Identification of Ultrasound-Sensitive Prognostic Markers of LAML and Construction of Prognostic Risk Model Based on WGCNA

BACKGROUND: Acute myeloid leukemia (LAML) is the most widely known acute leukemia in adults. Chemotherapy is the main treatment method, but eventually many individuals who have achieved remission relapse, the disease will ultimately transform into refractory leukemia. Therefore, for the improvement of the clinical outcome of patients, it is crucial to identify novel prognostic markers. METHODS: The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were utilized to retrieve RNA-Seq information and clinical follow-up details for patients with acute myeloid leukemia, respectively, whereas samples that received or did not receive ultrasound treatment were analyzed using differential expression analysis. For consistent clustering analysis, the ConsensusClusterPlus package was utilized, while by utilizing weighted correlation network analysis (WGCNA), important modules were found and the generation of the coexpression network of hub gene was generated using Cytoscape. CIBERSORT, ESTIMATE, and xCell algorithms of the “IOBR” R package were employed for the calculation of the relative quantity of immune infiltrating cells, whereas the mutation frequency of cells was estimated by means of the “maftools” R package. The pathway enrichment score was calculated using the single sample Gene Set Enrichment Analysis (ssGSEA) algorithm of the “Gene Set Variation Analysis (GSVA)” R package. The IC(50) value of the drug was predicted by utilizing the “pRRophetic.” The indications linked with prognosis were selected by means of the least absolute shrinkage and selection operator (Lasso) Cox analysis. RESULTS: Two categories of samples were created as follows: Cluster 1 and Cluster 2 depending on the differential gene consistent clustering of ultrasound treatment. The prognosis of patients in Cluster 2 was better than that in Cluster 1, and a considerable variation was observed in the immune microenvironment of Cluster 1 and Cluster 2. Lasso analysis finally obtained an 8-gene risk model (GASK1A, LPO, LTK, PRRT4, UGT3A2, BLOCK1S1, G6PD, and UNC93B1). The model acted as an independent risk factor for the patients' prognosis, and it showed good robustness in different datasets. Considerable variations were observed in the abundance of immune cell infiltration, genome mutation, pathway enrichment score, and chemotherapeutic drug resistance between the low and high-risk groups in accordance with the risk score (RS). Additionally, model-based RSs in the immunotherapy cohort were significantly different between complete remission (CR) and other response groups. CONCLUSION: The prognosis of people with LAML can be predicted using the 8-gene signature.