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EDITED BY Feng Jiang, Fudan University, China REVIEWED BY Xiaojing Chang, Second Hospital of Hebei Medical University, China Sammed Mandape, University of North Texas Health Science Center, United States
*CORRESPONDENCE Yali Zhang, zhangyl_2013@sina.com
“These authors have contributed equally to this work
SPECIALTY SECTION This article was submitted to Cancer Genetics and Oncogenomics, a section of the journal Frontiers in Genetics
RECEIVED 17 July 2022 ACCEPTED 29 November 2022 PUBLISHED 04 January 2023
CITATION Lu K, Yuan X, Zhao L, Wang B and Zhang Y (2023), 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. Front. Genet. 13:996180. doi: 10.3389/fgene.2022.996180
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@ 2023 Lu, Yuan, Zhao, Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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 Lut, Xingxing Yuan1, Lingling Zhao, Bingyu Wang and Yali Zhang*
Department of Gastroenterology, Heilongjiang Academy of Traditional Chinese Medicine, Harbin, China
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, pan-cancer analysis, tumor micro-environment, regulatory mechanism, ferroptosis
Introduction
Adrenal cortical carcinoma (ACC) is a rare malignant tumor with an annual incidence of one to two per million that can occur at any age and is more common in women (Cheng et al., 2021; Faron et al., 2022; Pitsava et al., 2022). It is an incidental adrenal tumor and one of the most common reasons for adrenalectomy, accounting for 14% of all spontaneous adrenal tumors (Alyateem and Nilubol, 2021). Although radical resection is the only option for the majority of ACC patients, postoperative survival remains low. As a result, understanding the molecular mechanism of ACC and identifying key target molecules can help predict tumor prognosis.
Currently, ACC is diagnosed using hormone detection and imaging, which plays a vital role in the initial diagnosis and prognostic detection and necessitates repeated detection (Mete et al., 2022). Efforts have been made for decades to discover new reliable, usable diagnostic and prognostic factors. Despite these achievements, 5-year mortality remains higher than 50% (Mizdrak et al., 2021). Accordingly, it is critical to discover new biomarkers that can predict patient outcomes and provide new treatment options.
Ferroptosis, a distinct mechanism of cell death caused by iron-dependent phospholipid peroxidation, has been shown to damage treatment-resistant cancer cells, particularly those in mesenchymal condition and prone to metastasis (Jiang et al., 2021). Correlative research has demonstrated that ferroptosis- related genes are linked to prognosis in various malignancies, including uveal melanoma, glioma, and adrenocortical tumors (Chen et al., 2021a; Luo and Ma, 2021; Zheng et al., 2021).
Aurora kinase A (AURKA) is a serine/threonine kinase family member, and its activation has been linked to several malignancies. Several studies have shown that highly expressed
AURKA can be used as a prognostic marker in various malignancies, including ACC (Du et al., 2021; Tang et al., 2021; Zhang et al., 2022).
Tumor samples from GEO databases were combined with standard models in this study. Differential expression analysis and ACC predictive analysis revealed significantly correlated genes. Pan-cancer analysis was used to study the expression of target genes in 40 different types of cancer. Then the correlations between target gene expression and tumor immune microenvironment, immune checkpoints, DNA repair genes, and methyltransferase were discovered.
Materials and methods
Data source
The GEO database (https://www.ncbi.nlm.nih.gov/geo) was used to download the RNA expression data for ACC from accession numbers GSE12368, GSE19750, and GSE75415, which contained 17 regular and 74 tumor tissues. All data were quantile normalized using a log2-scale transformation. The gene symbols found in multiple probes were calculated using their mean expression levels.
Ferroptosis-related genes
The “Limma” package of R software was used to investigate the differential expression genes (DEGs) of ACC (version: 3.42.2). p-values were adjusted to account for false-positive results. The number of highly expressed molecules in groups 1 (tumor) and 2 (standard control) that met the |log2(FC)|
>1&p. Adj0.05 threshold was counted. The DEGs were also visualized using the “ComplexHeatmap” and “ggplot2” packages. The DEGs and ferroptosis-related genes were then intersected to obtain ferroptosis-related genes with differential expression (DEFGs).
Functional analysis
Metascape Online (https://metascape.org/gp/index.html#/ main/step1) was used for available analysis. Metascape was used to perform functional analysis and build a PPI network using the ferroptosis-related genes. MCODE was used to reveal more densely connected regions.
Construction and prognostic value of IRSS
Univariate (Wei et al., 2022) Cox regression model is a semi- parametric regression model. The model’s dependent variables are survival results and survival time. It may examine the impact of several variables on survival time simultaneously. It does not require estimated data and can evaluate data with suppressed survival time. The least absolute shrinkage and selection operator (LASSO) is an L1-regularized linear regression approach. Using L1-regularization, part of the learned feature weights will be set to zero, achieving the goal of sparsity and feature selection (Tian et al., 2022). Univariate Cox regression analysis of DEFGs was used to identify significant prognosis-related genes, followed by LASSO regression analysis to obtain independent genes. A multivariate Cox regression analysis was also performed to obtain regression coefficients for independent prognostic factors. Finally, an immune risk score signature (IRSS) based on the Cox regression coefficient beta value was developed.
Survival analysis
One-way Cox was used to analyze the association of ACC expression with patient survival, and Xian Tao Academic created a forest plot of the correlation of overall survival and disease- specific survival of ARUKA in pan-cancer (https://www.xiantao. love).
Immune correlation analysis
The TIMER database was used to download data from multiple immune-infiltrating cells in 40 cancers, and the correlation between target gene expression and immune cell scores was examined separately. A lollipop graph of the correlation of target genes with immune cells in the cancer microenvironment and a diagram of the correlation of target
genes with immune scores, stromal scores, and computational scores in five cancers were drawn using Xian Tao Academic (https://www.xiantao.love).
Correlation analysis of DNA repair genes and methyltransferases
Using the TCGA expression profiling data, the correlation of DNA repair genes with target gene expression was assessed. The relationship between methyltransferases and the target gene was also investigated. Xian Tao Academic (https://www.xiantao.love) was used to create heat maps, with red dots indicating significant correlations.
Results
Results of DEGs screening in ACC
The information on the GEO database used is listed in Table 1. A total of 2,311 differentially expressed genes were identified following differential gene analysis: in GSE12368, the total number of molecules after filtering was 21,655, of which 849 IDs met the |log2(FC)|>1&p. Adj0.05 threshold. There were 170 highly expressed (logFC is positive) individuals in the standard group and 679 highly expressed (logFC is negative) individuals in the tumor group. The number of molecules in GSE19750 after filtering is 21,655, and 849 IDs meet the |log2(FC)| >1&p. Adj0.05 threshold.
Under this threshold, the regular group has a high expression (logFC is positive). The number was 170, with 679 having a high face (logFC is negative) in the tumor group. The number of molecules in GSE19750 after filtering is 21,655, and 849 IDs meet the |log2(FC)| >1&p. Adj0.05 threshold. Under this threshold, the usual group has a high expression (logFC is positive). The number was 170, with 679 highly expressed (logFC is negative) in the tumor group. After filtering in GSE75415, 12,548 molecules were obtained, of which 660 dysregulated genes satisfy |log2(FC)|>1&p. Adj0.05; under this threshold, the number of highly expressed (logFC is positive) genes in the standard group is equal to the number of highly expressed (logFC is positive) genes in the standard group. There were 258 in the tumor group, with 402 being highly expressed (logFC is negative) (Figure 1A). DEFGs was created by intersecting DEGs from GEO databases and ferroptosis- related genes (Figure 1B).
We used Metascape Online to perform a functional analysis to investigate ACC’s underlying mechanisms of ferroptosis signatures. The Gene Ontology (GO) analysis results show that these DEFGs were primarily enriched in response to
| Accession number | Platform | Samples | Experiment type |
|---|---|---|---|
| GSE75415 | GPL96 | 25 | expression profiling by array |
| GSE12368 | GPL570 | 18 | expression profiling by array |
| GSE19750 | GPL570 | 48 | expression profiling by array |
A
KONJ5
NCAPG TOPZA BACS
PMAIP1
Apelan
GRINZC
NPYIR
GRIA2
CCNB1 RRM2
SOLE
TCEAL2
MND1
IL32
OBH
FOXMI
CHGA
Group
PEG3-AS1
Group
BUB1
HOMER1
SST
GTF21
Group
SCNN1A
group1
CBXS
ZNF460
group1
COL11A1
ERICH3
SMCy HIST1H4E
TACC3
group2
UBEZS ASPM
group1
ASUS
CASA
SUR
PARK
group2
KIFZDA
group2
CYP1182
2
HEMES
BODILI
2
NEKS
BPPHI
PRI
2
BOTH
KLAA 1004
1
LUZPI
TMEFF2
1
TOP2A
TPX2
PTMS
SLC2AB
1
MUMIL1
RPS4Y1
0
0
ANGPT2
CENPK
CYP11B1
HSD382
CYP1182
0
LE
KIFZDA DEPDC1
-1
FAM1668
KONQ1
AADAC
-1
LMOD1
HOPX
-1
E
CDC25C
S100A8
11
1PX2
-2
FARBA
Ppare ADH18
-2
NOW
WNT4
DEPDC1B
HSD382
-2
AADIRE
FOXM!
ANIN
FMOD
ASPM
eyes
HIRZB
PIGDS
CSOCZ
HACE
L
CCNB2
OTL
CYP1182
COKI
OPR98
PDGFRA
SCG2
OFBP5
ZG16B
CALB!
CARTPT
LSP1
PNLIPRP3
PENK
CHGA
FATE1
TH
RARRE52
C3
MIR210HG
CHGA
PNMT
NR442
FOSB
GSE12368
GSE19750
GSE75415
B
C
GO:0034599: cellular response to oxidative stress
GO:0051348: negative regulation of transferase activity
GSE19750
GSE75415
R-HSA-8953897: Cellular responses to stimuli
GO:0034097: response to cytokine
GO:0007346: regulation of mitotic cell cycle
WP236: Adipogenesis
GSE12368
588
378
FerrDB
GO:0050727: regulation of inflammatory response
GO:1901652: response to peptide
hsa05167: Kaposi sarcoma-associated herpesvirus infection
118
49
17
R-HSA-9648895: Response of EIF2AK1 (HRI) to heme deficiency WP4313: Ferroptosis
GO:0002520: immune system development
77
2
GO:0080135: regulation of cellular response to stress
WP5044: Kynurenine pathway and links to cell senescence
462
217
R-HSA-9663891: Selective autophagy
3
WP2882: Nuclear receptors meta-pathway
GO:0048871: multicellular organismal homeostasis
132
10
M14: PID AURORA B PATHWAY
GO:0018958: phenol-containing compound metabolic process
2
2
WP231: TNF-alpha signaling pathway
GO:0031331: positive regulation of cellular catabolic process
R-HSA-556833: Metabolism of lipids
6
GO:0001889: liver development
GO:0006575: cellular modified amino acid metabolic process
WP5115: Network map of SARS-COV-2 signaling pathway
GO:0022411: cellular component disassembly
0
2
4
6
8
10
-log10(P)
D
E
TMNI
cellular response to oxidative stress
negative regulation of transferase activity
Cellular responses 10
response to cytokine
regulation of mitotic cell cycle
DOKONZA
Adipogenesis
regulation of inflammatory response
response to peptide
Kaposi sarcoma-associated herpesvirus infection
Response of EIF2AK1 (HRI) to heme deficiency Ferroptosis
KURKA
COKNIA
immune system development
regulation of cellular response to stress
Kynurenine pathway and links to cell senescence
Selective autophagy
Nuclear receptors meta-pathway
multicellular organismal homeostasis
PID AURORA B PATHWAY
phenol-containing compound metabolic process
ANPICS
TNF-alpha signaling pathway
NFAP3
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.
A
1.0
HELLS
1.0
FANCD2
1.0
SLC40A1
Low
High
Low
Low
0.8
High
High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Overall Survival HR = 8.83 (3.29-23.68)
0.2
0.2
Overall Survival HR = 5.10 (2.14-12.17)
Overall Survival HR = 0.23 (0.10-0.54)
0.0
P < 0.001
0.0
P < 0.001
0.0
P = 0.001
0
50
100
150
0
50
100
150
0
50
100
150
Time (months)
Time (months)
Time (months)
1.0
TNFAIP3
1.0
STMN1
Low
1.0
AURKA
Low
High
High
Low
High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Overall Survival HR = 5.74 (2.24-14.72)
0.2
Overall Survival HR = 10.62 (3.63-31.08)
0.2
Overall Survival HR = 6.55 (2.62-16.37)
0.0
P < 0.001
0.0
P < 0.001
0.0
P < 0.001
0
50
100
150
0
50
100
150
0
50
100
150
B
Time (months)
Time (months)
Time (months)
0
10
20
30
D
E
4
Risk score
3
66666666666666666666666666655533333322211110
6
6
6
3
0
Risk group
2
Low
0.6-
High
1
Coefficients
0.4-
Survival time
4000
0.2.
3000
Status
2000
1
0
0.0-
1000
₹
-0.2-
TNFAIP3
-5
-4
-3
-2
-1
-5
-4
-3
-2
-1
Log (
Log (1)
AURKA
HELLS
-2
-1
0
1
2
| Characteristics | Total(N) | HR(95% CI) Univariate analysis | P value Univariate analysis |
|---|---|---|---|
| HELLS(High vs. Low) | 79 | 8.829 (3.292-23.675) 1 | <0.001 |
| FANCD2(High vs. Low) | 79 | 5.098 (2.136-12.166) 1 | <0.001 |
| SLC40A1(High vs. Low) | 79 | 0.229 (0.097-0.542) | <0.001 |
| TNFAIP3(High vs. Low) | 79 | 5.742 (2.241-14.717) 1 | <0.001 |
| STMN1(High vs. Low) | 79 | 10.622 (3.631-31.076) I 1 | <0.001 |
| AURKA(High vs. Low) | 79 | 6.545 (2.617-16.369) 1 | <0.001 |
C
Partial Likelihood Deviance
9.5
9.0
8.5-
8.0-
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.
A
B
1.0
C
1.0
1.0
Low
High
0.8
0.8
Survival probability
0.8
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
AURKA
HR = 11.63 (3.96-34.12)
3-Year (AUC = 0.918)
5-Year (AUC = 0.902)
2-Year (AUC = 0.906)
3-Year (AUC = 0.920)
0.0
P < 0.001
0.0
7-Year (AUC = 0.853)
0.0
5-Year (AUC = 0.818)
0
1000
2000
3000
4000
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Time
1-Specificity (FPR)
1-Specificity (FPR)
D
E
6
10
ns
ns
The expression of AURKA Log2 (TPM+1)
5
The expression of AURKA Log2 (TPM+1)
8
ns
4
ns
6
3
2
4
1
2
0
T
Stage I
Stage II Stage III Stage IV Pathologic stage
Normal
Tumor
F
8
ns
The expression of AURKA Log2 (FPKM+1)
. .
6
Normal
Tumor
4
:
-
…
·
.
2
. …
-…
..
.
:
-
0
DE
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
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.
stimuli, oxidative stress responses, immune system processes, and negative regulators of transferase activity, as shown in Figures 1C, D. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, these d DEFGs were primarily enriched in ferroptosis, cellular responses to stimuli, selective autophagy, and EIE2AKI response to heme deficiency. As a result of these findings, we decided to investigate the
relationship between the ferroptosis-gene set and the tumor immune microenvironment. Furthermore, the MCODE plugin and the MetascapeOnline-based protein-protein interaction (PPI) network identified necessary modules in these filiform genes (Figure 1D). STMN1, CDKN2A, CDKN1A, MAP3K5, TNFAIP3, and AURKA are involved in seven edges and six nodes.
A
B
| Tumor | HR (95%CI) | p value | Tumor | HR (95%CI) | p value | |
|---|---|---|---|---|---|---|
| ACC | 5.48 (2.28-13.17) | <0.001 | ACC | 5.21 (2.14-12.69) | <0.001 | |
| BLCA | 1.31 (0.98-1.76) | 0.069 | BLCA | 1.51 (1.06-2.16) | 0.024 | |
| BRCA | 1.24 (0.90-1.71) | 0.184 | BRCA | 1.41 (0.92-2.18) | 0.114 | |
| CESC | 1.17 (0.74-1.86) | 0.505 | CESC | 1.25 (0.74-2.13) | 0.409 | |
| CHOL | 1.73 (0.65-4.65) | 0.275 | CHOL | 1.92 (0.66-5.56) | 0.231 | |
| COAD | 0.78 (0.53-1.15) | 0.205 | ||||
| COAD | 0.73 (0.44-1.20) | 0.21 | ||||
| COADREAD | 0.77 (0.54-1.08) | 0.133 | ||||
| DLBC | 0.80 (0.18-3.59) | 0.766 | COADREAD | 0.82 (0.52-1.28) | 0.373 | |
| ESAD | 1.85 (0.96-3.53) | 0.064 | DLBC | 1.14 (0.16-8.23) | 0.9 | |
| ESCA | 0.96 (0.59-1.58) | 0.886 | ESAD | 1.79 (0.84-3.81) | 0.13 | |
| ESCC | 0.46 (0.20-1.10) | 0.08 | ESCA | 0.97 (0.54-1.74) | 0.925 | |
| GBM | 1.20 (0.85-1.69) | 0.295 | ESCC | 0.68 (0.26-1.80) | 0.439 | |
| GBMLGG | 4.90 (3.68-6.54) | <0.001 | GBM | 1.24 (0.86-1.79) | 0.252 | |
| HNSC | 1.31 (1.00-1.71) | 0.05 | GBMLGG | 5.22 (3.84-7.10) | <0.001 | |
| KICH | 8.39 (1.05-67.12) | 0.045 | HNSC | 1.46 (1.03-2.07) | 0.033 | |
| KIRC | 1.61 (1.19-2.19) | 0.002 | KICH | 6.38 (0.77-52.97) | 0.086 | |
| KIRP | 2.73 (1.46-5.11) | 0.002 | KIRC | 2.28 (1.52-3.42) | <0.001 | |
| LAML | 0.98 (0.64-1.49) | 0.914 | KIRP | 7.40 (2.56-21.35) | <0.001 | |
| LGG | 2.60 (1.78-3.82) | <0.001 | 2.72 (1.81-4.07) | |||
| LGG | <0.001 | |||||
| LIHC | 1.83 (1.29-2.59) | 0.001 | ||||
| LIHC | 2.24 (1.41-3.54) | 0.001 | ||||
| LUAD | 1.36 (1.02-1.81) | 0.037 | ||||
| LUADLUSC | 1.12 (0.92-1.36) | 0.264 | LUAD | 1.45 (1.01-2.10) | 0.045 | |
| LUSC | 0.96 (0.73-1.26) | 0.779 | LUADLUSC | 1.23 (0.93-1.62) | 0.147 | |
| MESO | 3.46 (2.10-5.71) | <0.001 | LUSC | 1.41 (0.92-2.16) | 0.118 | |
| OS | 0.83 (0.45-1.54) | 0.557 | MESO | 3.77 (2.00-7.12) | <0.001 | |
| OSCC | 1.49 (1.08-2.06) | 0.016 | OSCC | 1.56 (1.03-2.36) | 0.034 | |
| OV | 0.98 (0.76-1.27) | 0.887 | OV | 0.92 (0.70-1.22) | 0.56 | |
| PAAD | 1.83 (1.20-2.78) | 0.005 | PAAD | 1.75 (1.09-2.81) | 0.02 | |
| PCPG | 7.53 (0.92-61.38) | 0.059 | PCPG | 5.50 (0.64-47.25) | 0.12 | |
| PRAD | 1.96 (0.50-7.61) | 0.331 | PRAD | 3.29 (0.37-29.58) | 0.289 | |
| READ | 0.97 (0.45-2.11) | 0.945 | READ | 1.85 (0.62-5.54) | 0.269 | |
| SARC | 1.30 (0.88-1.94) | 0.191 | SARC | 1.31 (0.85-2.03) | 0.225 | |
| SKCM | 1.29 (0.98-1.68) | 0.068 | ||||
| STAD | 0.93 (0.67-1.29) | 0.654 | SKCM | 1.29 (0.97-1.72) | 0.082 | |
| TGCT | 2.81 (0.29-27.06) | 0.371 | STAD | 0.89 (0.59-1.35 ... | 0.59 | |
| THCA | 1.31 (0.49-3.53) | 0.589 | TGCT | 1.83 (0.17-20.24 ... | 0.62 | |
| THYM | 0.17 (0.04-0.86) | 0.032 | THYM | 0.55 (0.07-4.19) | 0.566 | |
| UCEC | 2.13 (1.39-3.27) | 0.001 | UCEC | 3.07 (1.76-5.36) | <0.001 | |
| UCS | 1.16 (0.59-2.28) | 0.671 | UCS | 1.26 (0.60-2.63) | 0.542 | |
| UVM | 3.09 (1.26-7.61) | 0.014 | UVM | 4.11 (1.49-11.29) | 0.006 | |
0.0
2.5
5.0
7.5
10.0
0
2
4
6
8
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.
Construction and prognostic value of IRSS
The associations of 42 DEFGs with overall survival in ACC were calculated separately using univariate survival analysis. Six
genes were significantly related to ACC prognosis, including AURKA, TNFAIP, HELLS, STMN1, FANCD2, and SLC4OA1. The high expression of the six genes associated with poor prognosis in ACC, as shown in Figure 2A, greatly impacted
the overall survival of ACC patients and was followed by LASSO regression analysis.
LASSO regression can improve model accuracy and interpretability while also eliminating the issue of collinearity between independent variables (Yu et al., 2021). The results of Figures 2C, D determined that the model fit best when the penalty coefficient was 3, and the corresponding three immune genes, TNFAIP3, AURKA, and HELLS, were included in the model (Figure 2E).
Each patient’s risk score was calculated as previously described (Peng et al., 2022). Furthermore, the risk score of each ACC patient was directly computed using the above formula. The samples were then divided into high- and low-risk groups, which were then grouped based on the median. The KM curve results showed that the high-risk group had a worse prognosis than the low-risk group (Figure 3A, log-rank p 0.001; HR = 11.63% CI = 3.9634,12).
