Abstract
Patients with castration-resistant prostate cancer (CRPC) often develop drug resistance after treatment with enzalutamide. The goal of our study was to identify the key genes related to enzalutamide resistance in CRPC and to provide new gene targets for future research on improving the efficacy of enzalutamide. Differential expression genes (DEGs) associated with enzalutamide were obtained from the GSE151083 and GSE150807 datasets. We used R software, the DAVID database, protein–protein interaction networks, the Cytoscape program, and Gene Set Cancer Analysis for data analysis. The effect of RAD51 knockdown on prostate cancer (PCa) cell lines was demonstrated using Cell Counting Kit-8, clone formation, and transwell migration experiments. Six hub genes with prognostic values were screened (RAD51, BLM, DTL, RFC2, APOE, and EXO1), which were significantly associated with immune cell infiltration in PCa. High RAD51, BLM, EXO1, and RFC2 expression was associated with androgen receptor signaling pathway activation. Except for APOE, high expression of hub genes showed a significant negative correlation with the IC50 of Navitoclax and NPK76-II-72-1. RAD51 knockdown inhibited the proliferation and migration of PC3 and DU145 cell lines and promoted apoptosis. Additionally, 22Rv1 cell proliferation was more significantly inhibited with RAD51 knockdown than without RAD51 knockdown under enzalutamide treatment. Overall, six key genes associated with enzalutamide resistance were screened (RAD51, BLM, DTL, RFC2, APOE, and EXO1), which are potential therapeutic targets for enzalutamide-resistant PCa in the future.
1 Introduction
According to Global Cancer Statistics 2020, prostate cancer (PCa) is a common cancer type that causes death worldwide in men. Approximately 1.4 million people are diagnosed with PCa worldwide, resulting in 375,000 deaths annually, which is an upward trend compared to 2018 [1,2]. The androgen receptor (AR) signaling pathway is involved in the entire process of PCa progression, including transformation and metastasis [3]. Therefore, androgen-deprivation therapy (ADT) constitutes the first-line therapy for PCa [4]. However, there are limitations to ADT therapy, and almost all patients treated with this method inevitably end up with castration-resistant prostate cancer (CRPC) [5,6].
Enzalutamide is an FDA-approved targeted drug for treating CRPC [7]. Enzalutamide is a small molecule oral agent that functions as an AR signaling inhibitor by negatively affecting AR nuclear translocation, thereby inhibiting the transcription of related drivers located downstream of the AR cascade. This results in the induction of apoptosis and suppression of CRPC cell proliferation [8,9]. Many studies demonstrate the positive effects of enzalutamide in CRPC patients, such as significantly prolonging survival after chemotherapy [10]. However, insensitivity to enzalutamide is found in almost half of the patients [11]. In addition, although some patients do better on initial treatment with enzalutamide, acquired resistance occurring after 11.2 months is common [12]. Given that the underlying mechanisms of enzalutamide resistance remain largely unclear, there is an urgent need to explore the causes of resistance to improve further therapeutic efficacy in CRPC.
Enzalutamide resistance in CRPC is a complex process that is usually associated with abnormal expression of certain genes, which play an important role in the development of enzalutamide resistance [13]. Researchers have actively explored genes associated with enzalutamide resistance. For example, AR-V7 is the most common variant detected in circulating tumor cells of patients with CRPC, which is significantly increased after enzalutamide treatment, and the splicing factors hnRNPA1 and Malat1 have been identified to mediate enzalutamide resistance by promoting the production and expression of AR-V7 [14]. Furthermore, galectin-3, a member of the animal lectin family, significantly inhibited the therapeutic effect of enzalutamide by increasing the expression of KLK3 and TMPRSS2 [15]. Although we have identified several genes associated with enzalutamide resistance, many unknown genes are likely to remain unidentified. Therefore, exploring aberrant genes associated with enzalutamide resistance will help discover biomarkers of resistance susceptibility and develop advanced therapeutic targets to provide more treatment options for patients with CRPC.
With the generation and application of high-throughput sequencing technologies, many important genes and signaling pathways involved in the cancer process have been massively mined and stored in public databases. Researchers can access and download the needed genomic data from international public knowledge databases such as Gene Expression Omnibus (GEO) and TCGA for free [16]. In the present study, we obtained data on enzalutamide-resistant and -sensitive cell lines from the GEO database and identified key genes involved in enzalutamide resistance through bioinformatics analysis. These results may provide new directions and new therapeutic targets for advancing research on CRPC resistance to enzalutamide.
2 Materials and methods
2.1 Date acquisition
Considering that the acquisition of drug resistance in enzalutamide-sensitive PCa cells is a slow process, we used cells cultured long term in enzalutamide (≥6 months) as a screening criterion to select the data we needed. We downloaded the high-throughput sequencing data of two different PCa cell lines treated with enzalutamide from the GEO database (GSE151083 and GSE150807 datasets; https://www.ncbi.nlm.nih.gov/geo/). GSE150807 contains data on enzalutamide-sensitive and -resistant LNCaP cell lines, while GSE151083 contains data on enzalutamide-sensitive and -resistant C42B cell lines. We also downloaded data related to prostate adenocarcinoma from TCGA database, which included 501 PCa and 52 normal samples. In addition, GSE32269, containing 22 primary PCa (hormone-dependent) and 29 metastatic CRPC tissues, was used for follow-up validation.
2.2 Analysis of differential expression genes (DEGs) associated with enzalutamide resistance
The “limma package” was used to analyze the DEGs in the GSE150807 and GSE151083 datasets. All DEGs between the enzalutamide-resistant and enzalutamide-sensitive cells were identified and visualized. To screen DEGs, |log2FoldChange| > 1 and adjusted P-value <0.05 were used as thresholds. The results were visualized in volcano and heat map plots, with the heat map showing only the top 20 highest and lowest expressed genes. Screened DEGs from the GSE151083 and GSE150807 datasets were intersected in a Venn diagram using Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html), and overlapping DEGs common to both datasets were considered significantly associated with enzalutamide resistance.
2.3 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses
To explore the potential biological functions of the DEGs associated with enzalutamide resistance, GO and KEGG analyses of the DEGs were investigated through the DAVID online website. The downloaded results were further analyzed using an online web tool (https://www.bioinformatics.com.cn). P-values <0.05 were considered statistically significant.
2.4 Construction of protein–protein interaction (PPI) network and identification of candidate hub genes
The PPI network for DEGs associated with enzalutamide resistance was built using STRING (STRING version 11.5). Interactions with a score >0.7 were considered statistically significant. The PPI network was visualized using Cytoscape software (v3.9.1), and the hub genes were determined using the plugin cytohubba in Cytoscape. The top 20 genes in the network, ranked using the maximal clique centrality (MCC) method, were treated as candidate hub genes.
2.5 Survival analysis and expression validation to identify hub genes
Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/), a versatile online analysis site based on TCGA and Genotype-Tissue Expression (GTEx) databases, was used to validate the correlation between the disease-free survival (DFS) and expression levels of the 20 candidate hub genes. Fragments per kilobase of exon per million mapped fragment (FPKM) data associated with PCa from TCGA database were downloaded, and we analyzed the expression differences of the 20 candidate hub genes between 501 PCa tissues and 52 normal tissues using the Wilcoxon test method. Finally, we visualized the results using the beeswarm package in R software. Through the above analysis, the candidate hub genes with clinical prognostic value were identified as hub genes. P-values < 0.05 were considered statistically significant.
2.6 Clinical parameter analysis of hub genes
The N- and T-stage-related information of the samples were extracted from the data related to PCa downloaded from TCGA, and the correlation between the expression levels of the hub genes and the N- and T-stage was investigated using the Wilcoxon test and Kruskal–Wallis test. P-values < 0.05 were considered statistically significant.
2.7 Analysis of the relationship between hub genes and enzalutamide
With enzalutamide being an AR signaling inhibitor, we evaluated the potential relationship between hub gene expression and the AR signaling pathway via Gene Set Cancer Analysis (GSCA; http://bioinfo.life.hust.edu.cn/GSCA/#/), a versatile platform for data analysis. Moreover, we analyzed the expression differences of the six hub genes between primary PCa and metastatic CRPC (mCRPC) tissue samples using Wilcoxon test based on the GSE32269 dataset. P-value < 0.05 was considered statistically significant.
2.8 Exploration of the potential relationship between hub genes and immune cell infiltration
The immune infiltration and mRNA expression module in GSCA was used to estimate the association between the expression levels of the hub genes and the infiltration levels of macrophages, monocytes, and natural killer (NK) cells. FDR < 0.05 was considered statistically significant.
2.9 Drug sensitivity evaluation
We determined the correlation between the expression of hub genes and drug IC50 via Pearson correlation analysis based on the Genomics of Drug Sensitivity in Cancer (GDSC) database through the drug sensitivity module in GSCA. This is done to identify potential molecular compounds for targeted therapies. FDR < 0.05 was considered statistically significant.
2.10 Cell line and culture
PCa cell lines PC3, DU145, and 22Rv1 were purchased from the American Type Culture Collection (ATCC). They were cultured in RPMI-1640 medium (Gibco, USA) containing 10% Fetal Bovine Serum (Ausbian, Cat. No. WS500T) and 1% penicillin/streptomycin (Gibco, USA) and maintained at 37°C in a 5% CO2 environment.
2.11 Transient transfection of cells
For transient transfection, 5 × 104 cells/mL of PC3, DU145, and 22Rv1 cells were added to 6-well plates the day before transfection and cultured overnight. The next day, at about 30–40% confluence, the cells were transfected with Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) and small interfering RNA (siRNA) and then further incubated for 48 h. The siRNAs targeting RAD51 are shown in Table 1.
Sequences of the two siRNAs
| siRNA | siRNA sequence information |
|---|---|
| siRNA#1 | 5′-AAGCTATGTTCGCCATTAATT-3′ |
| siRNA#2 | 5′-CGCCCTTTACAGAACAGACTA-3′ |
2.12 RNA extraction and quantitative reverse transcription-polymerase chain reaction (qRT-PCR)
The collected PC3, DU145, and 22Rv1 cell pellets were treated with TRIzol reagent (Invitrogen, Thermo Scientific, Shanghai, China) for total RNA extraction. The general process was as follows: cells were treated with TRIzol reagent for 10 min, centrifuged at 12,000 rpm for 15 min at 4°C, and then the upper aqueous phase was extracted and mixed with isopropyl alcohol for 10 min, and centrifuged for another 10 min under the same conditions. The supernatant was removed, the residual liquid was washed with absolute ethanol, and then centrifuged at 7,500 rpm for 5 min to remove the supernatant to obtain the RNA precipitate. Finally, the RNA precipitate was dissolved in RNase-free water, and RNA was quantified using Nano drop 300 (ALLSHENG). Total RNA was subsequently reverse transcribed to obtain cDNA using a reverse transcription kit (TaKaRa Bio, Shiga, Japan). Finally, the relative expression of RAD51 was detected via qRT-PCR in a TK-6000 PCR system using a SYBR Green kit (TaKaRa Bio, Shiga, Japan). GAPDH expression was used for normalization of gene expression. Primers for qRT-PCR are listed in Table 2.
qRT-PCR primer pairs
| Name | Primer sequence information |
|---|---|
| RAD51-F | 5′-CAACCCATTTCACGGTTAGAGC-3′ |
| RAD51-R | 5′-TTCTTTGGCGCATAGGCAACA-3′ |
| GAPDH-F | 5′-GGAGCGAGATCCCTCCAAAAT-3′ |
| GAPDH-R | 5′-GGCTGTTGTCATACTTCTCATGG-3′ |
2.13 Western blotting detection
PC3, DU145, and 22Rv1 cells were lysed with RIPA buffer containing protease and phosphatase inhibitors to isolate proteins. Following separation via a 10% sodium dodecyl-sulfate polyacrylamide gel electrophoresis, the protein samples (30 µg) were transferred to nitrocellulose membranes. The membranes were blocked with 5% non-fat powdered milk and incubated with rabbit antibody against RAD51 (Proteintech, Cat#: 14961-1-AP) and mouse antibody against Actin (Abmart, Cat#: M20011) for 1 h at 37°C, followed by washing of the membranes. The fluorescently conjugated secondary antibodies (Licor, USA) were incubated with the membrane for 1 h at room temperature. Protein signals were detected by a two-color infrared laser imaging system (Odyssey CLx). Actin protein expression was used as an internal control.
2.14 Cell counting kit (CCK)-8 and colony formation assays
PC3 and DU145 cells were treated with siRNA for 48 h, and the cell proliferation ability was determined using the CCK-8 kit (Share-bio, SB-CCK8). The colony formation ability of PCa cells was determined by colony formation assay [17].
2.15 Apoptosis assay
Transiently transfected PC3 and DU145 cells were collected in 1.5 mL EP tubes, and 50 μL of diluted fluorescein isothiocyanate (FITC) was added to each tube. After 20 min, 250 μL of propidium iodide was added for 5 min according to the instructions of the FITC Annexin V apoptosis Detection Kit (BD Biosciences, San Diego, CA). Finally, the samples were analyzed using the BD LSRFortessa analyzer (BD Biosciences).
2.16 Cell migration assay
The transwell migration assay was set up in 24-well plates containing 500 μL of medium containing 10% FBS. The transwell units were placed in the wells, and 400 μL of serum-free medium containing 5 × 104 transiently transfected PC3 and DU145 cells was added to the corresponding upper chamber. The cells were incubated in a 5% CO2 incubator at 37°C for 24 h. Uninvaded cells on the membrane surface were gently removed using a wet swab, and the remaining cells were fixed with 4% paraformaldehyde for 15 min at room temperature before staining with 0.1% crystalline violet. Once the transwell units were dry, we examined them using an inverted microscope (Nikon). The invading cells were quantified using ImageJ software.
2.17 Drug treatments
RPMI-1640 medium containing 10% FBS and 1% penicillin/streptomycin was used to prepare 0, 5, 10, 20, 40, and 80 μM working concentrations of enzalutamide (Beyotime, Cat#: SC0074-10mM). The 22Rv1 cells siRNA-treated for 48 h were seeded at 1 × 104 cells/well in 96-well plates. After 24 h, the medium in the wells was removed and 200 μL of enzalutamide medium was added to the wells according to the principle of five replicate wells for each concentration. Cell viability was determined by CCK-8 kit after 48 h treatment.
