Circulating carnosine dipeptidase 1 associates with weight loss and poor prognosis in gastrointestinal cancer

Non-target metabolomics unravels the effect and mechanism of Lianpu Drink on spleen-stomach damp-heat syndrome

BACKGROUND

Lianpu Drink (LPY) is a classic prescription for treating spleen-stomach damp-heat syndrome (SSDHS), known for its ability to clear heat and eliminate dampness. However, the underlying mechanisms of LPY in treating SSDHS remain unclear.

OBJECTIVES

This study aims to use non-target metabolomics to unravel the effects and mechanisms of LPY on SSDHS.

METHODS

A metabolomics technique based on ultra-high-performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was used to identify the endogenous small-molecule metabolites in the urine of SSDHS model rats and find the metabolites associated with the LPY treatment of SSDHS. Furthermore, a network pharmacological analysis and molecular docking experiments were used to screen and validate the key metabolic pathways regulated by LPY.

RESULTS

LPY exerted therapeutic effects on SSDHS by increasing the levels of motilin and gastrin, reducing the rectal temperature, alleviating the pathological changes in gastric and colonic tissues, and regulating the metabolic pattern in SSDHS rats. A total of 25 different metabolites, including L-histidine, citric acid and isocitric acid, were identified as the potential biomarkers for SSDHS via metabolomics. Among them, 11 metabolites were substantially reversed by LPY, including L-histidine, citric acid, isocitric acid, pantothenic acid, homovanillic acid sulfate, hippuric acid, indole-3-carboxilic acid-O-sulphate, 6-hydroxy-5-methoxyindole glucuronide, 2-phenylethan-ol glucuronide, 3-hydroxydodecanedioic acid and 3-methoxy-4-hydroxy-phenylethyleneglyclol sulfate. The results of network pharmacological analysis and molecular docking experiments validated that LPY ameliorated SSDHS by regulating the citrate cycle and histidine metabolism.

CONCLUSION

We preliminarily investigated the effects and mechanisms of LPY on SSDHS at the level of endogenous small-molecule metabolites. Furthermore, this study provides a novel perspective for objectively evaluating the therapeutic effects, and exploring the mechanisms of Chinese medicinal formulas on SSDHS.

Profound perturbations are found in the proteome and metabolome in children with obesity after weight loss intervention

Background and aims

The mechanisms occur in children with obesity after lifestyle intervention remain poorly explained. Here, we investigated the serum proteomes and metabolomes of children with obesity who had undergone 30 days of weight loss intervention.

Methods and results

Serum samples and clinical parameters were collected before and after lifestyle alteration interventions. Proteomic and metabolomic profiling was used to identify the differentially expressed proteins and differentially abundant metabolites in response to weight loss intervention. Lifestyle alteration interventions significantly decreased BMI, waist circumference, hip circumference and body fat, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL) and high non-HDL cholesterol, but not TG and high-density lipoprotein cholesterol (HDL), in children with obesity. By comparing the multiomics data, we identified 43 proteins and 165 metabolites that were significantly differentially expressed in children with obesity before and after lifestyle alteration interventions. Using integrated -omics analysis, we obtained 7 KEGG pathways that were organically integrated based on the correlations between differentially expressed proteins (DEPs) and metabolites (DMs). Further interaction analysis identified 7 proteins as candidate DEPs and 9 metabolites as candidate DMs. Interestingly, we found that some of these candidate DEPs and candidate DMs were significantly correlated with clinical parameters.

Conclusion

Our results provide valuable proteome and metabolome data resources for better understanding weight loss-associated responses in children with obesity. In addition, we analyzed the number of significantly differentially expressed proteins and metabolites, shed new light on weight loss pathogenesis in children with obesity, and added potential therapeutic agents for obese children.

A proteomic classifier panel for early screening of colorectal cancer: a case control study

BACKGROUND

Diagnosis of colorectal cancer (CRC) during early stages can greatly improve patient outcome. Although technical advances in the field of genomics and proteomics have identified a number of candidate biomarkers for non-invasive screening and diagnosis, developing more sensitive and specific methods with improved cost-effectiveness and patient compliance has tremendous potential to help combat the disease.

