Construction of Cationic Azahelicenes: Regioselective Three-Component Annulation Using In Situ Activation Strategy.

Described herein is a strategy to construct cationic azahelicenes through the three-component annulation reaction of isoquinoline, indole, and 1,2-dichloroethane (DCE), in which DCE serves as an in situ activating agent for C1-H activation of isoquinoline, a vinyl equivalent, and a solvent. This in situ activation annulation reaction features a facile one-step synthesis and complete regioselectivity. The complete regioselectivity of C1 over C3 for the isoquinoline ring paves a path to the helical structure in a highly ordered sequence. One of the synthesized ionic [5]azahelicenium fluorophores exhibits the potential to serve as a mitochondria-targeted biomarker with good photostability and low cytotoxicity.

Exploring Novel Fentanyl Analogues Using a Graph-Based Transformer Model.

The structures of fentanyl and its analogues are easy to be modified and few types have been included in database so far, which allow criminals to avoid the supervision of relevant departments. This paper introduces a molecular graph-based transformer model, which is combined with a data augmentation method based on substructure replacement to generate novel fentanyl analogues. 140,000 molecules were generated, and after a set of screening, 36,799 potential fentanyl analogues were finally obtained. We calculated the molecular properties of 36,799 potential fentanyl analogues. The results showed that the model could learn some properties of original fentanyl molecules. We compared the generated molecules from transformer model and data augmentation method based on substructure replacement with those generated by the other two molecular generation models based on deep learning, and found that the model in this paper can generate more novel potential fentanyl analogues. Finally, the findings of the paper indicate that transformer model based on molecular graph helps us explore the structure of potential fentanyl molecules as well as understand distribution of original molecules of fentanyl.

Identification and Functional Analysis of Individual-Specific Subpathways in Lung Adenocarcinoma.

Small molecular networks within complex pathways are defined as subpathways. The identification of patient-specific subpathways can reveal the etiology of cancer and guide the development of personalized therapeutic strategies. The dysfunction of subpathways has been associated with the occurrence and development of cancer. Here, we propose a strategy to identify aberrant subpathways at the individual level by calculating the edge score and using the Gene Set Enrichment Analysis (GSEA) method. This provides a novel approach to subpathway analysis. We applied this method to the expression data of a lung adenocarcinoma (LUAD) dataset from The Cancer Genome Atlas (TCGA) database. We validated the effectiveness of this method in identifying LUAD-relevant subpathways and demonstrated its reliability using an independent Gene Expression Omnibus dataset (GEO). Additionally, survival analysis was applied to illustrate the clinical application value of the genes and edges in subpathways that were associated with the prognosis of patients and cancer immunity, which could be potential biomarkers. With these analyses, we show that our method could help uncover subpathways underlying lung adenocarcinoma.

Copper-Catalyzed Oxidative C-H Annulation of Quinolines with Dichloroethane toward Benzoquinoliziniums Using an In Situ Activation Strategy.

Described herein is a copper-catalyzed oxidative C-H annulation of quinolines with 1,2-chloroethane (DCE), providing a concise synthetic approach to benzoquinoliziniums. In this protocol, DCE not only serves as a solvent and an in situ activation agent of quinoline C2-H but also works as vinyl equivalents to constitute the six-membered azonia ring. Furthermore, the resultant benzoquinolizinium library exhibits good properties of binding to DNA and low cytotoxicity.

Revealing Prognosis-Related Pathways at the Individual Level by a Comprehensive Analysis of Different Cancer Transcription Data.

Identifying perturbed pathways at an individual level is important to discover the causes of cancer and develop individualized custom therapeutic strategies. Though prognostic gene lists have had success in prognosis prediction, using single genes that are related to the relevant system or specific network cannot fully reveal the process of tumorigenesis. We hypothesize that in individual samples, the disruption of transcription homeostasis can influence the occurrence, development, and metastasis of tumors and has implications for patient survival outcomes. Here, we introduced the individual-level pathway score, which can measure the correlation perturbation of the pathways in a single sample well. We applied this method to the expression data of 16 different cancer types from The Cancer Genome Atlas (TCGA) database. Our results indicate that different cancer types as well as their tumor-adjacent tissues can be clearly distinguished by the individual-level pathway score. Additionally, we found that there was strong heterogeneity among different cancer types and the percentage of perturbed pathways as well as the perturbation proportions of tumor samples in each pathway were significantly different. Finally, the prognosis-related pathways of different cancer types were obtained by survival analysis. We demonstrated that the individual-level pathway score (iPS) is capable of classifying cancer types and identifying some key prognosis-related pathways.