Elham Nazari , Reza ArefNezhad , Mahla Tabadkani , Amir Hossein Farzin , Mahmood Tara , Seyed Mahdi Hassanian , Majid Khazaei , Gordon A. Ferns , Hamed Tabesh , Amir Avan
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For studying internal relationships of each variables and as a risk factor, the correlation matrix was used. For implementation, Python programming language 3.7.2 was utilized and the coefficient of correlation between 0.7 and 1 was considered.</p></div><div><h3>Results</h3><p>Our finding revealed that there is a correlation between Ki_67 and cancer_family, between PR and ER, as well as a correlation between T and P53, CA153, ER, PR and cancer_family. Moreover, our result showed a relationship between Stage with p53, PR, CA153, T and N. Similarly, there was also a correlation between the genetic variables ABCB1 and CYP1B1, CDKN2 and CYP1B1, CDKN2 and ABCB1, CYP1B1 and Connexin37, ABCB1 and Connexin37, CDKN2A and CYP1B1, CDKN2A and ABCB1. The strong correlation of variables was seen stage T N in BC. However,the good correlation of variables was seen rs1764391, Dominnt108, p53, CA153, ER and PR in BC.</p></div><div><h3>Conclusion</h3><p>Our data provide a novel inside on the potential values of emerging markers in combination with current traditional markers as an approach in identification of high risk breast cancer patients.</p></div>","PeriodicalId":38190,"journal":{"name":"Meta Gene","volume":"30 ","pages":"Article 100947"},"PeriodicalIF":0.8000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.mgene.2021.100947","citationCount":"2","resultStr":"{\"title\":\"Using correlation matrix for the investigation the interaction of genes and traditional risk factor in breast cancer\",\"authors\":\"Elham Nazari , Reza ArefNezhad , Mahla Tabadkani , Amir Hossein Farzin , Mahmood Tara , Seyed Mahdi Hassanian , Majid Khazaei , Gordon A. 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引用次数: 2
摘要
尽管做了大量的努力,乳腺癌(BC)仍然是女性中最致命的癌症之一。在这里,我们探讨了与乳腺癌相关的传统标记(包括病理、临床和人口学参数)与5种遗传变异(如connexin37-rs1764391;ABCB1-rs2032582;CYP1B1-rs1056836;CDKN2A / B-rsrs10811661;CDKN2A/B-rs1333049)方法收集115例患者和230名健康人的40个变量(如病理[T, N, M],基因,生化参数[如CA153, ki_67, ER, CEA])并进行分析。为了研究各变量之间的内在关系,并作为风险因素,使用相关矩阵。在实现上,使用Python编程语言3.7.2,并考虑0.7 ~ 1的相关系数。结果Ki_67与cancer_family、PR与ER、T与P53、CA153、ER、PR、cancer_family存在相关性。此外,我们的结果还显示了Stage与p53、PR、CA153、T和n之间的关系。同样,ABCB1与CYP1B1、CDKN2与CYP1B1、CDKN2与ABCB1、CYP1B1与Connexin37、ABCB1与Connexin37、CDKN2A与CYP1B1、CDKN2A与ABCB1之间的遗传变量也存在相关性。在BC的T - N阶段,各变量之间存在较强的相关性。而在BC中,rs1764391、Dominnt108、p53、CA153、ER、PR等变量之间存在较好的相关性。结论我们的数据为新兴标志物与现有传统标志物结合作为乳腺癌高危患者鉴别方法的潜在价值提供了新的视角。
Using correlation matrix for the investigation the interaction of genes and traditional risk factor in breast cancer
Background
Despite extensive effort, breast cancer (BC) is still among the most lethal cancer in women. Here we explored the interaction of traditional markers (including pathological, clinical and demographical parameters) associated with breast cancer and 5 genetic variants (e.g., connexin37-rs1764391; ABCB1-rs2032582; CYP1B1-rs1056836; CDKN2A/B-rsrs10811661; CDKN2A/B-rs1333049) in BC.
Methods
Forty variables from 115 patients and 230 healthy individuals (e.g., pathology [T, N, M], genes, biochemical parameters [e.g., CA153, ki_67, ER, CEA] were collected and then analyzed. For studying internal relationships of each variables and as a risk factor, the correlation matrix was used. For implementation, Python programming language 3.7.2 was utilized and the coefficient of correlation between 0.7 and 1 was considered.
Results
Our finding revealed that there is a correlation between Ki_67 and cancer_family, between PR and ER, as well as a correlation between T and P53, CA153, ER, PR and cancer_family. Moreover, our result showed a relationship between Stage with p53, PR, CA153, T and N. Similarly, there was also a correlation between the genetic variables ABCB1 and CYP1B1, CDKN2 and CYP1B1, CDKN2 and ABCB1, CYP1B1 and Connexin37, ABCB1 and Connexin37, CDKN2A and CYP1B1, CDKN2A and ABCB1. The strong correlation of variables was seen stage T N in BC. However,the good correlation of variables was seen rs1764391, Dominnt108, p53, CA153, ER and PR in BC.
Conclusion
Our data provide a novel inside on the potential values of emerging markers in combination with current traditional markers as an approach in identification of high risk breast cancer patients.
Meta GeneBiochemistry, Genetics and Molecular Biology-Genetics
CiteScore
1.10
自引率
0.00%
发文量
20
期刊介绍:
Meta Gene publishes meta-analysis, polymorphism and population study papers that are relevant to both human and non-human species. Examples include but are not limited to: (Relevant to human specimens): 1Meta-Analysis Papers - statistical reviews of the published literature of human genetic variation (typically linked to medical conditionals and/or congenital diseases) 2Genome Wide Association Studies (GWAS) - examination of large patient cohorts to identify common genetic factors that influence health and disease 3Human Genetics Papers - original studies describing new data on genetic variation in smaller patient populations 4Genetic Case Reports - short communications describing novel and in formative genetic mutations or chromosomal aberrations (e.g., probands) in very small demographic groups (e.g., family or unique ethnic group). (Relevant to non-human specimens): 1Small Genome Papers - Analysis of genetic variation in organelle genomes (e.g., mitochondrial DNA) 2Microbiota Papers - Analysis of microbiological variation through analysis of DNA sequencing in different biological environments 3Ecological Diversity Papers - Geographical distribution of genetic diversity of zoological or botanical species.