Overexpression of HYOU1 is associated with cisplatin resistance and may depend on m6A modification in patients with cervical cancer
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- Published online on: November 26, 2024 https://doi.org/10.3892/ol.2024.14823
- Article Number: 77
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Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Cervical cancer (CC) is the fourth leading cause of cancer-associated mortalities among women worldwide despite advancements in diagnosis, prevention and treatment (1,2). The prognosis of patients with advanced or recurrent CC is poor, with a 1-year survival rate of only 10–20% (3). Chemotherapy is the standard treatment for patients with advanced or recurrent CC. Although the chemotherapeutic agent cisplatin (DDP) is the most effective drug for treating CC (4), resistance to DDP-based treatment limits the survival of patients with partial CC, leading to poor prognosis (4).
The mechanisms underlying DDP resistance in CC have been examined and strategies have been proposed to overcome the resistance (5–8). Previous studies show that reduced accumulation of intracellular platinum compounds (5), increased DNA damage repair (6), inactivation of apoptosis (7) and activation of the epithelial-mesenchymal transition (8) are associated with DDP resistance. In the previous number of decades, an increasing number of studies have shown that tumor cells hijack the unfolded protein response to induce chemotherapy resistance by activating the unfolded response sensors activated transcription factor 6, inositol-requiring transmembrane kinase/endoribonuclease 1α and protein kinase R-like endoplasmic reticulum kinase as well as their master regulator glucose regulated protein 78 (9–12). The hypoxia-upregulated 1 (HYOU1) gene encodes a chaperone protein in the endoplasmic reticulum (ER). Various stimuli, including hypoxia, impaired ubiquitination, proteasomal degradation and energy deficiency induce an unfolded protein response in the presence of ER stress, accompanied by the expression of ER molecular chaperones such as protein kinase-like ER kinase, inositol-requiring enzyme 1, and activating transcription factor 6α (13).
N6-methyladenosine (m6A), which is among the most prevalent and reversible internal RNA modifications in eukaryotic RNAs (14), occurs at the consensus motif RRACH (R is G, A or U; H is U, A or C) and regulates RNA transcription, splicing, degradation and translation (15). m6A modification of RNA is catalyzed by the m6A methyltransferase enzyme complexes (writers), removed by m6A demethylase enzymes (erasers) and recognized by specific proteins (readers) (16–20). Previous studies demonstrate that the m6A modification is involved in promoting the tumorigenesis, metastasis and drug resistance of different types of cancer (21–23). However, whether the m6A modification is involved in regulating DDP resistance in CC remains unclear.
The present study aimed to utilize bioinformatic methods to identify genes associated with DDP resistance in CC using various public databases. Using CRISPR data of CC cell lines and the gene expression profiles of CC samples, key genes associated with the survival and DDP resistance of CC were investigated. Furthermore, the association of key genes with the survival of patients with CC treated with DDP were also investigated using public datasets and in vitro experiments. Additionally, the m6A-associated genes involved in regulating dysregulated genes were investigated.
Materials and methods
Gene expression data of CC samples
The dataset associated with CC [The Cancer Genome Atlas (TCGA)-CC] was obtained by searching for the keywords ‘cervical cancer’ in TCGA (https://portal.gdc.cancer.gov/) database. The dataset (accession no. GSE56363) was obtained from the Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo/) database. The inclusion criteria were as follows: i) Patients with CC who received DDP; and ii) survival information or response status to DDP were recorded.
The expression profile of CC and clinical data were obtained by searching for ‘cervical cancer’ in the TCGA database from the Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/), and consisted of 178 CC tissues and three adjacent non-tumor tissues. Based on the clinical information of the patients in the TCGA-CC dataset, there were 43 patients with both the response status to DDP and overall survival (OS) status recorded. These patients were selected as the discovery set (TCGA-CC1 set; Table I) to identify the genes associated with DDP resistance. The 40 samples, which only recorded the OS of patients receiving DDP were used as the validation set (TCGA-CC2 set; Table I) to support the association of genes with DDP resistance. To exclude the prognostic association of the genes, the 95 patients that did not receive treatment were selected as the control set (TCGA-CC3 set; Table I) for survival analysis (24). GSE56363 consisted of 12 CC samples with complete response to DDP and 9 CC samples with non-complete response to DDP.
RNA-sequencing data were downloaded from TCGA via the GDC Data Portal (https://portal.gdc.cancer.gov/), which had been detected using the Illumina HiSeq 2000 platform. The fragments per kilobase of transcript per million mapped read values were log2-scaled plus 1 for gene expression level measurements.
Database
To identify key genes associated with CC cell survival, the CRISPR-Cas9 screening data of CC cell lines were downloaded from the DepMap portal (https://depmap.org/portal/) by selecting ‘Version: DepMap Public 21Q2’ and ‘CRISPR_gene_effect’ sections. The database recorded the gene essentiality scores [CRISPR-Cas9 gene knockout scores (CERES)] of genes in CC cell lines, which indicated the influence of knockout genes on the proliferation in CC cell lines (25,26). The lower the CERES score, the greater the effect after the gene knockout.
