
==== Front
J Cancer Res Clin Oncol
J Cancer Res Clin Oncol
Journal of Cancer Research and Clinical Oncology
0171-5216
1432-1335
Springer Berlin Heidelberg Berlin/Heidelberg

39294534
5952
10.1007/s00432-024-05952-7
Research
Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic model
Zhang Zhang 15088554408@163.com

Chen Fangfang
Deng Xiaoxiao
Department of Gynecology, The People’s Hospital of Pingyang, Wenzhou, 325400 China
19 9 2024
19 9 2024
2024
150 9 42321 7 2024
10 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Purpose

This study aims to utilize bioinformatics methods to systematically screen and identify susceptibility genes for cervical cancer, as well as to construct and validate an mitophagy-related genes (MRGs) diagnostic model. The objective is to increase the understanding of the disease’s pathogenesis and improve early diagnosis and treatment.

Method

We initially collected a large amount of genomic data, including gene expression profile and single nucleotide polymorphism (SNP) data, from the control group and Cervical cancer (CC) patients. Through bioinformatics analysis, which employs methods such as differential gene expression analysis and pathway enrichment analysis, we identified a set of candidate susceptibility genes associated with cervical cancer.

Results

MRGs were extracted from single-cell RNA sequencing data, and a network graph was constructed on the basis of intercellular interaction data. Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. Through bioinformatics analysis, we successfully identified a group of genes whose expression significantly differed during the development of CC and revealed the biological pathways in which these genes are involved. Moreover, our constructed clinical prognostic model demonstrated excellent performance in the validation phase, accurately predicting the clinical prognosis of patients.

Conclusion

This study delves into the susceptibility genes of cervical cancer through bioinformatics approaches and successfully builds a reliable clinical prognostic model. This study not only helps uncover potential pathogenic mechanisms of cervical cancer but also provides new directions for early diagnosis and treatment of the disease.

Keywords

Susceptibility genes
Cervical cancer
Bioinformatics analysis
Clinical prognosis model
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2024
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pmcIntroduction

Cervical cancer (CC) is a prevalent malignancy of the female reproductive tract, with the incidence of new cases steadily increasing each year, posing a significant threat to women’s health and safety (Tewari et al. 2022; Nguyen et al. 2022; Bhattacharjee et al. 2022). According to statistics, approximately 500,000 women worldwide receive a diagnosis of CC annually, with over 300,000 patients succumbing to the disease (Wu and Xi 2021). Despite recent advancements in the prevention, screening, and treatment of CC, the efficacy of therapy has not shown notable enhancement, and the prognosis remains grim for patients facing metastasis or recurrence (Zang et al. 2023). Identifying new prognostic indicators for CC to improve clinical treatment is clinically important and scientifically significant. RNA-binding proteins (RBPs) constitute a group of proteins that identify and attach to RNAs (both coding and noncoding) and work in collaboration with their binding regions (Wu et al. 2023; Shi et al. 2021). To date, 1,542 RNA-binding protein (RBP) genes have been identified through screening of the human genome (Wu et al. 2024). Relevant studies have indicated that RBPs play crucial roles as regulatory factors in tumor progression. RBPs are intricately involved in all stages of malignant tumor initiation and advancement. The molecular mechanisms through which RBPs operate include RNA selective splicing, polyadenylation, and transcription and translation regulation (Levinson et al. 2021; Wu et al. 2022a). Moreover, RBPs also form ribonucleoprotein complexes with intracellular proteins, coding or noncoding Rnas and affecting tumor progression (Wang et al. 2023). Although many studies have investigated RBPs, their function has not been analysed; in particular, the role of RBPs in CC has rarely been reported.

Therefore, this study collected CC expression profiles and clinical information from the tumor genome map (TCGA) database, screened the differentially expressed RBPs in tumor tissues and normal tissues, constructed an RBPs-based prognostic model through bioinformatics analysis, and verified it in the test set and training set. This model can effectively distinguish the difference in the prognosis of CC patients and optimize the prediction efficiency of the current TNM staging system, aiming to provide a new idea for the treatment decision of CC patients through this study.

