AURKA is a prognostic potential therapeutic target in skin cutaneous melanoma modulating the tumor microenvironment, apoptosis, and hypoxia

Abstract

Background: AURKA, Aurora kinase A encoding gene, is an important signaling hub gene for mitosis. In recent years, AURKA has been implicated in the occurrence and development of several cancers. However, its relationship with the tumor microenvironment in skin cutaneous melanoma (SKCM) and the molecular mechanisms underlying its effects are still unclear.

Method: We adopted a variety of bioinformatics methods to comprehensively analyze the potential carcinogenesis of AURKA in SKCM, and constructed a prognostic nomogram model. We also dentified an inhibitor targeting AURKA and verified its therapeutic effects against SKCM using the molecular docking technology.

Results: We found that abnormally high expression of AURKA was responsible for driving the occurrence and development of SKCM, and affected various pathological factors in SKCM. In addition, AURKA was established as an independent marker of poor SKCM prognosis. We also characterized the potential mechanisms by which AURKA manifests its effects in SKCM and found that AURKA inhibits the infiltration of CD8+ T cells and promotes hypoxia by activating the TGF-β signaling pathway. At the same time, the high AURKA expression group had higher tumor stemness index and promoted cell proliferation and metastasis. Finally, the small-molecule compound ZNC97018978 targeting AURKA screened by molecular docking technology can inhibit the proliferation, invasion and metastasis of SKCM. The possible mechanism is that ZNC97018978 induces apoptosis by arresting the cell cycle, thereby inhibiting cell proliferation.

Conclusion: AURKA is the core hub gene driving the occurrence and development of SKCM, and its expression is regulated by epigenetic modifications. AURKA can regulate the infiltration level of various immune cells in the tumor microenvironment, reshape the immunosuppressive tumor microenvironment, and apoptosis, and hypoxia. Thus, it is a prognostic biomarker and potential therapeutic target in SKCM. ZNC97018978 is an effective and safe inhibitor of AURKA in vitro; its safety and effectiveness in vivo as a potential treatment for cutaneous melanoma should be further determined.

Keywords: AURKA; Apoptosis; Hypoxia; Molecular docking technology; SKCM; Tumor microenvironment.

Fig. 1

Identification of SKCM hub genes. A–C Volcano plots of DEGs in GSE3189, GSE15605, and TCGA. D, E Venn diagram showing upregulated and downregulated DEGs across the three datasets. F–G Mutation status and visualization of the top ten hub genes. H, I Univariate Cox regression analysis of the overall survival and disease- specific survival of the top ten hub genes. J Correlation analysis of the top ten hub genes

Fig. 2

The relationship between AURKA expression and prognosis in SKCM. A Expression levels of SKCM cell lines in the CCLE database. AURKA mRNA levels in SKCM tissues and normal skin tissues in GSE3189 (B) and TCGA (D) datasets. C AURKA mRNA levels in primary and metastatic SKCM tissues in GSE46517. E, F Protein expression levels of AURKA in normal skin tissue and melanoma. G, H Immunohistochemical staining of AURKA in adjacent normal lung tissue. Representative images are shown. I AURKA mRNA differential expression in pan-cancer. J Overall survival curve of GSE22155 data; K, L Kaplan–Meier survival analysis of TCGA data of OS, DSS survival curve. M Multivariate Cox regression analysis. N, O Forest plot showing the correlation between AURKA expression and clinicopathological parameters in SKCM patients. *p < 0.05, **p < 0.01, ***p < 0.001

Fig. 3

AURKA gene mutation, methylation, and clinicopathological features analysis in SKCM patients. A Lollipop plot showing the distribution of AURKA gene mutations. B Lollipop plot showing the top ten somatic mutated genes in the AURKA- high and -low expression groups in SKCM. C Association of AURKA gene mutation with prognosis in SKCM patients. D Lollipop plot showing the top five mutated genes affecting AURKA expression. E Effects of AURKA mutation on six types of immune-infiltrating cells. F The correlation between AURKA copy number variation and infiltration abundance of six types of immune-infiltrating cells. G The relationship between AURKA methylation and its expression H AURKA promoter methylation prognostic analysis. I The correlation between AURKA expression and three methyltransferases (DNMT1, DNMT3A, and DNMT3B). J Correlation analysis between AURKA expression and three methyltransferases (DNMT1, DNMT3A, and DNMT3B) in pan-cancer. K Visualization between methylation levels and AURKA expression. L Relationship between AURKA expression and clinicopathological features

