Establishment of a lipid metabolism-related prognostic gene model for patients with acute myeloid leukemia_West China Medical Journal

Authors：

CUI Mengying ¹ , LIANG Yan ¹ , DONG Shaojuan ¹ , ZHU Danxia ² ,  BAO Ying ¹

1. Department of Hematology, Xiangyang First People’s Hospital, Hubei University of Medicine, Xiangyang, Hubei 441000, P. R. China;
2. Department of Oncology, the Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, P. R. China;

Corresponding author：

BAO Ying, Email: 949399787@qq.com

Keywords：

Bioinformatics; acute myeloid leukemia; fatty acid metabolism; immune infiltration; prognostic model

DOI：

10.7507/1002-0179.202502043

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To investigate the expression levels of fatty acid metabolism-related genes in acute myeloid leukemia (AML) and construct a prognostic risk regression model for AML. Methods Gene expression data from control groups and AML patients were downloaded from the GTEx database and The Cancer Genome Atlas (TCGA) database, followed by screening for differentially expressed genes (DEGs) between AML patients and controls. Fatty acid metabolism-related genes were obtained from the MSigDB database. The intersection of DEGs and fatty acid metabolism-related genes yielded fatty acid metabolism-associated DEGs. A protein-protein interaction network was constructed using the STRING database. Hub genes were analyzed via random forest, Kaplan-Meier survival, and Cox proportional hazards regression based on TCGA clinical data to establish a prognostic model and evaluate their diagnostic and prognostic significance. Immune cell infiltration differences between high- and low-risk groups were assessed using CIBERSORT algorithms to explore immune microenvironment variations and correlations with risk scores. Results A total of 60 fatty acid metabolism-related DEGs were identified. Further screening revealed 15 hub genes, among which four genes (HPGDS, CYP4F2, ACSL1, and EHHADH) were selected via integrated random forest, Cox regression, and Kaplan-Meier analyses to construct an AML prognostic lipid metabolism gene signature. Heatmaps demonstrated statistically significant differences in tumor-infiltrating immune cell proportions between risk groups. Conclusion The constructed lipid metabolism gene prognostic model may serve as a biomarker for overall survival in AML patients and provide new insights for immunotherapy drug development.

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5.	Shao F, Mao H, Luo T, et al. HPGDS is a novel prognostic marker associated with lipid metabolism and aggressiveness in lung adenocarcinoma. Front Oncol, 2022, 12: 894485.
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14.	Farge T, Saland E, de Toni F, et al. Chemotherapy-resistant human acute myeloid leukemia cells are not enriched for leukemic stem cells but require oxidative metabolism. Cancer Discov, 2017, 7(7): 716-735.
15.	Chen L, Tang S, Zhang FF, et al. CYP4A/20-HETE regulates ischemia-induced neovascularization via its actions on endothelial progenitor and preexisting endothelial cells. Am J Physiol Heart Circ Physiol, 2019, 316(6): H1468-H1479.
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18.	Panneerselvam S, Yesudhas D, Durai P, et al. A combined molecular docking/dynamics approach to probe the binding mode of cancer drugs with cytochrome P450 3A4. Molecules, 2015, 20(8): 14915-14935.
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20.	Nguyen NHK, Rafiee R, Tagmount A, et al. Genome-wide CRISPR/Cas9 screen identifies etoposide response modulators associated with clinical outcomes in pediatric AML. Blood Adv, 2023, 7(9): 1769-1783.

