Development of a lipid metabolism-related gene model to predict prognosis in patients with pancreatic cancer

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Multimodal Imaging of Pancreatic Cancer Microenvironment in Response to an Antiglycolytic Drug

Lipid metabolism and glycolysis play crucial roles in the progression and metastasis of cancer, and the use of 3-bromopyruvate (3-BP) as an antiglycolytic agent has shown promise in killing pancreatic cancer cells. However, developing an effective strategy to avoid chemoresistance requires the ability to probe the interaction of cancer drugs with complex tumor-associated microenvironments (TAMs). Unfortunately, no robust and multiplexed molecular imaging technology is currently available to analyze TAMs. In this study, the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody-functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer (PC) is demonstrated. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. These multimodal imaging techniques include surface-enhanced Raman spectroscopy (SERS), immunohistochemistry (IHC), polarized light microscopy, second harmonic generation (SHG) microscopy, fluorescence lifetime imaging microscopy (FLIM), and untargeted liquid chromatography and mass spectrometry (LC-MS) analysis. The study reveals the efficacy of 3-BP in treating pancreatic cancer and identifies drug treatment-induced lipid species remodeling and associated pathways through bioinformatics analysis.

Hepatic transcript profiling in beef cattle: Effects of rumen-protected niacin supplementation

The objective of our study was to assess the effect of rumen-protected niacin supplementation on the transcriptome of liver tissue in growing Angus × Simmental steers and heifers through RNA-seq analysis. Consequently, we wanted to assess the known role of niacin in the physiological processes of vasodilation, detoxification, and immune function in beef hepatic tissue. Normal weaned calves (~8 months old) were provided either a control diet or a diet supplemented with rumen-protected niacin (6 g/hd/d) for a 30-day period, followed by a liver biopsy. We observed a significant list of changes at the transcriptome level due to rumen-protected niacin supplementation. Several metabolic pathways revealed potential positive effects to the animal's liver metabolism due to administration of rumen-protected niacin; for example, a decrease in lipolysis, apoptosis, inflammatory responses, atherosclerosis, oxidative stress, fibrosis, and vasodilation-related pathways. Therefore, results from our study showed that the liver transcriptional machinery switched several metabolic pathways to a condition that could potentially benefit the health status of animals supplemented with rumen-protected niacin. In conclusion, based on the results of our study, we can suggest the utilization of rumen-protected niacin supplementation as a nutritional strategy could improve the health status of growing beef cattle in different beef production stages, such as backgrounding operations or new arrivals to a feedlot.

Establishment and validation of a ubiquitination-related gene signature associated with prognosis in pancreatic duct adenocarcinoma

Background

Patients with pancreatic duct adenocarcinoma (PDAC) have varied prognoses that depend on numerous variables. However, additional research is required to uncover the latent impact of ubiquitination-related genes (URGs) on determining PDAC patients' prognoses.

Methods

The URGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO) regression analysis of data from TCGA-PAAD. Verification analyses were conducted across TCGA-PAAD, GSE57495 and ICGC-PACA-AU to show the robustness of the signature. RT-qPCR was used to verify the expression of risk genes. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool.

Results

The URGs signature, comprised of three genes, was developed and was shown to be highly correlated with the prognoses of PAAD patients. The nomogram was established by combining the URGs signature with clinicopathological characteristics. We discovered that the URGs signature was remarkably superior than other individual predictors (age, grade, T stage, et al). Also, the immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, and StromalScores were elevated in the low-risk group. The immune cells that infiltrated the tissues were different between the two groups, as did the expression of immune-related genes.

Conclusion

The URGs signature could act as the biomarker of prognosis and selecting appropriate therapeutic drugs for PDAC patients.

Metabolic variation and oxidative stress response of blue mussels (Mytilus sp.) perturbed by norfloxacin exposure

Antibiotics are currently widely applied in agricultural cultivation, animal husbandry, and medical treatment, but the effects and ecological risks of antibiotics need to be further investigated. Norfloxacin is one of the most widely applied fluoroquinolone antibiotics and is commonly detected in aquatic ecosystems. In this study, the activities of catalase (CAT) and glutathione S-transferase (GST) in blue mussels (Mytilus sp.) exposed to norfloxacin (from 25 to 200 mg/L) for 2 d of acute exposure and 7 d of subacute exposure were measured. ¹H nuclear magnetic resonance (¹H-NMR)-based metabolomics was applied to identify the metabolites and to investigate the physiological metabolism of blue mussels (Mytilus sp.) under different concentrations of norfloxacin. The activity of the CAT enzyme was induced in acute exposure, while the activity of GST was inhibited in subacute exposure when the concentration of norfloxacin reached 200 mg/L. Orthogonal partial least squares discriminant analysis (OPLS-DA) revealed that the increased concentrations of norfloxacin might cause greater metabolic differences between the treatment and control groups and cause greater metabolic variation within the same treatment group. The contents of taurine in the 150 mg/L acute exposure group were 5.17 times higher than those in the control group. The pathway analysis indicated that exposure to high concentrations of norfloxacin disturbed different pathways involved in energy metabolism, amino acid metabolism, neuroregulation, and the regulation of osmotic pressure. These results may provide a molecular and metabolic view of the effects of norfloxacin and the regulatory mechanism of blue mussels when exposed to extremely high doses of antibiotics.

Identification of lipid metabolism-associated genes as prognostic biomarkers based on the immune microenvironment in hepatocellular carcinoma

Lipid metabolism has been associated with progression of various cancers. However, the underlying mechanisms of the impact of lipid metabolism-associated genes (LMAGs) on the tumor immune microenvironment have not been well-elucidated. This study aimed to determine the effects of lipid metabolism on the progression and development of hepatocellular carcinoma (HCC). Expression profiles and clinical data of 371 and 231 patients with HCC were obtained from the TCGA and Internal Cancer Genome Consortium (ICGC) databases, respectively. Using Cox regression and LASSO regression analyses, a prognostic risk model was constructed based on the LMAG data. The tumor mutation burden (TMB), immune cell infiltration levels, and immune response checkpoints of the identified risk groups were determined and compared. A total of two clusters were identified based on the LMAG expression, showing significant differences in tumor stage and immune cell infiltration. A prognostic risk model based on four LMAGs was constructed and proven to have a significant prognostic value. The 1-, 3-, and 5-year survival rates in the high-risk group were 62.2%, 20.5%, and 8.1%, respectively, whereas those in the low-risk group were 78.9%, 28.1%, and 13.5%, respectively. The survival differences between the two risk groups were likely associated with TP53 mutation status, TMB score, degree of immunocyte infiltration, and immune checkpoint level. Likewise, the expression level of every LMAG included in the model had the same effect on the overall survival and immune cell infiltration levels. More importantly, the prognostic value of the signature was verified in an independent ICGC cohort. Thus, the expression levels of LMAGs are closely related to the tumor microenvironment in HCC and may serve as promising biological indicators for prognosis and immune therapy in patients with HCC.