Cobalt-Catalyzed Cascade C-H Activation/Annulation Polymerizations toward Diversified and Multifunctional Sulfur-Containing Fused Heterocyclic Polymers.

Transition-metal-catalyzed C-H activation has greatly benefited the synthesis and development of functional polymer materials, and the construction of multifunctional fused (hetero)cyclic polymers via novel C-H activation-based polyannulations has emerged as a charming but challenging area in recent years. Herein, we report the first cobalt(III)-catalyzed cascade C-H activation/annulation polymerization (CAAP) approach that can efficiently transform readily available aryl thioamides and internal diynes into multifunctional sulfur-containing fused heterocyclic (SFH) polymers. Within merely 3 h, a series of SFH polymers bearing complex and multisubstituted S,N-doped polycyclic units are facilely and efficiently produced with high molecular weights (absolute Mn up to 220400) in excellent yields (up to 99%), which are hard to achieve by traditional methods. The intermediate-terminated SFH polymer can be used as a reactive macromonomer to controllably extend or modify polymer main chains. The structural diversity can be further enriched through facile S-oxidation and N-methylation reactions of the SFH polymers. Benefiting from the unique structures, the obtained polymers exhibit excellent solution processability, high thermal and morphological stability, efficient and readily tunable aggregate-state fluorescence, stimuli-responsive properties, and high and UV-modulatable refractive indices of up to 1.8464 at 632.8 nm. These properties allow the SFH polymers to be potentially applied in diverse fields, including metal ion detection, photodynamic killing of cancer cells, fluorescent photopatterning, and gradient-index optical materials.

High BLM Expression Predicts Poor Clinical Outcome and Contributes to Malignant Progression in Human Cholangiocarcinoma.

Molecular mechanisms underlying the tumorigenesis of a highly malignant cancer, cholangiocarcinoma (CCA), are still obscure. In our study, the CCA expression profile data were acquired from The Cancer Genome Atlas (TCGA) database, and differentially expressed genes (DEGs) in the TCGA-Cholangiocarcinoma (TCGA-CHOL) data set were utilized to construct a co-expression network via weighted gene co-expression network analysis (WGCNA). The blue gene module associated with the histopathologic grade of CCA was screened. Then, five candidate hub genes were screened by combining the co-expression network with protein-protein interaction (PPI) network. After progression and survival analyses, bloom syndrome helicase (BLM) was ultimately identified as a real hub gene. Moreover, the receiver operating characteristic (ROC) curve analysis suggested that BLM had a favorable diagnostic and predictive recurrence value for CCA. The gene set enrichment analysis (GSEA) results for a single hub gene revealed the importance of cell cycle-related pathways in the CCA progression and prognosis. Furthermore, we detected the BLM expression in vitro, and the results demonstrated that the expression level of BLM was much higher in the CCA tissues and cells relative to adjacent non-tumor samples and normal bile duct epithelial cells. Additionally, after further silencing the BLM expression by small interfering RNA (siRNA), the proliferation and migration ability of CCA cells were all inhibited, and the cell cycle was arrested. Altogether, a real hub gene (BLM) and cell cycle-related pathways were identified in the present study, and the gene BLM may be involved in the CCA progression and could act as a reliable biomarker for potential diagnosis and prognostic evaluation.

Development of a novel lipid metabolism-based risk score model in hepatocellular carcinoma patients.

BACKGROUND: Liver cancer is one of the most common malignancies worldwide. HCC (hepatocellular carcinoma) is the predominant pathological type of liver cancer, accounting for approximately 75-85 % of all liver cancers. Lipid metabolic reprogramming has emerged as an important feature of HCC. However, the influence of lipid metabolism-related gene expression in HCC patient prognosis remains unknown. In this study, we performed a comprehensive analysis of HCC gene expression data from TCGA (The Cancer Genome Atlas) to acquire further insight into the role of lipid metabolism-related genes in HCC patient prognosis. METHODS: We analyzed the mRNA expression profiles of 424 HCC patients from the TCGA database. GSEA(Gene Set Enrichment Analysis) was performed to identify lipid metabolism-related gene sets associated with HCC. We performed univariate Cox regression and LASSO(least absolute shrinkage and selection operator) regression analyses to identify genes with prognostic value and develop a prognostic model, which was tested in a validation cohort. We performed Kaplan-Meier survival and ROC (receiver operating characteristic) analyses to evaluate the performance of the model. RESULTS: We identified three lipid metabolism-related genes (ME1, MED10, MED22) with prognostic value in HCC and used them to calculate a risk score for each HCC patient. High-risk HCC patients exhibited a significantly lower survival rate than low-risk patients. Multivariate Cox regression analysis revealed that the 3-gene signature was an independent prognostic factor in HCC. Furthermore, the signature provided a highly accurate prediction of HCC patient prognosis. CONCLUSIONS: We identified three lipid-metabolism-related genes that are upregulated in HCC tissues and established a 3-gene signature-based risk model that can accurately predict HCC patient prognosis. Our findings support the strong links between lipid metabolism and HCC and may facilitate the development of new metabolism-targeted treatment approaches for HCC.