Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer

被引:11
|
作者
Zhou, Ying [1 ,2 ]
Zheng, Jianfeng [3 ]
Bai, Mengru [1 ,2 ]
Gao, Yuzhen [4 ]
Lin, Nengming [1 ,5 ]
机构
[1] Zhejiang Univ, Affiliated Hangzhou Peoples Hosp 1, Key Lab Clin Canc Pharmacol & Toxicol Res Zhejiang, Dept Clin Pharmacol,Sch Med,Canc Ctr, Hangzhou, Peoples R China
[2] Zhejiang Univ, Affiliated Hangzhou Peoples Hosp 1, Dept Clin Pharm, Sch Med, Hangzhou, Peoples R China
[3] Nanjing Med Univ, Affiliated Hangzhou Hosp, Dept Obstet & Gynecol, Hangzhou, Peoples R China
[4] Zhejiang Univ, Dept Clin Lab, Sir Run Run Shaw Hosp, Sch Med, Hangzhou, Zhejiang, Peoples R China
[5] Zhejiang Univ, Affiliated Hangzhou Peoples Hosp 1, Translat Med Res Ctr, Sch Med, Hangzhou, Peoples R China
来源
FRONTIERS IN ONCOLOGY | 2022年 / 12卷
基金
中国国家自然科学基金;
关键词
breast cancer; algorithm; programmed cell death; bioinformatic analyses; PRGs; CELL-DEATH; EXPRESSION; MECHANISMS; CISPLATIN; NETWORKS; SURVIVAL; PORE;
D O I
10.3389/fonc.2022.948169
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
BackgroundsPyroptosis, a newly pattern of specific programmed cell death, has been reported to participate in several cancers. However, the value of pyroptosis in breast cancer (BRCA) is still not clear. MethodsHerein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. After that, we performed clustering analysis by ConsensusClusterPlus. The PRGs with significant prognostic value were then screened through univariate cox regression and further evaluate by constructing a risk model by least absolute shrinkage and selection operator (LASSO) Cox regression. The immune and sensitivity to drugs were also predicted by comprehensive algorithms. Finally, real-time quantitative PCR (qPCR) was performed on two of the screened signature PRGs. ResultsA total of 49 PRGs were obtained from public database and 35 of them were significantly differentially expressed genes (DEGs). Cluster analysis was then performed to explore the relationship between DEGs with overall survival. After that, 6 optimal PRGs (GSDMC, IL-18, CHMP3, TP63, GZMB and CHMP6) were screened out to construct a prognostic signature, which divide BRCA patients into two risk groups. Risk scores were then confirmed to be independent prognostic factors in BRCA. Functional enrichment analyses showed that the signature were obviously associated with tumor-related and immune-associated pathways. 79 microenvironmental cells and 11 immune checkpoint genes were found disparate in two groups. Besides, tumor immune dysfunction and exclusion (TIDE) scores revealed that patients with higher risk scores are more sensitive to immune checkpoint blockade treatment. Patients in the low-risk group were more sensitive to Cytarabine, Docetaxel, Gefitinib, Paclitaxel, and Vinblastine. Inversely, patients in the high-risk group were more sensitive to Lapatinib. Finally, we found that, CHMP3 were down-regulated in both BRCA tissues and cell lines, while IL-18 were up-regulated. ConclusionPRGs play important roles in BRCA. Our study fills the gaps of 6 selected PRGs in BRCA, which were worthy for the further study as predict potential biomarkers and therapeutic targets.
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页数:14
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