Ferroptosis-induced Cardiotoxicity and Antitumor Drugs

Class III Phosphatidylinositol-3 Kinase/Vacuolar Protein Sorting 34 in Cardiovascular Health and Disease

Phosphatidylinositol-3 kinases (PI3Ks) play a critical role in maintaining cardiovascular health and the development of cardiovascular diseases (CVDs). Specifically, vacuolar Protein Sorting 34 (VPS34) or PIK3C3, the only member of Class III PI3K, plays an important role in CVD progression. The main function of VPS34 is inducing the production of phosphatidylinositol 3-phosphate, which, together with other essential structural and regulatory proteins in forming VPS34 complexes, further regulates the mammalian target of rapamycin activation, autophagy, and endocytosis. VPS34 is found to have crucial functions in the cardiovascular system, including dictating the proliferation and survival of vascular smooth muscle cells and cardiomyocytes and the formation of thrombosis. This review aims to summarize our current knowledge and recent advances in understanding the function and regulation of VPS34 in cardiovascular health and disease. We also discuss the current development of VPS34 inhibitors and their potential to treat CVDs.

Ferroptosis-Related Gene Signature for Prognosis Prediction in Acute Myeloid Leukemia and Potential Therapeutic Options

Background

Limited data were available to understand the significance of ferroptosis in leukemia prognosis, regardless of the genomic background.

Methods

RNA-seq data from 151 AML patients were analyzed from The Cancer Genome Atlas (TCGA) database, along with 70 healthy samples from the Genotype-Tissue Expression (GTEx) database. Ferroptosis-related genes (FRGs) features were constructed by multivariate COX regression analysis and risk scores were calculated for each sample and a novel prediction model was identified. The validation was carried out using data from 35 AML patients and 13 healthy controls in our cohort. Drug sensitivity analysis was conducted on various chemotherapeutic drugs.

Results

A signature of 10 FRGs was identified, as prognostic predictors for AML, and the risk scores were calculated to constructed the prognostic features of FRGs. Significantly lower overall survival was observed in the high-risk group. The predictive ability of these features for AML prognosis was confirmed using Cox regression analysis, ROC curves, and DCA. The prediction model performed well in our clinical practices, and had its potential superiority when comparing to classical NCCN risk stratification. Multiple chemotherapy drugs, including paclitaxel, dactinomycin, cisplatin, etc. had a lower IC50 in FRGs high-risk group than low-risk group.

Conclusion

The AML prognosis model based on FRGs accurately predicts AML prognosis and drug sensitivity, and the drugs identified worthy further investigation.

Identification of Dalbergiae Odoriferae Lignum active ingredients and potential mechanisms in the treatment of adriamycin-induced cardiotoxicity based on network pharmacology and experimental verification

The purpose of this study is to investigate the ingredients and mechanisms through which Dalbergiae Odoriferae Lignum (DOL) reduces adriamycin-induced cardiotoxicity. DOL's ingredients and drug targets were acquired from Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), and adriamycin-induced cardiotoxicity disease targets were gathered from GeneCards and National Center for Biotechnology Information (NCBI). The therapeutic targets of DOL against adriamycin-induced cardiotoxicity were identified by intersecting drug and disease targets. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted using R. Subsequently, core targets were determined and used for molecular docking with DOL ingredients. In vitro and in vivo experiments validated DOL's primary ingredients against adriamycin-induced cardiotoxicity efficacy. Western blot and immunohistochemistry verified its impact on target protein. After intersecting 530 drug targets and 51 disease targets, 19 therapeutic targets for DOL alleviated adriamycin-induced cardiotoxicity were received. Molecular docking demonstrated that DOL primary ingredient formononetin had a robust binding affinity for nitric oxide synthase 3 (NOS3). Experimental results showed that formononetin effectively mitigated adriamycin-induced cardiotoxicity. Additionally, western blot and immunohistochemistry showed that formononetin improved NOS3 expression. The network pharmacology and experimentation suggest that the primary ingredient of DOL, formononetin, may target NOS3 to act as a therapeutic agent for adriamycin-induced cardiotoxicity.

Ferroptosis and cuproptosis: Metal-dependent cell death pathways activated in response to classical chemotherapy - Significance for cancer treatment?

Apoptosis has traditionally been regarded as the desired cell death pathway activated by chemotherapeutic drugs due to its controlled and non-inflammatory nature. However, recent discoveries of alternative cell death pathways have paved the way for immune-stimulatory treatment approaches in cancer. Ferroptosis (dependent on iron) and cuproptosis (dependent on copper) hold promise for selective cancer cell targeting and overcoming drug resistance. Copper ionophores and iron-bearing nano-drugs show potential for clinical therapy as single agents and as adjuvant treatments. Here we review up-to-date evidence for the involvement of metal ion-dependent cell death pathways in the cytotoxicity of classical chemotherapeutic agents (alkylating agents, topoisomerase inhibitors, antimetabolites, and mitotic spindle inhibitors) and their combinations with cuproptosis and ferroptosis inducers, indicating the prospects, advantages, and obstacles of their use.

The Cardioprotective and Anticancer Effects of SGLT2 Inhibitors: JACC: CardioOncology State-of-the-Art Review

Sodium-glucose cotransporter-2 (SGLT2) inhibitors, originally approved for type 2 diabetes mellitus, have demonstrated efficacy in reducing cardiovascular events, particularly heart failure, in patients with and without diabetes. An intriguing research area involves exploring the potential application of SGLT2 inhibitors in cardio-oncology, aiming to mitigate the cardiovascular adverse events associated with anticancer treatments. These inhibitors present a unique dual nature, offering both cardioprotective effects and anticancer properties, conferring a double benefit for cardio-oncology patients. In this review, the authors first examine the established cardioprotective effects of SGLT2 inhibitors in heart failure and subsequently explore the existing body of evidence, including both preclinical and clinical studies, that supports the use of SGLT2 inhibitors in the context of cardio-oncology. The authors further discuss the mechanisms through which SGLT2 inhibitors protect against cardiovascular toxicity secondary to cancer treatment. Finally, they explore the potential anticancer effects of SGLT2 inhibitors along with their proposed mechanisms.

Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction

Currently, the main therapeutic methods for cancer include surgery, radiation therapy, and chemotherapy. However, chemotherapy still plays an important role in tumor therapy. Due to the variety of pathogenic factors, the development process of tumors is complex and regulated by many factors, and the treatment of a single drug is easy to cause the human body to produce a drug-resistant phenotype to specific drugs and eventually leads to treatment failure. In the process of clinical tumor treatment, the combination of multiple drugs can produce stronger anti-tumor effects by regulating multiple mechanisms and can reduce the problem of tumor drug resistance while reducing the toxic side effects of drugs. Therefore, it is still a great challenge to construct an efficient and accurate screening method that can systematically consider the synergistic anti- tumor effects of multiple drugs. However, anti-tumor drug synergy prediction is of importance in improving cancer treatment outcomes. However, identifying effective drug combinations remains a complex and challenging task. This review provides a comprehensive overview of cancer drug synergy therapy and the application of artificial intelligence (AI) techniques in cancer drug synergy prediction. In addition, we discuss the challenges and perspectives associated with deep learning approaches. In conclusion, the review of the AI techniques' application in cancer drug synergy prediction can further advance our understanding of cancer drug synergy and provide more effective treatment plans and reasonable drug use strategies for clinical guidance.