To validate the association of genes with DDP resistance, the gene expression profiles of CC cell lines and their half-maximal inhibitory concentration (IC50) values for DDP drugs were acquired from the Genomics of Drug Sensitivity in Cancer (GDSC; https://www.cancerrxgene.org; release-8.2) database (27) by selecting the ‘Cell Line Gene Expression Data’ and ‘Drug Sensitivity Data’ sections.
Relevant literature was used to identify 30 m6A-associated genes (28–31), including 11 methyltransferases, two demethylases and 17 reader proteins (Table II).
Cell culture
HeLa, a human CC cell line, was purchased from Macgene Biotechnology (https://www.macgene.com/). HeLa cells were routinely cultured in Dulbecco's modified Eagle's medium (DMEM; Wuhan Servicebio Technology Co., Ltd.), which was supplemented with 10% fetal bovine serum (FBS; Zhejiang Tianhang Biotechnology Co., Ltd.). Cells were grown at 37°C and 5% CO2 under humidified conditions and passaged upon reaching 80–90% confluency.
Cell viability assay
Cell viability was investigated using the Cell Counting Kit-8 (CCK-8; cat. no. C0038; Beyotime Institute of Biotechnology) assay. Cells were seeded at a density of 1×104 cells/ml in a 96-well plate at a volume of 100 ml/well. Various concentrations (0–100,000 nM) of DDP (cat. no. P4394; Sigma-Aldrich; Merck KGaA) were introduced into the culture medium, with a three-fold gradient to systematically probe the cytotoxic effects. After a 96-h incubation at 37°C, cell viability was quantified using the CCK-8 assay and measuring the absorbance, which was used to calculate the cell survival rate. The subsequent data were fitted to a dose-response curve to determine the IC50 of cell proliferation. The equation used to calculate inhibition (%) was: Inhibition (%)=[(Ac-As)/(Ac-Ab)] ×100. ‘As’ and ‘Ab’ represent the absorbance of the experimental wells and the wells with the highest concentration, respectively. ‘Ac’ represents the absorbance of the control wells.
DDP-resistant cells construction
HeLa cells were initially treated with 1 µM DDP which was increased to 2 µM after ~2 months and treatment was continued at this concentration for another 4 months until stabilization, resulting in DDP-resistant cells (HeLa/DDP). Subsequently, HeLa/DDP cells were seeded at a density of 5×105 cells/well into 6-well plates and maintained in culture medium containing 2 µM cisplatin at 37°C. Next, HeLa/DDP cells were cultured in the presence of increasing concentrations of DPP (cat. no. P4394; Sigma-Aldrich; Merck KGaA) to establish the IC50. The drug sensitivity of the cells were quantified by determining the IC50 using a cell viability assay. The resistance index (RI) was calculated as the ratio of the IC50 of the resistant cells to the IC50 of the parental cells, which served as a measurement of the relative resistance. An RI >3 indicated that the resistant cell line was less sensitive to the drug compared with the parental cell line.
Western blotting (WB)
WB was used to detect HYOU1 protein levels in three independent experiments. HeLa and HeLa/DDP cells were harvested and lysed in Whole Protein Extraction kit (cat. no. WLA019, Wanleibio Co., Ltd.) for 5 min. The supernatant was centrifuged at 4°C and 10,005 × g for 10 min and the protein concentration was determined using a bicinchoninic acid kit. Following this, 40 µg of protein from the supernatant was loaded per lane on a 10% gel and SDS-PAGE was carried out before the proteins were transferred to a PVDF membrane. Subsequently, the membrane was blocked with blocking buffer (cat. no. WLA066; Fast Blocking Western; Wanleibio Co., Ltd.) for 1 h at room temperature and then incubated with either the HYOU1 (cat. no. R383157; 1:500; Chengdu Zen-Bioscience Co., Ltd.) or the β-actin (cat. no. WL01372; 1:1,000; Wanleibio Co., Ltd.) primary antibody overnight at 4°C. The membranes were then rinsed with TBST (0.15% Tween20; Wanleibio Co., Ltd.) and incubated with a secondary antibody (cat. no. WLA023; 1:5,000; Goat Anti-Rabbit IgG/HRP; Wanleibio Co., Ltd.) for 45 min at 37°C. Subsequently, the membrane was washed with TBST six times and visualized using Ultrasensitive ECL Chemiluminescence Kit (cat. no. WLA006; Wanleibio Co., Ltd.) (32). The total protein concentration obtained was 2 µg/µl. The intensity of each band was quantified using Gel-Pro-Analyzer software (version 4.0; Media Cybernetics, Inc.).
Transfection
All small interfering RNA (siRNA), with a final concentration of 50 nM, were transiently transfected into HeLa/DDP cells using Lipofectamine®™ 3000 (Invitrogen; Thermo Fisher Scientific, Inc.) for 20 min to form transfection complexes at 37°C. Following a 6-h incubation, the transfection medium was replaced with fresh growth medium. DDP was added the next day and the culture was continued for 48 h at 37°C. Transfection efficiency was semi-quantified using WB. The siRNA sequences (Wanleibio Co., Ltd.) used were as follows: HYOU1 sense: 5′-AAGCUGCUGCGUGAGGCUAAUC-3′; anti-sense: 5′-GAUUAAGCCUCACGAGCAGCUU-3′; HYOU1 siRNA-2 sense: 5′-AGCUGGGGAAGAACAUCAAU-3′; anti-sense: 5′-AUUGUUCUUCCCAUCAUCG-3′; and siRNA negative control (NC) sense: 5′-AUAAACAUCGACUCAAU-3′; anti-sense: 5′-AUUGAGCUCGAUUGUUAU-3′.