METHODS

We enrolled three cohorts of 479 subjects, including 226 CRC cases, 197 healthy controls, and 56 advanced precancerous lesions (APC). In the discovery cohort, we used quantitative mass spectrometry to measure the expression profile of plasma proteins and applied machine-learning to select candidate proteins. We then developed a targeted mass spectrometry assay to measure plasma concentrations of seven proteins and a logistic regression classifier to distinguish CRC from healthy subjects. The classifier was further validated using two independent cohorts.

RESULTS

The seven-protein panel consisted of leucine rich alpha-2-glycoprotein 1 (LRG1), complement C9 (C9), insulin-like growth factor binding protein 2 (IGFBP2), carnosine dipeptidase 1 (CNDP1), inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3), serpin family A member 1 (SERPINA1), and alpha-1-acid glycoprotein 1 (ORM1). The panel classified CRC and healthy subjects with high accuracy, since the area under curve (AUC) of the training and testing cohort reached 0.954 and 0.958. The AUC of the two independent validation cohorts was 0.905 and 0.909. In one validation cohort, the panel had an overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 89.9%, 81.8%, 89.2%, and 82.9%, respectively. In another blinded validation cohort, the panel classified CRC from healthy subjects with a sensitivity of 81.5%, specificity of 97.9%, and overall accuracy of 92.0%. Finally, the panel was able to detect APC with a sensitivity of 49%.

CONCLUSIONS

This seven-protein classifier is a clear improvement compared to previously published blood-based protein biomarkers for detecting early-stage CRC, and is of translational potential to develop into a clinically useful assay.

Parathyroid hormone related protein (PTHrP) in patients with pancreatic carcinoma and overt signs of disease progression and host tissue wasting

BACKGROUND

Cancer-cachexia is a complex syndrome secondary to physiological mechanisms related to classical hormone and immune alterations, where contributions of neuro-endocrine involvement have been less evaluated. Therefore, the aim of our study was to explore relationships between PTHrP and whole body metabolism in patients with progressive pancreatic carcinoma; relevant to "fat tissue browning".

METHODS

Patient serum samples and clinical information were retrieved from earlier translational projects (1995-2005), at Sahlgrenska University Hospital in Gothenburg. Blood PTHrP levels were determined at Harvard medical School (2014). Patient data included: medical history, clinical laboratory tests, food diaries, resting metabolic expenditure, body composition, exercise capacity, Health-Related Quality of Life (SF-36) and mental disorders (HAD-scales).

RESULTS

Serum PTHrP was detectable in 17 % of all samples without significance to tumor stage. PTHrP-negativity at inclusion remained during follow-up. Mean PTHrP concentration was 262±274 pg/ml, without sex difference and elevation over time. PTHrP-positive and negative patients experienced similar body weight loss (%) at inclusion, with a trend to deviate at follow ups (16.8±8.2% vs. 13.1±8.2%, p<0.06), where PTHrP concentrations showed correlations to weight loss, handgrip strength and Karnofsky performance, without difference in exercise capacity. PTHrP-positivity was related to increased whole body fat oxidation (p<0.006-0.01) and reduced carbohydrate oxidation (p<0.01-0.03), independently of peripheral lipolysis. Metabolic alterations in PTHrP-positive patients were related to reduced Health Related Quality of life (SF: p<0.08, MH: p<0.02), and increased anxiety and depression (HAD 1-7: p<0.004; HAD 8-14: p<0.008).

CONCLUSION

Serum PTHrP positivity in patients with pancreatic carcinoma was related to altered whole body oxidative metabolism; perhaps induced by "browning" of fat cells?

