SCNN1A expression in triple-negative breast cancer: clinical implications for prognosis and neoadjuvant therapy response

SCNN1A在三阴性乳腺癌中的表达:对预后和新辅助治疗反应的临床意义

阅读:4

Abstract

BACKGROUND: This study aimed to identify differential genes between pathological complete response (pCR) and non-pCR following neoadjuvant chemotherapy in triple-negative breast cancer (TNBC). Additionally, the expression and clinical significance of the differential gene SCNN1A in TNBC were explored. METHODS: Differential genes related to prognosis following neoadjuvant chemotherapy in TNBC were identified using the GEO database. Core genes were selected through the Cytoscape visualization and support vector machine (SVM) feature selection. The prognostic significance of these genes was assessed via online databases. SCNN1A expression and its correlation with clinicopathological data and neoadjuvant chemotherapy response were analyzed in 283 TNBC patients from the First Affiliated Hospital of Bengbu Medical University using immunohistochemistry. RESULTS: Eleven core genes, including SCNN1A, were identified from 912 differential genes. High SCNN1A expression was associated with poor prognosis in TNBC patients via online database analysis. Gene set difference analysis (GSVA) and Gene set enrichment analysis (GSEA) revealed that SCNN1A was involved in several metabolic pathways. The clinical data indicated that high SCNN1A expression was associated with advanced T (p = 0.037) and N stages (p = 0.011), but not with age, HER2 status, Ki-67 expression, or histological grade. High SCNN1A expression was significantly more frequent in non-pCR patients compared to pCR patients, and high SCNN1A expression was associated with significantly lower overall survival (OS) and disease-free survival (DFS). CONCLUSION: SCNN1A overexpression is associated with poor prognosis and non-pCR status in TNBC patients undergoing neoadjuvant chemotherapy.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。