Analysis of the correlation between key gene mutations and breast cancer. (October 2021)

• Record Type: Journal Article
• Authors: Shen, Ziyuan; Zhu, Dongyue; Gu, Yunjing; Zhu, Ping
• Is Part Of: Journal of algorithms & computational technology. Volume 15(2021)
• Publication Date: 2021-10
• Subjects: Breast cancer -- key gene -- EIIP -- E difference value -- survival analysis; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/17483026211031163
• Languages: English
• ISSNs: 1748-3018

Abstract

The purpose of this study is to investigate whether mutations in key genes affect the prognosis of breast cancer patients, and base mutations is discussed by combining the physical properties of the electron–ion interaction pseudopotential. We study 994 breast cancer mutation samples, including 15, 015 mutation genes, and use social network algorithm to screen key genes. In order to analyze the relationship between electron–ion interaction pseudopotential and mutation of key genes, this paper proposes theE difference formula. It not only analyses gene mutation at the physical level, but also explains the mutation rule of single base. Simultaneously, we use the Kaplan–Meier to analyze the overall survival rate of breast cancer samples and use Tarone-Ware to test. The results showed that mutations in GATA3, PIK3CA, and TP53 are significant difference in overall survival rate of breast cancer (CI = 95 %, P < 0.05 ). Proteins often interact to form protein complexes, and driving protein function. Therefore, by constructing protein–protein interaction network and finding modular proteins of key genes, we can find genes closely related to key genes. Finally, the results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology function enrichment analysis show that key genes played an important role in metabolic pathways and biological functions. In clinical medicine, screening for key gene mutations may help predict survival in breast cancer patients.