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.

Questions remain about the relationship between type 2 diabetes mellitus (type 2 DM) and the risk of hepatocellular carcinoma (HCC), especially in patients with chronic liver diseases. We carried out a meta-analysis of cohort studies to explore these issues. We searched PubMed and EMBASE for studies on the association between type 2 DM and the risk of HCC through 30 September 2013. We included patients with chronic liver diseases. Summary relative risks with their corresponding 95% confidence intervals (CIs) were calculated using a random-effects model. A total of 21 cohort studies with 24 reports were included in our analysis. After a median duration of follow-up of 6.4 years, a total of 2528 HCC cases were identified in 35 202 participants. The summary relative risk of HCC with type 2 DM was 1.86 (95% CI 1.49-2.31) for patients with chronic liver disease, 1.90 (95% CI 1.37-2.63) for patients with hepatitis C virus infection, 1.93 (95% CI 1.35-2.76) for patients with cirrhosis, and 1.69 (95% CI 0.97-2.92) for patients with hepatitis B virus infection. Subgroup analyses indicated that the positive associations were independent of geographic location, duration of follow-up, and confounding factors such as smoking, alcohol use, and body mass index (BMI). Hepatitis C virus-infected or cirrhotic patients with the concomitant presence of type 2 DM would have a higher risk of developing HCC than those without DM. Therefore, these patients require more active monitoring of the development of HCC.

Cancer cachexia is a multifactorial syndrome affecting the skeletal muscle. Previous clinical trials showed that treatment with MEK inhibitor selumetinib resulted in skeletal muscle anabolism. However, it is conflicting that MAPK/ERK pathway controls the mass of the skeletal muscle. The current study investigated the therapeutic effect and mechanisms of selumetinib in amelioration of cancer cachexia. The classical cancer cachexia model was established via transplantation of CT26 colon adenocarcinoma cells into BALB/c mice. The effect of selumetinib on body weight, tumor growth, skeletal muscle, food intake, serum proinflammatory cytokines, E3 ligases, and MEK/ERK–related pathways was analyzed. Two independent experiments showed that 30 mg/kg/d selumetinib prevented the loss of body weight in murine cachexia mice. Muscle wasting was attenuated and the expression of E3 ligases, MuRF1 and Fbx32, was inhibited following selumetinib treatment of the gastrocnemius muscle. Furthermore, selumetinib efficiently reduced tumor burden without influencing the cancer cell proliferation, cumulative food intake, and serum cytokines. These results indicated that the role of selumetinib in attenuating muscle wasting was independent of cancer burden. Detailed analysis of the mechanism revealed AKT and mTOR were activated, while ERK, FoxO3a, and GSK3β were inhibited in the selumetinib -treated cachexia group. These indicated that selumetinib effectively prevented skeletal muscle wasting in cancer cachexia model through ERK inhibition and AKT activation in gastrocnemius muscle via cross-inhibition. The study not only elucidated the mechanism of MEK/ERK inhibition in skeletal muscle anabolism, but also validated selumetinib therapy as an effective intervention against cancer cachexia. Mol Cancer Ther; 16(2); 334–43. ©2016 AACR.

The effects of regular organic solvents on the metabolic activities of various human cytochromes P450 (P450s) have been reported. However, very little is known about their influence on metabolic activities mediated by P450s in the rat liver microsomes (RLM). The purpose of this study was to investigate the effects of organic solvents such as methanol, acetonitrile, dimethyl sulfoxide (DMSO), acetone, and ethanol on CYP1A, CYP2C, CYP2D, CYP2E, and CYP3A-mediated metabolism using RLM. The results showed that the activities of most rat P450 enzymes appeared to be organic solvent-dependent, and the metabolism of the tested probes were remarkably reduced when the concentration of organic solvents was up to 5% v/v, whereas most organic solvents demonstrated no significant interference when the concentration was below 1%, with the exception of DMSO. In addition, organic solvents exhibited different inhibitory effects, for example, CYP2D and CYP2E showed a significant reduction of activities at lower concentrations of organic solvents. Hence, this phenomenon should be taken into consideration when designing in vitro metabolism studies of new chemical entities. Therefore, we recommend acetonitrile as the most suitable solvent for RLM incubations, and the content of organic solvent should be kept lower than 1% v/v.