Analysis
Identification and validation of susceptibility modules and hub genes of adrenocortical carcinoma through WGCNA and machine learning
Yaoming Yang1 . Xinbao Wang1 . Liuqing Wu1 . Shihua Zhao1 . Ran Chen2 . Guoyong Yu1
Received: 24 September 2024 / Accepted: 15 April 2025
Published online: 03 May 2025
@ The Author(s) 2025 OPEN
Abstract
Purpose Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy characterized by rapid progression, significantly impacting patients’ quality of life. Analyzing gene co-expression modules offers valuable insights into the molecular mechanisms driving ACC progression. In this study, we applied Weighted Gene Co-Expression Network Analysis (WGCNA) to identify gene co-expression modules associated with ACC progression.
Methods Before conducting WGCNA, differential gene expression and immune infiltration analyses were performed on the GSE90713 dataset (available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi). Dynamic tree cutting was utilized to identify co-expression modules, which were subsequently analyzed to determine their correlations and associations with traits. A total of 21 co-expression modules were identified, with the yellow module demonstrating a strong correlation with the progression of ACC. Enrichment analysis was carried out on differentially expressed genes, the yellow module, cross-module interactions, and the final hub genes to identify the associated Biological Processes (BPs) and pathways relevant to ACC. Additionally, the CIBERSORT algorithm was employed to predict immune cell infiltration in ACC.
Results The enrichment analysis revealed that pathways associated with cell division, protein synthesis, and metabolism play significant roles in the progression of ACC. Additionally, CDK1, AURKA, CCNB2, BIRC5, CCNB1, TYMS, and TOP2A were identified as key regulatory hub genes. Survival analysis further demonstrated that elevated expression levels of these genes in ACC tissues are significantly correlated with lower overall survival rates in patients, underscoring their critical involvement in ACC development and progression.
Conclusion This study sheds light on the mechanisms underlying ACC progression and highlights potential therapeutic targets. By identifying specific immune cell subtypes associated with ACC, the findings may aid in developing immune modulation therapies aimed at preventing or treating ACC.
Keywords Adrenocortical carcinoma . Prognostic genes . Weighted gene co-expression network
1 Introduction
Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy, with an incidence rate of approximately two cases per million people. While ACC can manifest at any age, it demonstrates a bimodal age distribution, primarily affecting children under five years old and adults aged 40 to 50. The incidence is slightly higher in females compared to males [1-3]. Due to its varied morphology on imaging and the absence of specific
☒ Guoyong Yu, 18901133535@163.com | 1Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100007, China. 2School
of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.
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(2025) 16:663
| https://doi.org/10.1007/s12672-025-02396-4
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early symptoms, most cases of ACC are diagnosed at advanced stages (II or III), with a low survival rate ranging from 15 to 44%.
Currently, surgical resection remains the primary and most effective treatment option for ACC. However, even after complete resection, there is a substantial risk of recurrence, with rates ranging from 30 to 70% in patients who undergo radical surgery [1]. Prognostic factors influencing outcomes include tumor proliferative activity and excessive cortisol secretion. As a result, adjuvant therapy with mitotane is commonly administered to manage recurrence risks [4]. Despite advancements in imaging techniques, diagnostic methods for ACC are still limited, underscoring the need for simpler and more effective early diagnostic tools to improve patient outcomes [5]. Research has highlighted frequent mutations in the CTNNB1 and TP53 genes in ACC samples [6]. Specifically, about 80% of Brazilian ACC patients possess unique mutations in the TP53 gene [7]. This suggests that biomarkers hold significant potential for predicting patient survival and prognosis. Nevertheless, further evidence is required to identify additional biomarkers that can improve the diagnosis and treatment of ACC. ☒
Our study aimed to develop a polygenic signature and identify prognostic biomarkers that could enhance ACC prognosis prediction. We utilized transcriptomic sequencing data from the Gene Expression Omnibus (GEO) database to comprehensively identify and analyze genes implicated in adrenocortical carcinoma. Building on these findings, we explored the underlying molecular mechanisms to contribute to novel therapeutic strategies. Our research not only advances the understanding of ACC’s molecular landscape but also lays the foundation for targeted treatments, potentially improving patient outcomes in oncology.
2 Materials and methods
2.1 Data collection and processing
Gene expression profiles of adrenal cortex carcinoma (ACC) were obtained from the GEO database (https://www. ncbi.nlm.nih.gov/geo/). The selection criteria included: (1) adrenal cortex carcinoma, (2) samples containing both tumor and normal adrenal tissues, (3) array-based expression profiles, and (4) Homo sapiens data. Two GEO datasets were selected: GSE90713 [8] and GSE143383 [9].