Statistical analysis
An unpaired student's t-test was used to identify differentially expressed genes (DEGs) between tumor and normal samples. OS was defined as the time from the date of initial surgical resection to the date of mortality or last contact (censored), which was truncated to 60 months. As the number of responders and non-responders may not be equal, the ‘surv_cutpoint’ algorithm was used to determine the optimal cut-off to distinguish between the high and low expression levels of genes. Survival curves were drawn using the Kaplan-Meier method and statistically compared using the log-rank test. A univariate Cox regression model was used to analyze the association between clinical factors and OS. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox regression models.
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using gene set enrichment analysis (GSEA) from the web-based gene set analysis toolkit (WebGestalt; http://www.webgestalt.org) (33), with a cut-off value of <0.05 for the false discovery rate (FDR). The m6A sites of genes were predicted using a sequence-based RNA adenosine methylation site predictor (SRAMP) program (http://www.cuilab.cn/sramp/) (34) by inputting the sequences of the genes. The RMBase version 2.0 platform (http://rna.sysu.edu.cn/rmbase/) (35), a comprehensive resource for RNA modification data verified using methylated RNA immunoprecipitation sequencing, m6A-sequence and/or m6A-crosslinking immunoprecipitation arrays, was used to validate whether the predicted m6A sites underwent m6A modification. Subsequently, the interaction probabilities between predicted m6A site sequence motifs and the protein sequence of a m6A-associated gene were retrieved using the RNA-Protein Interaction Prediction (RPISeq) database (http://pridb.gdcb.iastate.edu/RPISeq/) (36). This database calculated the interaction probabilities using random forest (RF) and support vector machine (SVM) methods.
The correlation between gene expression levels and IC50 values for DDP in the GDSC database was estimated using Pearson correlation analysis and the ggplot2 package in R (https://cran.r-project.org/web/packages/ggplot2/index.html) was used to visualize the results. Comparisons between two groups were analyzed using the unpaired student's t-test. Comparisons among multiple groups were analyzed using one-way analysis of variance (ANOVA) and Tukey's test. P-values were adjusted using the Benjamini-Hochberg procedure for multiple testing (37) to control for the FDR. FDR <0.05 for multiple testing or P<0.05 was considered to indicate a statistically significant difference.
Results
HYOU1 is a gene that promotes CC survival and DDP resistance
Based on CRISPR-Cas9 screening data from CC cell lines, 699 genes were identified with potential impact on cell proliferation in CC cell lines, in which the CERES scores were <-1 in >75% of CC cell lines. Compared with normal samples, 3,309 DEGs were identified in 43 samples with CC derived from the TCGA-CC1 set (unpaired student's t-test; FDR <0.05 and log2(FC)>0; Fig. 1A). Furthermore, 401 DDP resistance genes were identified in the non-response group compared with those in the response group (unpaired student's t-test; P<0.05 and log2(FC)>0; Fig. 1B). Three genes, including HYOU1, nonsense-mediated mRNA decay factor (SMG5) and ankyrin repeat and LEM domain containing 2 (ANKLE2), were selected as they were significantly upregulated in samples with CC and in the non-response group when compared with normal samples and the response group, respectively (Fig. 1C).
The area under the curve of HYOU1, SMG5 and ANKLE2 for predicting the response and non-response status was 0.802, 0.815 and 0.775, respectively (Fig. 1D-F). Finally, for each gene, the mean expression level was used to stratify patients into high- and low-expression groups and a survival analysis was performed. The results showed that there was no significant difference in the OS between the two groups for the three genes [HYOU1 (high vs. low expression, 19 vs. 24; log-rank P=0.5412; HR=1.50; 95% CIs, 0.40–5.62), SMG5 (high vs. low expression, 23 vs. 20; log-rank P=0.5557; HR=1.51; 95% CIs, 0.38–6.07) and ANKLE2 (high vs. low expression, 20 vs. 23; log-rank P=0.6183; HR=1.40; 95% CIs, 0.37–5.22); Fig. S1].
