Abstract
Fanconi anemia (FA) is the predominant hereditary syndrome of bone marrow failure (BMF), distinguished by impairments in DNA repair mechanisms. The deficiency in the FANC pathway, which governs DNA repair and replication rescue, results in aberrant responses to DNA damage in individuals with FA. The objective of this study is to examine the involvement of the FANC core complex in BMF and ascertain nucleolar homeostasis-related genes by conducting transcriptome analysis on primary hematopoietic stem cells obtained from FA patients with FANCA and FANCC variants. In the present study, we analyzed scRNA-seq data obtained from both healthy donors and individuals diagnosed with FA in order to investigate the phenomenon of cell–cell communication. Through the implementation of trajectory analysis, the differentiation pathways of several progenitor cell types, such as HSC cells transitioning into LMPP, N, M, B-prog, and E cells, were elucidated. Moreover, by scrutinizing the inferred interactions, notable disparities in cell–cell communication were observed between FA patients and their healthy counterparts. Our analysis has unveiled heightened interactions involving TNFSF13B, MIF, IL16, and COL4A2 in patients with FA. Furthermore, we have developed a prognostic model for predicting the outcome of acute myeloid leukemia (AML) which has exhibited remarkable predictive precision, with an AUC exceeding 0.83 at the 3- and 5-year intervals following the baseline assessment. In summary, the prognostic model that has been developed provides a valuable instrument for forecasting outcomes of AML by utilizing the genes identified through both single-cell and bulk transcriptome analyses.
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Data Availability
The NCBI Gene Expression Omnibus (GEO: GSE157591) provides single-cell sequencing data.
Abbreviations
- AML:
-
Acute myeloid leukemia
- B-prog:
-
B cells progenitor
- E:
-
Erythroid progenitor
- EMP:
-
Erythroid–megakaryocyte progenitor
- FA:
-
Fanconi anemia
- FDR:
-
False discovery rate
- BMF:
-
Bone marrow failure
- HC:
-
Healthy donors
- HSPCs:
-
Hematopoietic stem and progenitor cells
- HSCs:
-
Hematopoietic stem cells
- LMPP:
-
Lymphoid-primed multipotent progenitor
- M:
-
Monocyte progenitor
- MK:
-
Megakaryocyte progenitor
- MK/E:
-
Common megakaryocyte/erythroid cluster
- N:
-
Neutrophil progenitor
- PA:
-
Patients with FA
- UMAP:
-
Uniform manifold approximation and projection
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It was a pleasure to receive insightful and critical comments from the editor and reviewers.
Funding
This study was funded by a grant from the Guangdong Postdoctoral Research Foundation (CN) (O0390302). This study was also funded by a grant from the Scientific Research Start-up Foundation for Talent Introduction Project of The Affiliated Hospital of Xuzhou Medical University (2024203018). The funders had no involvement in the design, collection, or analysis of the data.
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SCY conceived the study. SCY and LHY collected raw data and analyzed the scRNA-seq data. XHG performed the Cell–cell interaction analysis. FYZ performed the gene function and Trajectory analysis. FYZ and SCY wrote the manuscript. All authors read and approved the final manuscript.
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10528_2024_10678_MOESM1_ESM.tif
Supplementary figure 1 Specific ligand-receptor pairs differ between HC and PA. (A) The communication between MK/E (ligands) and other cells (receptors). (B) The communication between unanno-3 cells (ligands) and other cells (receptors). Red points show higher interoperability capacities, and bigger spots indicate greater significance. Supplementary file1 (TIF 30679 KB)
10528_2024_10678_MOESM2_ESM.docx
Supplementary file 2 Screening optimal prognosis-related predictors based on the genes of each analytical procedure. Supplementary file2 (DOCX 18 KB)
10528_2024_10678_MOESM3_ESM.tif
Supplementary figure 3 The regression equation employed to calculate the risk score for each patient. Supplementary file3 (TIF 679 KB)
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Zhang, F., Guo, X., Ye, L. et al. Identifying an AML Prognostic Model Using 10 Marker Genes from Single-Cell Transcriptome and Bulk Transcriptome Analysis. Biochem Genet (2024). https://doi.org/10.1007/s10528-024-10678-9
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DOI: https://doi.org/10.1007/s10528-024-10678-9