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Identification of prognostic risk factors of acute lymphoblastic leukemia based on mRNA expression profiling
Abstract:
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.
Insights
Identifying prognosis risk factors in adult acute lymphoblastic leukemia (ALL) is crucial. Four key genes (ALPK1, ACTN4, CALR, ZNF695) were found to be significant predictors of ALL patient outcomes.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Acute lymphoblastic leukemia (ALL) is a significant hematologic malignancy.
- Accurate prognostic markers are essential for tailoring treatment strategies in adult ALL patients.
Purpose of the Study:
- To identify novel gene expression-based prognosis risk factors in adult acute lymphoblastic leukemia (ALL).
- To develop and validate a predictive model for ALL patient prognosis.
Main Methods:
- Utilized mRNA microarray data from The Cancer Genome Atlas (TCGA) for adult ALL patients.
- Performed differential gene expression analysis to identify genes associated with prognosis.
- Constructed a sample dependency network and employed logistic regression to select prognosis-related genes.
- Developed a classification model and validated it on an independent dataset (GSE13280).
Main Results:
- Identified 578 down-regulated and 637 up-regulated differentially expressed genes (DEGs) associated with poor prognosis.
- A prognosis classification model using 13 genes achieved 82.7% precision in distinguishing prognosis status.
- Four specific genes—ALPK1, ACTN4, CALR, and ZNF695—were identified as key prognosis risk factors.
Conclusions:
- ALPK1, ACTN4, CALR, and ZNF695 represent potential novel prognosis risk factors for adult ALL.
- Gene expression profiling offers a promising avenue for predicting ALL patient outcomes.
- The developed model demonstrates potential for clinical application in ALL prognosis assessment.
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