2.18 Statistical analysis
Statistical analysis of the data was performed using R software. Comparisons of two independent samples were analyzed using Wilcoxon test, and comparisons of multiple independent samples were analyzed using the Kruskal–Wallis test or ANOVA. P-value <0.05 was considered statistically significant. The relationships between gene expression and signaling pathway activity, drug sensitivity, and immune infiltration were automatically generated by an online website, and FDR < 0.05 was considered statistically significant.
The design flow chart of this study. DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein–protein interaction; DFS, disease-free survival.
3 Results
3.1 Identification of DEGs associated with enzalutamide resistance
Two GEO datasets were used to identify DEGs associated with enzalutamide resistance. After analysis, 4,366 and 2,825 differential genes were identified in GSE150807 and GSE151083, respectively (Figure 1). Volcano and heat maps were used to visualize these DEGs (Figure 2a–d), with the heat map showing only the top 20 highest and lowest expressed genes with the largest differential expression multiplicity. The Venn diagram shows 1,208 overlapping DEGs associated with enzalutamide resistance found in both datasets (Figure 2e).
DEGs in enzalutamide-resistant and enzalutamide-sensitive PCa cell lines. (a and b) The heat map shows the top 20 DEGs in the GSE151083 and GSE150807 datasets with enzalutamide-resistant and enzalutamide-sensitive cell lines. (c and d) Volcano plot showing all DEGs in the GSE151083 and GSE150807 datasets with enzalutamide- resistant and enzalutamide-sensitive cell lines. (e) Venn diagram showing the overlapping DEGs in GSE151083 and GSE150807. DEGs, differentially expressed genes; PCa, prostate cancer.
3.2 GO and KEGG pathway analyses
To further explore the potential functions of DEGs, we performed GO and KEGG analysis. As shown in Figure 3, biological process analysis showed that DEGs were mainly enriched in signal transduction, positive regulation of gene expression, and drug response. Cellular component analysis showed that the nucleus, cytosol, cytoplasm, and plasma membrane were the most common classifications. For the molecular function, protein binding was significantly enriched. In addition, KEGG pathway analysis showed that these genes were involved in metabolic pathways and cancer-related pathways.
GO and KEGG analysis of DEGs. (a–d) Bubble plots showing the results of functional annotation analysis of 1,208 overlapping DEGs. (a) Biological process. (b) Cell component. (c) Molecular function. (d) KEGG pathways. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.
3.3 Construction of PPI network and identification of candidate hub genes
We uploaded 1,208 overlapping DEGs to the STRING database and the analyzed data were downloaded. The PPI network was then imported into Cytoscope and the top 20 genes with the highest degree of connectivity were derived by the MCC algorithm using the plugin cytohubba (Figure 4). These 20 genes – RAD51, PCNA, POLE, EXO1, MSH2, BLM, LIG1, RFC2, MCM6, BRCA1, GEN1, MCM7, CDK2, DTL, POLD4, RPS27A, PKM, LDHA, APP, and Apolipoprotein E (APOE) – were identified as candidate hub genes.
The PPI network shows the top 20 genes with the highest degree of connectivity calculated by the MCC algorithm in cytohubba from 1,208 overlapping genes. PPI, protein–protein interaction network; MCC, maximal clique centrality.
3.4 Identification of hub genes through expression and survival analyses of candidate genes
To screen for hub genes with clinical application, we explored the differences in expression of candidate hub genes in PCa and normal tissues, including DFS. We identified six genes, including RAD51, BLM, APOE, DTL, RFC2, and EXO1, with high expression associated with poorer DFS (Figure 5). These six genes with prognostic value were defined as hub genes.
Validation of the expression and DFS of top 6 genes. (a) RAD51. (b) BLM. (c) APOE. (d) DTL. (e) RFC2. (f) EXO1. The significance of differences between the two groups was determined using the Wilcoxon test. DFS, disease-free survival.
3.5 Clinical parameter analysis of hub genes
In order to explore whether the hub genes had clinical value, we focused on the relationship between their expression level and N- and T-stages. Our results showed that the expression levels of these six hub genes increased with the increase in N- and T-stages of PCa (P < 0.05; Figure 6).
Validation of the correlation between the expression levels of hub genes and N-stage and T-stage. (a) RAD51. (b) BLM. (c) APOE. (d) DTL. (e) RFC2. (f) EXO1. The significance of differences between the two groups was determined using the Wilcoxon test, and Kruskal–Wallis test was used to compare multiple groups.
3.6 Validation of the relationship between hub genes and enzalutamide
Since enzalutamide exerts its inhibitory effect on CRPC mainly through inhibition of AR signaling, we investigated the potential role of hub gene expression levels on the AR signaling pathway’s activity. RAD51, BLM, EXO1, and RFC2 (Figure 7a) activated the AR signaling pathway (FDR < 0.05), and their expression level correlated with AR pathway activity, while DTL and APOE had no significant effect on AR pathway activity (Figure 7b–g). Differential expression analysis on the dataset GSE32269 containing primary PCa and mCRPC tissue samples showed that the expression levels of RAD51, BLM, DTL, and APOE were significantly upregulated in mCRPC compared to those in primary PCa tissues (FDR < 0.05), while the expression of EXO1 and RFC2 were not significantly different (Figure 7h–m).
Validation of the relationship between the expression levels of hub genes and the AR signaling pathway. (a) The potential role of hub genes on signaling pathways in PCa is summarized in the figure. The red module indicates that expression of the gene has an activating effect on the signaling pathway, while the blue module indicates an inhibitory effect. (b–g) Relationship between higher and lower expression groups of hub genes and AR signaling pathway activity score. (b) RAD51. (c) BLM. (d) APOE. (e) DTL. (f) RFC2. (g) EXO1. (h–m) Validation of hub genes expression differences in localized Pca (22) and mCRPC (29) based on the GSE32269 dataset. (h) RAD51. (i) BLM. (j) APOE. (k) DTL. (l) RFC2. (m) EXO1. The significance of differences between the two groups was determined using the Wilcoxon test. AR signaling pathway, androgen receptor signaling pathway; PCa, prostate cancer; mCRPC, metastatic CRPC.
3.7 Validation of the relationship between hub genes and immune infiltration
We performed a literature review to investigate if these hub genes mediate CRPC resistance to enzalutamide by affecting the infiltration level of immune cells and found that NK cells, monocytes, and macrophages are associated with enzalutamide resistance [18,19]. In view of this, we analyzed the relationship between the expression of hub genes in PCa and the infiltration levels of NK cells, monocytes, and macrophages. As shown in Figure 8, the expression of RAD51, BLM, DTL, and EXO1 showed a significant positive correlation with the infiltration levels of monocytes and macrophages, while the expression of RFC2 and APOE only showed a significant positive correlation with the infiltration level of macrophages but not monocytes. In addition, the infiltration level of NK cells showed a significant negative correlation with the expression of BLM, DTL, and EXO1, but not with that of RAD51, and a positive correlation with RFC2 and APOE expression.
Spearman correlation of hub genes expression in PCa with macrophage, monocyte, and NK cell infiltration. (a) RAD51. (b) BLM. (c) APOE. (d) DTL. (e) RFC2. (f) EXO1. NK, natural killer.