It was hypothesized that the mean value may not be suitable for distinguishing patients with different responses to DDP. Therefore, the ‘surv_cutpoint’ algorithm was used to re-determine the optimal threshold for HYOU1 expression levels, which was 4.9094. Survival analysis using the TCGA-CC1 set showed that patients with high HYOU1 expression levels (>4.9094) had a significantly reduced OS compared with patients with low HYOU1 expression levels (<4.9094) following DDP treatment (high vs. low expression, 10 vs. 33; log-rank P=0.0456; HR=3.59; 95% CIs, 0.94–13.67; Fig. 1G). Similarly, the ‘surv_cutpoint’ algorithm was used to re-determine the optimal thresholds for SMG5 and ANKLE2, which were 4.6751 and 3.0310, respectively. However, high or low SMG5 expression levels (threshold, 4.6751) and ANKLE2 expression levels (threshold, 3.0310) did not indicate a significantly different OS in the TCGA-CC1 set [SMG5 (high vs. low expression, 30 vs. 13; log-rank P=0.0761; HR=291,560,949.96; 95% CIs, 0-infinity (inf); Fig. 1H) and ANKLE2 (high vs. low expression, 35 vs. 8; log-rank P=0.2170; HR=229,801,985.83; 95% CIs, 0-inf; Fig. 1I)]. Therefore, HYOU1 was selected for follow-up analyses as a gene associated with the survival of CC cells and DDP resistance.
Validation of the association of HYOU1 with DDP resistance in independent datasets
In the TCGA-CC2 set, the ‘surv_cutpoint’ algorithm was used to determine the optimal threshold for HYOU1, which was 4.9094. The 8 patients with high HYOU1 expression levels (>4.9094) demonstrated a significantly reduced OS compared with the 32 patients with low HYOU1 expression levels following DDP treatment (log-rank P=0.0012; HR=7.09; 95% CIs, 1.81–27.70; Fig. 2A). Using the TCGA-CC3 set, high and low HYOU1 expression levels did not indicate a significantly different OS in patients that did not receive DDP treatment (high vs. low expression, 19 vs. 76; log-rank P=0.6254; HR=1.49; 95% CIs, 0.30–7.38; Fig. 2B). Additionally, according to the GDSC database, the expression levels of HYOU1 were significantly positively correlated with the IC50 values of DDP in CC cell lines (Pearson's correlation analysis; P=0.0384; r=0.58; Fig. 2C).
To validate the effect of HYOU1 on the DDP resistance of CC, HeLa/DDP cells were constructed. The parental HeLa cells exhibited an IC50 of 1.65 µM. By contrast, the resistant cells had an IC50 of 15.51 µM, corresponding to an RI of 9. The IC50 values were determined using dose-response curves generated from cell viability assays (Fig. 2D). Using western blotting, the protein bands revealed an increased HYOU1 expression level in HeLa/DDP cells across three experiments compared with that in HeLa cells (Fig. 2E) and the semi-quantification values in Table SI further elucidates this. The results showed that the protein expression of HYOU1 was significantly increased in HeLa/DDP cells compared with that in parental HeLa cells (unpaired student's t-test; P=0.0002; Fig. 2F). To confirm the efficacy of HYOU1 knockdown, knockdown efficiency was assessed. Using WB analysis, a significant reduction in protein expression levels of HYOU1 was observed in the knockdown groups (one-way ANOVA; P<0.001; Fig. S2), indicating the success of HYOU1 knockdown. Based on this effective knockdown, it was further revealed that HYOU1 knockdown significantly reduced the viability of DDP treated cells compared with the control (one-way ANOVA; P<0.001; Fig. 2G). These results suggest that high HYOU1 expression levels are associated with resistance to DDP.
To further investigate the function of HYOU1, 2,952 genes that significantly correlated with the expression of HYOU1 were identified (Pearson correlation analysis; FDR <0.05; |r|>0.3). These genes were notably enriched in 12 functional pathways (GSEA; FDR <0.05; Fig. 2H). Among these functional pathways, ‘protein processing in endoplasmic reticulum’ and ‘N-glycan biosynthesis’ were significantly enriched in genes that positively correlated with HYOU1 and were involved in DDP resistance (9,13) (Fig. 2I). These results suggest that upregulated expression of HYOU1 is associated with the accumulation of unfolded proteins, and may enhance the stress response in the ER and induce DDP resistance.
m6A modification is enriched in HYOU1 and increases the stability of the transcript
Previous preliminary studies report that m6A modifications are present in almost all types of RNA molecules in the cell, and regulate the transcriptome to influence RNA splicing, translation, export, localization and stability (18–20). To investigate whether the expression of HYOU1 was regulated by m6A modification, the online tool SRAMP was used to predict m6A modification sites on HYOU1. This revealed six HYOU1 sequence motifs with high confidence (Fig. 3A; Table III).
The correlation between m6A-associated genes and the expression of HYOU1 using the TCGA-CC1 set was analyzed and eight m6A-associated genes were found that significantly correlated with the expression of HYOU1 (Pearson's correlation analysis; FDR <0.05; |r|>0.4; Fig. 3B; Table II). Among these genes, the expression of EIF3A was significantly upregulated in the non-response group compared with that of the response group (unpaired student's t-test; P=0.0399; FC=1.07; Fig. 3C). Furthermore, the ‘surv_cutpoint’ algorithm was used to determine the optimal thresholds for EIF3A, which was 5.2442. Survival analysis indicated that patients with high EIF3A expression levels (>5.2442) had a significantly reduced OS compared with patients with low EIF3A expression levels (<5.2442) following DDP treatment using TCGA-CC data integrated with TCGA-CC1 and TCGA-CC2 sets (high vs. low expression, 35 vs. 48; log-rank P=0.0310; HR=2.81; 95% CIs, 1.05–7.48; Fig. 3D). In an independent dataset of patients with CC (GSE56363), the expression of EIF3A was significantly increased in the non-response group compared with the response group (unpaired student's t-test; P=0.0228; FC=1.04; Fig. 3E).
