Cyclic sulfur compounds targeting macrophage polarization into M2/protumor phenotype and their anti-tumor effects

Mapping the landscape and exploring trends in macrophage-related research within non-small cell lung cancer: a comprehensive bibliometric analysis

Background

Macrophages play a pivotal role in the research landscape of non-small cell lung cancer (NSCLC), contributing significantly to understanding tumor progression, treatment resistance, and immunotherapy efficacy. In this study, we utilized bibliometric techniques to analyze shifts in research hotspots and trends within the field, while also forecasting future research directions. These insights aim to offer guidance for both clinical therapeutic interventions and foundational scientific inquiries.

Methods

All publications were released between 1993 and 2023 and focus on research pertaining to macrophages in the field of NSCLC. The articles were identified from the Web of Science Core Collection and analyzed using VOSviewer 1.6.19, CiteSpace 6.2.R2, and Scimago Graphica 1.0.35.

Result

A total of 361 articles authored by 3,072 researchers from 48 countries were included in the analysis. TAMs have gained increasing attention for their role in NSCLC development and as potential therapeutic targets. Modulating TAM behavior may offer avenues to suppress tumor growth and drug resistance, improving patient outcomes. International collaboration, particularly between China and the United States, accelerates progress in NSCLC research, benefiting patients worldwide. The research hotspot revolves around understanding the role of macrophages in immunotherapy, focusing on their contribution to tumor progression, therapeutic resistance, and potential as therapeutic targets in NSCLC.

Conclusions

The therapeutic significance of macrophages in the field of NSCLC is gaining increasing attention and recognition, highlighting their potential as key players in the development of novel treatment strategies. Future research will focus on understanding TAM molecular mechanisms, interactions with immune cells, and exploring novel therapies, with the aim of improving NSCLC treatment outcomes.

Development of White Cabbage, Coffee, and Red Onion Extracts as Natural Phosphodiesterase-4B (PDE4B) Inhibitors for Cognitive Dysfunction: In Vitro and In Silico Studies

Human cognition fundamentally depends on memory. Alzheimer's disease exhibits a strong correlation with a decline in this factor. Phosphodiesterase-4 B (PDE4B) plays a crucial role in neurodegenerative disorders, and its inhibition is one of the promising approaches for memory enhancement. This study aimed to identify secondary metabolites in white cabbage, coffee, and red onion extracts and identify their molecular interaction with PDE4B by in silico and in vitro experiments. Crushed white cabbage and red onion were macerated separately with ethanol to yield respective extracts, and ground coffee was boiled with water to produce aqueous extract. Thin layer chromatography (TLC)-densitometry was used to examine the phytochemicals present in white cabbage, coffee, and red onion extracts. Molecular docking studies were performed to know the interaction of test compounds with PDE4B. TLC-densitometry analysis showed that chlorogenic acid and quercetin were detected as major compounds in coffee and red onion extracts, respectively. In silico studies revealed that alpha-tocopherol (binding free energy (∆Gbind) = -38.00 kcal/mol) has the strongest interaction with PDE4B whereas chlorogenic acid (∆Gbind = -21.50 kcal/mol) and quercetin (∆Gbind = -17.25 kcal/mol) exhibited moderate interaction. In vitro assay showed that the combination extracts (cabbage, coffee, and red onion) had a stronger activity (half-maximal inhibitory concentration (IC50) = 0.12 ± 0.03 µM) than combination standards (sinigrin, chlorogenic acid, and quercetin) (IC50 = 0.17 ± 0.03 µM) and rolipram (IC50 = 0.15 ± 0.008 µM). Thus, the combination extracts are a promising cognitive enhancer by blocking PDE4B activity.

Tumor microenvironment-related multigene prognostic prediction model for breast cancer

BACKGROUND

Breast cancer is an invasive disease with complex molecular mechanisms. Prognosis-related biomarkers are still urgently needed to predict outcomes of breast cancer patients.

METHODS

Original data were download from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The analyses were performed using perl-5.32 and R-x64-4.1.1.

RESULTS

In this study, 1086 differentially expressed genes (DEGs) were identified in the TCGA cohort; 523 shared DEGs were identified in the TCGA and GSE10886 cohorts. Eight subtypes were estimated using non-negative matrix factorization clustering with significant differences seen in overall survival (OS) and progression-free survival (PFS) (P < 0.01). Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed to develop a related risk score related to the 17 DEGs; this score separated breast cancer into low- and high-risk groups with significant differences in survival (P < 0.01) and showed powerful effectiveness (TCGA all group: 1-year area under the curve [AUC] = 0.729, 3-year AUC = 0.778, 5-year AUC = 0.781). A nomogram prediction model was constructed using non-negative matrix factorization clustering, the risk score, and clinical characteristics. Our model was confirmed to be related with tumor microenvironment. Furthermore, DEGs in high-risk breast cancer were enriched in histidine metabolism (normalized enrichment score [NES] = 1.49, P < 0.05), protein export (NES = 1.58, P < 0.05), and steroid hormone biosynthesis signaling pathways (NES = 1.56, P < 0.05).

CONCLUSIONS

We established a comprehensive model that can predict prognosis and guide treatment.