2.2 Preprocessing and identification of differentially expressed genes
Differential expression analysis of gene expression data was performed using the ‘limma’ package in R language [10]. To address multiple hypothesis testing, we applied the False Discovery Rate (FDR) correction. This involved sorting the p-values of m genes and calculating FDR(i) =P(i) * m/i, while ensuring that any subsequent values greater than the preceding ones were equalized. Differentially Expressed Genes (DEGs) were identified using a threshold of |log2FC|> 1.5 and an FDR-adjusted p-value <0.05.
2.3 Immune infiltration analysis
To evaluate immune cell infiltration in ACC, the relative abundance of various immune cell subtypes was analyzed using the CIBERSORT deconvolution algorithm [11]. Gene expression profile data were used as input, along with the LM22 reference gene signature file, which encompasses expression patterns of 22 human hematopoietic cell types.
Subsequently, correlation analysis was performed to investigate the relationships among different immune cell subtypes and their associations with clinical characteristics. This analysis aimed to shed light on the potential roles these immune cells may play in the pathogenesis and progression of ACC.
2.4 Weighted gene co-expression network analysis
During the data preprocessing stage, outliers and outlying samples were first removed. Next, correlation coefficients between genes were computed to construct a correlation matrix. Using the scale-free network topology modeling
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function in the WGCNA package, a soft threshold of 6 was selected to transform the correlation matrix into a weighted adjacency matrix. Based on this weighted adjacency matrix, the topological overlap matrix (TOM) was calculated. In the module identification stage, hierarchical clustering analysis was performed on the genes, employing the dissimilarity coefficient as a measure of similarity derived from the TOM matrix.
2.5 Functional enrichment analysis
Using the DAVID functional annotation tool, we performed Gene Ontology (GO) enrichment analysis and KEGG pathway enrichment analysis on the key gene modules identified through the WGCNA. In the GO enrichment analysis, we examined three aspects: biological processes (BP), cellular components (CC), and molecular functions (MF). The significance threshold for the enrichment analysis results was set at p <0.05. The results of the GO term and KEGG pathway enrichment analyses were subsequently visualized using the “ggplot2” package in the R programming language.
2.6 Construction and module analysis of protein-protein interaction networks
To further investigate the interaction between differentially expressed genes (DEGs) and key gene modules, we extracted the intersection genes and entered them into the STRING (Search Tool for the Retrieval of Interacting Genes) database to construct a protein-protein interaction (PPI) network. The resulting PPI network was then visualized using Cytoscape network visualization software.
Following this, we conducted a topological analysis of the PPI network using the node degree centrality method through the CytoHubba plugin. This analysis ranked the importance of genes within the network and identified the top 10 most critical and highly connected hub genes. These highly connected node genes were subsequently selected as candidate hub genes.
2.7 Establishment of prognostic model and survival analysis
The Random Forest (RF) algorithm [12] was employed in the R programming language for feature selection of candidate hub genes derived from the PPI network analysis. The objective was to identify core genes exhibiting significant differential expression, which could serve as potential molecular biomarkers for the early diagnosis and prognosis prediction of ACC.
To assess the clinical relevance of the identified core genes, a receiver operating characteristic (ROC) curve analysis [13] was performed using the “ROC curve” package in R. The area under the curve (AUC) values were calculated for these genes, with higher AUC values indicating a greater potential for effective classification and diagnosis.
3 Results
3.1 Identification of differentially expressed genes
The GSE90713 microarray dataset comprises ACC samples from 42 patients and highlights the expression of CXCR4 in ACC. The GSE143383 dataset, derived from a comprehensive analysis of multiple studies, includes data from 416 patients registered in the ACC Registry in Germany, 45 patients from the COMETE study, 91 patients from the TCGA study, and 43 patients from the NIH study. Exon sequencing was performed on 43 tumor samples and 25 matched normal ACC samples, identifying significant gene mutations, including TP53 and CTNNB1. This dataset also served as a validation cohort, which consisted of five normal samples and 58 tumor samples. The search date for the data was January 28, 2024 (Fig. 1).
DEGs were identified from the GEO dataset GSE90713 using R analysis. The dataset comprised 5 normal samples and 58 tumor samples. A heatmap displaying the top 50 genes based on variance from this GEO dataset is presented in Fig. 2B. The criteria for identifying DEGs were established as |log2FC|> 1.5 and an adjusted p-value <0.05. In total, 288 genes were identified, including 71 that were upregulated and 217 that were downregulated (Fig. 2A).
