Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 35, 26 April 2024
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Establishment and validation of a prognostic model for major histocompatibility complex (MHC)-related genes in breast cancer
* Author to whom correspondence should be addressed.
Abstract
The major histocompatibility complex (MHC) is a group of genes involved in the immune system. In order to investigate this phenomenon, relevant sample data from human breast cancer can be downloaded from databases such as TCGA and GEO. Differential analysis of MHC-related genes that are differentially expressed (MHCRDEGs) can then be performed using single-factor Cox analysis. The identified characteristic genes can be subjected to differential analysis and protein interaction network analysis using multiple datasets. This analysis can aid in the selection of prognostic genes and the establishment of a clinically relevant MHCRDEG model, which can then be validated using multiple datasets. Through machine learning methods, six characteristic genes (LIFR, UGP2, F2RL2, SLC7A5, TUBA1C, IL12B) can be screened, and a diagnostic risk model can be developed. Finally, by comparing the results obtained from multiple datasets, four characteristic genes (LIFR, SLC7A5, TUBA1C, UGP2) can be identified. A clinical prognostic risk model can be established based on these genes, and its validity and accuracy can be confirmed using multiple datasets. This comprehensive study provides valuable insights into the underlying mechanisms of MHC-related genes in cancer.
Keywords
Breast cancer, Major Histocompatibility Complex, prognostic model, genes
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Modern Medicine and Global Health
- ISBN (Print)
- 978-1-83558-395-1
- ISBN (Online)
- 978-1-83558-396-8
- Published Date
- 26 April 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/35/20240909
- Copyright
- 26 April 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited