Dual inhibition of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) by resveratrol derivatives in cancer therapy: in silico approach

Targeting inflammation as cancer therapy

Inflammation has accompanied human beings since the emergence of wounds and infections. In the past decades, numerous efforts have been undertaken to explore the potential role of inflammation in cancer, from tumor development, invasion, and metastasis to the resistance of tumors to treatment. Inflammation-targeted agents not only demonstrate the potential to suppress cancer development, but also to improve the efficacy of other therapeutic modalities. In this review, we describe the highly dynamic and complex inflammatory tumor microenvironment, with discussion on key inflammation mediators in cancer including inflammatory cells, inflammatory cytokines, and their downstream intracellular pathways. In addition, we especially address the role of inflammation in cancer development and highlight the action mechanisms of inflammation-targeted therapies in antitumor response. Finally, we summarize the results from both preclinical and clinical studies up to date to illustrate the translation potential of inflammation-targeted therapies.

Drug discovery: In silico dry data can bypass biological wet data?

The recent and extraordinary increase in computer power, along with the availability of efficient algorithms based on artificial intelligence, has prompted a large number of inexperienced scientists to challenge the complex and yet competitive world of drug discovery, by pretending to identify new hits through the sole use of computer aided drug design (CADD). Does the golden era of dry data run the risk of overshadowing the importance of wet data and, in doing so, forget that in silico and biological data need each other in successful preclinical drug discovery programmes?

Dual COX-2/5-LOX inhibitors from Zanthoxylum simulans inhibit gastric cancer cells by cross-mediating thyroid, estrogen, and oxytocin signaling pathways

Cyclooxygenase 2 (COX-2) and 5-lipoxygenase (5-LOX) are overexpressed in gastric cancer cells, the dual inhibitors of which exhibit potential against metastasis and invasion with fewer side effects. To discover inhibitors targeting COX-2 and 5-LOX, we conducted ultrafiltration and enrichment calculation to screen candidates in quaternary alkaloids (QAs) from Zanthoxylum simulans through LC and LC-Q-TOF. For intensive peaks, peaks 19 (berberine) and 21 (chelerythrine) were observed as the most potent dual candidates and showed selective affinity to 5-LOX over COX-2. Peak 19 showed an enrichment at 4.36 for COX-2 and 22.81 for 5-LOX, while peak 21 showed an enrichment at 7.81 for COX-2 and 24.49 for 5-LOX. Molecular docking results revealed chelerythrine as a better dual inhibitor, showing time- and dose-dependent anti-proliferation against AGS cells. Bio-informatics strategies, such as Gene Expression Omnibus (GEO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), suggested that hormone pathways in gastric cancer cells might be mediated by chelerythrine. Further reviews and summaries helped outline the mechanisms by which COX-2/5-LOX inhibitors might promote apoptosis in gastric cancer cells via estrogen, thyroid, and oxytocin signaling pathways. Chelerythrine was also added to gastric cancer cells to verify the regulation of these three signaling pathways. As a result, significant calling back of thyroid-stimulating hormone receptor (TSHR), thyroid hormone α3 (TRα3), and thyroid hormone receptor β1 (TRβ1) and suppressing estrogen receptor α36 (ER-α36)-Src could benefit the anti-proliferation of chelerythrine. However, it was disappointing that regulation of estrogen receptor α66 (ER-α66), estrogen receptor β (ER-β), and oxytocin receptor (OTR) contributed inversely negative effects on anti-gastric cancer cells. At present, the integrative study not only revealed chelerythrine as the most potent dual COX-2/5-LOX inhibitor from QAs but also generally highlighted that comprehensive regulation of the estrogen, thyroid, and oxytocin pathway should be noted once gastric cancer cells were treated with inflammatory inhibitors.

Pan-Cancer Analysis of the LOX Family Reveals that LOX Affects Tumor Prognosis by Affecting Immune Infiltration

The lysyl oxidase (LOX) gene family encodes for a group of copper-dependent enzymes that play a crucial role in the cross-linking of collagen and elastin fibers in the extracellular matrix (ECM). Dysregulation of LOX gene expression has been implicated in various pathological conditions, including cancer. Several studies have shown that the LOX gene family is involved in cancer progression and metastasis. The goal of this article is to conduct a comprehensive analysis of the LOX family's role in pan-cancer multiplexes. We utilized pan-cancer multi-omics sequencing data from TCGA to investigate the relationship between LOX family genes and tumors at four different levels: mutation, copy number variation, methylation, and gene expression. In addition, we also examined the relationship between LOX family genes and tumors at the cell line level using tumor cell line sequencing data from CCLE. Taking into account the impact of LOX family genes on lung cancer, we developed a LOX family lung cancer prognostic model to forecast the disease's prognosis. Our findings revealed that LOXL2 had the highest mutation frequency in tumors, while all four LOX family genes experienced some degree of copy number variation in diverse tumors. We observed that LOX, LOXL1 to LOXL3 were predominantly highly expressed in tumors including LUAD. The expression trends of LOX and LOXL1 to LOXL3 were consistent across tumor cell lines, but differed somewhat from LOXL4. Utilizing 25 LOX family-related genes, we constructed a LOX family prognostic model that performed well in predicting the prognosis of lung cancer. Through pan-cancer analysis, we gain further knowledge of the role of LOX family genes in different tumors, offering a novel pathway for future research into the relationship between LOX family genes and tumors.