Feng Jing, Chen Zhiqiang, Wang Yu, Liu Yinghao, Zhao Danni, Gu Xiaodong
Department of Breast Radiotherapy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
Department of Breast Surgery, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
Clin Exp Med. 2025 May 3;25(1):137. doi: 10.1007/s10238-025-01661-8.
Growing evidence highlights the critical role of chromatin remodeling in tumor development and progression. This study explores the relationship between chromatin remodeling-related genes (CRRGs) and breast cancer (BRCA). Public databases were used to retrieve the TCGA-BRCA and GSE20685 datasets. CRRGs were sourced from prior studies. Prognosis-associated CRRGs were identified using univariate Cox regression analysis. TCGA-BRCA BRCA samples were grouped into CRRG-related subtypes through consensus clustering analysis. Differential expression analysis was conducted in TCGA-BRCA (BRAC vs. control) and among subtypes to identify differentially expressed genes (DEGs). Candidate genes were obtained through the intersection of these DEGs. Prognostic genes were selected using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Independent prognostic factors were identified, and a nomogram model was developed. Functional enrichment, immune infiltration, clinical relevance, and drug sensitivity analyses were subsequently performed. TCGA-BRCA BRCA samples were classified into two CRRG-related subtypes (clusters 1 and 2) based on prognosis-associated CRRGs. A total of 141 candidate genes were identified by intersecting 250 DEGs (cluster 1 vs. cluster 2) with 3,145 DEGs (BRCA vs. control). Five prognostic genes-LHX5, ZP2, GABRQ, APOA2, and CLCNKB-were selected, and a prognostic risk model was developed. In clinical samples, APOA2 (P = 0.0290) and GABRQ (P = 0.0132) expression were significantly up-regulated, CLCNKB (P < 0.0001) and ZP2 (P = 0.0445) expression were significantly down-regulated, while LHX5 (P = 0.1508) expression did not present a significant difference. A nomogram was created, and calibration and Receiver Operating Characteristic (ROC) curves demonstrated its superior predictive ability for BRCA. Gene Set Variation Analysis (GSVA) revealed 16 pathways, such as "mTORC1 signaling" and "E2F targets," were enriched in the high-risk group, while 9 pathways, including "estrogen response early" and "myogenesis," were enriched in the low-risk group. Additionally, significant differences in immune cell types, including CD8 T cells and follicular helper T cells, were observed between the two risk groups. The risk score was also significantly associated with six drugs, including AZD1332 1463 and SB505124 1194. This study presents a prognostic model based on five genes (LHX5, ZP2, GABRQ, APOA2, and CLCNKB) for BRCA, offering a novel perspective on the link between CRRGs and BRCA.
越来越多的证据凸显了染色质重塑在肿瘤发生和发展中的关键作用。本研究探讨了染色质重塑相关基因（CRRGs）与乳腺癌（BRCA）之间的关系。使用公共数据库检索TCGA - BRCA和GSE20685数据集。CRRGs来自先前的研究。采用单变量Cox回归分析确定与预后相关的CRRGs。通过一致性聚类分析将TCGA - BRCA乳腺癌样本分为CRRG相关亚型。在TCGA - BRCA（乳腺癌与对照）以及各亚型之间进行差异表达分析，以鉴定差异表达基因（DEGs）。通过这些DEGs的交集获得候选基因。使用单变量Cox和最小绝对收缩与选择算子（LASSO）回归分析选择预后基因。确定独立预后因素，并建立列线图模型。随后进行功能富集、免疫浸润、临床相关性和药物敏感性分析。基于与预后相关的CRRGs，将TCGA - BRCA乳腺癌样本分为两个CRRG相关亚型（簇1和簇2）。通过将250个DEGs（簇1与簇2）与3145个DEGs（乳腺癌与对照）相交，共鉴定出141个候选基因。选择了五个预后基因——LHX5、ZP2、GABRQ、APOA2和CLCNKB，并建立了预后风险模型。在临床样本中，APOA2（P = 0.0290）和GABRQ（P = 0.0132）表达显著上调，CLCNKB（P < 0.0001）和ZP2（P = 0.0445）表达显著下调，而LHX5（P = 0.1508）表达无显著差异。创建了列线图，校准和受试者工作特征（ROC）曲线证明其对乳腺癌具有卓越的预测能力。基因集变异分析（GSVA）显示，16条通路，如“mTORC1信号传导”和“E2F靶标”，在高风险组中富集，而9条通路，包括“早期雌激素反应”和“肌发生”，在低风险组中富集。此外，在两个风险组之间观察到免疫细胞类型的显著差异，包括CD8 T细胞和滤泡辅助性T细胞。风险评分还与六种药物显著相关，包括AZD1332 1463和SB505124 1194。本研究提出了一种基于五个基因（LHX5、ZP2、GABRQ、APOA2和CLCNKB）的乳腺癌预后模型，为CRRGs与乳腺癌之间的联系提供了新的视角。
