Tang Guohui, Zhang Zheng, Pang Bo, Li Ruonan, Liu Yuting, Cai Haotian, Wang Wenrui, Chen Changjie, Ou Yurong, Yang Qingling
Anhui Provincial Key Laboratory of Tumor Evolution and Intelligent Diagnosis and Treatment (Bengbu Medical University), Anhui, 233030, China.
Department of Life Sciences, Bengbu Medical University, Anhui, 233030, China.
Biochem Biophys Rep. 2025 Aug 6;43:102198. doi: 10.1016/j.bbrep.2025.102198. eCollection 2025 Sep.
Metabolic reprogramming and immune evasion synergistically drive breast carcinogenesis, but their combined impact remains unclear.
Transcriptomic data from the TCGA and GEO cohorts were integrated. Differentially expressed genes were identified, followed by WGCNA to detect immune-correlated co-expression modules. Immune-metabolism-related genes (IMGs) were screened using Genecards. Four machine learning algorithms (LASSO, SVM, RF, XGBoost) identified hub genes. The diagnostic value was evaluated by Kaplan-Meier and ROC analysis. CIBERSORT quantified immune microenvironment associations. The expression profiles of genes in different cells were plotted using single-cell RNA data. IHC validated protein expression in clinical samples.
Research has found that SELENOP and PKMYT1 are key immune metabolic hubs. Compared with normal tissues, the expression of SELENOP was significantly decreased (p < 0.05), while PKMYT1 showed an upward trend (p < 0.05). Both of these genes have demonstrated high accuracy in the diagnosis of breast cancer and can effectively predict the overall survival period of patients. Low SELENOP expression is associated with high PKMYT1 expression levels, which is significantly related to changes in immune infiltration and the expression patterns of checkpoint proteins. Immunohistochemical detection further confirmed that these genes were significantly correlated with histological grade, LAG-3, CD244, ER, PR and Her-2 and other indicators (p < 0.05).
SELENOP and PKMYT1 are novel immunomodulatory factors related to multiple pathological indicators of breast cancer and can be used as diagnostic biomarkers.
代谢重编程和免疫逃逸协同驱动乳腺癌的发生，但它们的联合影响仍不清楚。
整合来自TCGA和GEO队列的转录组数据。鉴定差异表达基因，随后进行加权基因共表达网络分析（WGCNA）以检测免疫相关的共表达模块。使用Genecards筛选免疫代谢相关基因（IMGs）。四种机器学习算法（LASSO、支持向量机、随机森林、XGBoost）鉴定枢纽基因。通过Kaplan-Meier和ROC分析评估诊断价值。CIBERSORT量化免疫微环境关联。使用单细胞RNA数据绘制不同细胞中基因的表达谱。免疫组化验证临床样本中的蛋白表达。
研究发现硒蛋白P（SELENOP）和M期周期蛋白依赖性激酶1抑制因子（PKMYT1）是关键的免疫代谢枢纽。与正常组织相比，SELENOP的表达显著降低（p<0.05），而PKMYT1呈上升趋势（p<0.05）。这两个基因在乳腺癌诊断中均显示出高准确性，并且可以有效预测患者的总生存期。低SELENOP表达与高PKMYT1表达水平相关，这与免疫浸润变化和检查点蛋白的表达模式显著相关。免疫组化检测进一步证实这些基因与组织学分级、淋巴细胞活化基因-3（LAG-3）、2B4蛋白（CD244）、雌激素受体（ER）、孕激素受体（PR）和人表皮生长因子受体2（Her-2）等指标显著相关（p<0.05）。
SELENOP和PKMYT1是与乳腺癌多种病理指标相关的新型免疫调节因子，可作为诊断生物标志物。
