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. 2022 Mar 4:2022:3199589.
doi: 10.1155/2022/3199589. eCollection 2022.

HCK is a Potential Prognostic Biomarker that Correlates with Immune Cell Infiltration in Acute Myeloid Leukemia

Fang Cheng  1   2 , Qiang Li  1   2 , Jinglin Wang  1   2 , Liu Wang  1   2 , Weiming Li  3 , Fang Zeng  1   2
Affiliations

HCK is a Potential Prognostic Biomarker that Correlates with Immune Cell Infiltration in Acute Myeloid Leukemia

Fang Cheng et al. Dis Markers. .

Abstract

Background: The tumor microenvironment (TME) plays a significant role in the progression and prognosis of acute myeloid leukemia (AML). This study is aimed at exploring TME-associated biomarkers and identify their potential mechanism in the microenvironment of AML.

Method: In this study, the stromal, immune, and ESTIMATE scores of AML patients were evaluated with the ESTIMATE and CIBERSORT algorithms; then, the AML samples were divided into high- and low-score groups. We evaluated the association between clinicopathological characteristics, survival rate, and the stromal/immune/ESTIMATE scores. Furthermore, we identified TME-associated differentially expressed genes (DEGs) then carried out pathway enrichment analysis, protein-protein interaction (PPI) network, Cox regression analysis, and Kaplan-Meier survival analysis to select the most crucial genes. In addition, we further explored the potential mechanism of HCK in the AML microenvironment.

Results: We identified 624 TME-associated DEGs and found that HCK was the most promising biomarker associated with AML. The results of the gene set enrichment analysis (GSEA) indicated that HCK was mainly involved in immune and inflammation-related signaling pathways. In addition, CIBERSORT analysis showed that HCK was closely related to tumor immune infiltration, with HCK expression associated with various infiltrating immune cells, including B cells, T cells, tumor-associated macrophages (TAM), NK cells, plasma cells, eosinophils, and neutrophils. Furthermore, HCK expression was closely related with ELN risk stratification in patients with AML.

Conclusion: HCK could regulate immune cell infiltration in the microenvironment of AML and may act as a potential biomarker for the treatment and prognosis of AML patients.

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Conflict of interest statement

The authors report no relevant conflict of interest.

Figures

Figure 1
Figure 1
Work flow of the study.
Figure 2
Figure 2
The correlation of the stromal, immune, and ESTIMATE scores with the overall survival rate of AML patients. (a) Stromal score group. (b) Immune score group. (c) ESTIMATE score group.
Figure 3
Figure 3
Correlation between scores and clinicopathological characteristics. (a) Correlation analysis of the stromal score, (b) immune score, and (c) ESTIMATE score with age, gender, and ELN risk stratification. 0 represents <65 years; 1 represents ≥65 years in the age subgroup; 0 represents male; 1 represents female in the gender subgroup.
Figure 4
Figure 4
Identification of TME-associated differentially expressed genes (DEGs). (a) Heatmaps of top 50 DEGs between the high-score group and low-score group based on the stromal score and (b) immune score. (c) Venn diagram of common upregulated TME-associated DEGs and (d) common downregulated TME-associated DEGs.
Figure 5
Figure 5
Functional enrichment analysis of common differentially expressed gene. (a) GO analysis. (b) KEGG pathway enrichment analysis.
Figure 6
Figure 6
The intersection of the PPI network and univariate Cox regression analysis. (a) PPI network. (b) Top 30 differentially expressed genes screened from the PPI network. (c) Univariate Cox regression analysis. (d) Venn diagram of common DEGs shared by the top 30 genes from the PPI network and the prognostic genes in Cox regression analysis.
Figure 7
Figure 7
Prognostic significance of the five hub genes. (a) The expression differences of the five hub genes in normal and tumor samples. (b–f) Survival analysis.
Figure 8
Figure 8
HCK was strongly correlated with prognosis in AML. The correlation of HCK expression with (a) age, (b) gender, and (c) ELN risk stratification. (d) GSEA analysis. 0 represents <65 years; 1 represents ≥65 years in the age subgroup; and 0 represents male; 1 represents female in the gender subgroup.
Figure 9
Figure 9
CIBERSORT algorithm. (a) The fractions of 22 types of tumor infiltratedimmune cells in AML. (b) Correlation with 22 types of tumor infiltrated immune cells.
Figure 10
Figure 10
Comparisons of 22 types of tumor infiltrated immune cells between high and low HCK-expression groups.
Figure 11
Figure 11
Correlation analysis between HCK and tumor-infiltrating immune cells.
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