FGF13 suppresses acute myeloid leukemia by regulating bone marrow niches

doi:10.1007/s11684-022-0944-z

Front. Med.

2022, Vol. 16

Issue (6) : 896-908 https://doi.org/10.1007/s11684-022-0944-z

RESEARCH ARTICLE

FGF13 suppresses acute myeloid leukemia by regulating bone marrow niches

Ran Li, Kai Xue(

), Junmin Li(

)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

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Abstract

Fibroblast growth factor 13 (FGF13) is aberrantly expressed in multiple cancer types, suggesting its essential role in tumorigenesis. Hence, we aimed to explore its definite role in the development of acute myeloid leukemia (AML) and emphasize its associations with bone marrow niches. Results showed that FGF13 was lowly expressed in patients with AML and that its elevated expression was related to prolonged overall survival (OS). Univariate and multivariate Cox regression analyses identified FGF13 as an independent prognostic factor. A prognostic nomogram integrating FGF13 and clinicopathologic variables was constructed to predict 1-, 3-, and 5-year OS. Gene mutation and functional analyses indicated that FGF13 was not associated with AML driver mutations but was related to bone marrow niches. As for immunity, FGF13 was remarkably associated with T cell count, immune checkpoint genes, and cytokines. In addition, FGF13 overexpression substantially inhibited the growth and significantly induced the early apoptosis of AML cells. The xenograft study indicated that FGF13 overexpression prolonged the survival of recipient mice. Overall, FGF13 could serve as an independent prognostic factor for AML, and it was closely related to the bone marrow microenvironment.

Keywords acute myeloid leukemia FGF13 prognosis immune-related genes bone marrow niches

Corresponding Author(s): Kai Xue,Junmin Li

Just Accepted Date: 29 July 2022 Online First Date: 01 September 2022 Issue Date: 16 January 2023

Cite this article:

Ran Li,Kai Xue,Junmin Li. FGF13 suppresses acute myeloid leukemia by regulating bone marrow niches[J]. Front. Med., 2022, 16(6): 896-908.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-022-0944-z
https://academic.hep.com.cn/fmd/EN/Y2022/V16/I6/896

Fig.1 FGF13 was lowly expressed in patients with AML. (A) Pan-cancer analysis of FGF13 expression based on the TCGA and GTEx databases. A box diagram was used to display these results. The horizontal in the middle of the box represents the median, and the upper and lower sides of the box represent the upper and lower quartiles, respectively. (B) WB detection of FGF13 in MNC and AML cells. (C) Kaplan–Meier survival curves for high- and low-FGF13 groups. *P < 0.05, ** P < 0.01, *** P < 0.001.

Fig.2 Association between FGF13 expression and clinical features. The relationship of FGF13 with age, gender, WBC count, cytogenetic risk (A), and AML driver gene mutations (B). *P < 0.05, ** P < 0.01, *** P < 0.001. ns, not significant.

Fig.3 Novel nomogram for AML prognosis. (A) Nomogram included age, cytogenetic risk, and FGF13 factors for predicting 1-, 3-, and 5-year OS. The model was visualized into a scale for predicting the occurrence of events, and the probability of the occurrence time of the individual is predicted by the score of each index of the individual. The value of the C-index is between 0.5 and 1, and the higher the C-index, the better the predictive power of the nomogram model is. (B) Calibration plot of the nomogram. The abscissa in the figure is the survival probability predicted by the model, and the ordinate is the survival probability observed. Each point represents the survival probability predicted by the model and the observed survival probability. Gray diagonal is the ideal line. The closer each line is to the diagonal, the better the fitting effect of the model is. (C) ROC analysis for the nomogram. The area value under the ROC curve is generally between 0.5 and 1. The closer the AUC is to 1, the better the predictive effect of the model is.

Fig.4 Functional enrichment analysis. (A) GO and KEGG analyses for DEGs. (B) GSEA analysis between high- and low-FGF13 expression groups. The results include upper, middle, and lower parts. Upper part: the peak on the left represents core molecules that were mainly concentrated in the high-FGF13 group, and the peak on the right represents core molecules that were mainly concentrated in the low-FGF13 group; middle part: each vertical line represents a molecule in the gene set; lower part: visualization of values given by the uploaded normalized data. (C) PPI analysis of top 300 DEGs. Red and blue rectangles represent upregulated and downregulated genes, respectively. (D) Scatter diagram for the association between FGF13 and hub genes.

Fig.5 Association between FGF13 expression and immunity. (A, B) Heatmaps for the association of FGF13 with immune checkpoint genes (A) and cytokines (B). Genes marked in red mean the association is significant. (C) Relationships between FGF13 expression and 24 immune cell types. The size of the circle and the height of the stick represent the degree of correlation, and the depth of the color represents the size of the P-value. (D, E) Enrichment scores of T cells (D) and T helper cells (E) between the high- and low-FGF13 expression groups. Enrichment scores were calculated using the “Estimate” package in R software based on the ssGSEA (single sample GSEA) concept. *P < 0.05, ** P < 0.01.

Fig.6 Association of FGF13 with MSI (A), TMB (B), and drug sensitivity (C). Radar chart shows the dimension of the correlation coefficient. According to the correlation coefficient from high to low, the top 6 drugs were displayed.

Fig.7 FGF13 inhibits the growth of human AML cells. The mRNA (A) and protein (B) of FGF13 detection in THP1 cells transduced with oe-NC or oe-FGF13. (C) Representative pictures of colonies from THP1 cells. (D) Cell growth assays in THP1 cells. (E) Representative flow cytometry plots of the percentage of apoptotic cells in THP1 cells with oe-NC or oe-FGF13. (F) Quantitative analysis for Fig. 7E. L, living cells; EA, early apoptosis; LA, late apoptosis. (G) Representative WB images showing the protein levels of Bcl-2 in THP1 cells with FGF overexpression. 5 × 10⁶ THP1 cells expressing oe-NC or oe-FGF13 were injected into sub-lethally irradiated NOD mice via tail vein. Leukemic mice were euthanized by CO₂ inhalation when they showed signs of systemic illness. BM cells were isolated from the femur and analyzed by labeling with anti-human CD45 antibody followed by flow cytometry analysis. (H) Kaplan–Meier survival curves of NOD mice injected with THP1 cells transduced with oe-NC or oe-FGF13. (I) Percentages of CD45⁺ cells and spleen weight (J) in recipient mice. ***P < 0.001. ns, not significant.

Fig.8 Diagram of the possible role of FGF13 in bone marrow niches, drawn using Figdraw. Tumor suppressive gene FGF13 is not associated with gene mutations but related to BM niches.

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