This analytical strategy integrates classical differential analysis methods with rigorous correction for multiple hypothesis testing, facilitating the precise identification of genes that exhibit significant and robust expression differences between normal and diseased states. This approach also controls the false positive rate in statistical analyses, laying the foundation for further investigation into their biological functions and molecular mechanisms.
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GEO
Downlodad of GSE90713
limma
Identification of DEGs
Highly related gene clusters (modules) — yellow
Crossover genes
DAVID GO/KEGG and Immune cell infiltration analysis
STRING PPI network Cytoscape
DAVID GO/KEGG
Hub Gene
RF
Final Hub Gene
Downlodad of GSE 143383
Validation
3.2 Weighted gene co-expression network analysis and identification of key modules
Key gene modules in ACC were identified using WGCNA. The height threshold was set at 130, resulting in the exclusion of GSM2411098 from the hierarchical clustering tree (Fig. 3A). Based on scale independence and mean connectivity, the soft-threshold power was established at 6 (Fig. 3B). Utilizing the dynamic tree cut package, a total of 21 modules were identified (Fig. 3C). Module-trait relationships were analyzed to determine the correlation between the identified modules and tumorigenesis (Fig. 3D). Notably, the yellow module exhibited a significant correlation with tumorigenesis, with a coefficient of 0.34 (Fig. 3).
This unsupervised clustering analysis effectively grouped highly co-expressed genes into distinct gene modules, thereby laying the groundwork for subsequent investigations into the relationships between these modules and phenotypic traits.
3.3 Exploration of hub genes
The yellow module genes were overlapped with DEGs to identify the crossover genes (Fig. 2C), resulting in a total of 56 intersecting genes.And a grouped dot plot (Tumor vs. Normal) is to display the expression levels of the 56 overlapping genes(Fig. 2D).For PPI network analysis, we utilized the STRING database. The CytoHubba software was employed to screen for key genes within the PPI network based on node degree, leading to the identification of 10 hub genes (Fig. 4B). These top 10 genes were selected, and the intersecting genes were recognized as candidate hub genes.
Next, we applied Recursive Feature Elimination (RFE) in conjunction with the RF algorithm to identify and retain the most influential features. RFE is a feature selection method that recursively considers progressively smaller subsets of features to determine the most significant ones. We implemented a tenfold cross-validation setup, with accuracy maximization as the evaluation criterion. This approach enabled us to identify the genes most critical for the classification of ACC.
To validate the effectiveness of the selected features, we plotted a line graph showing accuracy against the number of features (Fig. 4C), which indicated the optimal number of features that achieved the highest accuracy. This analysis confirmed the performance of the selected feature set in distinguishing between normal and ACC samples (Fig. 4).
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group
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group
MEGF10
SPINK13
Tumor
NSG1
NEFL
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Normal
A
25
B
ANO4
CTNNA2
CALB1
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15
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stable
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GBP2
STMINT
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BUB18
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APIS1
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Through PPI network analysis of the intersection between DEGs and key module genes, we not only clarified their mutual regulatory relationships at the molecular level but also uncovered potential core regulatory genes. This provides new insights for further research into their crucial roles in the pathogenesis and progression of the disease.
Utilizing the RF algorithm for feature selection, seven key genes associated with the progression of ACC were identified: CDK1, AURKA, CCNB2, BIRC5, CCNB1, TYMS, and TOP2A. These genes are believed to play significant regulatory roles in the development and progression of ACC.
3.4 Validation of prognostic model and survival analysis
The seven hub genes were subjected to ROC validation using R code. The results (Fig. 5A) demonstrated that the area under the curve (AUC) values for “CDK1,""AURKA,""CCNB2""BIRC5""CCNB1,""TYMS,” and “TOP2A” were 0.993, 0.982, 0.975, 0.979, 0.968, 0.926, and 0.972, respectively. These findings indicate a strong association of these genes with the progression and prognosis of ACC. To further validate the discriminatory power of the prognostic model using an independent dataset, we employed dataset GSE143383 for additional analysis. The results showed that the AUC values for the same seven genes CDK1, AURKA, CCNB2, BIRC5, CCNB1, TYMS, and TOP2A were 0.968, 0.905, 0.986, 0.965, 0.961, 0.993, and 0.961, respectively (Fig. 5B). These findings were consistent with the results of the prognostic model established in dataset GSE90713, further confirming the close relationship of these genes with ACC prognosis and progression. Overall, our analysis indicates that the prognostic model based on these seven key genes exhibits robust discriminatory ability for evaluating the prognosis and disease progression of ACC patients (Fig. 5). To further ensure the reliability of the validation results and avoid unreliable outcomes due to overfitting, we conducted an Out-Of-Bag (OOB) error estimation for the
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Sample clustering to detect outliers
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(3e-79)
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(4e-16)
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MEblack
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(2e-06)
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MEbrown
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MEturquoise
-0.061
0.061
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(48-17)
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MElightcyan
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MEgreen
(6e-280)
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(60-280)
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-0.28
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random forest model. The results showed that the OOB error rate was 3.23%, which is a relatively low level. This indirectly reflects that the model has good generalization ability, thereby enhancing our confidence in the reliability of the model.
