachexia in patients with cancer.

METHODS AND ANALYSIS

The proposed scoping review will follow the Arksey and O'Malley's methodological framework, Levac et al's recommendations for applying this framework, and Peters' enhancements of the framework. The key information from the selected studies will be extracted, including author, year of publication, cachexia definition, country/origin, study design, setting, population and sample size, biological specimens, independent variables, independent variables' measure and statistical analysis. A summary of metabolites will be divided into several sections depending on the biological specimen. Differential metabolites will be compared between paired groups, and the number and names of related metabolic pathways will be counted and described. The reporting of this scoping review will be in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. This is a scoping review protocol and describes the planned review process and provides data examples extracted from a pilot study to confirm the feasibility of further investigation of the subject.

ETHICS AND DISSEMINATION

An ethical approval is not required for this scoping review protocol, nor for the scoping review. The results of this scoping review will be disseminated through publication in a peer-reviewed journal, or presentation at a national or international conference.

Metabolomics and its Applications in Cancer Cachexia

Cancer cachexia (CC) is a complicated metabolic derangement and muscle wasting syndrome, affecting 50-80% cancer patients. So far, molecular mechanisms underlying CC remain elusive. Metabolomics techniques have been used to study metabolic shifts including changes of metabolite concentrations and disturbed metabolic pathways in the progression of CC, and expand further fundamental understanding of muscle loss. In this article, we aim to review the research progress and applications of metabolomics on CC in the past decade, and provide a theoretical basis for the study of prediction, early diagnosis, and therapy of CC.

Metabolomics as an Important Tool for Determining the Mechanisms of Human Skeletal Muscle Deconditioning

Muscle deconditioning impairs both locomotor function and metabolic health, and is associated with reduced quality life and increased mortality rates. Despite an appreciation of the existence of phenomena such as muscle anabolic resistance, mitophagy, and insulin resistance with age and disease in humans, little is known about the mechanisms responsible for these negative traits. With the complexities surrounding these unknowns and the lack of progress to date in development of effective interventions, there is a need for alternative approaches. Metabolomics is the study of the full array of metabolites within cells or tissues, which collectively constitute the metabolome. As metabolomics allows for the assessment of the cellular metabolic state in response to physiological stimuli, any chronic change in the metabolome is likely to reflect adaptation in the physiological phenotype of an organism. This, therefore, provides a holistic and unbiased approach that could be applied to potentially uncover important novel facets in the pathophysiology of muscle decline in ageing and disease, as well as identifying prognostic markers of those at risk of decline. This review will aim to highlight the current knowledge and potential impact of metabolomics in the study of muscle mass loss and deconditioning in humans and will highlight key areas for future research.

Biomarkers related to fatty acid oxidative capacity are predictive for continued weight loss in cachectic cancer patients

BACKGROUND

Cachexia is characterized by a negative protein and energy balance leading to loss of adipose tissue and muscle mass. Cancer cachexia negatively impacts treatment tolerability and prognosis. Supportive interventions should be initiated as early as possible. Biomarkers for early prediction of continuing weight loss during the course of disease are currently lacking.

METHODS

In this pilot, observational, cross-sectional, case-control study, cachectic cancer patients undergoing systemic first-line cancer treatment were matched 2:1 with healthy controls according to age, gender and body mass index. Alterations in amino acid and energy metabolism, as indicated by acylcarnitine levels, were analysed using mass spectrometry in plasma samples (PS) and dried blood specimen (DBS). Welch's two-sample t-test was used for comparative analysis of metabolites between cancer patients and healthy matched controls and to identify the metabolomic profiles related to weight loss across different time points. A linear regression model was applied to correlate weight loss and single metabolites as predictor variables. Finally, metabolite pathway enrichment analyses were performed.