3.8 Relationship between hub gene expression and drug sensitivity
Based on the data provided by the GDSC database, we analyzed the relationship between the expression levels of hub genes and tumor drug sensitivity (IC50) through the GSCA online data analysis website. A gene’s expression being positively correlated with drug sensitivity indicates drug resistance, and vice versa. The high expression of hub genes, except that of APOE, showed a significant negative correlation with the IC50 of Navitoclax and NPK76−II−72−1, while APOE expression showed a significant correlation with the IC50 of SB590885, Dabrafenib, and PLX4720 (Figure 9).
Correlation between GDSC drug (top 30) sensitivity and hub genes expression. The negative correlation indicates that high expression of the gene is sensitive to the drug, and vice versa. GDSC, Genomics of Drug Sensitivity in Cancer.
3.9 Knockdown of RAD51 inhibits the proliferation and migration of PCa cells and promotes apoptosis
To explore the role of RAD51 in PCa, we knocked down RAD51 in PC3, DU145, and 22Rv1 cell lines (Figures 10a and 11a). The CCK-8 and clone formation assays demonstrated that the knockdown of RAD51 inhibited the proliferative capacity of both PCa cell lines (Figure 10b and c). Moreover, transwell assays showed that knockdown of RAD51 inhibited the migratory ability of DU145 cells but had no effect on PC3 cells (Figure 10d). Apoptosis assays demonstrated that knockdown of RAD51 promoted apoptosis in two PCa cell lines (Figure 10e). In addition, knockdown of RAD51 inhibited the proliferation of 22Rv1 cells more significantly than no knockdown of RAD51 under enzalutamide treatment (Figure 11b).
Effect of RAD51 on the proliferation, migration, and apoptosis of PCa cell lines. (a) PC3 and DU145 cells were transfected with siRAD51 to verify the knockdown efficiency of RAD51 at the mRNA and protein expression levels (one-way analysis of variance). (b) CCK-8 detects the proliferation ability of PC3 and DU145 cells after RAD51 knockdown (one-way analysis of variance). (c) Clone formation assay detects the proliferation ability of PC3 and DU145 cells after RAD51 knockdown (one-way analysis of variance). (d) Transwell migration assay detects the proliferation ability of DU145 cell after RAD51 knockdown (100×) (one-way analysis of variance). (e) Flow cytometry assays detect the apoptosis of PC3 and DU145 cells after RAD51 knockdown (one-way analysis of variance). The significance of differences between multiple groups was determined by ANOVA. *P-value < 0.05, **P-value < 0.01, ***P-value < 0.001, ****P-value < 0.0001. PCa, prostate cancer.
Effect of RAD51 on the proliferation, migration, and apoptosis of PCa cell lines. (a) 22Rv1 cells were transfected with siRAD51 to verify the knockdown efficiency of RAD51 at the mRNA and protein expression levels (one-way analysis of variance). (b) Effect of RAD51 knockdown on the viability of 22Rv1 cells treated with different concentrations of enzalutamide for 48 h as detected using a CCK-8 assay. *P-value < 0.05, **P-value < 0.01, ***P-value < 0.001, ****P-value < 0.0001.
4 Discussion
An increasing number of studies have shown that abnormal expression of certain genes promotes CRPC resistance to enzalutamide [20,21,22]. In the present study, we analyzed six hub genes, RAD51, BLM, DTL, RFC2, APOE, and EXO1, which were significantly correlated with enzalutamide resistance, through bioinformatics analysis from multiple perspectives. The expression of RAD51, BLM, RFC2, and EXO1 was highly correlated with AR pathway activity, more so in the high-expression group than the low-expression group. Meanwhile, differential analysis of the GSE32269 dataset showed that the expression of RAD51, BLM, DTL, and APOE was higher in CRPC tissues than in primary PCa tissues. Curiously, the expression of RFC2 and EXO1 in CRPC tissues was not significantly different from that in primary PCa tissues. It is plausible that there was no increase in gene expression, but there may be other mechanisms by which these proteins increase in expression [23]. The AR signaling pathway, a key driver in the uncontrolled growth of malignant PCa cells, is reactivated in CRPC and contributes to CRPC progression [3]. Enzalutamide resistance is associated with the AR signaling pathway [24,25]. Therefore, inhibiting this pathway is a logical strategy for treating CRPC [26]. RAD51 and BLM were shown to be highly expressed in CRPC tissues in our study, and the expression levels were positively correlated with the promotion of AR signaling pathway activity. These results suggest that RAD51 and BLM likely mediate CRPC resistance to enzalutamide by promoting the AR signaling pathway. In addition, we verified that RAD51 knockdown can inhibit the proliferation and migratory ability of PCa cell lines and promote apoptosis. Not only that, the proliferation of 22Rv1 cells was more significantly inhibited under the treatment with enzalutamide when RAD51 was knocked down than when it was not, which further verified that RAD51 could promote enzalutamide resistance. This result is similar to the findings of Kohrt et al. [23], who identified 11 genes (TFAP2C, CAD, SPDEF, EIF6, GABRG2, CDC37, PSMD12, COL5A2, AR, MAP3K11, and ACAT1) whose loss resulted in decreased cell survival in response to enzalutamide. Further experimental validation revealed that three of these genes (ACAT1, MAP3K11, and PSMD12) could serve as proponents of enzalutamide resistance in vitro [23]. There is no doubt that these genes may serve as new therapeutic targets for enzalutamide-resistant PCa in the future.
Different cell types and extracellular components of the tumor microenvironment (TME) not only play a pivotal role in tumor progression, but also significantly influence therapeutic efficacy and mediate drug resistance [27]. Immune infiltration in the TME is associated with enzalutamide resistance [28,29]; we found from our literature review that enzalutamide-resistant CRPC cells were better able to recruit NK cells than ordinary PCa cells, and NK cells inhibited enzalutamide resistance in CRPC by targeting AR splice variant 7 (ARv7) [18]. Furthermore, enzalutamide induced neuroendocrine like (NE-like) PCa cells to enhance monocyte recruitment to the tumor region and promoted monocytes toward tumor-associated macrophages (TAMs). Further studies revealed that enzalutamide-treated C4-2 cells expressed higher levels of NSE and CHGA when co-cultured with NE-like PCa-activated THP-1 cells and sorted into monocytes. In addition, the survival rate of enzalutamide-treated C4-2 cells co-cultured with TAMs was higher than that of enzalutamide-treated cells alone [19]. In summary, NK cells can inhibit enzalutamide resistance in CRPC, while monocytes and macrophages have a facilitative effect on the survival of enzalutamide-resistant CRPC cells. Our results demonstrate that the expression levels of EXO1, BLM, and DTL significantly correlated with the infiltration levels of monocytes, macrophages, and NK cells. The expression levels of RAD51, EXO1, BLM, and DTL showed a significant positive correlation with the infiltration levels of monocytes and macrophages. The expression levels of EXO1, BLM, and DTL showed a significant negative correlation with the infiltration levels of NK cells, and finally, the expression levels of RFC2 and APOE were only significantly and positively correlated with the infiltration levels of macrophages. Collectively, our results suggest that the six hub genes may mediate enzalutamide resistance through the different immune cell infiltration pathways.