This integrated analytical strategy, which combines machine learning algorithms, differential analysis, and ROC curve assessment, facilitates the precise identification of differentially expressed core molecular biomarkers with clinical translational potential from high-throughput expression profile data. It provides critical insights for the early diagnosis and prognosis prediction of ACC while establishing a foundation for further exploration of the mechanistic roles of these biomarkers in the onset and progression of ACC.
Subsequently, we performed survival analysis on the identified hub genes using GEPIA2 (http://gepia2.cancer-pku. cn/#survival) and calculated the expression levels of these hub genes in ACC (Fig. 6).
3.5 Enrichment analysis
KEGG and GO enrichment analyses were conducted on the DEGs. The KEGG pathway analysis showed significant enrichment of these genes in several pathways, including “systemic lupus erythematosus,“steroid hormone biosynthesis,” and “Staphylococcus aureus infection” (Fig. 7A). In the GO-Biological Process (BP) category, these genes were associated with processes such as”regulation of complement activation,” “prostaglandin biosynthesis,” and “positive regulation
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CCNB2-auc0.986
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BIRC5-auc-0.979
CCNB1-auc0.968
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CCNB1-auc0.961
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of tumor necrosis factor production” (Fig. 7B). In the GO-Cellular Component (CC) category, notable enrichment was observed in the “insulin-like growth factor ternary complex,""insulin-like growth factor binding protein complex,” and “high-density lipoprotein particles” (Fig. 7C). For the GO-Molecular Function (MF) category, significant enrichment was noted in “monooxygenase activity,“iron ion binding,” and “insulin-like growth factor binding” (Fig. 7D).
Subsequently, we performed GO and KEGG analyses on the genes within the yellow module. The KEGG analysis indicated that these genes were enriched in pathways such as “viral life cycle-HIV-1,“terpenoid backbone biosynthesis,” and “steroid biosynthesis” (Fig. 7E). In the GO-BP category, these genes were involved in processes including “mitotic chromosome separation""mitotic cell cycle,” and “double-strand break repair via homologous recombination” (Fig. 7F). In the GO-CC category, these genes showed significant enrichment in the “spindle,“kinetochore,” and “nucleus” (Fig. 7G). Lastly, in the GO-MF category, they exhibited significant enrichment in “single-stranded DNA binding,""ATP-dependent single-stranded DNA helicase activity” and “RNA binding” (Fig. 7H).
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GO and KEGG enrichment analyses were performed on the intersecting genes between the yellow module and DEGs. The KEGG pathway analysis revealed that these genes were significantly enriched in several pathways, including “progesterone-mediated oocyte maturation,""the p53 signaling pathway,” “oocyte meiosis,""HTLV-I infection,""HIV-1 infection,""cellular senescence,” and “the cell cycle” (Fig. 71). In the BP category, these genes were involved in processes such as “female meiosis I spindle assembly,""regulation of cyclin-dependent protein serine/threonine kinase activity” and “positive regulation of mitotic spindle assembly and mitotic spindle organization” (Fig. 7J).
In the CC category, significant enrichment was observed in “spindle poles,” “spindle microtubules,” “spindles,” and “nuclei” (Fig. 7K). For the MF category, these genes were notably enriched in “protein serine/threonine kinase activity” “protein kinase binding,” and “protein kinase activity” (Fig. 7L).
This functional enrichment analysis contributes to a comprehensive interpretation of the biological functions of the genes within the relevant modules from a systems biology perspective. It elucidates the key biological processes, molecular functions, and metabolic regulatory networks in which these genes are involved, providing vital insights into
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KEGG Enrichment Results
KEGG Enrichment Results
KEGG Enrichment Results
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Coronavirus disease - COVID-19-
0.02
Fancon anemia pathway
15
007
Complement and coagulation cascades
0.01
20
25
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30
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15
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7
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BP Enrichment Results
BP Enrichment Results
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regulation of complement activation
F
.
mitotic sister chromatid segregation
PValan
J
spinde assemblyinvolved in female meiosis
+
prostaglandn biosynnete procesa
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mitotic cet cycle
44-08
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regulation of cydin dependent protein serne/threonine kinase actur
PValue
positive regulation oftumor necrosis factor production
.