1. Mishra SK, Millman SE, Zhang L. Metabolism in acute myeloid leukemia: mechanistic insights and therapeutic targets. Blood, 2023, 141(10): 1119-1135.
2. Bian X, Liu R, Meng Y, et al. Lipid metabolism and cancer. J Exp Med, 2021, 218(1): e20201606.
3. Lo Presti C, Yamaryo-Botté Y, Mondet J, et al. Variation in lipid species profiles among leukemic cells significantly impacts their sensitivity to the drug targeting of lipid metabolism and the prognosis of AML patients. Int J Mol Sci, 2023, 24(6): 5988.
4. Ouyang L, Qiu D, Fu X, et al. Overexpressing HPGDS in adipose-derived mesenchymal stem cells reduces inflammatory state and improves wound healing in type 2 diabetic mice. Stem Cell Res Ther, 2022, 13(1): 395.
5. Shao F, Mao H, Luo T, et al. HPGDS is a novel prognostic marker associated with lipid metabolism and aggressiveness in lung adenocarcinoma. Front Oncol, 2022, 12: 894485.
6. Solanki R, Zubbair Malik M, Alankar B, et al. Identification of novel biomarkers and potential molecular targets for uterine cancer using network-based approach. Pathol Res Pract, 2024, 260: 155431.
7. Johnson AL, Edson KZ, Totah RA, et al. Cytochrome P450 ω-hydroxylases in inflammation and cancer. Adv Pharmacol, 2015, 74: 223-262.
8. Houten SM, Denis S, Argmann CA, et al. Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids. J Lipid Res, 2012, 53(7): 1296-1303.
9. Qian F, Arner BE, Kelly KM, et al. Interleukin-4 treatment reduces leukemia burden in acute myeloid leukemia. FASEB J, 2022, 36(5): e22328.
10. Fu C, Kou R, Meng J, et al. m6A genotypes and prognostic signature for assessing the prognosis of patients with acute myeloid leukemia. BMC Med Genomics, 2023, 16(1): 191.
11. Zhou L, Jia X, Shang Y, et al. PRMT1 inhibition promotes ferroptosis sensitivity via ACSL1 upregulation in acute myeloid leukemia. Mol Carcinog, 2023, 62(8): 1119-1135.
12. 谢思雨, 李淼生, 江峰乐, 等. EHHADH 是肝细胞癌脂肪酸代谢通路的关键基因: 基于转录组分析. 南方医科大学学报, 2023, 43(5): 680-693.
13. Stevens BM, Jones CL, Pollyea DA, et al. Fatty acid metabolism underlies venetoclax resistance in acute myeloid leukemia stem cells. Nat Cancer, 2020, 1(12): 1176-1187.
14. Farge T, Saland E, de Toni F, et al. Chemotherapy-resistant human acute myeloid leukemia cells are not enriched for leukemic stem cells but require oxidative metabolism. Cancer Discov, 2017, 7(7): 716-735.
15. Chen L, Tang S, Zhang FF, et al. CYP4A/20-HETE regulates ischemia-induced neovascularization via its actions on endothelial progenitor and preexisting endothelial cells. Am J Physiol Heart Circ Physiol, 2019, 316(6): H1468-H1479.
16. Su M, Chang YT, Hernandez D, et al. Regulation of drug metabolizing enzymes in the leukaemic bone marrow microenvironment. J Cell Mol Med, 2019, 23(6): 4111-4117.
17. De la Garza-Salazar F, Peña-Lozano SP, Gómez-Almaguer D, et al. Orbital myeloid sarcoma treated with low-dose venetoclax and a potent cytochrome P450 inhibitor. J Oncol Pharm Pract, 2023, 29(2): 493-497.
18. Panneerselvam S, Yesudhas D, Durai P, et al. A combined molecular docking/dynamics approach to probe the binding mode of cancer drugs with cytochrome P450 3A4. Molecules, 2015, 20(8): 14915-14935.
19. Summerlin J, Wells DA, Anderson MK, et al. A review of current and emerging therapeutic options for hemophagocytic lymphohistiocytosis. Ann Pharmacother, 2023, 57(7): 867-879.
20. Nguyen NHK, Rafiee R, Tagmount A, et al. Genome-wide CRISPR/Cas9 screen identifies etoposide response modulators associated with clinical outcomes in pediatric AML. Blood Adv, 2023, 7(9): 1769-1783.

West China Medical Journal

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