|
1
|
Narum S, Kelley J, Sibbett B. Editorial 2024. Mol Ecol Resour 2024; 24:e13912. [PMID: 38095538 DOI: 10.1111/1755-0998.13912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
|
|
2
|
Chen C, Wang J, Dong C, Lim D, Feng Z. Development of a risk model to predict prognosis in breast cancer based on cGAS-STING-related genes. Front Genet 2023; 14:1121018. [PMID: 37051596 PMCID: PMC10083333 DOI: 10.3389/fgene.2023.1121018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 03/14/2023] [Indexed: 03/29/2023] Open
Abstract
Background: Breast cancer (BRCA) is regarded as a lethal and aggressive cancer with increasing morbidity and mortality worldwide. cGAS-STING signaling regulates the crosstalk between tumor cells and immune cells in the tumor microenvironment (TME), emerging as an important DNA-damage mechanism. However, cGAS-STING-related genes (CSRGs) have rarely been investigated for their prognostic value in breast cancer patients.Methods: Our study aimed to construct a risk model to predict the survival and prognosis of breast cancer patients. We obtained 1087 breast cancer samples and 179 normal breast tissue samples from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEX) database, 35 immune-related differentially expression genes (DEGs) from cGAS-STING-related genes were systematically assessed. The Cox regression was applied for further selection, and 11 prognostic-related DEGs were used to develop a machine learning-based risk assessment and prognostic model.Results: We successfully developed a risk model to predict the prognostic value of breast cancer patients and its performance acquired effective validation. The results derived from Kaplan-Meier analysis revealed that the low-risk score patients had better overall survival (OS). The nomogram that integrated the risk score and clinical information was established and had good validity in predicting the overall survival of breast cancer patients. Significant correlations were observed between the risk score and tumor-infiltrating immune cells, immune checkpoints and the response to immunotherapy. The cGAS-STING-related genes risk score was also relevant to a series of clinic prognostic indicators such as tumor staging, molecular subtype, tumor recurrence, and drug therapeutic sensibility in breast cancer patients.Conclusion: cGAS-STING-related genes risk model provides a new credible risk stratification method to improve the clinical prognostic assessment for breast cancer.
Collapse
Affiliation(s)
- Chen Chen
- Department of Occupational Health and Occupational Medicine, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Junxiao Wang
- Department of Occupational Health and Occupational Medicine, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chao Dong
- Department of Occupational Health and Occupational Medicine, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - David Lim
- Translational Health Research Institute, School of Health Sciences, Western Sydney University, Campbelltown, NSW, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Zhihui Feng
- Department of Occupational Health and Occupational Medicine, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- *Correspondence: Zhihui Feng,
| |
Collapse
|