6
M
double strand break regairvia homatopaus recombinaton
20-08
positive regulation of mitote cell cycle spindle assembly checkpoint
.
4e-04
35-44
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10
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14-00
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20-04
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V
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18-04
postive sagalition of ERX1 and ERK2 cascade
PValue
chromcapme segregation
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20-04
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70
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10
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CC Enrichment Results
CC Enrichment Results
CC Enrichment Results
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niukn-the grouin Sicher summary compila
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300
400
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25
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30
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50-04
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54-00
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40-04
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40-08
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3e-08
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36-04
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centroname
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.
10-04
0
20
40
60
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100
250
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300
400
10
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20
30
MF Enrichment Results
MF Enrichment Results
L
MF Enrichment Results
D
monpangtnase achit
H
single-stranded DNA binding .
protein serinastresseine kinase acuity
Count
iron ion binding
Count
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.
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1.00-00
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20
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0.0015
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200
0.04
8.0006
400
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.
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600
002
caloum ion binding
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10
20
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30
6
200
400
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600
2
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4
7
Count
·
5
their roles in the pathogenesis and progression of the disease. Moreover, visualizing the enrichment results enhances the clarity of the analysis findings.
3.6 Immune infiltration analysis of differentially expressed genes
Immune infiltration analysis was performed on the identified differentially expressed genes (Fig. 8A). This analysis revealed significant differential levels of M2 Macrophages, activated Dendritic cells, T follicular helper cells, and Monocytes in ACC tissues. These findings suggest that these immune cell subsets actively participate in the ACC tumor microenvironment, supporting the hypothesis that immune responses are crucial for the progression of ACC.
Subsequently, we conducted a correlation analysis among these immune cell types (Fig. 8B). The results indicated a positive correlation between activated Dendritic cells and T follicular helper cells, while a negative correlation was observed between activated Dendritic cells and M2 Macrophages. Additionally, T follicular helper cells showed a negative correlation with M2 Macrophages. In contrast, Monocytes did not exhibit significant correlations with the other cell types (Fig. 8).
This analytical approach combines advanced immune cell scoring algorithms with traditional correlation analysis, enabling a systematic characterization of immune cell infiltration patterns in the ACC tumor microenvironment. These insights are important for investigating the mechanistic roles of immune cells in the occurrence and development of ACC.
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T cells CD4 memory resting
A
TME Cell composition
B
Group ACC Normal
Monocytes
Dendritic cells resting
Macrophages M1
Monocytes
0.4
Dendritic cells resting
T cells CD4 memory resting
Cell composition
B cells memory
Macrophages M2
T cells CD8
NK cells activated
Mast cells resting
T cells CD4 memory activated
Macrophages M1
0.3
B cells memory
Mast cells activated
.
Macrophages M2
.
B cells naive
T cells CD8
T cells gamma delta
.
.
0.2
NK cells activated
·
.
.
Mast cells resting
T cells CD4 naive
·
B cells naive
.
-
.
0.1
Mast cells activated
*
·
.
T cells CD4 memory activated
·
·
.
.
T cells gamma delta
·
-
0.0
.
.
.
T cells CD4 naive
·
.
.
Macrophages M2
NK cells activated
Dendritic cells activated
T cells follicular helper
cells regulatory (Tregs)
T cells CD8
T cells regulatory (Tregs)
Monocytes
B cells naive
T cells CD4 memory resting
NK cells resting
Macrophages MO
T cells follicular helper
Mast cells resting
Macrophages MO
T cells CD4 naive
Plasma cells
Dendritic cells resting
Neutrophils
B cells memory
T cells CD4 memory activated
T cells gamma delta
NK cells resting
Macrophages M1
Mast cells activated
Eosinophils
Dendritic cells activated
NK cells resting
-
-
.
-
.
.
Plasma cells
Macrophages MO
.
·
.
- .
.
.
.
.
T cells follicular helper
.
.
.
·
T cells regulatory (Tregs)
.
. ☒
.
.
.
·
·
·
Plasma cells
.
.
·
Dendritic cells activated
..
·
.
.