Sequence docking prediction analyses with the RPISeq database confirmed, with high probabilities and confidence, that the EIF3A reader may bind with the six m6A site motifs of HYOU1 (interaction probabilities >0.5; Table IV; Fig. 3F), including the ‘3294’, ‘8651’, ‘10147’, ‘10786’, ‘11220’ and ‘11607’ sites. Furthermore, searching for the HYOU1 gene on the RMBase version 2.0 platform revealed that the m6A site (‘3294’) of HYOU1, which exhibited a high probability of binding with EIF3A, was modified by m6A modification (Table III).
 | Table IV.Probability of binding based on predictions using the RNA-protein interaction prediction database (http://pridb.gdcb.iastate.edu/RPISeq/). |
Discussion
Resistance to DDP-based chemotherapy is the leading cause of mortality for patients with CC. By integrating multidimensional publicly available data of CC, the present study identified HYOU1 as an important gene, the overexpression of which was associated with DDP resistance in patients with CC. The association between high HYOU1 expression levels and DDP resistance was revealed using data from 53 patients with CC and cell lines. Mechanistic analyses suggested that EIF3A overexpression might be associated with HYOU1 depending on the m6A modification and was associated with DDP resistance.
HYOU1 belongs to the heat shock protein 70 family and is expressed in numerous cell types, such as epithelial cells, neuronal cells and cardiomyocytes (38,39). It is induced by various types of stress, such as hypoxia, ER stress, ischemia and glucose deprivation (40). Previous studies reveal that HYOU1 is upregulated in various tumors (such as ovarian cancer and breast cancer) and is involved in tumorigenesis and tumor growth (41,42). The study by Liu and Wang (43) demonstrates that HYOU1 is upregulated in CC cell lines. In addition, the study by Zhou et al (44) indicates the expression of HYOU1 in the tissues of nasopharyngeal carcinoma, which is associated with poor prognosis. Additionally, HYOU1 is associated with the expansion and metastatic activity of epithelial ovarian tumor cell lines (41). However, the association of HYOU1 with DDP resistance has not yet been investigated. The present study was the first to demonstrate that HYOU1 was associated with DDP resistance in patients with CC. An independent cohort of patients with CC was used to indicate that high HYOU1 expression levels were associated with poor prognosis only in the patients that received DDP treatment. Additionally, pharmacogenomic data indicated that high HYOU1 expression levels were associated with high IC50 values of DDP. However, the correlation was not strong, which may be due to the small sample size and should be further validated in a large-scale dataset. In addition, the present study demonstrated that high HYOU1 expression levels were associated with resistance to DDP using WB experiments and knockdown experiments of HYOU1 in HeLa/DDP cells.
The m6A modification serves an important role in regulating RNA stability and participates in biological activities (such as response to stress and RNA stability) and clinical outcomes in patients with cancer (45,46). The present study found that m6A modifications were enriched within HYOU1 and that HYOU1 expression levels were significantly associated with the m6A reader, EIF3A. Analysis of TCGA-CC data showed that EIF3A was significantly associated with DDP resistance and poor survival in patients treated with DDP. Sequence docking indicated that EIF3A had docking activity with the m6A site sequence motifs of HYOU1. EIF3A is the largest subunit of EIF3, which is an important factor in translation initiation. EIF3A can bind with the 5′-untranslated region to promote the translation of cap-independent mRNAs (47). Expression of EIF3A may influence cancer cell proliferation as this malignant phenotype can be reversed by knocking down EIF3A in cancer cells (48). Previously, the study by Su et al (49), using ribosome profiling with HEK293T upon CRISPR-Cas9-induced methyltransferase-like protein 16 (METTL16; a methyltransferase) knockdown (GSE156796), reports that METTL16 directly interacts with EIF3A/B, thereby promoting the translation of >4,000 mRNA transcripts. The analysis of the data (49) reveals that METTL16 knockdown suppresses the translation efficiency of HYOU1 (log2(FC)=−1.21), suggesting that the dysregulation of HYOU1 might be dependent on the m6A modification. The study by Xu et al (50) demonstrates that variation in EIF3A contributes to platinum-based chemotherapy resistance in patients with lung cancer. To the best of our knowledge, the role of EIF3A in the DDP resistance of patients with CC has not been studied before. In the present study, it was demonstrated that EIF3A may promote DDP resistance in CC by inducing HYOU1 overexpression depending on the m6A modification.
However, there were limitations in the present study. Firstly, the associations of HYOU1 with DDP resistance needs to be validated using a larger number of patients with CC in future studies. Secondly, the underlying regulatory mechanism was only preliminarily investigated and it was found that EIF3A may promote DDP resistance in CC by inducing HYOU1 overexpression depending on the m6A modification. Further m6A RNA immunoprecipitation experiments in EIF3A-transfected and knockout cells are needed to validate the findings.