RESULTS

Eighteen cases (14 male and 4 female) and 36 paired controls were enrolled. There was a good correlation between baseline PS and DBS of healthy controls for the levels of most amino acids but not for acylcarnitine. Amino acid levels related to cancer metabolism were significantly altered in cancer patients compared with controls in both DBS and PS for arginine, citrulline, histidine and ornithine and in DBS only for asparagine, glutamine, methylhistidine, methionine, ornithine, serine, threonine and leucine/isoleucine. Metabolite enrichment analysis in PS of cancer patients revealed histidine metabolism activation (P = 0.0025). Baseline acylcarnitine analysis in DBS was indicative for alterations of the mitochondrial carnitine shuttle, related to β-oxidation: The ratio palmitoylcarnitine/acylcarnitine (Q2) and the ratio palmitoylcarnitine + octadecenoylcarnitine/acylcarnitine (Q3) were predictive for early weight loss (P < 0.0001) and weight loss during follow-up. Activation of tryptophan metabolism (P = 0.035) in DBS and PS and activation of serine/glycine metabolism (P = 0.017) in PS were also related to early weight loss and across successive time points.

CONCLUSIONS

We found alterations in amino acid levels most likely attributable to cancer metabolism itself in cancer patients compared with controls. Baseline DBS represent a valuable analyte to study energy metabolism related to cancer cachexia. Acylcarnitine patterns (Q2, Q3) predicted further weight loss in cachectic cancer patients undergoing systemic therapy, and pathway analyses indicated involvement of the serine/glycine and the tryptophan pathway in this condition. Validation in larger cohorts is warranted.

Systematic Survey of the Alteration of the Faecal Microbiota in Rats With Gastrointestinal Disorder and Modulation by Multicomponent Drugs

Gastrointestinal disorder (GID) is a global health disease which leads to heavy public medical burden. Disorders in the intestinal flora have been found in gastrointestinal disorder patients. However, the interaction between GID and the intestinal flora in faecal has not been studied comprehensively. In addition, multicomponent drugs represented by traditional Chinese medicine (TCM) are widely used for treating GID, but their modulation of the intestinal flora has not been investigated. Therefore, in this study, a high-throughput sequencing strategy was used to investigate alterations in the intestinal flora in a rat GID model, followed by an investigation of the modulation by a representative TCM, Xiaoerfupi (XEFP) granule. The results showed that in rats with GID, the relative abundances of Erysipelotrichaceae, Lachnospiraceae, Streptococcaceae increased and that of Ruminococcaceae decreased. At the macro level, the levels of LysoPC(16:0), LysoPC(20:2), LysoPC(15:0), LysoPC(20:2 (11Z, 14Z)), LysoPC(20:1), LysoPC(15:0), LysoPC(20:0) and LysoPE (0:0/20:0) in serum increased and levels of PC(36:4), PC(38:4), PC(o-36;4), PE (MonoMe(13,5)/MonoMe(11,5)) decreased. The imbalance of metabolites was restored by XEFP through ether lipid metabolism pathway. Increase in the phyla Firmicutes/Bacteroidetes (F/B) ratio of the GID rats was restored by XEFP as well. Moreover, XEFP can relief the symptoms of GID rats by increasing bacteria Ruminococcaceae and decreasing Streptococcaceae, Erysipelotrichaceae and Lachnospiraceae in faecal microbiota level. This study represents a comprehensive survey of the interaction between GID and the intestinal flora and a systematic evaluation of modulation by a multicomponent drug.

Metabolic Biomarkers for the Early Detection of Cancer Cachexia

Background: Cancer cachexia is a severe metabolic disorder characterized by progressive weight loss along with a dramatic loss in skeletal muscle and adipose tissue. Like cancer, cachexia progresses in stages starting with pre-cachexia to cachexia and finally to refractory cachexia. In the refractory stage, patients are no longer responsive to therapy and management of weight loss is no longer possible. It is therefore critical to detect cachexia as early as possible. In this study we applied a metabolomics approach to search for early biomarkers of cachexia.

Methods: Multi-platform metabolomics analyses were applied to the murine Colon-26 (C26) model of cachexia. Tumor bearing mice (n = 5) were sacrificed every other day over the 14-day time course and control mice (n = 5) were sacrificed every fourth day starting at day 2. Linear regression modeling of the data yielded metabolic trajectories that were compared with the trajectories of body weight and skeletal muscle loss to look for early biomarkers of cachexia.

Results: Weight loss in the tumor-bearing mice became significant at day 9 as did the loss of tibialis muscle. The loss of muscle in the gastrocnemius and quadriceps was significant at day 7. Reductions in amino acids were among the earliest metabolic biomarkers of cachexia. The earliest change was in methionine at day 4. Significant alterations in acylcarnitines and lipoproteins were also detected several days prior to weight loss.