-
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
4 Discussion
This study utilized the GEO dataset GSE90713 and integrated it with bioinformatics analysis to identify hub genes and prognostic markers crucial for the development and progression of ACC. By employing WGCNA, a systems biology tool, we identified DEGs and key modules within gene networks based on clustering and biological functional annotation results. An immune infiltration analysis of these DEGs revealed that immune responses play a significant role in ACC, underscoring the potential value of immunotherapy in its treatment. Further analysis, which examined the overlap between ke1y module genes and DEGs, involved PPI network analysis to identify 10 hub genes. Using the Random Forest (RF) method for validation, we identified seven key genes: CDK1, AURKA, CCNB2, BIRC5, CCNB1, TYMS and TOP2A. We developed a prognostic model based on these key genes and validated its accuracy in predicting ACC prognosis through receiver operating characteristic (ROC) curves. Consequently, these genes show promise as potential biomarkers and prognostic indicators for the diagnosis and treatment of ACC. To further validate our findings, we conducted an independent dataset validation using the GSE143383 GEO dataset, which revealed a strong correlation between these seven key genes and ACC prognosis and progression, thereby bolstering our research outcomes.
CDK1, a critical protein kinase that regulates the G2/M phase transition of the cell cycle, exhibits abnormal expression in various tumors. Previous studies have indicated its involvement in the transition from the G2 phase to mitosis, often demonstrating enhanced or overexpressed activity in cancer cells [14-16]. Overactivation of CDK1 can disrupt cell mitosis and increase genomic instability, thereby promoting tumor initiation and progression.A study employing immunohistochemistry examined ACC samples and discovered a significant elevation in the expression of CDK1 protein in ACC (p<0.01), corroborating the accuracy of our findings [17]. Previous research has linked CDK1 to recurrent tumors, suggesting its potential role in tumor recurrence and treatment resistance, which may also be relevant to ACC [18]. In our study, we observed elevated expression levels of CDK1, AURKA, CCNB2, and CCNB1 in ACC. AURKA, a member of the Aurora kinase family, plays a crucial role in regulating cell division during mitosis. It is overexpressed in multiple cancers and is involved in cancer cell proliferation, epithelial-mesenchymal transition (EMT), metastasis, apoptosis, and the self-renewal of cancer stem cells, thereby contributing to tumorigenesis [19]. Moreover, AURKA expression correlates positively with tumor immune infiltration in various cancers [20]. Other studies have found that compared to normal adrenals, the expression of AURKA in ACC is 5.2 times higher (p <0.001). Experimental evidence indicates that the inhibition of Aurora kinase and the Wnt/B-catenin pathway reduces the growth of adrenocortical cancer cells [21]. Furthermore, it has been established in prior research that AURKA is overexpressed in childhood adrenocortical tumors, aligning with the results of our study [22] CCNB1 and CCNB2 are members of the cyclin B protein family, with CCNB1 being particularly important for regulating G2/M phase progression of the
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cell cycle. Scholars have identified CCNB1 as a specific marker that distinguishes between adrenocortical carcinoma and adenoma, and CCNB2 was detected in formalin-fixed paraffin-embedded ACC samples [28]. CCNB1 inhibits separase activation, leading to delayed chromatid separation, while CCNB2 induces excessive activation of AURKA, resulting in accelerated centrosome separation and chromosome lagging. These findings suggest a causal relationship between the overexpression of B-type cyclins and tumor pathophysiology [23-26].Our findings further underline their critical role in ACC and suggest a causal relationship between the overexpression of B-type cyclins and the invasive behavior of ACC tumors. BIRC5, one of the inhibitors of apoptosis proteins (IAPs), inhibits apoptosis and promotes cell proliferation. It is essential for the mitotic checkpoint and is involved in cell differentiation, proliferation, and invasion. Although no progress has been made in linking BIRC5, TYMS, and TOP2A to ACC specifically, their further exploration in ACC research remains worthwhile.BIRC5 is highly expressed in most tumors, leading to poor prognosis for cancer patients [29, 30]. Thymidylate synthase (TYMS) is pivotal in thymidine biosynthesis and is located on chromosome 18p; it is a fundamental substrate for DNA synthesis.It is known that TYMS expression is elevated in renal cell carcinoma [31]. Its overexpression in malignant tumor cells is associated with adverse clinical outcomes in cancer. Topoisomerase 2a (TOP2A), a subtype of type II topoisomerase (TOP2), is essential for maintaining genomic integrity during various dynamic processes, such as transcription, replication, and cell division. Crucial for cell division, TOP2A is highly expressed during mitosis and is necessary for chromosome condensation and segregation during mitotic cell division.
