The usefulness of machine-learning-based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.

OBJECTIVE: To examine whether machine learning (ML) analyses involving clinical and 18F-FDG-PET-based radiomic features are helpful in predicting prognosis in patients with laryngeal cancer. METHODS: This retrospective study included 49 patients with laryngeal cancer who underwent18F-FDG-PET/CT before treatment, and these patients were divided into the training (n = 34) and testing (n = 15) cohorts.Seven clinical (age, sex, tumor size, T stage, N stage, Union for International Cancer Control stage, and treatment) and 40 18F-FDG-PET-based radiomic features were used to predict disease progression and survival. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were used for predicting disease progression. Two ML algorithms (cox proportional hazard and random survival forest [RSF] model) considering for time-to-event outcomes were used to assess progression-free survival (PFS), and prediction performance was assessed by the concordance index (C-index). RESULTS: Tumor size, T stage, N stage, GLZLM_ZLNU, and GLCM_Entropy were the five most important features for predicting disease progression.In both cohorts, the naïve Bayes model constructed by these five features was the best performing classifier (training: AUC = 0.805; testing: AUC = 0.842). The RSF model using the five features (tumor size, GLZLM_ZLNU, GLCM_Entropy, GLRLM_LRHGE and GLRLM_SRHGE) exhibited the highest performance in predicting PFS (training: C-index = 0.840; testing: C-index = 0.808). CONCLUSION: ML analyses involving clinical and 18F-FDG-PET-based radiomic features may help predict disease progression and survival in patients with laryngeal cancer. ADVANCES IN KNOWLEDGE: ML approach using clinical and 18F-FDG-PET-based radiomic features has the potential to predict prognosis of laryngeal cancer.

A large-scale targeted proteomics of plasma extracellular vesicles shows utility for prognosis prediction subtyping in colorectal cancer.

PURPOSE: The pathogenesis of cancers depends on the molecular background of each individual patient. Therefore, verifying as many biomarkers as possible and clarifying their relationships with each disease status would be very valuable. We performed a large-scale targeted proteomics analysis of plasma extracellular vesicles (EVs) that may affect tumor progression and/or therapeutic resistance. EXPERIMENTAL DESIGN: Plasma EVs from 59 were collected patients with colorectal cancer (CRC) and 59 healthy controls (HC) in cohort 1, and 150 patients with CRC in cohort 2 for the large-scale targeted proteomics analysis of 457 proteins as candidate CRC markers. The Mann-Whitney-Wilcoxon test and random forest model were applied in cohort 1 to select promising markers. Consensus clustering was applied to classify patients with CRC in cohort 2. The Kaplan-Meier method and Cox regression analysis were performed to identify potential molecular factors contributing to the overall survival (OS) of patients. RESULTS: In the analysis of cohort 1, 99 proteins were associated with CRC. The analysis of cohort 2 revealed two clusters showing significant differences in OS (p = 0.017). Twelve proteins, including alpha-1-acid glycoprotein 1 (ORM1), were suggested to be associated with the identified CRC subtypes, and ORM1 was shown to significantly contribute to OS, suggesting that ORM1 might be one of the factors closely related to the OS. CONCLUSIONS: The study identified two novel subtypes of CRC, which exhibit differences in OS, as well as important biomarker proteins that are closely related to the identified subtypes. Liquid biopsy assessment with targeted proteomics analysis was proposed to be crucial for predicting the CRC prognosis.

Capillary microsampling-based single-cell metabolomics by mass spectrometry and its applications in medicine and drug discovery.

Characterization of cellular metabolic states is a technical challenge in biomedicine. Cellular heterogeneity caused by inherent diversity in expression of metabolic enzymes or due to sensitivity of metabolic reactions to perturbations, necessitates single cell analysis of metabolism. Heterogeneity is typically seen in cancer and thus, single-cell metabolomics is expectedly useful in studying cancer progression, metastasis, and variations in cancer drug response. However, low sample volumes and analyte concentrations limit detection of critically important metabolites. Capillary microsampling-based mass spectrometry approaches are emerging as a promising solution for achieving single-cell omics. Herein, we focus on the recent advances in capillary microsampling-based mass spectrometry techniques for single-cell metabolomics. We discuss recent technical developments and applications to cancer medicine and drug discovery.

Versatile and multiplexed mass spectrometry-based absolute quantification with cell-free-synthesized internal standard peptides.

Preparation of stable isotope-labeled internal standard peptides is crucial for mass spectrometry (MS)-based targeted proteomics. Herein, we developed versatile and multiplexed absolute protein quantification method using MS. A previously developed method based on the cell-free peptide synthesis system, termed MS-based quantification by isotope-labeled cell-free products (MS-QBiC), was improved for multiple peptide synthesis in one-pot reaction. We pluralized the quantification tags used for the quantification of synthesized peptides and thus, made it possible to use cell-free synthesized isotope-labeled peptides as mixtures for the absolute quantification. The improved multiplexed MS-QBiC method was proved to be applied to clarify ribosomal proteins stoichiometry in the ribosomal subunit, one of the largest cellular complexes. The study demonstrates that the developed method enables the preparation of several dozens and even several hundreds of internal standard peptides within a few days for quantification of multiple proteins with only a single-run of MS analysis. SIGNIFICANCE: The developed method can be applied for the preparation of internal standard peptides without limiting the number of peptides to be synthesized, which may result in more practical screening of quantitatively reliable peptides, one of the fundamental steps in the reliable absolute quantification using MS. Furthermore, the method is highly versatile for proteome analysis of any organisms or species without any cDNA or SIL peptide libraries. The quantification can be finished in a few days including design and preparation of appropriate SIL peptides using small-scale batch cell-free reactions, which has a potential to be a part of the standard methodology in a field of quantitative proteomics.

