1
|
Yang Y, Du J, Huang YF, He W, Liu L, Li D, Chen R. Identification of TFR2 as a novel ferroptosis‑related gene that serves an important role in prognosis and progression of triple‑negative breast cancer. Oncol Lett 2024; 27:43. [PMID: 38106522 PMCID: PMC10722555 DOI: 10.3892/ol.2023.14176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023] Open
Abstract
Effective targeted therapeutic strategies for triple-negative breast cancer (TNBC), the most malignant subtype of breast cancer, are currently lacking. Ferroptosis has been reported to be associated with the onset and advancement of various cancer types, including TNBC. However, there are limited studies on the correlation between TNBC and ferroptosis-related genes. In addition, the potential biomarkers of ferroptosis in TNBC need further investigation. The present study aimed to assess the prognostic role of a novel ferroptosis-related gene signature in the context of TNBC. The signature was established utilizing The Cancer Genome Atlas dataset. This three-gene model [transferrin receptor 2 (TFR2), regulator of G protein signaling 4 and zinc finger protein 36] was developed utilizing least absolute shrinkage and selection operator regression analysis and demonstrated satisfactory predictive performance in TNBC. The area under the curve values of the receiver operating characteristic curves in this model concerning the 1-, 2- and 3-year survival prediction were 0.721, 0.840 and 0.856, respectively. The predictive performance of the model was verified using the TNBC dataset GSE25307. Gene set enrichment analysis (GSEA) demonstrated the enrichment of genes in the low-risk group in a number of important metabolic pathways. Single-sample GSEA demonstrated a variation in the expression levels of immune checkpoint molecules between the high- and low-risk groups. The inhibitory impact of TFR2 knockdown on the proliferative capacity of TNBC cells was verified through in vitro experiments. The data also demonstrated that TFR2 knockdown facilitated the ferroptosis of TNBC cells. Additional assessments indicated that the effects of TFR2 knockdown were partially reversed upon treatment with the ferroptosis inhibitor ferrostatin-1. In conclusion, in the present study, a novel and accurate ferroptosis-related predictive signature was established for TNBC with potential future clinical applications. To the best of our knowledge, the present study is the first to report that TFR2 regulated ferroptosis in TNBC cells in vitro.
Collapse
Affiliation(s)
- Yan Yang
- Department of Laboratory Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou 563003, P.R. China
- School of Laboratory Medicine, Zunyi Medical University, Zunyi, Guizhou 563003, P.R. China
- School of Forensic Medicine, Zunyi Medical University, Zunyi, Guizhou 563003, P.R. China
| | - Jie Du
- School of Laboratory Medicine, Zunyi Medical University, Zunyi, Guizhou 563003, P.R. China
| | - Yun-Fei Huang
- School of Laboratory Medicine, Zunyi Medical University, Zunyi, Guizhou 563003, P.R. China
| | - Wei He
- School of Laboratory Medicine, Zunyi Medical University, Zunyi, Guizhou 563003, P.R. China
| | - Li Liu
- Clinical Medical College, Zunyi Medical University, Zunyi, Guizhou 563003, P.R. China
| | - Dan Li
- Clinical Medical College, Zunyi Medical University, Zunyi, Guizhou 563003, P.R. China
| | - Rui Chen
- Department of General Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou 563003, P.R. China
| |
Collapse
|
2
|
Li J, He D, Li S, Xiao J, Zhu Z. Ferroptosis: the emerging player in remodeling triple-negative breast cancer. Front Immunol 2023; 14:1284057. [PMID: 37928550 PMCID: PMC10623117 DOI: 10.3389/fimmu.2023.1284057] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 10/11/2023] [Indexed: 11/07/2023] Open
Abstract
Triple-negative breast cancer (TNBC) is a highly heterogeneous breast tumor type that is highly malignant, invasive, and highly recurrent. Ferroptosis is a unique mode of programmed cell death (PCD) at the morphological, physiological, and molecular levels, mainly characterized by cell death induced by iron-dependent accumulation of lipid peroxides, which plays a substantial role in a variety of diseases, including tumors and inflammatory diseases. TNBC cells have been reported to display a peculiar equilibrium metabolic profile of iron and glutathione, which may increase the sensitivity of TNBC to ferroptosis. TNBC possesses a higher sensitivity to ferroptosis than other breast cancer types. Ferroptosis also occurred between immune cells and tumor cells, suggesting that regulating ferroptosis may remodel TNBC by modulating the immune response. Many ferroptosis-related genes or molecules have characteristic expression patterns and are expected to be diagnostic targets for TNBC. Besides, therapeutic strategies based on ferroptosis, including the isolation and extraction of natural drugs and the use of ferroptosis inducers, are urgent for TNBC personalized treatment. Thus, this review will explore the contribution of ferroptosis in TNBC progression, diagnosis, and treatment, to provide novel perspectives and therapeutic strategies for TNBC management.
