# Additional prognostic value of polymorphisms within the 3′-untranslated region of programmed cell death pathway genes in early-stage breast cancer

## TL;DR

This study identifies four genetic variants in PCD pathway genes that are linked to survival outcomes in early-stage breast cancer patients.

## Contribution

The study introduces four SNPs in PCD pathway genes as potential novel biomarkers for predicting breast cancer survival.

## Key findings

- Four SNPs (rs4900321, rs7150025, rs6753785, rs2213181) were significantly associated with various survival outcomes in breast cancer patients.
- Combining these SNPs showed a decreasing survival trend with more unfavorable genotypes.
- Incorporating these SNPs with clinicopathological variables improved survival prediction accuracy.

## Abstract

The programmed cell death (PCD) pathway plays an important role in restricting cancer cell survival and proliferation. However, limited studies have investigated the association between genetic variants in the 3′-untranslated region of the PCD pathway genes and breast cancer outcomes.

In this study, we genotyped 28 potentially functional single nucleotide polymorphisms (SNPs) in 23 PCD pathway genes in 1,177 patients with early-stage breast cancer (EBC) from a Han Chinese population. The median follow-up period was 174 months.

Among all the candidate SNPs, four independent SNPs (rs4900321 and rs7150025 in ATG2B, rs6753785 in BCL2L11, and rs2213181 in c-Kit) were associated with invasive disease-free survival (iDFS), distant disease-free survival (DDFS), breast cancer-specific survival (BCSS) and overall survival (OS), respectively. Further combined genotypes of these four SNPs revealed that the survival decreased as the number of unfavorable genotypes increased (Ptrend = 1.0 × 10−6, 8.5 × 10−8, 3.6 × 10−4, and 1.3 × 10−4 for iDFS, DDFS, BCSS, and OS, respectively). Receiver operating characteristic curve analysis demonstrated that incorporating unfavorable genotypes and clinicopathological variables improved the ability to predict EBC survival (P = 0.006, 0.004, 0.029, and 0.019 for iDFS, DDFS, BCSS, and OS, respectively). Additionally, rs6753785 and rs2213181 were associated with BCL2L11 and c-Kit mRNA expression, respectively.

Our results suggest that these four SNPs may act as novel biomarkers for EBC survival, possibly by modulating the expression of the corresponding genes.