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. 2023 Jan 4:13:996180.
doi: 10.3389/fgene.2022.996180. eCollection 2022.

Comprehensive pan-cancer analysis and the regulatory mechanism of AURKA, a gene associated with prognosis of ferroptosis of adrenal cortical carcinoma in the tumor micro-environment

Keqiang Lu  1 , Xingxing Yuan  1 , Lingling Zhao  1 , Bingyu Wang  1 , Yali Zhang  1
Affiliations

Comprehensive pan-cancer analysis and the regulatory mechanism of AURKA, a gene associated with prognosis of ferroptosis of adrenal cortical carcinoma in the tumor micro-environment

Keqiang Lu et al. Front Genet. .

Abstract

Background: The only curative option for patients with locally or locally advanced adrenocortical carcinoma is primary tumor curative sexual resection (ACC). However, overall survival remains low, with most deaths occurring within the first 2 years following surgery. The 5-year survival rate after surgery is less than 30%. As a result, more accurate prognosis-related predictive biomarkers must be investigated urgently to detect patients' disease status after surgery. Methods: Data from FerrDb were obtained to identify ferroptosis-related genes, and ACC gene expression profiles were collected from the GEO database to find differentially expressed ACC ferroptosis-related genes using differential expression analysis. The DEFGs were subjected to Gene Ontology gene enrichment analysis and KEGG signaling pathway enrichment analysis. PPI network building and predictive analysis were used to filter core genes. The expression of critical genes in ACC pathological stage and pan-cancer was then investigated. In recent years, immune-related factors, DNA repair genes, and methyltransferase genes have been employed in diagnosing and prognosis of different malignancies. Cancer cells are mutated due to DNA repair genes, and highly expressed DNA repair genes promote cancer. Dysregulation of methyltransferase genes and Immune-related factors, which are shown to be significantly expressed in numerous malignancies, also plays a crucial role in cancer. As a result, we investigated the relationship of AURKA with immunological checkpoints, DNA repair genes, and methyltransferases in pan-cancer. Result: The DEGs found in the GEO database were crossed with ferroptosis-related genes, yielding 42 differentially expressed ferroptosis-related genes. Six of these 42 genes, particularly AURKA, are linked to the prognosis of ACC. AURKA expression was significantly correlated with poor prognosis in patients with multiple cancers, and there was a significant positive correlation with Th2 cells. Furthermore, AURKA expression was positively associated with tumor immune infiltration in Lung adenocarcinoma (LUAD), Liver hepatocellular carcinoma (LIHC), Sarcoma (SARC), Esophageal carcinoma (ESCA), and Stomach adenocarcinoma (STAD), but negatively correlated with the immune score, matrix score, and calculated score in these tumors. Further investigation into the relationship between AURKA expression and immune examination gene expression revealed that AURKA could control the tumor-resistant pattern in most tumors by regulating the expression level of specific immune examination genes. Conclusion: AURKA may be an independent prognostic marker for predicting ACC patient prognosis. AURKA may play an essential role in the tumor microenvironment and tumor immunity, according to a pan-cancer analysis, and it has the potential to be a predictive biomarker for multiple cancers.

Keywords: AURKA; ferroptosis; pan-cancer analysis; regulatory mechanism; tumor micro-environment.

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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
Access to key genes. (A) Heatmap of differentially expressed genes in three GEO databases. (B) Venn diagram of differential ferroptosis genes. (C,D) Graph showing the GO and KEGG analysis based on the Metascape Online, bar plot, and network showing the distribution and relationship of the different functions. (E) PPI network and MCODE reveal hub genes in differential ferroptosis gene sets.
FIGURE 2
FIGURE 2
Establishment of ACC ferroptosis-related prognostic model. (A) six Significantly Differential Gene Survival Analysis Survival Chart. (B) Forest plot showing the results of a univariate Cox regression analysis. (C) Ten-fold cross-validation plot. (D) LASSO coefficient trajectory diagram. (E) The risk score, survival status and heat map of three key genes in patients.
FIGURE 3
FIGURE 3
Validation of the model. (A) Survival map of high and low risk patients. (B) 3-Gene time-dependent ROC plot. (C) Single-gene time-dependent ROC plot. (D) Expression of AURKA in normal population and ACC patients. (E) Expression of AURKA in ACC patients at different stages. (F) AURKA expression in a wide range of cancers.
FIGURE 4
FIGURE 4
Prognostic analysis of AURKA in pan-cancer. (A) Forest plot of overall survival prognostic analysis of AURKA in pan-cancer. (B) Disease-specific survival prognostic analysis of AURKA in pan-cancer.
FIGURE 5
FIGURE 5
Lollipop plot of AURKA’s association with immune cells in 12 cancers.
FIGURE 6
FIGURE 6
Correlation of AURKA expression level with immune score, stroma score, calculated score in LUAD (A), SARC (B), ACC (C), ESCA (D), STAD (E).
FIGURE 7
FIGURE 7
Heatmap of AURKA’s association with immune checkpoints in a broad range of cancers.
FIGURE 8
FIGURE 8
The relationship between AURKA expression and DNA repair gene and methyltransferase expression. (A) Heatmap of correlations between AURKA and DNA repair genes. (B) Heatmap of correlation between AURKA and methyltransferase genes.
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