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
. 2022 Nov 11:13:1043111.
doi: 10.3389/fimmu.2022.1043111. eCollection 2022.

Integrating RNA-seq and scRNA-seq to explore the biological significance of NAD + metabolism-related genes in the initial diagnosis and relapse of childhood B-cell acute lymphoblastic leukemia

Chao Lin  1 , Jia-Qi Xu  2 , Gui-Chao Zhong  3 , Hui Chen  4 , Hong-Man Xue  1 , Mo Yang  4 , Chun Chen  1
Affiliations

Integrating RNA-seq and scRNA-seq to explore the biological significance of NAD + metabolism-related genes in the initial diagnosis and relapse of childhood B-cell acute lymphoblastic leukemia

Chao Lin et al. Front Immunol. .

Abstract

Background: Nicotinamide Adenine Dinucleotide (NAD) depletion is reported to be a potential treatment for B-cell Acute Lymphoblastic Leukemia (B-ALL), but the mechanism of NAD metabolism-related genes (NMRGs) in B-ALL relapse remains unclear.

Methods: Transcriptome data (GSE3912), and single-cell sequencing data (GSE130116) of B-ALL patients were downloaded from Gene Expression Omnibus (GEO) database. NMRGs were sourced from Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Further, the differentially expressed NMRGs (DE-NMRGs) were selected from the analysis between initial diagnosis and relapse B-ALL samples, which further performed functional enrichment analyses. The biomarkers were obtained through random forest (RF) algorithm and repeated cross validation. Additionally, cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the immune cell differences between the initial diagnosis and relapse samples, and the correlations between biomarkers and gene markers of differential immune cells were analyzed. Furthermore, single cell RNA sequencing was conducted in the GSE130116 dataset to find key cell clusters. In addition, according to biomarkers expressions, cell clusters were categorized into high and low biomarker expression groups, and Gene Set Enrichment Analysis (GSEA) analysis was performed on them. Finally, the cell clusters with the highest expression of biomarkers were selected to explore the roles of biomarkers in different cell clusters and identify transcription factors (TFs) influencing biological markers.

Results: 23 DE-NMRGs were screened out, which were mainly enriched in nucleoside phosphate metabolic process, nucleotide metabolic process, and Nicotinate and nicotinamide metabolism. Moreover, 3 biomarkers (NADSYN1, SIRT3, and PARP6) were identified from the machine learning. CIBERSORT results demonstrated that four types of immune cells (B Cells naive, Monocyte, Neutrophils, and T cells CD4 memory Activated) were significantly different between the initial diagnosis and the relapse B-ALL samples, and there were strong correlations between biomarkers and differential immune cells such as positive correlation between NADSYN1 and B Cells naive. The single cell analyses showed that the biomarkers were highly expressed in common myeloid progenitors (CMP), granulocyte-macrophage progenitor (GMP), and megakaryocyte-erythroid progenitor (MEP) cell clusters. Gene set enrichment analysis (GSEA) results indicated that 55 GO terms and 3 KEGG pathways were enriched by the genes in high and low biomarker expression groups. It was found that TF CREB3L2(+) was significantly reduced in the high expression group, which may be the TF affecting biomarkers in the high expression group.

Conclusion: This study identified NADSYN1, SIRT3, and PARP6 as the biomarkers of B-ALL, explored biological significance of NMRGs in the initial diagnosis and relapse of B-ALL, and revealed mechanism of biomarkers at the level of the single cell.