Production and characterization of neurosecretory protein GM using Escherichia coli and Chinese Hamster Ovary cells.

Neurosecretory protein GL (NPGL) and neurosecretory protein GM (NPGM) are paralogs recently discovered in birds and in mammals. The post-translational products of NPGL and of NPGM genes include a signal peptide sequence, a glycine amidation signal, and a dibasic amino acid cleavage site. This suggests that the mature forms of NPGL and of NPGM are small proteins secreted in the hypothalamus and containing an amidated C-terminus. However, endogenous NPGL and NPGM have not yet been identified. Chicken NPGL and NPGM have two highly conserved Cys residues that are likely to form a disulfide bond, while mammalian NPGM has one additional Cys residue located between the two conserved Cys residues and the correct disulfide bond pattern is unclear. In this study, we prepared rat NPGM to elucidate the structure of its mature form. We first expressed the predicted mature NPGM, containing an extra C-terminal Gly, in Escherichia coli SHuffle cells, which are engineered to promote the formation of native disulfide bridges in recombinant proteins. We observed the presence of a disulfide bond between the N-terminal Cys residue and the second Cys residue, while the C-terminal Cys residue was free. Secondly, we transfected a construct containing the entire NPGM open reading frame into Chinese Hamster Ovary cells, and observed that NPGM was cleaved immediately after the signal peptide and that it was secreted into the medium. Furthermore, the protein presented a disulfide bond at the same location observed in recombinant NPGM.

Microwave-assisted solid-phase peptide synthesis of neurosecretory protein GL composed of 80 amino acid residues.

We recently identified a novel cDNA encoding a small secretory protein of 80 amino acid residues, termed neurosecretory protein GL (NPGL), from the chicken hypothalamus. Homologs of NPGL have been reported to be present in mammals, such as human and rat. NPGL is amidated at its C-terminus, contains an intramolecular disulfide bond, and is hydrophobic in nature. In this study, we have optimized the synthesis of the entire 80-amino acid peptide sequence of rat NPGL by microwave-assisted solid-phase peptide synthesis. NPGL was obtained with a 10% yield when the coupling reactions were performed using 1-[Bis(dimethylamino)methylene]-1H-1,2,3-triazolo[4,5-b]pyridinium-3-oxid hexafluorophosphate (HATU) at 50 °C for 5 min, and Fmoc deprotections were performed using 40% piperidine containing 0.1 M HOBt. Furthermore, the disulfide bond of NPGL was formed with 20% yield with the use of glutathione-containing redox buffer and 50% acetonitrile.

Central administration of chicken growth hormone-releasing hormone decreases food intake in chicks.

Growth hormone-releasing hormone (GHRH) is well known as a stimulator of growth hormone (GH) secretion. GHRH not only stimulates GH release but also modifies feeding behavior and energy homeostasis in rodents. In chickens (Gallus gallus domesticus), on the other hand, two types of GHRH, namely, chicken GHRH (cGHRH) and cGHRH-like peptide (cGHRH-LP), have been identified. The purpose of the present study was to investigate the effect of central injection of cGHRH and cGHRH-LP on feeding behavior in chicks. Intracerebroventricular (ICV) injection of both cGHRH and cGHRH-LP (0.04 to 1 nmol) significantly decreased food intake without any abnormal behavior in chicks. Furthermore, the feeding-inhibitory effect was not abolished by co-injection of the antagonist for pituitary adenylate cyclase-activating polypeptide (PACAP) or corticotropin-releasing hormone (CRH) receptors, suggesting that the anorexigenic effect of cGHRH and cGHRH-LP might not be related to the PACAP and CRH systems in the brain of chicks. Finally, 24-h food deprivation increased mRNA expression of cGHRH but not cGHRH-LP in the diencephalon. These results suggest that central cGHRH is related to inhibiting feeding behavior and energy homeostasis in chicks.

An isostructural G-G to A-A substitution within the HIV RRE RNA switches the specificity towards arginine-rich peptides.

The HIV Rev protein utilizes a short alpha-helical arginine-rich RNA-binding domain to bind deeply within the major groove of an internal loop region of the Rev-response element (RRE) RNA. A G48-G71 base-pair which covaries to an isostructural A48-A71 base pair has been shown to play an important structure role in Rev-RRE binding. On the other hand, a high affinity RRE-binding peptide aptamer, the K1 peptide, was shown to have low binding affinity towards the RRE A48-A71 mutant, suggesting that the K1 peptide was recognizing the G48-G71 base-pair. In this study, in an attempt to understand the basis for the recognition of the G48-G71 base-pair by the K1 peptide, the selection of peptides that bind to the RRE A48A71 (RREAA) mutant was carried out. As a result, a peptide specific for the mutant, the LDN1 peptide, was identified. The LDN1 peptide was found to bind to the internal loop region of the RREAA, as in the case of the K1-RRE interaction. However, amino acids important for LDN1-binding to RREAA, were found to be distinct from those important for K1-binding to the RRE. These results demonstrate how subtle changes in RNA structure can dramatically alter the peptide-binding specificity of an RNA.