Materials and methods

Data sources

The gene expression and associated clinical data utilized in this study were obtained from the UCSC website. These samples were corrected and combined via the limma software package, after which the differential expression of RBPs (DERBPs) was analysed. Among them, 1 542 RBPs were extracted from previous studies. RBP, an RNA-binding protein, was obtained via HITS_CLIP sequencing data, as shown in Table 1 below:Table 1 HITS_CLIP sequencing data analysis

ID	CircRNA ID	Chrom	RBP	
1	chr10:52,580,311|52,619,745	chr10	FUS_Human_GSE43308_HITS-CLIP	
2	chr10:52,580,311|52,619,745	chr10	FUS_Human_GSE43308_HITS- = CLIP	
3	chr10:52,580,311|52,619,745	chr10	FXR1_Human_GSE39682_PAR-CLIP	

The raw gene expression data from TCGA and GEO were downloaded and collated, and batch effect correction was performed on different datasets to ensure data consistency. R language and related bioinformatics packages were subsequently used for differential expression gene analysis to screen out the genes that were significantly expressed in cervical cancer. Subsequently, further functional annotation and pathway analysis of these differentially expressed genes were performed in combination with functional enrichment analysis methods such as WGCNA, GO, and KEGG analyses to identify possible cervical cancer susceptibility genes. Finally, a machine learning algorithm (such as LASSO regression) is applied to construct the mitophagy-related genes (MRGs) diagnostic model, and the predictive efficacy of the model is evaluated via an independent validation set.

Differential gene acquisition and functional enrichment analysis

DERBPs in normal cervical tissues and CC tissues were analysed via the limma software package. Robust biological biomarkers were detected, and enrichment analysis was conducted.

Protein interaction network (PPI) analysis

The STRING online website (http://string-db.org/) was used, and the core submodules in the PPI network were visually detected.

Screening of prognosis-related DERBPs

By matching the gene expression matrix and clinical information, samples without survival information were eliminated, and finally, 293 CC samples were included in the subsequent model construction. Univariate Cox regression analysis was utilized, and a survival software package was used to analyse DERBPs related to prognosis in the training cohort (P < 0.05). Following the initial analysis, least absolute shrinkage and selection operator (LASSO) regression was performed via glmnet software to identify additional determinants of prognosis related to the differential expression of DERBPs.

GSEA (gene set enrichment analysis)

We used gene set enrichment analysis (GSEA) to analyse the functional annotation and pathways associated with the RBPs. First, we sorted the gene expression data according to the amount of expression to construct a gene sequencing list. GSEA software was subsequently used to compare the predefined gene set with the sorted list to evaluate the enrichment degree of the gene set in the sorted list. By calculating the enrichment fraction (ES) and the adjusted P value, the significantly enriched gene sets were identified, and the biological processes and signalling pathways involved in the differential expression of RBP genes were revealed.

Model construction and evaluation

A risk score model was constructed via Cox regression according to previously obtained prognosis-related RBP genes. Patients in the training cohort were divided into high-risk and low-risk groups, and the area under the curve (AUC) was calculated. Univariate and multivariate Cox regression analyses were used to determine the associations between risk values and clinical features.

Single-gene survival analysis in the model

To explore the potential prognostic significance of key genes in the context of CC patients, the TCGA cohort was stratified according to the median expression level of each gene, and subsequent survival analysis was conducted at the individual gene level.

Single-cell sequencing

The gene expression matrix was extracted from single-cell RNA sequencing data GSM7574777, dimensionality was reduced, annotation was performed via the t-SNE algorithm, expression data of genes of interest were extracted, their expression levels were displayed via dot plots and heatmaps, a network graph based on cell‒cell interaction data was constructed, incoming and outgoing signal data of cell types were extracted, and signal patterns were displayed via heatmaps.

