Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Sep 16;14(9):1807.
doi: 10.3390/genes14091807.

Aldehyde Dehydrogenase Genes as Prospective Actionable Targets in Acute Myeloid Leukemia

Garrett M Dancik  1 , Lokman Varisli  2 , Veysel Tolan  2 , Spiros Vlahopoulos  3
Affiliations

Aldehyde Dehydrogenase Genes as Prospective Actionable Targets in Acute Myeloid Leukemia

Garrett M Dancik et al. Genes (Basel). .

Abstract

It has been previously shown that the aldehyde dehydrogenase (ALDH) family member ALDH1A1 has a significant association with acute myeloid leukemia (AML) patient risk group classification and that AML cells lacking ALDH1A1 expression can be readily killed via chemotherapy. In the past, however, a redundancy between the activities of subgroup members of the ALDH family has hampered the search for conclusive evidence to address the role of specific ALDH genes. Here, we describe the bioinformatics evaluation of all nineteen member genes of the ALDH family as prospective actionable targets for the development of methods aimed to improve AML treatment. We implicate ALDH1A1 in the development of recurrent AML, and we show that from the nineteen members of the ALDH family, ALDH1A1 and ALDH2 have the strongest association with AML patient risk group classification. Furthermore, we discover that the sum of the expression values for RNA from the genes, ALDH1A1 and ALDH2, has a stronger association with AML patient risk group classification and survival than either one gene alone does. In conclusion, we identify ALDH1A1 and ALDH2 as prospective actionable targets for the treatment of AML in high-risk patients. Substances that inhibit both enzymatic activities constitute potentially effective pharmaceutics.

Keywords: acute; aldehyde dehydrogenase; biomarkers; cancer bioinformatics; gene expression; leukemia; myeloid.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The methodological workflows conducted in this study. (A) Workflow to predict the implication of ALDH1A1 expression in chemotherapy resistance. (B) Workflow to reveal the association of ALDH1A1 expression level with risk groups and survival in AML patients.
Figure 2
Figure 2
ALDH1A1 RNA expression analysis in the TARGET cohort. (A) Comparison of ALDH1A1 expression between paired primary and recurrent tumors (N = 27), with red lines indicating higher expression in recurrent tumors, and blue lines indicating lower expression in recurrent tumors. (B) Comparison of ALDH1A1 expression in an additional set of independent primary (N = 92) and recurrent (N = 13) tumors. (C,D) Association of ALDH1A1 RNA expression (blue: low; red: high) with patient overall survival. p values comparing primary and recurrent tumors were calculated using the paired and independent two-sample t-tests for (A) and (B), respectively; p values comparing survival curves for high and low expressors were calculated using the log rank test. HR: hazard ratio.
Figure 3
Figure 3
Association of ALDH1A1 expression with stemness in AML. (A) Comparison of ALDH1A1 expression between LSC− and LSC+ samples (N = 227) in GSE76008. (B) Correlation between gene expression and LSC17 score for ALDH1A1 and LSC17 signature genes in 9 patient cohorts (described in Table 1). Genes are sorted based on median absolute correlation.
Figure 4
Figure 4
Proposed model of ALDH1A1 contributing to chemoresistance in AML. The model was constructed using the data presented here and in [1].
Figure 5
Figure 5
Evaluation of aldehyde dehydrogenase gene expression as a marker for risk in AML. The area under the “Receiver Operator Characteristic” curve (AUC) is used as a performance metric for how well gene expression separates low- and high- risk patients in 8 independent AML datasets. A value of 1 indicates perfect separation, while a value of 0.5 is the amount of separation expected by chance. Here, the Y-axis shows the values of AUC obtained with each gene, and the X-axis shows the ALDH genes examined.
Figure 6
Figure 6
Correlation and analysis of combined ALDH1A1 and ALDH2 expression as a marker of risk in AML. (A). Histogram of the correlation between ALDH1A1 and ALDH2 expression in 9 independent patient cohorts. (B) AUC values for of ALDH1A1, ALDH2, and their combined expression for distinguishing patients with low- and high-risk tumors.
Figure 7
Figure 7
Association of RNA expression and survival for ALDH1A1, ALDH2, and their combined expression in AML. Each hazard ratio (HR) is calculated by comparing survival curves for patients with high expression level (≥median) to patients with low (<median) expression level. HR > 1 corresponds to patients with high expression level having a higher risk. For each individual cohort, the HR and 95% confidence interval (CI) are denoted by the blue rectangles and whiskers, respectively. The size of the blue rectangles is proportional to the precision of the HR estimate. For the weighted average, the diamond represents the 95% CI. N = number of patients.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Dancik G.M., Varisli L., Vlahopoulos S.A. The Molecular Context of Oxidant Stress Response in Cancer Establishes ALDH1A1 as a Critical Target: What This Means for Acute Myeloid Leukemia. Int. J. Mol. Sci. 2023;24:9372. doi: 10.3390/ijms24119372. - DOI - PMC - PubMed
    1. Moreb J., Schweder M., Suresh A., Zucali J.R. Overexpression of the Human Aldehyde Dehydrogenase Class I Results in Increased Resistance to 4-Hydroperoxycyclophosphamide. Cancer Gene Ther. 1996;3:24–30. - PubMed
    1. Lambrou G.I., Hatziagapiou K., Vlahopoulos S. Inflammation and Tissue Homeostasis: The NF-ΚB System in Physiology and Malignant Progression. Mol. Biol. Rep. 2020;47:4047–4063. doi: 10.1007/s11033-020-05410-w. - DOI - PubMed
    1. Vlahopoulos S.A., Cen O., Hengen N., Agan J., Moschovi M., Critselis E., Adamaki M., Bacopoulou F., Copland J.A., Boldogh I., et al. Dynamic Aberrant NF-ΚB Spurs Tumorigenesis: A New Model Encompassing the Microenvironment. Cytokine Growth Factor Rev. 2015;26:389–403. doi: 10.1016/j.cytogfr.2015.06.001. - DOI - PMC - PubMed
    1. Vlahopoulos S.A. Aberrant Control of NF-ΚB in Cancer Permits Transcriptional and Phenotypic Plasticity, to Curtail Dependence on Host Tissue: Molecular Mode. Cancer Biol. Med. 2017;14:254–270. doi: 10.20892/j.issn.2095-3941.2017.0029. - DOI - PMC - PubMed

MeSH terms

Substances

Grants and funding

This research received no external funding.