ACC is a rare but highly heterogeneous malignancy with poor prognosis and high recurrence rates. Therefore, identifying key genes associated with the occurrence, development, and invasion of ACC is crucial for improving diagnosis, prognosis, and treatment. Recently, DLK1 (Delta-like non-canonical Notch ligand 1), a marker of adrenocortical stem/progenitor cells, has been shown to be re-expressed in ACC and correlated with tumor malignancy and recurrence risk [32]. This suggests that stem cell-related genes may play a significant role in the tumor biology of ACC. Research indicates that DLK1 expression in ACC is linked to stem cell characteristics, and DLK1 + cells exhibit higher steroidogenic potential and clonogenic capacity. CDK1 and CCNB1 play pivotal roles in the G2/M phase transition of the cell cycle, while BIRC5 is associated with cell survival and stem cell properties. Abnormal activation of these genes may endow ACC cells with stem cell-like characteristics, promoting tumor invasion and recurrence. Studies also highlight the correlation between DLK1 expression levels and recurrence-free survival in ACC patients, with its soluble ectodomain detectable in patient serum, aiding in the diagnosis and follow-up of ACC. This underscores DLK1’s potential as a prognostic and diagnostic marker. In our study, the high expression of CDK1 and AURKA is often associated with tumor aggressiveness and poor prognosis. We also discuss the expression levels of seven core genes in ACC and patient survival rates, further validating their prognostic value. This suggests that future research could explore the possibility of combining these genes with DLK1 to enhance the accuracy of prognostic predictions. Additionally, inhibiting DLK1 function may suppress the stem cell characteristics and invasive capabilities of tumor cells. The core genes in our study, such as CDK1 and CCNB1, which are key regulators of the cell cycle, have the potential to become therapeutic targets, and inhibitors of these genes are already undergoing clinical trials in various cancers. Future research can also investigate the interactions between these core genes and DLK1 to identify strategies for combination therapy. Continued exploration of related outcomes is encouraged, such as studying the functions of core genes in ACC cells using gene-editing techniques like CRISPR-Cas9 and exploring the interaction networks between these genes and DLK1. Furthermore, clinical trials can be conducted to evaluate the efficacy of inhibitors targeting these genes in the treatment of ACC.
In conclusion, Our analysis of the differential expression of these key genes in primary and recurrent ACC tumors provides valuable insights into their potential roles in tumor recurrence.The high expression levels of these key genes in ACC are closely linked to the onset and progression of the disease, indicating their potential as promising biomarkers and predictive factors for ACC diagnosis and treatment. Future studies should investigate the specific mechanisms by which these genes operate in ACC and evaluate their potential as therapeutic targets.
We conducted KEGG and GO enrichment analyses on DEGs in ACC tumors to elucidate their biological functions and involvement in signaling pathways. Our findings revealed significant enrichment of DEGs in various biological pathways associated with tumor development, thereby illuminating the molecular mechanisms underlying ACC. Notably, the enrichment of DEGs in the steroid hormone biosynthesis pathway underscores their critical roles in the pathogenesis of ACC. Given the essential regulatory functions of steroid hormones in cell growth, differentiation, and metabolism, dysregulated expression of these genes may drive malignant transformation and tumor proliferation.
Furthermore, the GO biological process (GO-BP) analysis emphasized the involvement of DEGs in regulating complement activation and prostaglandin biosynthesis, both of which are pivotal in immune response and inflammation.
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This suggests that DEGs may modulate the tumor microenvironment through immune and inflammatory responses, thereby promoting tumor progression.
The KEGG analysis of genes within the yellow module revealed enrichment in pathways such as the HIV-1 lifecycle. Although this pathway may appear unrelated to the direct pathological mechanisms of ACC, these findings could be connected to viral infection and host immune responses, providing a fresh perspective on the tumor immune microenvironment in ACC. Additionally, GO cellular component (GO-CC) and GO molecular function (GO-MF) analyses elucidated the roles of these genes in maintaining cellular structure and function, including spindle assembly and RNA binding, both of which are crucial for cell division and gene expression regulation.
Moreover, the analysis of intersecting genes between the yellow module and DEGs highlighted enrichment in pathways related to oocyte maturation and cell cycle regulation, suggesting their potential roles in reproductive and developmental processes. Aberrant expression of these genes may disrupt cell cycle regulation, thereby promoting tumor cell proliferation. Furthermore, the GO-MF analysis indicated significant enrichment of these genes in protein kinase activity, underscoring their potential roles in cellular signaling and regulation. Targeting these genes could provide new therapeutic strategies to overcome platinum resistance in ACC tumors.
In conclusion, our analysis not only highlighted the significant roles of DEGs and the genes within the yellow module in pathways and processes relevant to ACC tumors, but it also identified several potential molecular targets and avenues for future research. Subsequent studies can explore the mechanisms by which these genes contribute to the onset and progression of ACC tumors, as well as evaluate their viability as therapeutic targets.