In our analysis of the relationship between the expression of hub genes and drug sensitivity based on GDSC, high expression of RAD51, BLM, DTL, RFC2, and EXO1 was significantly associated with higher sensitivity to NPK76-II-72-1 and Navitoclax. Studies have demonstrated that CRPC patient-derived conditionally reprogrammed cells (CRCs) are sensitive to Navitoclax [30]. Navitoclax is an inhibitor of BCL-2 protein families, which are often overexpressed in cancer and associated with drug resistance [31]. BCL-2 family members, BCL-XL and MCL1, are involved in enzalutamide resistance through activation of PI3K/AKT signaling, leading to apoptosis evasion [32]. In addition, BCL-2 was found to be overexpressed in enzalutamide-sensitive cell lines, leading to the emergence of enzalutamide resistance. BCL-2 inhibitors suppressed the development of enzalutamide resistance in xenografts [33]. Through inhibition of the BCL-2 family of proteins, Navitoclax can induce apoptosis. Therefore, RAD51, BLM, DTL, RFC2, and EXO1 may be potential targets of Navitoclax. The combination of enzalutamide and Navitoclax may be a promising treatment regimen to counter enzalutamide resistance.
In this study, we identified a total of 1,208 DEGs, including the six key genes mentioned above, associated with enzalutamide resistance by analyzing the datasets GSE151083 and GSE150807. GO enrichment analysis showed that DEGs were mainly enriched in processes such as signal transduction, positive regulation of gene expression, and drug response. KEGG pathway analysis showed that DEGs were involved in the cancer metabolic pathways and pathways in cancer. In recent years, studies have reported that glucose and lipid metabolism are related to enzalutamide resistance [34,35,36].
RAD51 codes for an enzyme involved in double-stranded DNA break repair [37] is highly expressed in various types of tumors and is associated with poor clinical prognosis [38,39,40]. It can promote the occurrence and development of different tumor types [41,42]. Although RAD51 has been shown to be overexpressed in aggressive PCa [43], its functional role in PCa has not been investigated. Our study demonstrated that RAD51 knockdown in PCa cell lines PC3 and DU145 significantly reduced cell proliferation efficiency, promoted apoptosis, and inhibited migration. In addition, it has been well documented that RAD51 overexpression is associated with tumor resistance to chemotherapy [44,45,46,47]. These results suggest that RAD51 may be a key gene mediating enzalutamide resistance. Bloom syndrome protein (BLM) is an important enzyme in DNA metabolism [48,49]. Its expression in PCa tissues and PC3 cells is significantly higher than that in non-PCa tissues and benign prostatic hyperplasia cells. Knockdown of BLM inhibits PCa cell proliferation and promotes apoptosis, and overexpression can reverse tumor growth inhibition [50,51]. Studies have reported that EZH2 knockdown has an inhibitory effect on the growth of PCa cells, but overexpression of BLM can reverse this effect, and the expression of BLM is positively correlated with that of EZH2 [52]. In addition, it has been shown that EZH2 expression can inhibit CCN3 expression, and CCN3 expression inhibits AR signaling, thereby inhibiting the growth of enzalutamide-resistant PCa cells [53]. This evidence suggests that BLM may play an important role in promoting enzalutamide resistance through the EZH2-CCN3 axis. DTL, also known as CDT2, is mainly involved in the regulation of DNA replication and cell cycle [54,55]. It has been well documented that DTL promotes the development of certain cancers [56,57,58]. DTL was shown to be associated with AKT/mTOR activation, while inhibition of the mTOR pathway inhibited the proliferation of enzalutamide-resistant PCa cells, leading to AR or AR-V degradation [59,60]. Therefore, DTL may promote enzalutamide resistance through the AKT/mTOR pathway. EXO1, a damage-repair related gene, is associated with the clinical progression, metastasis, and survival prognosis of PCa [61,62]. Moreover, the high expression of EXO1 is related to the resistance of gastric and ovarian cancers to cisplatin [63,64]. APOE is a protein related to lipid metabolism that is abundantly secreted by hepatocytes and macrophages [65,66]. One study found that enzalutamide-treated C4-2B cells co-cultured with TAMs increased cell survival [19]. In addition, APOE has been reported to be highly expressed in a variety of tumor types and to promote cancer progression [67,68,69,70]. Replication factor C is a protein complex consisting of five subunits (RFC1-5) that mediates DNA replication and repair [71]. Forkhead box O1 has been reported to promote temozolomide resistance in gliomas through modulation of RFC2 [72]. Meanwhile, Forkhead box M1 (FoxM1) mediates temozolomide resistance in glioma cells by regulating the expression of RFC5 [73]. Recently, it has been increasingly reported that the high expression of RFC2 is associated with the proliferation, migration, and invasion of colorectal cancer and hepatocellular carcinoma. Although all six of the above mentioned hub genes were shown to be associated with cancer progression and we analyzed their relationship with enzalutamide resistance from a bioinformatics perspective, a limitation of the present study is that we did not demonstrate the role of these hub genes in enzalutamide resistance from an enzalutamide resistance cell model. Therefore, their specific role in enzalutamide resistance remains unclear, and future experiments are needed to demonstrate their functions.
5 Conclusion
We identified six key genes associated with enzalutamide resistance, RAD51, BLM, DTL, RFC2, APOE, and EXO1, and their role in enzalutamide resistance was validated via bioinformatic approaches in terms of signaling pathways and immune infiltration. In addition, we verified that RAD51 knockdown can inhibit the proliferation and migratory ability of PCa cell lines and promote apoptosis. Furthermore, the proliferation of 22Rv1 cells was more significantly inhibited with knockdown of RAD51 than without knockdown of RAD51 under enzalutamide treatment. RAD51 is a promotor of enzalutamide resistance. These genes and their protein products may serve as new therapeutic targets for enzalutamide-resistant PCa in the future.
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Funding information: This study was supported by the National Natural Science Foundation of China (82103178), Shanghai Sailing Program (20YF1438000 and 22YF1433800) and Shanghai Tenth People’s Hospital Climbing Talents Program (2021SYPDRC023).
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Author contributions: Conception and design: Wen Xu and Li Liu; administrative support: Fenyong Sun; provision of study materials: Jinliang Ni, Yue Zhang, and Mingyang Li; collection and assembly of data: Wen Xu, Zhongqi Cui, Nan Huang, Jie Luo, and Limei Sun; data analysis and interpretation: Wen Xu, Li Liu, and Zhongqi Cui; manuscript writing and final approval of manuscript: All authors. All authors reviewed and approved the manuscript for publication.
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Conflict of interest: The authors declare that they have no conflicts of interest.
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Data availability statement: This study used the following publicly available databases: TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/), GEPIA (http://gepia.cancer-pku.cn/), and GSCA (http://bioinfo.life.hust.edu.cn/GSCA/#/).