Collapse
Affiliation(s)
- Jie Li
- Department of Thyroid and Breast Surgery, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China
| | - Dejiao He
- Department of Nephrology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Sicheng Li
- Department of Plastic Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Jun Xiao
- Department of Breast Surgery, Yueyang Central Hospital, Yueyang, Hunan, China
| | - Zhanyong Zhu
- Department of Plastic Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| |
Collapse
|
3
|
Zhou H, Liu H, Yu Y, Yuan X, Xiao L. Informatics on Drug Repurposing for Breast Cancer. Drug Des Devel Ther 2023; 17:1933-1943. [PMID: 37405253 PMCID: PMC10315146 DOI: 10.2147/dddt.s417563] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/17/2023] [Indexed: 07/06/2023] Open
Abstract
Moving a new drug from bench to bedside is a long and arduous process. The tactic of drug repurposing, which solves "new" diseases with "old" existing drugs, is more efficient and economical than conventional ab-initio way for drug development. Information technology has dramatically changed the paradigm of biomedical research in the new century, and drug repurposing studies have been significantly accelerated by implementing informatics techniques related to genomics, systems biology and biophysics during the past few years. A series of remarkable achievements in this field comes with the practical applications of in silico approaches including transcriptomic signature matching, gene-connection-based scanning, and simulated structure docking in repositioning drug therapies against breast cancer. In this review, we systematically curated these impressive accomplishments with summarization of the main findings on potentially repurposable drugs, and provide our insights into the current issues as well as future directions of the field. With the prospective improvement in reliability, the computer-assisted repurposing strategy will play a more critical role in drug research and development.
Collapse
Affiliation(s)
- Hui Zhou
- Department of Lymphoma and Hematology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China
- Department of Lymphoma and Hematology, Hunan Cancer Hospital, Changsha, Hunan, People’s Republic of China
| | - Hongdou Liu
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
| | - Yan Yu
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
| | - Xiao Yuan
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
- Department of Laboratory Diagnosis, Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, Guangdong, People’s Republic of China
| | - Ling Xiao
- Department of Histology and Embryology of Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China
| |
Collapse
|
4
|
Kholod O, Basket W, Liu D, Mitchem J, Kaifi J, Dooley L, Shyu CR. Identification of Immuno-Targeted Combination Therapies Using Explanatory Subgroup Discovery for Cancer Patients with EGFR Wild-Type Gene. Cancers (Basel) 2022; 14:cancers14194759. [PMID: 36230688 PMCID: PMC9564073 DOI: 10.3390/cancers14194759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients’ heterogeneity, immune checkpoint inhibitors (ICIs) represent some the most promising therapeutic approaches. However, approximately 50% of cancer patients that are eligible for treatment with ICIs do not respond well, especially patients with no targetable mutations. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although matching a patient subgroup to a treatment option that can improve patients’ health outcomes remains a challenging task. (2) Methods: We extended our Subgroup Discovery algorithm to identify patient subpopulations that could potentially benefit from immuno-targeted combination therapies in four cancer types: head and neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), and skin cutaneous melanoma (SKCM). We employed the proportional odds model to identify significant drug targets and the corresponding compounds that increased the likelihood of stable disease versus progressive disease in cancer patients with the EGFR wild-type (WT) gene. (3) Results: Our pipeline identified six significant drug targets and thirteen specific compounds for cancer patients with the EGFR WT gene. Three out of six drug targets—FCGR2B, IGF1R, and KIT—substantially increased the odds of having stable disease versus progressive disease. Progression-free survival (PFS) of more than 6 months was a common feature among the investigated subgroups. (4) Conclusions: Our approach could help to better select responders for immuno-targeted combination therapies and improve health outcomes for cancer patients with no targetable mutations.
Collapse
Affiliation(s)
- Olha Kholod
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA
| | - William Basket
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA
| | - Danlu Liu
- Department of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65212, USA
| | - Jonathan Mitchem
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA
- Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA
- Harry S. Truman Memorial Veterans’ Hospital, Columbia, MO 65201, USA
| | - Jussuf Kaifi
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA
- Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA
| | - Laura Dooley
- Department of Otolaryngology, School of Medicine, University of Missouri, Columbia, MO 65212, USA
| | - Chi-Ren Shyu
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA
- Department of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65212, USA
- Correspondence:
| |
Collapse
|