Construction of 2DE Patterns of Plasma Proteins: Aspect of Potential Tumor Markers

The use of tumor markers aids in the early detection of cancer recurrence and prognosis. There is a hope that they might also be useful in screening tests for the early detection of cancer. Here, the question of finding ideal tumor markers, which should be sensitive, specific, and reliable, is an acute issue. Human plasma is one of the most popular samples as it is commonly collected in the clinic and provides noninvasive, rapid analysis for any type of disease including cancer. Many efforts have been applied in searching for “ideal” tumor markers, digging very deep into plasma proteomes. The situation in this area can be improved in two ways—by attempting to find an ideal single tumor marker or by generating panels of different markers. In both cases, proteomics certainly plays a major role. There is a line of evidence that the most abundant, so-called “classical plasma proteins”, may be used to generate a tumor biomarker profile. To be comprehensive these profiles should have information not only about protein levels but also proteoform distribution for each protein. Initially, the profile of these proteins in norm should be generated. In our work, we collected bibliographic information about the connection of cancers with levels of “classical plasma proteins”. Additionally, we presented the proteoform profiles (2DE patterns) of these proteins in norm generated by two-dimensional electrophoresis with mass spectrometry and immunodetection. As a next step, similar profiles representing protein perturbations in plasma produced in the case of different cancers will be generated. Additionally, based on this information, different test systems can be developed.

Gene Ontology (GO)-Driven Inference of Candidate Proteomic Markers Associated with Muscle Atrophy Conditions

Skeletal muscle homeostasis is essential for the maintenance of a healthy and active lifestyle. Imbalance in muscle homeostasis has significant consequences such as atrophy, loss of muscle mass, and progressive loss of functions. Aging-related muscle wasting, sarcopenia, and atrophy as a consequence of disease, such as cachexia, reduce the quality of life, increase morbidity and result in an overall poor prognosis. Investigating the muscle proteome related to muscle atrophy diseases has a great potential for diagnostic medicine to identify (i) potential protein biomarkers, and (ii) biological processes and functions common or unique to muscle wasting, cachexia, sarcopenia, and aging alone. We conducted a meta-analysis using gene ontology (GO) analysis of 24 human proteomic studies using tissue samples (skeletal muscle and adipose biopsies) and/or biofluids (serum, plasma, urine). Whilst there were few similarities in protein directionality across studies, biological processes common to conditions were identified. Here we demonstrate that the GO analysis of published human proteomics data can identify processes not revealed by single studies. We recommend the integration of proteomics data from tissue samples and biofluids to yield a comprehensive overview of the human skeletal muscle proteome. This will facilitate the identification of biomarkers and potential pathways of muscle-wasting conditions for use in clinics.

Metabolomic Biomarker Candidates for Skeletal Muscle Loss in the Collagen-Induced Arthritis (CIA) Model

There is no consensus for diagnosis or treatment of RA muscle loss. We aimed to investigate metabolites in arthritic mice urine as biomarkers of muscle loss. DBA1/J mice comprised collagen-induced arthritis (CIA) and control (CO) groups. Urine samples were collected at 0, 18, 35, 45, 55, and 65 days of disease and subjected to nuclear magnetic resonance spectroscopy. Metabolites were identified using Chenomx and Birmingham Metabolite libraries. The statistical model used principal component analysis, partial least-squares discriminant analysis, and partial least-squares regression analysis. Linear regression and Fisher's exact test via the MetaboAnalyst website were performed (VIP-score). Nearly 100 identified metabolites had CIA vs. CO and disease time-dependent differences (p < 0.05). Twenty-eight metabolites were muscle-associated: carnosine (VIPs 2.8 × 10²) and succinyl acetone (VIPs 1.0 × 10) showed high importance in CIA vs. CO models at day 65; CIA pair analysis showed histidine (VIPs 1.2 × 10²) days 55 vs. 65, histamine (VIPs 1.1 × 10²) days 55 vs. 65, and L-methionine (VIPs 1.1 × 10²) days 0 vs. 18. Carnosine was fatigue- (0.039) related, creatine was food intake- (-0.177) and body weight- (-0.039) related, and both metabolites were clinical score- (0.093; 0.050) and paw edema- (0.125; 0.026) related. Therefore, muscle metabolic alterations were detected in arthritic mice urine, enabling further validation in RA patient's urine, targeting prognosis, diagnosis, and monitoring of RA-mediated muscle loss.