References
[1] Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.10.3322/caac.21660Search in Google Scholar PubMed
[2] Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.10.3322/caac.21492Search in Google Scholar PubMed
[3] Heinlein CA, Chang C. Androgen receptor in prostate cancer. Endocr Rev. 2004;25(2):276–308.10.1210/er.2002-0032Search in Google Scholar PubMed
[4] Crawford ED, Schellhammer PF, McLeod DG, Moul JW, Higano CS, Shore N, et al. Androgen Receptor Targeted Treatments of Prostate Cancer: 35 Years of Progress with Antiandrogens. J Urol. 2018;200(5):956–66.10.1016/j.juro.2018.04.083Search in Google Scholar PubMed
[5] Trewartha D, Carter K. Advances in prostate cancer treatment. Nat Rev Drug Discov. 2013;12(11):823–4.10.1038/nrd4068Search in Google Scholar PubMed
[6] Davies A, Conteduca V, Zoubeidi A, Beltran H. Biological evolution of castration-resistant prostate cancer. Eur Urol Focus. 2019;5(2):147–54.10.1016/j.euf.2019.01.016Search in Google Scholar PubMed
[7] Tran C, Ouk S, Clegg NJ, Chen Y, Watson PA, Arora V, et al. Development of a second-generation antiandrogen for treatment of advanced prostate cancer. Science. 2009;324(5928):787–90.10.1126/science.1168175Search in Google Scholar PubMed PubMed Central
[8] Chen X, Liu J, Cheng L, Li C, Zhang Z, Bai Y, et al. Inhibition of noncanonical Wnt pathway overcomes enzalutamide resistance in castration-resistant prostate cancer. Prostate. 2020;80(3):256–66.10.1002/pros.23939Search in Google Scholar PubMed
[9] Davis ID, Martin AJ, Stockler MR, Begbie S, Chi KN, Chowdhury S, et al. Enzalutamide with standard first-line therapy in metastatic prostate cancer. N Engl J Med. 2019;381(2):121–31.10.1056/NEJMoa1903835Search in Google Scholar PubMed
[10] Scher HI, Fizazi K, Saad F, Taplin ME, Sternberg CN, Miller K, et al. Increased survival with enzalutamide in prostate cancer after chemotherapy. N Engl J Med. 2012;367(13):1187–97.10.1056/NEJMoa1207506Search in Google Scholar PubMed
[11] Maughan BL, Luber B, Nadal R, Antonarakis ES. Comparing sequencing of abiraterone and enzalutamide in Men with metastatic castration-resistant prostate cancer: A retrospective study. Prostate. 2017;77(1):33–40.10.1002/pros.23246Search in Google Scholar PubMed
[12] Tannock IF, de Wit R, Berry WR, Horti J, Pluzanska A, Chi KN, et al. Docetaxel plus prednisone or mitoxantrone plus prednisone for advanced prostate cancer. N Engl J Med. 2004;351(15):1502–12.10.1056/NEJMoa040720Search in Google Scholar PubMed
[13] Chen X, Wu Y, Wang X, Xu C, Wang L, Jian J, et al. CDK6 is upregulated and may be a potential therapeutic target in enzalutamide-resistant castration-resistant prostate cancer. Eur J Med Res. 2022;27(1):105.10.1186/s40001-022-00730-ySearch in Google Scholar PubMed PubMed Central
[14] Stone L. Prostate cancer: Escaping enzalutamide: Malat1 contributes to resistance. Nat Rev Urol. 2017;14(8):450.10.1038/nrurol.2017.91Search in Google Scholar PubMed
[15] Dondoo TO, Fukumori T, Daizumoto K, Fukawa T, Kohzuki M, Kowada M, et al. Galectin-3 is implicated in tumor progression and resistance to anti-androgen drug through regulation of androgen receptor signaling in prostate cancer. Anticancer Res. 2017;37(1):125–34.10.21873/anticanres.11297Search in Google Scholar PubMed
[16] Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: Archive for functional genomics data sets–update. Nucleic Acids Res. 2013;41(Database issue):D991–5.10.1093/nar/gks1193Search in Google Scholar PubMed PubMed Central
[17] Liu L, Wang J, Sun G, Wu Q, Ma J, Zhang X, et al. m(6)A mRNA methylation regulates CTNNB1 to promote the proliferation of hepatoblastoma. Mol Cancer. 2019;18(1):188.10.1186/s12943-019-1119-7Search in Google Scholar PubMed PubMed Central
[18] Lin SJ, Chou FJ, Li L, Lin CY, Yeh S, Chang C. Natural killer cells suppress enzalutamide resistance and cell invasion in the castration resistant prostate cancer via targeting the androgen receptor splicing variant 7 (ARv7). Cancer Lett. 2017;398:62–9.10.1016/j.canlet.2017.03.035Search in Google Scholar PubMed
[19] Wang C, Peng G, Huang H, Liu F, Kong DP, Dong KQ, et al. Blocking the feedback loop between neuroendocrine differentiation and macrophages improves the therapeutic effects of enzalutamide (MDV3100) on prostate Cancer. Clin Cancer Res: Off J Am Assoc Cancer Res. 2018;24(3):708–23.10.1158/1078-0432.CCR-17-2446Search in Google Scholar PubMed
[20] Li S, Fong KW, Gritsina G, Zhang A, Zhao JC, Kim J, et al. Activation of MAPK signaling by CXCR7 leads to enzalutamide resistance in prostate cancer. Cancer Res. 2019;79(10):2580–92.10.1158/0008-5472.CAN-18-2812Search in Google Scholar PubMed PubMed Central
[21] Gao L, Zhang W, Zhang J, Liu J, Sun F, Liu H, et al. KIF15-mediated stabilization of AR and AR-V7 contributes to enzalutamide resistance in prostate cancer. Cancer Res. 2021;81(4):1026–39.10.1158/0008-5472.CAN-20-1965Search in Google Scholar PubMed
[22] Lee HC, Ou CH, Huang YC, Hou PC, Creighton CJ, Lin YS, et al. YAP1 overexpression contributes to the development of enzalutamide resistance by induction of cancer stemness and lipid metabolism in prostate cancer. Oncogene. 2021;40(13):2407–21.10.1038/s41388-021-01718-4Search in Google Scholar PubMed PubMed Central
[23] Kohrt SE, Awadallah WN, Phillips RA, 3rd, Case TC, Jin R, Nanda JS, et al. Identification of genes required for enzalutamide resistance in castration-resistant prostate cancer cells in vitro. Mol Cancer Ther. 2021;20(2):398–409.10.1158/1535-7163.MCT-20-0244Search in Google Scholar PubMed PubMed Central
[24] Wang Y, Chen J, Wu Z, Ding W, Gao S, Gao Y, et al. Mechanisms of enzalutamide resistance in castration-resistant prostate cancer and therapeutic strategies to overcome it. Br J Pharmacol. 2021;178(2):239–61.10.1111/bph.15300Search in Google Scholar PubMed
[25] Hussain M, Fizazi K, Saad F, Rathenborg P, Shore N, Ferreira U, et al. Enzalutamide in men with nonmetastatic, castration-resistant prostate cancer. N Engl J Med. 2018;378(26):2465–74.10.1056/NEJMoa1800536Search in Google Scholar PubMed PubMed Central