In conclusion, HYOU1 was identified as a key gene associated with DDP resistance in CC. HYOU1 expression levels may serve as an indicator for assessing the suitability of DDP treatment as a therapeutic strategy. Mechanistically, EIF3A may induce HYOU1 overexpression depending on the m6A modifications in CC cells and may be a candidate to target for the treatment of patients with CC.
Supplementary Material
Supporting Data
Supporting Data
Acknowledgements
Not applicable.
Funding
This work was supported by grants from the Outstanding Youth Foundation of Heilongjiang Province of China (grant no. YQ2023H002).
Availability of data and materials
The data generated in the present study may be requested from the corresponding author.
Authors' contributions
RW, JD, MZ, ZW, SW, SL and LQ contributed to the conception and design of the present study. Material preparation, data collection and analysis were performed by RW, JD and MZ. ZW and SW prepared Fig. 1, Fig. 2, Fig. 3. LQ and SL confirm the authenticity of all the raw data. The first draft of the manuscript was written by LQ and SL and all authors commented on previous versions of the manuscript. All authors read and approved the final version of the manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Glossary
Abbreviations
Abbreviations:
|
CC |
cervical cancer |
|
DDP |
cisplatin |
|
HYOU1 |
hypoxia-upregulated 1 gene |
|
ER |
endoplasmic reticulum |
|
m6A |
N6-methyladenosine |
|
IC50 |
half-maximal inhibitory concentration |
|
TCGA |
The Cancer Genome Atlas |
|
OS |
overall survival |
|
GDSC |
Genomics of Drug Sensitivity in Cancer |
|
HRs |
hazard ratios |
|
CIs |
confidence intervals |
|
GSEA |
gene set enrichment analysis |
|
WebGestalt |
web-based gene set analysis toolkit |
|
FDR |
false discovery rates |
|
SMG5 |
nonsense-mediated mRNA decay factor |
|
SRAMP |
sequence-based RNA adenosine methylation site predictor |
|
WB |
western blotting |
|
RI |
resistance index |
References
|
Siegel RL, Miller KD, Wagle NS and Jemal A: Cancer statistics, 2023. CA Cancer J Clin. 73:17–48. 2023. View Article : Google Scholar : PubMed/NCBI | |
|
Siegel RL, Miller KD, Fuchs HE and Jemal A: Cancer statistics, 2021. CA Cancer J Clin. 71:7–33. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Zhu H, Luo H, Zhang W, Shen Z, Hu X and Zhu X: Molecular mechanisms of cisplatin resistance in cervical cancer. Drug Des Devel Ther. 10:1885–1895. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Lorusso D, Petrelli F, Coinu A, Raspagliesi F and Barni S: A systematic review comparing cisplatin and carboplatin plus paclitaxel-based chemotherapy for recurrent or metastatic cervical cancer. Gynecol Oncol. 133:117–123. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Kishimoto S, Kawazoe Y, Ikeno M, Saitoh M, Nakano Y, Nishi Y, Fukushima S and Takeuchi Y: Role of Na+, K+-ATPase alpha1 subunit in the intracellular accumulation of cisplatin. Cancer Chemother Pharmacol. 57:84–90. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Bai ZL, Wang YY, Zhe H, He JL and Hai P: ERCC1 mRNA levels can predict the response to cisplatin-based concurrent chemoradiotherapy of locally advanced cervical squamous cell carcinoma. Radiat Oncol. 7:2212012. View Article : Google Scholar : PubMed/NCBI | |
|
Yang X, Fraser M, Abedini MR, Bai T and Tsang BK: Regulation of apoptosis-inducing factor-mediated, cisplatin-induced apoptosis by Akt. Br J Cancer. 98:803–808. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Ashrafizadeh M, Zarrabi A, Hushmandi K, Kalantari M, Mohammadinejad R, Javaheri T and Sethi G: Association of the epithelial-mesenchymal transition (EMT) with cisplatin resistance. Int J Mol Sci. 21:40022020. View Article : Google Scholar : PubMed/NCBI | |
|
Avril T, Vauleon E and Chevet E: Endoplasmic reticulum stress signaling and chemotherapy resistance in solid cancers. Oncogenesis. 6:e3732017. View Article : Google Scholar : PubMed/NCBI | |
|
Visioli F, Wang Y, Alam GN, Ning Y, Rados PV, Nör JE and Polverini PJ: Glucose-regulated protein 78 (Grp78) confers chemoresistance to tumor endothelial cells under acidic stress. PLoS One. 9:e1010532014. View Article : Google Scholar : PubMed/NCBI | |
|
