Ma Haodi, Dong Yirui, Zheng Jiayu, Zhang Shunshun, Tang Siya, Wang Junxiang, Qu Zhifeng, Li Xiucheng, Zeng Li, Song Kena, Liu Chunyan, Shi Linlin, Yin Qinan, Zheng Xuewei
Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China.
Henan Engineering Research Center of Digital Pathology and Artificial Intelligence Diagnosis, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China.
IUBMB Life. 2025 Jan;77(1):e2922. doi: 10.1002/iub.2922.
Triple-negative breast cancer (TNBC) remains a significant global health challenge, emphasizing the need for precise identification of patients with specific therapeutic targets and those at high risk of metastasis. This study aimed to identify novel therapeutic targets for personalized treatment of TNBC patients by elucidating their roles in cell cycle regulation. Using weighted gene co-expression network analysis (WGCNA), we identified 83 hub genes by integrating gene expression profiles with clinical pathological grades. A machine learning-based integrative approach further pinpointed 12 prognostic genes, among which CDKN3 exhibited the highest hazard ratio and the most adverse impact on overall survival (OS) in BC patients. Additionally, CDKN3 was identified as an independent prognostic factor for OS prediction. CDKN3 overexpression was confirmed in BC patients and validated at both mRNA and protein levels in BC cells. Knockdown of CDKN3 significantly inhibited the migration and proliferation of BC cells. Cell cycle pathway analysis revealed significant enrichment in G2M-associated pathways in BC patients, with multi-transcriptomic data indicating a close association between enhanced G2M cell cycle activity and CDKN3 upregulation in basal cancer subtypes. Pseudotime analysis further suggested CDKN3 upregulation during the G2M phase at the terminal trajectory of basal cancer subtypes, implying that CDKN3 may drive BC cell progression by promoting G2M cell cycle activity. Mechanistically, CDKN3 knockdown induced G2M cell cycle arrest in TNBC cells by downregulating CCNB2. In conclusion, CDKN3 knockdown effectively inhibits TNBC by arresting the G2M cell cycle, underscoring CDKN3 as a promising therapeutic target in TNBC treatment.
三阴性乳腺癌（TNBC）仍然是一项重大的全球健康挑战，这凸显了精确识别具有特定治疗靶点的患者以及那些有高转移风险患者的必要性。本研究旨在通过阐明其在细胞周期调控中的作用来识别用于TNBC患者个性化治疗的新治疗靶点。使用加权基因共表达网络分析（WGCNA），我们通过整合基因表达谱与临床病理分级确定了83个枢纽基因。一种基于机器学习的综合方法进一步精确确定了12个预后基因，其中CDKN3在乳腺癌患者中表现出最高的风险比以及对总生存期（OS）最不利的影响。此外，CDKN3被确定为OS预测的独立预后因素。在乳腺癌患者中证实了CDKN3的过表达，并在乳腺癌细胞的mRNA和蛋白质水平上得到验证。敲低CDKN3可显著抑制乳腺癌细胞的迁移和增殖。细胞周期通路分析显示乳腺癌患者中G2M相关通路有显著富集，多组学数据表明在基底癌亚型中增强的G2M细胞周期活性与CDKN3上调密切相关。伪时间分析进一步表明在基底癌亚型的终末轨迹的G2M期CDKN3上调，这意味着CDKN3可能通过促进G2M细胞周期活性来驱动乳腺癌细胞进展。机制上，敲低CDKN3通过下调CCNB2诱导TNBC细胞发生G2M细胞周期阻滞。总之，敲低CDKN3通过阻滞G2M细胞周期有效抑制TNBC，强调CDKN3是TNBC治疗中一个有前景的治疗靶点。
