Cancer stem cells (CSCs) are considered to play a key role in the development and progression of pancreatic ductal adenocarcinoma (PDAC). However, little is known about lipid metabolism reprogramming in PDAC CSCs. Here, we assigned stemness indices, which were used to describe and quantify CSCs, to every patient from the Cancer Genome Atlas (TCGA-PAAD) database and observed differences in lipid metabolism between patients with high and low stemness indices. Then, tumor-repopulating cells (TRCs) cultured in soft 3D (three-dimensional) fibrin gels were demonstrated to be an available PDAC cancer stem-like cell (CSLCs) model. Comprehensive transcriptome and lipidomic analysis results suggested that fatty acid metabolism, glycerophospholipid metabolism, and, especially, the sphingolipid metabolism pathway were mostly associated with CSLCs properties. SPHK1 (sphingosine kinases 1), one of the genes involved in sphingolipid metabolism and encoding the key enzyme to catalyze sphingosine to generate S1P (sphingosine-1-phosphate), was identified to be the key gene in promoting the stemness of PDAC. In summary, we explored the characteristics of lipid metabolism both in patients with high stemness indices and in novel CSLCs models, and unraveled a molecular mechanism via which sphingolipid metabolism maintained tumor stemness. These findings may contribute to the development of a strategy for targeting lipid metabolism to inhibit CSCs in PDAC treatment.

Alzheimer’s disease (AD) is the most common type of dementia and is a serious disruption to normal life. Monoamine oxidase-B (MAO-B) is an important target for the treatment of AD. In this study, machine learning approaches were applied to investigate the identification model of MAO-B inhibitors. The results showed that the identification model for MAO-B inhibitors with K-nearest neighbor(KNN) algorithm had a prediction accuracy of 94.1% and 88.0% for the 10-fold cross-validation test and the independent test set, respectively. Secondly, a quantitative activity prediction model for MAO-B was investigated with the Topomer CoMFA model. Two separate cutting mode approaches were used to predict the activity of MAO-B inhibitors. The results showed that the cut model with q² = 0.612 (cross-validated correlation coefficient) and r² = 0.824 (non-cross-validated correlation coefficient) were determined for the training and test sets, respectively. In addition, molecular docking was employed to analyze the interaction between MAO-B and inhibitors. Finally, based on our proposed prediction model, 1-(4-hydroxyphenyl)-3-(2,4,6-trimethoxyphenyl)propan-1-one (LB) was predicted as a potential MAO-B inhibitor and was validated by a multi-spectroscopic approach including fluorescence spectra and ultraviolet spectrophotometry.

This retrospective study was designed to evaluate the safety and efficacy of adding a thalidomide‐based cocktail (the TCC cocktail—comprising thalidomide, carmofur, and compound mylabris capsule) to transarterial chemoembolization (TACE) for unresectable hepatocellular carcinoma (HCC) in real‐world practice. Data from 433 unresectable HCC patients treated with TACE at four hospitals were collected (from March 2014 to May 2018, followed up to July 2023) and overall survival (OS), overall response rate (ORR), disease control rate (DCR), and adverse events (AEs) were analyzed. After 1: 2 propensity score matching (PSM), 114 patients in the cocktail group (TACE + TCC) were compared with 228 patients in the control group (TACE alone). The median OS of the cocktail group was significantly better than that of the control group ( p < 0.001; 42.5 vs. 12.5 months) after PSM. ORR (29.8% vs. 21.1%, p = 0.081) and DCR (79.8% vs. 61.8%, p < 0.001) at 6 months were also better in the cocktail group after PSM. Moreover, no life‐threatening AEs (grade 4 or 5) occurred in either group. In this retrospective cohort, the addition of the TCC cocktail to TACE was associated with improved survival and tumor response, with a manageable safety profile.