The results of the immune infiltration analysis conducted in this study revealed significant differences in the infiltration levels of immune cell subtypes, including M2 macrophages, activated dendritic cells, follicular helper T cells, and monocytes, within the ACC tumor microenvironment. These findings support the hypothesis regarding the critical role of the immune response in the progression of ACC and align with the theoretical foundations of tumor immunology. M2 macrophages secrete various factors that promote tumor growth, such as VEGF and MMPs, which facilitate tumor cell migration, invasion, and angiogenesis [33, 34]. Furthermore, they suppress the activity of immune effector cells, including T cells and NK cells, thereby weakening the body’s anti-tumor immune response [35-37], which favors the malignant progression of the tumor. In contrast, activated dendritic cells and follicular helper T cells play crucial roles in initiating and regulating anti-tumor immune responses [38, 39], guiding T cells and B cells in their fight against cancer [40]. Monocytes may exhibit a dual role in the tumor microenvironment [41]. These results highlight the complex immune regulatory mechanisms involved in ACC pathogenesis. Further correlation analysis revealed a positive correlation between activated dendritic cells and follicular helper T cells, while both exhibited a negative correlation with M2 macrophages. Additionally, follicular helper T cells demonstrated a negative correlation with M2 macrophages. This suggests the existence of a regulatory network characterized by mutual inhibition or cooperation among different immune cell subtypes. For instance, dendritic cells and follicular helper T cells may undergo synergistic activation while simultaneously inhibiting M2 macrophages. These intricate regulatory relationships may play a significant role in the immune pathology of ACC.
The immune analysis results from this study confirm the critical role of the immune microenvironment in the development of ACC and provide initial insights into the potential regulatory networks and molecular mechanisms involved. A deeper understanding of this complex immune regulatory network will enhance our knowledge of ACC pathogenesis and support the development of immunotherapeutic strategies, thereby offering new treatment options for patients.
In summary, while our study has yielded significant discoveries, it also faces the limitations mentioned earlier. Addressing these limitations in future research endeavors will be essential for strengthening the theoretical foundations and practical guidance for precision diagnosis and treatment in ACC. However, our study has several limitations. First, data sourcing is restricted to the GEO database’s GSE90713 and GSE143383 datasets, which may introduce bias and lack general representativeness. Second, experimental validation is lacking; our findings are primarily derived from bioinformatics analysis, necessitating further experimental verification to substantiate the roles and clinical significance of these potential key genes and regulatory pathways in ACC. Third, the prospective application of these biomarkers requires additional evaluation. Despite identifying seven core genes with prognostic potential, their feasibility and accuracy as ACC biomarkers for diagnostics, molecular subtyping, and personalized treatment still need thorough evaluation and refinement. Lastly, the explanation of the immune regulatory network is not comprehensive. While we provide preliminary insights into the intricate tumor immune microenvironment of ACC, deeper studies into molecular mechanisms are imperative for a comprehensive elucidation of the complexities involved. To fully realize the potential of these genes as biomarkers, further preclinical research and large-scale clinical validation are necessary.
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5 Conclusion
In conclusion, this study conducted a comprehensive analysis of DEGs in ACC. A total of 288 DEGs were identified, among which seven genes-CDK1, AURKA, CCNB2, BIRC5, CCNB1, TYMS, and TOP2A-were pinpointed as potential key biomarkers. These genes demonstrated significant differential expression in ACC tissues, and their significance was further corroborated through validation with independent datasets. As potential biomarkers for ACC, these genes hold great promise in providing new molecular targets for early diagnosis, prognosis assessment, and treatment monitoring. They can serve as the basis for the development of novel molecular diagnostic reagents, thereby enabling more precise and timely detection of ACC. Moreover, these genes offer valuable insights into the molecular subtyping and personalized treatment of ACC, paving the way for the exploration of new targeted therapeutic strategies. Looking forward, future research should concentrate on investigating the expression characteristics of these molecular markers across different stages and subtypes of ACC, as well as examining their correlation with clinical outcomes and prognosis. Such endeavors will contribute to establishing a more robust foundation for precision medicine in ACC, ultimately enhancing our understanding and management of this complex disease.
Acknowledgements Not applicable.
Author contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Y. Y. X W. L.W S.Z. R.C and G.Y. The first draft of the manuscript was written by Y.Y and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding Basic and Clinical Research on Nephritis Anti-Failure Granules in Chronic Renal Interstitial Tubular Disease (601071)
Data availability The datasets [GSE 90713 and GSE 143383] for this study can be found in the [GEO database] (https://www.ncbi.nlm.nih. gov/geo/).
Ethical approval and consent to participate Not applicable.
Consent for publication Not applicable.
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativeco mmons.org/licenses/by-nc-nd/4.0/.
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