Mitochondrial function assay

Inoculate HeLa cells (1 × 10 ^ 4) into the upper chamber of Transwell culture plates (Corning) with a pore size of 8.0 μm. Then cells were treated with a 4% formaldehyde polyformaldehyde solution. In this study, in order to verify the function of the screened susceptibility genes for cervical cancer, we conducted functional experiments on cervical cancer cells. Firstly, the selected genes were knocked out using RNA interference technology, and siRNA was transfected into HeLa cells using Lipofectamine 2000 (Thermo Fisher Scientific). 48 h after transfection, qPCR and Western blot were used to verify the knockout efficiency. Subsequently, mitochondrial function was tested. Analyze the impact of gene knockout on the function of cervical cancer cells.

Statistical analysis

All the statistical analyses were conducted via SPSS version 23.0 or R software (v4.0.3). Survival differences between the two groups were assessed via the log-rank test, and the sensitivity and specificity of the prognostic model were evaluated via receiver operating characteristic (ROC) curve analysis. All the statistical tests were two-tailed, and significance was set at P < 0.05 unless otherwise specified.

The limma software package is a differential expression analysis tool that is specifically designed for microarray and RNA-Seq data and is based on a linear model and an empirical Bayesian statistical framework. Specifically, when we use limma for data analysis, we first fit the expression values of each gene across different groups through a linear model to address the complexity of the data under multiple experimental conditions. The estimated gene expression variance was subsequently adjusted via the empirical Bayesian method to increase the statistical efficiency in the case of small samples. This method can effectively reduce the uncertainty of variance estimation and improve the accuracy of the significance test.

Results

Differential gene analysis

Figure 1a shows box plots of 28 microRNA genes expressed locally in the CC and healthy control groups. There were 11 MRGs with p values < 0.05, among which 5 genes were upregulated in the cervical cancer group, whereas 6 genes were downregulated in the cervical cancer group. Figure 1b presents a heatmap of MRGs expression in the two groups. The correlations among MRGs in the cervical cancer group are shown in Fig. 1c, d, where MAP1LC3A, MAP1LC3B, and PINK1 are closely related to MRGs. A PPI network of the 28 MRGs was subsequently constructed (Fig. 1e, f).Fig. 1 The expression of mitophagy-related genes (MRGs) in cervical cancer was analysed via various approaches (a, b). Additionally, a correlation heatmap was generated to illustrate the relationships among MRGs, specifically in the CC group (c). Furthermore, protein‒protein interaction (PPI) networks were constructed to visualize the interactions among MRGs in CC (d) and to highlight the top 10 hub genes on the basis of MRGs (e). Statistical significance is denoted as follows: *P < 0.05, ** P < 0.01, *** P < 0.001, NS: not significant

LASSO analysis

LASSO regression analysis was performed on 28 MRGs, resulting in a cervical cancer diagnostic risk scoring model consisting of 23 MRGs (see Fig. 2). The ROC curve (AUC = 0.8912).Fig. 2 A disease model was constructed via LASSO analysis. LASSO coefficients were screened (a). A trajectory diagram of the LASSO variables was generated, where each curve represents the coefficient trajectory of an independent variable (b). Different trajectories correspond to varying LASSO coefficients as lambda changes. An ROC curve was generated for a cervical cancer diagnostic risk score model based on MRGs (c).

Enrichment gene analysis

On the basis of the differentially expressed MRGs, we conducted GOKEGG pathway enrichment analyses (Fig. 3). The GO annotation results indicated that the differentially expressed MRGs were associated primarily with biological processes (BPs), such as macroautophagy and autophagy. KEGG pathway enrichment analysis revealed that these genes were involved in processes such as ferroptosis (Fig. 4). Additionally, gene set enrichment analysis (GSEA) revealed enrichment of neutrophil degranulation via the Pathway Interaction Database (PID). Moreover, the gene set variation analysis (GSVA) results (Fig. 5) revealed enrichment of the GO terms microfibril and amino acid betaine metabolic process.Fig. 3 GO and KEGG enrichment gene analyses. This included the examination of biological processes (a), cellular components (b), molecular functions (c), and KEGG pathways (d). GO gene ontology, KEGG Kyoto Encyclopedia of Genes and Genomes

Fig. 4 Gene set enrichment analysis (GSEA) revealed differential enrichment of various signalling pathways in the cervical cancer samples (a–l). Significance was determined at a P value < 0.05 for pathway enrichment

Fig. 5 Changes in pathway activity were analysed in patients with cervical cancer via gene set variation analysis (GSVA). A volcano map was created to illustrate the differential GSVA enrichment between normal and cancerous samples (a). A cluster heatmap was generated to display the pathways of GSVA in the two groups (b), and a box plot was utilized to show the enrichment levels of pathways in the two groups (c). GSVA gene set variation analysis

WGCNA nanolysis

Unsupervised clustering was conducted by employing systematic WGCNA for partitioning. The identification and color assignment of these modules were achieved through merged dynamic tree cutting, resulting in a total of 13 distinct modules. Notably, the blue module exhibited a positive correlation with the samples (Fig. 6).Fig. 6 This study focused on WGCNA. A matrix was formed to depict the relationships among the modules and their characteristics (a, b). WGCNA was then employed to evaluate the correlation ® between external factors such as epileptic or normal conditions (c–h)

MRGs model analysis

Figure 7a displays the top six coexpressed genes. The Venn diagram in Fig. 7b compares the MRGs with the MEturquoise modules. We subsequently constructed a protein‒protein interaction (PPI) network via the STRING database (Fig. 7c) and imported the interactions into Cytoscape software for further analysis. By employing the CytoHubba plug-in within Cytoscape, we identified the top 10 hub genes, as depicted in Fig. 7d. Finally, the Sankey diagram in Fig. 7e illustrates the prediction of lncRNAs and miRNAs.Fig. 7 The interaction network was constructed via various approaches. An UpSet diagram was generated to illustrate the relationships between the gene coexpression modules and marker genes of interest (a). A Venn diagram was generated to depict the overlap between the MEturquoise modules, marker genes, and differentially expressed marker genes (b–e)

Clustering analysis

In this study, through bioinformatics analysis, we focused on screening cervical cancer susceptibility genes and exploring the construction of an MRGs diagnostic model. In the cluster analysis, we systematically integrated different classifications to reveal the expression patterns of CC susceptibility genes in multiple dimensions. This comprehensive classification method provides a new perspective for understanding the pathogenesis of cervical cancer. Moreover, we investigated the role of RBPs in the development of cervical cancer in depth. By analysing the relationships between RBPs and different classifications, we found that there are close associations between multiple RBPs and cervical cancer susceptibility genes. These findings provide important insight into the regulatory mechanism of RBPs in the development of cervical cancer (Fig. 8).Fig. 8 The molecular subtypes of cervical cancer were determined via gene expression levels. Unsupervised consensus clustering (a–f) of gene expression data from cervical cancer samples revealed the presence of 2–6 distinct clusters (g–h)

CIBERSORT was utilized to perform immune infiltration analysis

Our study assessed the abundance of immune cells in cervical cancer and normal tissue samples, as illustrated in Fig. 9a, b. The findings revealed a heightened presence of neutrophils (P < 0.01) in patients with cervical cancer; additionally, a correlation heatmap depicted the associations among the hub genes identified in the PPI network (Fig. 9c, d).Fig. 9 CIBERSORT analysis was performed to evaluate immune infiltration in cervical cancer. Histogram displaying the distribution of 22 immunocyte subgroups in CC samples (a) and an examination of differences in immune infiltration between control and CC samples (b); correlation heatmaps (c, d)

Single-cell sequencing

Figure 10A shows a t-SNE scatter plot, where different colors represent different types of cells in cervical cancer samples, including T cells, B cells, tumor cells, fibroblasts, monocytes, macrophages, neutrophils, and other cells. Tumor cells and T cells occupied significant positions in the plot, indicating their greater abundance in the cervical cancer samples. Figure 10B shows a gene expression dot plot displaying the expression levels of multiple genes in different cell types. Notably, certain genes are expressed at higher levels in specific cell types, for example, certain genes are expressed at higher levels in T cells than in other cell types. Figure 10C presents a gene expression heatmap, illustrating the expression levels of different genes in the cervical cancer samples. The color gradient from light to dark represents the intensity of gene expression, with some genes showing higher expression in tumor cells and lower expression in other cell types. These visualizations collectively reveal the complex patterns of different cell types and gene expression in cervical cancer samples, aiding in a deeper understanding of the biological characteristics of cervical cancer.Fig. 10 Single-cell sequencing. T-SNE clustering plot of cell types (a); expression dot plot of target genes in different cell types (b); heatmap of target gene expression (c); network plot of interactions between different cells (d); heatmap of signalling patterns in different cell types (e); heatmap of signalling patterns in different cell types (f)

Figure 10D depicts an interaction network among different cell types in cervical cancer samples. Nodes represent different cell types (such as T cells, B cells, and tumor cells), whereas edges represent their interactions. Tumor cells clearly have dense interactions with other cell types, particularly strong connections with T cells and fibroblasts, reflecting intricate cell communication within the tumor microenvironment. Figure 10E displays a heatmap of signalling patterns of different cell types, with a color gradient indicating signal strength. The results revealed that tumor cells exhibit strong signal outputs in multiple signalling pathways (such as the TGFβ, Wnt, and EGF pathways), suggesting the potential role of these pathways in regulating tumor cell functions. Figure 10F shows a heatmap of signalling patterns received by different cell types, with color gradients similarly representing signal strength. Tumor cells receive signals from mainly the TGFβ, Wnt, and EGF pathways, which may play crucial roles in tumor cell growth and survival. These visualizations collectively reveal the intricate signalling networks among cells in cervical cancer samples, offering important insights into cell communication within the tumor microenvironment.

In this study, immune cells in cervical cancer tissue were analysed in detail via single-cell sequencing. The results revealed that immune cells exhibit specific aggregation patterns in the tumor microenvironment, especially in the tumor margin region (Fig. 11). Atlas analysis further revealed the spatial distribution characteristics of different immune cell subpopulations and their interactions with tumor cells (Fig. 12). Cytospectral density analysis revealed that certain immune cell subpopulations dominated high-density regions, which may be related to the immune escape mechanism of tumors (Fig. 13).Fig. 11 Specific aggregation patterns of immune cells in the tumor microenvironment were analysed by single-cell sequencing

Fig. 12 The spatial distribution characteristics of different immune cell subsets were analysed via single-cell sequencing

Fig. 13 The cell spectral density was analysed via single-cell sequencing

Verification by in vitro cell experiments

Compared with the NC mimic group, the miRNA mimic group presented a significant reduction in the number of mitochondria in Hela cells, resulting in poor fluorescence intensity. This difference was statistically significant (P < 0.01) (Fig. 14A). Additionally, compared with that in the NC mimic group, the red fluorescence in the Hela miRNA mimic group decreased, whereas the intensity of green fluorescence increased. The statistical analysis revealed significant differences in the relative values of red and green fluorescence between the two groups (P < 0.001) (Fig. 14B). Mitochondrial permeability transition was assessed via the use of a calcein AM fluorescent probe. The results revealed a significant decrease in green fluorescence in Hela cells in the miRNA mimic group compared with the NC mimic group, with a statistically significant difference in fluorescence values between the two groups (P < 0.001) (Fig. 14C).Fig. 14 Effects of miR-431-5p overexpression on mitochondrial function in Hela cells. Mitochondrial number (a), mitochondrial potential (b), and mitochondrial mPTP (c). **P < 0.01, ***P < 0.001 vs. the NC mimic. mPTP mitochondrial permeability transition pore, NC negative control

Discussion

RBPs are considered to be significant factors that intensify cancer-causing mutations. Various studies have demonstrated that RBPs play pivotal roles in the onset and progression of different malignant tumors (Chen et al. 2022) and are closely related to the prognosis of tumor patients, as exemplified by Ueda et al. (Shukla et al. 2023). Researchers have confirmed that patients diagnosed with pancreatic ductal adenocarcinoma and exhibiting high ESRP1 expression tend to have longer survival periods than those with low ESRP1 expression. Previous studies have highlighted the significant role of RBPs in this context (Rodrigues et al. 2023; Wu et al. 2024 Feb 12), but comprehensive studies on the function and prognosis of RBPs in cervical carcinoma (CC) have not been reported.

This study systematically investigated the role and prognostic value of RBPs in CC. A total of 348 DERBPs were identified between normal and tumor tissues of CC patients via the TCGA and GTEx databases. The potential functions of these DERBPs were comprehensively analysed via bioinformatics methods, and protein‒protein interaction (PPI) networks were constructed. A prognostic model based on nine RBP genes was developed and validated through univariate Cox regression, LASSO regression, and Cox regression analyses. Furthermore, the potential prognostic markers in CC were further validated. Previous studies have indicated that dysregulated translation, RNA processing, and RNA metabolism play crucial roles in the pathogenesis and progression of human diseases (Wu et al. 2022b; Lu et al. 2022). Functional enrichment analysis revealed that DERBPs in CC were enriched mainly in biological processes such as translation initiation, mRNA catabolism and SRP-dependent cotranslation proteins. Moreover, KEGG analysis revealed that metabolic abnormalities of RBPs rich in ribosomes were involved mainly in the process of RNA splicing and degradation, and the results were consistent with previous research conclusions (Xu et al. 2021).

Studies have indicated that prognostic models for CC patients, which incorporate histone families, miRNAs, and long noncoding RNAs (lncRNAs), are highly effective at predicting patient survival rates (Wang et al. 2022; Liu et al. 2023; Vidrine et al. 2023). Although the role and prognostic effects of RBPs in CC have not been fully explored, these molecular subsets play critical roles in regulating the progression and development of malignant tumors. Owing to the limited ability of a single RBP to predict prognosis, this study undertook bioinformatics analysis to construct a prediction model. The analysis revealed that the high-risk group, determined by the median risk value, demonstrated the model’s effectiveness in predicting overall survival (OS) in CC patients. Furthermore, multivariate Cox regression analysis confirmed that the model could serve as an independent prognostic factor for CC patients. Subgroup survival analysis based on different clinical traits further validated the model’s applicability, with distinct survival curves accurately distinguishing between high-risk and low-risk patients. Notably, nomograms have been proven to offer greater accuracy in predicting the prognosis of various cancers than traditional staging systems do (Wu et al. 2022c; Friedman et al. 2023; Elias et al. 2023), enhancing the credibility and value of the risk model in clinical applications involving the use of nine RBPs to construct a nomogram for assessing individual patient survival risk. The nomogram’s calibration curve is relatively accurate for determining outcome events on the basis of individual patient data (Wu et al. 2024). The above information indicates that the model has a strong ability to predict the outcomes of patients with CC. It also affirms the essential role of RBPs, including those involved in CC, in the development and progression of human cancer.

In this study, we conducted strict data quality control to ensure the reliability and accuracy of the research results. First, we screened samples with complete clinical information and gene expression data from the TCGA database to ensure the comprehensiveness of the data. Second, through standardized processing and batch effect correction, we effectively reduce the technical deviation between different batches. In addition, we screened samples via multiple quality control indicators (e.g., RNA integrity, sequencing depth, etc.) to exclude low-quality data. Despite our various data quality control measures, potential data biases may still affect the results of the study. For example, heterogeneity of sample sources can lead to differences in gene expression, whereas technical differences between different sequencing platforms can also introduce biases. To address these challenges, we used multiple validation methods during the analysis, including cross-validation and leave-one validation, to assess the robustness of the model and minimize the impact of bias.

