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We aim to identify prognosis risk factors in acute lymphoblastic leukemia (ALL). mRNA microarray data of adult ALL patients were downloaded from TCGA database, whose mRNAs were isolated from bone marrow aspirate fluid mononuclear cells. Then the differentially expressed genes (DEGs) between good and poor prognosis samples were screened. Following that, the sample dependency network was constructed based on the Pearson connection coefficients of DEGs in the samples. The prognosis-related genes were collected using logistic regression analysis. A classifier for predict the prognosis of ALL patients was established, which was validated in another independent dataset GSE13280 including 173 ALL samples. A total of 578 down-regulated and 637 up-regulated DEGs for worse prognosis were identified. A sample dependency network was established, comprising 100 samples combined by 246 lines. 13 prognosis-related genes were selected to constructed the prognosis classification model, which had an overall precision of 82.7% on distinguishing prognosis status of ALL patients. Total 4 genes were found as the prognosis risk factors in predicting the prognosis of ALL samples, including ALPK1, ACTN4, CALR, and ZNF695. ALPK1, ACTN4, CALR, and ZNF695 were identified as the potential prognosis risk factors in adult ALL.

Identification of prognostic risk factors of acute lymphoblastic leukemia based on mRNA expression profiling