Fig. 4

DEGs and their functional enrichment analysis. A GSE46517 and GSE15605 validation cluster analysis results. B Based on TCGA database photos of that SKCM dataset for 54 AURKA-related genes Cluster analysis. C Volcano plot showing DEGs between the AURKA-high and -low groups. D–E AURKA is functionally enriched in single-cell GSE81383 and GSE72056. F Pathway analysis of 54 AURKA-related genes. G GSEA enrichment analysis between AURKA-high and -low groups. H Functional enrichment analysis between AURKA type I and AURKA type II group patients. I Tumor stemness index score between the AURKA type I and AURKA type II group patients. J–K Kaplan–Meier curve showing survival between the AURKA type I and AURKA type II groups

Fig. 5

Immune correlation analysis of AURKA in SKCM. A–D Analysis of the correlation between AURKA expression and immune cell infiltration abundance using the TIME, MCP-counter, xCell, and ssGSEA algorithms. E Correlation of AURKA with immune cell marker genes. F Analysis of the correlation between AURKA expression and stromal score, immune score, and ESTIMATE score using the ESTIMATE algorithm. G The correlation between AURKA and the infiltration level of four kinds of immunosuppressive cells in SKCM patients. H Differences between immune-infiltrating cells between the AURKA type I and AURKA type II group patients

Fig. 6

Correlation between AURKA expression and CD8+ T cell infiltration level and T cell characteristic genes, MHC molecular analysis. A, B The MHC signaling pathway was significantly enriched in the AURKA-low expression and AURKA type I groups. C, D GSEA analysis of the correlation between AURKA expression and CD8+ T cell infiltration level. E, F Molecular correlation analysis of AURKA and MHC. F, G Differential expression of MHC molecules in the AURKA-high and -low expression groups and between the AURKA types I and II groups. G Immune scores in the AURKA-high and -low groups. H, I Differential expression of MHC molecules in the AURKA-high and -low expression groups and between the AURKA types I and II groups. J GASElite database analysis of the correlation between AURKA expression and CD8+ T cell infiltration level. K TIMER2.0 database analysis of the correlation between AURKA expression and CD8+ T cell infiltration level. L, M Difference in the expression of T cell characteristic genes between the AURKA-high and -low expression groups and AURKA type I and type II groups. N Correlation between AURKA expression and the expression of T cell signature genes

Fig. 7

Correlation between AURKA expression and the TGF-β signaling pathway. A, B Difference in the expression of six important genes of the TGF-β signaling pathway in the AURKA-high and -low expression groups and the AURKA type I and type II groups. C Correlation between AURKA expression and the expression of important genes in the TGF-β signaling pathway analyzed by TMER2.0 database. D Correlation between AURKA expression and the expression of important genes in the TGF-β signaling pathway. E TMER2.0 database analysis of the correlation between AURKA expression and the expression of six important genes of the TGF-β signaling pathway in pan-cancer. F–I GSEA analysis showing high AURKA expression and enrichment of the TGF-β signaling pathway in AURKA type II patients. J Difference in the expression of six important genes of the TGF-β signaling pathway in the AURKA-high and -low expression groups and the AURKA type I and type II groups

Fig. 8

AURKA participates in the regulation of hypoxia, Relationship between 15 hypoxia signature genes and the tumor microenvironment. A, B GSVA and GSEA analysis of AURKA expression showing positive correlation of AURKA with hypoxia signaling pathways. C Correlation analysis of AURKA and 15 hypoxia signature genes. D Pan-cancer analysis of the association of AURKA with 15 hypoxia signature genes. E TMER2.0 analysis of the association of 15 hypoxia signature genes with AURKA in SKCM patients. F, G Differential expression of 15 hypoxia signature genes in AURKA-high and -low expression groups and between AURKA type I and type II. H, I TMER and EPIC algorithm analysis of the correlation between 15 hypoxia signature genes and immune cell infiltration levels. J, K Correlation of 15 hypoxia signature genes with MHC molecules and T cell signature genes

Fig. 9

Effects of small-molecule inhibitors ZNC97018978 of AURKA onA875 cells. A Schematic diagram of the docking of AURKA with ZNC97018978. B–D Transwell detection of changes in migration ability in the A875 and HaCaT cells. E Scratch assay detection of changes in migration ability in A875 cells. F RT-PCR detection of apoptosis and mRNA expression of cell cycle-related-factors in cells. G MTT detection of viability changes in A875 cells. H Cell cycle changes as detected by flow cytometry. I, J, L Western blot detection of AURKA, Caspase-3, Bax, Bcl-2, and CDK1 expression changes in the A875 and HaCaT cells. K Apoptosis as detected by flow cytometry