Hu R, Warri A, Jin L, Zwart A, Riggins RB, Fang HB and Clarke R: NF-kappaB signaling is required for XBP1 (unspliced and spliced)-mediated effects on antiestrogen responsiveness and cell fate decisions in breast cancer. Mol Cell Biol. 35:379–390. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Le Mercier M, Lefranc F, Mijatovic T, Debeir O, Haibe-Kains B, Bontempi G, Decaestecker C, Kiss R and Mathieu V: Evidence of galectin-1 involvement in glioma chemoresistance. Toxicol Appl Pharmacol. 229:172–183. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Rao S, Oyang L, Liang J, Yi P, Han Y, Luo X, Xia L, Lin J, Tan S, Hu J, et al: Biological function of HYOU1 in tumors and other diseases. Onco Targets Ther. 14:1727–1735. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Jiang X, Liu B, Nie Z, Duan L, Xiong Q, Jin Z, Yang C and Chen Y: The role of m6A modification in the biological functions and diseases. Signal Transduct Target Ther. 6:742021. View Article : Google Scholar : PubMed/NCBI | |
|
Fu Y, Dominissini D, Rechavi G and He C: Gene expression regulation mediated through reversible m(6)A RNA methylation. Nat Rev Genet. 15:293–306. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Knuckles P, Lence T, Haussmann IU, Jacob D, Kreim N, Carl SH, Masiello I, Hares T, Villaseñor R, Hess D, et al: Zc3h13/Flacc is required for adenosine methylation by bridging the mRNA-binding factor Rbm15/Spenito to the m(6)A machinery component Wtap/Fl(2)d. Genes Dev. 32:415–429. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Pendleton KE, Chen B, Liu K, Hunter OV, Xie Y, Tu BP and Conrad NK: The U6 snRNA m(6)A Methyltransferase METTL16 regulates SAM synthetase intron retention. Cell. 169:824–835. e142017. View Article : Google Scholar : PubMed/NCBI | |
|
Mendel M, Chen KM, Homolka D, Gos P, Pandey RR, McCarthy AA and Pillai RS: Methylation of structured rna by the m(6)A writer METTL16 Is essential for mouse embryonic development. Mol Cell. 71:986–1000. e112018. View Article : Google Scholar : PubMed/NCBI | |
|
Wei J, Liu F, Lu Z, Fei Q, Ai Y, He PC, Shi H, Cui X, Su R, Klungland A, et al: Differential m(6)A, m(6)A(m), and m(1)A demethylation mediated by FTO in the cell nucleus and cytoplasm. Mol Cell. 71:973–985. e52018. View Article : Google Scholar : PubMed/NCBI | |
|
Mauer J, Luo X, Blanjoie A, Jiao X, Grozhik AV, Patil DP, Linder B, Pickering BF, Vasseur JJ, Chen Q, et al: Reversible methylation of m(6)A(m) in the 5′ cap controls mRNA stability. Nature. 541:371–375. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Su Y, Wang B, Huang J, Huang M and Lin T: YTHDC1 positively regulates PTEN expression and plays a critical role in cisplatin resistance of bladder cancer. Cell Prolif. 56:e134042023. View Article : Google Scholar : PubMed/NCBI | |
|
Wu S, Yun J, Tang W, Familiari G, Relucenti M, Wu J, Li X, Chen H and Chen R: Therapeutic m(6)A eraser ALKBH5 mRNA-Loaded exosome-liposome hybrid nanoparticles inhibit progression of colorectal cancer in preclinical tumor models. ACS Nano. 17:11838–11854. 2023. View Article : Google Scholar : PubMed/NCBI | |
|
Niu Y, Wan A, Lin Z, Lu X and Wan G: N (6)-Methyladenosine modification: A novel pharmacological target for anti-cancer drug development. Acta Pharm Sin B. 8:833–843. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Qi L, Li Y, Qin Y, Shi G, Li T, Wang J, Chen L, Gu Y, Zhao W and Guo Z: An individualised signature for predicting response with concordant survival benefit for lung adenocarcinoma patients receiving platinum-based chemotherapy. Br J Cancer. 115:1513–1519. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Behan FM, Iorio F, Picco G, Gonçalves E, Beaver CM, Migliardi G, Santos R, Rao Y, Sassi F, Pinnelli M, et al: Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature. 568:511–516. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Meyers RM, Bryan JG, McFarland JM, Weir BA, Sizemore AE, Xu H, Dharia NV, Montgomery PG, Cowley GS, Pantel S, et al: Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat Genet. 49:1779–1784. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, et al: Genomics of drug sensitivity in cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41((Database issue)): D955–D961. 2013.PubMed/NCBI | |
|
Li Y, Xiao J, Bai J, Tian Y, Qu Y, Chen X, Wang Q, Li X, Zhang Y and Xu J: Molecular characterization and clinical relevance of m(6)A regulators across 33 cancer types. Mol Cancer. 18:1372019. View Article : Google Scholar : PubMed/NCBI | |
|
Liu J, Harada BT and He C: Regulation of gene expression by N(6)-methyladenosine in cancer. Trends Cell Biol. 29:487–499. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Huang H, Weng H and Chen J: m(6)A modification in coding and non-coding RNAs: Roles and therapeutic implications in cancer. Cancer Cell. 37:270–288. 2020. View Article : Google Scholar : PubMed/NCBI | |
|
Nombela P, Miguel-Lopez B and Blanco S: The role of m(6)A, m(5)C and Ψ RNA modifications in cancer: Novel therapeutic opportunities. Mol Cancer. 20:182021. View Article : Google Scholar : PubMed/NCBI | |
