Li Fu, Cai Jiao, Liu Jia, Yu Shi-Cang, Zhang Xi, Su Yi, Gao Lei
Medical Center of Hematology, Xinqiao Hospital, Army Medical University, Chongqing, China.
Department of Hematology and Hematopoietic Stem Cell Transplantation Centre, The General Hospital of Western Theater Command, Chengdu, China.
Front Oncol. 2022 Aug 10;12:925615. doi: 10.3389/fonc.2022.925615. eCollection 2022.
Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy. The bone marrow (BM) microenvironment in AML plays an important role in leukemogenesis, drug resistance and leukemia relapse. In this study, we aimed to identify reliable immune-related biomarkers for AML prognosis by multiomics analysis. We obtained expression profiles from The Cancer Genome Atlas (TCGA) database and constructed a LASSO-Cox regression model to predict the prognosis of AML using multiomics bioinformatic analysis data. This was followed by independent validation of the model in the GSE106291 (n=251) data set and mutated genes in clinical samples for predicting overall survival (OS). Molecular docking was performed to predict the most optimal ligands to the genes in prognostic model. The single-cell RNA sequence dataset GSE116256 was used to clarify the expression of the hub genes in different immune cell types. According to their significant differences in immune gene signatures and survival trends, we concluded that the immune infiltration-lacking subtype (IL type) is associated with better prognosis than the immune infiltration-rich subtype (IR type). Using the LASSO model, we built a classifier based on 5 hub genes to predict the prognosis of AML (risk score = -0.086×ADAMTS3 + 0.180×CD52 + 0.472×CLCN5 - 0.356×HAL + 0.368×ICAM3). In summary, we constructed a prognostic model of AML using integrated multiomics bioinformatic analysis that could serve as a therapeutic classifier.
急性髓系白血病（AML）是一种高度异质性的血液系统恶性肿瘤。AML中的骨髓（BM）微环境在白血病发生、耐药性和白血病复发中起重要作用。在本研究中，我们旨在通过多组学分析确定可靠的免疫相关生物标志物用于AML预后评估。我们从癌症基因组图谱（TCGA）数据库获取表达谱，并使用多组学生物信息分析数据构建LASSO-Cox回归模型来预测AML的预后。随后在GSE106291（n = 251）数据集中对该模型进行独立验证，并对临床样本中的突变基因进行分析以预测总生存期（OS）。进行分子对接以预测预后模型中基因的最佳配体。使用单细胞RNA序列数据集GSE116256来阐明枢纽基因在不同免疫细胞类型中的表达。根据它们在免疫基因特征和生存趋势上的显著差异，我们得出结论，免疫浸润缺乏亚型（IL型）比免疫浸润丰富亚型（IR型）的预后更好。使用LASSO模型，我们基于5个枢纽基因构建了一个分类器来预测AML的预后（风险评分 = -0.086×ADAMTS3 + 0.180×CD52 + 0.472×CLCN5 - 0.356×HAL + 0.368×ICAM3）。总之，我们使用综合多组学生物信息分析构建了AML的预后模型，该模型可作为一种治疗分类器。
