Tang Jiaqi, Luo Lin, Bosco Bakwatanisa, Li Ning, Huang Bin, Wu Rongrong, Lin Zihan, Hong Ming, Liu Wenjie, Wu Lingxiang, Wu Wei, Zhu Mengyan, Liu Quanzhong, Xia Peng, Yu Miao, Yao Diru, Lv Sali, Zhang Ruohan, Liu Wentao, Wang Qianghu, Li Kening
Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
Department of Hematology of the Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Northern Jiangsu Institute of Clinical Medicine, Huai'an, Jiangsu 223300, China.
J Biomed Res. 2024 May 29;38(4):397-412. doi: 10.7555/JBR.38.20240065.
Given the extremely high inter-patient heterogeneity of acute myeloid leukemia (AML), the identification of biomarkers for prognostic assessment and therapeutic guidance is critical. Cell surface markers (CSMs) have been shown to play an important role in AML leukemogenesis and progression. In the current study, we evaluated the prognostic potential of all human CSMs in 130 AML patients from The Cancer Genome Atlas (TCGA) based on differential gene expression analysis and univariable Cox proportional hazards regression analysis. By using multi-model analysis, including Adaptive LASSO regression, LASSO regression, and Elastic Net, we constructed a 9-CSMs prognostic model for risk stratification of the AML patients. The predictive value of the 9-CSMs risk score was further validated at the transcriptome and proteome levels. Multivariable Cox regression analysis showed that the risk score was an independent prognostic factor for the AML patients. The AML patients with high 9-CSMs risk scores had a shorter overall and event-free survival time than those with low scores. Notably, single-cell RNA-sequencing analysis indicated that patients with high 9-CSMs risk scores exhibited chemotherapy resistance. Furthermore, PI3K inhibitors were identified as potential treatments for these high-risk patients. In conclusion, we constructed a 9-CSMs prognostic model that served as an independent prognostic factor for the survival of AML patients and held the potential for guiding drug therapy.
鉴于急性髓系白血病（AML）患者间存在极高的异质性，识别用于预后评估和治疗指导的生物标志物至关重要。细胞表面标志物（CSMs）已被证明在AML的白血病发生和进展中起重要作用。在本研究中，我们基于差异基因表达分析和单变量Cox比例风险回归分析，评估了来自癌症基因组图谱（TCGA）的130例AML患者中所有人类CSMs的预后潜力。通过使用多模型分析，包括自适应LASSO回归、LASSO回归和弹性网络，我们构建了一个用于AML患者风险分层的9-CSMs预后模型。9-CSMs风险评分的预测价值在转录组和蛋白质组水平上得到了进一步验证。多变量Cox回归分析表明，风险评分是AML患者独立的预后因素。9-CSMs风险评分高的AML患者的总生存期和无事件生存期比评分低的患者短。值得注意的是，单细胞RNA测序分析表明，9-CSMs风险评分高的患者表现出化疗耐药性。此外，PI3K抑制剂被确定为这些高危患者的潜在治疗方法。总之，我们构建了一个9-CSMs预后模型，该模型是AML患者生存的独立预后因素，并具有指导药物治疗的潜力。