Metabolomic Biomarker Candidates for Skeletal Muscle Loss in the Collagen-Induced Arthritis (CIA) Model

There is no consensus for diagnosis or treatment of RA muscle loss. We aimed to investigate metabolites in arthritic mice urine as biomarkers of muscle loss. DBA1/J mice comprised collagen-induced arthritis (CIA) and control (CO) groups. Urine samples were collected at 0, 18, 35, 45, 55, and 65 days of disease and subjected to nuclear magnetic resonance spectroscopy. Metabolites were identified using Chenomx and Birmingham Metabolite libraries. The statistical model used principal component analysis, partial least-squares discriminant analysis, and partial least-squares regression analysis. Linear regression and Fisher’s exact test via the MetaboAnalyst website were performed (VIP-score). Nearly 100 identified metabolites had CIA vs. CO and disease time-dependent differences (p < 0.05). Twenty-eight metabolites were muscle-associated: carnosine (VIPs 2.8 × 102) and succinyl acetone (VIPs 1.0 × 10) showed high importance in CIA vs. CO models at day 65; CIA pair analysis showed histidine (VIPs 1.2 × 102) days 55 vs. 65, histamine (VIPs 1.1 × 102) days 55 vs. 65, and L-methionine (VIPs 1.1 × 102) days 0 vs. 18. Carnosine was fatigue- (0.039) related, creatine was food intake- (−0.177) and body weight- (−0.039) related, and both metabolites were clinical score- (0.093; 0.050) and paw edema- (0.125; 0.026) related. Therefore, muscle metabolic alterations were detected in arthritic mice urine, enabling further validation in RA patient’s urine, targeting prognosis, diagnosis, and monitoring of RA-mediated muscle loss.

Biomarkers for Cancer Cachexia: A Mini Review

Cancer cachexia is a common condition in many cancer patients, particularly those with advanced disease. Cancer cachexia patients are generally less tolerant to chemotherapies and radiotherapies, largely limiting their treatment options. While the search for treatments of this condition are ongoing, standards for the efficacy of treatments have yet to be developed. Current diagnostic criteria for cancer cachexia are primarily based on loss of body mass and muscle function. However, these criteria are rather limiting, and in time, when weight loss is noticeable, it may be too late for treatment. Consequently, biomarkers for cancer cachexia would be valuable adjuncts to current diagnostic criteria, and for assessing potential treatments. Using high throughput methods such as "omics approaches", a plethora of potential biomarkers have been identified. This article reviews and summarizes current studies of biomarkers for cancer cachexia.

Influencing Factors and Effects of Treatment on Quality of Life in Patients With Gastric Cancer-A Systematic Review

Background: Gastric cancer (GC) is one of the leading causes of death worldwide. It is associated with several disease-related impairments contributing to the psycho-social burden of those patients, such as deterioration of well-being and overall quality of life (QOL). The aim of this study is to present the wide range of factors potentially impacting patients' overall well-being and possible preventive interventions. Methods: This systematic review was conducted in October 2020 with a search in the PubMed, MedLine, PsycInfo, and Google Scholar databases. We used the keywords "gastric cancer," "gastric neoplasm," and each of them combined with "quality of life," "depression," and "anxiety" to identify all relevant articles reporting about potential impact factors influencing the overall well-being of patients suffering from gastric cancer. Results: Finally, 125,490 articles were found, of which 125,431 were excluded in several steps of screening. Inclusion criteria were studies carried out on human ≥18 years of age, studies in English or German language, clinical trials, registry-based studies, cohort studies, population-based studies, and certain titles and abstracts. After screening for eligibility 35 potential factors influencing overall well-being in patients with GC were identified and classified into 9 important categories: genetic condition, treatment method, blood markers, nutritional status, daily living, state of health, mental state, supportive care, and alternative treatment. Conclusion: Since various factors are involved in the development of patients' overall well-being, timely treatment of psycho-social impairments by physicians and psychologists is of enormous importance. Preventing psycho-social burden by improving patients' QOL should be of high importance in the treatment regimen of patients with GC.