[26] Schalken J, Fitzpatrick JM. Enzalutamide: Targeting the androgen signalling pathway in metastatic castration-resistant prostate cancer. BJU Int. 2016;117(2):215–25.10.1111/bju.13123Search in Google Scholar PubMed PubMed Central
[27] Wu T, Dai Y. Tumor microenvironment and therapeutic response. Cancer Lett. 2017;387:61–8.10.1016/j.canlet.2016.01.043Search in Google Scholar PubMed
[28] Ruan H, Bao L, Tao Z, Chen K. Flightless I homolog reverses enzalutamide resistance through PD-L1-mediated immune evasion in prostate cancer. Cancer Immunol Res. 2021;9(7):838–52.10.1158/2326-6066.CIR-20-0729Search in Google Scholar PubMed
[29] Madan RA, Karzai F, Donahue RN, Al-Harthy M, Bilusic M, Rosner II, et al. Clinical and immunologic impact of short-course enzalutamide alone and with immunotherapy in non-metastatic castration sensitive prostate cancer. J Immunother Cancer. 2021;9(3):e001556.10.1136/jitc-2020-001556Search in Google Scholar PubMed PubMed Central
[30] Saeed K, Rahkama V, Eldfors S, Bychkov D, Mpindi JP, Yadav B, et al. Comprehensive drug testing of patient-derived conditionally reprogrammed cells from castration-resistant prostate cancer. Eur Urol. 2017;71(3):319–27.10.1016/j.eururo.2016.04.019Search in Google Scholar PubMed
[31] Siddiqui WA, Ahad A, Ahsan H. The mystery of BCL2 family: BCL-2 proteins and apoptosis: An update. Arch Toxicol. 2015;89(3):289–317.10.1007/s00204-014-1448-7Search in Google Scholar PubMed
[32] Pilling AB, Hwang C. Targeting prosurvival BCL2 signaling through Akt blockade sensitizes castration-resistant prostate cancer cells to enzalutamide. Prostate. 2019;79(11):1347–59.10.1002/pros.23843Search in Google Scholar PubMed PubMed Central
[33] Liang Y, Jeganathan S, Marastoni S, Sharp A, Figueiredo I, Marcellus R, et al. Emergence of enzalutamide resistance in prostate cancer is associated with BCL-2 and IKKB dependencies. Clin Cancer Res Off J Am Assoc Cancer Res. 2021;27(8):2340–51.10.1158/1078-0432.CCR-20-3260Search in Google Scholar PubMed
[34] Basu HS, Wilganowski N, Robertson S, Reuben JM, Cohen EN, Zurita A, et al. Prostate cancer cells survive anti-androgen and mitochondrial metabolic inhibitors by modulating glycolysis and mitochondrial metabolic activities. Prostate. 2021;81(12):799–811.10.1002/pros.24146Search in Google Scholar PubMed
[35] Stoykova GE, Schlaepfer IR. Lipid metabolism and endocrine resistance in prostate cancer, and new opportunities for therapy. Int J Mol Sci. 2019;20(11):2626.10.3390/ijms20112626Search in Google Scholar PubMed PubMed Central
[36] Hoshi S, Meguro S, Imai H, Matsuoka Y, Yoshida Y, Onagi A, et al. Upregulation of glucocorticoid receptor-mediated glucose transporter 4 in enzalutamide-resistant prostate cancer. Cancer Sci. 2021;112(5):1899–910.10.1111/cas.14865Search in Google Scholar PubMed PubMed Central
[37] Scully R, Chen J, Plug A, Xiao Y, Weaver D, Feunteun J, et al. Association of BRCA1 with Rad51 in mitotic and meiotic cells. Cell. 1997;88(2):265–75.10.1016/S0092-8674(00)81847-4Search in Google Scholar
[38] Klein HL. The consequences of Rad51 overexpression for normal and tumor cells. DNA Repair. 2008;7(5):686–93.10.1016/j.dnarep.2007.12.008Search in Google Scholar PubMed PubMed Central
[39] Li Y, Wang WY, Xiao JH, Xu F, Liao DY, Xie L, et al. Overexpression of Rad51 predicts poor prognosis in colorectal cancer: Our experience with 54 patients. PLoS One. 2017;12(1):e0167868.10.1371/journal.pone.0167868Search in Google Scholar PubMed PubMed Central
[40] Alshareeda AT, Negm OH, Aleskandarany MA, Green AR, Nolan C, TigHhe PJ, et al. Clinical and biological significance of RAD51 expression in breast cancer: a key DNA damage response protein. Breast Cancer Res Treat. 2016;159(1):41–53.10.1007/s10549-016-3915-8Search in Google Scholar PubMed
[41] Zhang X, Ma N, Yao W, Li S, Ren Z. RAD51 is a potential marker for prognosis and regulates cell proliferation in pancreatic cancer. Cancer Cell Int. 2019;19:356.10.1186/s12935-019-1077-6Search in Google Scholar PubMed PubMed Central
[42] Chiu WC, Fang PT, Lee YC, Wang YY, Su YH, Hu SC, et al. DNA repair protein Rad51 induces tumor growth and metastasis in esophageal squamous cell carcinoma via a p38/Akt-dependent pathway. Ann Surgical Oncol. 2020;27(6):2090–101.10.1245/s10434-019-08043-xSearch in Google Scholar PubMed
[43] Mitra A, Jameson C, Barbachano Y, Sanchez L, Kote-Jarai Z, Peock S, et al. Overexpression of RAD51 occurs in aggressive prostatic cancer. Histopathology. 2009;55(6):696–704.10.1111/j.1365-2559.2009.03448.xSearch in Google Scholar PubMed PubMed Central
[44] Liu Y, Burness ML, Martin-Trevino R, Guy J, Bai S, Harouaka R, et al. RAD51 mediates resistance of cancer stem cells to PARP inhibition in triple-negative breast cancer. Clin Cancer Res Off J Am Assoc Cancer Res. 2017;23(2):514–22.10.1158/1078-0432.CCR-15-1348Search in Google Scholar PubMed
[45] Rajput M, Singh R, Singh N, Singh RP. EGFR-mediated Rad51 expression potentiates intrinsic resistance in prostate cancer via EMT and DNA repair pathways. Life Sci. 2021;286:120031.10.1016/j.lfs.2021.120031Search in Google Scholar PubMed
[46] Lee JO, Kang MJ, Byun WS, Kim SA, Seo IH, Han JA, et al. Metformin overcomes resistance to cisplatin in triple-negative breast cancer (TNBC) cells by targeting RAD51. Breast Cancer Res. 2019;21(1):115.10.1186/s13058-019-1204-2Search in Google Scholar PubMed PubMed Central
[47] Lin M, Xu M, Xu Z, Weng Z, Lin B, Lan Y, et al. LINC00200 contributes to the chemoresistance to oxaliplatin of gastric cancer cells via regulating E2F1/RAD51 axis. Hum Cell. 2021;34(4):1163–73.10.1007/s13577-021-00523-1Search in Google Scholar PubMed
[48] Mohaghegh P, Karow JK, Brosh RM, Jr, Bohr VA, Hickson ID. The Bloom’s and Werner’s syndrome proteins are DNA structure-specific helicases. Nucleic Acids Res. 2001;29(13):2843–9.10.1093/nar/29.13.2843Search in Google Scholar PubMed PubMed Central