|
Qiu F, Liu Q, Xia Y, Jin H, Lin Y and Zhao X: Circ_0000658 knockdown inhibits epithelial-mesenchymal transition in bladder cancer via miR-498-induced HMGA2 downregulation. J Exp Clin Cancer Res. 41:222022. View Article : Google Scholar : PubMed/NCBI | |
|
Liao Y, Wang J, Jaehnig EJ, Shi Z and Zhang B: WebGestalt 2019: Gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 47:W199–W205. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Zhou Y, Zeng P, Li YH, Zhang Z and Cui Q: SRAMP: Prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features. Nucleic Acids Res. 44:e912016. View Article : Google Scholar : PubMed/NCBI | |
|
Xuan JJ, Sun WJ, Lin PH, Zhou KR, Liu S, Zheng LL, Qu LH and Yang JH: RMBase v2.0: Deciphering the map of RNA modifications from epitranscriptome sequencing data. Nucleic Acids Res. 46((D1)): D327–D334. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Yi Y, Zhao Y, Huang Y and Wang D: A brief review of RNA-protein interaction database resources. Noncoding RNA. 3:62017.PubMed/NCBI | |
|
Hochberg Y and Benjamini Y: More powerful procedures for multiple significance testing. Stat Med. 9:811–818. 1990. View Article : Google Scholar : PubMed/NCBI | |
|
Tsukamoto Y, Kuwabara K, Hirota S, Ikeda J, Stern D, Yanagi H, Matsumoto M, Ogawa S and Kitamura Y: 150-kD oxygen-regulated protein is expressed in human atherosclerotic plaques and allows mononuclear phagocytes to withstand cellular stress on exposure to hypoxia and modified low density lipoprotein. J Clin Invest. 98:1930–1941. 1996. View Article : Google Scholar : PubMed/NCBI | |
|
Giffin L, Yan F, Major MB and Damania B: Modulation of Kaposi's sarcoma-associated herpesvirus interleukin-6 function by hypoxia-upregulated protein 1. J Virol. 88:9429–9441. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Kuwabara K, Matsumoto M, Ikeda J, Hori O, Ogawa S, Maeda Y, Kitagawa K, Imuta N, Kinoshita T and Stern DM: Purification and characterization of a novel stress protein, the 150-kDa oxygen-regulated protein (ORP150), from cultured rat astrocytes and its expression in ischemic mouse brain. J Biol Chem. 271:5025–5032. 1996. View Article : Google Scholar : PubMed/NCBI | |
|
Li X, Zhang NX, Ye HY, Song PP, Chang W, Chen L, Wang Z, Zhang L and Wang NN: HYOU1 promotes cell growth and metastasis via activating PI3K/AKT signaling in epithelial ovarian cancer and predicts poor prognosis. Eur Rev Med Pharmacol Sci. 23:4126–4135. 2019.PubMed/NCBI | |
|
Stojadinovic A, Hooke JA, Shriver CD, Nissan A, Kovatich AJ, Kao TC, Ponniah S, Peoples GE and Moroni M: HYOU1/Orp150 expression in breast cancer. Med Sci Monit. 13:BR231–BR239. 2007.PubMed/NCBI | |
|
Liu J and Wang Y: Long non-coding RNA KCNQ1OT1 facilitates the progression of cervical cancer and tumor growth through modulating miR-296-5p/HYOU1 axis. Bioengineered. 12:8753–8767. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Zhou Y, Liao Q, Li X, Wang H, Wei F, Chen J, Yang J, Zeng Z, Guo X, Chen P, et al: HYOU1, regulated by LPLUNC1, is up-regulated in nasopharyngeal carcinoma and associated with poor prognosis. J Cancer. 7:367–376. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Desrosiers R, Friderici K and Rottman F: Identification of methylated nucleosides in messenger RNA from Novikoff hepatoma cells. Proc Natl Acad Sci USA. 71:3971–3975. 1974. View Article : Google Scholar : PubMed/NCBI | |
|
Saletore Y, Meyer K, Korlach J, Vilfan ID, Jaffrey S and Mason CE: The birth of the Epitranscriptome: Deciphering the function of RNA modifications. Genome Biol. 13:1752012. View Article : Google Scholar : PubMed/NCBI | |
|
Meyer KD, Patil DP, Zhou J, Zinoviev A, Skabkin MA, Elemento O, Pestova TV, Qian SB and Jaffrey SR: 5′ UTR m(6)A promotes cap-independent translation. Cell. 163:999–1010. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Dong Z, Liu LH, Han B, Pincheira R and Zhang JT: Role of eIF3 p170 in controlling synthesis of ribonucleotide reductase M2 and cell growth. Oncogene. 23:3790–3801. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Su R, Dong L, Li Y, Gao M, He PC, Liu W, Wei J, Zhao Z, Gao L, Han L, et al: METTL16 exerts an m(6)A-independent function to facilitate translation and tumorigenesis. Nat Cell Biol. 24:205–216. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Xu X, Han L, Yang H, Duan L, Zhou B, Zhao Y, Qu J, Ma R, Zhou H and Liu Z: The A/G allele of eIF3a rs3740556 predicts platinum-based chemotherapy resistance in lung cancer patients. Lung Cancer. 79:65–72. 2013. View Article : Google Scholar : PubMed/NCBI |
