Using correlation matrix for the investigation the interaction of genes and traditional risk factor in breast cancer

Abstract

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.