Keywords: B-cell acute lymphoblastic leukemia; NAD metabolism-related genes; biomarkers; cell cluster; single cell RNA sequencing.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Identification of differentially expressed NAD+ metabolization-related genes (DE-NMRGs). (A) Volcano plot of DE-NMRGs between initially diagnosed and relapse samples, the red dots indicate up-regulated genes with the thresholds of |log2FC| > 0.5 and p < 0.05. (B) Heatmap of 23 DE-NMRGs in GSE3912. (C) The expression profile of 23 DE-NMRGs in GSE3912. * represents p < 0.05, ** represents p < 0.01, *** represents p< 0.001, **** represents p < 0.0001.
Figure 2
Figure 2
Identification of 3 biomarkers. (A) Box plot of importance of 11 DE-NMRGs through Random forest algorithm. (B) The expression profile of 3 biomarkers in initially diagnosed and relapse samples. **** represents p < 0.0001. (C) The expression levels of 3 biomarkers within different clinical traits (p < 0.05). * represents p < 0.05, ** represents p < 0.01, - represents no significant difference.
Figure 3
Figure 3
TME analysis in GSE3912. (A) Immune infiltration differences between initially diagnosed and relapse groups (p < 0.05). * represents p < 0.05, ** represents p < 0.01, ns represents no significance. (B) The correlation analysis of immune cells and NADSYN1, PARP6, SIRT3. (C) A Venn-diagram of the differentially expressed immune cells (DEIs) and significantly biomarkers-related immune cells to obtain key DEIs. (D) The correlation heatmaps of biomarkers and corresponding gene markers of key DEIs in initially diagnosed and relapse samples.
Figure 4
Figure 4
Single-cell analysis in GSE130116. (A) 26 clusters of core cells are identified in GSE130116 by UMAP2 algorithm. (B) The ratio of 26 clusters in initially diagnosed and relapse samples. (C) Heatmap of the expressions of top 5 gene markers in each cluster. (D) Functional annotations for 26 clusters by CellMarker. (E) Bubble plot of top 3 gene markers in each cell subset. (F) Visualization of inferred temporal trajectory of different cell types.
Figure 5
Figure 5
Ligand-receptor interaction predictions between 9 cell types. (A) Heatmaps of ligand–receptor interactions by CellphoneDB analysis. (B) Visualization of interaction pairs with p value <= 0.05 and log2 mean (Molecule 1, Molecule 2) >= 0.1.
Figure 6
Figure 6
Identification of key cell clusters. (A) The distribution of the biomarkers expressions in different cell types to identify key cell clusters. (B) Correlation between the two clusters (k = 2). (C) The expression levels of biomarkers in the high- and low- expression groups. (D, E). Gene Set Enrichment Analysis (GSEA) in the high- and low-expression groups.
Figure 7
Figure 7
Identification of key transcription factors (TFs) in high- and low- biomarker expression groups. (A) The distribution of top 10 TFs in different clusters. (B) The expression prfile of key TFs in high- and low- expression groups (p < 0.05).
Figure 8
Figure 8
qRT-PCR analysis. The expression levels of NADSYN1, PARP6, SIRT3.
See this image and copyright information in PMC

References

    1. Roberts KG, Mullighan CG. The biology of b-progenitor acute lymphoblastic leukemia. Cold Spring Harb Perspect Med (2020) 10(7):a034835. doi: 10.1101/cshperspect.a034835 - DOI - PMC - PubMed
    1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin (2021) 71(1):7–33. doi: 10.3322/caac.21654 - DOI - PubMed
    1. Jędraszek K, Malczewska M, Parysek-Wójcik K, Lejman M. Resistance mechanisms in pediatric b-cell acute lymphoblastic leukemia. Int J Mol Sci (2022) 23(6):3067. doi: 10.3390/ijms23063067 - DOI - PMC - PubMed
    1. Tasian SK, Loh ML, Hunger SP. Childhood acute lymphoblastic leukemia: Integrating genomics into therapy. Cancer (2015) 121(20):3577–90. doi: 10.1002/cncr.29573 - DOI - PMC - PubMed
    1. Schmiegelow K, Forestier E, Hellebostad M, Heyman M, Kristinsson J, Söderhäll S, et al. . Long-term results of nopho all-92 and all-2000 studies of childhood acute lymphoblastic leukemia. Leukemia (2010) 24(2):345–54. doi: 10.1038/leu.2009.251 - DOI - PubMed

Publication types