Direct adsorption sampling and ambient mass spectrometry analysis of tobacco smoke with porous paper strips.

Chemical analysis of atmospheric aerosols by conventional analytical methods is usually required to perform complicated and time-consuming sample preparation processes. In recent decades, ambient ionization mass spectrometry (AI-MS) methods have been proven to be simple, rapid, and effective analytical tools for direct analysis of various complex samples. In this work, we applied porous paper filters for direct adsorptive sampling of tobacco smoke, and then the sampled paper filters were performed the emitters of the paper spray ionization (PSI) device. An auto-sampling device was made to control the generation and collection of tobacco smoke. Nicotine, the typical compound of tobacco smoke, was used to optimize the key conditions of auto-sampling. Moreover, different types of tobacco smoke were also compared with multivariate variable analysis, and the makers of tobacco smoke from different sources of tobacco smoke were investigated. By using this method, direct sampling and analysis of a single tobacco sample can be completed within minutes. Overall, our results show that PSI-MS is a powerful tool that integrates collection, extraction, ionization, and identification analytes in smoke.

USP52 inhibits cell proliferation by stabilizing PTEN protein in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer. Ubiquitination is closely related to the development of lung cancer. However, the biological importance of newly discovered ubiquitin-specific peptidase (USP) 52 (USP52) in NSCLC remained unclear. Here, our findings identify USP52 as a novel tumor suppressor of NSCLC, the low expression of USP52 predicts a poor prognosis for NSCLC patients. The present study demonstrates that USP52 inhibits cancer cell proliferation through down-regulation of cyclin D1 (CCND1) as well as AKT/mTOR signaling pathway inhibition. Meanwhile, USP25 also suppresses NSCLC progression via enhancing phosphatase and tensin homolog (PTEN) stability in cancer cells, which further indicates the significance/importance of USP52 in NSCLC suppression.

A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer.

BACKGROUND We attempted to develop a prognostic model and characterize molecular subtypes for gastric cancer on the basis of ribonucleic acid (RNA)-binding proteins (RBPs). MATERIAL AND METHODS RNA sequence data of gastric cancer were obtained from The Cancer Genome Atlas. Univariate Cox regression analysis was used to screen survival-related RBPs, followed by least absolute shrinkage and selection operator Cox modeling. Overall and stratified survival analysis was carried out between high and low risk score groups, followed by receiver operator characteristic curve construction. Univariate and multivariate survival analysis was applied to assess its independent prognostic potential. A nomogram was constructed by combining age and the risk score, which was verified by calibration curves and decision curve analyses for 1-, 3-, and 5-year survival. Molecular subtypes were identified using nonnegative matrix factorization method. Clinical features of the identified subtypes were characterized on prognosis, drug sensitivity, and immune infiltration. An external Gene Expression Omnibus dataset was used to verify the above findings. RESULTS On the basis of 44 survival-related RBPs, a robust prognostic 15-RBP signature was constructed. Patients with high risk score had a poorer prognosis than those with low risk score. The risk score had good performance in predicting clinical outcomes for 1-, 3-, and 5-year survival. The signature was effectively independent of other clinical features. The nomogram model combining age and the 15-RBP prognostic model exhibited better practicality and reliability for prognosis. RBP expression data were utilized to define 2 distinct molecular subtypes obviously related to survival outcomes, chemotherapeutic drug sensitivity, and immune infiltration. CONCLUSIONS Our study provides a nomogram model that consists of age and a 15-RBP signature and identifies 2 molecular subtypes for gastric cancer that possess potential value for preclinical, clinical, and translational research on gastric cancer.