Conclusion: The results of this study demonstrate that metabolic alterations appear well in advance of observable weight loss. The earliest and most significant alterations were found in amino acids and lipoproteins. Validation of these results in other models of cachexia and in clinical studies will pave the way for a clinical diagnostic panel for the early detection of cachexia. Such a panel would provide a tremendous advance in cachectic patient management and in the design of clinical trials for new therapeutic interventions.

High levels of modified ceramides are a defining feature of murine and human cancer cachexia

BACKGROUND

Cancer cachexia (CCx) is a multifactorial energy-wasting syndrome reducing the efficiency of anti-cancer therapies, quality of life, and survival of cancer patients. In the past years, most studies focused on the identification of tumour and host-derived proteins contributing to CCx. However, there is still a lack of studies addressing the changes in bioactive lipids. The aim of this study was to identify specific lipid species as a hallmark of CCx by performing a broad range lipid analysis of plasma from well-established CCx mouse models as well as cachectic and weight stable cancer patients.

METHODS

Plasma from non-cachectic (PBS-injected mice, NC26 tumour-bearing mice), pre-cachectic and cachectic mice (C26 and LLC tumour-bearing mice, Apc^Min/+ mutant mice), and plasma from weight stable and cachectic patients with gastrointestinal cancer, were analysed using the Lipidyzer™ platform. In total, 13 lipid classes and more than 1100 lipid species, including sphingolipids, neutral and polar glycerolipids, were covered by the analysis. Correlation analysis between specific lipid species and readouts of CCx were performed. Lipidomics data were confirmed by gene expression analysis of metabolic organs to analyse enzymes involved in sphingolipid synthesis and degradation.

RESULTS

A decrease in several lysophosphatidylcholine (LPC) species and an increase in numerous sphingolipids including sphingomyelins (SMs), ceramides (CERs), hexosyl-ceramides (HCERs) and lactosyl-ceramides (LCERs), were mutual features of CCx in both mice and cancer patients. Notably, sphingolipid levels gradually increased during cachexia development. Key enzymes involved in ceramide synthesis were elevated in liver but not in adipose, muscle, or tumour tissues, suggesting that ceramide turnover in the liver is a major contributor to elevated sphingolipid levels in CCx. LPC(16:1), LPC(20:3), SM(16:0), SM(24:1), CER(16:0), CER(24:1), HCER(16:0), and HCER(24:1) were the most consistently affected lipid species between mice and humans and correlated negatively (LPCs) or positively (SMs, CERs and HCERs) with the severity of body weight loss.

CONCLUSIONS

High levels of sphingolipids, specifically ceramides and modified ceramides, are a defining feature of murine and human CCx and may contribute to tissue wasting and skeletal muscle atrophy through the inhibition of anabolic signals. The progressive increase in sphingolipids during cachexia development supports their potential as early biomarkers for CCx.

1H-NMR Based Serum Metabolomics Identifies Different Profile between Sarcopenia and Cancer Cachexia in Ageing Walker 256 Tumour-Bearing Rats

Sarcopenia among the older population has been growing over the last few years. In addition, the incidence of cancers increases with age and, consequently, the development of cachexia related cancer. Therefore, the elucidation of the metabolic derangements of sarcopenia and cachexia are important to improve the survival and life quality of cancer patients. We performed the ¹H-NMR based serum metabolomics in adult (A) and ageing (S) Walker 256 tumour-bearing rats in different stages of tumour evolution, namely intermediated (Wi) and advanced (Wa). Among 52 serum metabolites that were identified, 21 were significantly increased in S and 14 and 19 decreased in the Wi and Wa groups, respectively. The most impacted pathways by this metabolic alteration were related by amino acid biosynthesis and metabolism, with an upregulation in S group and downregulation in Wi and Wa groups. Taken together, our results suggest that the increase in metabolic profile in ageing rats is associated with the higher muscle protein degradation that releases several metabolites, especially amino acids into the serum. On the other hand, we hypothesise that the majority of metabolites released by muscle catabolism are used by tumours to sustain rapid cell proliferation and tumorigenesis.