Proteomic Analysis of Serum Differentially Expressed Proteins Between Allergic Bronchopulmonary Aspergillosis and Asthma

BACKGROUND

Allergic bronchopulmonary aspergillosis (ABPA) constantly develops in asthmatics, which has not been fully investigated.

OBJECTIVES

This study aimed to investigate serum differentially expressed proteins (DEPs) between ABPA and asthma using the new approach isobaric tags by relative and absolute quantitation (iTRAQ).

METHODS

Each 16 serum samples from ABPA or asthmatic subjects were pooled and screened using iTRAQ. After bioinformatic analysis, five candidate DEPs were validated in the enlarged serum samples from additional 21 ABPA, 31 asthmatic and 20 healthy subjects using ELISA. A receiver operating characteristic (ROC) curve was used to estimate the diagnostic power of carnosine dipeptidase 1 (CNDP1).

RESULTS

A total of 29 DEPs were screened out between ABPA and asthmatic groups. Over half of them were enriched in proteolysis and regulation of protein metabolic process. Further verification showed serum levels of immunoglobulin heavy constant gamma 1, α-1-acid glycoprotein 1, corticosteroid-binding globulin and vitronectin were neither differentially altered between ABPA and asthma nor consistent with the proteomic analysis. Only serum CNDP1 was significantly decreased in ABPA patients, compared with asthmatics and healthy controls (P < 0.01 and P < 0.05). The ROC analysis determined 10.73 ng/mL as the cutoff value of CNDP1, which could distinguish ABPA among asthmatics (AUC 0.770, 95%CI 0.632-0.875, P < 0.001).

CONCLUSIONS

This study firstly identified serological DEPs between ABPA and asthma using the new technique iTRAQ. Serum CNDP1 might assist the differential diagnosis of ABPA from asthma and serve as a new pathogenetic factor in fungal colonization and sensitization.

Preliminary study of the urinary proteome in Li and Han ethnic individuals from Hainan

Biomarkers indicate changes associated with disease. Blood is relatively stable due to the homeostatic mechanisms of the body; however, urine accumulates metabolites from changes in the body, making it a better source for early biomarker discovery. The Li ethnic group is a unique minority ethnic group that has only lived on Hainan Island for approximately 5,000 years. Studies have shown that various specific genetic variations are different between the Li and Han ethnic groups. However, whether the urinary proteome between these two ethnic groups is significantly different remains unknown. In this study, differential urinary proteins were identified in the Li and Han ethnic groups using liquid chromatography tandem mass spectrometry (LC-MS/MS). In total, 1,555 urinary proteins were identified. Twenty-five of the urinary proteins were statistically significantly different, 16 of which have been previously reported to be biomarkers of many diseases, and that these significantly different proteins were caused by ethnic differences rather than random differences. Ethnic group differences may be an influencing factor in urine proteome studies and should be considered when human urine samples are used for biomarker discovery.

Nutrigenomics: Opportunities & challenges for public health nutrition

The hierarchical information flow through DNA-RNA-protein-metabolite collectively referred to as ‘molecular fingerprint’ defines both health and disease. Environment and food (quality and quantity) are the key factors known to affect the health of an individual. The fundamental concepts are that the transition from a healthy condition to a disease phenotype must occur by concurrent alterations in the genome expression or by differences in protein synthesis, function and metabolites. In other words, the dietary components directly or indirectly modulate the molecular fingerprint and understanding of which is dealt with nutrigenomics. Although the fundamental principles of nutrigenomics remain similar to that of traditional research, a collection of comprehensive targeted/untargeted data sets in the context of nutrition offers the unique advantage of understanding complex metabolic networks to provide a mechanistic understanding of data from epidemiological and intervention studies. In this review the challenges and opportunities of nutrigenomic tools in addressing the nutritional problems of public health importance are discussed. The application of nutrigenomic tools provided numerous leads on biomarkers of nutrient intake, undernutrition, metabolic syndrome and its complications. Importantly, nutrigenomic studies also led to the discovery of the association of multiple genetic polymorphisms in relation to the variability of micronutrient absorption and metabolism, providing a potential opportunity for further research toward setting personalized dietary recommendations for individuals and population subgroups.

