Identification of Five Cytotoxicity-Related Genes Involved in the Progression of Triple-Negative Breast Cancer.

Abstract

Background: Breast cancer is one of the deadly tumors in women, and its incidence continues to increase. This study aimed to identify novel therapeutic molecules using RNA sequencing (RNA-seq) data of breast cancer from our hospital. Methods: 30 pairs of human breast cancer tissue and matched normal tissue were collected and RNA sequenced in our hospital. Differentially expressed genes (DEGs) were calculated with raw data by the R package "edgeR", and functionally annotated using R package "clusterProfiler". Tumor-infiltrating immune cells (TIICs) were estimated using a website tool TIMER 2.0. Effects of key genes on therapeutic efficacy were analyzed using RNA-seq data and drug sensitivity data from two databases: the Cancer Cell Line Encyclopedia (CCLE) and the Cancer Therapeutics Response Portal (CTRP). Results: There were 2,953 DEGs between cancerous and matched normal tissue, as well as 975 DEGs between primary breast cancer and metastatic breast cancer. These genes were primarily enriched in PI3K-Akt signaling pathway, calcium signaling pathway, cAMP signaling pathway, and cell cycle. Notably, CD8+ T cell, M0 macrophage, M1 macrophage, regulatory T cell and follicular helper T cell were significantly elevated in cancerous tissue as compared with matched normal tissue. Eventually, we found five genes (GALNTL5, MLIP, HMCN2, LRRN4CL, and DUOX2) were markedly corelated with CD8+ T cell infiltration and cytotoxicity, and associated with therapeutic response. Conclusion: We found five key genes associated with tumor progression, CD8+ T cell and therapeutic efficacy. The findings would provide potential molecular targets for the treatment of breast cancer.

• Authors: Yan Zhang, Gui-Hui Tong, Xu-Xuan Wei, Hai-Yang Chen, Tian Liang, Hong-Ping Tang, Chuan-An Wu, Guo-Ming Wen, Wei-Kang Yang, Li Liang, Hong Shen
• Journal: Frontiers in genetics (Front Genet) Vol. 12 Pg. 723477 ( 2021) ISSN: 1664-8021 [Print] Switzerland
• PMID: 35046993 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Zhang, Tong, Wei, Chen, Liang, Tang, Wu, Wen, Yang, Liang and Shen.