Tang Guohua, Wang Zhi, Geng Wei, Yu Yang, Zhang Yang
Department of Pharmacy, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China.
State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, China; Department of Hepatobiliary and Echinococcosis Surgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China.
J Genet Eng Biotechnol. 2025 Mar;23(1):100448. doi: 10.1016/j.jgeb.2024.100448. Epub 2024 Dec 24.
Few studies revealed that stromal genes regulate the tumor microenvironment (TME). However, identification of key-risk genes in the invasive ductal breast carcinoma-associated stroma (IDBCS) and their associations with the prediction of risk group remains lacking.
This study used the GSE9014, GSE10797, GSE8977, GSE33692, and TGGA BRCA datasets. We explored the differentially expressed transcriptional markers, hub genes, gene modules, and enriched KEGG pathways. We employed a variety of algorithms, such as the log-rank test, the LASSO-cox model, the univariate regression model, and the multivariate regression model, to predict prognostic-risk genes and the prognostic-risk model. Finally, we employed a molecular docking-based study to explore the interaction of sensitive drugs with prognostic-risk genes.
In comparing IDBCS and normal stroma, we discovered 1472 upregulated genes and 1400 downregulated genes (combined ES > 0585 and adjusted p-value < 0.05). The hub genes enrich cancer, immunity, and cellular signaling pathways. We explored the 12 key risk genes (ADAM8, CD86, CSRP1, DCTN2, EPHA1, GALNT10, IGFBP6, MIA, MMP11, RBM22, SLC39A4, and SYT2) in the IDBCS to identify the high-risk group and low-risk group patients. The high-risk group had a lower survival rate, and the constructed ROC curves evaluated the validity of the risk model. Expression validation and diagnostic efficacy revealed that the key stromal risk genes are consistently deregulated in the high-risk group and high stromal samples of the TCGA BRCA cohort. The expression of crucial risk genes, including CD86, CSRP1, EPHA1, GALNT10, IGFBP6, MIA, and RBM22 are associated with drug resistance and drug sensitivity. Finally, a molecular docking study explored several sensitive drugs (such as QL-XII-61, THZ-2-49, AZ628, NG-25, lapatinib, dasatinib, SB590885, and dabrafenib) interacted with these essential risk genes through hydrogen bonds and other chemical interactions.
Exploring essential prognostic-risk genes and their association with the prognosis, diagnostic efficacy, and risk-group prediction may provide substantial clues for targeting the breast cancer stromal key-risk genes.
少数研究表明基质基因可调节肿瘤微环境（TME）。然而，侵袭性导管乳腺癌相关基质（IDBCS）中关键风险基因的鉴定及其与风险组预测的关联仍不明确。
本研究使用了GSE9014、GSE10797、GSE8977、GSE33692和TGGA BRCA数据集。我们探索了差异表达的转录标志物、枢纽基因、基因模块和富集的KEGG通路。我们采用了多种算法，如对数秩检验、LASSO - cox模型、单变量回归模型和多变量回归模型，来预测预后风险基因和预后风险模型。最后，我们采用基于分子对接的研究来探索敏感药物与预后风险基因的相互作用。
在比较IDBCS和正常基质时，我们发现1472个上调基因和1400个下调基因（合并效应量>0.585且调整后的p值<0.05）。枢纽基因富集癌症、免疫和细胞信号通路。我们在IDBCS中探索了12个关键风险基因（ADAM8、CD86、CSRP1、DCTN2、EPHA1、GALNT10、IGFBP6、MIA、MMP11、RBM22、SLC39A4和SYT2）以识别高危组和低危组患者。高危组的生存率较低，构建的ROC曲线评估了风险模型的有效性。表达验证和诊断效能显示，关键基质风险基因在TCGA BRCA队列的高危组和高基质样本中持续失调。关键风险基因的表达，包括CD86、CSRP1、EPHA1、GALNT10、IGFBP6、MIA和RBM22与耐药性和药物敏感性相关。最后，分子对接研究探索了几种敏感药物（如QL - XII - 61、THZ - 2 - 49、AZ628、NG - 25、拉帕替尼、达沙替尼、SB590885和达拉非尼）通过氢键和其他化学相互作用与这些关键风险基因相互作用。
探索关键预后风险基因及其与预后、诊断效能和风险组预测的关联可能为靶向乳腺癌基质关键风险基因提供重要线索。
