What does this research mean for the field?

Specific genetic factors have causal relationships with acute myeloid leukemia risk, including protective effects from COL11A2, MTHFD1, and SERPINA10, and risk-increasing effects from genes such as PDE5A, which exhibit distinct cell-specific expression patterns across hematopoietic lineages. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to understand the causal relationships between genetic factors and acute myeloid leukemia (AML) risk for future therapeutic strategies.

June 2, 2026Open Access

Integrative Analysis of Genetic Risk Factors for Acute Myeloid Leukemia Using Mendelian Randomization and Single‐Cell RNA Sequencing Validation

Key Points

This research aims to understand the causal relationships between genetic factors and acute myeloid leukemia (AML) risk for future therapeutic strategies.
Conducted a Mendelian randomization analysis of 10 genetic exposures on AML risk using multiple analytical methods.
Performed single-cell RNA sequencing to validate gene expression patterns and cellular heterogeneity in AML.
Executed quality control and clustering analysis to characterize expression profiles of identified risk genes.
MR analysis identified three genes with significant protective effects: COL11A2 (OR: 0.425–0.481), MTHFD1 (OR: 0.142–0.151), and SERPINA10 (OR: 0.426–0.560).
Seven genes were linked to increased risk of AML, notably PDE5A with an OR exceeding 8.0.
Single-cell analysis revealed 17 distinct cell populations and 14 cell types with unique expression patterns of risk genes.

Abstract

Background Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy with complex genetic underpinnings. Understanding the causal relationships between genetic factors and AML risk is crucial for developing targeted therapeutic strategies. Methods We conducted a comprehensive Mendelian randomization (MR) analysis to evaluate causal effects of 10 genetic exposures on AML risk using multiple analytical methods including inverse‐variance weighted, weighted median, simple mode, weighted mode, and MR‐Egger regression. Single‐cell RNA sequencing analysis was performed to validate gene expression patterns and investigate cellular heterogeneity in AML. Quality control, clustering analysis, and cell type annotation were conducted to characterize the expression profiles of identified risk genes. Results MR analysis revealed heterogeneous causal effects across genetic exposures. Three genes demonstrated significant protective effects: COL11A2 (OR: 0.425–0.481), MTHFD1 (OR: 0.142–0.151), and SERPINA10 (OR: 0.426–0.560). Seven genes showed risk‐increasing effects: SPATA20 , PDE5A , ANXA11 , FUT10 , TXNL4B , RNASET2 , and TCL1A , with PDE5A showing the strongest risk association (OR > 8.0). Single‐cell analysis identified 17 distinct cell populations and 14 cell types, revealing cell‐specific expression patterns of these risk genes across different hematopoietic lineages. Conclusions This integrative approach provides robust evidence for causal relationships between specific genetic factors and AML risk, offering insights into disease mechanisms and potential therapeutic targets at the cellular level.

Integrative Analysis of Genetic Risk Factors for Acute Myeloid Leukemia Using Mendelian Randomization and Single‐Cell RNA Sequencing Validation

Key Points

Abstract

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