Targeting Ferroptosis Suppressor Protein 1 in Cancer Therapy: Implications and Perspectives, with Emphasis on Head and Neck Cancer.
Critical Reviews in Oncology/Hematology(2024)
Department of Otorhinolaryngology-Head and Neck Surgery
Abstract
The diverse functions of ferroptosis suppressor protein 1 (FSP1/AIFM2) in cancer have positioned it as a promising therapeutic target across various malignancies, including head and neck cancer (HNC). Initially characterized as a potential tumor suppressor due to its involvement in apoptosis and ferroptosis, recent studies have revealed its complex role in tumor growth, metabolism, and therapy resistance. Pharmacological inhibition of FSP1 shows potential in sensitizing cancer cells to ferroptosis and overcoming resistance to conventional therapies, offering new avenues for precision medicine approaches. Identifying novel FSP1 inhibitors and their synergistic effects with existing therapies presents exciting opportunities for therapeutic development. However, translating preclinical findings into clinical practice requires the refinement of FSP1 inhibitors, robust biomarkers for patient stratification, and further investigations into the molecular mechanisms underlying FSP1-mediated therapy resistance. Integrating FSP1-targeted therapies into comprehensive treatment regimens holds promise for improving outcomes in cancer patients and advancing the field of precision oncology.
MoreTranslated text
Key words
Ferroptosis,Ferroptosis suppressor protein 1,Resistance,Head and neck cancer,Therapy
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest