
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12271-2
10.1016/j.heliyon.2024.e36240
e36240
Research Article
Methylation-related differentially expressed genes as potential prognostic biomarkers for cervical cancer
Chen Yili chenyli33@mail.sysu.edu.cn
ab1⁎
Zou Qiaojian ab1
Chen Qianrun ab1
Wang Shuyi c
Du Qiqiao ab
Mai Qiuwen ab
Wang Xiaojun ab
Lin Xiaoying ab
Du Liu d
Yao Shuzhong yaoshuzh@mail.sysu.edu.cn
ab⁎
Liu Junxiu liujxiu@mail.sysu.edu.cn
ab⁎
a Department of Obstetrics and Gynecology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510000, China
b Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, Guangzhou, 510000, China
c Department of Obstetrics and Gynecology, Qingdao Municipal Hospital, Qingdao, 266000, China
d Department of Ultrasonic Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510000, China
⁎ Corresponding authors. Department of Obstetrics and Gynecology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510000, China. chenyli33@mail.sysu.edu.cnyaoshuzh@mail.sysu.edu.cnliujxiu@mail.sysu.edu.cn
1 These authors contributed equally to this work.

14 8 2024
15 9 2024
14 8 2024
10 17 e3624016 1 2024
16 6 2024
12 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Aim

To discover novel methylation-related differentially expressed genes (MRDEGs) for cervical cancer, with a focus on their potential for early diagnosis and prognostic assessment.

Materials & methods

We integrated data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. TCGA-MRDEGs were identified by analyzing differentially methylated genes (DMGs) and their correlation with gene expression. We examined GEO datasets GSE39001, GSE9750, and GSE46306 for GEO-MRDEGs. Overlapping MRDEGs were subjected to overall survival (OS) analysis to identify prognostic markers. The expression and methylation levels of these genes were validated in a total of 30 tissue samples, comprising 20 from cervical cancer patients and 10 from normal cervical tissues, using qRT-PCR and MassARRAY EpiTYPER Assay.

Results

A total of 314 TCGA-MRDEGs and 40 GEO-MRDEGs were identified. Intersection analysis yielded 10 overlapping MRDEGs. Notably, NOVA1, GSTM5, TRHDE, and CXCL12 were found to have reduced expression and increased methylation in cervical cancer, which correlated with poor prognosis. The methylation status and expression levels of these genes were confirmed in tissue specimens.

Conclusion

We identified four MRDEGs as potential prognostic biomarkers for cervical cancer. Their clinical utility is highlighted, but further validation in larger cohorts is required to establish their clinical significance.

Keywords

Cervical cancer
DNA methylation
Differentially expressed genes
Prognosis
Biomarker
==== Body
pmc1 Introduction

Cervical cancer remains a formidable adversary in global health, emerging as the fourth most prevalent malignancy among women worldwide. It was responsible for an estimated 604,000 new cases and 342,000 deaths in 2020 [1]. Notably, its morbidity and mortality rates rank second in countries with a low Human Development Index (HDI) [1]. Persistent high-risk human papillomavirus (HR-HPV) infection is widely acknowledged as the primary etiological factor. Despite significant strides in HPV testing, liquid-based cytology, and the HPV vaccination campaign, cervical cancer continues to be a leading cause of cancer-related mortality in several countries [[1], [2], [3]]. This underscores the exigency for innovative diagnostic biomarkers and therapeutic targets to enhance early detection and treatment efficacy.

The advent of epigenetics has unveiled a new frontier in our understanding of cancer biology. Epigenetic alterations, potential heritable changes influencing gene expression without altering the primary nucleotide sequence of DNA, play a crucial role in tumor-related biological behaviors. Recent studies have elucidated that these epigenetic modifications significantly impact key aspects of cancer, including cell growth, differentiation, transformation, and apoptosis [4,5]. The spectrum of common epigenetic changes encompasses DNA methylation, histone modification, chromosome remodeling, chromosome inactivation, genomic imprinting, and the regulatory influence of non-coding RNA.

DNA methylation stands out as the paramount and crucial form of epigenetic modification, primarily responsible for repressing gene expression through methylation on cytosine residues. Extensive evidence has demonstrated its intricate connection with tumor biological behaviors [6,7]. Abnormal methylation patterns in cancer cells have been identified as potential biomarkers for early diagnosis and prognosis in cervical cancer.

Research on DNA methylation in the early diagnosis of cervical cancer has shown promising progress [8]. Multiple studies are focusing on exploring DNA methylation markers to enhance the accuracy of early detection [9]. Specifically, research on diagnostic DNA methylation markers for both HPV-positive and negative cervical tumors suggests that highly methylated PAX1/SOX1 plays a crucial role in cancer development [10,11]. Recent studies have also found the effectiveness of FAM19A4, ASCL1, ZNF671 and other DNA methylation markers in cervical cancer screening, further highlighting the potential of DNA methylation as a tool for early diagnosis [[12], [13], [14]]. The diagnostic specificity and sensitivity of these genes still require further validation. Currently, no authoritative DNA methylation marker is established for early diagnosis of cervical cancer [15,16]. While existing diagnostic methods have improved detection rates, the need for biomarkers that can predict patient outcomes remains unmet. We hypothesize that DNA methylation markers, given their stability and potential for early detection, could serve as valuable prognostic indicators.

In this study, we integrated gene expression data and DNA methylation profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Through bioinformatics analysis, we identified candidate genes and conducted a survival analysis to pinpoint four potential methylation-related biomarkers with prognostic significance for cervical cancer. To substantiate our findings, we employed quantitative real-time polymerase chain reaction (qRT-PCR) and MassARRAY EpiTYPER Assay for validation.

2 Methods

2.1 Identification of TCGA-MRDEGs

In order to identify methylation-related differentially expressed genes (MRDEGs) with TCGA data (TCGA-MRDEGs), the differentially methylated genes (DMGs) and methylation-expression correlation data were downloaded from the DNMIVD (http://119.3.41.228/dnmivd/index/) [17,18]. We then further selected DMGs according to the adjusted P < 0.05, while screened out methylation-expression correlated genes according to Pearson P < 0.01 and Spearman P < 0.01. Simultaneously, differentially expressed genes (DEGs) data of TCGA were downloaded from the GEPIA (http://gepia.cancer-pku.cn) and screened by the adjusted P < 0.05 and |log2FC| > 1 [19]. Ultimately, we identified the overlap of selected DMGs, DEGs and methylation-expression correlated genes to acquire TCGA-MRDEGs (Fig. 1, Fig. 2A).Fig. 1 Overview and flowchart of the study.

Fig. 1

Fig. 2 Venn diagrams of the DMGs mining process. (A) Venn diagram of DEGs, DMGs and methylation-expression correlated genes in the TCGA database. “Spearman” denotes genes obtained through Spearman analysis (P < 0.001), and “Pearson” indicates those gained via Pearson analysis (P < 0.001). (B) Venn diagram of DMGs and DEGs between GSE39001, GSE9750, and GSE46306. (C) Intersection of MRDEGs between TCGA and GEO databases.

Fig. 2

2.2 Identification of GEO-MRDEGs

For determining MRDEGs with GEO data (GEO-MRDEGs), two gene expression profile datasets (GSE39001 and GSE9750) and one methylation profile dataset (GSE46306) on the GEO database (https://www.ncbi.nlm.nih.gov/geo/) were selected for analysis. Specifically, GSE39001, utilizing the GPL201 platform ([HG-Focus] Affymetrix Human HG-Focus Target Array), comprised 12 normal cervix samples and 43 cervical cancer samples. Meanwhile, GSE9750, based on the GPL96 platform ([HG-U133A] Affymetrix Human Genome U133A Array), consisted of 24 normal cervix and 33 cervical cancer samples. Additionally, GSE46306, utilizing the GPL13534 platform (Illumina HumanMethylation450 BeadChip (HumanMethylation450_15017482)), encompassed six normal cervix and 20 cervical cancer samples.

All three data sets were categorized into normal group and cervical cancer group for analysis. The specific criteria of adjusted P < 0.05, GSE39001 and GSE9750 were analyzed using the “limma” package to obtain DEGs, while GSE46306 adopted the “minfi” package to acquire DMGs. Ultimately, we determined the intersection of DEGs and DMGs to obtain GEO-MRDEGs (Fig. 1, Fig. 2B).

2.3 Identification of overlapping MRDEGs

After acquiring TCGA-MRDEGs (n = 314) and GEO-MRDEGs (n = 40), we identified their intersection to extract overlapping MRDEGs (n = 10), which were then subjected to a further survival analysis for comprehensive evaluation (Fig. 1, Fig. 2C).

2.4 Overall survival (OS) analysis

The OS analysis of overlapping MRDEGs was conducted using the Kaplan‐Meier (KM) plotter (http://kmplot.com/analysis/index.php) [20], which contained 304 patients with cervical squamous cell carcinoma from the TCGA. The KM curves were plotted based on gene expression. P < 0.05 was considered to be statistically significant.

2.5 Visualization of gene methylation and expression

DNMIVD, UALCAN (http://ualcan.path.uab.edu/analysis.html) [21], and GEPIA were employed to confirm and graphically represent the methylation status and expression patterns of the four potential prognostic biomarkers. Additionally, the “ggplot2” package was used to visualize their respective gene expression patterns in GSE39001 and GSE9750 datasets. Moreover, KM plotter was harnessed to illustrate the OS curves. To delve deeper, the MEXPRESS database (https://www.mexpress.be/) [22,23] was aided in elucidating the precise methylation sites of candidate genes and exploring the correlation between methylation and gene expression.

2.6 Tissue specimens

From August 2018 to March 2019, we obtained 20 cervical cancer tissue specimens and 10 normal cervix tissue specimens from the First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China). The inclusion criteria of cervical cancer specimens were: (1) Patients with cervical cancer confirmed by pathological biopsy; (2) Patients had not received radiotherapy, chemotherapy, or immunotherapy. The exclusion criteria of cervical cancer specimens were: (1) Patients with a history of other malignant tumors; (2) Patients with heart, liver, kidney, or other organ failures; (3) Pregnant or breastfeeding women; (4) Patients who had undergone cervical surgery for other reasons. Normal cervix specimens were taken from patients with non-malignant tumors who were undergoing hysterectomy. The exclusion criteria of normal cervix specimens were the same as those of cervical cancer specimens. The specimens were immediately frozen in liquid nitrogen and stored at −80 °C until RNA extraction. All patients gave informed consent and signed an informed consent form. All specimens were approved by the ethics review committee of the First Affiliated Hospital of Sun Yat-sen University before being used in this study, the ethics approval number is [2023]757.

2.7 RNA isolation and qRT-PCR

First, we extracted total RNA using TRIzol reagent (Code No.9108, Takara, Japan). Then, with PrimeScript™ RT Master Mix (RR036A, Takara), we used 500 ng of RNA for the reverse transcription reaction and employed TB Green® Premix Ex Taq™ (Tli RNaseH Plus) (RR420A, Takara) for the qPCR reaction according to manufacturer's protocol. The primers used in this study were all synthesized by GENEWIZ (Suzhou, China) and the sequences are shown in Table 1.Table 1 Primer sequences of qRT-PCR primers used in this study.

Table 1Name	Primers	sequences(5′-3′)	
NOVA1	Forward	GCTTTCGAAGGCAGCAATTG	
Reverse	TGTGTATAGCCATGCTTGCC	
GSTM5	Forward	TGTGTGTGTGTGTGTGTTGG	
Reverse	AGAGGGCAGAAATGACCAAGG	
TRHDE	Forward	TGGCACTGACAACTGTGTTC	
Reverse	AAGCAACGTTTGGAGAGCTG	
CXCL12	Forward	TCAACCTGCCTGACATTTGG	
Reverse	AAAACCCACAAGTGCTTGCC	

2.8 MassARRAY EpiTYPER assay

The methylation levels of particular CpG sites located in the promoter region of candidate genes were verified using MassARRAY EpiTYPER (Sequenom, San Diego, CA). Briefly, Genomic DNA was extracted from tissues using Universal Column Genome Extraction Kit (DP3302, BioTeke Corporation, China) according to the manufacturer's protocol. DNA quantification and integrity were determined by the Nanodrop spectrophotometer (Thermo Fisher Scientific, Wilmington, DE) and the agarose electrophoresis, respectively. Bisulfite conversion reaction was perfomed using an EpiTect Bisulfite Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. PCR primers were designed using EpiDesigner software as shown in Table 2. PCR products were incubated with Shrimp Alkaline Phosphatase (SAP). After in vitro transcription and RNaseA digestion, small RNA fragments with CpG sites were acquired for the reverse reaction. DNA methylation of CpG were measured by MassARRAY platform and the methylation ratios of the products were calculated using Epityper software Version 1.0 (Agena, San Diego, CA, USA). Methylation levels ranging from 0 (completely nonmethylated) to 1 (fully methylated) are presented. The average methylation value of all CpG units was calculated as a representation of the region-specific gene methylation level. The detection and analysis were conducted by OE Biotech Co., Ltd (Shanghai, China).Table 2 PCR primer sequences for MassARRAY EpiTYPER Assay.

Table 2Name	Primers	sequences(5′-3′)	
NOVA1	Forward	aggaagagagGTGGGATTTTGGTTTAGTTGGTATT	
Reverse	cagtaatacgactcactatagggagaaggctAAAAAAAACCCACACCTAACTTTATTT	
GSTM5	Forward	aggaagagagTTAGAGTTATGGGTATGGTGTTGGT	
Reverse	cagtaatacgactcactatagggagaaggctAAACCACCACTTTTTAATCTAACCC	
TRHDE	Forward	aggaagagagTAGTAATTTTTTTTGTGGTTGTGGG	
Reverse	cagtaatacgactcactatagggagaaggctCTAAAAAAACCTTCTCCACCCTAAA	
CXCL12	Forward	aggaagagagGTTTTTGTGATAGGGTTTTATTGGA	
Reverse	cagtaatacgactcactatagggagaaggctTTAACTCATTTCACCATTAAAAAATC	

2.9 Enrichment analysis and mutation analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the “clusterProfiler” package. P < 0.05 was considered to be statistically significant. MRDEGs (n = 314) obtained from the TCGA database were used to establish the protein-protein interaction (PPI) network using the STRING database (v.11.0; http://string‐db.org) [24] and Cytoscape software (v.3.7.2). The mutations of the four prognostic MRDEGs in cervical cancer tissues in the TCGA database were analyzed using the cBioPortal database (http://www.cbioportal.org/) [25,26].

2.10 Statistics analysis

The expression values are expressed as the mean ± standard deviation, and statistical differences between the mean values were determined by Mann Whitney test analysis. Methylation-expression correlation was determined through Pearson correlation analysis or Spearman correlation analysis. Survival curves were plotted via the Kaplan–Meier method and compared using the log-rank test. Unless otherwise specified, P < 0.05 is considered statistically significant.

3 Results

3.1 Identification of TCGA-MRDEGs

For the TCGA data, we downloaded from GEPIA and selected 6320 DEGs based on adjusted P < 0.05 (Supplementary Table S1). Simultaneously, we downloaded from DNMIVD and obtained 1503 DMGs according to adjusted P < 0.05 (Supplementary Table S2). In addition, 8823 and 7494 methylation-expression correlated genes were identified according to the adjusted P < 0.05 of Pearson and Spearman correlation analysis, respectively (Supplementary Table S3). Then we determined the intersection of DEGs, DMGs, and methylation-expression correlated genes to acquire 314 TCGA-MRDEGs (Fig. 1, Fig. 2A).

3.2 Identification of GEO-MRDEGs

In analyzing the GEO data, we have successfully identified 1228 differentially methylated genes (DMGs) from the GSE46306 datasets, as detailed in Supplementary Table S4. Furthermore, a comprehensive analysis of the GSE39001 and GSE9750 datasets revealed a total of 1519 and 3497 differentially expressed genes (DEGs), respectively, as outlined in Supplementary Table S5 and Supplementary Table S6.The normalization of these three data sets and the heatmaps of DEGs are presented in Supplementary Fig. S1. We then obtained 40 GEO-MRDEGs by integrating the results of these three data sets (Fig. 1, Fig. 2B).

3.3 Identification of overlapping MRDEGs

After conducting an intersection analysis of the TCGA-MRDEGs and GEO-MRDEGs, we identified 10 overlapping methylation-regulated differentially expressed genes (MRDEGs). These genes are TCP11, NAP1L3, EDNRB, NOVA1, AIM2, NT5E, GSTM5, TRHDE, CXCL12, and NEFM, as depicted in Fig. 1, Fig. 2C.

3.4 The OS analysis of overlapping MRDEGs

To further assess the clinical diagnostic significance of these 10 overlapping MRDEGs, we employed the KM plotter to conduct an overall survival (OS) analysis. Utilizing the automatically determined optimal cutoff, patients were stratified into two groups: high expression and low expression. Setting a statistical significance threshold of P < 0.05, we discovered that the expression levels of four MRDEGs, namely NOVA1, GSTM5, TRHDE, and CXCL12, were intricately linked to patients' OS. Specifically, the OS curves indicated that a low expression of these four genes was significantly associated with a poorer prognosis for the patients (Fig. 3).The OS curves of the other six genes with no significant correlation are shown in Supplementary Fig. S2. The information related to DNA methylation, mRNA expression and OS analysis of these four MRDEGs are shown in Table 3.Fig. 3 Overall Survival (OS) analysis for four prognostic MRDEGs. (A–D) Kaplan-Meier curves for NOVA1, GSTM5, TRHDE, and CXCL12.

Fig. 3

Table 3 Detail information of MRDEGs in TCGA database.

Table 3MRDEGs	DNA methylation	mRNA expression	Overall survival	
status	adj. P value	status	adj. P value	adj. P value	
NOVA1	Hypermethylated	0.00144104	Downregulated	6.83E-69	0.0019	
GSTM5	Hypermethylated	0.000000112	Downregulated	6.41E-68	0.0086	
TRHDE	Hypermethylated	0.00394126	Downregulated	3.58E-47	0.029	
CXCL12	Hypermethylated	0.000813116	Downregulated	1.18E-30	0.036	

3.5 The methylation status and expression levels of MRDEGs

In order to better verify the role of these MRDEGs in carcinogenesis, multiple databases were applied to validate their differential expression and differential methylation between cervical cancer tissues and normal cervix tissues. For the TCGA database, we first employed DNMIVD and UALCAN to verify and visualize the high methylation levels of the four MRDEGs respectively (Fig. 4, Fig. 5A–D). In addition, we used DNMIVD, UALCAN, and GEPIA to validate their low expression levels (Fig. 4, Fig. 5, Fig. 6A–D). Furthermore, we also analyzed the expression of these four MRDEGs in GSE39001 and GSE9750, further supporting their low expression (Fig. 6E–L). We then applied UALCAN to perform pan-cancer analysis on these four MRDEGs and found that their expression in multiple cancer tissues was lower than for normal tissues, especially GSTM5 and CXCL12 (Fig. 5I–L). Moreover, we utilized the MEXPRESS database to visualize DNA methylation and expression of candidate genes. It could be seen that most of the methylation occurred in the promoter region of the transcript, and the absolute part of the DNA methylation was negatively correlated with the expression of the candidate genes (Supplementary Fig. S3).Fig. 4 Methylation status (A–D) and mRNA expression level (E–H) of four prognostic MRDEGs in DNMIVD database. P values are indicated on each figure.

Fig. 4

Fig. 5 The methylation status (A–D), mRNA expression level (E–H) and their respective pan-cancer analysis (I–L) of four prognostic MRDEGs in normal tissues and cancer tissues in UALCAN database.

Fig. 5

Fig. 6 The mRNA expression of four prognostic MRDEGs in TCGA and GEO databases. The mRNA expression of four MRDEGs in GEPIA (based on TCGA, A-D), GSE39001 (E–H) and GSE9750 (I–L). “*” represents a significant difference in mRNA expression between normal tissues and tumor tissues (P < 0.05).

Fig. 6

3.6 Validation of methylation status and expression levels of MRDEGs via tissue specimen analysis

To enhance the credibility of the biomarkers, we gathered 10 normal cervix specimens and 20 cervical cancer specimens for qRT-PCR analysis. This method was selected for its sensitivity and specificity in quantifying the expression levels of the four MRDEGs, thereby allowing for a robust comparison between tumor and normal specimens. Our findings unequivocally demonstrated that the expression levels of the four MRDEGs in tumor specimens were significantly lower than those in normal specimens. This aligns seamlessly with the information obtained from both the TCGA and GEO databases (Fig. 7A–D).Fig. 7 Verification of mRNA and methylation expression levels of four prognostic MRDEGs. (A–D) The qRT-PCR results of four MRDEGs in normal specimens and cervical cancer specimens. “****” represents a significant difference in mRNA expression between normal tissues and tumor tissues (P < 0.0001). (E–H) Comparison of total methylation levels in the promoter region of MRDEG between tumor and normal tissues. P-values are indicated on each figure.

Fig. 7

Subsequently, we conducted methylation analysis using MassARRAY EpiTYPER Assay on three normal cervix specimens and three cervical cancer specimens. The rationale for choosing this method lies in its ability to provide quantitative and high-throughput methylation data across multiple CpG sites, which is essential for understanding the epigenetic changes associated with cervical cancer. Our findings indicated a substantial increase in total DNA methylation levels for the four MRDEGs in tumor specimens compared to normal cervical samples (Fig. 7E–H). Further granularity is provided by the detailed examination of DNA methylation at various CpG sites, elucidated in Fig. 8.Fig. 8 Comparative analysis of CpG site methylation levels in MRDEG between tumor and normal tissues. Symbols “*”, “**”, and “***” denote statistical significance with P-values less than 0.05, 0.01, and 0.001, respectively.

Fig. 8

3.7 Enrichment analysis of TCGA-MRDEGs

A comprehensive list of 314 TCGA-MRDEGs was curated for GO and KEGG enrichment analyses. GO analysis revealed significant enrichment of MRDEGs in extracellular matrix organization, extracellular structure organization, and collagen-containing extracellular matrix (Supplementary Fig. S4A). Meanwhile, KEGG enrichment analysis indicated these MRDEGs were mainly related to herpes simplex virus 1 infection, cytokine-cytokine receptor interaction, and rheumatoid arthritis (Supplementary Fig. S4B). Genes intimately linked to the GO-enriched functions are presented in Supplementary Fig. S4C, while genes associated with KEGG-enriched pathways are depicted in Supplementary Fig. S4D. Additionally, the PPI network of MRDEGs consisted of 196 nodes and 355 edges. Except for CXCL12, the other three candidate genes exhibited a notable lack of connections with other MRDEGs (Supplementary Fig. S4E).

3.8 Mutation analysis of the prognostic MRDEGs

The mutation analysis revealed that despite the hypermethylation observed in the genes, the mutation frequency among the four MRDEGs was exceptionally low. Notably, TRHDE exhibited the highest mutation rate at 1.8%, whereas the remaining genes displayed even lower frequencies (Supplementary Fig. S5A). This finding aligns closely with previous reports suggesting that methylation can influence gene expression without altering the underlying genetic sequence. The precise mutation sites and types for each gene are detailed in Supplementary Figs. S5B–E.

4 Discussion

Despite HR-HPV's long-standing recognition as the primary cause of cervical cancer, its occurrence and development constitute a complex process, influenced by both inherent factors and external environmental elements. The precise molecular mechanism remains elusive. Despite the ongoing promotion of HPV testing and liquid-based cytology, which has somewhat decreased morbidity and mortality, cervical cancer remains a prevalent malignancy in women globally [27].

Epigenetic modifications, particularly DNA methylation, play a crucial role in tumor-related biological behaviors, increasingly important in early cancer diagnosis. DNA methylation offers advantages over clinical biomarkers. Firstly, DNA methylation alterations can occur in the pre-cancerous stage, aiding in the early diagnosis of cervical cancer. Secondly, DNA methylation is a stable biomarker, less influenced by external factors, aiding in the long-term monitoring of cervical cancer progression and treatment outcomes [28]. Prior to our research, numerous studies concentrated on early cervical cancer diagnosis involving methylation-related genes, including PAX1, FAM19A4, SOX1, MAL and CADM1 [14,[29], [30], [31]]. It is evident that DNA methylation serves as a reliable biomarker for the early diagnosis and prognosis of cervical cancer.

However, the current findings may represent just the beginning of DNA methylation research, with more biomarkers yet to be uncovered [32,33]. The accuracy and reliability of these genes as diagnostic tools are yet to be thoroughly confirmed. At present, there is no universally accepted DNA methylation signature for the prompt identification of cervical cancer. While existing diagnostic methods have improved detection rates, the need for biomarkers that can predict patient outcomes remains unmet. We are looking for DNA methylation markers, given their stability and potential for early detection, could serve as valuable prognostic indicators.

Our study builds upon previous research by identifying novel MRDEGs in cervical cancer. Specifically, we found that NOVA1, GSTM5, TRHDE, and CXCL12 exhibit significant hypermethylation associated with cervical cancer progression, which corroborates with some existing studies that have reported similar findings in other cancer types [[34], [35], [36], [37], [38]]. These markers offer enhanced predictive power for disease progression and patient outcomes, guiding personalized treatment approaches. Incorporating these biomarkers into screening could bolster early detection efforts, with implications for non-invasive sampling methods, thereby increasing patient compliance and timely intervention. Additionally, their use in monitoring treatment efficacy and cancer recurrence could enable real-time adjustments to clinical management strategies. In summary, the integration of DNA methylation biomarkers into clinical practice holds promise for advancing cervical cancer diagnosis, prognosis, and treatment personalization.

Nevertheless, our study faced certain limitations. Although we confirmed the methylation levels of the identified MRDEGs using the MassARRAY EpiTYPER Assay, the limited number of tissue samples may affect the generalizability of the results. Further research should expand the sample size to enhance the clinical translational potential of the findings. Additionally, conducting a more in-depth analysis to investigate the impact of HPV infection, clinical stage, and treatment on the study results would be intriguing.

Moving forward, conducting additional validation studies in larger, diverse patient cohorts is essential to confirm the clinical utility of these biomarkers. Furthermore, investigating the mechanistic roles of these genes in cervical cancer progression could offer valuable insights for developing targeted therapies and personalized treatment strategies. Overall, our findings have established a preliminary theoretical foundation for the future clinical translation of these biomarkers, potentially aiding in the diagnosis, prognosis, and treatment outcomes for cervical cancer patients.

5 Conclusion

In conclusion, our study successfully identified and validated four MRDEGs with significant prognostic value for cervical cancer. These findings, derived from comprehensive analyses of various databases and tissue specimens, emphasize the potential of these genes as valuable biomarkers for cervical cancer. Moving forward, it is essential to conduct further validation studies in larger and more diverse patient cohorts, as well as to investigate the mechanistic roles of these genes, in order to confirm the clinical utility of these biomarkers. In summary, our research has laid a foundational theoretical groundwork that paves the way for the future clinical application of these biomarkers. This advancement holds the promise of contributing to the enhancement of diagnostic accuracy, prognostic assessment, and the overall therapeutic outcomes for individuals afflicted with cervical cancer.

Funding

This work was supported by grants from the 10.13039/501100001809 National Natural Science Foundation of China, China (No. 82303390 to YC; No. 82072884 to JL; No. 82072874 to SY; No. 82303920 to QD), 10.13039/501100002858 China Postdoctoral Science Foundation, China (No. 2022M713591 to YC), 10.13039/501100021171 Guangdong Basic and Applied Basic Research Foundation, China (No. 2022A1515111226 to YC), Guangzhou Science and Technology Project, China (No. 202201011238 to LD).

Data availability

Data included in article/supp. material/referenced in article.

CRediT authorship contribution statement

Yili Chen: Writing – review & editing, Writing – original draft, Project administration, Funding acquisition. Qiaojian Zou: Methodology, Investigation. Qianrun Chen: Methodology, Investigation. Shuyi Wang: Methodology, Investigation. Qiqiao Du: Writing – review & editing, Validation. Qiuwen Mai: Methodology, Investigation. Xiaojun Wang: Methodology, Investigation. Xiaoying Lin: Method. Liu Du: Writing – review & editing. Shuzhong Yao: Writing – review & editing, Conceptualization. Junxiu Liu: Writing – review & editing, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

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Acknowledgments

We thank the 10.13039/501100001809 National Natural Science Foundation of China for the grant funding. We also acknowledge the contributions from TCGA, GEO, DNMIVD, GEPIA, STRING, UALCAN, and cBioPortal. In addition, we thank the developers of all the R packages mentioned in our study.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36240.
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