Identification of Survival-Related Genes in Acute Myeloid Leukemia (AML) Based on Cytogenetically Normal AML Samples Using Weighted Gene Coexpression Network Analysis.
Tingting ChenJuan ZhangYinying WangHe-Bing ZhouPublished in: Disease markers (2022)
The prognosis of acute myeloid leukemia (AML) remains a challenge. In this study, we applied the weighted gene coexpression network analysis (WGCNA) to find survival-specific genes in AML based on 42 adult CN-AML samples from The Cancer Genome Atlas (TCGA) database. Eighteen hub genes ( ABCA 13, ANXA 3, ARG 1, BTNL 8, C11orf 42, CEACAM 1, CEACAM 3, CHI 3 L 1, CRISP 2, CYP 4 F 3, GPR 84, HP , LTF , MMP 8, OLR 1, PADI 2, RGL 4, and RILPL 1) were found to be related to AML patient survival time. We then compared the hub gene expression levels between AML peripheral blood (PB) samples ( n = 162) and control healthy whole blood samples ( n = 337). Seventeen of the hub genes showed lower expression levels in AML PB samples. The gene expression analysis was also done among AML BM (bone marrow) samples of different stages: diagnosis ( n = 142), posttreatment ( n = 42), and recurrent ( n = 12) stages. The results showed a significant increase of ANXA 3, CEACM 1, RGL4 , RILPL 1, and HP in posttreatment samples compared to diagnosis and/or recurrent samples. Transcription factor (TF) prediction of the hub genes suggested LTF as the top hit, overlapping 10 hub genes, while LTF itself is just one of the hub genes. Also, 3671 correlation links were shown between 128 mRNAs and 209 lncRNAs found in survival time-related modules. Generally, we identified candidate mRNA biomarkers based on CN-AML data which can be extensively used in AML prognosis. In addition, we mapped their potential regulatory mechanisms with correlated lncRNAs, providing new insights into potential targets for therapies in AML.
Keyphrases
- network analysis
- acute myeloid leukemia
- genome wide identification
- genome wide
- bioinformatics analysis
- allogeneic hematopoietic stem cell transplantation
- transcription factor
- gene expression
- genome wide analysis
- bone marrow
- dna methylation
- poor prognosis
- heavy metals
- squamous cell carcinoma
- acute lymphoblastic leukemia
- long non coding rna
- mesenchymal stem cells
- deep learning
- computed tomography
- climate change
- electronic health record