The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess IRSS’s prognostic predictive value in ACC patients. The receiver operating characteristic curves are referred to as ROC curves, with sensitivity as the ordinate and 1-specificity as the abscissa (DeLong et al., 1988). The AUC is a probability value ranging from 0.5 to -1 that is used to evaluate the accuracy of the model prediction; a more extensive area indicates higher accuracy. In the current study, the greater its value, the greater the agreement between predicted and actual overall survival.
The area under the curve (AUC) was 0.918 (3-year OS), 0.902 (5-year OS), and 0.853 (7-year OS), as shown in Figure 3B, indicating that the prediction model was well established. We also created ROC curves for the effect of AURKA alone on survival time in ACC patients, with AUCs of 0.906 (2-year OS), 0.920 (3-year OS), and 0.818 (5-year OS) (Figure 3C). The above results demonstrated the model’s robustness and accuracy in predicting patient prognosis. Simultaneously, we discovered that AURKA’s single-gene and polygenic prognostic models have similar prediction results. AURKA is a common intersection of ferroptosis- related genes and three differentially expressed gene sets in the GEO database. As a result, we make the bold assumption that AURKA is a crucial gene associated with ferroptosis prognosis in ACC. Then, we looked at AURKA’s pan-cancer expression and its relationship to ACC pathological stage.
Expression of AURKA in pan-cancer
The expression level of AURKA was higher in ACC tissue (Figure 3D), and the expression level of AURKA in different stages of ACC was shown in Figure 3E, indicating that the expression level of AURK increased with the progression of ACC. We then investigated AURKA expression in pan- cancer, and the results show that AURKA was highly expressed in all 31 tumors except PCPG and THCA (Figure 3F).
Prognostic analysis of AURKA expression in ACC and other cancers
The correlation of AURKA expression with overall survival and disease-specific survival in 40 TCGA tumors was calculated using univariate survival analysis. AURKA expression, as shown in Figure 4A, significantly impacted overall survival in multiple cancers.
In addition to COAD, COADREAD, DLBC, ESCC, and THYM, forest plot results revealed that high AURKA expression was associated with poor patient prognosis. Figure 4B depicts the correlation of AURKA expression with disease-specific survival, demonstrating that in ACC, GBMLGG, KICH, KIRC, KIRP, and LGG, patients with high AURKA expression had significantly lower disease-specific survival than patients with common AURKA expression. Overall, the findings suggest that AURKA could be used to predict the prognosis of ACC and other cancers.
Correlation of AURKA with immune cells in the pan-cancer microenvironment
It has been studied whether AURKA expression correlates with immune infiltration in ACC or other types of cancer. The findings revealed that AURKA expression is associated with the level of immune infiltration in various tumors. Particularly Th2 cells. AURKA was significantly positively correlated with Th2 cells in all 40 cancers studied, and it was the first positive correlation. We also chose 12 cancers to map the relationship between AURKA and immune cells in these cancer microenvironments (GBM, LUSC, LUAD, TGCT, CESC, COADREAD, SARC, ACC, KICH, ESAD, STAD, READ). Figure 5 shows that, in addition to Th2 cells, many other immune cells were negatively correlated with AURKA. The killer CD8+T regulated by Th1 was the main focus of the previous immunotherapy study for AURKA. Perhaps Th1- executing B Cells will have an unanticipated effect on AURKA targeted therapy. AURKA may also inhibit other immune cells in the tumor microenvironment, though the specific mechanism is unknown.
Xiantao Academic then created a correlation chart of AURKA expression levels in LUAD, SARC, ACC, ESCA, STAD, immune score, matrix score, and calculation score, which were all negatively correlated (Figure 6).
Correlation of AURKA expression with immune checkpoints
More than 40 common immune checkpoint genes were analyzed, as was the relationship between AURKA expression and immune checkpoint gene expression. Figure 7 depicts the results. AURKA was
GBM
LUSC
LUAD
Th2 cells
Th2 oulis
Th2 cells
T helper cells
T helper cells
Tgd
Tom
Tgđ
NK CD55dim cells
Tgd
Tem
T helper cells
NK cells
Tem
aDC
CD8 T cels
aDC
TReg
TReg
P value
Th17 cells
P value
Cytotoxic cells
P value
Tem
0.75