Through rigorous data quality control and multiple validation methods, we strive to ensure the reliability of our findings. However, future studies should further validate our findings on the basis of larger sample sizes and multicenter data to fully evaluate the clinical applicability and robustness of the model.

Posttranscriptional regulation is a complex and ongoing process. It remains unclear whether alterations in genes associated with RBPs adequately represent RBP function; therefore, this study has certain limitations (Andalib et al. 2023; Malla and Kamal 2021; Wu et al. 2021). First, this prognostic model is based only on TCGA cohort data and needs to be validated in clinical patient cohorts and multicenter prospective studies (Ding et al. 2023; Wu et al. 2024; Yang et al. 2023). Moreover, additional in vitro and in vivo experimental investigations are essential to elucidate the underlying molecular mechanisms comprehensively for enhanced implementation in clinical settings(Zhu et al. 2022).

In this study, we explored the biological processes and signalling pathways associated with RBPs through gene set enrichment analysis (GSEA). The GSEA results revealed that the differentially expressed genes associated with RBPs were significantly enriched in several key biological processes and pathways, such as cell cycle regulation, DNA repair, and RNA metabolism. The discovery of these enriched pathways will help us to further understand the potential mechanism of RBPs in the occurrence and development of cervical cancer, provide new research directions and targets, and provide theoretical support for clinical diagnosis and treatment.

In this study, although we screened and identified cervical cancer susceptibility genes through bioinformatics methods and constructed MRGs diagnostic models, several limitations still need to be discussed. Biases in data sources may affect the generalizability of the results. Our data were drawn primarily from the TCGA database, whose samples were drawn primarily from Western populations, which may limit the applicability of the results to other ethnicities. In addition, the clinical information and pathological types of samples in the TCGA database may not be comprehensive enough, further limiting the wide application of study results. Sample selection limitations are also important factors. Our study had a relatively limited sample size and failed to cover all possible clinical features and pathology types, which could lead to bias in the results for some specific subpopulations. Therefore, future studies need to verify the reliability and applicability of the model through multicenter and multiethnic large-scale samples. Finally, our analytical approach is based on a number of assumptions, such as the linear relationship of gene expression and the applicability of specific algorithms. These assumptions may have influenced the results. Although we used a variety of validation methods to reduce the bias of these hypotheses, further optimization and validation of the analytical methods are needed in future studies.

This study comprehensively investigated the role and prognostic significance of RBPs in cervical carcinoma (CC). A risk model was developed using the expression levels of RBPs, and a column graph was constructed to offer a novel reference for personalized treatment strategies and the prediction of clinical outcomes among CC patients.

Acknowledgements

The authors would like to express their gratitude to AJE for the expert linguistic services provided.

Author contributions

Zhang Zhang authored the manuscript, whereas Fangfang Chen and Xiaoxiao Deng were responsible for data collection and study guidance. All the authors contributed to the manuscript’s review, editing, and approval, ensuring critical revision for important intellectual content. They collectively gave final approval for publication and agreed to be accountable for all aspects of the research.

Funding

None.

Data availability

The data underpinning the findings of this study can be accessed by contacting the corresponding author with a reasonable request.

Declarations

Conflict of Interests

The author did not encounter any conflicts throughout the preparation of the article.

Ethics approval and consent to participate

The present study was approved by the Ethics Committee of Pingyang Hospital, Wenzhou Medical University (LW-2024-03).

Consent for publications

The author has reviewed and approved the final manuscript for publication.

Informed Consent

The researchers affirm that no patients were involved in this study.

Institutional Review Board Statement

None.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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