[49] Thompson ER, Doyle MA, Ryland GL, Rowley SM, Choong DY, Tothill RW, et al. Exome sequencing identifies rare deleterious mutations in DNA repair genes FANCC and BLM as potential breast cancer susceptibility alleles. PLoS Genet. 2012;8(9):e1002894.10.1371/journal.pgen.1002894Search in Google Scholar PubMed PubMed Central
[50] Chen K, Xu H, Zhao J. Bloom syndrome protein activates AKT and PRAS40 in prostate cancer cells. Oxid Med Cell Longev. 2019;2019:3685817.10.1155/2019/3685817Search in Google Scholar PubMed PubMed Central
[51] Qian X, Feng S, Xie D, Feng D, Jiang Y, Zhang X. RecQ helicase BLM regulates prostate cancer cell proliferation and apoptosis. Oncol Lett. 2017;14(4):4206–12.10.3892/ol.2017.6704Search in Google Scholar PubMed PubMed Central
[52] Ruan Y, Xu H, Ji X, Zhao J. BLM interaction with EZH2 regulates MDM2 expression and is a poor prognostic biomarker for prostate cancer. Am J Cancer Res. 2021;11(4):1347–68.Search in Google Scholar
[53] Armstrong CM, Gao AC. CCN3-EZH2-AR feedback loop: New targets for enzalutamide and castration resistant prostate cancer. J Cell Commun Signal. 2017;11(1):89–91.10.1007/s12079-017-0378-6Search in Google Scholar PubMed PubMed Central
[54] Cheung WM, Chu AH, Chu PW, Ip NY. Cloning and expression of a novel nuclear matrix-associated protein that is regulated during the retinoic acid-induced neuronal differentiation. J Biol Chem. 2001;276(20):17083–91.10.1074/jbc.M010802200Search in Google Scholar PubMed
[55] Slenn TJ, Morris B, Havens CG, Freeman RM, Jr, Takahashi TS, Walter JC. Thymine DNA glycosylase is a CRL4Cdt2 substrate. J Biol Chem. 2014;289(33):23043–55.10.1074/jbc.M114.574194Search in Google Scholar PubMed PubMed Central
[56] Liu S, Gu L, Wu N, Song J, Yan J, Yang S, et al. Overexpression of DTL enhances cell motility and promotes tumor metastasis in cervical adenocarcinoma by inducing RAC1-JNK-FOXO1 axis. Cell Death Dis. 2021;12(10):929.10.1038/s41419-021-04179-5Search in Google Scholar PubMed PubMed Central
[57] Cui H, Wang Q, Lei Z, Feng M, Zhao Z, Wang Y, et al. DTL promotes cancer progression by PDCD4 ubiquitin-dependent degradation. J Exp Clin Cancer Research CR. 2019;38(1):350.10.1186/s13046-019-1358-xSearch in Google Scholar PubMed PubMed Central
[58] Kiran S, Dar A, Singh SK, Lee KY, Dutta A. The deubiquitinase USP46 is essential for proliferation and tumor growth of HPV-transformed cancers. Mol Cell. 2018;72(5):823–35.e5.10.1016/j.molcel.2018.09.019Search in Google Scholar PubMed PubMed Central
[59] Luo Y, He Z, Liu W, Zhou F, Liu T, Wang G. DTL is a prognostic biomarker and promotes bladder cancer progression through regulating the AKT/mTOR axis. Oxid Med Cell Longev. 2022;2022:3369858.10.1155/2022/3369858Search in Google Scholar PubMed PubMed Central
[60] Kong Y, Cheng L, Mao F, Zhang Z, Zhang Y, Farah E, et al. Inhibition of cholesterol biosynthesis overcomes enzalutamide resistance in castration-resistant prostate cancer (CRPC). J Biol Chem. 2018;293(37):14328–41.10.1074/jbc.RA118.004442Search in Google Scholar PubMed PubMed Central
[61] Tran PT, Erdeniz N, Symington LS, Liskay RM. EXO1-A multi-tasking eukaryotic nuclease. DNA Repair. 2004;3(12):1549–59.10.1016/j.dnarep.2004.05.015Search in Google Scholar PubMed
[62] Goellner EM, Putnam CD, Kolodner RD. Exonuclease 1-dependent and independent mismatch repair. DNA Repair. 2015;32:24–32.10.1016/j.dnarep.2015.04.010Search in Google Scholar PubMed PubMed Central
[63] Mao P, Wu S, Fan Y. Upregulation of exonuclease 1 caused by homology-dependent repair confers cisplatin resistance to gastric cancer cells. Can J Physiol Pharmacol. 2022;100(9):903–14.10.1139/cjpp-2022-0139Search in Google Scholar PubMed
[64] He D, Li T, Sheng M, Yang B. Exonuclease 1 (Exo1) participates in mammalian non-homologous end joining and contributes to drug resistance in ovarian cancer. Med Sci Monit Int Med J Exp Clin Res. 2020;26:e918751.10.12659/MSM.918751Search in Google Scholar PubMed PubMed Central
[65] Tall AR, Yvan-Charvet L. Cholesterol, inflammation and innate immunity. Nat Rev Immunol. 2015;15(2):104–16.10.1038/nri3793Search in Google Scholar PubMed PubMed Central
[66] Mahley RW. Apolipoprotein E: From cardiovascular disease to neurodegenerative disorders. J Mol Med (Berlin, Ger). 2016;94(7):739–46.10.1007/s00109-016-1427-ySearch in Google Scholar PubMed PubMed Central
[67] Trost Z, Marc J, Sok M, Cerne D. Increased apolipoprotein E gene expression and protein concentration in lung cancer tissue do not contribute to the clinical assessment of non-small cell lung cancer patients. Arch Med Res. 2008;39(7):663–7.10.1016/j.arcmed.2008.06.009Search in Google Scholar PubMed
[68] Sakashita K, Tanaka F, Zhang X, Mimori K, Kamohara Y, Inoue H, et al. Clinical significance of ApoE expression in human gastric cancer. Oncol Rep. 2008;20(6):1313–9.Search in Google Scholar
[69] Su WP, Chen YT, Lai WW, Lin CC, Yan JJ, Su WC. Apolipoprotein E expression promotes lung adenocarcinoma proliferation and migration and as a potential survival marker in lung cancer. Lung Cancer (Amsterdam, Netherlands). 2011;71(1):28–33.10.1016/j.lungcan.2010.04.009Search in Google Scholar PubMed
[70] Jayakar SK, Loudig O, Brandwein-Gensler M, Kim RS, Ow TJ, Ustun B, et al. Apolipoprotein E promotes invasion in oral squamous cell carcinoma. Am J Pathol. 2017;187(10):2259–72.10.1016/j.ajpath.2017.06.016Search in Google Scholar PubMed PubMed Central
[71] Ohashi E, Tsurimoto T. Functions of multiple clamp and clamp-loader complexes in eukaryotic DNA replication. Adv Exp Med Biol. 2017;1042:135–62.10.1007/978-981-10-6955-0_7Search in Google Scholar PubMed
[72] Qiu X, Tan G, Wen H, Lian L, Xiao S. Forkhead box O1 targeting replication factor C subunit 2 expression promotes glioma temozolomide resistance and survival. Ann Transl Med. 2021;9(8):692.10.21037/atm-21-1523Search in Google Scholar PubMed PubMed Central
[73] Peng WX, Han X, Zhang CL, Ge L, Du FY, Jin J, et al. FoxM1-mediated RFC5 expression promotes temozolomide resistance. Cell Biol Toxicol. 2017;33(6):527–37.10.1007/s10565-017-9381-1Search in Google Scholar PubMed
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