NK CD56dim cells
PDC
0.50
Eosinophils
0.2
Tem
0.4
Neutrophils -
0.3
NK CD56dim cells
0.26
NK CD56bright cells
0.1
Thi cells
02
B cells
0.00
TReg
Tom -
0.1
0.0
NK CD56bright cells
Correlation
Cytotoxic cells
Correlation
NK CD55bright cells
Correlation
Thi cells
0.2
T cells
0.1
T cells -
0.2
TFH
0.4
CDB T cells
0.2
Th17 cells B cells
0.4
T cells
0.6
TFH
0.3
0.6
Mast cells
Neutrophils
Macrophages CD8 T cells
aDC
Th1 cells
DC
NK cells
pDC
Cytotoxic cells
Macrophages
NK cells -
Th17 cels
B cells
TFH-
iDC
PDC
DC
Neutrophils
DC
Eosinophils
Macrophages
iDC
iDC
Eosinophils
Mast cells
Mast cells
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
-0.4
-0.2
0.0
0.2
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Correlation
Correlation
Correlation
TGCT
CESC
COADREAD
Th2 cells
Th2 cells
Th2 cells
NK CD56bright cels
T helper cells
T helper cells
Tom
Tcm
Th17 cells
NK cells
Tgd
NK CD66dim cells
T helper cells
Eosinophils
aDC
Tgd
P value
Tem
Tgd -
TReg
0.75
Th17 cells
P value
Tem
aDC
0.50
NK cells
0.8
Neutrophils
P value
Macrophages Mast cells
NK CD56bright cells
0.6
0.25
0.4
Eosinophils
0.75
aDC
0.2
TReg-
0.50
DC
Macrophages
B cells
0.25
NK CD56dim cells
Correlation
0.1
NK CD56dim cells
Correlation
DC
T cells
02
Thi cells
0.1
Tem -
Correlation
Neutrophils
0.3
Neutrophils
0.2
Th1 cells-
0.1
Thi cells
0.4
TFH
0.3
Macrophages CD8 T cells-
0.2
Th17 cells
0.5
TReg
Eosinophils
T cells
Mast cells
iDC
DC
Cytotoxic cells
Cytotoxic cells
CDB T cells
T cells
B cells
IDC
TFH -
CDB T cells
Cytotoxic cells
PDC
TFH
B cells
NK CD56bright cells
Tem
Mast cells
NK cells
PDC
PDC
IDC
-0.4
-0.2
0.0
0.2
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Correlation
Correlation
Correlation
SARC
ACC
KICH
Th2 cells
Th2 cells
Th2 cells
T helper cells
aDC
aDC
TReg
T helper o
T helper cells
Neutrophils
Tgd
Tcm
Th1 cells
TReg
Macrophages
NK CD56bright cels
Tcm
Eosinophils
aDC
P value
NK cells
P value
Neutrophils
P value
Macrophages
0.8
0.6
Tem
0,6
NK cells
0.8
NK CD56dim cels
0.8
0.4
IDC
0.4
B cells
0.4
Th17 cells
0.2
DC
0.2
TReg
0.2
Tgd
0.0
Neutrophils
0.0
TFH
T cells
Correlation
NK CD56bright cells Macrophages
Correlation
Tgd
Correlation
DC
0.2
0.2
NK CD56dim cells
0.1
IDC
0.4
PDC
0.4
Mast cells
0.2
Tom
0.6
NK CD56dim cells
0.6
Th1 cells
0.3
TFH
Th17 cell
T cells
0.4
B cells
Eosinophils
PDC -
Tem
Th1 cells
Tem -
Cytotoxic cells
T cells
NK CD55bright cells
Mast cells
B cells
CD8 T cells
CD8 T cells
TFH
iDC
Eosinophils
CDB T cells Mast cells
Th17 cells
NK cells
DC
PDC
Cytotoxic cells
Cytotoxic cells
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0,8
-0,4
-0.2
0.0
0.2
0.4
Correlation
Correlation
Correlation
ESAD
STAD
READ
Th2 cells
Th2 cells
Th2 cells
Tgd
Th17 cells
T helper cells
Th17 cells
NK CD56bright cells
aDC-
T helper cels
T helper cells
Tom -
NK CD56bright cells
NK CD56dim cells
Tgd -
NK cels
aDC
Macrophages NK CD56dim cells
aDC
P value
Neutrophils
P value
P value
Neutrophils
0.6
0,75
Thi cells
0.75
DC-
0.4
Tgd
TReg
0.50
0.25
TReg
0.50
Tem
0.2
Th1 cells
0.25
Eosinophils
Macrophages
0.00
Th17 cells
Correlation
Correlation
B cells
Thi cells
Tem -
Comelation
Macrophages
0.1
Eosinophils
0,1
0.1
Tom
0.2
Tcm
0.2
Neutrophils
02
iDC
0.3
iDC
T cells
0.3
0.4
Tem
0.3
0.4
PDC
TReg
TFH
DC
0.4
PDC
Cytotoxic cells
Mast cells
TFH
DC
CD8 T cells
T cells
T cells
Cytotoxic cells
NK CD56dim cells
B cells
TFH -
B cels
NK cells
Eosinophils
Cytotoxic cells
CDB T cells
NK CD56bright cells-
CDB T cells
Mast cells
NIK cells
Mast cells
PDC
iDC
-0.4
-0.2
0.0
0.2
0.4
-0.4
-0.2
0.0
0.2
0.4
-0.4
-0.2
0.0
0.2
0.4
Correlation
Correlation
Correlation
FIGURE 5 Lollipop plot of AURKA’s association with immune cells in 12 cancers.
A
2000
4000
3000
StromalScore
1000
ESTIMATEScore
ImmuneScore
2000
2000
.
0
1000
0
-1000
Spearman
Spearman
0
Dearman
r= - 0.224
r =- 0.219
-2000
P < 0.001
-2000
o
r= - 0.182
P < 0.001
-1000
P <0.001
2
4
6
8
2
4
6
8
2
4
6
8
The expression of AURKA Log2 (TPM+1)
The expression of AURKA Log2 (TPM+1)
The expression of AURKA Log2 (TPM+1)
B
6000
4000
2000
.
StromalScore
ESTIMATEScore
4000
3000
.
ImmuneScore
1000
2000
2000
1000
0
0
0
Spearman
-2000
Spearman
-1000
Spearman
-1000
r= - 0.284
-0.199
r= - 0.119
P < 0.001
P = 0.001
2
-2000
P = 0.053
3
4
5
6
7
2
3
4
5
6
7
2
3
4
5
6
7
The expression of AURKA Log2 (TPM+1)
The expression of AURKA Log2 (TPM+1)
The expression of AURKA Log2 (TPM+1)
C
.
4000
2000
.
1000
StromalScore
ESTIMATEScore
2000
ImmuneScore
1000
0
0
.
0
.
-1000
.
00
.
Spearman
®
F-0.258
-2000
Spearman
Spearman
P = 0.022
= - 0.270
-1000
= - 0.292
-2000
P = 0.017
.
