To explore the clinical role of QPRT in breast cancer. The gene expression, methylation levels and prognostic value of QPRT in breast cancer was analyzed using TCGA data. Validation was performed using the data from GEO dataset and TNMPLOT database. Meta analysis method was used to pool the survival data for QPRT. The predictive values of QPRT for different drugs were retrieved from the ROC plot. The expression differences of QPRT in acquired drug-resistant and sensitive cell lines were analyzed using GEO datasets. GO and KEGG enrichment analysis were conducted for those genes which were highly co-expressed with QPRT in tissue based on TCGA data and which changed after QPRT knockdown. Timer2.0 was utilized to explore the correlation between QPRT and immune cells infiltration, and the Human Protein Atlas was used to analyse QPRT's single-cell sequencing data across different human tissues. The expression of QPRT in different types of macrophages, and the expression of QPRT were analysed after coculturing HER2+âbreast cancer cells with macrophages. Additionally, TargetScan, Comparative Toxicogenomics and the connectivity map were used to research miRNAs and drugs that could regulate QPRT expression. Cytoscape was used to map the interaction networks between QPRT and other proteins. QPRT was highly expressed in breast cancer tissue and highly expressed in HER2+âbreast cancer patients (Pâ<â0.01). High QPRT expression levels were associated with worse OS, DMFS, and RFS (Pâ<â0.01). Two sites (cg02640602 and cg06453916) were found to be potential regulators of breast cancer (Pâ<â0.01). QPRT might predict survival benefits in breast cancer patients who received taxane or anthracycline. QPRT was associated with tumour immunity, especially in macrophages. QPRT may influence the occurrence and progression of breast cancer through the PI3K-AKT signalling pathway, Wnt signalling pathway, and cell cycle-related molecules.
A comprehensive analysis of the role of QPRT in breast cancer.
对QPRT在乳腺癌中的作用进行全面分析
阅读:5
作者:Yan Yiqing, Li Lun, Wang Zixin, Pang Jian, Guan Xinyu, Yuan Yunchang, Xia Zhenkun, Yi Wenjun
| 期刊: | Scientific Reports | 影响因子: | 3.900 |
| 时间: | 2023 | 起止号: | 2023 Sep 18; 13(1):15414 |
| doi: | 10.1038/s41598-023-42566-4 | 研究方向: | 肿瘤 |
| 疾病类型: | 乳腺癌 | ||
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