Journal: International Journal of Clinical Practice
Article Title: Machine Learning-Based Integrated Analysis of PANoptosis Patterns in Acute Myeloid Leukemia Reveals a Signature Predicting Survival and Immunotherapy
doi: 10.1155/2024/5113990
Figure Lengend Snippet: Identification of the diverse patterns based on the PANoptosis gene list. (a) A total of 226 PANoptosis-related genes belonging to necroptosis, apoptosis, and pyroptosis were collected for downstream analysis. (b) The univariate Cox regression analysis and Spearman's sum rank test were performed in the HOVON cohort, which was set as the training cohort. 28 genes with prognostic prediction were screened out with p value <0.05. (c) An interaction network of prognostic PANoptosis-related genes. (d) The 618 patients in HOVON cohort were divided into three clusters, according to the consensus clustering analysis based on the transcriptome profile of PANoptosis-related genes. (e) The KM curves showing the differential overall survival among the three subgroups. (f) A heat map showing the expression of the prognostic PANoptosis related genes in different subgroups. The two-sided p value <0.05 was considered significant for all statistical analyses and shown as ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, and ∗∗∗∗ p < 0.0001.
Article Snippet: Transcriptome data of human cancer cell lines (CCLs) were downloaded from the Broad Institute-Cancer Cell Line Encyclopedia project (CCLE, https://sites.broadinstitute.org/ccle/ ) and the CERES scores, the score to evaluate the dependency of the certain gene in the CCL, which indicated that the score was negative correlation with the possible significance of the gene in cell proliferation of the certain CCL, were downloaded from the dependency map portal (DepMap, https://depmap.org/portal/ ).
Techniques: Expressing