P = 0.009
2
4
6
2
4
6
2
4
6
The expression of AURKA Log2 (TPM+1)
The expression of AURKA Log2 (TPM+1)
The expression of AURKA Log2 (TPM+1)
D
2000
.
6000
3000
1000
4000
StromalScore
ESTIMATEScore
ImmuneScore
2000
0
2000
1000
-1000
0
.
.
0
Spearman
®
-2000
Spearman
·
-2000
0.188
9-0.214
Spearman
-1000
45-0.217
P = 0.017
-4000
P = 0.006
P = 0.006
3
4
5
6
7
3
4
5
6
7
3
4
5
6
7
The expression of AURKA Log2 (TPM+1)
The expression of AURKA Log2 (TPM+1)
The expression of AURKA Log2 (TPM+1)
E
2000
3000
.
4000
StromalScore
1000
ESTIMATEScore
.
ImmuneScore
2000
2000
.
0
1000
®
0
-1000
0
Spearmar
Spearman
Spearman
T= - 0,422
= - 0.421
r= - 0,334
-2000
P < 0.001
-2000
··· P < 0.001
-1000
P < 0.001
3
5
7
9
3
5
7
9
3
5
7
9
The expression of AURKA Log2 (TPM+1)
The expression of AURKA Log2 (TPM+1)
The expression of AURKA Log2 (TPM+1)
BTLA
CD200
TNFRSF14
NRP1
LAIR1
TNFSF4
CD244
LAG3
ICOS
CD40LG
CTLA4
CD48
CD28
CD200R1
HAVCR2
ADORA2A
CD276
KIR3DL1
CD80
PDCD1
Correlation
1.0
LGALS9
0.5
CD160
0.0
TNFSF14
-0.5
IDO2
-1.0
ICOSLG
TMIGD2
VTCN1
IDO1
PDCD1LG2
HHLA2
TNFSF18
BTNL2
CD70
TNFSF9
TNFRSF8
CD27
TNFRSF25
VSIR
TNFRSF4
CD40
TNFRSF18
TNFSF15
TIGIT
CD274
CD86
CD44
TNFRSF9
ACC
p1 LIHC
p2
COAD
p3
THYM
p4
E
p5
PAAD
p6
LUSC
p7
GBMLGG
p8
BRCA
p9
PRAD
p10
KIRC
1
ESCA
p12
LUAD
p13
UCEC
p14
HNSC
p15
THCA
p16
COADREAD
p17
BLCA
p18
FIGURE 7 Heatmap of AURKA’s association with immune checkpoints in a broad range of cancers.
A
MSH2
MSH3
MSH6
Correlation
1.0
MLH1
0.5
PMS2
0.0
EPCAM
-0.5
MGMT
-1.0
ALKBH2
ALKBH3
ACC
p1
LIHC
p2
COAD
p3
THYM
p4
STAD
p5
PAAD
p6
LUSC
p7
GBMLGG
p8
BRCA
p9
PRAD
p10
KIRC
p11
ESCA
p12
LUAD
p13
UCEC
p14
HNSC
p15
THCA
p16
COADREAD
p17
BLCA
p18
B
Correlation
DNMT1
1.0
0.5
DNMT3A
0.0
DNMT3B
-0.5
ACC
p1
LIHC
p2
COAD
p3
THYM
p4
STAD
p5
PAAD
p6
LUSC
p7
GBMLGG
p8
BRCA
p9
PRAD
p10
KIRC
p11
ESCA
p12
LUAD
p13
UCEC
p14
HNSC
p15
THCA
p16
COADREAD
p17
BLCA
p18
-1.0
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.
positively correlated with the presentation of immune checkpoint genes in many cancers, which supports our findings in Figure 5. Meanwhile, we discovered that AURKA was significantly negatively associated with most checkpoint genes in thymic carcinoma. The thymus is the site of T cell maturation and a mechanism that inhibits the AURKA-mediated increase in immune checkpoint expression, protecting T cells in the thymus.
The relationship between AURKA expression and DNA repair gene and methyltransferase expression
AURKA was found to be associated with DNA repair genes as well as methyltransferase genes in several common cancers, as shown in Figures 8A, B. AURKA may have an indirect effect on cancer development and progression by modulating epigenetic status.
Discussion
Although the incidence of adrenal cortical carcinoma is very low, it is one of the most aggressive solid tumors with a poor
prognosis (Yeoh et al., 2022). Furthermore, the recurrence of ACC patients after surgery is still common. As a result, more biomarkers are required for more accurate predictive detection in ACC patients to improve the detection of postoperative risk. The discovery of predictive cancer biomarkers can aid in predicting each patient’s prognosis (Mizdrak et al., 2021; Lippert et al., 2022; Waszut and Taylor, 2022). Using robust rank analysis and a PPI network, XiaoH et al. identified five genes (TOP2A, NDC80, CEP55, CDKN3, and CDK1) that could predict the prognosis of ACC (Xiao et al., 2018). Giordano et al.’s laid the groundwork for ACC molecular classification and prediction, as well as a rich source of potential diagnostic and prognostic markers (Xu et al., 2019).
Ferroptosis is a new iron-dependent programmed cell death method discovered that can induce cell death by promoting cellular lipid peroxidation. It is involved in the occurrence and development of many diseases and plays an essential regulatory role in disease processes. Related studies have shown that ferroptosis plays a role in the progression of various cancers. For example, inhibiting glutathione synthesis in ccRCC in clear cell renal cell carcinoma can induce ferroptosis and inhibit tumor growth (Miess et al., 2018); According to other research (Liu et al., 2020; Chen et al., 2021b; Lu et al., 2022),
ferroptosis attenuates the viability of glioma cells, and activation of ferroptosis inhibits glioma cell proliferation. Inhibition of ferroptosis accelerates glioma proliferation and metastasis and promotes angiogenesis and malignant transformation of gliomas. One study discovered that ferroptosis sensitivity was significantly increased in adrenocortical carcinoma and proposed ferroptosis induction as a treatment option for ACC (Belavgeni et al., 2019).