Carnosinase concentration, activity, and CNDP1 genotype in patients with type 2 diabetes with and without nephropathy

This study assessed if serum carnosinase (CNDP1) activity and concentration in patients with type 2 diabetes mellitus (T2D) with diabetic nephropathy (DN) differs from those without nephropathy. In a cross-sectional design 127 patients with T2D with DN ((CTG)₅ homozygous patients n = 45) and 145 patients with T2D without nephropathy ((CTG)₅ homozygous patients n = 47) were recruited. Univariate and multivariate regression analyses were performed to predict factors relevant for serum CNDP1 concentration. CNDP1 (CTG)₅ homozygous patients with T2D with DN had significantly lower CNDP1 concentrations (30.4 ± 18.3 vs 51.2 ± 17.6 µg/ml, p < 0.05) and activity (1.25 ± 0.5 vs 2.53 ± 1.1 µmol/ml/h, p < 0.05) than those without nephropathy. This applied for patients with DN on the whole, irrespective of (CTG)₅ homozygosity. In the multivariate regression analyses, lower serum CNDP1 concentrations correlated with impaired renal function and to a lesser extend with the CNDP1 genotype (95% CI of regression coefficients: eGFR: 0.10-1.94 (p = 0.001); genotype: - 0.05 to 5.79 (p = 0.055)). Our study demonstrates that serum CNDP1 concentrations associate with CNDP1 genotype and renal function in patients with T2D. Our data warrant further studies using large cohorts to confirm these findings and to delineate the correlation between low serum CNDP1 concentrations and renal function deterioration in patients with T2D.

Serum Carnosinase-1 and Albuminuria Rather than the CNDP1 Genotype Correlate with Urinary Carnosinase-1 in Diabetic and Nondiabetic Patients with Chronic Kidney Disease

BACKGROUND

Carnosinase-1 (CN-1) can be detected in 24 h urine of healthy individuals and patients with type 2 diabetes (T2DM). We aimed to assess whether urinary CN-1 is also reliably measured in spot urine and investigated its association with renal function and the albumin/creatinine ratio (ACR). We also assessed associations between the CNDP1 (CTG) _n genotype and CN-1 concentrations in serum and urine.

METHODS

Patients with T2DM (n = 85) and nondiabetic patients with chronic kidney disease (CKD) (n = 26) stratified by albuminuria (ACR ≤ 300 mg/g or ACR > 300 mg/g) recruited from the nephrology clinic and healthy subjects (n = 24) were studied.

RESULTS

Urinary CN-1 was more frequently detected and displayed higher concentrations in patients with ACR > 300 mg/g as compared to those with ACR ≤ 300 mg/g irrespective of the baseline disease (T2DM: 554 ng/ml [IQR 212-934 ng/ml] vs. 31 ng/ml [IQR 31-63 ng/ml] (p < 0.0001) and nondiabetic CKD: 197 ng/ml [IQR 112-739] vs. 31 ng/ml [IQR 31-226 ng/ml] (p = 0.015)). A positive correlation between urinary CN-1 and ACR was found (r = 0.68, p < 0.0001). Multivariate linear regression analysis revealed that ACR and serum CN-1 concentrations but not eGFR or the CNDP1 genotype are independent predictors of urinary CN-1, explaining 47% of variation of urinary CN-1 concentrations (R ² = 0.47, p < 0.0001).

CONCLUSION

These results confirm and extend previous findings on urinary CN-1 concentrations, suggesting that assessment of CN-1 in spot urine is as reliable as in 24 h urine and may indicate that urinary CN-1 in macroalbuminuric patients is primarily serum-derived and not locally produced.

Current applications of antibody microarrays

The concept of antibody microarrays is one of the most versatile approaches within multiplexed immunoassay technologies. These types of arrays have increasingly become an attractive tool for the exploratory detection and study of protein abundance, function, pathways, and potential drug targets. Due to the properties of the antibody microarrays and their potential use in basic research and clinical analytics, various types of antibody microarrays have already been developed. In spite of the growing number of studies utilizing this technique, few reviews about antibody microarray technology have been presented to reflect the quality and future uses of the generated data. In this review, we provide a summary of the recent applications of antibody microarray techniques in basic biology and clinical studies, providing insights into the current trends and future of protein analysis.

