Cancer is a serious disease with high mortality and morbidity worldwide. Natural products have served as a major source for developing new anticancer drugs during recent decades. Magnolol, a representative natural phenolic lignan isolated from Magnolia officinali, has attracted considerable attention for its anticancer properties in recent years. Accumulating preclinical studies have demonstrated the tremendous therapeutic potential of magnolol via a wide range of pharmacological mechanisms against cancer. In this review, we summarized the latest advances in preclinical studies investigating anticancer properties of magnolol and described the important signaling pathways explaining its underlying mechanisms. Magnolol was capable of inhibiting cancer growth and metastasis against various cancer types. Magnolol exerted anticancer effects through inhibiting proliferation, inducing cell cycle arrest, provoking apoptosis, restraining migration and invasion, and suppressing angiogenesis. Multiple signaling pathways were also involved in the pharmacological actions of magnolol against cancer, such as PI3K/Akt/mTOR signaling, MAPK signaling and NF-κB signaling. Based on this existing evidence summarized in the review, we have conclusively confirmed magnolol had a multi-target anticancer effect against heterogeneous cancer disease. It is promising to develop magnolol as a drug candidate for cancer therapy in the future.

Cancer is a major global public health problem with high morbidity. Depression is known to be a high-frequency complication of cancer diseases that decreases patients’ life quality and increases the mortality rate. Therefore, antidepressants are often used as a complementary treatment during cancer therapy. During recent decades, various studies have shown that the combination of antidepressants and anticancer drugs increases treatment efficiency. In recent years, further emerging evidence has suggested that the modulation of autophagy serves as one of the primary anticancer mechanisms for antidepressants to suppress tumor growth. In this review, we introduce the anticancer potential of antidepressants, including tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), selective serotonin reuptake inhibitors (SSRIs), and serotonin-norepinephrine reuptake inhibitors (SNRIs). In particular, we focus on their autophagy-modulating mechanisms for regulating autophagosome formation and lysosomal degradation. We also discuss the prospect of repurposing antidepressants as anticancer agents. It is promising to repurpose antidepressants for cancer therapy in the future.

Background: Rapid advances in transcriptomic profiles have resulted in recognizing IRLs (immune-related long noncoding RNAs), as modulators of the expression of genes related to immune cells that mediate immune inhibition as well as immune stimulatory, indicating LncRNAs play fundamental roles in immune modulation. Hence, we establish an IRL classifier to precisely predict prognosis and immunotherapeutic efficiency in laryngeal squamous cell carcinoma (LSCC). Methods: LSCC RNA-seq (RNA sequencing) datasets, somatic mutation data, and corresponding clinicopathologic information were acquired from TCGA (the Cancer Genome Atlas) and Gene Expression Omnibus (GEO) databases. Spearman correlation analysis identified LncRNAs associated with immune-related genes (IRG). Based on Lasso penalized regression and random forest (RF), we constructed an IRL classifier associated with prognosis. GEO database was utilized to validate the IRL classifier. The predictive precision and clinical application of the IRL classifier were assessed and compared to clinicopathologic features. The immune cell infiltration of LSCC was calculated via CIBERSORTx tools and ssGSEA (single-sample gene set enrichment analysis). Then, we systematically correlated the IRL classifier with immunological characteristics from multiple perspectives, such as immune-related cells infiltrating, tumor microenvironment (TME) scoring, microsatellite instability (MSI), tumor mutation burden (TMB), and chemokines. Finally, the TIDE (tumor immune dysfunction and exclusion) algorithm was used to predict response to immunotherapy. Results: Based on machine learning approach, three prognosis-related IRLs (BARX1-DT, KLHL7-DT, and LINC02154) were selected to build an IRL classifier. The IRL classifier could availably classify patients into the low-risk and high-risk groups based on the different endpoints, including recurrence-free survival (RFS) and overall survival (OS). In terms of predictive ability and clinical utility, the IRL classifier was superior to other clinical characteristics. Encouragingly, similar results were observed in the GEO databases. Immune infiltration analysis displayed immune cells that are significantly richer in low-risk group, CD8 T cells and activated NK cells via CIBERSORTx algorithm as well as activated CD8 T cell via ssGSEA. Additionally, compared with the high-risk group, immune score, CD8 T effector was higher in the low-risk group, yet stromal score, score of p53 signaling pathway and TGFher in the Tx algorithm, was lower in the low-risk group. Corresponding results were confirmed in GEO dataset. Finally, TIDE analysis uncovered that the IRL classifier may be effectually predict the clinical response of immunotherapy in LSCC. Conclusion: Based on BARX1-DT, KLHL7-DT, and LINC02154, the IRL classifier was established, which can be used to predict the prognosis, immune infiltration status, and immunotherapy response in LSCC patients and might facilitate personalized counseling for immunotherapy.

Peroxynitrite (ONOO⁻), a highly reactive nitrogen species, which plays a crucial role in numerous physiological and pathological processes of cell functionalization. The anomalous concentration of ONOO⁻ may result in a range of diseases, such as arthritis, neurological disorders, and even cancer. Therefore, it is urgent to develop a simple and effective tool to monitor the fluctuation of ONOO⁻ levels in biological systems. Herein, an ultrafast fluorescent probe (HND-ONOO) is proposed to detect ONOO⁻, which displays brilliant fluorescence in less than 30s with a large Stokes shift. Furthermore, the probe exhibited the lower detection limit (48nM) and satisfactory results in differentiating ONOO⁻ from other related species. The probe that possesses good biocompatibility and low toxicity was employed to monitor the level of exogenous and endogenous ONOO⁻ in living cells. Thus, the probe HND-ONOO could be served as a potential imaging tool to visualize intracellular ONOO⁻ and understand the relationship between ONOO⁻ and inflammation.