We obtained six critical genes in this study by crossing the up-regulated genes in ACC with the genes associated with overall survival in ACC. Three of them were chosen to build a polygenic model. The AURKA prediction model and the polygenic model produced very similar results. Meanwhile, AURKA is the point of convergence for the ferroptosis-related gene set and the GEO databases. As a result, we boldly identified AURKA as a critical gene in ACC ferroptosis. We then looked at AURKA expression in ACC and other cancers to see if it had any predictive value. The findings revealed that AURKA was highly expressed in ACC and most cancers and that its expression level increased as ACC progressed. It is consistent with previous research findings (Naso et al., 2021; Sankhe et al., 2021; Ng et al., 2022; Wang et al., 2022). Related studies have also shown high levels of AURKA as an indicator of poor prognosis in bladder cancer. It is also associated with the development and prognosis of rectal cancer, hepatocellular carcinoma, and head and neck cancer (Lu et al., 2021; Tsepenko et al., 2021; Guo et al., 2022; Huang et al., 2022). This research discovered that high AURKA was related to a bad prognosis in various malignancies by creating a deep forest graph and feeding back the association between AURKA, overall survival, and disease-specific survival. It gives compelling evidence that ARUKA may be used to predict the prognosis of ACC and other malignancies.
In addition, we investigated the relationship between AURKA and immune cells in the pan-cancer microenvironment. We discovered that AURKA had a substantial positive link with Th2 cells in all 40 malignancies studied, and these were all the first positive correlations. We next chose 12 malignancies to investigate the association between AURKA and immune cells in them, finding that all immune cells except Th2 cells were adversely connected with AURKA. Previous research (Bustos- Moran et al., 2019; Sun et al., 2021; Long and Zhang, 2022) has shown that AURKA may impact T cells, reshape the immunosuppressive tumor microenvironment, apoptosis, and hypoxia and hence contribute to immunological control, particularly CD8+ T cells that govern Th1 regulation. For example, studies (Han et al., 2020) suggest that decreasing Aurora-A activity or deleting the AURKA gene might boost IL10-induced infiltration and growth of CD8+ T cells in malignancies. Th1-executing B Cells may have unanticipated impacts on AURKA targeted treatment. AURKA may also block other immune cells in the tumor microenvironment, albeit the particular mechanism is unknown. We next looked at the relationship between AURKA expression level and immunological score, stromal score, and computational score in
five malignancies (ACC, SARC, LUAD, ESCA, and STAD), which were all shown to be negatively linked. AURKA has also been identified to affect tumor immunological patterns in diverse malignancies by controlling the expression of particular immune checkpoint genes, according to subsequent research. AURKA was shown to be favorably connected with the indication of immune checkpoint genes, which supports our prior results from a pan-cancer immunological correlation study. The discovery of immunological checkpoints opens up new avenues for tumor therapy. Immune checkpoint inhibitors have been employed in treating many tumors recently, and their effectiveness and safety have been objectively validated (Cai et al., 2022; Minegishi et al., 2022). In addition, we discovered an intriguing phenomenon. AURKA was strongly inversely related to most checkpoint genes in thymic cancer. The thymus is the location of T cell maturation. Thymic cancer has a mechanism that blocks the AURKA-mediated rise in immune checkpoint expression, safeguarding T cells in the thymus.
DNA repair capacity, which is primarily determined by repair gene expression levels, is the first line of defense against genotoxic stress, which causes metabolic changes, inflammation, and cancer, and is also required for maintaining genome stability and protecting cells from endogenous and exogenous DNA traumatic injuring (Shao et al., 2022; Zuo et al., 2022).
This study looked at nine DNA repair genes: MSH2, MSH3, MSH6, MLH1, PMS2, EPCAM, MGMT, ALKBH2, and ALKBH3. In most malignancies, AURKA expression was strongly positively linked with DNA repair genes, according to the findings. Furthermore, the results of this study revealed that the levels of ARUKA and the methyltransferase gene expression exhibited a substantial positive link in a range of malignancies.
Conclusion
To summarize, we did differential expression analysis on the GEO database data, obtaining DEFGs by intersecting with ferroptosis-related genes and exploring some information from them. The significant result is that AURKA is a critical gene for the prognosis of ferroptosis in ACC and can be exploited as an ACC biomarker. The expression of ARUKA is connected with the tumor microenvironment and the number of immune cells in the pan-cancer study, which can impact cancer growth by controlling the level of immune cells, DNA repair, and DNA methylation. This result can only be reached from bioinformatics research, and thus further biological tests are required to demonstrate ARUKA’s probable relevant activities, action mechanisms, and signaling pathways in ACC ferroptosis. It is believed that this work would aid in related research while providing additional biological information about the mechanism of AURKA in tumor immunity and the tumor microenvironment in future research.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.
Author contributions
KL and YZ designed the study; KL and XY wrote the initial manuscript; XY, BW, and YZ collected data; KL. YZ, XY, BW, and YZ analyzed data and contributed in writing the revised manuscript.
Funding
This research was supported by Natural Science Foundation of Heilongjiang Province (LH2019H095) and Outstanding Young Talents Project of Heilongjiang Natural Science Foundation (YQ2022H015).
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene. 2022.996180/full#supplementary-material
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