Serum and urine metabolomics study reveals a distinct diagnostic model for cancer cachexia

BACKGROUND

Cachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance-based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model.

METHODS

Eighty-four cancer cachexia patients, 33 pre-cachectic patients, 105 weight-stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model.

RESULTS

Forty-six cancer cachexia patients, 22 pre-cachectic patients, 68 weight-stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre-cachectic patients, 37 weight-stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age-matched and sex-matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = -400.53 - 481.88 × log(Carnosine) -239.02 × log(Leucine) + 383.92 × log(Phenyl acetate), and the result showed 94.64% accuracy in the validation set.

CONCLUSIONS

This metabolomics study revealed a distinct metabolic profile of cancer cachexia and established and validated a diagnostic model. This research provided a feasible diagnostic tool for identifying at-risk populations through the detection of serum metabolites.

An analysis of the types of recently published research in the field of cachexia

Introduction Cachexia is a common complication of many and varied chronic disease processes, yet it has received very little attention as an area of clinical research effort until recently. We sought to survey the contemporary literature on published research into cachexia to define where it is being published and the proportion of output classified into the main types of research output. Methods I searched the PubMed listings under the topic research term "cachexia" and related terms for articles published in the calendar years of 2015 and 2016, regardless of language. Searches were conducted and relevant papers extracted by two observers, and disagreements were resolved by consensus. Results There were 954 publications, 370 of which were review articles or commentaries, 254 clinical observations or non-randomised trials, 246 original basic science reports and only 26 were randomised controlled trials. These articles were published in 478 separate journals but with 36% of them being published in a core set of 23 journals. The H-index of these papers was 25 and there were 147 papers with 10 or more citations. Of the top 100 cited papers, 25% were published in five journals. Of the top cited papers, 48% were review articles, 18% were original basic science, and 7% were randomised clinical trials. Discussion This analysis shows a steady but modest increase in publications concerning cachexia with a strong pipeline of basic science research but still a relative lack of randomised clinical trials, with none exceeding 1000 patients. Research in cachexia is still in its infancy, but the solid basic science effort offers hope that translation into randomised controlled clinical trials may eventually lead to effective therapies for this troubling and complex clinical disease process.

The prognostic role of nutrition risk score (NRS) in patients with metastatic or recurrent esophageal squamous cell carcinoma (ESCC)

The purpose of this study was to elucidate the prognostic value of nutritional risk score (NRS) in patients with metastatic or recurrent ESCC. A total of 187 patients who undergoing S1 based or paclitaxel based salvage chemotherapy were enrolled in this retrospective study. Nutritional status was evaluated by NRS. The relationship between NRS and clinicopathological variables and post-treatment outcomes were assessed by univariate and multivariate analysis. NRS was significantly associated with weight loss (P<0.001), BMI (P<0.001), chemotherapy regimens (P=0.038) and treatment response (P=0.013). The Kaplan-Meier survival curves indicated that patients with NRS ≥ 3 had worse overall survival (OS) compared to patients with NRS < 3 (P<0.001). Multivariable regression revealed that weight loss, NRS and treatment response were three prognostic factors (P<0.05). These results suggest that NRS is a promising indicator of poor prognosis in patients with metastatic or recurrent ESCC who received S1 based or paclitaxel based salvage chemotherapy.

