Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic model

Abstract

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: Bioinformatics analysis; Cervical cancer; Clinical prognosis model; Susceptibility genes.

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

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).

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

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)

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)

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)

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)

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)

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

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