Biomarkers of cancer cachexia

Cachexia is a complex multifactorial syndrome, characterized by loss of skeletal muscle and fat mass, which affects the majority of advanced cancer patients and is associated with poor prognosis. Interestingly, reversing muscle loss in animal models of cancer cachexia leads to prolong survival. Therefore, detecting cachexia and maintaining muscle mass represent a major goal in the care of cancer patients. However, early diagnosis of cancer cachexia is currently limited for several reasons. Indeed, cachexia development is variable according to tumor and host characteristics. In addition, safe, accessible and non-invasive tools to detect skeletal muscle atrophy are desperately lacking in clinical practice. Finally, the precise molecular mechanisms and the key players involved in cancer cachexia remain poorly characterized. The need for an early diagnosis of cancer cachexia supports therefore the quest for a biomarker that might reflect skeletal muscle atrophy process. Current research offers different promising ways to identify such a biomarker. Initially, the quest for a biomarker of cancer cachexia has mostly focused on mediators of muscle atrophy, produced by both tumor and host, in an attempt to define new therapeutic approaches. In another hand, molecules released by the muscle into the circulation during the atrophy process have been also considered as potential biomarkers. More recently, several "omics" studies are emerging to identify new muscular or circulating markers of cancer cachexia. Some genetic markers could also contribute to identify patients more susceptible to develop cachexia. This article reviews our current knowledge regarding potential biomarkers of cancer cachexia.

Omics and cachexia

PURPOSE OF REVIEW

The purpose of this review is to recapture recent advances in cachexia-related diseases, mainly cancer cachexia, and treatment using genomic, transcriptomics, proteomic, and metabolomics-related techniques.

RECENT FINDINGS

From recent studies in the cancer cachexia field it is clear that the tumor has a direct effect on distant organs via its secretome. The affected pathways on the other hand were largely known from earlier studies with changes in energy-related pathways (mainly lipid metabolism) and the protein degradation pathways. Treatment-oriented studies use mostly rodent models and in-vivo cultures and it is too early for human studies.

SUMMARY

Omics tools are powerful if used in the right way. Omics research has identified the tumor as an important player in cancer cachexia and some interesting novel treatments have been found in experimental models.

The Plasma Proteome Is Associated with Anthropometric Status of Undernourished Nepalese School-Aged Children

Background: Malnutrition affects body growth, size, and composition of children. Yet, few functional biomarkers are known to be associated with childhood morphology.

Objective: This cross-sectional study examined associations of anthropometric indicators of height, musculature, and fat mass with plasma proteins by using proteomics in a population cohort of school-aged Nepalese children.

Methods: Height, weight, midupper arm circumference (MUAC), triceps and subscapular skinfolds, upper arm muscle area (AMA), and arm fat area (AFA) were assessed in 500 children 6–8 y of age. Height-for-age z scores (HAZs), weight-for-age z scores (WAZs), and body mass index–for-age z scores (BAZs) were derived from the WHO growth reference. Relative protein abundance was quantified by using tandem mass spectrometry. Protein-anthropometry associations were evaluated by linear mixed-effects models and identified as having a false discovery rate (q) <5%.

Results: Among 982 proteins, 1, 10, 14, and 17 proteins were associated with BAZ, HAZ, MUAC, and AMA, respectively (q < 0.05). Insulin-like growth factor (IGF)-I, 2 IGF-binding proteins, and carnosinase-1 were associated with both HAZ and AMA. Proteins involved in nutrient transport, activation of innate immunity, and bone mineralization were associated with HAZ. Several extracellular matrix proteins were positively associated with AMA alone. The proteomes of MUAC and AMA substantially overlapped, whereas no proteins were associated with AFA or triceps and subscapular skinfolds. Myosin light-chain kinase, possibly reflecting leakage from muscle, was inversely associated with BAZ. The proteome of WAZ was the largest (n = 33) and most comprehensive, including proteins involved in neural development and oxidative stress response, among others.

Conclusions: Plasma proteomics confirmed known biomarkers of childhood growth and revealed novel proteins associated with lean mass in chronically undernourished children. Identified proteins may serve as candidates for assessing growth and nutritional status of children in similar undernourished settings. The antenatal micronutrient supplementation trial yielding the study cohort of children was registered at clinicaltrials.gov as NCT00115271.

PDGFB, a new candidate plasma biomarker for venous thromboembolism: results from the VEREMA affinity proteomics study

High-throughput affinity plasma proteomic profiling can identify candidate plasma biomarkers for VTE. Elevated plasma PDGFB levels are identified as associated with VTE in 2 independent case control studies.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.